Package: a4 Version: 1.38.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo, Rgraphviz, GOstats License: GPL-3 MD5sum: 2ab2a7beb73de4275b3c3025110c0165 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Umbrella package is available for the entire Automated Affymetrix Array Analysis suite of package. biocViews: Microarray Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4 git_branch: RELEASE_3_12 git_last_commit: 5b7a908 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/a4_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/a4_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/a4_1.38.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4/inst/doc/a4vignette.R dependencyCount: 72 Package: a4Base Version: 1.38.0 Depends: a4Preproc, a4Core Imports: methods, graphics, grid, Biobase, annaffy, mpm, genefilter, limma, multtest, glmnet, gplots Suggests: Cairo, ALL, hgu95av2.db, nlcv Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 0ca9c7ec1fce79158a2fdc9beea9dcb2 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Base utility functions are available for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray Author: Willem Talloen [aut], Tine Casneuf [aut], An De Bondt [aut], Steven Osselaer [aut], Hinrich Goehlmann [aut], Willem Ligtenberg [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud git_url: https://git.bioconductor.org/packages/a4Base git_branch: RELEASE_3_12 git_last_commit: 4add242 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/a4Base_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/a4Base_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/a4Base_1.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 dependencyCount: 64 Package: a4Classif Version: 1.38.0 Depends: a4Core, a4Preproc Imports: methods, Biobase, ROCR, pamr, glmnet, varSelRF, utils, graphics, stats Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 9e4fb68faa11fcf1b8a73cf6d2cafeae NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Functionalities for classification of Affymetrix microarray data, integrating within the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, GeneExpression, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Classif git_branch: RELEASE_3_12 git_last_commit: c4d0588 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/a4Classif_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/a4Classif_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/a4Classif_1.38.0.tgz vignettes: vignettes/a4Classif/inst/doc/a4Classif-vignette.html vignetteTitles: a4Classif package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Classif/inst/doc/a4Classif-vignette.R dependsOnMe: a4 dependencyCount: 30 Package: a4Core Version: 1.38.0 Imports: Biobase, glmnet, methods, stats Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 138cb050616b377254cc31b8465f7103 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Utility functions for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Classification Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Core git_branch: RELEASE_3_12 git_last_commit: a027dcd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/a4Core_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/a4Core_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/a4Core_1.38.0.tgz vignettes: vignettes/a4Core/inst/doc/a4Core-vignette.html vignetteTitles: a4Core package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Core/inst/doc/a4Core-vignette.R dependsOnMe: a4, a4Base, a4Classif, nlcv dependencyCount: 18 Package: a4Preproc Version: 1.38.0 Imports: BiocGenerics, Biobase Suggests: ALL, hgu95av2.db, knitr, rmarkdown License: GPL-3 MD5sum: 6baf7e20eefb67058564649e6c1e4046 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Utility functions to pre-process data for the Automated Affymetrix Array Analysis set of packages. biocViews: Microarray, Preprocessing Author: Willem Talloen [aut], Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Preproc git_branch: RELEASE_3_12 git_last_commit: c93c223 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/a4Preproc_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/a4Preproc_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/a4Preproc_1.38.0.tgz vignettes: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.html vignetteTitles: a4Preproc package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Preproc/inst/doc/a4Preproc-vignette.R dependsOnMe: a4, a4Base, a4Classif suggestsMe: graphite dependencyCount: 7 Package: a4Reporting Version: 1.38.0 Imports: methods, xtable Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 89870e9d224d0d79ec5776bd7faae199 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Utility functions to facilitate the reporting of the Automated Affymetrix Array Analysis Reporting set of packages. biocViews: Microarray Author: Tobias Verbeke [aut], Laure Cougnaud [cre] Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/a4Reporting git_branch: RELEASE_3_12 git_last_commit: cd3cf24 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/a4Reporting_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/a4Reporting_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/a4Reporting_1.38.0.tgz vignettes: vignettes/a4Reporting/inst/doc/a4reporting-vignette.html vignetteTitles: a4Reporting package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/a4Reporting/inst/doc/a4reporting-vignette.R dependsOnMe: a4 dependencyCount: 4 Package: ABAEnrichment Version: 1.20.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), gtools (>= 3.5.0), ABAData (>= 0.99.2), data.table (>= 1.10.4), GOfuncR (>= 1.1.2), grDevices, stats, graphics, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 34a8c3647a5569782604855fd377d3d3 NeedsCompilation: yes Title: Gene expression enrichment in human brain regions Description: The package ABAEnrichment is designed to test for enrichment of user defined candidate genes in the set of expressed genes in different human brain regions. The core function 'aba_enrich' integrates the expression of the candidate gene set (averaged across donors) and the structural information of the brain using an ontology, both provided by the Allen Brain Atlas project. 'aba_enrich' interfaces the ontology enrichment software FUNC to perform the statistical analyses. Additional functions provided in this package like 'get_expression' and 'plot_expression' facilitate exploring the expression data, and besides the standard candidate vs. background gene set enrichment, also three additional tests are implemented, e.g. for cases when genes are ranked instead of divided into candidate and background. biocViews: GeneSetEnrichment, GeneExpression Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ABAEnrichment git_branch: RELEASE_3_12 git_last_commit: 608433a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ABAEnrichment_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ABAEnrichment_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ABAEnrichment_1.20.0.tgz vignettes: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html vignetteTitles: Introduction to ABAEnrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R suggestsMe: ABAData dependencyCount: 48 Package: ABarray Version: 1.58.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 8f8954226989bc24307a95f22c62a5ce NeedsCompilation: no Title: Microarray QA and statistical data analysis for Applied Biosystems Genome Survey Microrarray (AB1700) gene expression data. Description: Automated pipline to perform gene expression analysis for Applied Biosystems Genome Survey Microarray (AB1700) data format. Functions include data preprocessing, filtering, control probe analysis, statistical analysis in one single function. A GUI interface is also provided. The raw data, processed data, graphics output and statistical results are organized into folders according to the analysis settings used. biocViews: Microarray, OneChannel, Preprocessing Author: Yongming Andrew Sun Maintainer: Yongming Andrew Sun git_url: https://git.bioconductor.org/packages/ABarray git_branch: RELEASE_3_12 git_last_commit: 6e6a0ce git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ABarray_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ABarray_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ABarray_1.58.0.tgz vignettes: vignettes/ABarray/inst/doc/ABarray.pdf, vignettes/ABarray/inst/doc/ABarrayGUI.pdf vignetteTitles: ABarray gene expression, ABarray gene expression GUI interface hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 17 Package: abseqR Version: 1.8.0 Depends: R (>= 3.5.0) Imports: ggplot2, RColorBrewer, circlize, reshape2, VennDiagram, plyr, flexdashboard, BiocParallel (>= 1.1.25), png, grid, gridExtra, rmarkdown, knitr, vegan, ggcorrplot, ggdendro, plotly, BiocStyle, stringr, utils, methods, grDevices, stats, tools, graphics Suggests: testthat License: GPL-3 | file LICENSE MD5sum: 77bd617f97afbae2f7c081c85acf8297 NeedsCompilation: no Title: Reporting and data analysis functionalities for Rep-Seq datasets of antibody libraries Description: AbSeq is a comprehensive bioinformatic pipeline for the analysis of sequencing datasets generated from antibody libraries and abseqR is one of its packages. abseqR empowers the users of abseqPy (https://github.com/malhamdoosh/abseqPy) with plotting and reporting capabilities and allows them to generate interactive HTML reports for the convenience of viewing and sharing with other researchers. Additionally, abseqR extends abseqPy to compare multiple repertoire analyses and perform further downstream analysis on its output. biocViews: Sequencing, Visualization, ReportWriting, QualityControl, MultipleComparison Author: JiaHong Fong [cre, aut], Monther Alhamdoosh [aut] Maintainer: JiaHong Fong URL: https://github.com/malhamdoosh/abseqR SystemRequirements: pandoc (>= 1.19.2.1) VignetteBuilder: knitr BugReports: https://github.com/malhamdoosh/abseqR/issues git_url: https://git.bioconductor.org/packages/abseqR git_branch: RELEASE_3_12 git_last_commit: 0a4616b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/abseqR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/abseqR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/abseqR_1.8.0.tgz vignettes: vignettes/abseqR/inst/doc/abseqR.pdf vignetteTitles: Introduction to abseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/abseqR/inst/doc/abseqR.R dependencyCount: 109 Package: ABSSeq Version: 1.44.0 Depends: R (>= 2.10), methods Imports: locfit, limma Suggests: edgeR License: GPL (>= 3) MD5sum: ce91402bb383fbcd1b669e44db5f118a NeedsCompilation: no Title: ABSSeq: a new RNA-Seq analysis method based on modelling absolute expression differences Description: Inferring differential expression genes by absolute counts difference between two groups, utilizing Negative binomial distribution and moderating fold-change according to heterogeneity of dispersion across expression level. biocViews: DifferentialExpression Author: Wentao Yang Maintainer: Wentao Yang git_url: https://git.bioconductor.org/packages/ABSSeq git_branch: RELEASE_3_12 git_last_commit: c202b4a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ABSSeq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ABSSeq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ABSSeq_1.44.0.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABSSeq/inst/doc/ABSSeq.R importsMe: metaseqR2 dependencyCount: 9 Package: acde Version: 1.20.0 Depends: R(>= 3.3), boot(>= 1.3) Imports: stats, graphics Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 76dbae5991e03d486463f3775ba089de NeedsCompilation: no Title: Artificial Components Detection of Differentially Expressed Genes Description: This package provides a multivariate inferential analysis method for detecting differentially expressed genes in gene expression data. It uses artificial components, close to the data's principal components but with an exact interpretation in terms of differential genetic expression, to identify differentially expressed genes while controlling the false discovery rate (FDR). The methods on this package are described in the vignette or in the article 'Multivariate Method for Inferential Identification of Differentially Expressed Genes in Gene Expression Experiments' by J. P. Acosta, L. Lopez-Kleine and S. Restrepo (2015, pending publication). biocViews: DifferentialExpression, TimeCourse, PrincipalComponent, GeneExpression, Microarray, mRNAMicroarray Author: Juan Pablo Acosta, Liliana Lopez-Kleine Maintainer: Juan Pablo Acosta git_url: https://git.bioconductor.org/packages/acde git_branch: RELEASE_3_12 git_last_commit: cefb4f2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/acde_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/acde_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/acde_1.20.0.tgz vignettes: vignettes/acde/inst/doc/acde.pdf vignetteTitles: Identification of Differentially Expressed Genes with Artificial Components hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/acde/inst/doc/acde.R importsMe: coexnet dependencyCount: 3 Package: ACE Version: 1.8.0 Depends: R (>= 3.4) Imports: Biobase, QDNAseq, ggplot2, grid, stats, utils, methods, grDevices, GenomicRanges Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 8c5dbed3ce3919d4c5cea19b006f98f5 NeedsCompilation: no Title: Absolute Copy Number Estimation from Low-coverage Whole Genome Sequencing Description: Uses segmented copy number data to estimate tumor cell percentage and produce copy number plots displaying absolute copy numbers. biocViews: CopyNumberVariation, DNASeq, Coverage, WholeGenome, Visualization, Sequencing Author: Jos B Poell Maintainer: Jos B Poell URL: https://github.com/tgac-vumc/ACE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ACE git_branch: RELEASE_3_12 git_last_commit: 6a04454 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ACE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ACE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ACE_1.8.0.tgz vignettes: vignettes/ACE/inst/doc/ACE_vignette.html vignetteTitles: ACE vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACE/inst/doc/ACE_vignette.R dependencyCount: 80 Package: aCGH Version: 1.68.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, grDevices, graphics, methods, stats, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: e4a24c8087c72fb38dcb3b06cbcfb9f8 NeedsCompilation: yes Title: Classes and functions for Array Comparative Genomic Hybridization data Description: Functions for reading aCGH data from image analysis output files and clone information files, creation of aCGH S3 objects for storing these data. Basic methods for accessing/replacing, subsetting, printing and plotting aCGH objects. biocViews: CopyNumberVariation, DataImport, Genetics Author: Jane Fridlyand , Peter Dimitrov Maintainer: Peter Dimitrov git_url: https://git.bioconductor.org/packages/aCGH git_branch: RELEASE_3_12 git_last_commit: 91f41a3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/aCGH_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/aCGH_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/aCGH_1.68.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aCGH/inst/doc/aCGH.R dependsOnMe: CRImage importsMe: ADaCGH2, snapCGH suggestsMe: beadarraySNP dependencyCount: 17 Package: ACME Version: 2.46.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: f389bd392848370ceca8a1a7950ad597 NeedsCompilation: yes Title: Algorithms for Calculating Microarray Enrichment (ACME) Description: ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory. biocViews: Technology, Microarray, Normalization Author: Sean Davis Maintainer: Sean Davis URL: http://watson.nci.nih.gov/~sdavis git_url: https://git.bioconductor.org/packages/ACME git_branch: RELEASE_3_12 git_last_commit: 68f45c9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ACME_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ACME_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ACME_2.46.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ACME/inst/doc/ACME.R suggestsMe: oligo dependencyCount: 7 Package: ADaCGH2 Version: 2.30.0 Depends: R (>= 3.2.0), parallel, ff, GLAD Imports: bit, ffbase, DNAcopy, tilingArray, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: 81b47493f2488d1013ff401d411d7053 NeedsCompilation: yes Title: Analysis of big data from aCGH experiments using parallel computing and ff objects Description: Analysis and plotting of array CGH data. Allows usage of Circular Binary Segementation, wavelet-based smoothing (both as in Liu et al., and HaarSeg as in Ben-Yaacov and Eldar), HMM, BioHMM, GLAD, CGHseg. Most computations are parallelized (either via forking or with clusters, including MPI and sockets clusters) and use ff for storing data. biocViews: Microarray, CopyNumberVariants Author: Ramon Diaz-Uriarte and Oscar M. Rueda . Wavelet-based aCGH smoothing code from Li Hsu and Douglas Grove . Imagemap code from Barry Rowlingson . HaarSeg code from Erez Ben-Yaacov; downloaded from . Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/adacgh2 git_url: https://git.bioconductor.org/packages/ADaCGH2 git_branch: RELEASE_3_12 git_last_commit: 1d62234 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ADaCGH2_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ADaCGH2_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ADaCGH2_2.30.0.tgz vignettes: vignettes/ADaCGH2/inst/doc/ADaCGH2-long-examples.pdf, vignettes/ADaCGH2/inst/doc/ADaCGH2.pdf, vignettes/ADaCGH2/inst/doc/benchmarks.pdf vignetteTitles: ADaCGH2-long-examples.pdf, ADaCGH2 Overview, benchmarks.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADaCGH2/inst/doc/ADaCGH2.R dependencyCount: 93 Package: ADAM Version: 1.6.0 Depends: R(>= 3.5), stats, utils, methods Imports: Rcpp (>= 0.12.18), GO.db (>= 3.6.0), KEGGREST (>= 1.20.2), knitr, pbapply (>= 1.3-4), dplyr (>= 0.7.6), DT (>= 0.4), stringr (>= 1.3.1), SummarizedExperiment (>= 1.10.1) LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 1ca8104600f009d43c6a5b06889c145f NeedsCompilation: yes Title: ADAM: Activity and Diversity Analysis Module Description: ADAM is a GSEA R package created to group a set of genes from comparative samples (control versus experiment) belonging to different species according to their respective functions (Gene Ontology and KEGG pathways as default) and show their significance by calculating p-values referring togene diversity and activity. Each group of genes is called GFAG (Group of Functionally Associated Genes). biocViews: GeneSetEnrichment, Pathways, KEGG Author: André Luiz Molan , Giordano Bruno Sanches Seco , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ADAM git_branch: RELEASE_3_12 git_last_commit: 8799f99 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ADAM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ADAM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ADAM_1.6.0.tgz vignettes: vignettes/ADAM/inst/doc/ADAM.html vignetteTitles: "Using ADAM" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAM/inst/doc/ADAM.R dependsOnMe: ADAMgui dependencyCount: 84 Package: ADAMgui Version: 1.6.0 Depends: R(>= 3.6), stats, utils, methods, ADAM Imports: GO.db (>= 3.5.0), dplyr (>= 0.7.6), shiny (>= 1.1.0), stringr (>= 1.3.1), stringi (>= 1.2.4), varhandle (>= 2.0.3), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), ggpubr (>= 0.1.8), ggsignif (>= 0.4.0), reshape2 (>= 1.4.3), RColorBrewer (>= 1.1-2), colorRamps (>= 2.3), DT (>= 0.4), data.table (>= 1.11.4), gridExtra (>= 2.3), shinyjs (>= 1.0), knitr, testthat Suggests: BiocStyle License: GPL (>= 2) MD5sum: e8bfe302f8065014beef910524fdce5e NeedsCompilation: no Title: Activity and Diversity Analysis Module Graphical User Interface Description: ADAMgui is a Graphical User Interface for the ADAM package. The ADAMgui package provides 2 shiny-based applications that allows the user to study the output of the ADAM package files through different plots. It's possible, for example, to choose a specific GFAG and observe the gene expression behavior with the plots created with the GFAGtargetUi function. Features such as differential expression and foldchange can be easily seen with aid of the plots made with GFAGpathUi function. biocViews: GeneSetEnrichment, Pathways, KEGG Author: Giordano Bruno Sanches Seco , André Luiz Molan , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho URL: TBA VignetteBuilder: knitr BugReports: https://github.com/jrybarczyk/ADAMgui/issues git_url: https://git.bioconductor.org/packages/ADAMgui git_branch: RELEASE_3_12 git_last_commit: 75782fe git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ADAMgui_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ADAMgui_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ADAMgui_1.6.0.tgz vignettes: vignettes/ADAMgui/inst/doc/ADAMgui.html vignetteTitles: "Using ADAMgui" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ADAMgui/inst/doc/ADAMgui.R dependencyCount: 176 Package: adductomicsR Version: 1.6.0 Depends: R (>= 3.6), adductData, ExperimentHub, AnnotationHub Imports: parallel (>= 3.3.2), data.table (>= 1.10.4), OrgMassSpecR (>= 0.4.6), foreach (>= 1.4.3), mzR (>= 2.14.0), ade4 (>= 1.7.6), rvest (>= 0.3.2), pastecs (>= 1.3.18), reshape2 (>= 1.4.2), pracma (>= 2.0.4), DT (>= 0.2), fpc (>= 2.1.10), doSNOW (>= 1.0.14), fastcluster (>= 1.1.22), RcppEigen (>= 0.3.3.3.0), bootstrap (>= 2017.2), smoother (>= 1.1), dplyr (>= 0.7.5), zoo (>= 1.8), stats (>= 3.5.0), utils (>= 3.5.0), graphics (>= 3.5.0), grDevices (>= 3.5.0), methods (>= 3.5.0), datasets (>= 3.5.0) Suggests: knitr (>= 1.15.1), rmarkdown (>= 1.5), Rdisop (>= 1.34.0), testthat License: Artistic-2.0 MD5sum: 071100cb204d7d5501519a33b9d38f6a NeedsCompilation: no Title: Processing of adductomic mass spectral datasets Description: Processes MS2 data to identify potentially adducted peptides from spectra that has been corrected for mass drift and retention time drift and quantifies MS1 level mass spectral peaks. biocViews: MassSpectrometry,Metabolomics,Software,ThirdPartyClient,DataImport, GUI Author: Josie Hayes Maintainer: Josie Hayes VignetteBuilder: knitr BugReports: https://github.com/JosieLHayes/adductomicsR/issues git_url: https://git.bioconductor.org/packages/adductomicsR git_branch: RELEASE_3_12 git_last_commit: 4f15169 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/adductomicsR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/adductomicsR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/adductomicsR_1.6.0.tgz vignettes: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.html vignetteTitles: Adductomics workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adductomicsR/inst/doc/adductomicsRWorkflow.R dependencyCount: 130 Package: ADImpute Version: 1.0.0 Depends: R (>= 4.0) Imports: ArgumentCheck, BiocParallel, data.table, DrImpute, kernlab, MASS, Matrix, methods, rsvd, S4Vectors, SAVER, SingleCellExperiment, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 + file LICENSE MD5sum: 79238349d4322161781bc585bc2390db NeedsCompilation: no Title: Adaptive Dropout Imputer (ADImpute) Description: Single-cell RNA sequencing (scRNA-seq) methods are typically unable to quantify the expression levels of all genes in a cell, creating a need for the computational prediction of missing values (‘dropout imputation’). Most existing dropout imputation methods are limited in the sense that they exclusively use the scRNA-seq dataset at hand and do not exploit external gene-gene relationship information. Here we propose two novel methods: a gene regulatory network-based approach using gene-gene relationships learnt from external data and a baseline approach corresponding to a sample-wide average. ADImpute can implement these novel methods and also combine them with existing imputation methods (currently supported: DrImpute, SAVER). ADImpute can learn the best performing method per gene and combine the results from different methods into an ensemble. biocViews: GeneExpression, Network, Preprocessing, Sequencing, SingleCell, Transcriptomics Author: Ana Carolina Leote [cre, aut] () Maintainer: Ana Carolina Leote VignetteBuilder: knitr BugReports: https://github.com/anacarolinaleote/ADImpute/issues git_url: https://git.bioconductor.org/packages/ADImpute git_branch: RELEASE_3_12 git_last_commit: 5676910 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ADImpute_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ADImpute_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ADImpute_1.0.0.tgz vignettes: vignettes/ADImpute/inst/doc/ADImpute_tutorial.html vignetteTitles: ADImpute tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ADImpute/inst/doc/ADImpute_tutorial.R dependencyCount: 51 Package: adSplit Version: 1.60.0 Depends: R (>= 2.1.0), methods (>= 2.1.0) Imports: AnnotationDbi, Biobase (>= 1.5.12), cluster (>= 1.9.1), GO.db (>= 1.8.1), graphics, grDevices, KEGG.db (>= 1.8.1), methods, multtest (>= 1.6.0), stats (>= 2.1.0) Suggests: golubEsets (>= 1.0), vsn (>= 1.5.0), hu6800.db (>= 1.8.1) License: GPL (>= 2) Archs: i386, x64 MD5sum: f522e1c3c24276d524bc56dfb4da5427 NeedsCompilation: yes Title: Annotation-Driven Clustering Description: This package implements clustering of microarray gene expression profiles according to functional annotations. For each term genes are annotated to, splits into two subclasses are computed and a significance of the supporting gene set is determined. biocViews: Microarray, Clustering Author: Claudio Lottaz, Joern Toedling Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/adSplit.shtml git_url: https://git.bioconductor.org/packages/adSplit git_branch: RELEASE_3_12 git_last_commit: de5abcc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/adSplit_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/adSplit_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/adSplit_1.60.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/adSplit/inst/doc/tr_2005_02.R dependencyCount: 37 Package: AffiXcan Version: 1.8.0 Depends: R (>= 3.6), SummarizedExperiment Imports: MultiAssayExperiment, BiocParallel, crayon Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 55ac29056d3a356545e5e148a347088a NeedsCompilation: no Title: A Functional Approach To Impute Genetically Regulated Expression Description: Impute a GReX (Genetically Regulated Expression) for a set of genes in a sample of individuals, using a method based on the Total Binding Affinity (TBA). Statistical models to impute GReX can be trained with a training dataset where the real total expression values are known. biocViews: GeneExpression, Transcription, GeneRegulation, DimensionReduction, Regression, PrincipalComponent Author: Alessandro Lussana [aut, cre] Maintainer: Alessandro Lussana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AffiXcan git_branch: RELEASE_3_12 git_last_commit: 00f1fbc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AffiXcan_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AffiXcan_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AffiXcan_1.8.0.tgz vignettes: vignettes/AffiXcan/inst/doc/AffiXcan.html vignetteTitles: AffiXcan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffiXcan/inst/doc/AffiXcan.R dependencyCount: 54 Package: affxparser Version: 1.62.0 Depends: R (>= 2.14.0) Suggests: R.oo (>= 1.22.0), R.utils (>= 2.7.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: f4f9f08e8609de090844140cf15bb089 NeedsCompilation: yes Title: Affymetrix File Parsing SDK Description: Package for parsing Affymetrix files (CDF, CEL, CHP, BPMAP, BAR). It provides methods for fast and memory efficient parsing of Affymetrix files using the Affymetrix' Fusion SDK. Both ASCII- and binary-based files are supported. Currently, there are methods for reading chip definition file (CDF) and a cell intensity file (CEL). These files can be read either in full or in part. For example, probe signals from a few probesets can be extracted very quickly from a set of CEL files into a convenient list structure. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms, OneChannel Author: Henrik Bengtsson [aut], James Bullard [aut], Robert Gentleman [ctb], Kasper Daniel Hansen [aut, cre], Jim Hester [ctb], Martin Morgan [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/affxparser BugReports: https://github.com/HenrikBengtsson/affxparser/issues git_url: https://git.bioconductor.org/packages/affxparser git_branch: RELEASE_3_12 git_last_commit: b3e988e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affxparser_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affxparser_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affxparser_1.62.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder importsMe: affyILM, cn.farms, crossmeta, EventPointer, GCSscore, GeneRegionScan, ITALICS, oligo suggestsMe: TIN, aroma.affymetrix, aroma.apd dependencyCount: 0 Package: affy Version: 1.68.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocManager, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: be0bf944c76571002c49bf9fb722870d NeedsCompilation: yes Title: Methods for Affymetrix Oligonucleotide Arrays Description: The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it. biocViews: Microarray, OneChannel, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Benjamin Milo Bolstad , and Crispin Miller with contributions from Magnus Astrand , Leslie M. Cope , Robert Gentleman, Jeff Gentry, Conrad Halling , Wolfgang Huber, James MacDonald , Benjamin I. P. Rubinstein, Christopher Workman , John Zhang Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affy git_branch: RELEASE_3_12 git_last_commit: 1664399 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affy_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affy_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affy_1.68.0.tgz vignettes: vignettes/affy/inst/doc/affy.pdf, vignettes/affy/inst/doc/builtinMethods.pdf, vignettes/affy/inst/doc/customMethods.pdf, vignettes/affy/inst/doc/vim.pdf vignetteTitles: 1. Primer, 2. Built-in Processing Methods, 3. Custom Processing Methods, 4. Import Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affy/inst/doc/affy.R, vignettes/affy/inst/doc/builtinMethods.R, vignettes/affy/inst/doc/customMethods.R, vignettes/affy/inst/doc/vim.R dependsOnMe: affyContam, AffyExpress, affyPara, affyPLM, affyQCReport, AffyRNADegradation, altcdfenvs, arrayMvout, ArrayTools, bgx, Cormotif, DrugVsDisease, dualKS, ExiMiR, farms, frmaTools, gcrma, logitT, maskBAD, MLP, panp, prebs, qpcrNorm, RefPlus, Risa, RPA, SCAN.UPC, simpleaffy, sscore, webbioc, affydata, ALLMLL, AmpAffyExample, bronchialIL13, ccTutorial, CLL, curatedBladderData, curatedOvarianData, ecoliLeucine, Hiiragi2013, MAQCsubset, MAQCsubsetAFX, mvoutData, PREDAsampledata, SpikeIn, SpikeInSubset, XhybCasneuf, RobLoxBioC importsMe: affycoretools, affyILM, affylmGUI, affyQCReport, arrayQualityMetrics, ArrayTools, CAFE, ChIPXpress, coexnet, Cormotif, crossmeta, Doscheda, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, makecdfenv, mimager, MSnbase, PECA, plier, puma, pvac, Rnits, simpleaffy, STATegRa, tilingArray, TurboNorm, vsn, rat2302frmavecs, DeSousa2013, signatureSearchData, bapred, IsoGene suggestsMe: AnnotationForge, ArrayExpress, beadarray, beadarraySNP, BiocCaseStudies, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, ExpressionView, factDesign, GeneRegionScan, limma, made4, piano, PREDA, qcmetrics, runibic, siggenes, TCGAbiolinks, estrogen, ffpeExampleData, arrays, aroma.affymetrix, hexbin, maGUI dependencyCount: 12 Package: affycomp Version: 1.66.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 455dc620afe5a4f092d8439ecfa10abd NeedsCompilation: no Title: Graphics Toolbox for Assessment of Affymetrix Expression Measures Description: The package contains functions that can be used to compare expression measures for Affymetrix Oligonucleotide Arrays. biocViews: OneChannel, Microarray, Preprocessing Author: Rafael A. Irizarry and Zhijin Wu with contributions from Simon Cawley Maintainer: Rafael A. Irizarry git_url: https://git.bioconductor.org/packages/affycomp git_branch: RELEASE_3_12 git_last_commit: 388d01a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affycomp_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affycomp_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affycomp_1.66.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycomp/inst/doc/affycomp.R dependsOnMe: affycompData dependencyCount: 7 Package: AffyCompatible Version: 1.50.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: ccf9b9dcb9f3ada54790d83e8a96ccf6 NeedsCompilation: no Title: Affymetrix GeneChip software compatibility Description: This package provides an interface to Affymetrix chip annotation and sample attribute files. The package allows an easy way for users to download and manage local data bases of Affynmetrix NetAffx annotation files. The package also provides access to GeneChip Operating System (GCOS) and GeneChip Command Console (AGCC)-compatible sample annotation files. biocViews: Infrastructure, Microarray, OneChannel Author: Martin Morgan, Robert Gentleman Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/AffyCompatible git_branch: RELEASE_3_12 git_last_commit: 3b12d12 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AffyCompatible_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AffyCompatible_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AffyCompatible_1.50.0.tgz vignettes: vignettes/AffyCompatible/inst/doc/MAGEAndARR.pdf, vignettes/AffyCompatible/inst/doc/NetAffxResource.pdf vignetteTitles: Retrieving MAGE and ARR sample attributes, Annotation retrieval with NetAffxResource hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyCompatible/inst/doc/MAGEAndARR.R, vignettes/AffyCompatible/inst/doc/NetAffxResource.R dependencyCount: 18 Package: affyContam Version: 1.48.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata Suggests: hgu95av2cdf License: Artistic-2.0 MD5sum: 89d0db140ed87963c373b00920e09a02 NeedsCompilation: no Title: structured corruption of affymetrix cel file data Description: structured corruption of cel file data to demonstrate QA effectiveness biocViews: Infrastructure Author: V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/affyContam git_branch: RELEASE_3_12 git_last_commit: 88387a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affyContam_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affyContam_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affyContam_1.48.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyContam/inst/doc/affyContam.R dependencyCount: 15 Package: affycoretools Version: 1.62.0 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR, RSQLite, BiocGenerics, DBI, Glimma Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl, rmarkdown License: Artistic-2.0 MD5sum: 3f9abcb58faa3646b7d86a695f466c8b NeedsCompilation: no Title: Functions useful for those doing repetitive analyses with Affymetrix GeneChips Description: Various wrapper functions that have been written to streamline the more common analyses that a core Biostatistician might see. biocViews: ReportWriting, Microarray, OneChannel, GeneExpression Author: James W. MacDonald Maintainer: James W. MacDonald VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/affycoretools git_branch: RELEASE_3_12 git_last_commit: c9779e4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affycoretools_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affycoretools_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affycoretools_1.62.0.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.html vignetteTitles: Creating annotated output with \Biocpkg{affycoretools} and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R suggestsMe: EnMCB dependencyCount: 184 Package: AffyExpress Version: 1.56.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: dff97fbd0f3b28ef60767fd18788a8b3 NeedsCompilation: no Title: Affymetrix Quality Assessment and Analysis Tool Description: The purpose of this package is to provide a comprehensive and easy-to-use tool for quality assessment and to identify differentially expressed genes in the Affymetrix gene expression data. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu , Xuejun Arthur Li Maintainer: Xuejun Arthur Li git_url: https://git.bioconductor.org/packages/AffyExpress git_branch: RELEASE_3_12 git_last_commit: e070858 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AffyExpress_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AffyExpress_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AffyExpress_1.56.0.tgz vignettes: vignettes/AffyExpress/inst/doc/AffyExpress.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyExpress/inst/doc/AffyExpress.R dependencyCount: 14 Package: affyILM Version: 1.42.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, Biobase Suggests: AffymetrixDataTestFiles, hgfocusprobe License: GPL-3 MD5sum: ee5d544d5ee6b960053fd28235c6c3e7 NeedsCompilation: no Title: Linear Model of background subtraction and the Langmuir isotherm Description: affyILM is a preprocessing tool which estimates gene expression levels for Affymetrix Gene Chips. Input from physical chemistry is employed to first background subtract intensities before calculating concentrations on behalf of the Langmuir model. biocViews: Microarray, OneChannel, Preprocessing Author: K. Myriam Kroll, Fabrice Berger, Gerard Barkema, Enrico Carlon Maintainer: Myriam Kroll and Fabrice Berger git_url: https://git.bioconductor.org/packages/affyILM git_branch: RELEASE_3_12 git_last_commit: b97b297 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affyILM_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affyILM_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affyILM_1.42.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyILM/inst/doc/affyILM.R dependencyCount: 23 Package: affyio Version: 1.60.0 Depends: R (>= 2.6.0) Imports: zlibbioc, methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: fed8be49f728011047085c7f117d37c0 NeedsCompilation: yes Title: Tools for parsing Affymetrix data files Description: Routines for parsing Affymetrix data files based upon file format information. Primary focus is on accessing the CEL and CDF file formats. biocViews: Microarray, DataImport, Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyio git_url: https://git.bioconductor.org/packages/affyio git_branch: RELEASE_3_12 git_last_commit: ee20528 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affyio_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affyio_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affyio_1.60.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPara, makecdfenv, SCAN.UPC, sscore importsMe: affy, affylmGUI, crlmm, ExiMiR, gcrma, oligo, oligoClasses, puma suggestsMe: BufferedMatrixMethods dependencyCount: 2 Package: affylmGUI Version: 1.64.0 Imports: grDevices, graphics, stats, utils, tcltk, tkrplot, limma, affy, affyio, affyPLM, gcrma, BiocGenerics, AnnotationDbi, BiocManager, R2HTML, xtable License: GPL (>=2) MD5sum: bff5631e9c9fcbba42d6fa3d3436a8dc NeedsCompilation: no Title: GUI for limma Package with Affymetrix Microarrays Description: A Graphical User Interface (GUI) for analysis of Affymetrix microarray gene expression data using the affy and limma packages. biocViews: GUI, GeneExpression, Transcription, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [cre,aut], Gordon Smyth [aut], Ken Simpson [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/affylmGUI/ git_url: https://git.bioconductor.org/packages/affylmGUI git_branch: RELEASE_3_12 git_last_commit: 85d506f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affylmGUI_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affylmGUI_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affylmGUI_1.64.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf, vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files, about.html, CustMenu.html, index.html, windowsFocus.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R dependencyCount: 44 Package: affyPara Version: 1.50.0 Depends: R (>= 2.5.0), methods, affy (>= 1.20.0), snow (>= 0.2-3), vsn (>= 3.6.0), aplpack (>= 1.1.1), affyio Suggests: affydata Enhances: affy License: GPL-3 MD5sum: a496d4c1c2190d788ca460f875c2bc02 NeedsCompilation: no Title: Parallelized preprocessing methods for Affymetrix Oligonucleotide Arrays Description: The package contains parallelized functions for exploratory oligonucleotide array analysis. The package is designed for large numbers of microarray data. biocViews: Microarray, Preprocessing Author: Markus Schmidberger , Esmeralda Vicedo , Ulrich Mansmann Maintainer: Markus Schmidberger URL: http://www.ibe.med.uni-muenchen.de git_url: https://git.bioconductor.org/packages/affyPara git_branch: RELEASE_3_12 git_last_commit: 7c6df5a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affyPara_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affyPara_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affyPara_1.50.0.tgz vignettes: vignettes/affyPara/inst/doc/affyPara.pdf, vignettes/affyPara/inst/doc/vsnStudy.pdf vignetteTitles: Parallelized affy functions for preprocessing, Simulation Study for VSN Add-On Normalization and Subsample Size hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPara/inst/doc/affyPara.R, vignettes/affyPara/inst/doc/vsnStudy.R dependencyCount: 50 Package: affyPLM Version: 1.66.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: i386, x64 MD5sum: 63a3e5296e25ebfb18808fd9c1426354 NeedsCompilation: yes Title: Methods for fitting probe-level models Description: A package that extends and improves the functionality of the base affy package. Routines that make heavy use of compiled code for speed. Central focus is on implementation of methods for fitting probe-level models and tools using these models. PLM based quality assessment tools. biocViews: Microarray, OneChannel, Preprocessing, QualityControl Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/affyPLM git_url: https://git.bioconductor.org/packages/affyPLM git_branch: RELEASE_3_12 git_last_commit: f0780c3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affyPLM_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affyPLM_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affyPLM_1.66.0.tgz vignettes: vignettes/affyPLM/inst/doc/AffyExtensions.pdf, vignettes/affyPLM/inst/doc/MAplots.pdf, vignettes/affyPLM/inst/doc/QualityAssess.pdf, vignettes/affyPLM/inst/doc/ThreeStep.pdf vignetteTitles: affyPLM: Fitting Probe Level Models, affyPLM: Advanced use of the MAplot function, affyPLM: Model Based QC Assessment of Affymetrix GeneChips, affyPLM: the threestep function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyPLM/inst/doc/AffyExtensions.R, vignettes/affyPLM/inst/doc/MAplots.R, vignettes/affyPLM/inst/doc/QualityAssess.R, vignettes/affyPLM/inst/doc/ThreeStep.R dependsOnMe: RefPlus, bapred importsMe: affylmGUI, affyQCReport, arrayQualityMetrics, mimager suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, piano, aroma.affymetrix dependencyCount: 22 Package: affyQCReport Version: 1.68.0 Depends: Biobase (>= 1.13.16), affy, lattice Imports: affy, affyPLM, Biobase, genefilter, graphics, grDevices, lattice, RColorBrewer, simpleaffy, stats, utils, xtable Suggests: tkWidgets (>= 1.5.23), affydata (>= 1.4.1) License: LGPL (>= 2) MD5sum: b5f02ceb8d83d2ab8561d6ca3dfa7159 NeedsCompilation: no Title: QC Report Generation for affyBatch objects Description: This package creates a QC report for an AffyBatch object. The report is intended to allow the user to quickly assess the quality of a set of arrays in an AffyBatch object. biocViews: Microarray,OneChannel,QualityControl Author: Craig Parman , Conrad Halling , Robert Gentleman Maintainer: Craig Parman git_url: https://git.bioconductor.org/packages/affyQCReport git_branch: RELEASE_3_12 git_last_commit: 34b42a1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/affyQCReport_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/affyQCReport_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/affyQCReport_1.68.0.tgz vignettes: vignettes/affyQCReport/inst/doc/affyQCReport.pdf vignetteTitles: affyQCReport: Methods for Generating Affymetrix QC Reports hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affyQCReport/inst/doc/affyQCReport.R suggestsMe: BiocCaseStudies dependencyCount: 57 Package: AffyRNADegradation Version: 1.36.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: 50dc949339f021c82cf93e73cc743b06 NeedsCompilation: no Title: Analyze and correct probe positional bias in microarray data due to RNA degradation Description: The package helps with the assessment and correction of RNA degradation effects in Affymetrix 3' expression arrays. The parameter d gives a robust and accurate measure of RNA integrity. The correction removes the probe positional bias, and thus improves comparability of samples that are affected by RNA degradation. biocViews: GeneExpression, Microarray, OneChannel, Preprocessing, QualityControl Author: Mario Fasold Maintainer: Mario Fasold git_url: https://git.bioconductor.org/packages/AffyRNADegradation git_branch: RELEASE_3_12 git_last_commit: 89662b9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AffyRNADegradation_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AffyRNADegradation_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AffyRNADegradation_1.36.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyRNADegradation/inst/doc/vignette.R dependencyCount: 13 Package: AGDEX Version: 1.38.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: 5b7586cd4ace352fd7b779f7f68f0a3f NeedsCompilation: no Title: Agreement of Differential Expression Analysis Description: A tool to evaluate agreement of differential expression for cross-species genomics biocViews: Microarray, Genetics, GeneExpression Author: Stan Pounds ; Cuilan Lani Gao Maintainer: Cuilan lani Gao git_url: https://git.bioconductor.org/packages/AGDEX git_branch: RELEASE_3_12 git_last_commit: 7e2c1f5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AGDEX_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AGDEX_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AGDEX_1.38.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AGDEX/inst/doc/AGDEX.R dependencyCount: 40 Package: aggregateBioVar Version: 1.0.0 Depends: R (>= 4.0) Imports: stats, methods, S4Vectors, SummarizedExperiment, SingleCellExperiment, Matrix, tibble, rlang Suggests: BiocStyle, magick, knitr, rmarkdown, testthat, BiocGenerics, DESeq2, magrittr, dplyr, ggplot2, cowplot, ggtext, RColorBrewer, pheatmap, viridis License: GPL-3 MD5sum: 970014a4c12acc26c41ec6b6037b65bf NeedsCompilation: no Title: Differential Gene Expression Analysis for Multi-subject scRNA-seq Description: For single cell RNA-seq data collected from more than one subject (e.g. biological sample or technical replicates), this package contains tools to summarize single cell gene expression profiles at the level of subject. A SingleCellExperiment object is taken as input and converted to a list of SummarizedExperiment objects, where each list element corresponds to an assigned cell type. The SummarizedExperiment objects contain aggregate gene-by-subject count matrices and inter-subject column metadata for individual subjects that can be processed using downstream bulk RNA-seq tools. biocViews: Software, SingleCell, RNASeq, Transcriptomics, Transcription, GeneExpression, DifferentialExpression Author: Jason Ratcliff [aut, cre] (), Andrew Thurman [aut], Michael Chimenti [ctb], Alejandro Pezzulo [ctb] Maintainer: Jason Ratcliff URL: https://github.com/jasonratcliff/aggregateBioVar VignetteBuilder: knitr BugReports: https://github.com/jasonratcliff/aggregateBioVar/issues git_url: https://git.bioconductor.org/packages/aggregateBioVar git_branch: RELEASE_3_12 git_last_commit: ca130de git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/aggregateBioVar_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/aggregateBioVar_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/aggregateBioVar_1.0.0.tgz vignettes: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.html vignetteTitles: Multi-subject scRNA-seq Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/aggregateBioVar/inst/doc/multi-subject-scRNA-seq.R dependencyCount: 40 Package: agilp Version: 3.22.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: fdc7edc2388e338b870d9641f94867da NeedsCompilation: no Title: Agilent expression array processing package Description: More about what it does (maybe more than one line) Author: Benny Chain Maintainer: Benny Chain git_url: https://git.bioconductor.org/packages/agilp git_branch: RELEASE_3_12 git_last_commit: 7d089d5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/agilp_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/agilp_3.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/agilp_3.22.0.tgz vignettes: vignettes/agilp/inst/doc/agilp_manual.pdf vignetteTitles: An R Package for processing expression microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/agilp/inst/doc/agilp_manual.R dependencyCount: 0 Package: AgiMicroRna Version: 2.40.0 Depends: R (>= 2.10),methods,Biobase,limma,affy (>= 1.22),preprocessCore,affycoretools Imports: Biobase Suggests: geneplotter,marray,gplots,gtools,gdata,codelink License: GPL-3 MD5sum: d622a4a728945657a3af50ca1133c207 NeedsCompilation: no Title: Processing and Differential Expression Analysis of Agilent microRNA chips Description: Processing and Analysis of Agilent microRNA data biocViews: Microarray, AgilentChip, OneChannel, Preprocessing, DifferentialExpression Author: Pedro Lopez-Romero Maintainer: Pedro Lopez-Romero git_url: https://git.bioconductor.org/packages/AgiMicroRna git_branch: RELEASE_3_12 git_last_commit: cfa4acb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AgiMicroRna_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AgiMicroRna_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AgiMicroRna_2.40.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.R dependencyCount: 185 Package: AIMS Version: 1.22.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d96a1df8ff126b86c99eec448d68e029 NeedsCompilation: no Title: AIMS : Absolute Assignment of Breast Cancer Intrinsic Molecular Subtype Description: This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data. biocViews: ImmunoOncology, Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS git_url: https://git.bioconductor.org/packages/AIMS git_branch: RELEASE_3_12 git_last_commit: 34a3897 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AIMS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AIMS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AIMS_1.22.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AIMS/inst/doc/AIMS.R dependsOnMe: genefu dependencyCount: 12 Package: ALDEx2 Version: 1.22.0 Depends: methods, stats, zCompositions Imports: BiocParallel, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, multtest Suggests: testthat, BiocStyle, knitr, rmarkdown License: file LICENSE MD5sum: 407754f1cacbc288534af0a0d301b526 NeedsCompilation: no Title: Analysis Of Differential Abundance Taking Sample Variation Into Account Description: A differential abundance analysis for the comparison of two or more conditions. Useful for analyzing data from standard RNA-seq or meta-RNA-seq assays as well as selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, optimized for three or more experimental replicates. The method infers biological and sampling variation to calculate the expected false discovery rate, given the variation, based on a Wilcoxon Rank Sum test and Welch's t-test (via aldex.ttest), a Kruskal-Wallis test (via aldex.kw), a generalized linear model (via aldex.glm), or a correlation test (via aldex.corr). All tests report p-values and Benjamini-Hochberg corrected p-values. biocViews: DifferentialExpression, RNASeq, Transcriptomics, GeneExpression, DNASeq, ChIPSeq, Bayesian, Sequencing, Software, Microbiome, Metagenomics, ImmunoOncology Author: Greg Gloor, Andrew Fernandes, Jean Macklaim, Arianne Albert, Matt Links, Thomas Quinn, Jia Rong Wu, Ruth Grace Wong, Brandon Lieng Maintainer: Greg Gloor URL: https://github.com/ggloor/ALDEx_bioc VignetteBuilder: knitr BugReports: https://github.com/ggloor/ALDEx_bioc/issues git_url: https://git.bioconductor.org/packages/ALDEx2 git_branch: RELEASE_3_12 git_last_commit: ac7f0ab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ALDEx2_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ALDEx2_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ALDEx2_1.22.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.pdf vignetteTitles: ANOVA-Like Differential Expression tool for high throughput sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R dependsOnMe: omicplotR suggestsMe: propr dependencyCount: 40 Package: alevinQC Version: 1.6.1 Depends: R (>= 4.0) Imports: rmarkdown (>= 2.5), tools, methods, ggplot2, GGally, dplyr, rjson, shiny, shinydashboard, DT, stats, utils, tximport (>= 1.17.4), cowplot, rlang Suggests: knitr, BiocStyle, testthat License: MIT + file LICENSE MD5sum: c5580782dde2c0ca120f359bde77cc85 NeedsCompilation: no Title: Generate QC Reports For Alevin Output Description: Generate QC reports summarizing the output from an alevin run. Reports can be generated as html or pdf files, or as shiny applications. biocViews: QualityControl, SingleCell Author: Charlotte Soneson [aut, cre] (), Avi Srivastava [aut] Maintainer: Charlotte Soneson URL: https://github.com/csoneson/alevinQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/alevinQC/issues git_url: https://git.bioconductor.org/packages/alevinQC git_branch: RELEASE_3_12 git_last_commit: 528a726 git_last_commit_date: 2021-02-02 Date/Publication: 2021-02-02 source.ver: src/contrib/alevinQC_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/alevinQC_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/alevinQC_1.6.1.tgz vignettes: vignettes/alevinQC/inst/doc/alevinqc.html vignetteTitles: alevinQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/alevinQC/inst/doc/alevinqc.R dependencyCount: 89 Package: AllelicImbalance Version: 1.28.0 Depends: R (>= 4.0.0), grid, GenomicRanges (>= 1.31.8), SummarizedExperiment (>= 0.2.0), GenomicAlignments (>= 1.15.6) Imports: methods, BiocGenerics, AnnotationDbi, BSgenome (>= 1.47.3), VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Rsamtools (>= 1.99.3), GenomicFeatures (>= 1.31.3), Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb, nlme Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 5543aa09b19da1478052ac1d7cac58e7 NeedsCompilation: no Title: Investigates Allele Specific Expression Description: Provides a framework for allelic specific expression investigation using RNA-seq data. biocViews: Genetics, Infrastructure, Sequencing Author: Jesper R Gadin, Lasse Folkersen Maintainer: Jesper R Gadin URL: https://github.com/pappewaio/AllelicImbalance VignetteBuilder: knitr BugReports: https://github.com/pappewaio/AllelicImbalance/issues git_url: https://git.bioconductor.org/packages/AllelicImbalance git_branch: RELEASE_3_12 git_last_commit: ac5d13c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AllelicImbalance_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AllelicImbalance_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AllelicImbalance_1.28.0.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.R dependencyCount: 143 Package: AlphaBeta Version: 1.4.0 Depends: R (>= 3.6.0) Imports: dplyr (>= 0.7), data.table (>= 1.10), stringr (>= 1.3), utils (>= 3.6.0), gtools (>= 3.8.0), optimx (>= 2018-7.10), expm (>= 0.999-4), stats (>= 3.6), BiocParallel (>= 1.18), igraph (>= 1.2.4), graphics (>= 3.6), ggplot2 (>= 3.2), grDevices (>= 3.6), plotly (>= 4.9) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 92be31dd6d18dcf094531d28ce23d544 NeedsCompilation: no Title: Computational inference of epimutation rates and spectra from high-throughput DNA methylation data in plants Description: AlphaBeta is a computational method for estimating epimutation rates and spectra from high-throughput DNA methylation data in plants. The method has been specifically designed to: 1. analyze 'germline' epimutations in the context of multi-generational mutation accumulation lines (MA-lines). 2. analyze 'somatic' epimutations in the context of plant development and aging. biocViews: Epigenetics, FunctionalGenomics, Genetics, MathematicalBiology Author: Yadollah Shahryary Dizaji [cre, aut], Frank Johannes [aut], Rashmi Hazarika [aut] Maintainer: Yadollah Shahryary Dizaji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlphaBeta git_branch: RELEASE_3_12 git_last_commit: d67f156 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AlphaBeta_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AlphaBeta_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AlphaBeta_1.4.0.tgz vignettes: vignettes/AlphaBeta/inst/doc/AlphaBeta.pdf vignetteTitles: AlphaBeta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlphaBeta/inst/doc/AlphaBeta.R dependencyCount: 78 Package: alpine Version: 1.16.0 Depends: R (>= 3.3) Imports: Biostrings, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, SummarizedExperiment, GenomicFeatures, speedglm, splines, graph, RBGL, stringr, stats, methods, graphics, GenomeInfoDb, S4Vectors Suggests: knitr, testthat, alpineData, rtracklayer, ensembldb, BSgenome.Hsapiens.NCBI.GRCh38, RColorBrewer License: GPL (>=2) MD5sum: 51d73baad1f928e13cab9594fb748722 NeedsCompilation: no Title: alpine Description: Fragment sequence bias modeling and correction for RNA-seq transcript abundance estimation. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, GeneExpression, Transcription, Coverage, BatchEffect, Normalization, Visualization, QualityControl Author: Michael Love, Rafael Irizarry Maintainer: Michael Love VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alpine git_branch: RELEASE_3_12 git_last_commit: aee3977 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/alpine_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/alpine_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/alpine_1.16.0.tgz vignettes: vignettes/alpine/inst/doc/alpine.html vignetteTitles: alpine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alpine/inst/doc/alpine.R dependencyCount: 93 Package: ALPS Version: 1.4.0 Depends: R (>= 3.6) Imports: assertthat, BiocParallel, ChIPseeker, corrplot, data.table, dplyr, GenomicRanges, GGally, genefilter, gghalves, ggplot2, ggseqlogo, Gviz, magrittr, org.Hs.eg.db, plyr, reshape2, rtracklayer, stats, stringr, tibble, tidyr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, utils Suggests: knitr, rmarkdown, ComplexHeatmap, circlize, testthat License: MIT + file LICENSE MD5sum: 388e2fa02e7b4618a2787ae6949e35c4 NeedsCompilation: no Title: AnaLysis routines for ePigenomicS data Description: The package provides analysis and publication quality visualization routines for genome-wide epigenomics data such as histone modification or transcription factor ChIP-seq, ATAC-seq, DNase-seq etc. The functions in the package can be used with any type of data that can be represented with bigwig files at any resolution. The goal of the ALPS is to provide analysis tools for most downstream analysis without leaving the R environment and most tools in the package require a minimal input that can be prepared with basic R, unix or excel skills. biocViews: Epigenetics, Sequencing, ChIPSeq, ATACSeq, Visualization, Transcription, HistoneModification Author: Venu Thatikonda, Natalie Jäger Maintainer: Venu Thatikonda URL: https://github.com/itsvenu/ALPS VignetteBuilder: knitr BugReports: https://github.com/itsvenu/ALPS/issues git_url: https://git.bioconductor.org/packages/ALPS git_branch: RELEASE_3_12 git_last_commit: efa0189 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ALPS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ALPS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ALPS_1.4.0.tgz vignettes: vignettes/ALPS/inst/doc/ALPS-vignette.html vignetteTitles: ALPS-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALPS/inst/doc/ALPS-vignette.R dependencyCount: 184 Package: AlpsNMR Version: 3.0.6 Depends: R (>= 4.0), dplyr (>= 0.7.5), future (>= 1.10.0), magrittr (>= 1.5) Imports: utils, graphics, stats, grDevices, signal (>= 0.7-6), assertthat (>= 0.2.0), rlang (>= 0.3.0.1), stringr (>= 1.3.1), tibble(>= 1.3.4), tidyr (>= 1.0.0), readxl (>= 1.1.0), plyr (>= 1.8.4), purrr (>= 0.2.5), glue (>= 1.2.0), reshape2 (>= 1.4.3), GGally (>= 1.4.0), mixOmics (>= 6.3.2), matrixStats (>= 0.54.0), writexl (>= 1.0), fs (>= 1.2.6), rmarkdown (>= 1.10), speaq (>= 2.4.0), htmltools (>= 0.3.6), ggrepel (>= 0.8.0), pcaPP (>= 1.9-73), furrr (>= 0.1.0), ggplot2 (>= 3.1.0), baseline (>= 1.2-1), zip (>= 2.0.4), tidyselect (>= 0.2.5), BiocParallel, SummarizedExperiment, S4Vectors Suggests: DT (>= 0.5), testthat (>= 2.0.0), plotly (>= 4.7.1), ChemoSpec, knitr License: file LICENSE MD5sum: ffe4d24845034865edfae20ba301be2d NeedsCompilation: no Title: Automated spectraL Processing System for NMR Description: Reads Bruker NMR data directories both zipped and unzipped. It provides automated and efficient signal processing for untargeted NMR metabolomics. It is able to interpolate the samples, detect outliers, exclude regions, normalize, detect peaks, align the spectra, integrate peaks, manage metadata and visualize the spectra. After spectra proccessing, it can apply multivariate analysis on extracted data. Efficient plotting with 1-D data is also available. Basic reading of 1D ACD/Labs exported JDX samples is also available. biocViews: Software, Preprocessing, Visualization, Classification, Cheminformatics, Metabolomics, DataImport Author: Ivan Montoliu Roura [aut], Sergio Oller Moreno [aut] (), Francisco Madrid Gambin [aut] (), Luis Fernandez [aut, cre] (), Héctor Gracia Cabrera [aut], Santiago Marco Colás [aut] (), Nestlé Institute of Health Sciences [cph], Institute for Bioengineering of Catalonia [cph] Maintainer: Luis Fernandez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AlpsNMR git_branch: RELEASE_3_12 git_last_commit: 18e74a2 git_last_commit_date: 2021-03-31 Date/Publication: 2021-03-31 source.ver: src/contrib/AlpsNMR_3.0.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/AlpsNMR_3.0.6.zip mac.binary.ver: bin/macosx/contrib/4.0/AlpsNMR_3.0.6.tgz vignettes: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.html vignetteTitles: Introduction to AlpsNMR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AlpsNMR/inst/doc/introduction-to-alpsnmr.R dependencyCount: 149 Package: alsace Version: 1.26.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice, knitr License: GPL (>= 2) MD5sum: a14b095aa81f32539f1ea059a5f07348 NeedsCompilation: no Title: ALS for the Automatic Chemical Exploration of mixtures Description: Alternating Least Squares (or Multivariate Curve Resolution) for analytical chemical data, in particular hyphenated data where the first direction is a retention time axis, and the second a spectral axis. Package builds on the basic als function from the ALS package and adds functionality for high-throughput analysis, including definition of time windows, clustering of profiles, retention time correction, etcetera. Author: Ron Wehrens Maintainer: Ron Wehrens URL: https://github.com/rwehrens/alsace VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/alsace git_branch: RELEASE_3_12 git_last_commit: 40a7640 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/alsace_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/alsace_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/alsace_1.26.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: tofsims dependencyCount: 8 Package: altcdfenvs Version: 2.52.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 122819753c9cc68d05d1754a8972003d NeedsCompilation: no Title: alternative CDF environments (aka probeset mappings) Description: Convenience data structures and functions to handle cdfenvs biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Annotation, ProprietaryPlatforms, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/altcdfenvs git_branch: RELEASE_3_12 git_last_commit: 21329ab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/altcdfenvs_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/altcdfenvs_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/altcdfenvs_2.52.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, Modifying existing CDF environments to make alternative CDF environments, Alternative CDF environments for 2(or more)-genomes chips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/altcdfenvs/inst/doc/altcdfenvs.R, vignettes/altcdfenvs/inst/doc/modify.R, vignettes/altcdfenvs/inst/doc/ngenomeschips.R importsMe: Harshlight dependencyCount: 23 Package: AMARETTO Version: 1.6.0 Depends: R (>= 3.6), impute, doParallel, grDevices, dplyr, methods, ComplexHeatmap Imports: callr (>= 3.0.0.9001), Matrix, Rcpp, BiocFileCache, DT, MultiAssayExperiment, circlize, curatedTCGAData, foreach, glmnet, httr, limma, matrixStats, readr, reshape2, tibble, rmarkdown, graphics, grid, parallel, stats, knitr, ggplot2, gridExtra, utils LinkingTo: Rcpp Suggests: testthat, MASS, knitr License: Apache License (== 2.0) + file LICENSE MD5sum: c8d80cc092bd6f2c6ed90ed49ccc6d9c NeedsCompilation: no Title: Regulatory Network Inference and Driver Gene Evaluation using Integrative Multi-Omics Analysis and Penalized Regression Description: Integrating an increasing number of available multi-omics cancer data remains one of the main challenges to improve our understanding of cancer. One of the main challenges is using multi-omics data for identifying novel cancer driver genes. We have developed an algorithm, called AMARETTO, that integrates copy number, DNA methylation and gene expression data to identify a set of driver genes by analyzing cancer samples and connects them to clusters of co-expressed genes, which we define as modules. We applied AMARETTO in a pancancer setting to identify cancer driver genes and their modules on multiple cancer sites. AMARETTO captures modules enriched in angiogenesis, cell cycle and EMT, and modules that accurately predict survival and molecular subtypes. This allows AMARETTO to identify novel cancer driver genes directing canonical cancer pathways. biocViews: StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,Transcription,Preprocessing,BatchEffect,DataImport,mRNAMicroarray,MicroRNAArray,Regression,Clustering,RNASeq,CopyNumberVariation,Sequencing,Microarray,Normalization,Network,Bayesian,ExonArray,OneChannel,TwoChannel,ProprietaryPlatforms,AlternativeSplicing,DifferentialExpression,DifferentialSplicing,GeneSetEnrichment,MultipleComparison,QualityControl,TimeCourse Author: Jayendra Shinde, Celine Everaert, Shaimaa Bakr, Mohsen Nabian, Jishu Xu, Vincent Carey, Nathalie Pochet and Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMARETTO git_branch: RELEASE_3_12 git_last_commit: 0deca3f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AMARETTO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AMARETTO_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AMARETTO_1.6.0.tgz vignettes: vignettes/AMARETTO/inst/doc/amaretto.html vignetteTitles: "1. Introduction" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/AMARETTO/inst/doc/amaretto.R dependencyCount: 150 Package: AMOUNTAIN Version: 1.16.0 Depends: R (>= 3.3.0) Imports: stats Suggests: BiocStyle, qgraph, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 882b00225eb678432a5f10455420d6eb NeedsCompilation: yes Title: Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach Description: A pure data-driven gene network, weighted gene co-expression network (WGCN) could be constructed only from expression profile. Different layers in such networks may represent different time points, multiple conditions or various species. AMOUNTAIN aims to search active modules in multi-layer WGCN using a continuous optimization approach. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, Shan He, Zhisong Pan and Guyu Hu Maintainer: Dong Li SystemRequirements: gsl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AMOUNTAIN git_branch: RELEASE_3_12 git_last_commit: 2bf8cd6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AMOUNTAIN_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AMOUNTAIN_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AMOUNTAIN_1.16.0.tgz vignettes: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AMOUNTAIN/inst/doc/AMOUNTAIN.R importsMe: MODA dependencyCount: 1 Package: amplican Version: 1.12.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.22.0), Biostrings (>= 2.44.2), data.table (>= 1.10.4-3) Imports: Rcpp, utils (>= 3.4.1), S4Vectors (>= 0.14.3), ShortRead (>= 1.34.0), IRanges (>= 2.10.2), GenomicRanges (>= 1.28.4), GenomeInfoDb (>= 1.12.2), BiocParallel (>= 1.10.1), gtable (>= 0.2.0), gridExtra (>= 2.2.1), ggplot2 (>= 2.2.0), ggthemes (>= 3.4.0), waffle (>= 0.7.0), stringr (>= 1.2.0), stats (>= 3.4.1), matrixStats (>= 0.52.2), Matrix (>= 1.2-10), dplyr (>= 0.7.2), rmarkdown (>= 1.6), knitr (>= 1.16), clusterCrit (>= 1.2.7) LinkingTo: Rcpp Suggests: testthat, BiocStyle, GenomicAlignments License: GPL-3 Archs: i386, x64 MD5sum: 3e3f9928d738b3e40a4c3c407e54c734 NeedsCompilation: yes Title: Automated analysis of CRISPR experiments Description: `amplican` performs alignment of the amplicon reads, normalizes gathered data, calculates multiple statistics (e.g. cut rates, frameshifts) and presents results in form of aggregated reports. Data and statistics can be broken down by experiments, barcodes, user defined groups, guides and amplicons allowing for quick identification of potential problems. biocViews: ImmunoOncology, Technology, Alignment, qPCR, CRISPR Author: Kornel Labun [aut], Eivind Valen [cph, cre] Maintainer: Eivind Valen URL: https://github.com/valenlab/amplican VignetteBuilder: knitr BugReports: https://github.com/valenlab/amplican/issues git_url: https://git.bioconductor.org/packages/amplican git_branch: RELEASE_3_12 git_last_commit: f593f59 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/amplican_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/amplican_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/amplican_1.12.0.tgz vignettes: vignettes/amplican/inst/doc/amplicanFAQ.html, vignettes/amplican/inst/doc/amplicanOverview.html, vignettes/amplican/inst/doc/example_amplicon_report.html, vignettes/amplican/inst/doc/example_barcode_report.html, vignettes/amplican/inst/doc/example_group_report.html, vignettes/amplican/inst/doc/example_guide_report.html, vignettes/amplican/inst/doc/example_id_report.html, vignettes/amplican/inst/doc/example_index.html vignetteTitles: amplican FAQ, amplican overview, example amplicon_report report, example barcode_report report, example group_report report, example guide_report report, example id_report report, example index report hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/amplican/inst/doc/amplicanOverview.R, vignettes/amplican/inst/doc/example_amplicon_report.R, vignettes/amplican/inst/doc/example_barcode_report.R, vignettes/amplican/inst/doc/example_group_report.R, vignettes/amplican/inst/doc/example_guide_report.R, vignettes/amplican/inst/doc/example_id_report.R, vignettes/amplican/inst/doc/example_index.R dependencyCount: 99 Package: Anaquin Version: 2.14.0 Depends: R (>= 3.3), ggplot2 (>= 2.2.0) Imports: ggplot2, ROCR, knitr, qvalue, locfit, methods, stats, utils, plyr, DESeq2 Suggests: RUnit, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: a74e9b36afa787b300c18eda79287f3b NeedsCompilation: no Title: Statistical analysis of sequins Description: The project is intended to support the use of sequins (synthetic sequencing spike-in controls) owned and made available by the Garvan Institute of Medical Research. The goal is to provide a standard open source library for quantitative analysis, modelling and visualization of spike-in controls. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, GeneExpression, Software Author: Ted Wong Maintainer: Ted Wong URL: www.sequin.xyz VignetteBuilder: knitr BugReports: https://github.com/student-t/RAnaquin/issues git_url: https://git.bioconductor.org/packages/Anaquin git_branch: RELEASE_3_12 git_last_commit: d0a34c9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Anaquin_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Anaquin_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Anaquin_2.14.0.tgz vignettes: vignettes/Anaquin/inst/doc/Anaquin.pdf vignetteTitles: Anaquin - Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Anaquin/inst/doc/Anaquin.R dependencyCount: 106 Package: ANCOMBC Version: 1.0.5 Imports: stats, MASS, nloptr, Rdpack, phyloseq, microbiome Suggests: knitr, microbiome, tidyverse, testthat, DT, magrittr, qwraps2 (>= 0.5.0) License: Artistic-2.0 MD5sum: 52251bf22bda0e8fce84df9dac3062e4 NeedsCompilation: no Title: Analysis of compositions of microbiomes with bias correction Description: ANCOMBC is a package for normalizing the microbial observed abundance data due to unequal sampling fractions across samples, and identifying taxa (e.g. phyla, families, genera, species, etc.) that are differentially abundant with respect to the covariate of interest (e.g. study groups) between two or more groups of multiple samples. biocViews: DifferentialExpression, Microbiome, Normalization, Sequencing, Software Author: Huang Lin [cre, aut] (), Shyamal Das Peddada [aut] () Maintainer: Huang Lin URL: https://github.com/FrederickHuangLin/ANCOMBC VignetteBuilder: knitr BugReports: https://github.com/FrederickHuangLin/ANCOMBC/issues git_url: https://git.bioconductor.org/packages/ANCOMBC git_branch: RELEASE_3_12 git_last_commit: c2662df git_last_commit_date: 2021-03-09 Date/Publication: 2021-03-09 source.ver: src/contrib/ANCOMBC_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/ANCOMBC_1.0.5.zip mac.binary.ver: bin/macosx/contrib/4.0/ANCOMBC_1.0.5.tgz vignettes: vignettes/ANCOMBC/inst/doc/ANCOMBC.html vignetteTitles: ANCOMBC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANCOMBC/inst/doc/ANCOMBC.R dependencyCount: 87 Package: AneuFinder Version: 1.18.0 Depends: R (>= 3.5), GenomicRanges, ggplot2, cowplot, AneuFinderData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, Rsamtools, bamsignals, DNAcopy, ecp, Biostrings, GenomicAlignments, reshape2, ggdendro, ggrepel, ReorderCluster, mclust Suggests: knitr, BiocStyle, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: i386, x64 MD5sum: d67cbe167b89bee51a352b7c07026c81 NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: AneuFinder implements functions for copy-number detection, breakpoint detection, and karyotype and heterogeneity analysis in single-cell whole genome sequencing and strand-seq data. biocViews: ImmunoOncology, Software, Sequencing, SingleCell, CopyNumberVariation, GenomicVariation, HiddenMarkovModel, WholeGenome Author: Aaron Taudt, Bjorn Bakker, David Porubsky Maintainer: Aaron Taudt URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AneuFinder git_branch: RELEASE_3_12 git_last_commit: 76ec9af git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AneuFinder_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AneuFinder_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AneuFinder_1.18.0.tgz vignettes: vignettes/AneuFinder/inst/doc/AneuFinder.pdf vignetteTitles: A quick introduction to AneuFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AneuFinder/inst/doc/AneuFinder.R dependencyCount: 89 Package: ANF Version: 1.12.0 Imports: igraph, Biobase, survival, MASS, stats, RColorBrewer Suggests: ExperimentHub, SNFtool, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 2c4efae5e0b793084a6c2c81bacdf761 NeedsCompilation: no Title: Affinity Network Fusion for Complex Patient Clustering Description: This package is used for complex patient clustering by integrating multi-omic data through affinity network fusion. biocViews: Clustering, GraphAndNetwork, Network Author: Tianle Ma, Aidong Zhang Maintainer: Tianle Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ANF git_branch: RELEASE_3_12 git_last_commit: 39a01f6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ANF_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ANF_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ANF_1.12.0.tgz vignettes: vignettes/ANF/inst/doc/ANF.html vignetteTitles: Cancer Patient Clustering with ANF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ANF/inst/doc/ANF.R suggestsMe: HarmonizedTCGAData dependencyCount: 18 Package: animalcules Version: 1.6.0 Depends: R (>= 4.0.0) Imports: assertthat, shiny, shinyjs, DESeq2, caret, plotly, ggplot2, rentrez, reshape2, covr, ape, vegan, dplyr, magrittr, MultiAssayExperiment, SummarizedExperiment, S4Vectors (>= 0.23.19), XML, forcats, scales, lattice, glmnet, tsne, DMwR, plotROC, DT, reactable, utils, limma, methods, stats, tibble, biomformat, umap, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: f334a80a985374fd1e2b391775f79982 NeedsCompilation: no Title: Interactive microbiome analysis toolkit Description: animalcules is an R package for utilizing up-to-date data analytics, visualization methods, and machine learning models to provide users an easy-to-use interactive microbiome analysis framework. It can be used as a standalone software package or users can explore their data with the accompanying interactive R Shiny application. Traditional microbiome analysis such as alpha/beta diversity and differential abundance analysis are enhanced, while new methods like biomarker identification are introduced by animalcules. Powerful interactive and dynamic figures generated by animalcules enable users to understand their data better and discover new insights. biocViews: Microbiome, Metagenomics, Coverage, Visualization Author: Yue Zhao [aut, cre] (), Anthony Federico [aut] (), W. Evan Johnson [aut] () Maintainer: Yue Zhao URL: https://github.com/compbiomed/animalcules VignetteBuilder: knitr BugReports: https://github.com/compbiomed/animalcules/issues git_url: https://git.bioconductor.org/packages/animalcules git_branch: RELEASE_3_12 git_last_commit: 6a2f3dd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/animalcules_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/animalcules_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/animalcules_1.6.0.tgz vignettes: vignettes/animalcules/inst/doc/animalcules.html vignetteTitles: animalcules hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/animalcules/inst/doc/animalcules.R dependencyCount: 168 Package: annaffy Version: 1.62.0 Depends: R (>= 2.5.0), methods, Biobase, GO.db, KEGG.db Imports: AnnotationDbi (>= 0.1.15), DBI Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: 4c2a1b19cd8db0b88d93467426b604f4 NeedsCompilation: no Title: Annotation tools for Affymetrix biological metadata Description: Functions for handling data from Bioconductor Affymetrix annotation data packages. Produces compact HTML and text reports including experimental data and URL links to many online databases. Allows searching biological metadata using various criteria. biocViews: OneChannel, Microarray, Annotation, GO, Pathways, ReportWriting Author: Colin A. Smith Maintainer: Colin A. Smith git_url: https://git.bioconductor.org/packages/annaffy git_branch: RELEASE_3_12 git_last_commit: ad9c37e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/annaffy_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/annaffy_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/annaffy_1.62.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annaffy/inst/doc/annaffy.R dependsOnMe: webbioc importsMe: a4Base suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, metaMA dependencyCount: 28 Package: annmap Version: 1.32.0 Depends: R (>= 2.15.0), methods, GenomicRanges Imports: DBI, RMySQL (>= 0.6-0), digest, Biobase, grid, lattice, Rsamtools, genefilter, IRanges, BiocGenerics Suggests: RUnit, rjson, Gviz License: GPL-2 MD5sum: c58d08654ea3c3ddfeb5dcf0fbd16aa5 NeedsCompilation: no Title: Genome annotation and visualisation package pertaining to Affymetrix arrays and NGS analysis. Description: annmap provides annotation mappings for Affymetrix exon arrays and coordinate based queries to support deep sequencing data analysis. Database access is hidden behind the API which provides a set of functions such as genesInRange(), geneToExon(), exonDetails(), etc. Functions to plot gene architecture and BAM file data are also provided. Underlying data are from Ensembl. biocViews: Annotation, Microarray, OneChannel, ReportWriting, Transcription, Visualization Author: Tim Yates Maintainer: Chris Wirth URL: http://annmap.cruk.manchester.ac.uk git_url: https://git.bioconductor.org/packages/annmap git_branch: RELEASE_3_12 git_last_commit: 4e527dd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/annmap_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/annmap_1.32.0.tgz vignettes: vignettes/annmap/inst/doc/annmap.pdf, vignettes/annmap/inst/doc/cookbook.pdf, vignettes/annmap/inst/doc/INSTALL.pdf vignetteTitles: annmap primer, The Annmap Cookbook, annmap installation instruction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 65 Package: annotate Version: 1.68.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), httr Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), IRanges, rae230a.db, rae230aprobe, tkWidgets, GO.db, org.Hs.eg.db, org.Mm.eg.db, hom.Hs.inp.db, humanCHRLOC, Rgraphviz, RUnit, License: Artistic-2.0 MD5sum: c06830f9bcfc0ae87023bfe03acb319e NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/annotate git_branch: RELEASE_3_12 git_last_commit: 98cdb12 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/annotate_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/annotate_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/annotate_1.68.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useHomology.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using the homology package, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotate/inst/doc/annotate.R, vignettes/annotate/inst/doc/chromLoc.R, vignettes/annotate/inst/doc/GOusage.R, vignettes/annotate/inst/doc/prettyOutput.R, vignettes/annotate/inst/doc/query.R, vignettes/annotate/inst/doc/useDataPkgs.R, vignettes/annotate/inst/doc/useHomology.R, vignettes/annotate/inst/doc/useProbeInfo.R dependsOnMe: ChromHeatMap, GeneAnswers, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, PCpheno, phenoTest, PREDA, RpsiXML, sampleClassifier, ScISI, SemDist, Neve2006, PREDAsampledata importsMe: CAFE, Category, categoryCompare, CNEr, codelink, debrowser, DrugVsDisease, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, MGFR, phenoTest, qpgraph, ScISI, systemPipeR, tigre, UMI4Cats, geneExpressionFromGEO, GOxploreR suggestsMe: BiocCaseStudies, BiocGenerics, biomaRt, GenomicRanges, GSAR, GSEAlm, hmdbQuery, maigesPack, metagenomeSeq, MLP, pageRank, pcxn, RnBeads, siggenes, SummarizedExperiment, adme16cod.db, ag.db, ath1121501.db, bovine.db, canine.db, canine2.db, celegans.db, chicken.db, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, drosgenome1.db, drosophila2.db, ecoli2.db, GGHumanMethCancerPanelv1.db, h10kcod.db, h20kcod.db, hcg110.db, hgfocus.db, hgu133a.db, hgu133a2.db, hgu133b.db, hgu133plus2.db, hgu219.db, hgu95a.db, hgu95av2.db, hgu95b.db, hgu95c.db, hgu95d.db, hgu95e.db, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133b.db, hu35ksuba.db, hu35ksubb.db, hu35ksubc.db, hu35ksubd.db, hu6800.db, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, LAPOINTE.db, lumiHumanAll.db, lumiMouseAll.db, lumiRatAll.db, m10kcod.db, m20kcod.db, mgu74a.db, mgu74av2.db, mgu74b.db, mgu74bv2.db, mgu74c.db, mgu74cv2.db, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, miRBaseVersions.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430b.db, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse4302.db, mouse430a2.db, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubb.db, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, mwgcod.db, Norway981.db, nugohs1a520180.db, nugomm1a520177.db, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, POCRCannotation.db, porcine.db, r10kcod.db, rae230a.db, rae230b.db, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat2302.db, rgu34a.db, rgu34b.db, rgu34c.db, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, ri16cod.db, RnAgilentDesign028282.db, rnu34.db, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rwgcod.db, SHDZ.db, u133x3p.db, xlaevis.db, yeast2.db, ygs98.db, zebrafish.db, clValid, maGUI, MOSS, optCluster dependencyCount: 37 Package: AnnotationDbi Version: 1.52.0 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.29.2), Biobase (>= 1.17.0), IRanges Imports: DBI, RSQLite, S4Vectors (>= 0.9.25) Suggests: hgu95av2.db, GO.db, org.Sc.sgd.db, org.At.tair.db, KEGG.db, RUnit, TxDb.Hsapiens.UCSC.hg19.knownGene, hom.Hs.inp.db, org.Hs.eg.db, reactome.db, AnnotationForge, graph, EnsDb.Hsapiens.v75, BiocStyle, knitr License: Artistic-2.0 MD5sum: 847d056d51f439e6b4aa2a72c8216e26 NeedsCompilation: no Title: Manipulation of SQLite-based annotations in Bioconductor Description: Implements a user-friendly interface for querying SQLite-based annotation data packages. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Hervé Pagès, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationDbi VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik BugReports: https://github.com/Bioconductor/AnnotationDbi/issues git_url: https://git.bioconductor.org/packages/AnnotationDbi git_branch: RELEASE_3_12 git_last_commit: c4e0ca9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AnnotationDbi_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnnotationDbi_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AnnotationDbi_1.52.0.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: 2. (Deprecated) How to use bimaps from the ".db" annotation packages, 1. Introduction To Bioconductor Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.R, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.R dependsOnMe: annotate, AnnotationForge, AnnotationFuncs, ASpli, attract, Category, chimera, ChromHeatMap, customProDB, deco, DEXSeq, EGSEA, eisa, EpiTxDb, ExpressionView, GenomicFeatures, goProfiles, GSReg, ipdDb, miRNAtap, MLP, OrganismDbi, pathRender, proBAMr, RpsiXML, safe, SemDist, topGO, adme16cod.db, ag.db, agprobe, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501probe, barley1probe, bovine.db, bovine.db0, bovineprobe, bsubtilisprobe, canine.db, canine.db0, canine2.db, canine2probe, canineprobe, celegans.db, celegansprobe, chicken.db, chicken.db0, chickenprobe, chimp.db0, citrusprobe, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottonprobe, DO.db, drosgenome1.db, drosgenome1probe, drosophila2.db, drosophila2probe, ecoli2.db, ecoli2probe, ecoliasv2probe, ecoliK12.db0, ecoliprobe, ecoliSakai.db0, fly.db0, GGHumanMethCancerPanelv1.db, GO.db, h10kcod.db, h20kcod.db, hcg110.db, hcg110probe, hgfocus.db, hgfocusprobe, hgu133a.db, hgu133a2.db, hgu133a2probe, hgu133aprobe, hgu133atagprobe, hgu133b.db, hgu133bprobe, hgu133plus2.db, hgu133plus2probe, hgu219.db, hgu219probe, hgu95a.db, hgu95aprobe, hgu95av2.db, hgu95av2probe, hgu95b.db, hgu95bprobe, hgu95c.db, hgu95cprobe, hgu95d.db, hgu95dprobe, hgu95e.db, hgu95eprobe, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hom.At.inp.db, hom.Ce.inp.db, hom.Dm.inp.db, hom.Dr.inp.db, hom.Hs.inp.db, hom.Mm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133aprobe, hthgu133b.db, hthgu133bprobe, hthgu133pluspmprobe, htmg430aprobe, htmg430bprobe, htmg430pmprobe, htrat230pmprobe, htratfocusprobe, hu35ksuba.db, hu35ksubaprobe, hu35ksubb.db, hu35ksubbprobe, hu35ksubc.db, hu35ksubcprobe, hu35ksubd.db, hu35ksubdprobe, hu6800.db, hu6800probe, huex10stprobeset.db, huex10sttranscriptcluster.db, HuExExonProbesetLocation, HuExExonProbesetLocationHg18, HuExExonProbesetLocationHg19, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1probe, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, IlluminaHumanMethylation450kprobe, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, KEGG.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizeprobe, malaria.db0, medicagoprobe, mgu74a.db, mgu74aprobe, mgu74av2.db, mgu74av2probe, mgu74b.db, mgu74bprobe, mgu74bv2.db, mgu74bv2probe, mgu74c.db, mgu74cprobe, mgu74cv2.db, mgu74cv2probe, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, mirna10probe, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430aprobe, moe430b.db, moe430bprobe, moex10stprobeset.db, moex10sttranscriptcluster.db, MoExExonProbesetLocation, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1probe, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302probe, mouse430a2.db, mouse430a2probe, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubaprobe, mu11ksubb.db, mu11ksubbprobe, Mu15v1.db, mu19ksuba.db, mu19ksubb.db, mu19ksubc.db, Mu22v3.db, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180probe, nugomm1a520177.db, nugomm1a520177probe, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Mxanthus.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, paeg1aprobe, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelesprobe, POCRCannotation.db, poplarprobe, porcine.db, porcineprobe, primeviewprobe, r10kcod.db, rae230a.db, rae230aprobe, rae230b.db, rae230bprobe, raex10stprobeset.db, raex10sttranscriptcluster.db, RaExExonProbesetLocation, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1probe, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302probe, rattoxfxprobe, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34aprobe, rgu34b.db, rgu34bprobe, rgu34c.db, rgu34cprobe, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesusprobe, ri16cod.db, riceprobe, RnAgilentDesign028282.db, rnu34.db, rnu34probe, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34probe, rwgcod.db, saureusprobe, SHDZ.db, soybeanprobe, sugarcaneprobe, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test3probe, tomatoprobe, u133x3p.db, u133x3pprobe, vitisviniferaprobe, wheatprobe, worm.db0, xenopus.db0, xenopuslaevisprobe, xlaevis.db, xlaevis2probe, xtropicalisprobe, yeast.db0, yeast2.db, yeast2probe, ygs98.db, ygs98probe, zebrafish.db, zebrafish.db0, zebrafishprobe, GGdata, tinesath1probe, rnaseqGene importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, annotatr, artMS, beadarray, BiocSet, biomaRt, BioNet, biovizBase, bumphunter, BUSpaRse, CancerMutationAnalysis, CARNIVAL, categoryCompare, ccmap, cellity, chimeraviz, chipenrich, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, consensusDE, crisprseekplus, CrispRVariants, crossmeta, csaw, debrowser, derfinder, DominoEffect, DOSE, EDASeq, eegc, eisaR, EnrichmentBrowser, ensembldb, erma, esATAC, ExpressionView, FRASER, GA4GHshiny, gage, GAPGOM, genefilter, geneplotter, GeneTonic, geneXtendeR, GenVisR, GGBase, ggbio, GGtools, GlobalAncova, globaltest, GmicR, GOfuncR, GOpro, GOSemSim, goseq, GOSim, goSTAG, GOstats, goTools, gpart, gQTLstats, graphite, GSEABase, GSEABenchmarkeR, Gviz, gwascat, ideal, IMAS, InPAS, interactiveDisplay, isomiRs, IVAS, karyoploteR, LRBaseDbi, lumi, mAPKL, MCbiclust, mdgsa, MeSHDbi, meshes, MesKit, MetaboSignal, methyAnalysis, methylGSA, methylumi, MIGSA, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, MSEADbi, MSnID, multiGSEA, multiMiR, NanoMethViz, NanoStringQCPro, nanotatoR, Onassis, ontoProc, ORFik, Organism.dplyr, PADOG, pathview, pcaExplorer, PCpheno, PGA, phantasus, phenoTest, proActiv, psichomics, pwOmics, qpgraph, QuasR, ReactomePA, REDseq, regutools, restfulSE, rgsepd, ribosomeProfilingQC, RNAAgeCalc, rrvgo, rTRM, SBGNview, ScISI, scPipe, scruff, scTensor, SGSeq, signatureSearch, simplifyEnrichment, singleCellTK, SLGI, SMITE, SpidermiR, StarBioTrek, SubCellBarCode, TCGAutils, tenXplore, tigre, trackViewer, trena, tximeta, Ularcirc, UniProt.ws, VariantAnnotation, VariantFiltering, ViSEAGO, adme16cod.db, ag.db, agcdf, anopheles.db0, arabidopsis.db0, ath1121501.db, ath1121501cdf, barley1cdf, bovine.db, bovine.db0, bovinecdf, bsubtiliscdf, canine.db, canine.db0, canine2.db, canine2cdf, caninecdf, celegans.db, celeganscdf, chicken.db, chicken.db0, chickencdf, chimp.db0, citruscdf, clariomdhumanprobeset.db, clariomdhumantranscriptcluster.db, clariomshumanhttranscriptcluster.db, clariomshumantranscriptcluster.db, clariomsmousehttranscriptcluster.db, clariomsmousetranscriptcluster.db, clariomsrathttranscriptcluster.db, clariomsrattranscriptcluster.db, cottoncdf, cyp450cdf, DO.db, drosgenome1.db, drosgenome1cdf, drosophila2.db, drosophila2cdf, ecoli2.db, ecoli2cdf, ecoliasv2cdf, ecolicdf, ecoliK12.db0, ecoliSakai.db0, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, fly.db0, GenomicState, GGHumanMethCancerPanelv1.db, GO.db, gp53cdf, h10kcod.db, h20kcod.db, hcg110.db, hcg110cdf, hgfocus.db, hgfocuscdf, hgu133a.db, hgu133a2.db, hgu133a2cdf, hgu133acdf, hgu133atagcdf, hgu133b.db, hgu133bcdf, hgu133plus2.db, hgu133plus2cdf, hgu219.db, hgu219cdf, hgu95a.db, hgu95acdf, hgu95av2.db, hgu95av2cdf, hgu95b.db, hgu95bcdf, hgu95c.db, hgu95ccdf, hgu95d.db, hgu95dcdf, hgu95e.db, hgu95ecdf, hguatlas13k.db, hgubeta7.db, hguDKFZ31.db, hgug4100a.db, hgug4101a.db, hgug4110b.db, hgug4111a.db, hgug4112a.db, hgug4845a.db, hguqiagenv3.db, hi16cod.db, hivprtplus2cdf, hom.At.inp.db, hom.Ce.inp.db, hom.Dm.inp.db, hom.Dr.inp.db, hom.Hs.inp.db, hom.Mm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db, Homo.sapiens, hs25kresogen.db, Hs6UG171.db, HsAgilentDesign026652.db, Hspec, hspeccdf, hta20probeset.db, hta20transcriptcluster.db, hthgu133a.db, hthgu133acdf, hthgu133b.db, hthgu133bcdf, hthgu133pluspmcdf, htmg430acdf, htmg430bcdf, htmg430pmcdf, htrat230pmcdf, htratfocuscdf, hu35ksuba.db, hu35ksubacdf, hu35ksubb.db, hu35ksubbcdf, hu35ksubc.db, hu35ksubccdf, hu35ksubd.db, hu35ksubdcdf, hu6800.db, hu6800cdf, hu6800subacdf, hu6800subbcdf, hu6800subccdf, hu6800subdcdf, huex10stprobeset.db, huex10sttranscriptcluster.db, hugene10stprobeset.db, hugene10sttranscriptcluster.db, hugene10stv1cdf, hugene11stprobeset.db, hugene11sttranscriptcluster.db, hugene20stprobeset.db, hugene20sttranscriptcluster.db, hugene21stprobeset.db, hugene21sttranscriptcluster.db, human.db0, HuO22.db, hwgcod.db, IlluminaHumanMethylation27k.db, illuminaHumanv1.db, illuminaHumanv2.db, illuminaHumanv2BeadID.db, illuminaHumanv3.db, illuminaHumanv4.db, illuminaHumanWGDASLv3.db, illuminaHumanWGDASLv4.db, illuminaMousev1.db, illuminaMousev1p1.db, illuminaMousev2.db, illuminaRatv1.db, indac.db, JazaeriMetaData.db, KEGG.db, LAPOINTE.db, lumiHumanAll.db, lumiHumanIDMapping, lumiMouseAll.db, lumiMouseIDMapping, lumiRatAll.db, lumiRatIDMapping, m10kcod.db, m20kcod.db, maizecdf, malaria.db0, medicagocdf, mgu74a.db, mgu74acdf, mgu74av2.db, mgu74av2cdf, mgu74b.db, mgu74bcdf, mgu74bv2.db, mgu74bv2cdf, mgu74c.db, mgu74ccdf, mgu74cv2.db, mgu74cv2cdf, mguatlas5k.db, mgug4104a.db, mgug4120a.db, mgug4121a.db, mgug4122a.db, mi16cod.db, mirbase.db, miRBaseVersions.db, mirna102xgaincdf, mirna10cdf, mirna20cdf, miRNAtap.db, mm24kresogen.db, MmAgilentDesign026655.db, moe430a.db, moe430acdf, moe430b.db, moe430bcdf, moex10stprobeset.db, moex10sttranscriptcluster.db, mogene10stprobeset.db, mogene10sttranscriptcluster.db, mogene10stv1cdf, mogene11stprobeset.db, mogene11sttranscriptcluster.db, mogene20stprobeset.db, mogene20sttranscriptcluster.db, mogene21stprobeset.db, mogene21sttranscriptcluster.db, mouse.db0, mouse4302.db, mouse4302cdf, mouse430a2.db, mouse430a2cdf, mpedbarray.db, mta10probeset.db, mta10transcriptcluster.db, mu11ksuba.db, mu11ksubacdf, mu11ksubb.db, mu11ksubbcdf, Mu15v1.db, mu19ksuba.db, mu19ksubacdf, mu19ksubb.db, mu19ksubbcdf, mu19ksubc.db, mu19ksubccdf, Mu22v3.db, mu6500subacdf, mu6500subbcdf, mu6500subccdf, mu6500subdcdf, Mus.musculus, mwgcod.db, Norway981.db, nugohs1a520180.db, nugohs1a520180cdf, nugomm1a520177.db, nugomm1a520177cdf, OperonHumanV3.db, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, paeg1acdf, PartheenMetaData.db, pedbarrayv10.db, pedbarrayv9.db, PFAM.db, pig.db0, plasmodiumanophelescdf, POCRCannotation.db, PolyPhen.Hsapiens.dbSNP131, poplarcdf, porcine.db, porcinecdf, primeviewcdf, r10kcod.db, rae230a.db, rae230acdf, rae230b.db, rae230bcdf, raex10stprobeset.db, raex10sttranscriptcluster.db, ragene10stprobeset.db, ragene10sttranscriptcluster.db, ragene10stv1cdf, ragene11stprobeset.db, ragene11sttranscriptcluster.db, ragene20stprobeset.db, ragene20sttranscriptcluster.db, ragene21stprobeset.db, ragene21sttranscriptcluster.db, rat.db0, rat2302.db, rat2302cdf, rattoxfxcdf, Rattus.norvegicus, reactome.db, rgu34a.db, rgu34acdf, rgu34b.db, rgu34bcdf, rgu34c.db, rgu34ccdf, rguatlas4k.db, rgug4105a.db, rgug4130a.db, rgug4131a.db, rhesus.db0, rhesuscdf, ri16cod.db, ricecdf, RmiR.Hs.miRNA, RmiR.hsa, RnAgilentDesign028282.db, rnu34.db, rnu34cdf, Roberts2005Annotation.db, rta10probeset.db, rta10transcriptcluster.db, rtu34.db, rtu34cdf, rwgcod.db, saureuscdf, SHDZ.db, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, soybeancdf, sugarcanecdf, targetscan.Hs.eg.db, targetscan.Mm.eg.db, test1cdf, test2cdf, test3cdf, tomatocdf, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, u133aaofav2cdf, u133x3p.db, u133x3pcdf, vitisviniferacdf, wheatcdf, worm.db0, xenopus.db0, xenopuslaeviscdf, xlaevis.db, xlaevis2cdf, xtropicaliscdf, ye6100subacdf, ye6100subbcdf, ye6100subccdf, ye6100subdcdf, yeast.db0, yeast2.db, yeast2cdf, ygs98.db, ygs98cdf, zebrafish.db, zebrafish.db0, zebrafishcdf, celldex, chipenrich.data, DeSousa2013, ppiData, scRNAseq, aliases2entrez, BiSEp, DIscBIO, jetset, MARVEL, MetaIntegrator, pathfindR, prioGene, pulseTD, RobLoxBioC, WGCNA suggestsMe: APAlyzer, bambu, BiocCaseStudies, BiocGenerics, BiocOncoTK, CellTrails, cicero, cola, DEGreport, edgeR, enrichplot, esetVis, FELLA, FGNet, fgsea, GA4GHclient, gCrisprTools, GeneAnswers, GeneRegionScan, GenomicRanges, iSEEu, limma, MutationalPatterns, oligo, OUTRIDER, piano, Pigengene, pRoloc, qcmetrics, R3CPET, recount, RGalaxy, sigPathway, SummarizedExperiment, TFutils, tidybulk, topconfects, weitrix, wiggleplotr, BloodCancerMultiOmics2017, curatedAdipoChIP, RforProteomics, conos, cRegulome, DGCA, pagoda2, rliger dependencyCount: 25 Package: AnnotationFilter Version: 1.14.0 Depends: R (>= 3.4.0) Imports: utils, methods, GenomicRanges, lazyeval Suggests: BiocStyle, knitr, testthat, RSQLite, org.Hs.eg.db License: Artistic-2.0 MD5sum: 9713a855b78fed1a44f2a89227f02033 NeedsCompilation: no Title: Facilities for Filtering Bioconductor Annotation Resources Description: This package provides class and other infrastructure to implement filters for manipulating Bioconductor annotation resources. The filters will be used by ensembldb, Organism.dplyr, and other packages. biocViews: Annotation, Infrastructure, Software Author: Martin Morgan [aut], Johannes Rainer [aut], Joachim Bargsten [ctb], Daniel Van Twisk [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/AnnotationFilter VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationFilter/issues git_url: https://git.bioconductor.org/packages/AnnotationFilter git_branch: RELEASE_3_12 git_last_commit: 6ee3a13 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AnnotationFilter_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnnotationFilter_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AnnotationFilter_1.14.0.tgz vignettes: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.html vignetteTitles: Facilities for Filtering Bioconductor Annotation resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFilter/inst/doc/AnnotationFilter.R dependsOnMe: chimeraviz, ensembldb, Organism.dplyr importsMe: biovizBase, BUSpaRse, ggbio, QFeatures, TVTB suggestsMe: TFutils, TxRegInfra, wiggleplotr dependencyCount: 18 Package: AnnotationForge Version: 1.32.0 Depends: R (>= 3.5.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors, RCurl Suggests: biomaRt, httr, GenomeInfoDb (>= 1.17.1), Biostrings, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, hom.Hs.inp.db, GO.db, BiocStyle, knitr, BiocManager License: Artistic-2.0 MD5sum: 412436fe2c2dfc0929bca1bfeabaee08 NeedsCompilation: no Title: Tools for building SQLite-based annotation data packages Description: Provides code for generating Annotation packages and their databases. Packages produced are intended to be used with AnnotationDbi. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/AnnotationForge VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationForge/issues git_url: https://git.bioconductor.org/packages/AnnotationForge git_branch: RELEASE_3_12 git_last_commit: 3d17c2a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AnnotationForge_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnnotationForge_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AnnotationForge_1.32.0.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html vignetteTitles: Creating probe packages, AnnotationForge: Creating select Interfaces for custom Annotation resources, SQLForge: An easy way to create a new annotation package with a standard database schema., Making New Organism Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationForge/inst/doc/makeProbePackage.R, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.R, vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.R, vignettes/AnnotationForge/inst/doc/SQLForge.R importsMe: AnnotationHubData, GOstats, ViSEAGO, GGHumanMethCancerPanelv1.db suggestsMe: AnnotationDbi, AnnotationHub dependencyCount: 29 Package: AnnotationFuncs Version: 1.40.0 Depends: R (>= 2.7.0), AnnotationDbi Imports: DBI Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db License: GPL-2 MD5sum: f63968ee8a66966da0e0a84acf5c2994 NeedsCompilation: no Title: Annotation translation functions Description: Functions for handling translating between different identifieres using the Biocore Data Team data-packages (e.g. org.Bt.eg.db). biocViews: AnnotationData, Software Author: Stefan McKinnon Edwards Maintainer: Stefan McKinnon Edwards URL: http://www.iysik.com/index.php?page=annotation-functions git_url: https://git.bioconductor.org/packages/AnnotationFuncs git_branch: RELEASE_3_12 git_last_commit: 90cc52b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AnnotationFuncs_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnnotationFuncs_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AnnotationFuncs_1.40.0.tgz vignettes: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf vignetteTitles: Annotation mapping functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.R importsMe: bioCancer dependencyCount: 26 Package: AnnotationHub Version: 2.22.1 Depends: BiocGenerics (>= 0.15.10), BiocFileCache (>= 1.5.1) Imports: utils, methods, grDevices, RSQLite, BiocManager, BiocVersion, curl, rappdirs, AnnotationDbi (>= 1.31.19), S4Vectors, interactiveDisplayBase, httr, yaml, dplyr Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment, ExperimentHub, gdsfmt, rmarkdown Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: 999a1c414c84f396c38e17ab5fdafe45 NeedsCompilation: yes Title: Client to access AnnotationHub resources Description: This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, Ensembl) can be discovered. The resource includes metadata about each resource, e.g., a textual description, tags, and date of modification. The client creates and manages a local cache of files retrieved by the user, helping with quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/AnnotationHub/issues git_url: https://git.bioconductor.org/packages/AnnotationHub git_branch: RELEASE_3_12 git_last_commit: 73fe9c7 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/AnnotationHub_2.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnnotationHub_2.22.1.zip mac.binary.ver: bin/macosx/contrib/4.0/AnnotationHub_2.22.1.tgz vignettes: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/CreateAnAnnotationPackage.html, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.html vignetteTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, AnnotationHub: Creating An AnnotationHub Package, Troubleshooting The Hubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.R, vignettes/AnnotationHub/inst/doc/AnnotationHub.R, vignettes/AnnotationHub/inst/doc/TroubleshootingTheCache.R dependsOnMe: adductomicsR, AnnotationHubData, ExperimentHub, hipathia, ipdDb, ProteomicsAnnotationHubData, EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3, EuPathDB, GenomicState, org.Mxanthus.db, phastCons30way.UCSC.hg38, curatedMetagenomicData, MetaGxBreast, MetaGxOvarian, NestLink, sesameData, tartare, annotation, sequencing importsMe: annotatr, circRNAprofiler, customCMPdb, dmrseq, ENCODExplorer, GenomicScores, GSEABenchmarkeR, MSnID, psichomics, pwOmics, regutools, REMP, restfulSE, scmeth, scTensor, TSRchitect, tximeta, Ularcirc, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, grasp2db, metaboliteIDmapping, adductData, alpineData, biscuiteerData, celldex, chipseqDBData, curatedTCGAData, depmap, DropletTestFiles, FieldEffectCrc, HCAData, HMP16SData, HMP2Data, mcsurvdata, MetaGxPancreas, scRNAseq, SingleCellMultiModal, spatialLIBD, TENxBrainData, TENxBUSData, TENxPBMCData, TCGAWorkflow suggestsMe: BgeeCall, Chicago, CINdex, clusterProfiler, CNVRanger, COCOA, DNAshapeR, dupRadar, ensembldb, epiNEM, EpiTxDb, epivizrChart, epivizrData, GenomicRanges, GOSemSim, gwascat, maser, MIRA, MSnbase, multicrispr, OrganismDbi, recountmethylation, VariantAnnotation, AHEnsDbs, ENCODExplorerData, HarmonizedTCGAData dependencyCount: 75 Package: AnnotationHubData Version: 1.20.2 Depends: R (>= 3.2.2), methods, utils, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub (>= 2.15.15) Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocManager, biocViews, AnnotationDbi, Biobase, Biostrings, DBI, GenomeInfoDb (>= 1.15.4), OrganismDbi, RSQLite, AnnotationForge, futile.logger (>= 1.3.0), XML, RCurl Suggests: RUnit, knitr, BiocStyle, grasp2db, GenomeInfoDbData, rmarkdown License: Artistic-2.0 MD5sum: 9cd89c7bc91e4c4ea7766ff5182dbbf2 NeedsCompilation: no Title: Transform public data resources into Bioconductor Data Structures Description: These recipes convert a wide variety and a growing number of public bioinformatic data sets into easily-used standard Bioconductor data structures. biocViews: DataImport Author: Martin Morgan [ctb], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Paul Shannon [ctb], Lori Shepherd [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnnotationHubData git_branch: RELEASE_3_12 git_last_commit: 60ff8b9 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/AnnotationHubData_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnnotationHubData_1.20.2.zip mac.binary.ver: bin/macosx/contrib/4.0/AnnotationHubData_1.20.2.tgz vignettes: vignettes/AnnotationHubData/inst/doc/CreateAnAnnotationPackage.html, vignettes/AnnotationHubData/inst/doc/IntroductionToAnnotationHubData.html vignetteTitles: AnnotationHub: Creating An AnnotationHub Package, Introduction to AnnotationHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ExperimentHubData importsMe: AHEnsDbs, EuPathDB suggestsMe: GenomicState dependencyCount: 117 Package: annotationTools Version: 1.64.0 Imports: Biobase, stats Suggests: BiocStyle License: GPL MD5sum: ee15858c805ec7da39d7ae7a3e65b4e2 NeedsCompilation: no Title: Annotate microarrays and perform cross-species gene expression analyses using flat file databases Description: Functions to annotate microarrays, find orthologs, and integrate heterogeneous gene expression profiles using annotation and other molecular biology information available as flat file database (plain text files). biocViews: Microarray, Annotation Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/annotationTools git_branch: RELEASE_3_12 git_last_commit: 8b4f650 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/annotationTools_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/annotationTools_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/annotationTools_1.64.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R dependencyCount: 7 Package: annotatr Version: 1.16.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, AnnotationHub, dplyr, GenomicFeatures, GenomicRanges, GenomeInfoDb (>= 1.10.3), ggplot2, IRanges, methods, readr, regioneR, reshape2, rtracklayer, S4Vectors (>= 0.23.10), stats, utils Suggests: BiocStyle, devtools, knitr, org.Dm.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, rmarkdown, roxygen2, testthat, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.refGene License: GPL-3 MD5sum: ae5b221e3e477b331ed97a2bd2b88042 NeedsCompilation: no Title: Annotation of Genomic Regions to Genomic Annotations Description: Given a set of genomic sites/regions (e.g. ChIP-seq peaks, CpGs, differentially methylated CpGs or regions, SNPs, etc.) it is often of interest to investigate the intersecting genomic annotations. Such annotations include those relating to gene models (promoters, 5'UTRs, exons, introns, and 3'UTRs), CpGs (CpG islands, CpG shores, CpG shelves), or regulatory sequences such as enhancers. The annotatr package provides an easy way to summarize and visualize the intersection of genomic sites/regions with genomic annotations. biocViews: Software, Annotation, GenomeAnnotation, FunctionalGenomics, Visualization Author: Raymond G. Cavalcante [aut, cre], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://www.github.com/rcavalcante/annotatr/issues git_url: https://git.bioconductor.org/packages/annotatr git_branch: RELEASE_3_12 git_last_commit: e5b2b37 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/annotatr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/annotatr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/annotatr_1.16.0.tgz vignettes: vignettes/annotatr/inst/doc/annotatr-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotatr/inst/doc/annotatr-vignette.R importsMe: dmrseq, scmeth dependencyCount: 132 Package: anota Version: 1.38.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: e8659cc703c94586999656d714cccca9 NeedsCompilation: no Title: ANalysis Of Translational Activity (ANOTA). Description: Genome wide studies of translational control is emerging as a tool to study verious biological conditions. The output from such analysis is both the mRNA level (e.g. cytosolic mRNA level) and the levl of mRNA actively involved in translation (the actively translating mRNA level) for each mRNA. The standard analysis of such data strives towards identifying differential translational between two or more sample classes - i.e. differences in actively translated mRNA levels that are independent of underlying differences in cytosolic mRNA levels. This package allows for such analysis using partial variances and the random variance model. As 10s of thousands of mRNAs are analyzed in parallell the library performs a number of tests to assure that the data set is suitable for such analysis. biocViews: GeneExpression, DifferentialExpression, Microarray, Sequencing Author: Ola Larsson , Nahum Sonenberg , Robert Nadon Maintainer: Ola Larsson git_url: https://git.bioconductor.org/packages/anota git_branch: RELEASE_3_12 git_last_commit: 22f55f9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/anota_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/anota_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/anota_1.38.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota/inst/doc/anota.R dependsOnMe: tRanslatome dependencyCount: 51 Package: anota2seq Version: 1.12.0 Depends: R (>= 3.4.0), methods Imports: multtest,qvalue,limma,DESeq2,edgeR,RColorBrewer, grDevices, graphics, stats, utils, SummarizedExperiment Suggests: BiocStyle,knitr License: GPL-3 MD5sum: d9eb935903371b9f35a9d6b3e3fa823b NeedsCompilation: no Title: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq Description: anota2seq provides analysis of translational efficiency and differential expression analysis for polysome-profiling and ribosome-profiling studies (two or more sample classes) quantified by RNA sequencing or DNA-microarray. Polysome-profiling and ribosome-profiling typically generate data for two RNA sources; translated mRNA and total mRNA. Analysis of differential expression is used to estimate changes within each RNA source (i.e. translated mRNA or total mRNA). Analysis of translational efficiency aims to identify changes in translation efficiency leading to altered protein levels that are independent of total mRNA levels (i.e. changes in translated mRNA that are independent of levels of total mRNA) or buffering, a mechanism regulating translational efficiency so that protein levels remain constant despite fluctuating total mRNA levels (i.e. changes in total mRNA that are independent of levels of translated mRNA). anota2seq applies analysis of partial variance and the random variance model to fulfill these tasks. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Microarray,GenomeWideAssociation, BatchEffect, Normalization, RNASeq, Sequencing, GeneRegulation, Regression Author: Christian Oertlin , Julie Lorent , Ola Larsson Maintainer: Christian Oertlin , Julie Lorent VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/anota2seq git_branch: RELEASE_3_12 git_last_commit: 4435c78 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/anota2seq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/anota2seq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/anota2seq_1.12.0.tgz vignettes: vignettes/anota2seq/inst/doc/anota2seq.pdf vignetteTitles: Generally applicable transcriptome-wide analysis of translational efficiency using anota2seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/anota2seq/inst/doc/anota2seq.R dependencyCount: 98 Package: antiProfiles Version: 1.30.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: d865919be4b15669bb59804f99fcbbd7 NeedsCompilation: no Title: Implementation of gene expression anti-profiles Description: Implements gene expression anti-profiles as described in Corrada Bravo et al., BMC Bioinformatics 2012, 13:272 doi:10.1186/1471-2105-13-272. biocViews: GeneExpression,Classification Author: Hector Corrada Bravo, Rafael A. Irizarry and Jeffrey T. Leek Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/antiProfiles git_url: https://git.bioconductor.org/packages/antiProfiles git_branch: RELEASE_3_12 git_last_commit: 7247cce git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/antiProfiles_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/antiProfiles_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/antiProfiles_1.30.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/antiProfiles/inst/doc/antiProfiles.R dependencyCount: 9 Package: AnVIL Version: 1.2.1 Depends: R (>= 3.6), dplyr Imports: stats, utils, methods, futile.logger, jsonlite, httr, curl, rapiclient (>= 0.1.3), tibble, tidyselect, rlang, BiocManager Suggests: knitr, rmarkdown, testthat, withr, readr License: Artistic-2.0 MD5sum: dd337b534380ef6a9ef37e34bf859e2e NeedsCompilation: no Title: Bioconductor on the AnVIL compute environment Description: The AnVIL is cloud computing resource developed in part by the National Human Genome Research Institute. The AnVIL package provides end-user and devloper functionality. For the end-user, AnVIL provides fast binary package installation, utitlities for working with Terra / AnVIL table and data resources, and convenient functions for file movement to and from Google cloud storage. For developers, AnVIL provides programatic access to the Terra, Leonardo, Dockstore, and Gen3 RESTful programming interface, including helper functions to transform JSON responses to more formats more amenable to manipulation in R. biocViews: Infrastructure Author: Martin Morgan [aut, cre], Nitesh Turaga [aut], BJ Stubbs [ctb], Vincent Carey [ctb], Marcel Ramos [ctb], Sweta Gopaulakrishnan [ctb], Valerie Obenchain [ctb] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVIL git_branch: RELEASE_3_12 git_last_commit: 16197ca git_last_commit_date: 2021-04-29 Date/Publication: 2021-04-29 source.ver: src/contrib/AnVIL_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnVIL_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/AnVIL_1.2.1.tgz vignettes: vignettes/AnVIL/inst/doc/BiocDockstore.html, vignettes/AnVIL/inst/doc/Introduction.html vignetteTitles: Dockstore and Bioconductor for AnVIL, Introduction to the AnVIL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVIL/inst/doc/BiocDockstore.R, vignettes/AnVIL/inst/doc/Introduction.R dependsOnMe: cBioPortalData, HCABrowser, HCAMatrixBrowser importsMe: AnVILPublish dependencyCount: 37 Package: AnVILBilling Version: 1.0.1 Depends: R (>= 4.0) Imports: methods, DT, shiny, bigrquery, shinytoastr, DBI, magrittr, dplyr, lubridate, plotly, ggplot2 Suggests: testthat, knitr, BiocStyle License: Artistic-2.0 MD5sum: a681decb8e5842422c077ef0dd490b2d NeedsCompilation: no Title: Provide functions to retrieve and report on usage expenses in NHGRI AnVIL (anvilproject.org). Description: AnVILBilling helps monitor AnVIL-related costs in R, using queries to a BigQuery table to which costs are exported daily. Functions are defined to help categorize tasks and associated expenditures, and to visualize and explore expense profiles over time. This package will be expanded to help users estimate costs for specific task sets. biocViews: Infrastructure, Software Author: BJ Stubbs [aut], Vince Carey [aut, cre] Maintainer: Vince Carey VignetteBuilder: knitr BugReports: https://github.com/vjcitn/AnVILBilling/issues git_url: https://git.bioconductor.org/packages/AnVILBilling git_branch: RELEASE_3_12 git_last_commit: 5bda8a2 git_last_commit_date: 2020-11-19 Date/Publication: 2020-11-19 source.ver: src/contrib/AnVILBilling_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnVILBilling_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/AnVILBilling_1.0.1.tgz vignettes: vignettes/AnVILBilling/inst/doc/billing.html vignetteTitles: Software for reckoning AnVIL/terra usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILBilling/inst/doc/billing.R dependencyCount: 89 Package: AnVILPublish Version: 1.0.0 Imports: AnVIL, httr, rmarkdown, whisker, tools, utils, stats, Suggests: knitr, BiocStyle, BiocManager License: Artistic-2.0 MD5sum: 834623540e07e2600dd7ab2c2dd98db7 NeedsCompilation: no Title: Publish Packages and Other Resources to AnVIL Workspaces Description: Use this package to create or update AnVIL workspaces from resources such as R / Bioconductor packages. The metadata about the package (e.g., select information from the package DESCRIPTION file and from vignette YAML headings) are used to populate the 'DASHBOARD'. Vignettes are translated to python notebooks ready for evaluation in AnVIL. biocViews: Infrastructure, Software Author: Martin Morgan [aut, cre] () Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AnVILPublish git_branch: RELEASE_3_12 git_last_commit: fe3b8cb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AnVILPublish_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AnVILPublish_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AnVILPublish_1.0.0.tgz vignettes: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.html vignetteTitles: Publishing R / Bioconductor packages to AnVIL Workspaces hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnVILPublish/inst/doc/AnVILPublishIntro.R dependencyCount: 51 Package: APAlyzer Version: 1.4.0 Depends: R (>= 3.5.0) Imports: GenomicRanges, GenomicFeatures, GenomicAlignments, DESeq, ggrepel, SummarizedExperiment, Rsubread, stats, ggplot2, methods, rtracklayer, ensembldb, VariantAnnotation, dplyr, tidyr, repmis Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, AnnotationDbi, TBX20BamSubset, Rsamtools, testthat License: LGPL-3 MD5sum: 25b1ffe4ca0418c58094b0265653d3c8 NeedsCompilation: no Title: A toolkit for APA analysis using RNA-seq data Description: Perform 3'UTR APA, Intronic APA and gene expression analysis using RNA-seq data. biocViews: Sequencing, RNASeq, DifferentialExpression, GeneExpression, GeneRegulation, Annotation, DataImport, Software Author: Ruijia Wang [cre, aut] (), Bin Tian [aut] Maintainer: Ruijia Wang URL: https://github.com/RJWANGbioinfo/APAlyzer/ VignetteBuilder: knitr BugReports: https://github.com/RJWANGbioinfo/APAlyzer/issues git_url: https://git.bioconductor.org/packages/APAlyzer git_branch: RELEASE_3_12 git_last_commit: 4247a57 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/APAlyzer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/APAlyzer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/APAlyzer_1.4.0.tgz vignettes: vignettes/APAlyzer/inst/doc/APAlyzer.html vignetteTitles: APAlyzer: toolkit for RNA-seq APA analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/APAlyzer/inst/doc/APAlyzer.R dependencyCount: 121 Package: apComplex Version: 2.56.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: bad7c657681efe5f797836e71814057c NeedsCompilation: no Title: Estimate protein complex membership using AP-MS protein data Description: Functions to estimate a bipartite graph of protein complex membership using AP-MS data. biocViews: ImmunoOncology, NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/apComplex git_branch: RELEASE_3_12 git_last_commit: 1054e71 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/apComplex_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/apComplex_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/apComplex_2.56.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apComplex/inst/doc/apComplex.R dependsOnMe: ScISI suggestsMe: BiocCaseStudies dependencyCount: 33 Package: apeglm Version: 1.12.0 Imports: emdbook, SummarizedExperiment, GenomicRanges, methods, stats, utils, Rcpp LinkingTo: Rcpp, RcppEigen, RcppNumerical Suggests: DESeq2, airway, knitr, rmarkdown, testthat License: GPL-2 Archs: i386, x64 MD5sum: 4d99d808bceafeddcdd095c82d59d493 NeedsCompilation: yes Title: Approximate posterior estimation for GLM coefficients Description: apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, GeneExpression, Bayesian Author: Anqi Zhu [aut, cre], Joshua Zitovsky [ctb], Joseph Ibrahim [aut], Michael Love [aut] Maintainer: Anqi Zhu VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/apeglm git_branch: RELEASE_3_12 git_last_commit: e18a461 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/apeglm_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/apeglm_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/apeglm_1.12.0.tgz vignettes: vignettes/apeglm/inst/doc/apeglm.html vignetteTitles: Effect size estimation with apeglm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/apeglm/inst/doc/apeglm.R dependsOnMe: rnaseqGene importsMe: debrowser, DiffBind suggestsMe: bambu, BRGenomics, DESeq2, fishpond, NanoporeRNASeq dependencyCount: 37 Package: appreci8R Version: 1.8.0 Imports: shiny, shinyjs, DT, VariantAnnotation, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens, SNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, rsnps, Biostrings, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.hs37d5, MafDb.gnomADex.r2.1.hs37d5, COSMIC.67, rentrez, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137, seqinr, openxlsx, Rsamtools, stringr, utils, stats, GenomicRanges, S4Vectors, GenomicFeatures, IRanges, GenomicScores, SummarizedExperiment Suggests: GO.db, org.Hs.eg.db License: LGPL-3 MD5sum: 8fba4ab7a10cc431fe22ab26ee500c6d NeedsCompilation: no Title: appreci8R: an R/Bioconductor package for filtering SNVs and short indels with high sensitivity and high PPV Description: The appreci8R is an R version of our appreci8-algorithm - A Pipeline for PREcise variant Calling Integrating 8 tools. Variant calling results of our standard appreci8-tools (GATK, Platypus, VarScan, FreeBayes, LoFreq, SNVer, samtools and VarDict), as well as up to 5 additional tools is combined, evaluated and filtered. biocViews: VariantDetection, GeneticVariability, SNP, VariantAnnotation, Sequencing, Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/appreci8R git_branch: RELEASE_3_12 git_last_commit: 078ead0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/appreci8R_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/appreci8R_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/appreci8R_1.8.0.tgz vignettes: vignettes/appreci8R/inst/doc/appreci8R.pdf vignetteTitles: Using appreci8R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/appreci8R/inst/doc/appreci8R.R dependencyCount: 152 Package: aroma.light Version: 3.20.0 Depends: R (>= 2.15.2) Imports: stats, R.methodsS3 (>= 1.7.1), R.oo (>= 1.23.0), R.utils (>= 2.9.0), matrixStats (>= 0.55.0) Suggests: princurve (>= 2.1.4) License: GPL (>= 2) MD5sum: c653c33f5827d8038e514c542de169ad NeedsCompilation: no Title: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types Description: Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes. biocViews: Infrastructure, Microarray, OneChannel, TwoChannel, MultiChannel, Visualization, Preprocessing Author: Henrik Bengtsson [aut, cre, cph], Pierre Neuvial [ctb], Aaron Lun [ctb] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, https://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues git_url: https://git.bioconductor.org/packages/aroma.light git_branch: RELEASE_3_12 git_last_commit: 02cde7f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/aroma.light_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/aroma.light_3.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/aroma.light_3.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq, scone, PSCBS suggestsMe: TIN, aroma.affymetrix, aroma.cn, aroma.core dependencyCount: 8 Package: ArrayExpress Version: 1.50.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: 8902ac3f4e92a60d514058e750d3e340 NeedsCompilation: no Title: Access the ArrayExpress Microarray Database at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet Description: Access the ArrayExpress Repository at EBI and build Bioconductor data structures: ExpressionSet, AffyBatch, NChannelSet biocViews: Microarray, DataImport, OneChannel, TwoChannel Author: Audrey Kauffmann, Ibrahim Emam, Michael Schubert Maintainer: Suhaib Mohammed git_url: https://git.bioconductor.org/packages/ArrayExpress git_branch: RELEASE_3_12 git_last_commit: 3a8e996 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ArrayExpress_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ArrayExpress_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ArrayExpress_1.50.0.tgz vignettes: vignettes/ArrayExpress/inst/doc/ArrayExpress.pdf vignetteTitles: ArrayExpress: Import and convert ArrayExpress data sets into R object hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpress/inst/doc/ArrayExpress.R dependsOnMe: DrugVsDisease, maEndToEnd suggestsMe: Hiiragi2013, bapred dependencyCount: 56 Package: ArrayExpressHTS Version: 1.40.0 Depends: sampling, Rsamtools (>= 1.99.0), snow Imports: Biobase, BiocGenerics, Biostrings, DESeq, GenomicRanges, Hmisc, IRanges (>= 2.13.11), R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rhtslib (>= 1.15.3) License: Artistic License 2.0 MD5sum: 2c15364d9346d886e44a2be87173f584 NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: ImmunoOncology, RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/ArrayExpressHTS git_branch: RELEASE_3_12 git_last_commit: 8e951b9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ArrayExpressHTS_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/ArrayExpressHTS_1.40.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.R dependencyCount: 139 Package: arrayMvout Version: 1.48.0 Depends: R (>= 2.6.0), tools, methods, utils, parody, Biobase, affy, lumi Imports: simpleaffy, mdqc, affyContam, Suggests: MAQCsubset, mvoutData, lumiBarnes, affyPLM, affydata, hgu133atagcdf License: Artistic-2.0 MD5sum: edfab1003511948e0744a0441705f4a7 NeedsCompilation: no Title: multivariate outlier detection for expression array QA Description: This package supports the application of diverse quality metrics to AffyBatch instances, summarizing these metrics via PCA, and then performing parametric outlier detection on the PCs to identify aberrant arrays with a fixed Type I error rate biocViews: Infrastructure, Microarray, QualityControl Author: Z. Gao, A. Asare, R. Wang, V. Carey Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/arrayMvout git_branch: RELEASE_3_12 git_last_commit: 3d9d8e4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/arrayMvout_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/arrayMvout_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/arrayMvout_1.48.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayMvout/inst/doc/arrayMvout.R dependencyCount: 158 Package: arrayQuality Version: 1.68.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: b84c6d7fe7381e7d4b34581fb30bca5e NeedsCompilation: no Title: Assessing array quality on spotted arrays Description: Functions for performing print-run and array level quality assessment. biocViews: Microarray,TwoChannel,QualityControl,Visualization Author: Agnes Paquet and Jean Yee Hwa Yang Maintainer: Agnes Paquet URL: http://arrays.ucsf.edu/ git_url: https://git.bioconductor.org/packages/arrayQuality git_branch: RELEASE_3_12 git_last_commit: e40f17d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/arrayQuality_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/arrayQuality_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/arrayQuality_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: arrayQualityMetrics Version: 3.46.0 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, utils, vsn (>= 3.23.3), XML, svglite Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: d4c53b78030f1c1e3961bc43e96ea876 NeedsCompilation: no Title: Quality metrics report for microarray data sets Description: This package generates microarray quality metrics reports for data in Bioconductor microarray data containers (ExpressionSet, NChannelSet, AffyBatch). One and two color array platforms are supported. biocViews: Microarray, QualityControl, OneChannel, TwoChannel, ReportWriting Author: Audrey Kauffmann, Wolfgang Huber Maintainer: Mike Smith VignetteBuilder: knitr BugReports: https://github.com/grimbough/arrayQualityMetrics/issues git_url: https://git.bioconductor.org/packages/arrayQualityMetrics git_branch: RELEASE_3_12 git_last_commit: 2bc0f4e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/arrayQualityMetrics_3.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/arrayQualityMetrics_3.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/arrayQualityMetrics_3.46.0.tgz vignettes: vignettes/arrayQualityMetrics/inst/doc/aqm.pdf, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.pdf vignetteTitles: Advanced topics: Customizing arrayQualityMetrics reports and programmatic processing of the output, Introduction: microarray quality assessment with arrayQualityMetrics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/arrayQualityMetrics/inst/doc/aqm.R, vignettes/arrayQualityMetrics/inst/doc/arrayQualityMetrics.R dependsOnMe: maEndToEnd importsMe: EGAD dependencyCount: 124 Package: ArrayTools Version: 1.50.0 Depends: R (>= 2.7.0), affy (>= 1.23.4), Biobase (>= 2.5.5), methods Imports: affy, Biobase, graphics, grDevices, limma, methods, stats, utils, xtable Suggests: simpleaffy, R2HTML, affydata, affyPLM, genefilter, annaffy, gcrma, hugene10sttranscriptcluster.db License: LGPL (>= 2.0) MD5sum: f90dd1a6994808f25d57fd13b31ff169 NeedsCompilation: no Title: geneChip Analysis Package Description: This package is designed to provide solutions for quality assessment and to detect differentially expressed genes for the Affymetrix GeneChips, including both 3' -arrays and gene 1.0-ST arrays. The package generates comprehensive analysis reports in HTML format. Hyperlinks on the report page will lead to a series of QC plots, processed data, and differentially expressed gene lists. Differentially expressed genes are reported in tabular format with annotations hyperlinked to online biological databases. biocViews: Microarray, OneChannel, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Xiwei Wu, Arthur Li Maintainer: Arthur Li git_url: https://git.bioconductor.org/packages/ArrayTools git_branch: RELEASE_3_12 git_last_commit: 01c515f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ArrayTools_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ArrayTools_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ArrayTools_1.50.0.tgz vignettes: vignettes/ArrayTools/inst/doc/ArrayTools.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTools/inst/doc/ArrayTools.R dependencyCount: 15 Package: ARRmNormalization Version: 1.30.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: a9a7b6f7102328aedbc70e423d6e67eb NeedsCompilation: no Title: Adaptive Robust Regression normalization for Illumina methylation data Description: Perform the Adaptive Robust Regression method (ARRm) for the normalization of methylation data from the Illumina Infinium HumanMethylation 450k assay. biocViews: DNAMethylation, TwoChannel, Preprocessing, Microarray Author: Jean-Philippe Fortin, Celia M.T. Greenwood, Aurelie Labbe. Maintainer: Jean-Philippe Fortin git_url: https://git.bioconductor.org/packages/ARRmNormalization git_branch: RELEASE_3_12 git_last_commit: ce62ab3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ARRmNormalization_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ARRmNormalization_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ARRmNormalization_1.30.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.R dependencyCount: 1 Package: artMS Version: 1.8.3 Depends: R (>= 3.6.0) Imports: AnnotationDbi, biomaRt, bit64, circlize, cluster, ComplexHeatmap, corrplot, data.table, dplyr, factoextra, FactoMineR, getopt, ggdendro, ggplot2, gplots, ggrepel, gProfileR, graphics, grDevices, grid, limma, MSstats, openxlsx, org.Hs.eg.db, org.Mm.eg.db, PerformanceAnalytics, pheatmap, plotly, plyr, RColorBrewer, scales, seqinr, stats, stringr, tidyr, UpSetR, utils, VennDiagram, yaml Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) + file LICENSE MD5sum: 1cbb891e3c085677edca21fd1f447607 NeedsCompilation: no Title: Analytical R tools for Mass Spectrometry Description: artMS provides a set of tools for the analysis of proteomics label-free datasets. It takes as input the MaxQuant search result output (evidence.txt file) and performs quality control, relative quantification using MSstats, downstream analysis and integration. artMS also provides a set of functions to re-format and make it compatible with other analytical tools, including, SAINTq, SAINTexpress, Phosfate, and PHOTON. Check [http://artms.org](http://artms.org) for details. biocViews: Proteomics, DifferentialExpression, BiomedicalInformatics, SystemsBiology, MassSpectrometry, Annotation, QualityControl, GeneSetEnrichment, Clustering, Normalization, ImmunoOncology, MultipleComparison Author: David Jimenez-Morales [aut, cre] (), Alexandre Rosa Campos [aut, ctb] (), John Von Dollen [aut], Nevan Krogan [aut] (), Danielle Swaney [aut, ctb] () Maintainer: David Jimenez-Morales URL: http://artms.org VignetteBuilder: knitr BugReports: https://github.com/biodavidjm/artMS/issues git_url: https://git.bioconductor.org/packages/artMS git_branch: RELEASE_3_12 git_last_commit: 43a054f git_last_commit_date: 2021-04-06 Date/Publication: 2021-04-13 source.ver: src/contrib/artMS_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/artMS_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.0/artMS_1.8.3.tgz vignettes: vignettes/artMS/inst/doc/artMS_vignette.html vignetteTitles: Learn to use artMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/artMS/inst/doc/artMS_vignette.R dependencyCount: 198 Package: ASAFE Version: 1.16.0 Depends: R (>= 3.2) Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 1145236591d973ea06651fad4434e19b NeedsCompilation: no Title: Ancestry Specific Allele Frequency Estimation Description: Given admixed individuals' bi-allelic SNP genotypes and ancestry pairs (where each ancestry can take one of three values) for multiple SNPs, perform an EM algorithm to deal with the fact that SNP genotypes are unphased with respect to ancestry pairs, in order to estimate ancestry-specific allele frequencies for all SNPs. biocViews: SNP, GenomeWideAssociation, LinkageDisequilibrium, BiomedicalInformatics, Genetics, ExperimentalDesign Author: Qian Zhang Maintainer: Qian Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASAFE git_branch: RELEASE_3_12 git_last_commit: a3c9236 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ASAFE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASAFE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ASAFE_1.16.0.tgz vignettes: vignettes/ASAFE/inst/doc/ASAFE.pdf vignetteTitles: ASAFE (Ancestry Specific Allele Frequency Estimation) hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASAFE/inst/doc/ASAFE.R dependencyCount: 0 Package: ASEB Version: 1.34.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 380e4f496717acdf82a931f9555c4d85 NeedsCompilation: yes Title: Predict Acetylated Lysine Sites Description: ASEB is an R package to predict lysine sites that can be acetylated by a specific KAT-family. biocViews: Proteomics Author: Likun Wang and Tingting Li . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/ASEB git_branch: RELEASE_3_12 git_last_commit: 21baac1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ASEB_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASEB_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ASEB_1.34.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASEB/inst/doc/ASEB.R dependencyCount: 3 Package: ASGSCA Version: 1.24.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: ad017053e56917850529108d665e7dcb NeedsCompilation: no Title: Association Studies for multiple SNPs and multiple traits using Generalized Structured Equation Models Description: The package provides tools to model and test the association between multiple genotypes and multiple traits, taking into account the prior biological knowledge. Genes, and clinical pathways are incorporated in the model as latent variables. The method is based on Generalized Structured Component Analysis (GSCA). biocViews: StructuralEquationModels Author: Hela Romdhani, Stepan Grinek , Heungsun Hwang and Aurelie Labbe. Maintainer: Hela Romdhani git_url: https://git.bioconductor.org/packages/ASGSCA git_branch: RELEASE_3_12 git_last_commit: 7f9f2f0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ASGSCA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASGSCA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ASGSCA_1.24.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASGSCA/inst/doc/ASGSCA.R suggestsMe: matrixpls dependencyCount: 9 Package: ASICS Version: 2.6.1 Depends: R (>= 3.5) Imports: BiocParallel, ggplot2, glmnet, grDevices, gridExtra, methods, mvtnorm, PepsNMR, plyr, quadprog, ropls, stats, SummarizedExperiment, utils, Matrix, zoo Suggests: knitr, rmarkdown, BiocStyle, testthat, ASICSdata License: GPL (>= 2) MD5sum: 4106a96038f9cff25f70fe295d53ac78 NeedsCompilation: no Title: Automatic Statistical Identification in Complex Spectra Description: With a set of pure metabolite reference spectra, ASICS quantifies concentration of metabolites in a complex spectrum. The identification of metabolites is performed by fitting a mixture model to the spectra of the library with a sparse penalty. The method and its statistical properties are described in Tardivel et al. (2017) . biocViews: Software, DataImport, Cheminformatics, Metabolomics Author: Gaëlle Lefort [aut, cre], Rémi Servien [aut], Patrick Tardivel [aut], Nathalie Vialaneix [aut] Maintainer: Gaëlle Lefort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ASICS git_branch: RELEASE_3_12 git_last_commit: 2165d0b git_last_commit_date: 2020-10-28 Date/Publication: 2020-10-28 source.ver: src/contrib/ASICS_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASICS_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ASICS_2.6.1.tgz vignettes: vignettes/ASICS/inst/doc/ASICS.html, vignettes/ASICS/inst/doc/ASICSUsersGuide.html vignetteTitles: ASICS, ASICS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASICS/inst/doc/ASICS.R, vignettes/ASICS/inst/doc/ASICSUsersGuide.R dependencyCount: 87 Package: ASpediaFI Version: 1.4.0 Depends: R (>= 3.6.0), SummarizedExperiment, ROCR Imports: BiocParallel, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, IVAS, Rsamtools, biomaRt, limma, S4Vectors, stats, DRaWR, GenomeInfoDb, Gviz, Matrix, dplyr, fgsea, reshape2, igraph, graphics, e1071, methods, rtracklayer, scales, grid, ggplot2, mGSZ, utils Suggests: knitr License: GPL-3 MD5sum: 3e188facc0514e0cc4113490a8153361 NeedsCompilation: no Title: ASpedia-FI: Functional Interaction Analysis of Alternative Splicing Events Description: This package provides functionalities for a systematic and integrative analysis of alternative splicing events and their functional interactions. biocViews: AlternativeSplicing, Annotation, Coverage, GeneExpression, GeneSetEnrichment, GraphAndNetwork, KEGG, Network, NetworkInference, Pathways, Reactome, Transcription, Sequencing, Visualization Author: Doyeong Yu, Kyubin Lee, Daejin Hyung, Soo Young Cho, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr BugReports: https://github.com/nachoryu/ASpediaFI git_url: https://git.bioconductor.org/packages/ASpediaFI git_branch: RELEASE_3_12 git_last_commit: d59c4a0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ASpediaFI_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASpediaFI_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ASpediaFI_1.4.0.tgz vignettes: vignettes/ASpediaFI/inst/doc/ASpediaFI.pdf vignetteTitles: ASpediaFI.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpediaFI/inst/doc/ASpediaFI.R dependencyCount: 170 Package: ASpli Version: 2.0.0 Depends: methods, grDevices, stats, utils, parallel, edgeR, limma, AnnotationDbi Imports: GenomicRanges, GenomicFeatures, BiocGenerics, IRanges, GenomicAlignments, Gviz, S4Vectors, Rsamtools, BiocStyle, igraph, htmltools, data.table, UpSetR, tidyr, DT, MASS, grid, graphics License: GPL MD5sum: f7df37943398c0e259854ae9b4784e26 NeedsCompilation: no Title: Analysis of Alternative Splicing Using RNA-Seq Description: Integrative pipeline for the analysis of alternative splicing using RNAseq. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, TimeCourse, RNASeq, GenomeAnnotation, Sequencing, Alignment Author: Estefania Mancini, Andres Rabinovich, Javier Iserte, Marcelo Yanovsky and Ariel Chernomoretz Maintainer: Estefania Mancini git_url: https://git.bioconductor.org/packages/ASpli git_branch: RELEASE_3_12 git_last_commit: 641dc04 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ASpli_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASpli_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ASpli_2.0.0.tgz vignettes: vignettes/ASpli/inst/doc/ASpli.pdf vignetteTitles: ASpli hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASpli/inst/doc/ASpli.R dependencyCount: 155 Package: AssessORF Version: 1.8.0 Depends: R (>= 3.5.0), DECIPHER (>= 2.10.0) Imports: Biostrings, GenomicRanges, IRanges, graphics, grDevices, methods, stats, utils Suggests: AssessORFData, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 5d8775a400935a481bf5701aec819e80 NeedsCompilation: no Title: Assess Gene Predictions Using Proteomics and Evolutionary Conservation Description: In order to assess the quality of a set of predicted genes for a genome, evidence must first be mapped to that genome. Next, each gene must be categorized based on how strong the evidence is for or against that gene. The AssessORF package provides the functions and class structures necessary for accomplishing those tasks, using proteomic hits and evolutionarily conserved start codons as the forms of evidence. biocViews: ComparativeGenomics, GenePrediction, GenomeAnnotation, Genetics, Proteomics, QualityControl, Visualization Author: Deepank Korandla [aut, cre], Erik Wright [aut] Maintainer: Deepank Korandla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AssessORF git_branch: RELEASE_3_12 git_last_commit: 3cb4cb1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AssessORF_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AssessORF_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AssessORF_1.8.0.tgz vignettes: vignettes/AssessORF/inst/doc/UsingAssessORF.pdf vignetteTitles: Using AssessORF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AssessORF/inst/doc/UsingAssessORF.R suggestsMe: AssessORFData dependencyCount: 36 Package: ASSET Version: 2.8.0 Depends: MASS, msm, rmeta Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 39cae307eebf07e8614010d4f41b8c54 NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and subtypes biocViews: Software Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: Samsiddhi Bhattacharjee git_url: https://git.bioconductor.org/packages/ASSET git_branch: RELEASE_3_12 git_last_commit: aecd936 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ASSET_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASSET_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ASSET_2.8.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSET/inst/doc/vignette.R dependsOnMe: REBET dependencyCount: 15 Package: ASSIGN Version: 1.26.0 Depends: R (>= 3.4) Imports: gplots, graphics, grDevices, msm, Rlab, stats, sva, utils, ggplot2, yaml Suggests: testthat, BiocStyle, lintr, knitr, rmarkdown License: MIT + file LICENSE MD5sum: d0fa1584cd00633d501382bf96632c4c NeedsCompilation: no Title: Adaptive Signature Selection and InteGratioN (ASSIGN) Description: ASSIGN is a computational tool to evaluate the pathway deregulation/activation status in individual patient samples. ASSIGN employs a flexible Bayesian factor analysis approach that adapts predetermined pathway signatures derived either from knowledge-based literature or from perturbation experiments to the cell-/tissue-specific pathway signatures. The deregulation/activation level of each context-specific pathway is quantified to a score, which represents the extent to which a patient sample encompasses the pathway deregulation/activation signature. biocViews: Software, GeneExpression, Pathways, Bayesian Author: Ying Shen, Andrea H. Bild, W. Evan Johnson, and Mumtehena Rahman Maintainer: Ying Shen , W. Evan Johnson , David Jenkins , Mumtehena Rahman URL: https://compbiomed.github.io/ASSIGN/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/ASSIGN/issues git_url: https://git.bioconductor.org/packages/ASSIGN git_branch: RELEASE_3_12 git_last_commit: a522dee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ASSIGN_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ASSIGN_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ASSIGN_1.26.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.html vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R importsMe: TBSignatureProfiler dependencyCount: 90 Package: ATACseqQC Version: 1.14.4 Depends: R (>= 3.4), BiocGenerics, S4Vectors Imports: BSgenome, Biostrings, ChIPpeakAnno, IRanges, GenomicRanges, GenomicAlignments, GenomeInfoDb, GenomicScores, graphics, grid, limma, Rsamtools (>= 1.31.2), randomForest, rtracklayer, stats, motifStack, preseqR, utils, KernSmooth, edgeR Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, MotifDb, trackViewer, testthat License: GPL (>= 2) MD5sum: 84c22259fa29e28d845fcc5aaae1572f NeedsCompilation: no Title: ATAC-seq Quality Control Description: ATAC-seq, an assay for Transposase-Accessible Chromatin using sequencing, is a rapid and sensitive method for chromatin accessibility analysis. It was developed as an alternative method to MNase-seq, FAIRE-seq and DNAse-seq. Comparing to the other methods, ATAC-seq requires less amount of the biological samples and time to process. In the process of analyzing several ATAC-seq dataset produced in our labs, we learned some of the unique aspects of the quality assessment for ATAC-seq data.To help users to quickly assess whether their ATAC-seq experiment is successful, we developed ATACseqQC package partially following the guideline published in Nature Method 2013 (Greenleaf et al.), including diagnostic plot of fragment size distribution, proportion of mitochondria reads, nucleosome positioning pattern, and CTCF or other Transcript Factor footprints. biocViews: Sequencing, DNASeq, ATACSeq, GeneRegulation, QualityControl, Coverage, NucleosomePositioning, ImmunoOncology Author: Jianhong Ou, Haibo Liu, Feng Yan, Jun Yu, Michelle Kelliher, Lucio Castilla, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ATACseqQC git_branch: RELEASE_3_12 git_last_commit: 0728a45 git_last_commit_date: 2020-11-05 Date/Publication: 2020-11-05 source.ver: src/contrib/ATACseqQC_1.14.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/ATACseqQC_1.14.4.zip mac.binary.ver: bin/macosx/contrib/4.0/ATACseqQC_1.14.4.tgz vignettes: vignettes/ATACseqQC/inst/doc/ATACseqQC.html vignetteTitles: ATACseqQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ATACseqQC/inst/doc/ATACseqQC.R dependencyCount: 154 Package: atSNP Version: 1.6.1 Depends: R (>= 3.6) Imports: BSgenome, BiocFileCache, BiocParallel, Rcpp, data.table, ggplot2, grDevices, graphics, grid, motifStack, rappdirs, stats, testthat, utils, lifecycle LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 00cd5fd0c7aadc6e9e327f7eeaa78a69 NeedsCompilation: yes Title: Affinity test for identifying regulatory SNPs Description: atSNP performs affinity tests of motif matches with the SNP or the reference genomes and SNP-led changes in motif matches. biocViews: Software, ChIPSeq, GenomeAnnotation, MotifAnnotation, Visualization Author: Chandler Zuo [aut], Sunyoung Shin [aut, cre], Sunduz Keles [aut] Maintainer: Sunyoung Shin URL: https://github.com/sunyoungshin/atSNP VignetteBuilder: knitr BugReports: https://github.com/sunyoungshin/atSNP/issues git_url: https://git.bioconductor.org/packages/atSNP git_branch: RELEASE_3_12 git_last_commit: 7864975 git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-28 source.ver: src/contrib/atSNP_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/atSNP_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/atSNP_1.6.1.tgz vignettes: vignettes/atSNP/inst/doc/atsnp-vignette.html vignetteTitles: atsnp-vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/atSNP/inst/doc/atsnp-vignette.R dependencyCount: 121 Package: attract Version: 1.42.0 Depends: R (>= 3.4.0), AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db, utils, methods Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: 9a866c233e81a1db7c613cb1493418d7 NeedsCompilation: no Title: Methods to Find the Gene Expression Modules that Represent the Drivers of Kauffman's Attractor Landscape Description: This package contains the functions to find the gene expression modules that represent the drivers of Kauffman's attractor landscape. The modules are the core attractor pathways that discriminate between different cell types of groups of interest. Each pathway has a set of synexpression groups, which show transcriptionally-coordinated changes in gene expression. biocViews: ImmunoOncology, KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/attract git_branch: RELEASE_3_12 git_last_commit: e6d6973 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/attract_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/attract_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/attract_1.42.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{attract} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/attract/inst/doc/attract.R dependencyCount: 66 Package: AUCell Version: 1.12.0 Imports: data.table, graphics, grDevices, GSEABase, methods, mixtools, R.utils, shiny, stats, SummarizedExperiment, BiocGenerics, S4Vectors, utils Suggests: Biobase, BiocStyle, doSNOW, dynamicTreeCut, DT, GEOquery, knitr, NMF, plyr, R2HTML, rmarkdown, reshape2, plotly, rbokeh, devtools, Rtsne, tsne, testthat, zoo Enhances: doMC, doRNG, doParallel, foreach License: GPL-3 MD5sum: a553f72079110ccd3aacf6df113ece18 NeedsCompilation: no Title: AUCell: Analysis of 'gene set' activity in single-cell RNA-seq data (e.g. identify cells with specific gene signatures) Description: AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched within the expressed genes for each cell. The distribution of AUC scores across all the cells allows exploring the relative expression of the signature. Since the scoring method is ranking-based, AUCell is independent of the gene expression units and the normalization procedure. In addition, since the cells are evaluated individually, it can easily be applied to bigger datasets, subsetting the expression matrix if needed. biocViews: SingleCell, GeneSetEnrichment, Transcriptomics, Transcription, GeneExpression, WorkflowStep, Normalization Author: Sara Aibar, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium. Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/AUCell git_branch: RELEASE_3_12 git_last_commit: 2286fa3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AUCell_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AUCell_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AUCell_1.12.0.tgz vignettes: vignettes/AUCell/inst/doc/AUCell.html vignetteTitles: AUCell: Identifying cells with active gene sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AUCell/inst/doc/AUCell.R importsMe: RcisTarget dependencyCount: 83 Package: Autotuner Version: 1.4.0 Depends: R (>= 4.0.0), methods, Biobase, MSnbase (>= 2.14.2) Imports: RColorBrewer, mzR, assertthat, scales, entropy, cluster, grDevices, graphics, stats, utils Suggests: testthat (>= 2.1.0), covr, devtools, knitr, rmarkdown, mtbls2 License: MIT + file LICENSE MD5sum: c0fc849c0983d8bbe25ad90924eaa66b NeedsCompilation: no Title: Automated parameter selection for untargeted metabolomics data processing Description: This package is designed to help faciliate data processing in untargeted metabolomics. To do this, the algorithm contained within the package performs statistical inference on raw data to come up with the best set of parameters to process the raw data. biocViews: MassSpectrometry, Metabolomics Author: Craig McLean Maintainer: Craig McLean URL: https://github.com/crmclean/Autotuner/ VignetteBuilder: knitr BugReports: https://github.com/crmclean/Autotuner/issues git_url: https://git.bioconductor.org/packages/Autotuner git_branch: RELEASE_3_12 git_last_commit: 9d402d7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Autotuner_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Autotuner_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Autotuner_1.4.0.tgz vignettes: vignettes/Autotuner/inst/doc/Autotuner.html vignetteTitles: Autotuner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Autotuner/inst/doc/Autotuner.R dependencyCount: 78 Package: AWFisher Version: 1.4.0 Depends: R (>= 3.6) Imports: edgeR, limma, stats Suggests: knitr, tightClust License: GPL-3 Archs: i386, x64 MD5sum: e9ceac75f1aa84ee9b5472793b46b79e NeedsCompilation: yes Title: An R package for fast computing for adaptively weighted fisher's method Description: Implementation of the adaptively weighted fisher's method, including fast p-value computing, variability index, and meta-pattern. biocViews: StatisticalMethod, Software Author: Zhiguang Huo Maintainer: Zhiguang Huo VignetteBuilder: knitr BugReports: https://github.com/Caleb-Huo/AWFisher/issues git_url: https://git.bioconductor.org/packages/AWFisher git_branch: RELEASE_3_12 git_last_commit: f83e5c2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/AWFisher_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/AWFisher_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/AWFisher_1.4.0.tgz vignettes: vignettes/AWFisher/inst/doc/AWFisher.html vignetteTitles: AWFisher hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AWFisher/inst/doc/AWFisher.R dependencyCount: 11 Package: BaalChIP Version: 1.16.0 Depends: R (>= 3.3.1), GenomicRanges, IRanges, Rsamtools, Imports: GenomicAlignments, GenomeInfoDb, doParallel, parallel, doBy, reshape2, scales, coda, foreach, ggplot2, methods, utils, graphics, stats Suggests: RUnit, BiocGenerics, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 40b8379ac46a8f9f648b1cbeed0010e8 NeedsCompilation: no Title: BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes Description: The package offers functions to process multiple ChIP-seq BAM files and detect allele-specific events. Computes allele counts at individual variants (SNPs/SNVs), implements extensive QC steps to remove problematic variants, and utilizes a bayesian framework to identify statistically significant allele- specific events. BaalChIP is able to account for copy number differences between the two alleles, a known phenotypical feature of cancer samples. biocViews: Software, ChIPSeq, Bayesian, Sequencing Author: Ines de Santiago, Wei Liu, Ke Yuan, Martin O'Reilly, Chandra SR Chilamakuri, Bruce Ponder, Kerstin Meyer, Florian Markowetz Maintainer: Ines de Santiago VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BaalChIP git_branch: RELEASE_3_12 git_last_commit: c466b6c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BaalChIP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BaalChIP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BaalChIP_1.16.0.tgz vignettes: vignettes/BaalChIP/inst/doc/BaalChIP.html vignetteTitles: Analyzing ChIP-seq and FAIRE-seq data with the BaalChIP package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaalChIP/inst/doc/BaalChIP.R dependencyCount: 104 Package: BAC Version: 1.50.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: 1a51fd1bbc5fb79477bb525521cdc3cb NeedsCompilation: yes Title: Bayesian Analysis of Chip-chip experiment Description: This package uses a Bayesian hierarchical model to detect enriched regions from ChIP-chip experiments biocViews: Microarray, Transcription Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/BAC git_branch: RELEASE_3_12 git_last_commit: 102457c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BAC_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BAC_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BAC_1.50.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAC/inst/doc/BAC.R dependencyCount: 0 Package: bacon Version: 1.18.0 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: i386, x64 MD5sum: 672c21f15bd4b4b0b5efbd9c10fd0eb8 NeedsCompilation: yes Title: Controlling bias and inflation in association studies using the empirical null distribution Description: Bacon can be used to remove inflation and bias often observed in epigenome- and transcriptome-wide association studies. To this end bacon constructs an empirical null distribution using a Gibbs Sampling algorithm by fitting a three-component normal mixture on z-scores. biocViews: ImmunoOncology, StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bacon git_branch: RELEASE_3_12 git_last_commit: 8eb9db7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bacon_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bacon_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bacon_1.18.0.tgz vignettes: vignettes/bacon/inst/doc/bacon.html vignetteTitles: Controlling bias and inflation in association studies using the empirical null distribution hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R dependencyCount: 47 Package: BADER Version: 1.28.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: 5ae919eeebd7622855f3881dc84ad7dd NeedsCompilation: yes Title: Bayesian Analysis of Differential Expression in RNA Sequencing Data Description: For RNA sequencing count data, BADER fits a Bayesian hierarchical model. The algorithm returns the posterior probability of differential expression for each gene between two groups A and B. The joint posterior distribution of the variables in the model can be returned in the form of posterior samples, which can be used for further down-stream analyses such as gene set enrichment. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker git_url: https://git.bioconductor.org/packages/BADER git_branch: RELEASE_3_12 git_last_commit: 68f8939 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BADER_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BADER_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BADER_1.28.0.tgz vignettes: vignettes/BADER/inst/doc/BADER.pdf vignetteTitles: Analysing RNA-Seq data with the "BADER" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BADER/inst/doc/BADER.R dependencyCount: 0 Package: BadRegionFinder Version: 1.18.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 273caa5bdcac547a6e970b1d39851629 NeedsCompilation: no Title: BadRegionFinder: an R/Bioconductor package for identifying regions with bad coverage Description: BadRegionFinder is a package for identifying regions with a bad, acceptable and good coverage in sequence alignment data available as bam files. The whole genome may be considered as well as a set of target regions. Various visual and textual types of output are available. biocViews: Coverage, Sequencing, Alignment, WholeGenome, Classification Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BadRegionFinder git_branch: RELEASE_3_12 git_last_commit: 54deba4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BadRegionFinder_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BadRegionFinder_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BadRegionFinder_1.18.0.tgz vignettes: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.pdf vignetteTitles: Using BadRegionFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BadRegionFinder/inst/doc/BadRegionFinder.R dependencyCount: 90 Package: BAGS Version: 2.30.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 013a0749ec0109b3cf2484cb419e4928 NeedsCompilation: yes Title: A Bayesian Approach for Geneset Selection Description: R package providing functions to perform geneset significance analysis over simple cross-sectional data between 2 and 5 phenotypes of interest. biocViews: Bayesian Author: Alejandro Quiroz-Zarate Maintainer: Alejandro Quiroz-Zarate git_url: https://git.bioconductor.org/packages/BAGS git_branch: RELEASE_3_12 git_last_commit: c96396d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BAGS_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BAGS_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BAGS_2.30.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BAGS/inst/doc/BAGS.R dependencyCount: 8 Package: ballgown Version: 2.22.0 Depends: R (>= 3.1.1), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.9.39), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 85b3b71837d733f1bca7276436b73069 NeedsCompilation: no Title: Flexible, isoform-level differential expression analysis Description: Tools for statistical analysis of assembled transcriptomes, including flexible differential expression analysis, visualization of transcript structures, and matching of assembled transcripts to annotation. biocViews: ImmunoOncology, RNASeq, StatisticalMethod, Preprocessing, DifferentialExpression Author: Jack Fu [aut], Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Jack Fu VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues git_url: https://git.bioconductor.org/packages/ballgown git_branch: RELEASE_3_12 git_last_commit: 3bb5d43 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ballgown_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ballgown_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ballgown_2.22.0.tgz vignettes: vignettes/ballgown/inst/doc/ballgown.html vignetteTitles: Flexible isoform-level differential expression analysis with Ballgown hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R dependsOnMe: vasp, VaSP importsMe: RNASeqR suggestsMe: polyester, variancePartition dependencyCount: 76 Package: bambu Version: 1.0.3 Depends: R(>= 4.0.0), SummarizedExperiment(>= 1.1.6), S4Vectors(>= 0.22.1), IRanges Imports: BiocGenerics, BiocParallel, data.table, dplyr, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, stats, glmnet, Rsamtools, methods, Rcpp LinkingTo: Rcpp, RcppArmadillo Suggests: AnnotationDbi, Biostrings, BiocFileCache, ggplot2, ComplexHeatmap, circlize, ggbio, gridExtra, knitr, rmarkdown, testthat, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, ExperimentHub (>= 1.15.3), DESeq2, NanoporeRNASeq, BSgenome, apeglm, utils, DEXSeq Enhances: parallel License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: bcc80890252d4bff4da822df5dd894ca NeedsCompilation: yes Title: Reference-guided isoform reconstruction and quantification for long read RNA-Seq data Description: bambu is a R package for multi-sample transcript discovery and quantification using long read RNA-Seq data. You can use bambu after read alignment to obtain expression estimates for known and novel transcripts and genes. The output from bambu can directly be used for visualisation and downstream analysis such as differential gene expression or transcript usage. biocViews: Alignment, Coverage, DifferentialExpression, FeatureExtraction, GeneExpression, GenomeAnnotation, GenomeAssembly, ImmunoOncology, MultipleComparison, Normalization, RNASeq, Regression, Sequencing, Software, Transcription, Transcriptomics Author: Ying Chen [cre, aut], Yuk Kei Wan [aut], Jonathan Goeke [aut] Maintainer: Ying Chen URL: https://github.com/GoekeLab/bambu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bambu git_branch: RELEASE_3_12 git_last_commit: 7140dd6 git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-27 source.ver: src/contrib/bambu_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/bambu_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.0/bambu_1.0.3.tgz vignettes: vignettes/bambu/inst/doc/bambu.html vignetteTitles: bambu hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bambu/inst/doc/bambu.R suggestsMe: NanoporeRNASeq dependencyCount: 97 Package: bamsignals Version: 1.22.0 Depends: R (>= 3.2.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 690ef79539fca84b2c37d91dc70f9619 NeedsCompilation: yes Title: Extract read count signals from bam files Description: This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data. biocViews: DataImport, Sequencing, Coverage, Alignment Author: Alessandro Mammana [aut, cre], Johannes Helmuth [aut] Maintainer: Johannes Helmuth URL: https://github.com/lamortenera/bamsignals SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/lamortenera/bamsignals/issues git_url: https://git.bioconductor.org/packages/bamsignals git_branch: RELEASE_3_12 git_last_commit: 5f53396 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bamsignals_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bamsignals_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bamsignals_1.22.0.tgz vignettes: vignettes/bamsignals/inst/doc/bamsignals.html vignetteTitles: Introduction to the bamsignals package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R importsMe: AneuFinder, chromstaR, karyoploteR, normr, hoardeR dependencyCount: 19 Package: BANDITS Version: 1.6.0 Depends: R (>= 3.6.0) Imports: Rcpp, doRNG, MASS, data.table, R.utils, doParallel, parallel, foreach, methods, stats, graphics, ggplot2, DRIMSeq, BiocParallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, testthat, tximport, BiocStyle, GenomicFeatures, Biostrings License: GPL (>= 3) Archs: i386, x64 MD5sum: a15062b892049d4422fd374f3afcd284 NeedsCompilation: yes Title: BANDITS: Bayesian ANalysis of DIfferenTial Splicing Description: BANDITS is a Bayesian hierarchical model for detecting differential splicing of genes and transcripts, via differential transcript usage (DTU), between two or more conditions. The method uses a Bayesian hierarchical framework, which allows for sample specific proportions in a Dirichlet-Multinomial model, and samples the allocation of fragments to the transcripts. Parameters are inferred via Markov chain Monte Carlo (MCMC) techniques and a DTU test is performed via a multivariate Wald test on the posterior densities for the average relative abundance of transcripts. biocViews: DifferentialSplicing, AlternativeSplicing, Bayesian, Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization Author: Simone Tiberi [aut, cre], Mark D. Robinson [aut]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/BANDITS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/BANDITS/issues git_url: https://git.bioconductor.org/packages/BANDITS git_branch: RELEASE_3_12 git_last_commit: 5446a54 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BANDITS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BANDITS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BANDITS_1.6.0.tgz vignettes: vignettes/BANDITS/inst/doc/BANDITS.html vignetteTitles: BANDITS: Bayesian ANalysis of DIfferenTial Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BANDITS/inst/doc/BANDITS.R dependencyCount: 78 Package: banocc Version: 1.14.0 Depends: R (>= 3.5.1), rstan (>= 2.17.4) Imports: coda (>= 0.18.1), mvtnorm, stringr Suggests: knitr, rmarkdown, methods, testthat License: MIT + file LICENSE MD5sum: f64538e79438b3e50a4600d9163ea4ca NeedsCompilation: no Title: Bayesian ANalysis Of Compositional Covariance Description: BAnOCC is a package designed for compositional data, where each sample sums to one. It infers the approximate covariance of the unconstrained data using a Bayesian model coded with `rstan`. It provides as output the `stanfit` object as well as posterior median and credible interval estimates for each correlation element. biocViews: ImmunoOncology, Metagenomics, Software, Bayesian Author: Emma Schwager [aut, cre], Curtis Huttenhower [aut] Maintainer: George Weingart , Curtis Huttenhower VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/banocc git_branch: RELEASE_3_12 git_last_commit: cb11766 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/banocc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/banocc_1.13.0.zip mac.binary.ver: bin/macosx/contrib/4.0/banocc_1.14.0.tgz vignettes: vignettes/banocc/inst/doc/banocc-vignette.html vignetteTitles: BAnOCC (Bayesian Analysis of Compositional Covariance) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/banocc/inst/doc/banocc-vignette.R dependencyCount: 67 Package: basecallQC Version: 1.14.0 Depends: R (>= 3.4), stats, utils, methods, rmarkdown, knitr, prettydoc, yaml Imports: ggplot2, stringr, XML, raster, dplyr, data.table, tidyr, magrittr, DT, lazyeval, ShortRead Suggests: testthat, BiocStyle License: GPL (>= 3) MD5sum: 0187ef8e4c9bca29680e5ae92e738ee9 NeedsCompilation: no Title: Working with Illumina Basecalling and Demultiplexing input and output files Description: The basecallQC package provides tools to work with Illumina bcl2Fastq (versions >= 2.1.7) software.Prior to basecalling and demultiplexing using the bcl2Fastq software, basecallQC functions allow the user to update Illumina sample sheets from versions <= 1.8.9 to >= 2.1.7 standards, clean sample sheets of common problems such as invalid sample names and IDs, create read and index basemasks and the bcl2Fastq command. Following the generation of basecalled and demultiplexed data, the basecallQC packages allows the user to generate HTML tables, plots and a self contained report of summary metrics from Illumina XML output files. biocViews: Sequencing, Infrastructure, DataImport, QualityControl Author: Thomas Carroll and Marian Dore Maintainer: Thomas Carroll SystemRequirements: bcl2Fastq (versions >= 2.1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basecallQC git_branch: RELEASE_3_12 git_last_commit: 0930609 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/basecallQC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/basecallQC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/basecallQC_1.14.0.tgz vignettes: vignettes/basecallQC/inst/doc/basecallQC.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basecallQC/inst/doc/basecallQC.R dependencyCount: 104 Package: BaseSpaceR Version: 1.34.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: 8939cc3681cf2f775c4864fc714a1857 NeedsCompilation: no Title: R SDK for BaseSpace RESTful API Description: A rich R interface to Illumina's BaseSpace cloud computing environment, enabling the fast development of data analysis and visualisation tools. biocViews: Infrastructure, DataRepresentation, ConnectTools, Software, DataImport, HighThroughputSequencing, Sequencing, Genetics Author: Adrian Alexa Maintainer: Jared O'Connell git_url: https://git.bioconductor.org/packages/BaseSpaceR git_branch: RELEASE_3_12 git_last_commit: 40c323f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BaseSpaceR_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BaseSpaceR_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BaseSpaceR_1.34.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.R dependencyCount: 4 Package: Basic4Cseq Version: 1.26.0 Depends: R (>= 3.4), Biostrings, GenomicAlignments, caTools, GenomicRanges, grDevices, graphics, stats, utils Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 0eb7594adbec7598fa670a82c5c7d0a4 NeedsCompilation: no Title: Basic4Cseq: an R/Bioconductor package for analyzing 4C-seq data Description: Basic4Cseq is an R/Bioconductor package for basic filtering, analysis and subsequent visualization of 4C-seq data. Virtual fragment libraries can be created for any BSGenome package, and filter functions for both reads and fragments and basic quality controls are included. Fragment data in the vicinity of the experiment's viewpoint can be visualized as a coverage plot based on a running median approach and a multi-scale contact profile. biocViews: ImmunoOncology, Visualization, QualityControl, Sequencing, Coverage, Alignment, RNASeq, SequenceMatching, DataImport Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Basic4Cseq git_branch: RELEASE_3_12 git_last_commit: aa3db3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Basic4Cseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Basic4Cseq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Basic4Cseq_1.26.0.tgz vignettes: vignettes/Basic4Cseq/inst/doc/vignette.pdf vignetteTitles: Basic4Cseq: an R/Bioconductor package for the analysis of 4C-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Basic4Cseq/inst/doc/vignette.R dependencyCount: 44 Package: BASiCS Version: 2.2.4 Depends: R (>= 4.0), SingleCellExperiment Imports: Biobase, BiocGenerics, coda, cowplot, ggExtra, ggplot2, graphics, grDevices, MASS, methods, Rcpp (>= 0.11.3), S4Vectors, scran, stats, stats4, SummarizedExperiment, viridis, utils, Matrix, matrixStats, assertthat, reshape2, BiocParallel, hexbin LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, magick License: GPL (>= 2) Archs: i386, x64 MD5sum: 3d9ccf7c09030d183d401065e527ebe6 NeedsCompilation: yes Title: Bayesian Analysis of Single-Cell Sequencing data Description: Single-cell mRNA sequencing can uncover novel cell-to-cell heterogeneity in gene expression levels in seemingly homogeneous populations of cells. However, these experiments are prone to high levels of technical noise, creating new challenges for identifying genes that show genuine heterogeneous expression within the population of cells under study. BASiCS (Bayesian Analysis of Single-Cell Sequencing data) is an integrated Bayesian hierarchical model to perform statistical analyses of single-cell RNA sequencing datasets in the context of supervised experiments (where the groups of cells of interest are known a priori, e.g. experimental conditions or cell types). BASiCS performs built-in data normalisation (global scaling) and technical noise quantification (based on spike-in genes). BASiCS provides an intuitive detection criterion for highly (or lowly) variable genes within a single group of cells. Additionally, BASiCS can compare gene expression patterns between two or more pre-specified groups of cells. Unlike traditional differential expression tools, BASiCS quantifies changes in expression that lie beyond comparisons of means, also allowing the study of changes in cell-to-cell heterogeneity. The latter can be quantified via a biological over-dispersion parameter that measures the excess of variability that is observed with respect to Poisson sampling noise, after normalisation and technical noise removal. Due to the strong mean/over-dispersion confounding that is typically observed for scRNA-seq datasets, BASiCS also tests for changes in residual over-dispersion, defined by residual values with respect to a global mean/over-dispersion trend. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, DifferentialExpression, Bayesian, CellBiology, ImmunoOncology Author: Catalina Vallejos [aut, cre], Nils Eling [aut], Alan O'Callaghan [aut], Sylvia Richardson [ctb], John Marioni [ctb] Maintainer: Catalina Vallejos URL: https://github.com/catavallejos/BASiCS SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/catavallejos/BASiCS/issues git_url: https://git.bioconductor.org/packages/BASiCS git_branch: RELEASE_3_12 git_last_commit: 33c1ef0 git_last_commit_date: 2021-04-14 Date/Publication: 2021-04-15 source.ver: src/contrib/BASiCS_2.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/BASiCS_2.2.4.zip mac.binary.ver: bin/macosx/contrib/4.0/BASiCS_2.2.4.tgz vignettes: vignettes/BASiCS/inst/doc/BASiCS.html vignetteTitles: Introduction to BASiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BASiCS/inst/doc/BASiCS.R suggestsMe: splatter dependencyCount: 122 Package: BasicSTARRseq Version: 1.18.0 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: e9f793090f051b5bf1d32f3c956805f5 NeedsCompilation: no Title: Basic peak calling on STARR-seq data Description: Basic peak calling on STARR-seq data based on a method introduced in "Genome-Wide Quantitative Enhancer Activity Maps Identified by STARR-seq" Arnold et al. Science. 2013 Mar 1;339(6123):1074-7. doi: 10.1126/science. 1232542. Epub 2013 Jan 17. biocViews: PeakDetection, GeneRegulation, FunctionalPrediction, FunctionalGenomics, Coverage Author: Annika Buerger Maintainer: Annika Buerger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BasicSTARRseq git_branch: RELEASE_3_12 git_last_commit: acae287 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BasicSTARRseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BasicSTARRseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BasicSTARRseq_1.18.0.tgz vignettes: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.pdf vignetteTitles: BasicSTARRseq.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BasicSTARRseq/inst/doc/BasicSTARRseq.R dependencyCount: 38 Package: basilisk Version: 1.2.1 Imports: utils, methods, parallel, reticulate, filelock, basilisk.utils Suggests: knitr, rmarkdown, BiocStyle, testthat, callr License: GPL-3 MD5sum: 92cb0bb483defd6e91d71566275325c2 NeedsCompilation: no Title: Freezing Python Dependencies Inside Bioconductor Packages Description: Installs a self-contained conda instance that is managed by the R/Bioconductor installation machinery. This aims to provide a consistent Python environment that can be used reliably by Bioconductor packages. Functions are also provided to enable smooth interoperability of multiple Python environments in a single R session. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph], Vince Carey [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk git_branch: RELEASE_3_12 git_last_commit: 27516b7 git_last_commit_date: 2020-12-16 Date/Publication: 2020-12-16 source.ver: src/contrib/basilisk_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/basilisk_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/basilisk_1.2.1.tgz vignettes: vignettes/basilisk/inst/doc/motivation.html vignetteTitles: Motivation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk/inst/doc/motivation.R importsMe: BiocSklearn, dasper, densvis, MOFA2, snifter, velociraptor, zellkonverter dependencyCount: 17 Package: basilisk.utils Version: 1.2.2 Imports: utils, methods, rappdirs, filelock Suggests: knitr, rmarkdown, BiocStyle, testthat, BiocFileCache License: GPL-3 MD5sum: 76f6748b9b70f84042c8c1977b31042d NeedsCompilation: no Title: Basilisk Installation Utilities Description: Implements utilities for installation of the basilisk package, primarily for creation of the underlying Conda instance. This allows us to avoid re-writing the same R code in both the configure script (for centrally administered R installations) and in the lazy installation mechanism (for distributed package binaries). It is highly unlikely that developers - or, heaven forbid, end-users! - will need to interact with this package directly; they should be using the basilisk package instead. biocViews: Infrastructure Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/basilisk.utils git_branch: RELEASE_3_12 git_last_commit: d4795d1 git_last_commit_date: 2021-01-27 Date/Publication: 2021-01-27 source.ver: src/contrib/basilisk.utils_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/basilisk.utils_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/basilisk.utils_1.2.2.tgz vignettes: vignettes/basilisk.utils/inst/doc/purpose.html vignetteTitles: _basilisk_ installation utilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/basilisk.utils/inst/doc/purpose.R importsMe: basilisk dependencyCount: 4 Package: batchelor Version: 1.6.3 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, Rcpp, stats, methods, utils, igraph, BiocNeighbors, BiocSingular, Matrix, DelayedArray, DelayedMatrixStats, BiocParallel, scuttle, ResidualMatrix LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, scran, scater, bluster, scRNAseq License: GPL-3 Archs: i386, x64 MD5sum: f906463fadbe422f84efae14e53ac4a0 NeedsCompilation: yes Title: Single-Cell Batch Correction Methods Description: Implements a variety of methods for batch correction of single-cell (RNA sequencing) data. This includes methods based on detecting mutually nearest neighbors, as well as several efficient variants of linear regression of the log-expression values. Functions are also provided to perform global rescaling to remove differences in depth between batches, and to perform a principal components analysis that is robust to differences in the numbers of cells across batches. biocViews: Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Normalization Author: Aaron Lun [aut, cre], Laleh Haghverdi [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/batchelor git_branch: RELEASE_3_12 git_last_commit: b108a53 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/batchelor_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/batchelor_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.0/batchelor_1.6.3.tgz vignettes: vignettes/batchelor/inst/doc/correction.html, vignettes/batchelor/inst/doc/extension.html vignetteTitles: 1. Correcting batch effects, 2. Extending methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/batchelor/inst/doc/correction.R, vignettes/batchelor/inst/doc/extension.R importsMe: ChromSCape, singleCellTK suggestsMe: TSCAN, bcTSNE, RaceID dependencyCount: 52 Package: BatchQC Version: 1.18.0 Depends: R (>= 3.5.0) Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva, corpcor, moments, matrixStats, ggvis, heatmaply, reshape2, limma, grDevices, graphics, stats, methods, Matrix Suggests: testthat License: GPL (>= 2) MD5sum: 0c8a7e36e6f12adfcedaede74344d8b6 NeedsCompilation: no Title: Batch Effects Quality Control Software Description: Sequencing and microarray samples often are collected or processed in multiple batches or at different times. This often produces technical biases that can lead to incorrect results in the downstream analysis. BatchQC is a software tool that streamlines batch preprocessing and evaluation by providing interactive diagnostics, visualizations, and statistical analyses to explore the extent to which batch variation impacts the data. BatchQC diagnostics help determine whether batch adjustment needs to be done, and how correction should be applied before proceeding with a downstream analysis. Moreover, BatchQC interactively applies multiple common batch effect approaches to the data, and the user can quickly see the benefits of each method. BatchQC is developed as a Shiny App. The output is organized into multiple tabs, and each tab features an important part of the batch effect analysis and visualization of the data. The BatchQC interface has the following analysis groups: Summary, Differential Expression, Median Correlations, Heatmaps, Circular Dendrogram, PCA Analysis, Shape, ComBat and SVA. biocViews: BatchEffect, GraphAndNetwork, Microarray, PrincipalComponent, Sequencing, Software, Visualization, QualityControl, RNASeq, Preprocessing, DifferentialExpression, ImmunoOncology Author: Solaiappan Manimaran , W. Evan Johnson , Heather Selby , Claire Ruberman , Kwame Okrah , Hector Corrada Bravo Maintainer: Solaiappan Manimaran URL: https://github.com/mani2012/BatchQC SystemRequirements: pandoc (http://pandoc.org/installing.html) for generating reports from markdown files. VignetteBuilder: knitr BugReports: https://github.com/mani2012/BatchQC/issues git_url: https://git.bioconductor.org/packages/BatchQC git_branch: RELEASE_3_12 git_last_commit: 448a06a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BatchQC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BatchQC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BatchQC_1.18.0.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf, vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html vignetteTitles: BatchQC_usage_advanced, BatchQC_examples, BatchQCIntro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R dependencyCount: 153 Package: BayesKnockdown Version: 1.16.0 Depends: R (>= 3.3) Imports: stats, Biobase License: GPL-3 MD5sum: 9dac30f80238b3241f024c015303a471 NeedsCompilation: no Title: BayesKnockdown: Posterior Probabilities for Edges from Knockdown Data Description: A simple, fast Bayesian method for computing posterior probabilities for relationships between a single predictor variable and multiple potential outcome variables, incorporating prior probabilities of relationships. In the context of knockdown experiments, the predictor variable is the knocked-down gene, while the other genes are potential targets. Can also be used for differential expression/2-class data. biocViews: NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: William Chad Young Maintainer: William Chad Young git_url: https://git.bioconductor.org/packages/BayesKnockdown git_branch: RELEASE_3_12 git_last_commit: e11dfe6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BayesKnockdown_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BayesKnockdown_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BayesKnockdown_1.16.0.tgz vignettes: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.pdf vignetteTitles: BayesKnockdown.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesKnockdown/inst/doc/BayesKnockdown.R dependencyCount: 7 Package: BayesSpace Version: 1.0.0 Depends: R (>= 4.0.0), SingleCellExperiment Imports: Rcpp (>= 1.0.4.6), stats, purrr, scater, scran, SummarizedExperiment, coda, rhdf5, S4Vectors, Matrix, assertthat, mclust, RCurl, DirichletReg, xgboost, utils, ggplot2, scales, BiocFileCache LinkingTo: Rcpp, RcppArmadillo, RcppDist, RcppProgress Suggests: testthat, knitr, rmarkdown, igraph, spatialLIBD, dplyr, viridis, patchwork, RColorBrewer, Seurat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 4dbd44a3b6fc2e719dae0c4c1b06d69d NeedsCompilation: yes Title: Clustering and Resolution Enhancement of Spatial Transcriptomes Description: Tools for clustering and enhancing the resolution of spatial gene expression experiments. BayesSpace clusters a low-dimensional representation of the gene expression matrix, incorporating a spatial prior to encourage neighboring spots to cluster together. The method can enhance the resolution of the low-dimensional representation into "sub-spots", for which features such as gene expression or cell type composition can be imputed. biocViews: Software, Clustering, Transcriptomics, GeneExpression, SingleCell, ImmunoOncology, DataImport Author: Edward Zhao [aut], Matt Stone [aut, cre], Xing Ren [ctb], Raphael Gottardo [ctb] Maintainer: Matt Stone URL: edward130603.github.io/BayesSpace SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/edward130603/BayesSpace/issues git_url: https://git.bioconductor.org/packages/BayesSpace git_branch: RELEASE_3_12 git_last_commit: 6398aec git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BayesSpace_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BayesSpace_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BayesSpace_1.0.0.tgz vignettes: vignettes/BayesSpace/inst/doc/BayesSpace.html vignetteTitles: BayesSpace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BayesSpace/inst/doc/BayesSpace.R dependencyCount: 130 Package: bayNorm Version: 1.8.0 Depends: R (>= 3.5), Imports: Rcpp (>= 0.12.12), BB, foreach, iterators, doSNOW, Matrix, parallel, MASS, locfit, fitdistrplus, stats, methods, graphics, grDevices, SingleCellExperiment, SummarizedExperiment, BiocParallel, utils LinkingTo: Rcpp, RcppArmadillo,RcppProgress Suggests: knitr, rmarkdown, BiocStyle, devtools, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 664fbd6b8462f6aea409be190c1acaf9 NeedsCompilation: yes Title: Single-cell RNA sequencing data normalization Description: bayNorm is used for normalizing single-cell RNA-seq data. biocViews: ImmunoOncology, Normalization, RNASeq, SingleCell,Sequencing Author: Wenhao Tang [aut, cre], Franois Bertaux [aut], Philipp Thomas [aut], Claire Stefanelli [aut], Malika Saint [aut], Samuel Marguerat [aut], Vahid Shahrezaei [aut] Maintainer: Wenhao Tang URL: https://github.com/WT215/bayNorm VignetteBuilder: knitr BugReports: https://github.com/WT215/bayNorm/issues git_url: https://git.bioconductor.org/packages/bayNorm git_branch: RELEASE_3_12 git_last_commit: 430ae33 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bayNorm_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bayNorm_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bayNorm_1.8.0.tgz vignettes: vignettes/bayNorm/inst/doc/bayNorm.html vignetteTitles: Introduction to bayNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bayNorm/inst/doc/bayNorm.R dependencyCount: 48 Package: baySeq Version: 2.24.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, parallel Imports: edgeR Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 9955039abe561d5f55370918dcfef57f NeedsCompilation: no Title: Empirical Bayesian analysis of patterns of differential expression in count data Description: This package identifies differential expression in high-throughput 'count' data, such as that derived from next-generation sequencing machines, calculating estimated posterior likelihoods of differential expression (or more complex hypotheses) via empirical Bayesian methods. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/baySeq git_branch: RELEASE_3_12 git_last_commit: 4221784 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/baySeq_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/baySeq_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/baySeq_2.24.0.tgz vignettes: vignettes/baySeq/inst/doc/baySeq_generic.pdf, vignettes/baySeq/inst/doc/baySeq.pdf vignetteTitles: Advanced baySeq analyses, baySeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/baySeq/inst/doc/baySeq_generic.R, vignettes/baySeq/inst/doc/baySeq.R dependsOnMe: clusterSeq, Rcade, segmentSeq, TCC importsMe: EDDA, metaseqR, metaseqR2, riboSeqR suggestsMe: compcodeR dependencyCount: 25 Package: BBCAnalyzer Version: 1.20.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 50009d5cfa94fa1db13e0f3adeba50d1 NeedsCompilation: no Title: BBCAnalyzer: an R/Bioconductor package for visualizing base counts Description: BBCAnalyzer is a package for visualizing the relative or absolute number of bases, deletions and insertions at defined positions in sequence alignment data available as bam files in comparison to the reference bases. Markers for the relative base frequencies, the mean quality of the detected bases, known mutations or polymorphisms and variants called in the data may additionally be included in the plots. biocViews: Sequencing, Alignment, Coverage, GeneticVariability, SNP Author: Sarah Sandmann Maintainer: Sarah Sandmann git_url: https://git.bioconductor.org/packages/BBCAnalyzer git_branch: RELEASE_3_12 git_last_commit: 1ab650d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BBCAnalyzer_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BBCAnalyzer_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BBCAnalyzer_1.20.0.tgz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.R dependencyCount: 90 Package: BCRANK Version: 1.52.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: 1b3cedde8f800c5716223979b3783e57 NeedsCompilation: yes Title: Predicting binding site consensus from ranked DNA sequences Description: Functions and classes for de novo prediction of transcription factor binding consensus by heuristic search biocViews: MotifDiscovery, GeneRegulation Author: Adam Ameur Maintainer: Adam Ameur git_url: https://git.bioconductor.org/packages/BCRANK git_branch: RELEASE_3_12 git_last_commit: 4976ef5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BCRANK_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BCRANK_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BCRANK_1.52.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BCRANK/inst/doc/BCRANK.R dependencyCount: 15 Package: bcSeq Version: 1.12.0 Depends: R (>= 3.4.0) Imports: Rcpp (>= 0.12.12), Matrix, Biostrings LinkingTo: Rcpp, Matrix Suggests: knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 0a8102195fbec03926d0a4bb9d95b3ad NeedsCompilation: yes Title: Fast Sequence Mapping in High-Throughput shRNA and CRISPR Screens Description: This Rcpp-based package implements a highly efficient data structure and algorithm for performing alignment of short reads from CRISPR or shRNA screens to reference barcode library. Sequencing error are considered and matching qualities are evaluated based on Phred scores. A Bayes' classifier is employed to predict the originating barcode of a read. The package supports provision of user-defined probability models for evaluating matching qualities. The package also supports multi-threading. biocViews: ImmunoOncology, Alignment, CRISPR, Sequencing, SequenceMatching, MultipleSequenceAlignment, Software, ATACSeq Author: Jiaxing Lin [aut, cre], Jeremy Gresham [aut], Jichun Xie [aut], Kouros Owzar [aut], Tongrong Wang [ctb], So Young Kim [ctb], James Alvarez [ctb], Jeffrey S. Damrauer [ctb], Scott Floyd [ctb], Joshua Granek [ctb], Andrew Allen [ctb], Cliburn Chan [ctb] Maintainer: Jiaxing Lin URL: https://github.com/jl354/bcSeq VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/bcSeq git_branch: RELEASE_3_12 git_last_commit: 6fc7a71 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bcSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bcSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bcSeq_1.12.0.tgz vignettes: vignettes/bcSeq/inst/doc/bcSeq.pdf vignetteTitles: bcSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bcSeq/inst/doc/bcSeq.R dependencyCount: 19 Package: BDMMAcorrect Version: 1.8.0 Depends: R (>= 3.5), vegan, ellipse, ggplot2, ape, SummarizedExperiment Imports: Rcpp (>= 0.12.12), RcppArmadillo, RcppEigen, stats LinkingTo: Rcpp, RcppArmadillo, RcppEigen Suggests: knitr, rmarkdown, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: f21073130041f39170320e9891690d2f NeedsCompilation: yes Title: Meta-analysis for the metagenomic read counts data from different cohorts Description: Metagenomic sequencing techniques enable quantitative analyses of the microbiome. However, combining the microbial data from these experiments is challenging due to the variations between experiments. The existing methods for correcting batch effects do not consider the interactions between variables—microbial taxa in microbial studies—and the overdispersion of the microbiome data. Therefore, they are not applicable to microbiome data. We develop a new method, Bayesian Dirichlet-multinomial regression meta-analysis (BDMMA), to simultaneously model the batch effects and detect the microbial taxa associated with phenotypes. BDMMA automatically models the dependence among microbial taxa and is robust to the high dimensionality of the microbiome and their association sparsity. biocViews: ImmunoOncology, BatchEffect, Microbiome, Bayesian Author: ZHENWEI DAI Maintainer: ZHENWEI DAI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BDMMAcorrect git_branch: RELEASE_3_12 git_last_commit: 0e87f21 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BDMMAcorrect_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BDMMAcorrect_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BDMMAcorrect_1.8.0.tgz vignettes: vignettes/BDMMAcorrect/inst/doc/Vignette.pdf vignetteTitles: BDMMAcorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BDMMAcorrect/inst/doc/Vignette.R dependencyCount: 64 Package: beachmat Version: 2.6.4 Imports: methods, DelayedArray (>= 0.15.14), BiocGenerics, Matrix Suggests: testthat, BiocStyle, knitr, rmarkdown, rcmdcheck, BiocParallel License: GPL-3 MD5sum: 3f67e380686b3a3d41ab18074bba80c3 NeedsCompilation: yes Title: Compiling Bioconductor to Handle Each Matrix Type Description: Provides a consistent C++ class interface for reading from and writing data to a variety of commonly used matrix types. Ordinary matrices and several sparse/dense Matrix classes are directly supported, third-party S4 classes may be supported by external linkage, while all other matrices are handled by DelayedArray block processing. biocViews: DataRepresentation, DataImport, Infrastructure Author: Aaron Lun [aut, cre], Hervé Pagès [aut], Mike Smith [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beachmat git_branch: RELEASE_3_12 git_last_commit: 7d9dc63 git_last_commit_date: 2020-12-19 Date/Publication: 2020-12-20 source.ver: src/contrib/beachmat_2.6.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/beachmat_2.6.4.zip mac.binary.ver: bin/macosx/contrib/4.0/beachmat_2.6.4.tgz vignettes: vignettes/beachmat/inst/doc/external.html, vignettes/beachmat/inst/doc/input.html, vignettes/beachmat/inst/doc/linking.html, vignettes/beachmat/inst/doc/output.html vignetteTitles: 4. Supporting arbitrary matrix classes (v2), 2. Reading data from R matrices in C++ (v2), 1. Developer guide, 3. Writing data into R matrix objects (v2) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beachmat/inst/doc/external.R, vignettes/beachmat/inst/doc/input.R, vignettes/beachmat/inst/doc/linking.R, vignettes/beachmat/inst/doc/output.R importsMe: BiocSingular, DropletUtils, scran, scuttle, SingleR suggestsMe: bsseq, glmGamPoi, mbkmeans, PCAtools, scCB2, TSCAN linksToMe: BiocSingular, bsseq, DropletUtils, glmGamPoi, mbkmeans, PCAtools, scran, scuttle, SingleR dependencyCount: 16 Package: beadarray Version: 2.40.0 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), hexbin Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio, methods, ggplot2 Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: MIT + file LICENSE Archs: i386, x64 MD5sum: 6444e90ccb814fddb89e43f6cd2f9c05 NeedsCompilation: yes Title: Quality assessment and low-level analysis for Illumina BeadArray data Description: The package is able to read bead-level data (raw TIFFs and text files) output by BeadScan as well as bead-summary data from BeadStudio. Methods for quality assessment and low-level analysis are provided. biocViews: Microarray, OneChannel, QualityControl, Preprocessing Author: Mark Dunning, Mike Smith, Jonathan Cairns, Andy Lynch, Matt Ritchie Maintainer: Mark Dunning VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/beadarray git_branch: RELEASE_3_12 git_last_commit: 785579b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/beadarray_2.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/beadarray_2.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/beadarray_2.40.0.tgz vignettes: vignettes/beadarray/inst/doc/beadarray.pdf, vignettes/beadarray/inst/doc/beadlevel.pdf, vignettes/beadarray/inst/doc/beadsummary.pdf, vignettes/beadarray/inst/doc/ImageProcessing.pdf vignetteTitles: beadarray.pdf, beadlevel.pdf, beadsummary.pdf, ImageProcessing.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/beadarray/inst/doc/beadarray.R, vignettes/beadarray/inst/doc/beadlevel.R, vignettes/beadarray/inst/doc/beadsummary.R, vignettes/beadarray/inst/doc/ImageProcessing.R dependsOnMe: beadarrayExampleData, beadarrayFilter importsMe: arrayQualityMetrics, blima, epigenomix, BeadArrayUseCases, RobLoxBioC suggestsMe: beadarraySNP, lumi, blimaTestingData, maGUI dependencyCount: 75 Package: beadarraySNP Version: 1.56.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: 7a353a552dabbe06e5f2aa5ded53cceb NeedsCompilation: no Title: Normalization and reporting of Illumina SNP bead arrays Description: Importing data from Illumina SNP experiments and performing copy number calculations and reports. biocViews: CopyNumberVariation, SNP, GeneticVariability, TwoChannel, Preprocessing, DataImport Author: Jan Oosting Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/beadarraySNP git_branch: RELEASE_3_12 git_last_commit: 49500dd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/beadarraySNP_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/beadarraySNP_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/beadarraySNP_1.56.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/beadarraySNP/inst/doc/beadarraySNP.R dependencyCount: 19 Package: BeadDataPackR Version: 1.42.0 Imports: stats, utils Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: 30f2b7b761d40ca7af3499f4e619e797 NeedsCompilation: yes Title: Compression of Illumina BeadArray data Description: Provides functionality for the compression and decompression of raw bead-level data from the Illumina BeadArray platform. biocViews: Microarray Author: Mike Smith, Andy Lynch Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BeadDataPackR git_branch: RELEASE_3_12 git_last_commit: 95371b9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BeadDataPackR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BeadDataPackR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BeadDataPackR_1.42.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.R importsMe: beadarray dependencyCount: 2 Package: BEARscc Version: 1.10.0 Depends: R (>= 3.5.0) Imports: ggplot2, SingleCellExperiment, data.table, stats, utils, graphics, compiler Suggests: testthat, cowplot, knitr, rmarkdown, BiocStyle, NMF License: GPL-3 MD5sum: cf98be15a4e47d6f65b5e467a9f1fffd NeedsCompilation: no Title: BEARscc (Bayesian ERCC Assesstment of Robustness of Single Cell Clusters) Description: BEARscc is a noise estimation and injection tool that is designed to assess putative single-cell RNA-seq clusters in the context of experimental noise estimated by ERCC spike-in controls. biocViews: ImmunoOncology, SingleCell, Clustering, Transcriptomics Author: David T. Severson Maintainer: Benjamin Schuster-Boeckler VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BEARscc git_branch: RELEASE_3_12 git_last_commit: e20aa4b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BEARscc_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BEARscc_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BEARscc_1.10.0.tgz vignettes: vignettes/BEARscc/inst/doc/BEARscc.pdf vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEARscc/inst/doc/BEARscc.R dependencyCount: 59 Package: BEAT Version: 1.28.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 7a395ee24a4c7853ca59ef94cf1db887 NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: ImmunoOncology, Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman git_url: https://git.bioconductor.org/packages/BEAT git_branch: RELEASE_3_12 git_last_commit: 5406d3d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BEAT_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BEAT_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BEAT_1.28.0.tgz vignettes: vignettes/BEAT/inst/doc/BEAT.pdf vignetteTitles: Analysing single-cell BS-Seq data with the "BEAT" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEAT/inst/doc/BEAT.R dependencyCount: 47 Package: BEclear Version: 2.6.0 Depends: BiocParallel (>= 1.14.2) Imports: futile.logger, Rdpack, Matrix, data.table (>= 1.11.8), Rcpp, outliers, abind, stats, graphics, utils, methods LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, pander License: GPL-3 Archs: i386, x64 MD5sum: 0611c95acf2f35c253847eb09d192029 NeedsCompilation: yes Title: Correction of batch effects in DNA methylation data Description: Provides functions to detect and correct for batch effects in DNA methylation data. The core function is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers. biocViews: BatchEffect, DNAMethylation, Software, Preprocessing, StatisticalMethod Author: David Rasp [aut, cre] (), Markus Merl [aut], Ruslan Akulenko [aut] Maintainer: David Rasp URL: https://github.com/uds-helms/BEclear SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/uds-helms/BEclear/issues git_url: https://git.bioconductor.org/packages/BEclear git_branch: RELEASE_3_12 git_last_commit: c0d806a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BEclear_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BEclear_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BEclear_2.6.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.html vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BEclear/inst/doc/BEclear.R dependencyCount: 23 Package: BgeeCall Version: 1.6.2 Depends: R (>= 3.6) Imports: GenomicFeatures, rhdf5, tximport, Biostrings, rtracklayer, biomaRt, jsonlite, methods, grDevices, graphics, stats, utils, rslurm Suggests: knitr, testthat, rmarkdown, AnnotationHub, httr License: GPL-3 MD5sum: 373edd5e15bc23dff0d9eab04f84e9fe NeedsCompilation: no Title: Automatic RNA-Seq present/absent gene expression calls generation Description: BgeeCall allows to generate present/absent gene expression calls without using an arbitrary cutoff like TPM<1. Calls are generated based on reference intergenic sequences. These sequences are generated based on expression of all RNA-Seq libraries of each species integrated in Bgee (https://bgee.org). biocViews: Software, GeneExpression, RNASeq Author: Julien Wollbrett [aut, cre], Julien Roux [aut], Sara Fonseca Costa [ctb], Marc Robinson Rechavi [ctb], Frederic Bastian [aut] Maintainer: Julien Wollbrett URL: https://github.com/BgeeDB/BgeeCall SystemRequirements: kallisto VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeCall/issues git_url: https://git.bioconductor.org/packages/BgeeCall git_branch: RELEASE_3_12 git_last_commit: 1b74dcf git_last_commit_date: 2020-12-10 Date/Publication: 2020-12-10 source.ver: src/contrib/BgeeCall_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/BgeeCall_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.0/BgeeCall_1.6.2.tgz vignettes: vignettes/BgeeCall/inst/doc/bgeecall-manual.html vignetteTitles: automatic RNA-Seq present/absent gene expression calls generation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BgeeCall/inst/doc/bgeecall-manual.R dependencyCount: 94 Package: BgeeDB Version: 2.16.0 Depends: R (>= 3.6.0), topGO, tidyr Imports: data.table, curl, RCurl, digest, methods, stats, utils, dplyr, RSQLite, graph, Biobase Suggests: knitr, BiocStyle, testthat, rmarkdown License: GPL-3 + file LICENSE MD5sum: 35577e1735d11627917234d044f96181 NeedsCompilation: no Title: Annotation and gene expression data retrieval from Bgee database. TopAnat, an anatomical entities Enrichment Analysis tool for UBERON ontology Description: A package for the annotation and gene expression data download from Bgee database, and TopAnat analysis: GO-like enrichment of anatomical terms, mapped to genes by expression patterns. biocViews: Software, DataImport, Sequencing, GeneExpression, Microarray, GO, GeneSetEnrichment Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Julien Wollbrett , Julien Roux , Andrea Komljenovic , Frederic Bastian URL: https://github.com/BgeeDB/BgeeDB_R VignetteBuilder: knitr BugReports: https://github.com/BgeeDB/BgeeDB_R/issues git_url: https://git.bioconductor.org/packages/BgeeDB git_branch: RELEASE_3_12 git_last_commit: ce6dd8f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BgeeDB_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BgeeDB_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BgeeDB_2.16.0.tgz vignettes: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html vignetteTitles: BgeeDB,, an R package for retrieval of curated expression datasets and for gene list enrichment tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R importsMe: psygenet2r, RITAN dependencyCount: 54 Package: BGmix Version: 1.50.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: 20854d28bc9da2ac7ee58a8651aed4d0 NeedsCompilation: yes Title: Bayesian models for differential gene expression Description: Fully Bayesian mixture models for differential gene expression biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Alex Lewin, Natalia Bochkina Maintainer: Alex Lewin git_url: https://git.bioconductor.org/packages/BGmix git_branch: RELEASE_3_12 git_last_commit: 3e2c6f2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BGmix_1.50.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/BGmix_1.50.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BGmix/inst/doc/BGmix.R dependencyCount: 2 Package: bgx Version: 1.56.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Imports: Rcpp (>= 0.11.0) LinkingTo: Rcpp Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: 3dc07c72f5966c9f8c3acef1c3f853a3 NeedsCompilation: yes Title: Bayesian Gene eXpression Description: Bayesian integrated analysis of Affymetrix GeneChips biocViews: Microarray, DifferentialExpression Author: Ernest Turro, Graeme Ambler, Anne-Mette K Hein Maintainer: Ernest Turro git_url: https://git.bioconductor.org/packages/bgx git_branch: RELEASE_3_12 git_last_commit: 45eff47 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bgx_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bgx_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bgx_1.56.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgx/inst/doc/bgx.R dependencyCount: 23 Package: BHC Version: 1.42.0 License: GPL-3 Archs: i386, x64 MD5sum: 09a3a174549dc146fb34beb4fee7b59b NeedsCompilation: yes Title: Bayesian Hierarchical Clustering Description: The method performs bottom-up hierarchical clustering, using a Dirichlet Process (infinite mixture) to model uncertainty in the data and Bayesian model selection to decide at each step which clusters to merge. This avoids several limitations of traditional methods, for example how many clusters there should be and how to choose a principled distance metric. This implementation accepts multinomial (i.e. discrete, with 2+ categories) or time-series data. This version also includes a randomised algorithm which is more efficient for larger data sets. biocViews: Microarray, Clustering Author: Rich Savage, Emma Cooke, Robert Darkins, Yang Xu Maintainer: Rich Savage git_url: https://git.bioconductor.org/packages/BHC git_branch: RELEASE_3_12 git_last_commit: 8e8e9b4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BHC_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BHC_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BHC_1.42.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BHC/inst/doc/bhc.R dependencyCount: 0 Package: BicARE Version: 1.48.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: fe7a2f752dfcf493a8304468f575fa11 NeedsCompilation: yes Title: Biclustering Analysis and Results Exploration Description: Biclustering Analysis and Results Exploration biocViews: Microarray, Transcription, Clustering Author: Pierre Gestraud Maintainer: Pierre Gestraud URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/BicARE git_branch: RELEASE_3_12 git_last_commit: 15e7609 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BicARE_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BicARE_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BicARE_1.48.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BicARE/inst/doc/BicARE.R dependsOnMe: RcmdrPlugin.BiclustGUI importsMe: miRSM dependencyCount: 48 Package: BiFET Version: 1.10.0 Imports: stats, poibin, GenomicRanges Suggests: testthat, knitr License: GPL-3 MD5sum: eb76762d53f0dbfdea2c3d1bcaedb396 NeedsCompilation: no Title: Bias-free Footprint Enrichment Test Description: BiFET identifies TFs whose footprints are over-represented in target regions compared to background regions after correcting for the bias arising from the imbalance in read counts and GC contents between the target and background regions. For a given TF k, BiFET tests the null hypothesis that the target regions have the same probability of having footprints for the TF k as the background regions while correcting for the read count and GC content bias. For this, we use the number of target regions with footprints for TF k, t_k as a test statistic and calculate the p-value as the probability of observing t_k or more target regions with footprints under the null hypothesis. biocViews: ImmunoOncology, Genetics, Epigenetics, Transcription, GeneRegulation, ATACSeq, DNaseSeq, RIPSeq, Software Author: Ahrim Youn [aut, cre], Eladio Marquez [aut], Nathan Lawlor [aut], Michael Stitzel [aut], Duygu Ucar [aut] Maintainer: Ahrim Youn VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiFET git_branch: RELEASE_3_12 git_last_commit: 6562e26 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiFET_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiFET_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiFET_1.10.0.tgz vignettes: vignettes/BiFET/inst/doc/BiFET.html vignetteTitles: "A Guide to using BiFET" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiFET/inst/doc/BiFET.R dependencyCount: 18 Package: BiGGR Version: 1.26.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph, limSolve License: file LICENSE MD5sum: fb79f157b492f6ff7f91d9a5a8c26f95 NeedsCompilation: no Title: Constraint based modeling in R using metabolic reconstruction databases Description: This package provides an interface to simulate metabolic reconstruction from the BiGG database(http://bigg.ucsd.edu/) and other metabolic reconstruction databases. The package facilitates flux balance analysis (FBA) and the sampling of feasible flux distributions. Metabolic networks and estimated fluxes can be visualized with hypergraphs. biocViews: Systems Biology,Pathway,Network,GraphAndNetwork, Visualization,Metabolomics Author: Anand K. Gavai, Hannes Hettling Maintainer: Anand K. Gavai , Hannes Hettling URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/BiGGR git_branch: RELEASE_3_12 git_last_commit: dde16eb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiGGR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiGGR_1.26.0.zip vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R dependencyCount: 26 Package: bigmelon Version: 1.16.0 Depends: R (>= 3.3), wateRmelon (>= 1.25.0), gdsfmt (>= 1.0.4), methods, minfi (>= 1.21.0), Biobase, methylumi Imports: stats, utils, GEOquery, graphics, BiocGenerics Suggests: BiocGenerics, RUnit, BiocStyle, minfiData, parallel, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, bumphunter License: GPL-3 MD5sum: 3458fcd386880c8b99acddea3e3c90d4 NeedsCompilation: no Title: Illumina methylation array analysis for large experiments Description: Methods for working with Illumina arrays using gdsfmt. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl, MethylationArray, DataImport, CpGIsland Author: Tyler J. Gorrie-Stone [cre, aut], Ayden Saffari [aut], Karim Malki [aut], Leonard C. Schalkwyk [aut] Maintainer: Tyler J. Gorrie-Stone git_url: https://git.bioconductor.org/packages/bigmelon git_branch: RELEASE_3_12 git_last_commit: e87386b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bigmelon_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bigmelon_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bigmelon_1.16.0.tgz vignettes: vignettes/bigmelon/inst/doc/bigmelon.pdf vignetteTitles: The bigmelon Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigmelon/inst/doc/bigmelon.R dependencyCount: 161 Package: bigmemoryExtras Version: 1.38.0 Depends: R (>= 2.12), bigmemory (>= 4.5.31) Imports: methods Suggests: testthat, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 OS_type: unix MD5sum: 0c162388c855072256556bf914857fc0 NeedsCompilation: no Title: An extension of the bigmemory package with added safety, convenience, and a factor class Description: This package defines a "BigMatrix" ReferenceClass which adds safety and convenience features to the filebacked.big.matrix class from the bigmemory package. BigMatrix protects against segfaults by monitoring and gracefully restoring the connection to on-disk data and it also protects against accidental data modification with a filesystem-based permissions system. We provide utilities for using BigMatrix-derived classes as assayData matrices within the Biobase package's eSet family of classes. BigMatrix provides some optimizations related to attaching to, and indexing into, file-backed matrices with dimnames. Additionally, the package provides a "BigMatrixFactor" class, a file-backed matrix with factor properties. biocViews: Infrastructure, DataRepresentation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/bigmemoryExtras VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bigmemoryExtras git_branch: RELEASE_3_12 git_last_commit: 3731aac git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bigmemoryExtras_1.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/bigmemoryExtras_1.38.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: bigPint Version: 1.6.0 Depends: R (>= 3.6.0) Imports: DelayedArray (>= 0.12.2), dplyr (>= 0.7.2), GGally (>= 1.3.2), ggplot2 (>= 2.2.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), grid (>= 3.5.0), gridExtra (>= 2.3), hexbin (>= 1.27.1), Hmisc (>= 4.0.3), htmlwidgets (>= 0.9), methods (>= 3.5.2), plotly (>= 4.7.1), plyr (>= 1.8.4), RColorBrewer (>= 1.1.2), reshape (>= 0.8.7), shiny (>= 1.0.5), shinycssloaders (>= 0.2.0), shinydashboard (>= 0.6.1), stats (>= 3.5.0), stringr (>= 1.3.1), SummarizedExperiment (>= 1.16.1), tidyr (>= 0.7.0), utils (>= 3.5.0) Suggests: BiocGenerics (>= 0.29.1), data.table (>= 1.11.8), EDASeq (>= 2.14.0), edgeR (>= 3.22.2), gtools (>= 3.5.0), knitr (>= 1.13), matrixStats (>= 0.53.1), rmarkdown (>= 1.10), roxygen2 (>= 3.0.0), RUnit (>= 0.4.32), tibble (>= 1.4.2), License: GPL-3 MD5sum: c4ba758e50ad33ebb7e4a278b447d235 NeedsCompilation: no Title: Big multivariate data plotted interactively Description: Methods for visualizing large multivariate datasets using static and interactive scatterplot matrices, parallel coordinate plots, volcano plots, and litre plots. Includes examples for visualizing RNA-sequencing datasets and differentially expressed genes. biocViews: Clustering, DataImport, DifferentialExpression, GeneExpression, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Sequencing, Software, Transcription, Visualization Author: Lindsay Rutter [aut, cre], Dianne Cook [aut] Maintainer: Lindsay Rutter URL: https://github.com/lindsayrutter/bigPint VignetteBuilder: knitr BugReports: https://github.com/lindsayrutter/bigPint/issues git_url: https://git.bioconductor.org/packages/bigPint git_branch: RELEASE_3_12 git_last_commit: e7062bb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bigPint_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bigPint_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bigPint_1.6.0.tgz vignettes: vignettes/bigPint/inst/doc/bioconductor.html, vignettes/bigPint/inst/doc/manuscripts.html, vignettes/bigPint/inst/doc/summarizedExperiment.html vignetteTitles: "bigPint Vignette", "Recommended RNA-seq pipeline", "Data metrics object" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bigPint/inst/doc/bioconductor.R, vignettes/bigPint/inst/doc/manuscripts.R, vignettes/bigPint/inst/doc/summarizedExperiment.R dependencyCount: 125 Package: bioassayR Version: 1.28.3 Depends: R (>= 3.5.0), DBI (>= 0.3.1), RSQLite (>= 1.0.0), methods, Matrix, rjson, BiocGenerics (>= 0.13.8) Imports: XML, ChemmineR Suggests: BiocStyle, RCurl, biomaRt, cellHTS2, knitr, knitcitations, knitrBootstrap, testthat, ggplot2, rmarkdown License: Artistic-2.0 MD5sum: 36d766d326b3c558b7ddf2d6f83754e9 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: bioassayR is a computational tool that enables simultaneous analysis of thousands of bioassay experiments performed over a diverse set of compounds and biological targets. Unique features include support for large-scale cross-target analyses of both public and custom bioassays, generation of high throughput screening fingerprints (HTSFPs), and an optional preloaded database that provides access to a substantial portion of publicly available bioactivity data. biocViews: ImmunoOncology, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Daniela Cassol URL: https://github.com/girke-lab/bioassayR VignetteBuilder: knitr BugReports: https://github.com/girke-lab/bioassayR/issues git_url: https://git.bioconductor.org/packages/bioassayR git_branch: RELEASE_3_12 git_last_commit: bc3e252 git_last_commit_date: 2021-04-18 Date/Publication: 2021-04-19 source.ver: src/contrib/bioassayR_1.28.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/bioassayR_1.28.3.zip mac.binary.ver: bin/macosx/contrib/4.0/bioassayR_1.28.3.tgz vignettes: vignettes/bioassayR/inst/doc/bioassayR.html vignetteTitles: bioassayR Introduction and Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R dependencyCount: 69 Package: Biobase Version: 2.50.0 Depends: R (>= 2.10), BiocGenerics (>= 0.27.1), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: fc0fb1f68f2aed8b314c8347afb2257c NeedsCompilation: yes Title: Biobase: Base functions for Bioconductor Description: Functions that are needed by many other packages or which replace R functions. biocViews: Infrastructure Author: R. Gentleman, V. Carey, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Biobase BugReports: https://github.com/Bioconductor/Biobase/issues git_url: https://git.bioconductor.org/packages/Biobase git_branch: RELEASE_3_12 git_last_commit: 9927f90 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Biobase_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Biobase_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Biobase_2.50.0.tgz vignettes: vignettes/Biobase/inst/doc/BiobaseDevelopment.pdf, vignettes/Biobase/inst/doc/esApply.pdf, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.pdf vignetteTitles: Notes for eSet developers, esApply Introduction, An introduction to Biobase and ExpressionSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biobase/inst/doc/BiobaseDevelopment.R, vignettes/Biobase/inst/doc/esApply.R, vignettes/Biobase/inst/doc/ExpressionSetIntroduction.R dependsOnMe: ACME, affy, affycomp, affyContam, affycoretools, affyPLM, affyQCReport, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, ArrayTools, Autotuner, BAGS, beadarray, beadarraySNP, bgx, BicARE, bigmelon, BiocCaseStudies, BioMVCClass, BioQC, biosigner, BLMA, BrainStars, CAMERA, cancerclass, casper, Category, categoryCompare, CCPROMISE, cellHTS2, CGHbase, CGHcall, CGHregions, chimera, clippda, clusterStab, CMA, cn.farms, codelink, convert, copa, covEB, covRNA, DEXSeq, DFP, diggit, doppelgangR, DSS, dualKS, dyebias, EBarrays, EDASeq, edge, EGSEA, eisa, epigenomix, epivizrData, ExiMiR, ExpressionAtlas, fabia, factDesign, fastseg, flowBeads, frma, gaga, GeneAnswers, GeneExpressionSignature, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, geNetClassifier, GEOquery, GOexpress, goProfiles, GOstats, GSEABase, GSEABenchmarkeR, GSEAlm, GWASTools, hapFabia, HELP, hopach, HTqPCR, HybridMTest, iCheck, IdeoViz, idiogram, InPAS, INSPEcT, isobar, iterativeBMA, IVAS, lumi, macat, made4, mAPKL, massiR, MEAL, metagenomeFeatures, metagenomeSeq, metavizr, MethPed, methyAnalysis, methylumi, Mfuzz, MiChip, microbiomeExplorer, mimager, MIMOSA, MineICA, MiRaGE, miRcomp, MLInterfaces, monocle, MSnbase, Mulcom, MultiDataSet, multtest, NanoStringDiff, NOISeq, nondetects, normalize450K, NormqPCR, oligo, omicRexposome, OrderedList, OTUbase, OutlierD, pandaR, panp, pcaMethods, pcot2, pdInfoBuilder, pepStat, phenoTest, PLPE, PREDA, pRolocGUI, PROMISE, qpcrNorm, qPLEXanalyzer, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, RefPlus, rexposome, Ringo, Risa, Rmagpie, RNAinteract, rnaSeqMap, Rnits, ropls, RpsiXML, RTopper, RUVSeq, safe, SCAN.UPC, SeqGSEA, SigCheck, siggenes, simpleaffy, simulatorZ, singleCellTK, SpeCond, SPEM, spkTools, splineTimeR, STROMA4, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, tspair, twilight, UNDO, variancePartition, VegaMC, viper, vsn, wateRmelon, webbioc, xcms, XDE, yarn, EuPathDB, affycompData, ALL, bcellViper, beadarrayExampleData, bladderbatch, brgedata, cancerdata, CCl4, ceu1kg, ceuhm3, CLL, colonCA, CRCL18, curatedBreastData, curatedMetagenomicData, davidTiling, diggitdata, DLBCL, dressCheck, dsQTL, etec16s, fabiaData, fibroEset, gaschYHS, GGdata, golubEsets, GSE62944, GSVAdata, harbChIP, Hiiragi2013, hmyriB36, HumanAffyData, humanStemCell, Iyer517, kidpack, leeBamViews, leukemiasEset, lumiBarnes, lungExpression, MAQCsubset, MAQCsubsetAFX, MAQCsubsetILM, MetaGxBreast, MetaGxOvarian, miRNATarget, msd16s, mvoutData, Neve2006, PREDAsampledata, ProData, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, pumadata, rcellminerData, RUVnormalizeData, SpikeInSubset, TCGAcrcmiRNA, TCGAcrcmRNA, tweeDEseqCountData, yeastCC, maEndToEnd, countTransformers, crmn, dGAselID, GExMap, GWASbyCluster, lmQCM, MM2Sdata, MMDvariance, permGPU, propOverlap, statVisual importsMe: a4Base, a4Classif, a4Core, a4Preproc, ABarray, ACE, aCGH, adSplit, affyILM, affyQCReport, AgiMicroRna, ANF, annmap, annotate, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, ArrayTools, attract, ballgown, BASiCS, BayesKnockdown, BgeeDB, biobroom, bioCancer, biocViews, BioNet, biscuiteer, BiSeq, blima, BrainStars, bsseq, BubbleTree, CAFE, canceR, Cardinal, CellScore, CellTrails, CGHnormaliter, ChIPQC, ChIPXpress, ChromHeatMap, chromswitch, cicero, clipper, CluMSID, cn.mops, COCOA, coexnet, cogena, combi, ConsensusClusterPlus, consensusDE, consensusOV, coRdon, CoreGx, crlmm, crossmeta, ctgGEM, cummeRbund, cycle, cydar, CytoML, CytoTree, ddCt, debCAM, deco, DEGreport, DESeq2, destiny, diffloop, discordant, EBarrays, ecolitk, EGAD, ensembldb, erma, esetVis, ExiMiR, farms, ffpe, FindMyFriends, flowClust, flowCore, flowFP, flowMatch, flowMeans, flowSpecs, flowSpy, flowStats, flowUtils, flowViz, flowWorkspace, FourCSeq, FRASER, frma, frmaTools, FunciSNP, GAPGOM, gCrisprTools, gcrma, GCSscore, genbankr, geneClassifiers, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GENESIS, GenomicFeatures, GenomicInteractions, GenomicScores, GEOsubmission, gep2pep, gespeR, GGBase, ggbio, GGtools, girafe, GISPA, GlobalAncova, globaltest, gmapR, gQTLstats, GSRI, GSVA, Gviz, Harshlight, HEM, HTqPCR, HTSFilter, imageHTS, ImmuneSpaceR, infinityFlow, IsoGeneGUI, isomiRs, iterClust, kissDE, lapmix, LiquidAssociation, LRBaseDbi, maanova, MAGeCKFlute, makecdfenv, maSigPro, MAST, mBPCR, MeSHDbi, metaseqR2, methyAnalysis, MethylAid, methylCC, methylumi, mfa, MiChip, microbiomeDASim, MIGSA, minfi, MinimumDistance, MiPP, MIRA, miRSM, missMethyl, MLSeq, MMAPPR2, MOFA, mogsa, MoonlightR, MOSim, MSEADbi, MSnID, MultiAssayExperiment, multiscan, mzR, NanoStringQCPro, NormalyzerDE, npGSEA, nucleR, oligoClasses, omicade4, ontoProc, oposSOM, oppar, OrganismDbi, panp, PCpheno, phantasus, PharmacoGx, phemd, phyloseq, piano, plethy, plgem, plier, podkat, POMA, POST, ppiStats, prebs, PrInCE, proBatch, proFIA, progeny, pRoloc, PROMISE, PROPS, ProteomicsAnnotationHubData, PSEA, psygenet2r, puma, pvac, pvca, pwOmics, qcmetrics, QDNAseq, QFeatures, qpgraph, quantro, QuasR, qusage, RadioGx, randPack, RGalaxy, RIVER, Rmagpie, rols, ROTS, rqubic, rScudo, Rtpca, Rtreemix, RUVnormalize, SAGx, scmap, scTGIF, SeqVarTools, ShortRead, SigsPack, sigsquared, SimBindProfiles, simpleaffy, singscore, SLGI, SomaticSignatures, spkTools, SPONGE, STATegRa, subSeq, synapter, TEQC, TFBSTools, timecourse, TMixClust, TnT, ToPASeq, topdownr, ToxicoGx, tradeSeq, TTMap, twilight, uSORT, VanillaICE, VariantAnnotation, VariantFiltering, VariantTools, vidger, vulcan, wateRmelon, wpm, XBSeq, Xeva, BloodCancerMultiOmics2017, ccTutorial, ceu1kgv, cgdv17, DeSousa2013, Fletcher2013a, hgu133plus2CellScore, IHWpaper, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, mcsurvdata, pRolocdata, RNAinteractMAPK, seqc, signatureSearchData, yri1kgv, ExpressionNormalizationWorkflow, bapred, BisqueRNA, CDSeq, ClassComparison, ClassDiscovery, FMradio, geneExpressionFromGEO, HiResTEC, IntegratedJM, IsoGene, maGUI, MetaIntegrator, nlcv, NMF, PerseusR, pulseTD, ragt2ridges, rmRNAseq, RobLox, RobLoxBioC, RPPanalyzer, ssizeRNA, TailRank suggestsMe: AUCell, BiocCaseStudies, BiocCheck, BiocGenerics, BiocOncoTK, BSgenome, CellMapper, cellTree, clustComp, coseq, CountClust, DART, dcanr, dearseq, edgeR, EnMCB, EpiDISH, epivizr, epivizrChart, epivizrStandalone, farms, genefu, GENIE3, GenomicRanges, GSAR, GSgalgoR, Heatplus, interactiveDisplay, kebabs, les, limma, M3Drop, mCSEA, messina, msa, multiClust, OSAT, PCAtools, pkgDepTools, RcisTarget, ReactomeGSA, ROC, RTCGA, scater, scmeth, scran, SeqArray, slinky, spatialHeatmap, stageR, survcomp, TargetScore, TCGAbiolinks, TFutils, TimeSeriesExperiment, tkWidgets, TypeInfo, vbmp, widgetTools, biotmleData, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, ccTutorial, dorothea, dyebiasexamples, HMP16SData, HMP2Data, mammaPrintData, mAPKLData, methyvimData, RegParallel, rheumaticConditionWOLLBOLD, seventyGeneData, yeastExpData, yeastRNASeq, amap, aroma.affymetrix, BaseSet, clValid, CrossValidate, distrDoc, dnet, dplR, exp2flux, GenAlgo, hexbin, HTSCluster, Modeler, multiclassPairs, NACHO, optCluster, ordPens, Patterns, pkgmaker, propr, rknn, Seurat, sigminer, SourceSet dependencyCount: 6 Package: biobroom Version: 1.22.0 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, testthat, magrittr, edgeR, qvalue, knitr, data.table, MSnbase, SummarizedExperiment License: LGPL MD5sum: 05f8e479e94468757246d48bffdfa7be NeedsCompilation: no Title: Turn Bioconductor objects into tidy data frames Description: This package contains methods for converting standard objects constructed by bioinformatics packages, especially those in Bioconductor, and converting them to tidy data. It thus serves as a complement to the broom package, and follows the same the tidy, augment, glance division of tidying methods. Tidying data makes it easy to recombine, reshape and visualize bioinformatics analyses. biocViews: MultipleComparison, DifferentialExpression, Regression, GeneExpression, Proteomics, DataImport Author: Andrew J. Bass, David G. Robinson, Steve Lianoglou, Emily Nelson, John D. Storey, with contributions from Laurent Gatto Maintainer: John D. Storey and Andrew J. Bass URL: https://github.com/StoreyLab/biobroom VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/biobroom/issues git_url: https://git.bioconductor.org/packages/biobroom git_branch: RELEASE_3_12 git_last_commit: 8fe86f2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biobroom_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biobroom_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biobroom_1.22.0.tgz vignettes: vignettes/biobroom/inst/doc/biobroom_vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R importsMe: TPP dependencyCount: 33 Package: biobtreeR Version: 1.2.0 Imports: httr, httpuv, stringi,jsonlite,methods,utils Suggests: BiocStyle, knitr,testthat License: MIT + file LICENSE MD5sum: d08933d0ab39a128325ce3f0e259f94a NeedsCompilation: no Title: Using biobtree tool from R Description: The biobtreeR package provides an interface to [biobtree](https://github.com/tamerh/biobtree) tool which covers large set of bioinformatics datasets and allows search and chain mappings functionalities. biocViews: Annotation Author: Tamer Gur Maintainer: Tamer Gur URL: https://github.com/tamerh/biobtreeR VignetteBuilder: knitr BugReports: https://github.com/tamerh/biobtreeR/issues git_url: https://git.bioconductor.org/packages/biobtreeR git_branch: RELEASE_3_12 git_last_commit: e217ab5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biobtreeR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biobtreeR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biobtreeR_1.2.0.tgz vignettes: vignettes/biobtreeR/inst/doc/biobtreeR.html vignetteTitles: The biobtreeR users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biobtreeR/inst/doc/biobtreeR.R dependencyCount: 19 Package: bioCancer Version: 1.18.0 Depends: R (>= 3.5.0), radiant.data (>= 1.0.6), cgdsr(>= 1.2.6), XML(>= 3.99) Imports: shiny (>= 1.0.5), AlgDesign (>= 1.1.7.3), import (>= 1.1.0), methods, shinythemes, Biobase, geNetClassifier, AnnotationFuncs, org.Hs.eg.db, DOSE, clusterProfiler, reactome.db, ReactomePA, DiagrammeR(>= 1.0.5), visNetwork, htmlwidgets, plyr, tibble, DT (>= 0.12), dplyr (>= 0.8.5) Suggests: BiocStyle, rmarkdown, knitr, testthat (>= 0.10.0) License: AGPL-3 | file LICENSE MD5sum: fb4ddd2128564ed5dfba6aa038eef1ca NeedsCompilation: no Title: Interactive Multi-Omics Cancers Data Visualization and Analysis Description: bioCancer is a Shiny App to visualize and analyse interactively Multi-Assays of Cancer Genomic Data. biocViews: GUI, DataRepresentation, Network, MultipleComparison, Pathways, Reactome, Visualization,GeneExpression,GeneTarget Author: Karim Mezhoud [aut, cre] Maintainer: Karim Mezhoud URL: http://kmezhoud.github.io/bioCancer VignetteBuilder: knitr BugReports: https://github.com/kmezhoud/bioCancer/issues git_url: https://git.bioconductor.org/packages/bioCancer git_branch: RELEASE_3_12 git_last_commit: caddbd9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bioCancer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bioCancer_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bioCancer_1.18.0.tgz vignettes: vignettes/bioCancer/inst/doc/bioCancer.html vignetteTitles: bioCancer: Interactive Multi-OMICS Cancers Data Visualization and Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/bioCancer/inst/doc/bioCancer.R dependencyCount: 205 Package: BiocCaseStudies Version: 1.52.0 Depends: tools, methods, utils, Biobase Suggests: affy (>= 1.17.3), affyPLM (>= 1.15.1), affyQCReport (>= 1.17.0), ALL (>= 1.4.3), annaffy (>= 1.11.1), annotate (>= 1.17.3), AnnotationDbi (>= 1.1.6), apComplex (>= 2.5.0), Biobase (>= 1.17.5), bioDist (>= 1.11.3), biocGraph (>= 1.1.1), biomaRt (>= 1.13.5), CCl4 (>= 1.0.6), CLL (>= 1.2.4), Category (>= 2.5.0), class (>= 7.2-38), cluster (>= 1.11.9), convert (>= 1.15.0), gcrma (>= 2.11.1), genefilter (>= 1.17.6), geneplotter (>= 1.17.2), GO.db (>= 2.0.2), GOstats (>= 2.5.0), graph (>= 1.17.4), GSEABase (>= 1.1.13), hgu133a.db (>= 2.0.2), hgu95av2.db, hgu95av2cdf (>= 2.0.0), hgu95av2probe (>= 2.0.0), hopach (>= 1.13.0), KEGG.db (>= 2.0.2), kohonen (>= 2.0.2), lattice (>= 0.17.2), latticeExtra (>= 0.3-1), limma (>= 2.13.1), MASS (>= 7.2-38), MLInterfaces (>= 1.13.17), multtest (>= 1.19.0), org.Hs.eg.db (>= 2.0.2), ppiStats (>= 1.5.4), randomForest (>= 4.5-20), RBGL (>= 1.15.6), RColorBrewer (>= 1.0-2), Rgraphviz (>= 1.17.11), vsn (>= 3.4.0), weaver (>= 1.5.0), xtable (>= 1.5-2), yeastExpData (>= 0.9.11) License: Artistic-2.0 MD5sum: 400995f3c28a4094975ec3934a8b6a40 NeedsCompilation: no Title: BiocCaseStudies: Support for the Case Studies Monograph Description: Software and data to support the case studies. biocViews: Infrastructure Author: R. Gentleman, W. Huber, F. Hahne, M. Morgan, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocCaseStudies git_branch: RELEASE_3_12 git_last_commit: 5b6b517 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocCaseStudies_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocCaseStudies_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocCaseStudies_1.52.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: BiocCheck Version: 1.26.0 Depends: R (>= 3.5.0) Imports: biocViews (>= 1.33.7), BiocManager, stringdist, graph, httr, tools, optparse, codetools, methods, utils, knitr Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown, devtools (>= 1.4.1), usethis, BiocStyle Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: ed1d5453f9b79d9e28625bb19e08a0da NeedsCompilation: no Title: Bioconductor-specific package checks Description: Executes Bioconductor-specific package checks. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre], Lori Shepherd [aut], Daniel von Twisk [ctb], Kevin Rue [ctb], Marcel Ramos [ctb], Leonardo Collado-Torres [ctb], Federico Marini [ctb] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocCheck/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocCheck git_branch: RELEASE_3_12 git_last_commit: f80471c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocCheck_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocCheck_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocCheck_1.26.0.tgz vignettes: vignettes/BiocCheck/inst/doc/BiocCheck.html vignetteTitles: BiocCheck: Ensuring Bioconductor package guidelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R importsMe: ExperimentHubData suggestsMe: packFinder, preciseTAD, SpectralTAD, curatedMetagenomicData, HMP16SData, HMP2Data dependencyCount: 40 Package: BiocDockerManager Version: 1.2.1 Depends: R (>= 4.0) Imports: httr, whisker, readr, dplyr, utils, methods, memoise Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 716c25ca443356bff6f8121950816879 NeedsCompilation: no Title: Access Bioconductor docker images Description: Package works analogous to BiocManager but for docker images. Use the BiocDockerManager package to install and manage docker images provided by the Bioconductor project. A convenient package to install images, update images and find which Bioconductor based docker images are available. biocViews: Software, Infrastructure, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Nitesh Turaga [aut] Maintainer: Bioconductor Package Maintainer SystemRequirements: docker VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocDockerManager/issues git_url: https://git.bioconductor.org/packages/BiocDockerManager git_branch: RELEASE_3_12 git_last_commit: 05cb39d git_last_commit_date: 2021-03-19 Date/Publication: 2021-03-20 source.ver: src/contrib/BiocDockerManager_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocDockerManager_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocDockerManager_1.2.1.tgz vignettes: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.html vignetteTitles: BiocDockerManager Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocDockerManager/inst/doc/BiocDockerManager.R dependencyCount: 38 Package: BiocFileCache Version: 1.14.0 Depends: R (>= 3.4.0), dbplyr (>= 1.0.0) Imports: methods, stats, utils, dplyr, RSQLite, DBI, rappdirs, curl, httr Suggests: testthat, knitr, BiocStyle, rmarkdown, rtracklayer License: Artistic-2.0 MD5sum: bec46391d4461ea384a385ad1773b010 NeedsCompilation: no Title: Manage Files Across Sessions Description: This package creates a persistent on-disk cache of files that the user can add, update, and retrieve. It is useful for managing resources (such as custom Txdb objects) that are costly or difficult to create, web resources, and data files used across sessions. biocViews: DataImport Author: Lori Shepherd [aut, cre], Martin Morgan [aut] Maintainer: Lori Shepherd VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocFileCache/issues git_url: https://git.bioconductor.org/packages/BiocFileCache git_branch: RELEASE_3_12 git_last_commit: cdcde4b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocFileCache_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocFileCache_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocFileCache_1.14.0.tgz vignettes: vignettes/BiocFileCache/inst/doc/BiocFileCache.html vignetteTitles: BiocFileCache: Managing File Resources Across Sessions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocFileCache/inst/doc/BiocFileCache.R dependsOnMe: AnnotationHub, ExperimentHub, RcwlPipelines importsMe: AMARETTO, atSNP, BayesSpace, BiocPkgTools, biomaRt, BrainSABER, brendaDb, cbaf, cBioPortalData, CellBench, customCMPdb, dasper, EnrichmentBrowser, EpiTxDb, GAPGOM, GenomicScores, GSEABenchmarkeR, gwascat, HCABrowser, HCAExplorer, HCAMatrixBrowser, MBQN, ontoProc, Organism.dplyr, psichomics, recount3, recountmethylation, regutools, rpx, TFutils, tximeta, UMI4Cats, UniProt.ws, waddR, org.Mxanthus.db, PANTHER.db, SingleCellMultiModal, spatialLIBD suggestsMe: bambu, basilisk.utils, BiocOncoTK, BiocSet, HumanTranscriptomeCompendium, MethReg, Nebulosa, progeny, seqsetvis, structToolbox, TCGAutils, TimeSeriesExperiment, HighlyReplicatedRNASeq, MethylSeqData, scRNAseq, TENxBrainData, TENxPBMCData, chipseqDB, fluentGenomics, simpleSingleCell dependencyCount: 45 Package: BiocGenerics Version: 0.36.1 Depends: R (>= 4.0.0), methods, utils, graphics, stats, parallel Imports: methods, utils, graphics, stats, parallel Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, DelayedArray, Biostrings, Rsamtools, AnnotationDbi, affy, affyPLM, DESeq2, flowClust, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: 8d9c499f9137c64259f2277f41005e1c NeedsCompilation: no Title: S4 generic functions used in Bioconductor Description: The package defines S4 generic functions used in Bioconductor. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/BiocGenerics BugReports: https://github.com/Bioconductor/BiocGenerics/issues git_url: https://git.bioconductor.org/packages/BiocGenerics git_branch: RELEASE_3_12 git_last_commit: 0b1c4b9 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/BiocGenerics_0.36.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocGenerics_0.36.1.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocGenerics_0.36.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, 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MeSH.Eco.55989.eg.db, MeSH.Eco.ED1a.eg.db, MeSH.Eco.IAI39.eg.db, MeSH.Eco.K12.MG1655.eg.db, MeSH.Eco.O157.H7.Sakai.eg.db, MeSH.Eco.UMN026.eg.db, MeSH.Eqc.eg.db, MeSH.Gga.eg.db, MeSH.Gma.eg.db, MeSH.Hsa.eg.db, MeSH.Laf.eg.db, MeSH.Lma.eg.db, MeSH.Mdo.eg.db, MeSH.Mes.eg.db, MeSH.Mga.eg.db, MeSH.Miy.eg.db, MeSH.Mml.eg.db, MeSH.Mmu.eg.db, MeSH.Mtr.eg.db, MeSH.Nle.eg.db, MeSH.Oan.eg.db, MeSH.Ocu.eg.db, MeSH.Oni.eg.db, MeSH.Osa.eg.db, MeSH.Pab.eg.db, MeSH.Pae.PAO1.eg.db, MeSH.PCR.db, MeSH.Pfa.3D7.eg.db, MeSH.Pto.eg.db, MeSH.Ptr.eg.db, MeSH.Rno.eg.db, MeSH.Sce.S288c.eg.db, MeSH.Sco.A32.eg.db, MeSH.Sil.eg.db, MeSH.Spu.eg.db, MeSH.Ssc.eg.db, MeSH.Syn.eg.db, MeSH.Tbr.9274.eg.db, MeSH.Tgo.ME49.eg.db, MeSH.Tgu.eg.db, MeSH.Vvi.eg.db, MeSH.Xla.eg.db, MeSH.Xtr.eg.db, MeSH.Zma.eg.db, ConnectivityMap, FieldEffectCrc, grndata, HarmanData, microRNAome, MIGSAdata, pwrEWAS.data, RegParallel, sesameData, adjclust, aroma.affymetrix, asteRisk, BioMedR, gkmSVM, MetaIntegrator, NutrienTrackeR, openSkies, pagoda2, polyRAD, Rediscover, Seurat dependencyCount: 5 Package: biocGraph Version: 1.52.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: b35480b0326d069401956232bfb006b2 NeedsCompilation: no Title: Graph examples and use cases in Bioinformatics Description: This package provides examples and code that make use of the different graph related packages produced by Bioconductor. biocViews: Visualization, GraphAndNetwork Author: Li Long , Robert Gentleman , Seth Falcon Florian Hahne Maintainer: Florian Hahne git_url: https://git.bioconductor.org/packages/biocGraph git_branch: RELEASE_3_12 git_last_commit: 0ffc853 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biocGraph_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biocGraph_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biocGraph_1.52.0.tgz vignettes: vignettes/biocGraph/inst/doc/biocGraph.pdf, vignettes/biocGraph/inst/doc/layingOutPathways.pdf vignetteTitles: Examples of plotting graphs Using Rgraphviz, HOWTO layout pathways hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocGraph/inst/doc/biocGraph.R, vignettes/biocGraph/inst/doc/layingOutPathways.R suggestsMe: BiocCaseStudies, EnrichmentBrowser dependencyCount: 45 Package: BiocIO Version: 1.0.1 Depends: R (>= 4.0) Imports: BiocGenerics, GenomicRanges, RCurl, S4Vectors, methods, tools Suggests: testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 7965729dbc75a4ea0c5f2a3ab8a16a4a NeedsCompilation: no Title: Standard Input and Output for Bioconductor Packages Description: Implements `import()` and `export()` standard generics for importing and exporting biological data formats. `import()` supports whole-file as well as chunk-wise iterative import. The `import()` interface optionally provides a standard mechanism for 'lazy' access via `filter()` (on row or element-like components of the file resource), `select()` (on column-like components of the file resource) and `collect()`. The `import()` interface optionally provides transparent access to remote (e.g. via https) as well as local access. Developers can register a file extension, e.g., `.loom` for dispatch from character-based URIs to specific `import()` / `export()` methods based on classes representing file types, e.g., `LoomFile()`. biocViews: Annotation,DataImport Author: Martin Morgan [aut], Michael Lawrence [aut], Daniel Van Twisk [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocIO/issues git_url: https://git.bioconductor.org/packages/BiocIO git_branch: RELEASE_3_12 git_last_commit: 724abe7 git_last_commit_date: 2020-11-09 Date/Publication: 2020-11-09 source.ver: src/contrib/BiocIO_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocIO_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocIO_1.0.1.tgz vignettes: vignettes/BiocIO/inst/doc/BiocIO.html vignetteTitles: BiocIO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocIO/inst/doc/BiocIO.R importsMe: BiocSet dependencyCount: 17 Package: BiocNeighbors Version: 1.8.2 Imports: Rcpp, S4Vectors, BiocParallel, stats, methods, Matrix LinkingTo: Rcpp, RcppHNSW Suggests: testthat, BiocStyle, knitr, rmarkdown, FNN, RcppAnnoy, RcppHNSW License: GPL-3 Archs: i386, x64 MD5sum: f3e819df44e8bb7dc3bf7543985ed7a8 NeedsCompilation: yes Title: Nearest Neighbor Detection for Bioconductor Packages Description: Implements exact and approximate methods for nearest neighbor detection, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Exact searches can be performed using the k-means for k-nearest neighbors algorithm or with vantage point trees. Approximate searches can be performed using the Annoy or HNSW libraries. Searching on either Euclidean or Manhattan distances is supported. Parallelization is achieved for all methods by using BiocParallel. Functions are also provided to search for all neighbors within a given distance. biocViews: Clustering, Classification Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocNeighbors git_branch: RELEASE_3_12 git_last_commit: 889bc91 git_last_commit_date: 2020-12-06 Date/Publication: 2020-12-07 source.ver: src/contrib/BiocNeighbors_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocNeighbors_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocNeighbors_1.8.2.tgz vignettes: vignettes/BiocNeighbors/inst/doc/approx.html, vignettes/BiocNeighbors/inst/doc/exact.html, vignettes/BiocNeighbors/inst/doc/range.html vignetteTitles: 2. Detecting approximate nearest neighbors, 1. Detecting exact nearest neighbors, 3. Detecting neighbors within range hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocNeighbors/inst/doc/approx.R, vignettes/BiocNeighbors/inst/doc/exact.R, vignettes/BiocNeighbors/inst/doc/range.R importsMe: batchelor, bluster, CellMixS, cydar, CytoTree, flowSpy, scater, scDblFinder, scran, SingleR suggestsMe: TSCAN dependencyCount: 21 Package: BiocOncoTK Version: 1.10.0 Depends: R (>= 3.6.0), methods, utils Imports: ComplexHeatmap, S4Vectors, bigrquery, shiny, stats, httr, rjson, dplyr, magrittr, grid, DT, GenomicRanges, IRanges, ggplot2, SummarizedExperiment, DBI, GenomicFeatures, curatedTCGAData, scales, ggpubr, plyr, car, graph, Rgraphviz Suggests: knitr, dbplyr, org.Hs.eg.db, MultiAssayExperiment, BiocStyle, ontoProc, ontologyPlot, pogos, GenomeInfoDb, restfulSE (>= 1.3.7), BiocFileCache, TxDb.Hsapiens.UCSC.hg19.knownGene, Biobase, TxDb.Hsapiens.UCSC.hg18.knownGene, reshape2, testthat, AnnotationDbi, FDb.InfiniumMethylation.hg19, EnsDb.Hsapiens.v75 License: Artistic-2.0 MD5sum: da47a752e1a508c7f0f666b792ab3124 NeedsCompilation: no Title: Bioconductor components for general cancer genomics Description: Provide a central interface to various tools for genome-scale analysis of cancer studies. biocViews: CopyNumberVariation, CpGIsland, DNAMethylation, GeneExpression, GeneticVariability, SNP, Transcription, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocOncoTK git_branch: RELEASE_3_12 git_last_commit: 4267875 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocOncoTK_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocOncoTK_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocOncoTK_1.10.0.tgz vignettes: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.html, vignettes/BiocOncoTK/inst/doc/curatedMSIData.html, vignettes/BiocOncoTK/inst/doc/maptcga.html vignetteTitles: BiocOncoTK -- cancer oriented components for Bioconductor, curatedMSIData overview, "Mapping TCGA tumor codes to NCIT" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocOncoTK/inst/doc/BiocOncoTK.R, vignettes/BiocOncoTK/inst/doc/curatedMSIData.R, vignettes/BiocOncoTK/inst/doc/maptcga.R dependencyCount: 198 Package: BioCor Version: 1.14.0 Depends: R (>= 3.4.0) Imports: BiocParallel, Matrix, methods, GSEABase Suggests: reactome.db, org.Hs.eg.db, WGCNA, GOSemSim, testthat, knitr, rmarkdown, BiocStyle, airway, DESeq2, boot, targetscan.Hs.eg.db, Hmisc, spelling License: MIT + file LICENSE MD5sum: 026616bc167963e4aa803c21fa660fad NeedsCompilation: no Title: Functional similarities Description: Calculates functional similarities based on the pathways described on KEGG and REACTOME or in gene sets. These similarities can be calculated for pathways or gene sets, genes, or clusters and combined with other similarities. They can be used to improve networks, gene selection, testing relationships... biocViews: StatisticalMethod, Clustering, GeneExpression, Network, Pathways, NetworkEnrichment, SystemsBiology Author: Lluís Revilla Sancho [aut, cre] (), Pau Sancho-Bru [ths] (), Juan José Salvatella Lozano [ths] () Maintainer: Lluís Revilla Sancho URL: https://llrs.github.io/BioCor/ VignetteBuilder: knitr BugReports: https://github.com/llrs/BioCor/issues git_url: https://git.bioconductor.org/packages/BioCor git_branch: RELEASE_3_12 git_last_commit: a64a874 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BioCor_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BioCor_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BioCor_1.14.0.tgz vignettes: vignettes/BioCor/inst/doc/BioCor_1_basics.html, vignettes/BioCor/inst/doc/BioCor_2_advanced.html vignetteTitles: About BioCor, Advanced usage of BioCor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BioCor/inst/doc/BioCor_1_basics.R, vignettes/BioCor/inst/doc/BioCor_2_advanced.R dependencyCount: 51 Package: BiocParallel Version: 1.24.1 Depends: methods Imports: stats, utils, futile.logger, parallel, snow LinkingTo: BH Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr, batchtools, data.table License: GPL-2 | GPL-3 Archs: i386, x64 MD5sum: e4c72f1bb1b4592cc2a013b8f14c9e85 NeedsCompilation: yes Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut], Nitesh Turaga [aut], Aaron Lun [ctb] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocParallel SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocParallel/issues git_url: https://git.bioconductor.org/packages/BiocParallel git_branch: RELEASE_3_12 git_last_commit: f713caa git_last_commit_date: 2020-11-06 Date/Publication: 2020-11-06 source.ver: src/contrib/BiocParallel_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocParallel_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocParallel_1.24.1.tgz vignettes: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.pdf, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf vignetteTitles: 2. Introduction to BatchtoolsParam, 3. Errors,, Logs and Debugging, 1. Introduction to BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocParallel/inst/doc/BiocParallel_BatchtoolsParam.R, vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.R, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.R dependsOnMe: bacon, BEclear, Cardinal, ClassifyR, clusterSeq, consensusSeekeR, CopywriteR, deco, DEWSeq, DEXSeq, DMCFB, DMCHMM, doppelgangR, DSS, FRASER, GenomicFiles, hiReadsProcessor, INSPEcT, matter, MBASED, metagene, metagene2, ncGTW, Oscope, OUTRIDER, PCAN, periodicDNA, pRoloc, Rqc, ShortRead, SigCheck, Spectra, STROMA4, SummarizedBenchmark, sva, variancePartition, xcms, sequencing importsMe: abseqR, ADImpute, AffiXcan, ALDEx2, AlphaBeta, ALPS, AlpsNMR, amplican, ASICS, ASpediaFI, atSNP, bambu, BANDITS, BASiCS, batchelor, bayNorm, BiocNeighbors, BioCor, BiocSingular, BioMM, BioNetStat, biotmle, biscuiteer, bluster, brendaDb, bsseq, CAGEfightR, CAGEr, cellbaseR, CellBench, CellMixS, ChIPexoQual, ChIPQC, ChromSCape, chromswitch, chromVAR, CNVRanger, CoGAPS, consensusDE, contiBAIT, CoreGx, coseq, cpvSNP, CRISPRseek, CrispRVariants, csaw, cydar, dasper, dcGSA, debCAM, DEComplexDisease, DelayedMatrixStats, derfinder, DEScan2, DESeq2, DEsingle, DiffBind, dmrseq, DOSE, DRIMSeq, DropletUtils, Dune, EMDomics, erma, ERSSA, escape, fgsea, FindMyFriends, flowcatchR, flowSpecs, GDCRNATools, GenoGAM, GenomicAlignments, genotypeeval, gmapR, gscreend, GSEABenchmarkeR, GSVA, GUIDEseq, h5vc, HiCBricks, HiCcompare, HTSeqGenie, HTSFilter, iasva, icetea, ideal, IMAS, InPAS, IntEREst, IONiseR, IPO, ISAnalytics, KinSwingR, LineagePulse, loci2path, LowMACA, MACPET, mbkmeans, MCbiclust, metabomxtr, metaseqR2, MethCP, MethylAid, methylGSA, methylInheritance, methyvim, MetNet, MIGSA, minfi, mixOmics, MMAPPR2, MOGAMUN, motifbreakR, MPRAnalyze, MSnbase, MSstatsSampleSize, multiHiCcompare, muscat, NBAMSeq, NBSplice, NPARC, OmicsLonDA, ORFik, OVESEG, PAIRADISE, PCAtools, PharmacoGx, pipeComp, pram, PrecisionTrialDrawer, proActiv, proFIA, profileplyr, qpgraph, qsea, QuasR, RadioGx, Rcwl, recount, RegEnrich, REMP, RJMCMCNucleosomes, RNAmodR, Rsamtools, RUVcorr, scater, scClassify, scDblFinder, scDD, scde, SCFA, scHOT, scMerge, SCnorm, scone, scoreInvHap, scPCA, scran, scRecover, scruff, scTHI, scuttle, sesame, SEtools, sigFeature, signatureSearch, singleCellTK, SingleR, singscore, SNPhood, soGGi, SpectralTAD, spicyR, splatter, SplicingGraphs, srnadiff, TAPseq, TarSeqQC, TBSignatureProfiler, TFBSTools, TMixClust, ToxicoGx, TPP2D, tradeSeq, trena, Trendy, TSRchitect, TVTB, TxRegInfra, VariantFiltering, VariantTools, velociraptor, waddR, weitrix, zinbwave, IHWpaper, DysPIA, enviGCMS suggestsMe: beachmat, chimera, DelayedArray, glmGamPoi, HDF5Array, netSmooth, omicsPrint, PureCN, randRotation, RcisTarget, scGPS, SeqArray, systemPipeR, TFutils, TileDBArray, tofsims, TSCAN, universalmotif, curatedMetagenomicData, MethylAidData, TENxBrainData, TENxPBMCData, CAGEWorkflow, conos, Corbi, pagoda2, phase1RMD, survBootOutliers, wrTopDownFrag dependencyCount: 10 Package: BiocPkgTools Version: 1.8.2 Depends: htmlwidgets Imports: BiocFileCache, BiocManager, biocViews, tibble, methods, rlang, tidyselect, stringr, rvest, rex, dplyr, xml2, rappdirs, readr, httr, htmltools, DT, tools, utils, igraph, tidyr, jsonlite, gh, RBGL, graph, magrittr Suggests: BiocStyle, knitr, rmarkdown, testthat, tm, SnowballC, visNetwork, clipr, blastula, kableExtra, DiagrammeR, SummarizedExperiment License: MIT + file LICENSE MD5sum: 66539d6e55c51119d2919303f88b1317 NeedsCompilation: no Title: Collection of simple tools for learning about Bioc Packages Description: Bioconductor has a rich ecosystem of metadata around packages, usage, and build status. This package is a simple collection of functions to access that metadata from R. The goal is to expose metadata for data mining and value-added functionality such as package searching, text mining, and analytics on packages. biocViews: Software, Infrastructure Author: Shian Su [aut, ctb], Lori Shepherd [ctb], Marcel Ramos [ctb], Felix G.M. Ernst [ctb], Charlotte Soneson [ctb], Martin Morgan [ctb], Vince Carey [ctb], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://github.com/seandavi/BiocPkgTools SystemRequirements: mailsend-go VignetteBuilder: knitr BugReports: https://github.com/seandavi/BiocPkgTools/issues/new git_url: https://git.bioconductor.org/packages/BiocPkgTools git_branch: RELEASE_3_12 git_last_commit: f427c50 git_last_commit_date: 2021-04-13 Date/Publication: 2021-04-14 source.ver: src/contrib/BiocPkgTools_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocPkgTools_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocPkgTools_1.8.2.tgz vignettes: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.html vignetteTitles: Overview of BiocPkgTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocPkgTools/inst/doc/BiocPkgTools.R dependencyCount: 87 Package: BiocSet Version: 1.4.0 Depends: R (>= 3.6), dplyr Imports: methods, tibble, utils, rlang, plyr, S4Vectors, BiocIO, AnnotationDbi, KEGGREST, ontologyIndex, tidyr Suggests: GSEABase, airway, org.Hs.eg.db, DESeq2, limma, BiocFileCache, GO.db, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 0150d9b36f17de32a0b54985c65316bd NeedsCompilation: no Title: Representing Different Biological Sets Description: BiocSet displays different biological sets in a triple tibble format. These three tibbles are `element`, `set`, and `elementset`. The user has the abilty to activate one of these three tibbles to perform common functions from the dplyr package. Mapping functionality and accessing web references for elements/sets are also available in BiocSet. biocViews: GeneExpression, GO, KEGG, Software Author: Kayla Morrell [aut, cre], Martin Morgan [aut], Kevin Rue-Albrecht [ctb], Lluís Revilla Sancho [ctb] Maintainer: Kayla Morrell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSet git_branch: RELEASE_3_12 git_last_commit: 5397e23 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocSet_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocSet_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocSet_1.4.0.tgz vignettes: vignettes/BiocSet/inst/doc/BiocSet.html vignetteTitles: BiocSet: Representing Element Sets in the Tidyverse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSet/inst/doc/BiocSet.R dependsOnMe: RegEnrich suggestsMe: dearseq dependencyCount: 63 Package: BiocSingular Version: 1.6.0 Imports: BiocGenerics, S4Vectors, Matrix, methods, utils, DelayedArray, BiocParallel, irlba, rsvd, Rcpp, beachmat LinkingTo: Rcpp, beachmat Suggests: testthat, BiocStyle, knitr, rmarkdown, ResidualMatrix License: GPL-3 Archs: i386, x64 MD5sum: 30d0a953f1f1ab8ac213368ff22a6766 NeedsCompilation: yes Title: Singular Value Decomposition for Bioconductor Packages Description: Implements exact and approximate methods for singular value decomposition and principal components analysis, in a framework that allows them to be easily switched within Bioconductor packages or workflows. Where possible, parallelization is achieved using the BiocParallel framework. biocViews: Software, DimensionReduction, PrincipalComponent Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/BiocSingular SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/LTLA/BiocSingular/issues git_url: https://git.bioconductor.org/packages/BiocSingular git_branch: RELEASE_3_12 git_last_commit: 11baf10 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocSingular_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocSingular_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocSingular_1.6.0.tgz vignettes: vignettes/BiocSingular/inst/doc/decomposition.html, vignettes/BiocSingular/inst/doc/representations.html vignetteTitles: 1. SVD and PCA, 2. Matrix classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSingular/inst/doc/decomposition.R, vignettes/BiocSingular/inst/doc/representations.R importsMe: batchelor, NewWave, PCAtools, scater, scDblFinder, scMerge, scran, scry, SingleR, velociraptor suggestsMe: ResidualMatrix, splatter, HCAData dependencyCount: 27 Package: BiocSklearn Version: 1.12.0 Depends: R (>= 3.5.0), reticulate, methods, SummarizedExperiment, knitr Imports: basilisk, Rcpp Suggests: testthat, restfulSE, HDF5Array, BiocStyle License: Artistic-2.0 MD5sum: 84059d5e8a774aec8ea4bf82b7a4bf1d NeedsCompilation: no Title: interface to python sklearn via Rstudio reticulate Description: This package provides interfaces to selected sklearn elements, and demonstrates fault tolerant use of python modules requiring extensive iteration. biocViews: StatisticalMethod, DimensionReduction, Infrastructure Author: Vince Carey Maintainer: VJ Carey SystemRequirements: python (>= 2.7), sklearn, numpy, pandas, h5py VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocSklearn git_branch: RELEASE_3_12 git_last_commit: 285cf6f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocSklearn_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocSklearn_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocSklearn_1.12.0.tgz vignettes: vignettes/BiocSklearn/inst/doc/BiocSklearn.html vignetteTitles: BiocSklearn overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocSklearn/inst/doc/BiocSklearn.R dependencyCount: 46 Package: BiocStyle Version: 2.18.1 Imports: bookdown, knitr (>= 1.30), rmarkdown (>= 1.2), stats, utils, yaml, BiocManager Suggests: BiocGenerics, RUnit, htmltools License: Artistic-2.0 MD5sum: de2f3ccd71c505f2d0f04c5de8481856 NeedsCompilation: no Title: Standard styles for vignettes and other Bioconductor documents Description: Provides standard formatting styles for Bioconductor PDF and HTML documents. Package vignettes illustrate use and functionality. biocViews: Software Author: Andrzej Oleś, Martin Morgan, Wolfgang Huber Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues git_url: https://git.bioconductor.org/packages/BiocStyle git_branch: RELEASE_3_12 git_last_commit: 956f065 git_last_commit_date: 2020-11-24 Date/Publication: 2020-11-24 source.ver: src/contrib/BiocStyle_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocStyle_2.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocStyle_2.18.1.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle2.pdf, vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.html vignetteTitles: Bioconductor LaTeX Style 2.0, Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocStyle/inst/doc/AuthoringRmdVignettes.R, vignettes/BiocStyle/inst/doc/LatexStyle2.R dependsOnMe: sangeranalyseR, org.Mxanthus.db, curatedBreastData, cytofWorkflow, methylationArrayAnalysis, rnaseqGene, RnaSeqGeneEdgeRQL importsMe: abseqR, ASpli, BiocWorkflowTools, BPRMeth, BubbleTree, chimeraviz, COMPASS, deco, DiscoRhythm, geneXtendeR, Melissa, MSnID, PathoStat, PhyloProfile, rebook, regionReport, Rmmquant, Rqc, scTensor, scTGIF, srnadiff, FieldEffectCrc, simpleSingleCell, MetaClean, SNPassoc suggestsMe: ABAEnrichment, ACE, ADAMgui, ADImpute, AffiXcan, affycoretools, aggregateBioVar, ALDEx2, alevinQC, AllelicImbalance, AMOUNTAIN, amplican, AneuFinder, animalcules, AnnotationDbi, AnnotationFilter, AnnotationForge, AnnotationHub, AnnotationHubData, annotationTools, annotatr, AnVILBilling, AnVILPublish, APAlyzer, arrayQualityMetrics, artMS, ASGSCA, ASICS, AssessORF, ASSIGN, ATACseqQC, atSNP, AUCell, BaalChIP, bacon, bamsignals, BANDITS, basecallQC, BASiCS, basilisk, basilisk.utils, batchelor, bayNorm, baySeq, beachmat, beadarray, BeadDataPackR, BEARscc, BEclear, BgeeDB, 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hpar, HTSFilter, HumanTranscriptomeCompendium, ideal, iGC, IgGeneUsage, igvR, IHW, illuminaio, imageHTS, IMAS, Imetagene, immunoClust, infercnv, InPAS, INSPEcT, InTAD, InteractionSet, InterMineR, IONiseR, IRanges, ISAnalytics, iSEE, iSEEu, isomiRs, IVAS, karyoploteR, kissDE, LACE, ldblock, lefser, LinkHD, Linnorm, lipidr, loci2path, LOLA, LoomExperiment, LowMACA, lpsymphony, LRBaseDbi, MACPET, made4, MAGeCKFlute, mAPKL, marr, maser, MAST, MatrixRider, matter, MBASED, mbkmeans, MBttest, MCbiclust, mCSEA, mdgsa, MEAL, MEAT, MEDIPS, megadepth, messina, metabolomicsWorkbenchR, MetaboSignal, metagene, metagene2, metagenomeFeatures, metavizr, methimpute, methInheritSim, MethPed, MethReg, MethylAid, methylCC, methylInheritance, MethylMix, methylSig, methyvim, MetNet, mfa, microbiome, MIGSA, mimager, minfi, MIRA, miRcomp, miRmine, miRSM, miRspongeR, missMethyl, missRows, mixOmics, MLSeq, MMAPPR2, MMDiff2, MMUPHin, MODA, Modstrings, MOGAMUN, mogsa, MOMA, MoonlightR, MOSim, motifbreakR, MotifDb, 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AssessORFData, benchmarkfdrData2019, blimaTestingData, BloodCancerMultiOmics2017, bodymapRat, CardinalWorkflows, celldex, CellMapperData, chipenrich.data, chipseqDBData, CLLmethylation, clustifyrdatahub, CopyhelpeR, COSMIC.67, curatedBladderData, curatedCRCData, curatedMetagenomicData, curatedOvarianData, curatedTCGAData, depmap, derfinderData, DmelSGI, dorothea, DropletTestFiles, DuoClustering2018, ELMER.data, furrowSeg, GeuvadisTranscriptExpr, GSE62944, HarmanData, HCAData, HD2013SGI, HDCytoData, HelloRangesData, HighlyReplicatedRNASeq, Hiiragi2013, HMP16SData, HMP2Data, HumanAffyData, IHWpaper, JctSeqData, mCSEAdata, MetaGxPancreas, MethylAidData, MethylSeqData, methyvimData, minionSummaryData, MMAPPR2data, MouseGastrulationData, MSMB, msqc1, MSstatsBioData, muscData, nanotubes, NestLink, OnassisJavaLibs, optimalFlowData, parathyroidSE, pasilla, PasillaTranscriptExpr, PCHiCdata, PepsNMRData, rcellminerData, RforProteomics, RGMQLlib, RNAmodR.Data, RnaSeqSampleSizeData, sampleClassifierData, SCLCBam, scRNAseq, Single.mTEC.Transcriptomes, spatialLIBD, systemPipeRdata, TabulaMurisData, tartare, TCGAbiolinksGUI.data, TENxBrainData, TENxBUSData, TENxPBMCData, timecoursedata, TimerQuant, tissueTreg, TMExplorer, VariantToolsData, yriMulti, zebrafishRNASeq, annotation, arrays, BiocMetaWorkflow, CAGEWorkflow, chipseqDB, csawUsersGuide, EGSEA123, eQTL, ExpressionNormalizationWorkflow, generegulation, highthroughputassays, liftOver, maEndToEnd, proteomics, recountWorkflow, RNAseq123, sequencing, SingscoreAMLMutations, variants, asteRisk, EHRtemporalVariability, ggBubbles, i2dash, magmaR, MetaIntegrator, multiclassPairs, NutrienTrackeR, openSkies, PlackettLuce, Rediscover, SourceSet dependencyCount: 25 Package: biocthis Version: 1.0.10 Imports: BiocManager, fs, glue, rlang, styler, usethis (>= 2.0.1) Suggests: BiocStyle, covr, devtools, knitr, pkgdown, RefManageR, rmarkdown, sessioninfo, testthat, utils License: Artistic-2.0 MD5sum: c90ef87f786be1b83bae952114bc5f43 NeedsCompilation: no Title: Automate package and project setup for Bioconductor packages Description: This package expands the usethis package with the goal of helping automate the process of creating R packages for Bioconductor or making them Bioconductor-friendly. biocViews: Software, ReportWriting Author: Leonardo Collado-Torres [aut, cre] (), Marcel Ramos [ctb] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/biocthis VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/biocthis git_url: https://git.bioconductor.org/packages/biocthis git_branch: RELEASE_3_12 git_last_commit: 52ecca4 git_last_commit_date: 2021-02-24 Date/Publication: 2021-02-26 source.ver: src/contrib/biocthis_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/4.0/biocthis_1.0.10.zip mac.binary.ver: bin/macosx/contrib/4.0/biocthis_1.0.10.tgz vignettes: vignettes/biocthis/inst/doc/biocthis_dev_notes.html, vignettes/biocthis/inst/doc/biocthis.html vignetteTitles: biocthis developer notes, Introduction to biocthis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocthis/inst/doc/biocthis_dev_notes.R, vignettes/biocthis/inst/doc/biocthis.R dependencyCount: 54 Package: BiocVersion Version: 3.12.0 Depends: R (>= 4.0.0) License: Artistic-2.0 MD5sum: 2cdaf7530d99b42d26b1f0e71df76d4e NeedsCompilation: no Title: Set the appropriate version of Bioconductor packages Description: This package provides repository information for the appropriate version of Bioconductor. biocViews: Infrastructure Author: Martin Morgan [aut], Marcel Ramos [ctb], Bioconductor Package Maintainer [ctb, cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/BiocVersion git_branch: master git_last_commit: 23b9719 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/BiocVersion_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocVersion_3.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocVersion_3.12.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHub suggestsMe: BiocManager dependencyCount: 0 Package: biocViews Version: 1.58.1 Depends: R (>= 3.6.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, RUnit, BiocManager Suggests: BiocGenerics, knitr, commonmark License: Artistic-2.0 MD5sum: fbb414f25b205851f82ed12656d8570a NeedsCompilation: no Title: Categorized views of R package repositories Description: Infrastructure to support 'views' used to classify Bioconductor packages. 'biocViews' are directed acyclic graphs of terms from a controlled vocabulary. There are three major classifications, corresponding to 'software', 'annotation', and 'experiment data' packages. biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora, Lori Shepherd Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/BiocViews BugReports: https://github.com/Bioconductor/BiocViews/issues git_url: https://git.bioconductor.org/packages/biocViews git_branch: RELEASE_3_12 git_last_commit: eb78bba git_last_commit_date: 2020-11-16 Date/Publication: 2020-11-17 source.ver: src/contrib/biocViews_1.58.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/biocViews_1.58.1.zip mac.binary.ver: bin/macosx/contrib/4.0/biocViews_1.58.1.tgz vignettes: vignettes/biocViews/inst/doc/createReposHtml.pdf, vignettes/biocViews/inst/doc/HOWTO-BCV.pdf vignetteTitles: biocViews-CreateRepositoryHTML, biocViews-HOWTO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biocViews/inst/doc/createReposHtml.R, vignettes/biocViews/inst/doc/HOWTO-BCV.R dependsOnMe: Risa importsMe: AnnotationHubData, BiocCheck, BiocPkgTools, ExperimentHubData, monocle, sigFeature, RforProteomics suggestsMe: packFinder dependencyCount: 17 Package: BiocWorkflowTools Version: 1.16.0 Depends: R (>= 3.4) Imports: BiocStyle, bookdown, git2r, httr, knitr, rmarkdown, rstudioapi, stringr, tools, utils, usethis License: MIT + file LICENSE MD5sum: 021b33849fa4d00fac4187a27a24e81b NeedsCompilation: no Title: Tools to aid the development of Bioconductor Workflow packages Description: Provides functions to ease the transition between Rmarkdown and LaTeX documents when authoring a Bioconductor Workflow. biocViews: Software, ReportWriting Author: Mike Smith [aut, cre], Andrzej Oleś [aut] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BiocWorkflowTools git_branch: RELEASE_3_12 git_last_commit: b35a1f1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiocWorkflowTools_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiocWorkflowTools_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiocWorkflowTools_1.16.0.tgz vignettes: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.html vignetteTitles: Converting Rmarkdown to F1000Research LaTeX Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiocWorkflowTools/inst/doc/Generate_F1000_Latex.R dependsOnMe: RNAseq123 suggestsMe: BiocMetaWorkflow, CAGEWorkflow, recountWorkflow, SingscoreAMLMutations dependencyCount: 53 Package: bioDist Version: 1.62.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: d408ca6dfea806b66a1d1d18b00ce4df NeedsCompilation: no Title: Different distance measures Description: A collection of software tools for calculating distance measures. biocViews: Clustering, Classification Author: B. Ding, R. Gentleman and Vincent Carey Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/bioDist git_branch: RELEASE_3_12 git_last_commit: 81311a7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bioDist_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bioDist_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bioDist_1.62.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioDist/inst/doc/bioDist.R importsMe: CHETAH, PhyloProfile, TBSignatureProfiler suggestsMe: BiocCaseStudies dependencyCount: 8 Package: biomaRt Version: 2.46.3 Depends: methods Imports: utils, XML, AnnotationDbi, progress, stringr, httr, openssl, BiocFileCache, rappdirs, xml2 Suggests: annotate, BiocStyle, knitr, rmarkdown, testthat, mockery License: Artistic-2.0 MD5sum: 78bda33a44d8fe0a3b6e0d7bcf1fab34 NeedsCompilation: no Title: Interface to BioMart databases (i.e. Ensembl) Description: In recent years a wealth of biological data has become available in public data repositories. Easy access to these valuable data resources and firm integration with data analysis is needed for comprehensive bioinformatics data analysis. biomaRt provides an interface to a growing collection of databases implementing the BioMart software suite (). The package enables retrieval of large amounts of data in a uniform way without the need to know the underlying database schemas or write complex SQL queries. The most prominent examples of BioMart databases are maintain by Ensembl, which provides biomaRt users direct access to a diverse set of data and enables a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck [aut], Wolfgang Huber [aut], Sean Davis [ctb], Francois Pepin [ctb], Vince S Buffalo [ctb], Mike Smith [ctb, cre] () Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biomaRt git_branch: RELEASE_3_12 git_last_commit: 607b632 git_last_commit_date: 2021-02-08 Date/Publication: 2021-02-09 source.ver: src/contrib/biomaRt_2.46.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/biomaRt_2.46.3.zip mac.binary.ver: bin/macosx/contrib/4.0/biomaRt_2.46.3.tgz vignettes: vignettes/biomaRt/inst/doc/accessing_ensembl.html, vignettes/biomaRt/inst/doc/biomaRt.html vignetteTitles: Accessing Ensembl annotation with biomaRt, The biomaRt users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/accessing_ensembl.R, vignettes/biomaRt/inst/doc/biomaRt.R dependsOnMe: BrainSABER, chromPlot, coMET, customProDB, DrugVsDisease, genefu, GenomicOZone, MineICA, PPInfer, PSICQUIC, RepViz, VegaMC, annotation importsMe: ArrayExpressHTS, artMS, ASpediaFI, BadRegionFinder, BgeeCall, branchpointer, BUSpaRse, ChIPpeakAnno, CHRONOS, cTRAP, dagLogo, DEXSeq, diffloop, DominoEffect, EDASeq, ELMER, FRASER, GDCRNATools, GeneAccord, GenomicFeatures, GenVisR, gespeR, glmSparseNet, GOexpress, goSTAG, gpart, Gviz, isobar, mCSEA, MEDIPS, MetaboSignal, metaseqR, metaseqR2, methyAnalysis, MGFR, MouseFM, OncoScore, oposSOM, pcaExplorer, PGA, phenoTest, PrecisionTrialDrawer, pRoloc, ProteoMM, psygenet2r, pwOmics, R453Plus1Toolbox, ramwas, recoup, rgsepd, RIPAT, RNAither, scPipe, seq2pathway, SeqGSEA, SPLINTER, SWATH2stats, TCGAbiolinks, TFEA.ChIP, TimiRGeN, transcriptogramer, trena, ViSEAGO, XCIR, yarn, TCGAWorkflow, biomartr, BioVenn, GOxploreR, kangar00, liayson, robustSingleCell, snplist suggestsMe: AnnotationForge, bioassayR, BiocCaseStudies, celda, cellTree, chromstaR, ClusterJudge, ctgGEM, FELLA, GeneAnswers, h5vc, MAGeCKFlute, martini, massiR, MethReg, MineICA, MiRaGE, MutationalPatterns, netSmooth, oligo, OrganismDbi, piano, Pigengene, progeny, PubScore, R3CPET, Rcade, RnBeads, rTRM, scater, ShortRead, SIM, sincell, SummarizedBenchmark, systemPipeR, trackViewer, wiggleplotr, zinbwave, BloodCancerMultiOmics2017, ccTutorial, leeBamViews, RegParallel, RforProteomics, BED, BioInsight, cinaR, DGEobj, DGEobj.utils, loose.rock, Patterns, R.SamBada, SNPassoc dependencyCount: 60 Package: biomformat Version: 1.18.0 Depends: R (>= 3.2), methods Imports: plyr (>= 1.8), jsonlite (>= 0.9.16), Matrix (>= 1.2), rhdf5 Suggests: testthat (>= 0.10), knitr (>= 1.10), BiocStyle (>= 1.6), rmarkdown (>= 0.7) License: GPL-2 MD5sum: 6a7ab79cb1cfc1efe5120b2595a21de7 NeedsCompilation: no Title: An interface package for the BIOM file format Description: This is an R package for interfacing with the BIOM format. This package includes basic tools for reading biom-format files, accessing and subsetting data tables from a biom object (which is more complex than a single table), as well as limited support for writing a biom-object back to a biom-format file. The design of this API is intended to match the python API and other tools included with the biom-format project, but with a decidedly "R flavor" that should be familiar to R users. This includes S4 classes and methods, as well as extensions of common core functions/methods. biocViews: ImmunoOncology, DataImport, Metagenomics, Microbiome Author: Paul J. McMurdie and Joseph N Paulson Maintainer: Paul J. McMurdie URL: https://github.com/joey711/biomformat/, http://biom-format.org/ VignetteBuilder: knitr BugReports: https://github.com/joey711/biomformat/issues git_url: https://git.bioconductor.org/packages/biomformat git_branch: RELEASE_3_12 git_last_commit: dc18859 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biomformat_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biomformat_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biomformat_1.18.0.tgz vignettes: vignettes/biomformat/inst/doc/biomformat.html vignetteTitles: The biomformat package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R importsMe: animalcules, microbiomeExplorer, phyloseq, metacoder suggestsMe: metagenomeSeq, MicrobiotaProcess, PLNmodels dependencyCount: 14 Package: BioMM Version: 1.6.0 Depends: R (>= 3.6) Imports: stats, utils, grDevices, lattice, BiocParallel, glmnet, rms, precrec, nsprcomp, ranger, e1071, ggplot2, vioplot, CMplot, imager, topGO, xlsx Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 247f14aeff976fe0fa96b03666854fb2 NeedsCompilation: no Title: BioMM: Biological-informed Multi-stage Machine learning framework for phenotype prediction using omics data Description: The identification of reproducible biological patterns from high-dimensional omics data is a key factor in understanding the biology of complex disease or traits. Incorporating prior biological knowledge into machine learning is an important step in advancing such research. We have proposed a biologically informed multi-stage machine learing framework termed BioMM specifically for phenotype prediction based on omics-scale data where we can evaluate different machine learning models with prior biological meta information. biocViews: Genetics, Classification, Regression, Pathways, GO, Software Author: Junfang Chen and Emanuel Schwarz Maintainer: Junfang Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioMM git_branch: RELEASE_3_12 git_last_commit: bb432b4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BioMM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BioMM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BioMM_1.6.0.tgz vignettes: vignettes/BioMM/inst/doc/BioMMtutorial.html vignetteTitles: BioMMtutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioMM/inst/doc/BioMMtutorial.R dependencyCount: 135 Package: BioMVCClass Version: 1.58.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 59422a610062cd71aef26c5d0e796d57 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes That Use Biobase Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/BioMVCClass git_branch: RELEASE_3_12 git_last_commit: 58f7377 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BioMVCClass_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BioMVCClass_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BioMVCClass_1.58.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: biomvRCNS Version: 1.30.0 Depends: IRanges, GenomicRanges, Gviz Imports: methods, mvtnorm Suggests: cluster, parallel, GenomicFeatures, dynamicTreeCut, Rsamtools, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) Archs: i386, x64 MD5sum: d05548e7b5ef7ff319fc28d5ddc213ed NeedsCompilation: yes Title: Copy Number study and Segmentation for multivariate biological data Description: In this package, a Hidden Semi Markov Model (HSMM) and one homogeneous segmentation model are designed and implemented for segmentation genomic data, with the aim of assisting in transcripts detection using high throughput technology like RNA-seq or tiling array, and copy number analysis using aCGH or sequencing. biocViews: aCGH, CopyNumberVariation, Microarray, Sequencing, Sequencing, Visualization, Genetics Author: Yang Du Maintainer: Yang Du git_url: https://git.bioconductor.org/packages/biomvRCNS git_branch: RELEASE_3_12 git_last_commit: 0cb8b43 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biomvRCNS_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biomvRCNS_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biomvRCNS_1.30.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomvRCNS/inst/doc/biomvRCNS.R dependencyCount: 139 Package: BioNet Version: 1.50.0 Depends: R (>= 2.10.0), graph, RBGL Imports: igraph (>= 1.0.1), AnnotationDbi, Biobase Suggests: rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML License: GPL (>= 2) MD5sum: 3d404ef66179f6bb0de55614c98d7f39 NeedsCompilation: no Title: Routines for the functional analysis of biological networks Description: This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork. biocViews: Microarray, DataImport, GraphAndNetwork, Network, NetworkEnrichment, GeneExpression, DifferentialExpression Author: Marcus Dittrich and Daniela Beisser Maintainer: Marcus Dittrich URL: http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ git_url: https://git.bioconductor.org/packages/BioNet git_branch: RELEASE_3_12 git_last_commit: a4e5179 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BioNet_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BioNet_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BioNet_1.50.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioNet/inst/doc/Tutorial.R importsMe: SMITE dependencyCount: 35 Package: BioNetStat Version: 1.10.5 Depends: R (>= 3.5), shiny, igraph, shinyBS, pathview, DT Imports: BiocParallel, RJSONIO, whisker, yaml, pheatmap, ggplot2, plyr, utils, stats, RColorBrewer, Hmisc, psych, knitr, rmarkdown, markdown License: GPL (>= 3) MD5sum: 97a4e9902dabf2cfb85132088b2d1c74 NeedsCompilation: no Title: Biological Network Analysis Description: A package to perform differential network analysis, differential node analysis (differential coexpression analysis), network and metabolic pathways view. biocViews: Network, NetworkInference, Pathways, GraphAndNetwork, Sequencing, Microarray, Metabolomics, Proteomics, GeneExpression, RNASeq, SystemsBiology, DifferentialExpression, GeneSetEnrichment, ImmunoOncology Author: Vinícius Jardim, Suzana Santos, André Fujita, and Marcos Buckeridge Maintainer: Vinicius Jardim URL: http://github.com/jardimViniciusC/BioNetStat VignetteBuilder: knitr, rmarkdown BugReports: http://github.com/jardimViniciusC/BioNetStat/issues git_url: https://git.bioconductor.org/packages/BioNetStat git_branch: RELEASE_3_12 git_last_commit: 14777c5 git_last_commit_date: 2021-04-28 Date/Publication: 2021-04-29 source.ver: src/contrib/BioNetStat_1.10.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/BioNetStat_1.10.5.zip mac.binary.ver: bin/macosx/contrib/4.0/BioNetStat_1.10.5.tgz vignettes: vignettes/BioNetStat/inst/doc/BNS_tutorial_by_command_line_us.html, vignettes/BioNetStat/inst/doc/vignette.html vignetteTitles: 2. R console tutorial, 1. Interface tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 136 Package: BioQC Version: 1.18.0 Depends: Biobase Imports: edgeR, Rcpp, methods, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, lattice, latticeExtra, rbenchmark, gplots, gridExtra, org.Hs.eg.db, ineq, covr License: GPL (>=3) Archs: i386, x64 MD5sum: 3e4884fc125c3a7b211d8b39e90c4a1c NeedsCompilation: yes Title: Detect tissue heterogeneity in expression profiles with gene sets Description: BioQC performs quality control of high-throughput expression data based on tissue gene signatures. It can detect tissue heterogeneity in gene expression data. The core algorithm is a Wilcoxon-Mann-Whitney test that is optimised for high performance. biocViews: GeneExpression,QualityControl,StatisticalMethod, GeneSetEnrichment Author: Jitao David Zhang , Laura Badi, Gregor Sturm, Roland Ambs Maintainer: Jitao David Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioQC git_branch: RELEASE_3_12 git_last_commit: 93ea3cd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BioQC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BioQC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BioQC_1.18.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc-efficiency.html, vignettes/BioQC/inst/doc/bioqc-signedGenesets.html, vignettes/BioQC/inst/doc/bioqc.html vignetteTitles: BioQC Alogrithm: Speeding up the Wilcoxon-Mann-Whitney Test, Using BioQC with signed genesets, BioQC: Detect tissue heterogeneity in gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioQC/inst/doc/bioqc-efficiency.R, vignettes/BioQC/inst/doc/bioqc-signedGenesets.R, vignettes/BioQC/inst/doc/bioqc.R dependencyCount: 14 Package: biosigner Version: 1.18.2 Depends: Biobase, ropls Imports: methods, e1071, MultiDataSet, randomForest Suggests: BioMark, BiocGenerics, BiocStyle, golubEsets, hu6800.db, knitr, omicade4, rmarkdown, testthat License: CeCILL MD5sum: e53bb5acebe48ea8947cc5f9f7728b86 NeedsCompilation: no Title: Signature discovery from omics data Description: Feature selection is critical in omics data analysis to extract restricted and meaningful molecular signatures from complex and high-dimension data, and to build robust classifiers. This package implements a new method to assess the relevance of the variables for the prediction performances of the classifier. The approach can be run in parallel with the PLS-DA, Random Forest, and SVM binary classifiers. The signatures and the corresponding 'restricted' models are returned, enabling future predictions on new datasets. A Galaxy implementation of the package is available within the Workflow4metabolomics.org online infrastructure for computational metabolomics. biocViews: Classification, FeatureExtraction, Transcriptomics, Proteomics, Metabolomics, Lipidomics Author: Philippe Rinaudo , Etienne Thevenot Maintainer: Philippe Rinaudo , Etienne Thevenot VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/biosigner git_branch: RELEASE_3_12 git_last_commit: 8a38d87 git_last_commit_date: 2020-11-21 Date/Publication: 2020-11-22 source.ver: src/contrib/biosigner_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/biosigner_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.0/biosigner_1.18.2.tgz vignettes: vignettes/biosigner/inst/doc/biosigner-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner-vignette.R dependencyCount: 67 Package: Biostrings Version: 2.58.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.31.5), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2) Imports: methods, utils, grDevices, graphics, stats, crayon LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.13.14), BSgenome.Celegans.UCSC.ce2 (>= 1.3.11), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.11), BSgenome.Hsapiens.UCSC.hg18, drosophila2probe, hgu95av2probe, hgu133aprobe, GenomicFeatures (>= 1.3.14), hgu95av2cdf, affy (>= 1.41.3), affydata (>= 1.11.5), RUnit Enhances: Rmpi License: Artistic-2.0 Archs: i386, x64 MD5sum: b8348ec1e83b25c77a0a67a372857ffa NeedsCompilation: yes Title: Efficient manipulation of biological strings Description: Memory efficient string containers, string matching algorithms, and other utilities, for fast manipulation of large biological sequences or sets of sequences. biocViews: SequenceMatching, Alignment, Sequencing, Genetics, DataImport, DataRepresentation, Infrastructure Author: H. Pagès, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pagès URL: https://bioconductor.org/packages/Biostrings BugReports: https://github.com/Bioconductor/Biostrings/issues git_url: https://git.bioconductor.org/packages/Biostrings git_branch: RELEASE_3_12 git_last_commit: 0ec1a54 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Biostrings_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Biostrings_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Biostrings_2.58.0.tgz vignettes: vignettes/Biostrings/inst/doc/Biostrings2Classes.pdf, vignettes/Biostrings/inst/doc/BiostringsQuickOverview.pdf, vignettes/Biostrings/inst/doc/matchprobes.pdf, vignettes/Biostrings/inst/doc/MultipleAlignments.pdf, vignettes/Biostrings/inst/doc/PairwiseAlignments.pdf vignetteTitles: A short presentation of the basic classes defined in Biostrings 2, Biostrings Quick Overview, Handling probe sequence information, Multiple Alignments, Pairwise Sequence Alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Biostrings/inst/doc/Biostrings2Classes.R, vignettes/Biostrings/inst/doc/matchprobes.R, vignettes/Biostrings/inst/doc/MultipleAlignments.R, vignettes/Biostrings/inst/doc/PairwiseAlignments.R dependsOnMe: altcdfenvs, amplican, Basic4Cseq, BRAIN, BSgenome, chimeraviz, ChIPanalyser, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, GOTHiC, HelloRanges, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, minfi, Modstrings, MotifDb, msa, muscle, oligo, periodicDNA, PGA, pqsfinder, PWMEnrich, qrqc, QSutils, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, rRDP, Rsamtools, RSVSim, sangeranalyseR, sangerseqR, SCAN.UPC, SELEX, seqbias, ShortRead, SICtools, SimFFPE, Structstrings, systemPipeR, topdownr, triplex, FDb.FANTOM4.promoters.hg19, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, harbChIP, JASPAR2014, NestLink, generegulation, sequencing, CleanBSequences, FcircSEC, ionflows, pagoo, SimRAD, STRMPS, SubVis importsMe: AffyCompatible, AllelicImbalance, alpine, AneuFinder, AnnotationHubData, appreci8R, ArrayExpressHTS, AssessORF, ATACseqQC, BBCAnalyzer, BCRANK, bcSeq, BEAT, BgeeCall, biovizBase, brainflowprobes, branchpointer, BSgenome, bsseq, BUMHMM, BUSpaRse, CellaRepertorium, ChIPpeakAnno, ChIPseqR, ChIPsim, chromVAR, circRNAprofiler, CNEr, CNVfilteR, compEpiTools, consensusDE, coRdon, CrispRVariants, customProDB, dada2, dagLogo, DAMEfinder, decompTumor2Sig, diffHic, DNAshapeR, DominoEffect, EDASeq, ensembldb, ensemblVEP, EpiTxDb, esATAC, eudysbiome, EventPointer, FastqCleaner, FindMyFriends, FourCSeq, GA4GHclient, gcapc, gcrma, genbankr, GeneRegionScan, GenoGAM, genomation, GenomicAlignments, GenomicFeatures, GenomicScores, genphen, GenVisR, ggbio, GGtools, girafe, gmapR, gmoviz, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiLDA, HiTC, HTSeqGenie, icetea, idpr, IMMAN, IntEREst, InterMineR, IONiseR, ipdDb, IsoformSwitchAnalyzeR, KEGGREST, LowMACA, LymphoSeq, MACPET, MADSEQ, MatrixRider, MDTS, MEDIPS, MEDME, MesKit, metagenomeFeatures, metaseqR2, methimpute, methylPipe, MicrobiotaProcess, microRNA, MMDiff2, motifbreakR, motifcounter, motifmatchr, motifStack, MSnID, MSstatsPTM, multicrispr, musicatk, MutationalPatterns, ngsReports, nucleR, oligoClasses, OmaDB, openPrimeR, ORFik, OTUbase, packFinder, pdInfoBuilder, PhyloProfile, phyloseq, pipeFrame, podkat, polyester, primirTSS, proBAMr, procoil, ProteomicsAnnotationHubData, PureCN, Pviz, qPLEXanalyzer, qrqc, qsea, QuasR, r3Cseq, ramwas, RCAS, Rcpi, regioneR, regutools, REMP, Repitools, rfaRm, rGADEM, ribosomeProfilingQC, RNAmodR, RNAprobR, RNASeqR, Rqc, rtracklayer, sarks, scmeth, SCOPE, scoreInvHap, scRepertoire, scruff, SeqArray, seqcombo, seqPattern, seqplots, SGSeq, signeR, SigsPack, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SPLINTER, sscu, StructuralVariantAnnotation, synapter, SynExtend, SynMut, TAPseq, TarSeqQC, TFBSTools, transite, trena, tRNA, tRNAdbImport, tRNAscanImport, TVTB, tximeta, Ularcirc, UMI4Cats, universalmotif, VariantAnnotation, VariantExperiment, VariantFiltering, VariantTools, wavClusteR, YAPSA, EuPathDB, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1, pd.atdschip.tiling, PhyloProfileData, ActiveDriverWGS, alakazam, BALCONY, BASiNET, biomartr, BioMedR, crispRdesignR, CSESA, deepredeff, EncDNA, ensembleTax, ExomeDepth, genBaRcode, ggmsa, hoardeR, ICAMS, immuneSIM, kibior, microbial, MicroSEC, PACVr, PredCRG, ptm, RAPIDR, seqmagick, simMP, SMITIDstruct, vhcub, viromeBrowser suggestsMe: annotate, AnnotationForge, AnnotationHub, bambu, BANDITS, BiocGenerics, BRGenomics, CSAR, eisaR, exomeCopy, GenomicFiles, GenomicRanges, genoset, GWASTools, maftools, methrix, methylumi, MiRaGE, nuCpos, RNAmodR.AlkAnilineSeq, rpx, rSWeeP, rTRM, splatter, treeio, XVector, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, BeadArrayUseCases, AhoCorasickTrie, apcluster, bbl, bio3d, DDPNA, gkmSVM, maGUI, MLPA, msaR, NameNeedle, phangorn, polyRAD, protr, rDNAse, sigminer, Signac linksToMe: DECIPHER, kebabs, MatrixRider, Rsamtools, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 14 Package: BioTIP Version: 1.4.0 Depends: R (>= 3.6) Imports: igraph, cluster, psych, stringr, GenomicRanges, Hmisc, MASS Suggests: knitr, markdown, base, rmarkdown, ggplot2 License: GPL-2 MD5sum: 6ee7877db01e47302304dd3f0ff04cb8 NeedsCompilation: no Title: BioTIP: An R package for characterization of Biological Tipping-Point Description: Adopting tipping-point theory to transcriptome profiles to unravel disease regulatory trajectory. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Software Author: Zhezhen Wang, Andrew Goldstein, Yuxi Sun, Biniam Feleke, Qier An, Antonio Feliciano, Xinan Yang Maintainer: Yuxi (Jennifer) Sun , Zhezhen Wang , and X Holly Yang URL: https://github.com/xyang2uchicago/BioTIP VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BioTIP git_branch: RELEASE_3_12 git_last_commit: 7fbbf0f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BioTIP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BioTIP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BioTIP_1.4.0.tgz vignettes: vignettes/BioTIP/inst/doc/BioTIP.html vignetteTitles: BioTIP- an R package for characterization of Biological Tipping-Point hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioTIP/inst/doc/BioTIP.R dependencyCount: 85 Package: biotmle Version: 1.14.0 Depends: R (>= 3.4) Imports: stats, methods, dplyr, tibble, ggplot2, ggsci, superheat, assertthat, future, doFuture, drtmle (>= 1.0.4), S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, limma Suggests: testthat, knitr, rmarkdown, BiocStyle, arm, earth, xgboost, SuperLearner, Matrix, DBI, biotmleData (>= 1.1.1) License: file LICENSE MD5sum: c381a87856819945db1f847ba2c2279c NeedsCompilation: no Title: Targeted Learning with Moderated Statistics for Biomarker Discovery Description: Tools for differential expression biomarker discovery based on microarray and next-generation sequencing data that leverage efficient semiparametric estimators of the average treatment effect for variable importance analysis. Estimation and inference of the (marginal) average treatment effects of potential biomarkers are computed by targeted minimum loss-based estimation, with joint, stable inference constructed across all biomarkers using a generalization of moderated statistics for use with the estimated efficient influence function. The procedure accommodates the use of ensemble machine learning for the estimation of nuisance functions. biocViews: Regression, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq, ImmunoOncology Author: Nima Hejazi [aut, cre, cph] (), Alan Hubbard [aut, ths] (), Mark van der Laan [aut, ths] (), Weixin Cai [ctb] () Maintainer: Nima Hejazi URL: https://code.nimahejazi.org/biotmle VignetteBuilder: knitr BugReports: https://github.com/nhejazi/biotmle/issues git_url: https://git.bioconductor.org/packages/biotmle git_branch: RELEASE_3_12 git_last_commit: a929ef5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biotmle_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biotmle_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biotmle_1.14.0.tgz vignettes: vignettes/biotmle/inst/doc/exposureBiomarkers.html vignetteTitles: Identifying Biomarkers from an Exposure Variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/biotmle/inst/doc/exposureBiomarkers.R dependencyCount: 115 Package: biovizBase Version: 1.38.0 Depends: R (>= 3.5.0), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.23.19), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.23.21), SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4), ensembldb (>= 1.99.13), AnnotationFilter (>= 0.99.8), rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: c807018b70433dbd774d88bd235e0fe2 NeedsCompilation: yes Title: Basic graphic utilities for visualization of genomic data. Description: The biovizBase package is designed to provide a set of utilities, color schemes and conventions for genomic data. It serves as the base for various high-level packages for biological data visualization. This saves development effort and encourages consistency. biocViews: Infrastructure, Visualization, Preprocessing Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/biovizBase git_branch: RELEASE_3_12 git_last_commit: d0f3362 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biovizBase_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biovizBase_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biovizBase_1.38.0.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biovizBase/inst/doc/intro.R dependsOnMe: CAFE, qrqc importsMe: BubbleTree, ChIPexoQual, ggbio, Gviz, karyoploteR, Pviz, qrqc, Rqc suggestsMe: CINdex, derfinderPlot, R3CPET, regionReport, StructuralVariantAnnotation, TxRegInfra, Signac dependencyCount: 136 Package: BiRewire Version: 3.22.0 Depends: igraph, slam, tsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 6cab9e85b4f35b7492cc0159e24e76ee NeedsCompilation: yes Title: High-performing routines for the randomization of a bipartite graph (or a binary event matrix), undirected and directed signed graph preserving degree distribution (or marginal totals) Description: Fast functions for bipartite network rewiring through N consecutive switching steps (See References) and for the computation of the minimal number of switching steps to be performed in order to maximise the dissimilarity with respect to the original network. Includes functions for the analysis of the introduced randomness across the switching steps and several other routines to analyse the resulting networks and their natural projections. Extension to undirected networks and directed signed networks is also provided. Starting from version 1.9.7 a more precise bound (especially for small network) has been implemented. Starting from version 2.2.0 the analysis routine is more complete and a visual montioring of the underlying Markov Chain has been implemented. Starting from 3.6.0 the library can handle also matrices with NA (not for the directed signed graphs). biocViews: Network Author: Andrea Gobbi [aut], Francesco Iorio [aut], Giuseppe Jurman [cbt], Davide Albanese [cbt], Julio Saez-Rodriguez [cbt]. Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire git_url: https://git.bioconductor.org/packages/BiRewire git_branch: RELEASE_3_12 git_last_commit: 161ba9f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiRewire_3.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiRewire_3.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiRewire_3.22.0.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiRewire/inst/doc/BiRewire.R dependencyCount: 13 Package: biscuiteer Version: 1.4.0 Depends: R (>= 3.6), biscuiteerData, bsseq Imports: readr, qualV, Matrix, impute, HDF5Array, S4Vectors, Rsamtools, data.table, Biobase, GenomicRanges, BiocGenerics, VariantAnnotation, DelayedMatrixStats, SummarizedExperiment, GenomeInfoDb, Mus.musculus, Homo.sapiens, matrixStats, rtracklayer, QDNAseq, dmrseq, methods, utils, R.utils, gtools, BiocParallel Suggests: DSS, covr, knitr, rlang, scmeth, pkgdown, roxygen2, testthat, QDNAseq.hg19, QDNAseq.mm10 License: GPL-3 MD5sum: 2ce1be55396046233b06aa62b89fcdbb NeedsCompilation: no Title: Convenience Functions for Biscuit Description: A test harness for bsseq loading of Biscuit output, summarization of WGBS data over defined regions and in mappable samples, with or without imputation, dropping of mostly-NA rows, age estimates, etc. biocViews: DataImport, MethylSeq, DNAMethylation Author: Tim Triche, Jr. [aut, cre], Wanding Zhou [aut], Ben Johnson [aut], Jacob Morrison [aut], Lyong Heo [aut] Maintainer: "Jacob Morrison" URL: https://github.com/trichelab/biscuiteer VignetteBuilder: knitr BugReports: https://github.com/trichelab/biscuiteer/issues git_url: https://git.bioconductor.org/packages/biscuiteer git_branch: RELEASE_3_12 git_last_commit: 7142015 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/biscuiteer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/biscuiteer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/biscuiteer_1.4.0.tgz vignettes: vignettes/biscuiteer/inst/doc/biscuiteer.html vignetteTitles: Biscuiteer User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biscuiteer/inst/doc/biscuiteer.R dependencyCount: 184 Package: BiSeq Version: 1.30.0 Depends: R (>= 2.15.2), methods, S4Vectors, IRanges (>= 1.17.24), GenomicRanges, SummarizedExperiment (>= 0.2.0), Formula Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, rtracklayer, parallel, betareg, lokern, Formula, globaltest License: LGPL-3 MD5sum: 6627d7cd20646d311fa227d68fe1bfb7 NeedsCompilation: no Title: Processing and analyzing bisulfite sequencing data Description: The BiSeq package provides useful classes and functions to handle and analyze targeted bisulfite sequencing (BS) data such as reduced-representation bisulfite sequencing (RRBS) data. In particular, it implements an algorithm to detect differentially methylated regions (DMRs). The package takes already aligned BS data from one or multiple samples. biocViews: Genetics, Sequencing, MethylSeq, DNAMethylation Author: Katja Hebestreit, Hans-Ulrich Klein Maintainer: Katja Hebestreit git_url: https://git.bioconductor.org/packages/BiSeq git_branch: RELEASE_3_12 git_last_commit: e946e2d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BiSeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BiSeq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BiSeq_1.30.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiSeq/inst/doc/BiSeq.R dependsOnMe: RRBSdata dependencyCount: 79 Package: BitSeq Version: 1.34.0 Depends: Rsamtools (>= 1.99.3) Imports: S4Vectors, IRanges LinkingTo: Rhtslib (>= 1.15.5) Suggests: edgeR, DESeq, BiocStyle License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: a111e93aabf8a31a6c718166f9c182dd NeedsCompilation: yes Title: Transcript expression inference and differential expression analysis for RNA-seq data Description: The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/BitSeq git_branch: RELEASE_3_12 git_last_commit: 46472fc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BitSeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BitSeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BitSeq_1.34.0.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BitSeq/inst/doc/BitSeq.R dependencyCount: 29 Package: blacksheepr Version: 1.4.0 Depends: R (>= 3.6) Imports: grid, stats, grDevices, utils, circlize, viridis, RColorBrewer, ComplexHeatmap, SummarizedExperiment, pasilla Suggests: testthat (>= 2.1.0), knitr, BiocStyle, rmarkdown, curl License: MIT + file LICENSE MD5sum: 06d27c44936a133e4425b10d754d522a NeedsCompilation: no Title: Outlier Analysis for pairwise differential comparison Description: Blacksheep is a tool designed for outlier analysis in the context of pairwise comparisons in an effort to find distinguishing characteristics from two groups. This tool was designed to be applied for biological applications such as phosphoproteomics or transcriptomics, but it can be used for any data that can be represented by a 2D table, and has two sub populations within the table to compare. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, DifferentialExpression, Transcriptomics Author: MacIntosh Cornwell [aut], RugglesLab [cre] Maintainer: RugglesLab VignetteBuilder: knitr BugReports: https://github.com/ruggleslab/blackSheepR/issues git_url: https://git.bioconductor.org/packages/blacksheepr git_branch: RELEASE_3_12 git_last_commit: 558236a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/blacksheepr_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/blacksheepr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/blacksheepr_1.4.0.tgz vignettes: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.html vignetteTitles: Outlier Analysis using blacksheepr - Phosphoprotein hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/blacksheepr/inst/doc/blacksheepr_vignette.R dependencyCount: 69 Package: blima Version: 1.24.0 Depends: R(>= 3.3) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), Rcpp (>= 0.12.8), BiocGenerics, grDevices, stats, graphics LinkingTo: Rcpp Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi, knitr License: GPL-3 Archs: i386, x64 MD5sum: 575461a9e61404bc4492d025edd907dc NeedsCompilation: yes Title: Tools for the preprocessing and analysis of the Illumina microarrays on the detector (bead) level Description: Package blima includes several algorithms for the preprocessing of Illumina microarray data. It focuses to the bead level analysis and provides novel approach to the quantile normalization of the vectors of unequal lengths. It provides variety of the methods for background correction including background subtraction, RMA like convolution and background outlier removal. It also implements variance stabilizing transformation on the bead level. There are also implemented methods for data summarization. It also provides the methods for performing T-tests on the detector (bead) level and on the probe level for differential expression testing. biocViews: Microarray, Preprocessing, Normalization, DifferentialExpression, GeneRegulation, GeneExpression Author: Vojtěch Kulvait Maintainer: Vojtěch Kulvait URL: https://bitbucket.org/kulvait/blima VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/blima git_branch: RELEASE_3_12 git_last_commit: 9a0d605 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/blima_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/blima_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/blima_1.24.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/blima/inst/doc/blima.R suggestsMe: blimaTestingData dependencyCount: 76 Package: BLMA Version: 1.14.0 Depends: ROntoTools, GSA, PADOG, limma, graph, stats, utils, parallel, Biobase, metafor, methods Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 1b938a45905638f1ff18e992eea44781 NeedsCompilation: no Title: BLMA: A package for bi-level meta-analysis Description: Suit of tools for bi-level meta-analysis. The package can be used in a wide range of applications, including general hypothesis testings, differential expression analysis, functional analysis, and pathway analysis. biocViews: GeneSetEnrichment, Pathways, DifferentialExpression, Microarray Author: Tin Nguyen , Hung Nguyen , and Sorin Draghici Maintainer: Hung Nguyen git_url: https://git.bioconductor.org/packages/BLMA git_branch: RELEASE_3_12 git_last_commit: 85de4fa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BLMA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BLMA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BLMA_1.14.0.tgz vignettes: vignettes/BLMA/inst/doc/BLMA.pdf vignetteTitles: BLMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BLMA/inst/doc/BLMA.R importsMe: multiHiCcompare dependencyCount: 68 Package: bluster Version: 1.0.0 Imports: stats, methods, utils, Matrix, Rcpp, igraph, S4Vectors, BiocParallel, BiocNeighbors LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, dynamicTreeCut, scRNAseq, scuttle, scater, scran, pheatmap, viridis License: GPL-3 Archs: i386, x64 MD5sum: 5ad40283ceec7ada36a181897ec42d32 NeedsCompilation: yes Title: Clustering Algorithms for Bioconductor Description: Wraps common clustering algorithms in an easily extended S4 framework. Backends are implemented for hierarchical, k-means and graph-based clustering. Several utilities are also provided to compare and evaluate clustering results. biocViews: ImmunoOncology, Software, GeneExpression, Transcriptomics, SingleCell, Clustering Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/bluster git_branch: RELEASE_3_12 git_last_commit: 3727538 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bluster_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bluster_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bluster_1.0.0.tgz vignettes: vignettes/bluster/inst/doc/clusterRows.html, vignettes/bluster/inst/doc/diagnostics.html vignetteTitles: 1. Clustering algorithms, 2. Clustering diagnostics hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bluster/inst/doc/clusterRows.R, vignettes/bluster/inst/doc/diagnostics.R importsMe: mbkmeans, scDblFinder, scran suggestsMe: batchelor, scDblFinder dependencyCount: 25 Package: bnbc Version: 1.12.0 Depends: R (>= 3.5.0), methods, BiocGenerics, SummarizedExperiment, GenomicRanges Imports: Rcpp (>= 0.12.12), IRanges, rhdf5, data.table, GenomeInfoDb, S4Vectors, matrixStats, preprocessCore, sva, parallel, EBImage, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 25feb70cea499ecebf4aa62010cb7258 NeedsCompilation: yes Title: Bandwise normalization and batch correction of Hi-C data Description: Tools to normalize (several) Hi-C data from replicates. biocViews: HiC, Preprocessing, Normalization, Software Author: Kipper Fletez-Brant [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Kipper Fletez-Brant URL: https://github.com/hansenlab/bnbc VignetteBuilder: knitr BugReports: https://github.com/hansenlab/bnbc/issues git_url: https://git.bioconductor.org/packages/bnbc git_branch: RELEASE_3_12 git_last_commit: 191bf12 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bnbc_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bnbc_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bnbc_1.12.0.tgz vignettes: vignettes/bnbc/inst/doc/bnbc.html vignetteTitles: bnbc User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bnbc/inst/doc/bnbc.R dependencyCount: 85 Package: BPRMeth Version: 1.16.0 Depends: R (>= 3.5.0), GenomicRanges Imports: assertthat, methods, MASS, doParallel, parallel, e1071, earth, foreach, randomForest, stats, IRanges, S4Vectors, data.table, graphics, truncnorm, mvtnorm, Rcpp (>= 0.12.14), matrixcalc, magrittr, kernlab, ggplot2, cowplot, BiocStyle LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE Archs: i386, x64 MD5sum: 7117088b31d7d695b4b77e301913fbef NeedsCompilation: yes Title: Model higher-order methylation profiles Description: The BPRMeth package is a probabilistic method to quantify explicit features of methylation profiles, in a way that would make it easier to formally use such profiles in downstream modelling efforts, such as predicting gene expression levels or clustering genomic regions or cells according to their methylation profiles. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: Chantriolnt-Andreas Kapourani [aut, cre] Maintainer: Chantriolnt-Andreas Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BPRMeth git_branch: RELEASE_3_12 git_last_commit: e048536 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BPRMeth_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BPRMeth_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BPRMeth_1.16.0.tgz vignettes: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.html vignetteTitles: BPRMeth: Model higher-order methylation profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BPRMeth/inst/doc/BPRMeth_vignette.R dependsOnMe: Melissa dependencyCount: 90 Package: BRAIN Version: 1.36.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: dba40e2aa51d466b692a13b841261178 NeedsCompilation: no Title: Baffling Recursive Algorithm for Isotope distributioN calculations Description: Package for calculating aggregated isotopic distribution and exact center-masses for chemical substances (in this version composed of C, H, N, O and S). This is an implementation of the BRAIN algorithm described in the paper by J. Claesen, P. Dittwald, T. Burzykowski and D. Valkenborg. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald git_url: https://git.bioconductor.org/packages/BRAIN git_branch: RELEASE_3_12 git_last_commit: 2637aed git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BRAIN_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BRAIN_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BRAIN_1.36.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRAIN/inst/doc/BRAIN-vignette.R suggestsMe: cleaver, synapter, RforProteomics dependencyCount: 19 Package: brainflowprobes Version: 1.4.1 Depends: R (>= 3.6.0) Imports: Biostrings (>= 2.52.0), BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), bumphunter (>= 1.26.0), cowplot (>= 1.0.0), derfinder (>= 1.18.1), derfinderPlot (>= 1.18.1), GenomicRanges (>= 1.36.0), ggplot2 (>= 3.1.1), RColorBrewer (>= 1.1), utils, grDevices, GenomicState (>= 0.99.7) Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: 359b433d602961918d871be53ce6555c NeedsCompilation: no Title: Plots and annotation for choosing BrainFlow target probe sequence Description: Use these functions to characterize genomic regions for BrainFlow target probe design. biocViews: Coverage, Visualization, ExperimentalDesign, Transcriptomics, FlowCytometry, GeneTarget Author: Amanda Price [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: Amanda Price URL: https://github.com/LieberInstitute/brainflowprobes VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/brainflowprobes git_url: https://git.bioconductor.org/packages/brainflowprobes git_branch: RELEASE_3_12 git_last_commit: 49252c4 git_last_commit_date: 2020-12-18 Date/Publication: 2020-12-19 source.ver: src/contrib/brainflowprobes_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/brainflowprobes_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.0/brainflowprobes_1.4.1.tgz vignettes: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.html vignetteTitles: brainflowprobes users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/brainflowprobes/inst/doc/brainflowprobes-vignette.R dependencyCount: 182 Package: BrainSABER Version: 1.0.0 Depends: R (>= 4.0), biomaRt, SummarizedExperiment Imports: data.table, lsa, methods, S4Vectors, utils, BiocFileCache Suggests: BiocStyle, ComplexHeatmap, fastcluster, heatmaply, knitr, plotly License: Artistic-2.0 MD5sum: c024071d6f05ba6476deb9516faa516f NeedsCompilation: no Title: Brain Span Atlas in Biobase Expressionset R toolset Description: The Allen Institute for Brain Science provides an RNA sequencing (RNA-Seq) data resource for studying transcriptional mechanisms involved in human brain development known as BrainSpan. BrainSABER is an R package that facilitates comparison of user data with the various developmental stages and brain structures found in the BrainSpan atlas by generating dynamic similarity heatmaps for the two data sets. It also provides a self-validating container for user data. biocViews: GeneExpression, Visualization, Sequencing Author: Carrie Minette and Evgeni Radichev Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BrainSABER git_branch: RELEASE_3_12 git_last_commit: 378073c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BrainSABER_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BrainSABER_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BrainSABER_1.0.0.tgz vignettes: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.html vignetteTitles: BrainSABER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainSABER/inst/doc/Installing_and_Using_BrainSABER.R dependencyCount: 78 Package: BrainStars Version: 1.34.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: 701d6a4fe98985ffa7c2b687e4355931 NeedsCompilation: no Title: query gene expression data and plots from BrainStars (B*) Description: This package can search and get gene expression data and plots from BrainStars (B*). BrainStars is a quantitative expression database of the adult mouse brain. The database has genome-wide expression profile at 51 adult mouse CNS regions. biocViews: Microarray, OneChannel, DataImport Author: Itoshi NIKAIDO Maintainer: Itoshi NIKAIDO git_url: https://git.bioconductor.org/packages/BrainStars git_branch: RELEASE_3_12 git_last_commit: 87655cb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BrainStars_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BrainStars_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BrainStars_1.34.0.tgz vignettes: vignettes/BrainStars/inst/doc/BrainStars.pdf vignetteTitles: BrainStars hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrainStars/inst/doc/BrainStars.R dependencyCount: 10 Package: branchpointer Version: 1.16.0 Depends: caret, R(>= 3.4) Imports: plyr, kernlab, gbm, stringr, cowplot, ggplot2, biomaRt, Biostrings, parallel, utils, stats, BSgenome.Hsapiens.UCSC.hg38, rtracklayer, GenomicRanges, GenomeInfoDb, IRanges, S4Vectors, data.table Suggests: knitr, BiocStyle License: BSD_3_clause + file LICENSE MD5sum: 75721e4e70f571684548d3dfda13d0cd NeedsCompilation: no Title: Prediction of intronic splicing branchpoints Description: Predicts branchpoint probability for sites in intronic branchpoint windows. Queries can be supplied as intronic regions; or to evaluate the effects of mutations, SNPs. biocViews: Software, GenomeAnnotation, GenomicVariation, MotifAnnotation Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/branchpointer git_branch: RELEASE_3_12 git_last_commit: 853ea53 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/branchpointer_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/branchpointer_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/branchpointer_1.16.0.tgz vignettes: vignettes/branchpointer/inst/doc/branchpointer.pdf vignetteTitles: Using Branchpointer for annotation of intronic human splicing branchpoints hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/branchpointer/inst/doc/branchpointer.R dependencyCount: 132 Package: breakpointR Version: 1.8.0 Depends: R (>= 3.5), GenomicRanges, cowplot, breakpointRdata Imports: methods, utils, grDevices, stats, S4Vectors, GenomeInfoDb (>= 1.12.3), IRanges, Rsamtools, GenomicAlignments, ggplot2, BiocGenerics, gtools, doParallel, foreach Suggests: knitr, BiocStyle, testthat License: file LICENSE MD5sum: eacd509d8c0fce6fcd91fdeb40492196 NeedsCompilation: no Title: Find breakpoints in Strand-seq data Description: This package implements functions for finding breakpoints, plotting and export of Strand-seq data. biocViews: Software, Sequencing, DNASeq, SingleCell, Coverage Author: David Porubsky, Ashley Sanders, Aaron Taudt Maintainer: David Porubsky URL: https://github.com/daewoooo/BreakPointR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/breakpointR git_branch: RELEASE_3_12 git_last_commit: 8542f20 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/breakpointR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/breakpointR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/breakpointR_1.8.0.tgz vignettes: vignettes/breakpointR/inst/doc/breakpointR.pdf vignetteTitles: How to use breakpointR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/breakpointR/inst/doc/breakpointR.R dependencyCount: 74 Package: brendaDb Version: 1.4.0 Imports: dplyr, Rcpp, tibble, stringr, magrittr, purrr, BiocParallel, crayon, utils, tidyr, curl, xml2, grDevices, rlang, BiocFileCache, rappdirs LinkingTo: Rcpp Suggests: testthat, BiocStyle, knitr, rmarkdown, devtools License: MIT + file LICENSE Archs: i386, x64 MD5sum: dcc1c651d61e2a96a5aac2d3cc42b8ee NeedsCompilation: yes Title: The BRENDA Enzyme Database Description: R interface for importing and analyzing enzyme information from the BRENDA database. biocViews: ThirdPartyClient, Annotation, DataImport Author: Yi Zhou [aut, cre] () Maintainer: Yi Zhou URL: https://github.com/y1zhou/brendaDb SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/y1zhou/brendaDb/issues git_url: https://git.bioconductor.org/packages/brendaDb git_branch: RELEASE_3_12 git_last_commit: cbc88bb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/brendaDb_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/brendaDb_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/brendaDb_1.4.0.tgz vignettes: vignettes/brendaDb/inst/doc/brendaDb.html vignetteTitles: brendaDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/brendaDb/inst/doc/brendaDb.R dependencyCount: 59 Package: BRGenomics Version: 1.2.0 Depends: R (>= 4.0), rtracklayer, GenomeInfoDb, S4Vectors Imports: GenomicRanges, parallel, IRanges, stats, Rsamtools, GenomicAlignments, DESeq2, SummarizedExperiment, utils, methods Suggests: BiocStyle, knitr, rmarkdown, testthat, apeglm, remotes, ggplot2, reshape2, Biostrings License: Artistic-2.0 MD5sum: ee1b271530cae5872b2151e1a7f97d87 NeedsCompilation: no Title: Tools for the Efficient Analysis of High-Resolution Genomics Data Description: This package provides useful and efficient utilites for the analysis of high-resolution genomic data using standard Bioconductor methods and classes. BRGenomics is feature-rich and simplifies a number of post-alignment processing steps and data handling. Emphasis is on efficient analysis of multiple datasets, with support for normalization and blacklisting. Included are functions for: spike-in normalizing data; generating basepair-resolution readcounts and coverage data (e.g. for heatmaps); importing and processing bam files (e.g. for conversion to bigWig files); generating metaplots/metaprofiles (bootstrapped mean profiles) with confidence intervals; conveniently calling DESeq2 without using sample-blind estimates of genewise dispersion; among other features. biocViews: Software, DataImport, Sequencing, Coverage, RNASeq, ATACSeq, ChIPSeq, Transcription, GeneRegulation, GeneExpression, Normalization Author: Mike DeBerardine [aut, cre] Maintainer: Mike DeBerardine URL: https://mdeber.github.io VignetteBuilder: knitr BugReports: https://github.com/mdeber/BRGenomics/issues git_url: https://git.bioconductor.org/packages/BRGenomics git_branch: RELEASE_3_12 git_last_commit: 79bb3d5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BRGenomics_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BRGenomics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BRGenomics_1.2.0.tgz vignettes: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.html, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.html, vignettes/BRGenomics/inst/doc/GettingStarted.html, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.html, vignettes/BRGenomics/inst/doc/ImportingProcessingData.html, vignettes/BRGenomics/inst/doc/Overview.html, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.html, vignettes/BRGenomics/inst/doc/SequenceExtraction.html, vignettes/BRGenomics/inst/doc/SignalCounting.html, vignettes/BRGenomics/inst/doc/SpikeInNormalization.html vignetteTitles: Analyzing Multiple Datasets, DESeq2 with Global Perturbations, Getting Started, Importing and Modifying Annotations, Importing and Processing Data, Overview, Profile Plots and Bootstrapping, Sequence Extraction, Signal Counting, Spike-in Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BRGenomics/inst/doc/AnalyzingMultipleDatasets.R, vignettes/BRGenomics/inst/doc/DESeq2WithGlobalPerturbations.R, vignettes/BRGenomics/inst/doc/GettingStarted.R, vignettes/BRGenomics/inst/doc/ImportingModifyingAnnotations.R, vignettes/BRGenomics/inst/doc/ImportingProcessingData.R, vignettes/BRGenomics/inst/doc/ProfilePlotsAndBootstrapping.R, vignettes/BRGenomics/inst/doc/SequenceExtraction.R, vignettes/BRGenomics/inst/doc/SignalCounting.R, vignettes/BRGenomics/inst/doc/SpikeInNormalization.R dependencyCount: 95 Package: bridge Version: 1.54.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: 0d880b0f47bfd60409ecb3ddd6b28f25 NeedsCompilation: yes Title: Bayesian Robust Inference for Differential Gene Expression Description: Test for differentially expressed genes with microarray data. This package can be used with both cDNA microarrays or Affymetrix chip. The packge fits a robust Bayesian hierarchical model for testing for differential expression. Outliers are modeled explicitly using a $t$-distribution. The model includes an exchangeable prior for the variances which allow different variances for the genes but still shrink extreme empirical variances. Our model can be used for testing for differentially expressed genes among multiple samples, and can distinguish between the different possible patterns of differential expression when there are three or more samples. Parameter estimation is carried out using a novel version of Markov Chain Monte Carlo that is appropriate when the model puts mass on subspaces of the full parameter space. biocViews: Microarray,OneChannel,TwoChannel,DifferentialExpression Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/bridge git_branch: RELEASE_3_12 git_last_commit: e19b141 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bridge_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bridge_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bridge_1.54.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bridge/inst/doc/bridge.R dependencyCount: 1 Package: BridgeDbR Version: 2.0.2 Depends: R (>= 3.3.0), rJava Imports: curl Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: 0a0a255573472dcef79da45990f3fb2a NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R. It uses GitHub, Zenodo, and Figshare if you use this package to download identifier mappings files. biocViews: Software, Annotation, Metabolomics, Cheminformatics Author: Christ Leemans , Egon Willighagen , Anwesha Bohler , Lars Eijssen Maintainer: Egon Willighagen URL: https://github.com/bridgedb/BridgeDbR VignetteBuilder: knitr BugReports: https://github.com/bridgedb/BridgeDbR/issues git_url: https://git.bioconductor.org/packages/BridgeDbR git_branch: RELEASE_3_12 git_last_commit: a66f65e git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-26 source.ver: src/contrib/BridgeDbR_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/BridgeDbR_2.0.2.zip mac.binary.ver: bin/macosx/contrib/4.0/BridgeDbR_2.0.2.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.html vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R dependencyCount: 3 Package: BrowserViz Version: 2.12.0 Depends: R (>= 3.5.0), jsonlite (>= 1.5), httpuv(>= 1.5.0) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: 5fe6a589b3da2e196c6b911d09c9488d NeedsCompilation: no Title: BrowserViz: interactive R/browser graphics using websockets and JSON Description: Interactvive graphics in a web browser from R, using websockets and JSON. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/BrowserViz/ VignetteBuilder: knitr BugReports: https://github.com/paul-shannon/BrowserViz/issues git_url: https://git.bioconductor.org/packages/BrowserViz git_branch: RELEASE_3_12 git_last_commit: e62f0d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BrowserViz_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BrowserViz_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BrowserViz_2.12.0.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.html vignetteTitles: "BrowserViz: support programmatic access to javascript apps running in your web browser" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: igvR, RCyjs dependencyCount: 14 Package: BSgenome Version: 1.58.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.28), IRanges (>= 2.13.16), GenomeInfoDb (>= 1.25.6), GenomicRanges (>= 1.31.10), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7) Imports: methods, utils, stats, matrixStats, BiocGenerics, S4Vectors, IRanges, XVector (>= 0.29.3), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocManager, Biobase, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: 6034657e50ed6a6f4a8ac7188f69e721 NeedsCompilation: no Title: Software infrastructure for efficient representation of full genomes and their SNPs Description: Infrastructure shared by all the Biostrings-based genome data packages. biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/BSgenome BugReports: https://github.com/Bioconductor/BSgenome/issues git_url: https://git.bioconductor.org/packages/BSgenome git_branch: RELEASE_3_12 git_last_commit: 3a4926e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BSgenome_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BSgenome_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BSgenome_1.58.0.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BSgenome/inst/doc/BSgenomeForge.R, vignettes/BSgenome/inst/doc/GenomeSearching.R dependsOnMe: ChIPanalyser, GOTHiC, HelloRanges, MEDIPS, periodicDNA, REDseq, rGADEM, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Hsapiens.1000genomes.hs37d5, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, leeBamViews, annotation importsMe: AllelicImbalance, appreci8R, ATACseqQC, atSNP, BEAT, bsseq, BUSpaRse, CAGEr, chromVAR, cleanUpdTSeq, CRISPRseek, crisprseekplus, diffHic, dpeak, enrichTF, esATAC, EventPointer, FRASER, gcapc, genomation, GenVisR, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, IsoformSwitchAnalyzeR, MADSEQ, methrix, MethylSeekR, MMDiff2, motifbreakR, motifmatchr, msgbsR, multicrispr, musicatk, MutationalPatterns, ORFik, PING, pipeFrame, podkat, qsea, QuasR, R453Plus1Toolbox, RareVariantVis, RCAS, regioneR, REMP, Repitools, ribosomeProfilingQC, RNAmodR, scmeth, SCOPE, seqplots, SigsPack, SparseSignatures, TAPseq, TFBSTools, trena, tRNAscanImport, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, BSgenome.Alyrata.JGI.v1, BSgenome.Amellifera.BeeBase.assembly4, BSgenome.Amellifera.NCBI.AmelHAv3.1, BSgenome.Amellifera.UCSC.apiMel2, BSgenome.Amellifera.UCSC.apiMel2.masked, BSgenome.Aofficinalis.NCBI.V1, BSgenome.Athaliana.TAIR.04232008, BSgenome.Athaliana.TAIR.TAIR9, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau3.masked, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau4.masked, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau6.masked, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Carietinum.NCBI.v1, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Celegans.UCSC.ce6, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam2.masked, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Cfamiliaris.UCSC.canFam3.masked, BSgenome.Cjacchus.UCSC.calJac3, BSgenome.Creinhardtii.JGI.v5.6, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm2.masked, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Dmelanogaster.UCSC.dm3.masked, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer5.masked, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer6.masked, BSgenome.Drerio.UCSC.danRer7, BSgenome.Drerio.UCSC.danRer7.masked, BSgenome.Dvirilis.Ensembl.dvircaf1, BSgenome.Ecoli.NCBI.20080805, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Gaculeatus.UCSC.gasAcu1.masked, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal3.masked, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Ggallus.UCSC.galGal4.masked, BSgenome.Ggallus.UCSC.galGal5, BSgenome.Ggallus.UCSC.galGal6, BSgenome.Hsapiens.NCBI.GRCh38, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Hsapiens.UCSC.hg17.masked, BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mdomestica.UCSC.monDom5, BSgenome.Mfascicularis.NCBI.5.0, BSgenome.Mfuro.UCSC.musFur1, BSgenome.Mmulatta.UCSC.rheMac10, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac2.masked, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmulatta.UCSC.rheMac3.masked, BSgenome.Mmulatta.UCSC.rheMac8, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Mmusculus.UCSC.mm8.masked, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Osativa.MSU.MSU7, BSgenome.Ppaniscus.UCSC.panPan1, BSgenome.Ppaniscus.UCSC.panPan2, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro2.masked, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Ptroglodytes.UCSC.panTro3.masked, BSgenome.Ptroglodytes.UCSC.panTro5, BSgenome.Ptroglodytes.UCSC.panTro6, BSgenome.Rnorvegicus.UCSC.rn4, BSgenome.Rnorvegicus.UCSC.rn4.masked, BSgenome.Rnorvegicus.UCSC.rn5, BSgenome.Rnorvegicus.UCSC.rn5.masked, BSgenome.Rnorvegicus.UCSC.rn6, BSgenome.Scerevisiae.UCSC.sacCer1, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Sscrofa.UCSC.susScr11, BSgenome.Sscrofa.UCSC.susScr3, BSgenome.Sscrofa.UCSC.susScr3.masked, BSgenome.Tgondii.ToxoDB.7.0, BSgenome.Tguttata.UCSC.taeGut1, BSgenome.Tguttata.UCSC.taeGut1.masked, BSgenome.Tguttata.UCSC.taeGut2, BSgenome.Vvinifera.URGI.IGGP12Xv0, BSgenome.Vvinifera.URGI.IGGP12Xv2, BSgenome.Vvinifera.URGI.IGGP8X, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, ActiveDriverWGS, deconstructSigs, ICAMS, simMP suggestsMe: bambu, Biostrings, biovizBase, ChIPpeakAnno, chipseq, eisaR, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, genoset, maftools, metaseqR, metaseqR2, MiRaGE, proActiv, PWMEnrich, QDNAseq, recoup, rtracklayer, SNPlocs.Hsapiens.dbSNP.20101109, gkmSVM, sigminer, Signac dependencyCount: 40 Package: bsseq Version: 1.26.0 Depends: R (>= 4.0), methods, BiocGenerics, GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5) Imports: IRanges (>= 2.23.9), GenomeInfoDb, scales, stats, graphics, Biobase, locfit, gtools, data.table (>= 1.11.8), S4Vectors (>= 0.27.12), R.utils (>= 2.0.0), DelayedMatrixStats (>= 1.5.2), permute, limma, DelayedArray (>= 0.15.16), Rcpp, BiocParallel, BSgenome, Biostrings, utils, HDF5Array (>= 1.15.19), rhdf5 LinkingTo: Rcpp, beachmat Suggests: testthat, bsseqData, BiocStyle, rmarkdown, knitr, Matrix, doParallel, rtracklayer, BSgenome.Hsapiens.UCSC.hg38, beachmat (>= 1.5.2), BatchJobs License: Artistic-2.0 Archs: i386, x64 MD5sum: 4ee553ffae2ab33d239e24d1c6b429eb NeedsCompilation: yes Title: Analyze, manage and store bisulfite sequencing data Description: A collection of tools for analyzing and visualizing bisulfite sequencing data. biocViews: DNAMethylation Author: Kasper Daniel Hansen [aut, cre], Peter Hickey [aut] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues git_url: https://git.bioconductor.org/packages/bsseq git_branch: RELEASE_3_12 git_last_commit: fae3229 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bsseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bsseq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bsseq_1.26.0.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.html, vignettes/bsseq/inst/doc/bsseq.html vignetteTitles: Analyzing WGBS data with bsseq, bsseq User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bsseq/inst/doc/bsseq_analysis.R, vignettes/bsseq/inst/doc/bsseq.R dependsOnMe: biscuiteer, dmrseq, DSS, bsseqData importsMe: DMRcate, MethCP, methylCC, methylSig, MIRA, NanoMethViz, scmeth, tcgaWGBSData.hg19 suggestsMe: methrix, tissueTreg dependencyCount: 68 Package: BubbleTree Version: 2.20.0 Depends: R (>= 3.5), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.31.6), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, e1071, methods, grDevices, stats, utils Suggests: knitr, rmarkdown License: LGPL (>= 3) MD5sum: 1356d57241492e5cd6eac0adc844c364 NeedsCompilation: no Title: BubbleTree: an intuitive visualization to elucidate tumoral aneuploidy and clonality in somatic mosaicism using next generation sequencing data Description: CNV analysis in groups of tumor samples. biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BubbleTree git_branch: RELEASE_3_12 git_last_commit: e891780 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BubbleTree_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BubbleTree_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BubbleTree_2.20.0.tgz vignettes: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html vignetteTitles: BubbleTree Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R dependencyCount: 149 Package: BufferedMatrix Version: 1.54.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: b98e64c249935c00d3c8f84f407722d1 NeedsCompilation: yes Title: A matrix data storage object held in temporary files Description: A tabular style data object where most data is stored outside main memory. A buffer is used to speed up access to data. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/BufferedMatrix git_url: https://git.bioconductor.org/packages/BufferedMatrix git_branch: RELEASE_3_12 git_last_commit: eae3841 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BufferedMatrix_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BufferedMatrix_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BufferedMatrix_1.54.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.R dependsOnMe: BufferedMatrixMethods linksToMe: BufferedMatrixMethods dependencyCount: 1 Package: BufferedMatrixMethods Version: 1.54.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: 8086690d0d6ee1270c60b58d4dc56dfe NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods git_url: https://git.bioconductor.org/packages/BufferedMatrixMethods git_branch: RELEASE_3_12 git_last_commit: 5f9d4f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BufferedMatrixMethods_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BufferedMatrixMethods_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BufferedMatrixMethods_1.54.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 2 Package: BUMHMM Version: 1.14.0 Depends: R (>= 3.4) Imports: devtools, stringi, gtools, stats, utils, SummarizedExperiment, Biostrings, IRanges Suggests: testthat, knitr, BiocStyle License: GPL-3 MD5sum: 6a05bdc2990404078d731514d12bce0d NeedsCompilation: no Title: Computational pipeline for computing probability of modification from structure probing experiment data Description: This is a probabilistic modelling pipeline for computing per- nucleotide posterior probabilities of modification from the data collected in structure probing experiments. The model supports multiple experimental replicates and empirically corrects coverage- and sequence-dependent biases. The model utilises the measure of a "drop-off rate" for each nucleotide, which is compared between replicates through a log-ratio (LDR). The LDRs between control replicates define a null distribution of variability in drop-off rate observed by chance and LDRs between treatment and control replicates gets compared to this distribution. Resulting empirical p-values (probability of being "drawn" from the null distribution) are used as observations in a Hidden Markov Model with a Beta-Uniform Mixture model used as an emission model. The resulting posterior probabilities indicate the probability of a nucleotide of having being modified in a structure probing experiment. biocViews: ImmunoOncology, GeneticVariability, Transcription, GeneExpression, GeneRegulation, Coverage, Genetics, StructuralPrediction, Transcriptomics, Bayesian, Classification, FeatureExtraction, HiddenMarkovModel, Regression, RNASeq, Sequencing Author: Alina Selega (alina.selega@gmail.com), Sander Granneman, Guido Sanguinetti Maintainer: Alina Selega VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUMHMM git_branch: RELEASE_3_12 git_last_commit: 704c38c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BUMHMM_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BUMHMM_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BUMHMM_1.14.0.tgz vignettes: vignettes/BUMHMM/inst/doc/BUMHMM.pdf vignetteTitles: An Introduction to the BUMHMM pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUMHMM/inst/doc/BUMHMM.R dependencyCount: 100 Package: bumphunter Version: 1.32.0 Depends: R (>= 3.5), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi, stats Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 463f42d907c48779cb67fe0e99a6bb89 NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison, ImmunoOncology Author: Rafael A. Irizarry [cre, aut], Martin Aryee [aut], Kasper Daniel Hansen [aut], Hector Corrada Bravo [aut], Shan Andrews [ctb], Andrew E. Jaffe [ctb], Harris Jaffee [ctb], Leonardo Collado-Torres [ctb] Maintainer: Rafael A. Irizarry URL: https://github.com/ririzarr/bumphunter git_url: https://git.bioconductor.org/packages/bumphunter git_branch: RELEASE_3_12 git_last_commit: 8d9d889 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/bumphunter_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/bumphunter_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/bumphunter_1.32.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bumphunter/inst/doc/bumphunter.R dependsOnMe: minfi importsMe: brainflowprobes, DAMEfinder, derfinder, dmrseq, epivizr, methylCC, methyvim, rnaEditr, GenomicState, recountWorkflow suggestsMe: bigmelon, derfinderPlot, epivizrData, regionReport dependencyCount: 96 Package: BUS Version: 1.46.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: 1fb5b7476fbee8d15766d0ef28b0bc05 NeedsCompilation: yes Title: Gene network reconstruction Description: This package can be used to compute associations among genes (gene-networks) or between genes and some external traits (i.e. clinical). biocViews: Preprocessing Author: Yin Jin, Hesen Peng, Lei Wang, Raffaele Fronza, Yuanhua Liu and Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/BUS git_branch: RELEASE_3_12 git_last_commit: 1e5d5fa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BUS_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BUS_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BUS_1.46.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUS/inst/doc/bus.R dependencyCount: 3 Package: BUScorrect Version: 1.8.0 Depends: R (>= 3.5.0) Imports: gplots, methods, grDevices, stats, SummarizedExperiment Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 6f0257db94ca2c5ccff72b288dcae0a2 NeedsCompilation: yes Title: Batch Effects Correction with Unknown Subtypes Description: High-throughput experimental data are accumulating exponentially in public databases. However, mining valid scientific discoveries from these abundant resources is hampered by technical artifacts and inherent biological heterogeneity. The former are usually termed "batch effects," and the latter is often modelled by "subtypes." The R package BUScorrect fits a Bayesian hierarchical model, the Batch-effects-correction-with-Unknown-Subtypes model (BUS), to correct batch effects in the presence of unknown subtypes. BUS is capable of (a) correcting batch effects explicitly, (b) grouping samples that share similar characteristics into subtypes, (c) identifying features that distinguish subtypes, and (d) enjoying a linear-order computation complexity. biocViews: GeneExpression, StatisticalMethod, Bayesian, Clustering, FeatureExtraction, BatchEffect Author: Xiangyu Luo , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/BUScorrect git_branch: RELEASE_3_12 git_last_commit: 8b66f5e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/BUScorrect_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/BUScorrect_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/BUScorrect_1.8.0.tgz vignettes: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.pdf vignetteTitles: BUScorrect_user_guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BUScorrect/inst/doc/BUScorrect_user_guide.R dependencyCount: 30 Package: BUSpaRse Version: 1.4.2 Depends: R (>= 3.6) Imports: AnnotationDbi, AnnotationFilter, biomaRt, BiocGenerics, Biostrings, BSgenome, dplyr, ensembldb, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, IRanges, magrittr, Matrix, methods, plyranges, Rcpp, S4Vectors, stats, stringr, tibble, tidyr, utils, zeallot LinkingTo: Rcpp, RcppArmadillo, RcppProgress, BH Suggests: knitr, rmarkdown, testthat, BiocStyle, TENxBUSData, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38, EnsDb.Hsapiens.v86 License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 492eb2fed7f2c8aafc416039c4a3bb11 NeedsCompilation: yes Title: kallisto | bustools R utilities Description: The kallisto | bustools pipeline is a fast and modular set of tools to convert single cell RNA-seq reads in fastq files into gene count or transcript compatibility counts (TCC) matrices for downstream analysis. Central to this pipeline is the barcode, UMI, and set (BUS) file format. This package serves the following purposes: First, this package allows users to manipulate BUS format files as data frames in R and then convert them into gene count or TCC matrices. Furthermore, since R and Rcpp code is easier to handle than pure C++ code, users are encouraged to tweak the source code of this package to experiment with new uses of BUS format and different ways to convert the BUS file into gene count matrix. Second, this package can conveniently generate files required to generate gene count matrices for spliced and unspliced transcripts for RNA velocity. Here biotypes can be filtered and scaffolds and haplotypes can be removed, and the filtered transcriptome can be extracted and written to disk. Third, this package implements utility functions to get transcripts and associated genes required to convert BUS files to gene count matrices, to write the transcript to gene information in the format required by bustools, and to read output of bustools into R as sparses matrices. biocViews: SingleCell, RNASeq, WorkflowStep Author: Lambda Moses [aut, cre] (), Lior Pachter [aut, ths] () Maintainer: Lambda Moses URL: https://github.com/BUStools/BUSpaRse SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/BUStools/BUSpaRse/issues git_url: https://git.bioconductor.org/packages/BUSpaRse git_branch: RELEASE_3_12 git_last_commit: ac591a3 git_last_commit_date: 2021-03-01 Date/Publication: 2021-03-01 source.ver: src/contrib/BUSpaRse_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/BUSpaRse_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/BUSpaRse_1.4.2.tgz vignettes: vignettes/BUSpaRse/inst/doc/sparse-matrix.html, vignettes/BUSpaRse/inst/doc/tr2g.html vignetteTitles: Converting BUS format into sparse matrix, Transcript to gene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BUSpaRse/inst/doc/sparse-matrix.R, vignettes/BUSpaRse/inst/doc/tr2g.R dependencyCount: 114 Package: CAFE Version: 1.26.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 45decc9ec1f8a3e31da6a7aa6c983da9 NeedsCompilation: no Title: Chromosmal Aberrations Finder in Expression data Description: Detection and visualizations of gross chromosomal aberrations using Affymetrix expression microarrays as input biocViews: GeneExpression, Microarray, OneChannel, GeneSetEnrichment Author: Sander Bollen Maintainer: Sander Bollen git_url: https://git.bioconductor.org/packages/CAFE git_branch: RELEASE_3_12 git_last_commit: 2dd3923 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CAFE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CAFE_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CAFE_1.26.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAFE/inst/doc/CAFE-manual.R dependencyCount: 155 Package: CAGEfightR Version: 1.10.0 Depends: R (>= 3.5), GenomicRanges (>= 1.30.1), rtracklayer (>= 1.38.2), SummarizedExperiment (>= 1.8.1) Imports: pryr(>= 0.1.3), assertthat(>= 0.2.0), methods(>= 3.6.3), Matrix(>= 1.2-12), BiocGenerics(>= 0.24.0), S4Vectors(>= 0.16.0), IRanges(>= 2.12.0), GenomeInfoDb(>= 1.14.0), GenomicFeatures(>= 1.29.11), GenomicAlignments(>= 1.22.1), BiocParallel(>= 1.12.0), GenomicFiles(>= 1.14.0), Gviz(>= 1.22.2), InteractionSet(>= 1.9.4), GenomicInteractions(>= 1.15.1) Suggests: knitr, rmarkdown, BiocStyle, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-3 + file LICENSE MD5sum: d7267cefef3e47c8dd6e0a673f13aa5c NeedsCompilation: no Title: Analysis of Cap Analysis of Gene Expression (CAGE) data using Bioconductor Description: CAGE is a widely used high throughput assay for measuring transcription start site (TSS) activity. CAGEfightR is an R/Bioconductor package for performing a wide range of common data analysis tasks for CAGE and 5'-end data in general. Core functionality includes: import of CAGE TSSs (CTSSs), tag (or unidirectional) clustering for TSS identification, bidirectional clustering for enhancer identification, annotation with transcript and gene models, correlation of TSS and enhancer expression, calculation of TSS shapes, quantification of CAGE expression as expression matrices and genome brower visualization. biocViews: Software, Transcription, Coverage, GeneExpression, GeneRegulation, PeakDetection, DataImport, DataRepresentation, Transcriptomics, Sequencing, Annotation, GenomeBrowsers, Normalization, Preprocessing, Visualization Author: Malte Thodberg Maintainer: Malte Thodberg URL: https://github.com/MalteThodberg/CAGEfightR VignetteBuilder: knitr BugReports: https://github.com/MalteThodberg/CAGEfightR/issues git_url: https://git.bioconductor.org/packages/CAGEfightR git_branch: RELEASE_3_12 git_last_commit: 2ed5872 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CAGEfightR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CAGEfightR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CAGEfightR_1.10.0.tgz vignettes: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.html vignetteTitles: Introduction to CAGEfightR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CAGEfightR/inst/doc/Introduction_to_CAGEfightR.R dependsOnMe: CAGEWorkflow suggestsMe: nanotubes dependencyCount: 144 Package: CAGEr Version: 1.32.1 Depends: methods, MultiAssayExperiment, R (>= 3.5.0) Imports: beanplot, BiocGenerics, BiocParallel, BSgenome, data.table, DelayedArray, formula.tools, GenomeInfoDb, GenomicAlignments, GenomicRanges (>= 1.37.16), ggplot2 (>= 2.2.0), gtools, IRanges (>= 2.18.0), KernSmooth, memoise, plyr, Rsamtools, reshape, rtracklayer, S4Vectors (>= 0.27.5), som, stringdist, stringi, SummarizedExperiment, utils, vegan, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, DESeq2, FANTOM3and4CAGE, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 382b5f3ffa0750706dc37bb0979e19f1 NeedsCompilation: no Title: Analysis of CAGE (Cap Analysis of Gene Expression) sequencing data for precise mapping of transcription start sites and promoterome mining Description: Preprocessing of CAGE sequencing data, identification and normalization of transcription start sites and downstream analysis of transcription start sites clusters (promoters). biocViews: Preprocessing, Sequencing, Normalization, FunctionalGenomics, Transcription, GeneExpression, Clustering, Visualization Author: Vanja Haberle Maintainer: Vanja Haberle , Charles Plessy , Damir Baranasic , Sarvesh Nikumbh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CAGEr git_branch: RELEASE_3_12 git_last_commit: 034e4bf git_last_commit_date: 2021-01-15 Date/Publication: 2021-01-15 source.ver: src/contrib/CAGEr_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/CAGEr_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.0/CAGEr_1.32.1.tgz vignettes: vignettes/CAGEr/inst/doc/CAGE_Resources.html, vignettes/CAGEr/inst/doc/CAGEexp.html vignetteTitles: Use of CAGE resources with CAGEr, CAGEr: an R package for CAGE data analysis and promoterome mining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGE_Resources.R, vignettes/CAGEr/inst/doc/CAGEexp.R suggestsMe: seqPattern dependencyCount: 96 Package: calm Version: 1.4.0 Imports: mgcv, stats, graphics Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 6a6886838997ffda4517824cd7782486 NeedsCompilation: no Title: Covariate Assisted Large-scale Multiple testing Description: Statistical methods for multiple testing with covariate information. Traditional multiple testing methods only consider a list of test statistics, such as p-values. Our methods incorporate the auxiliary information, such as the lengths of gene coding regions or the minor allele frequencies of SNPs, to improve power. biocViews: Bayesian, DifferentialExpression, GeneExpression, Regression, Microarray, Sequencing, RNASeq, MultipleComparison, Genetics, ImmunoOncology, Metabolomics, Proteomics, Transcriptomics Author: Kun Liang [aut, cre] Maintainer: Kun Liang VignetteBuilder: knitr BugReports: https://github.com/k22liang/calm/issues git_url: https://git.bioconductor.org/packages/calm git_branch: RELEASE_3_12 git_last_commit: 7c0651b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/calm_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/calm_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/calm_1.4.0.tgz vignettes: vignettes/calm/inst/doc/calm_intro.html vignetteTitles: Userguide for calm package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/calm/inst/doc/calm_intro.R dependencyCount: 11 Package: CAMERA Version: 1.46.0 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5) Imports: methods, xcms, RBGL, graph, graphics, grDevices, stats, utils, Hmisc, igraph Suggests: faahKO, RUnit, BiocGenerics Enhances: Rmpi, snow License: GPL (>= 2) Archs: i386, x64 MD5sum: b574c6897e7d490ac06bba131d30aeb9 NeedsCompilation: yes Title: Collection of annotation related methods for mass spectrometry data Description: Annotation of peaklists generated by xcms, rule based annotation of isotopes and adducts, isotope validation, EIC correlation based tagging of unknown adducts and fragments biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Hendrik Treutler, Steffen Neumann {ckuhl|htreutle|sneumann}@ipb-halle.de, rtautenh@scripps.edu Maintainer: Steffen Neumann URL: http://msbi.ipb-halle.de/msbi/CAMERA/ BugReports: https://github.com/sneumann/CAMERA/issues/new git_url: https://git.bioconductor.org/packages/CAMERA git_branch: RELEASE_3_12 git_last_commit: 89886d3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CAMERA_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CAMERA_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CAMERA_1.46.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf, vignettes/CAMERA/inst/doc/compoundQuantilesVignette.pdf, vignettes/CAMERA/inst/doc/IsotopeDetectionVignette.pdf vignetteTitles: Molecule Identification with CAMERA, Atom count expectations with compoundQuantiles, Isotope pattern validation with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, IPO, LOBSTAHS, MAIT, metaMS, PtH2O2lipids suggestsMe: cliqueMS, msPurity, RMassBank, mtbls2 dependencyCount: 125 Package: canceR Version: 1.24.0 Depends: R (>= 3.4), tcltk, tcltk2, cgdsr Imports: GSEABase, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils, grDevices Suggests: testthat (>= 0.10.0), R.rsp License: GPL-2 MD5sum: b727e188cce8d52dfb1437873ae76340 NeedsCompilation: no Title: A Graphical User Interface for accessing and modeling the Cancer Genomics Data of MSKCC Description: The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC). biocViews: GUI, GeneExpression, Software Author: Karim Mezhoud. Nuclear Safety & Security Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud VignetteBuilder: R.rsp git_url: https://git.bioconductor.org/packages/canceR git_branch: RELEASE_3_12 git_last_commit: 84f757f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/canceR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/canceR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/canceR_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 151 Package: cancerclass Version: 1.34.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: 89ba92f8e8f9007767c0a63b08e7078a NeedsCompilation: yes Title: Development and validation of diagnostic tests from high-dimensional molecular data Description: The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements. biocViews: Cancer, Microarray, Classification, Visualization Author: Jan Budczies, Daniel Kosztyla Maintainer: Daniel Kosztyla git_url: https://git.bioconductor.org/packages/cancerclass git_branch: RELEASE_3_12 git_last_commit: fcec215 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cancerclass_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cancerclass_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cancerclass_1.34.0.tgz vignettes: vignettes/cancerclass/inst/doc/vignette_cancerclass.pdf vignetteTitles: Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cancerclass/inst/doc/vignette_cancerclass.R dependencyCount: 8 Package: CancerInSilico Version: 2.10.0 Depends: R (>= 3.4), Rcpp Imports: methods, utils, graphics, stats LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, BiocStyle, Rtsne, viridis, rgl, gplots License: GPL-2 Archs: i386, x64 MD5sum: 531b6aed5aa3e2001b32233ae9a0c8cb NeedsCompilation: yes Title: An R interface for computational modeling of tumor progression Description: The CancerInSilico package provides an R interface for running mathematical models of tumor progresson and generating gene expression data from the results. This package has the underlying models implemented in C++ and the output and analysis features implemented in R. biocViews: ImmunoOncology, MathematicalBiology, SystemsBiology, CellBiology, BiomedicalInformatics, GeneExpression, RNASeq, SingleCell Author: Thomas D. Sherman, Raymond Cheng, Elana J. Fertig Maintainer: Thomas D. Sherman , Elana J. Fertig VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CancerInSilico git_branch: RELEASE_3_12 git_last_commit: 3419b7b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CancerInSilico_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CancerInSilico_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CancerInSilico_2.10.0.tgz vignettes: vignettes/CancerInSilico/inst/doc/CancerInSilico.html vignetteTitles: The CancerInSilico Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerInSilico/inst/doc/CancerInSilico.R dependencyCount: 6 Package: CancerMutationAnalysis Version: 1.32.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 87f515dbecf94e4f5f9aa6e2349aa374 NeedsCompilation: yes Title: Cancer mutation analysis Description: This package implements gene and gene-set level analysis methods for somatic mutation studies of cancer. The gene-level methods distinguish between driver genes (which play an active role in tumorigenesis) and passenger genes (which are mutated in tumor samples, but have no role in tumorigenesis) and incorporate a two-stage study design. The gene-set methods implement a patient-oriented approach, which calculates gene-set scores for each sample, then combines them across samples; a gene-oriented approach which uses the Wilcoxon test is also provided for comparison. biocViews: Genetics, Software Author: Giovanni Parmigiani, Simina M. Boca Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/CancerMutationAnalysis git_branch: RELEASE_3_12 git_last_commit: afd4481 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CancerMutationAnalysis_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CancerMutationAnalysis_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CancerMutationAnalysis_1.32.0.tgz vignettes: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.pdf vignetteTitles: CancerMutationAnalysisTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.R dependencyCount: 62 Package: CancerSubtypes Version: 1.16.0 Depends: R (>= 3.5.0), sigclust, NMF Imports: SNFtool, iCluster, cluster, impute, limma, ConsensusClusterPlus, grDevices, survival Suggests: BiocGenerics, knitr, RTCGA.mRNA License: GPL (>= 2) MD5sum: fd4ff8efda2642cceebf976073e980c5 NeedsCompilation: no Title: Cancer subtypes identification, validation and visualization based on multiple genomic data sets Description: CancerSubtypes integrates the current common computational biology methods for cancer subtypes identification and provides a standardized framework for cancer subtype analysis based multi-omics data, such as gene expression, miRNA expression, DNA methylation and others. biocViews: Clustering, Software, Visualization, GeneExpression Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/CancerSubtypes VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/CancerSubtypes/issues git_url: https://git.bioconductor.org/packages/CancerSubtypes git_branch: RELEASE_3_12 git_last_commit: 5228b58 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CancerSubtypes_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CancerSubtypes_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CancerSubtypes_1.16.0.tgz vignettes: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.html vignetteTitles: CancerSubtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CancerSubtypes/inst/doc/CancerSubtypes-vignette.R dependencyCount: 74 Package: CAnD Version: 1.22.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 40651bfe36d64db191a5bf5c9bd6fb0f NeedsCompilation: no Title: Perform Chromosomal Ancestry Differences (CAnD) Analyses Description: Functions to perform the CAnD test on a set of ancestry proportions. For a particular ancestral subpopulation, a user will supply the estimated ancestry proportion for each sample, and each chromosome or chromosomal segment of interest. A p-value for each chromosome as well as an overall CAnD p-value will be returned for each test. Plotting functions are also available. biocViews: Genetics, StatisticalMethod, GeneticVariability, SNP Author: Caitlin McHugh, Timothy Thornton Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/CAnD git_branch: RELEASE_3_12 git_last_commit: 26d6a21 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CAnD_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CAnD_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CAnD_1.22.0.tgz vignettes: vignettes/CAnD/inst/doc/CAnD.pdf vignetteTitles: Detecting heterogenity in population structure across chromosomes with the "CAnD" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAnD/inst/doc/CAnD.R dependencyCount: 41 Package: caOmicsV Version: 1.20.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: d9bbdce6e2b80d2cdeabc8413ed73dc0 NeedsCompilation: no Title: Visualization of multi-dimentional cancer genomics data Description: caOmicsV package provides methods to visualize multi-dimentional cancer genomics data including of patient information, gene expressions, DNA methylations, DNA copy number variations, and SNP/mutations in matrix layout or network layout. biocViews: ImmunoOncology, Visualization, Network, RNASeq Author: Henry Zhang Maintainer: Henry Zhang git_url: https://git.bioconductor.org/packages/caOmicsV git_branch: RELEASE_3_12 git_last_commit: bd4a780 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/caOmicsV_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/caOmicsV_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/caOmicsV_1.20.0.tgz vignettes: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.pdf vignetteTitles: Intrudoction_to_caOmicsV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.R dependencyCount: 14 Package: Cardinal Version: 2.8.0 Depends: BiocGenerics, BiocParallel, EBImage, graphics, methods, S4Vectors (>= 0.27.3), stats, ProtGenerics Imports: Biobase, dplyr, irlba, lattice, Matrix, matter, magrittr, mclust, nlme, parallel, signal, sp, stats4, utils, viridisLite Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: e358ff115c2546a210a758a29a5401c2 NeedsCompilation: yes Title: A mass spectrometry imaging toolbox for statistical analysis Description: Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification. biocViews: Software, Infrastructure, Proteomics, Lipidomics, MassSpectrometry, ImagingMassSpectrometry, ImmunoOncology, Normalization, Clustering, Classification, Regression Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: http://www.cardinalmsi.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Cardinal git_branch: RELEASE_3_12 git_last_commit: 2df8fad git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Cardinal_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Cardinal_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Cardinal_2.8.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-2-guide.html, vignettes/Cardinal/inst/doc/Cardinal-2-stats.html vignetteTitles: 1. Cardinal 2: User guide for mass spectrometry imaging analysis, 2. Cardinal 2: Statistical methods for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-2-guide.R, vignettes/Cardinal/inst/doc/Cardinal-2-stats.R dependsOnMe: CardinalWorkflows dependencyCount: 64 Package: CARNIVAL Version: 1.2.0 Depends: R (>= 4.0) Imports: doParallel, readr, viper, AnnotationDbi, Category, ggplot2, UniProt.ws, lpSolve, igraph Suggests: knitr, readxl, testthat (>= 2.1.0) License: Apache License (== 3.0) | file LICENSE MD5sum: 13b2e8b50ca7ffd63b5ebb33d97cf06e NeedsCompilation: no Title: A CAusal Reasoning tool for Network Identification (from gene expression data) using Integer VALue programming Description: An upgraded causal reasoning tool from Melas et al in R with updated assignments of TFs' weights from PROGENy scores. Optimization parameters can be freely adjusted and multiple solutions can be obtained and aggregated. biocViews: Transcriptomics, GeneExpression, Network Author: Enio Gjerga Panuwat Trairatphisan Anika Liu Alberto Valdeolivas Nikolas Peschke Maintainer: Enio Gjerga URL: https://github.com/saezlab/CARNIVAL VignetteBuilder: knitr BugReports: https://github.com/saezlab/CARNIVAL/issues git_url: https://git.bioconductor.org/packages/CARNIVAL git_branch: RELEASE_3_12 git_last_commit: b0141d1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CARNIVAL_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CARNIVAL_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CARNIVAL_1.2.0.tgz vignettes: vignettes/CARNIVAL/inst/doc/CARNIVAL.html vignetteTitles: narray Usage Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CARNIVAL/inst/doc/CARNIVAL.R dependencyCount: 102 Package: casper Version: 2.24.2 Depends: R (>= 3.6.0), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics (>= 0.31.6), coda, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors (>= 0.9.25), sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: i386, x64 MD5sum: 3ff36f2b9b2b85605b6a54c028148997 NeedsCompilation: yes Title: Characterization of Alternative Splicing based on Paired-End Reads Description: Infer alternative splicing from paired-end RNA-seq data. The model is based on counting paths across exons, rather than pairwise exon connections, and estimates the fragment size and start distributions non-parametrically, which improves estimation precision. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/casper git_branch: RELEASE_3_12 git_last_commit: f2c5903 git_last_commit_date: 2020-12-15 Date/Publication: 2020-12-15 source.ver: src/contrib/casper_2.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/casper_2.24.2.zip mac.binary.ver: bin/macosx/contrib/4.0/casper_2.24.2.tgz vignettes: vignettes/casper/inst/doc/casper.pdf, vignettes/casper/inst/doc/DesignRNASeq.pdf vignetteTitles: Manual for the casper library, DesignRNASeq.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/casper/inst/doc/casper.R dependencyCount: 103 Package: CATALYST Version: 1.14.1 Depends: R (>= 4.0), SingleCellExperiment Imports: circlize, ComplexHeatmap, ConsensusClusterPlus, cowplot, data.table, dplyr, drc, flowCore, FlowSOM, ggplot2, ggrepel, ggridges, graphics, grDevices, grid, gridExtra, magrittr, Matrix, matrixStats, methods, nnls, purrr, RColorBrewer, reshape2, Rtsne, SummarizedExperiment, S4Vectors, scales, scater, stats Suggests: BiocStyle, diffcyt, flowWorkspace, ggcyto, knitr, openCyto, rmarkdown, testthat, uwot License: GPL (>=2) MD5sum: 98bc6734e914c5c760f0c95a21bc1921 NeedsCompilation: no Title: Cytometry dATa anALYSis Tools Description: Mass cytometry (CyTOF) uses heavy metal isotopes rather than fluorescent tags as reporters to label antibodies, thereby substantially decreasing spectral overlap and allowing for examination of over 50 parameters at the single cell level. While spectral overlap is significantly less pronounced in CyTOF than flow cytometry, spillover due to detection sensitivity, isotopic impurities, and oxide formation can impede data interpretability. We designed CATALYST (Cytometry dATa anALYSis Tools) to provide a pipeline for preprocessing of cytometry data, including i) normalization using bead standards, ii) single-cell deconvolution, and iii) bead-based compensation. biocViews: Clustering, DifferentialExpression, ExperimentalDesign, FlowCytometry, ImmunoOncology, MassSpectrometry, Normalization, Preprocessing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Vito R.T. Zanotelli [aut], Stéphane Chevrier [aut, dtc], Mark D. Robinson [aut, fnd], Bernd Bodenmiller [fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/CATALYST VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/CATALYST/issues git_url: https://git.bioconductor.org/packages/CATALYST git_branch: RELEASE_3_12 git_last_commit: 0c0f428 git_last_commit_date: 2021-04-28 Date/Publication: 2021-04-28 source.ver: src/contrib/CATALYST_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/CATALYST_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/CATALYST_1.14.1.tgz vignettes: vignettes/CATALYST/inst/doc/differential.html, vignettes/CATALYST/inst/doc/preprocessing.html vignetteTitles: "2. Differential discovery", "1. Preprocessing" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CATALYST/inst/doc/differential.R, vignettes/CATALYST/inst/doc/preprocessing.R dependsOnMe: cytofWorkflow suggestsMe: diffcyt dependencyCount: 224 Package: Category Version: 2.56.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, DBI Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGG.db, karyoploteR, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 MD5sum: 45b5c28e9d7435eaa71b4f7f51599707 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category (gene set enrichment) analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: Robert Gentleman [aut], Seth Falcon [ctb], Deepayan Sarkar [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Category git_branch: RELEASE_3_12 git_last_commit: ad478ca git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Category_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Category_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Category_2.56.0.tgz vignettes: vignettes/Category/inst/doc/Category.pdf, vignettes/Category/inst/doc/ChromBand.pdf vignetteTitles: Using Categories to Analyze Microarray Data, Using Chromosome Bands as Categories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Category/inst/doc/Category.R, vignettes/Category/inst/doc/ChromBand.R dependsOnMe: GOstats, PCpheno importsMe: CARNIVAL, categoryCompare, cellHTS2, eisa, GmicR, interactiveDisplay, meshr, miRLAB, PCpheno, phenoTest, ppiStats, RDAVIDWebService, scTensor suggestsMe: BiocCaseStudies, qpgraph, RnBeads, maGUI dependencyCount: 49 Package: categoryCompare Version: 1.34.0 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCy3 (>= 1.99.29), methods, grDevices, utils Suggests: knitr, GO.db, KEGG.db, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter License: GPL-2 MD5sum: f2300d4129b50cf4f37c83ba30fa95f3 NeedsCompilation: no Title: Meta-analysis of high-throughput experiments using feature annotations Description: Calculates significant annotations (categories) in each of two (or more) feature (i.e. gene) lists, determines the overlap between the annotations, and returns graphical and tabular data about the significant annotations and which combinations of feature lists the annotations were found to be significant. Interactive exploration is facilitated through the use of RCytoscape (heavily suggested). biocViews: Annotation, GO, MultipleComparison, Pathways, GeneExpression Author: Robert M. Flight Maintainer: Robert M. Flight URL: https://github.com/rmflight/categoryCompare SystemRequirements: Cytoscape (>= 3.6.1) (if used for visualization of results, heavily suggested) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues git_url: https://git.bioconductor.org/packages/categoryCompare git_branch: RELEASE_3_12 git_last_commit: b4f5da5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/categoryCompare_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/categoryCompare_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/categoryCompare_1.34.0.tgz vignettes: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html vignetteTitles: categoryCompare: High-throughput data meta-analysis using gene annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R dependencyCount: 65 Package: CausalR Version: 1.22.0 Depends: R (>= 3.2.0) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 74f47a38604f84e66cb4fd0be7d9c5dc NeedsCompilation: no Title: Causal network analysis methods Description: Causal network analysis methods for regulator prediction and network reconstruction from genome scale data. biocViews: ImmunoOncology, SystemsBiology, Network, GraphAndNetwork, Network Inference, Transcriptomics, Proteomics, DifferentialExpression, RNASeq, Microarray Author: Glyn Bradley, Steven Barrett, Chirag Mistry, Mark Pipe, David Wille, David Riley, Bhushan Bonde, Peter Woollard Maintainer: Glyn Bradley , Steven Barrett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CausalR git_branch: RELEASE_3_12 git_last_commit: 03ea8be git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CausalR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CausalR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CausalR_1.22.0.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R dependencyCount: 11 Package: cbaf Version: 1.12.1 Depends: R (>= 3.5.0) Imports: BiocFileCache, RColorBrewer, cgdsr, genefilter, gplots, grDevices, stats, utils, xlsx Suggests: knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: f6525428227c68ac45ce6122fc9014e2 NeedsCompilation: no Title: Automated functions for comparing various omic data from cbioportal.org Description: This package contains functions that allow analysing and comparing omic data across various cancers/cancer subgroups easily. So far, it is compatible with RNA-seq, microRNA-seq, microarray and methylation datasets that are stored on cbioportal.org. biocViews: Software, AssayDomain, DNAMethylation, GeneExpression, Transcription, ResearchField, BiomedicalInformatics, ComparativeGenomics, Epigenetics, Genetics, Transcriptomics Author: Arman Shahrisa [aut, cre, cph], Maryam Tahmasebi Birgani [aut] Maintainer: Arman Shahrisa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cbaf git_branch: RELEASE_3_12 git_last_commit: 75ed7d8 git_last_commit_date: 2020-12-06 Date/Publication: 2020-12-07 source.ver: src/contrib/cbaf_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/cbaf_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/cbaf_1.12.1.tgz vignettes: vignettes/cbaf/inst/doc/cbaf.html vignetteTitles: cbaf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cbaf/inst/doc/cbaf.R dependencyCount: 74 Package: cBioPortalData Version: 2.2.11 Depends: R (>= 4.0.0), AnVIL, MultiAssayExperiment Imports: BiocFileCache (>= 1.5.3), digest, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, methods, readr, RaggedExperiment, RTCGAToolbox (>= 2.19.7), S4Vectors, SummarizedExperiment, stats, tibble, tidyr, TCGAutils (>= 1.9.4), utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: AGPL-3 MD5sum: b8c8d49c99f19a55f64986787cfe89d0 NeedsCompilation: no Title: Exposes and makes available data from the cBioPortal web resources Description: The cBioPortalData package takes compressed resources from repositories such as cBioPortal and assembles a MultiAssayExperiment object with Bioconductor classes. biocViews: Software, Infrastructure, ThirdPartyClient Author: Levi Waldron [aut], Marcel Ramos [aut, cre] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/cBioPortalData/issues git_url: https://git.bioconductor.org/packages/cBioPortalData git_branch: RELEASE_3_12 git_last_commit: 48d683a git_last_commit_date: 2021-04-21 Date/Publication: 2021-04-22 source.ver: src/contrib/cBioPortalData_2.2.11.tar.gz win.binary.ver: bin/windows/contrib/4.0/cBioPortalData_2.2.11.zip mac.binary.ver: bin/macosx/contrib/4.0/cBioPortalData_2.2.11.tgz vignettes: vignettes/cBioPortalData/inst/doc/cBioPortalData.html, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.html vignetteTitles: cBioPortal User Guide, cBioPortal Quick-start Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cBioPortalData/inst/doc/cBioPortalData.R, vignettes/cBioPortalData/inst/doc/cBioPortalRClient.R dependencyCount: 110 Package: ccfindR Version: 1.10.0 Depends: R (>= 3.6.0) Imports: stats, S4Vectors, utils, methods, Matrix, SummarizedExperiment, SingleCellExperiment, Rtsne, graphics, grDevices, gtools, RColorBrewer, ape, Rmpi, irlba, Rcpp, Rdpack (>= 0.7) LinkingTo: Rcpp, RcppEigen Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 2842b3d0d0ce6550ed4dc91b52be07e9 NeedsCompilation: yes Title: Cancer Clone Finder Description: A collection of tools for cancer genomic data clustering analyses, including those for single cell RNA-seq. Cell clustering and feature gene selection analysis employ Bayesian (and maximum likelihood) non-negative matrix factorization (NMF) algorithm. Input data set consists of RNA count matrix, gene, and cell bar code annotations. Analysis outputs are factor matrices for multiple ranks and marginal likelihood values for each rank. The package includes utilities for downstream analyses, including meta-gene identification, visualization, and construction of rank-based trees for clusters. biocViews: Transcriptomics, SingleCell, ImmunoOncology, Bayesian, Clustering Author: Jun Woo [aut, cre], Jinhua Wang [aut] Maintainer: Jun Woo URL: http://dx.doi.org/10.26508/lsa.201900443 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccfindR git_branch: RELEASE_3_12 git_last_commit: 3576737 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ccfindR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ccfindR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ccfindR_1.10.0.tgz vignettes: vignettes/ccfindR/inst/doc/ccfindR.html vignetteTitles: ccfindR: single-cell RNA-seq analysis using Bayesian non-negative matrix factorization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ccfindR/inst/doc/ccfindR.R suggestsMe: MutationalPatterns dependencyCount: 38 Package: ccmap Version: 1.16.0 Imports: AnnotationDbi (>= 1.36.2), BiocManager (>= 1.30.4), ccdata (>= 1.1.2), doParallel (>= 1.0.10), data.table (>= 1.10.4), foreach (>= 1.4.3), parallel (>= 3.3.3), xgboost (>= 0.6.4), lsa (>= 0.73.1) Suggests: crossmeta, knitr, rmarkdown, testthat, lydata License: MIT + file LICENSE MD5sum: eab5a9dc1e5f7e458734c13f5cda2444 NeedsCompilation: no Title: Combination Connectivity Mapping Description: Finds drugs and drug combinations that are predicted to reverse or mimic gene expression signatures. These drugs might reverse diseases or mimic healthy lifestyles. biocViews: GeneExpression, Transcription, Microarray, DifferentialExpression Author: Alex Pickering Maintainer: Alex Pickering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccmap git_branch: RELEASE_3_12 git_last_commit: 9020a98 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ccmap_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ccmap_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ccmap_1.16.0.tgz vignettes: vignettes/ccmap/inst/doc/ccmap-vignette.html vignetteTitles: ccmap vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccmap/inst/doc/ccmap-vignette.R dependencyCount: 42 Package: CCPROMISE Version: 1.16.0 Depends: R (>= 3.3.0), stats, methods, CCP, PROMISE, Biobase, GSEABase, utils License: GPL (>= 2) MD5sum: d878fdf1506553d7f614a15af4d3e1a9 NeedsCompilation: no Title: PROMISE analysis with Canonical Correlation for Two Forms of High Dimensional Genetic Data Description: Perform Canonical correlation between two forms of high demensional genetic data, and associate the first compoent of each form of data with a specific biologically interesting pattern of associations with multiple endpoints. A probe level analysis is also implemented. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/CCPROMISE git_branch: RELEASE_3_12 git_last_commit: f273cb2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CCPROMISE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CCPROMISE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CCPROMISE_1.16.0.tgz vignettes: vignettes/CCPROMISE/inst/doc/CCPROMISE.pdf vignetteTitles: An introduction to CCPROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CCPROMISE/inst/doc/CCPROMISE.R dependencyCount: 42 Package: ccrepe Version: 1.26.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: f5a61ecbc5a4ba691b4fb80fc0871a1d NeedsCompilation: no Title: ccrepe_and_nc.score Description: The CCREPE (Compositionality Corrected by REnormalizaion and PErmutation) package is designed to assess the significance of general similarity measures in compositional datasets. In microbial abundance data, for example, the total abundances of all microbes sum to one; CCREPE is designed to take this constraint into account when assigning p-values to similarity measures between the microbes. The package has two functions: ccrepe: Calculates similarity measures, p-values and q-values for relative abundances of bugs in one or two body sites using bootstrap and permutation matrices of the data. nc.score: Calculates species-level co-variation and co-exclusion patterns based on an extension of the checkerboard score to ordinal data. biocViews: ImmunoOncology, Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ccrepe git_branch: RELEASE_3_12 git_last_commit: 718ab4b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ccrepe_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ccrepe_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ccrepe_1.26.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ccrepe/inst/doc/ccrepe.R dependencyCount: 1 Package: celaref Version: 1.8.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: MAST, ggplot2, Matrix, dplyr, magrittr, stats, utils, rlang, BiocGenerics, S4Vectors, readr, tibble, DelayedArray Suggests: limma, parallel, knitr, rmarkdown, ExperimentHub, testthat License: GPL-3 MD5sum: 170a2abb32df21001cf3e0db69d614a2 NeedsCompilation: no Title: Single-cell RNAseq cell cluster labelling by reference Description: After the clustering step of a single-cell RNAseq experiment, this package aims to suggest labels/cell types for the clusters, on the basis of similarity to a reference dataset. It requires a table of read counts per cell per gene, and a list of the cells belonging to each of the clusters, (for both test and reference data). biocViews: SingleCell Author: Sarah Williams [aut, cre] Maintainer: Sarah Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/celaref git_branch: RELEASE_3_12 git_last_commit: 446dc60 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/celaref_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/celaref_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/celaref_1.8.0.tgz vignettes: vignettes/celaref/inst/doc/celaref_doco.html vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/celaref/inst/doc/celaref_doco.R dependencyCount: 76 Package: celda Version: 1.6.1 Depends: R (>= 3.6) Imports: plyr, foreach, ggplot2, RColorBrewer, grid, scales, gtable, grDevices, graphics, matrixStats, doParallel, digest, methods, reshape2, MAST, S4Vectors, data.table, Rcpp, RcppEigen, uwot, enrichR, stringi, SummarizedExperiment, MCMCprecision, ggrepel, Rtsne, withr, dendextend, ggdendro, pROC, scater (>= 1.14.4), scran, SingleCellExperiment, dbscan, DelayedArray, Seurat, stringr, Matrix, ComplexHeatmap, multipanelfigure, circlize LinkingTo: Rcpp, RcppEigen Suggests: testthat, knitr, roxygen2, rmarkdown, biomaRt, covr, BiocManager, BiocStyle, M3DExampleData, TENxPBMCData License: MIT + file LICENSE Archs: i386, x64 MD5sum: 7e7b9a8f9977c86b4b59f119e9600adb NeedsCompilation: yes Title: CEllular Latent Dirichlet Allocation Description: Utilizing Bayesian hierarchical models to analyze single-cell genomic data. biocViews: SingleCell, GeneExpression, Clustering, Sequencing, Bayesian Author: Joshua Campbell [aut, cre], Sean Corbett [aut], Yusuke Koga [aut], Shiyi Yang [aut], Eric Reed [aut], Zhe Wang [aut] Maintainer: Joshua Campbell VignetteBuilder: knitr BugReports: https://github.com/campbio/celda/issues git_url: https://git.bioconductor.org/packages/celda git_branch: RELEASE_3_12 git_last_commit: c18034f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/celda_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/celda_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/celda_1.6.1.tgz vignettes: vignettes/celda/inst/doc/celda.pdf, vignettes/celda/inst/doc/decontX.pdf vignetteTitles: Analysis of single-cell genomic data with celda, Estimate and remove cross-contamination from ambient RNA in single-cell data with DecontX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/celda/inst/doc/celda.R, vignettes/celda/inst/doc/decontX.R importsMe: singleCellTK dependencyCount: 213 Package: CellaRepertorium Version: 1.0.0 Depends: R (>= 4.0) Imports: dplyr, tibble, stringr, Biostrings, Rcpp, reshape2, methods, rlang (>= 0.3), purrr, Matrix, S4Vectors, BiocGenerics, tidyr, forcats, progress, stats, utils LinkingTo: Rcpp Suggests: testthat, readr, knitr, rmarkdown, ggplot2, BiocStyle, ggdendro, broom, lme4, RColorBrewer, SingleCellExperiment, scater, broom.mixed, cowplot License: GPL-3 Archs: i386, x64 MD5sum: aa2607ae12d30350ebdc2e92c3ddea55 NeedsCompilation: yes Title: Data structures, clustering and testing for single cell immune receptor repertoires (scRNAseq RepSeq/AIRR-seq) Description: Methods to cluster and analyze high-throughput single cell immune cell repertoires, especially from the 10X Genomics VDJ solution. Contains an R interface to CD-HIT (Li and Godzik 2006). Methods to visualize and analyze paired heavy-light chain data. Tests for specific expansion, as well as omnibus oligoclonality under hypergeometric models. biocViews: RNASeq, Transcriptomics, SingleCell, TargetedResequencing, Technology, ImmunoOncology, Clustering Author: Andrew McDavid [aut, cre], Yu Gu [aut], Erik VonKaenel [aut], Thomas Lin Pedersen [ctb] Maintainer: Andrew McDavid URL: https://github.com/amcdavid/CellaRepertorium VignetteBuilder: knitr BugReports: https://github.com/amcdavid/CellaRepertorium/issues git_url: https://git.bioconductor.org/packages/CellaRepertorium git_branch: RELEASE_3_12 git_last_commit: 4e5b113 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CellaRepertorium_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CellaRepertorium_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CellaRepertorium_1.0.0.tgz vignettes: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.html, vignettes/CellaRepertorium/inst/doc/cr-overview.html, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.html, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.html vignetteTitles: Clustering and differential usage of repertoire CDR3 sequences, An Introduction to CellaRepertorium, Quality control and Exploration of UMI-based repertoire data, Combining Repertoire with Expression with SingleCellExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellaRepertorium/inst/doc/cdr3_clustering.R, vignettes/CellaRepertorium/inst/doc/cr-overview.R, vignettes/CellaRepertorium/inst/doc/mouse_tcell_qc.R, vignettes/CellaRepertorium/inst/doc/repertoire_and_expression.R dependencyCount: 46 Package: cellbaseR Version: 1.14.0 Depends: R(>= 3.4) Imports: methods, jsonlite, httr, data.table, pbapply, tidyr, R.utils, Rsamtools, BiocParallel, foreach, utils, parallel, doParallel Suggests: BiocStyle, knitr, rmarkdown, Gviz, VariantAnnotation License: Apache License (== 2.0) MD5sum: 3ecf58ad741080cd19447e53ea214fad NeedsCompilation: no Title: Querying annotation data from the high performance Cellbase web Description: This R package makes use of the exhaustive RESTful Web service API that has been implemented for the Cellabase database. It enable researchers to query and obtain a wealth of biological information from a single database saving a lot of time. Another benefit is that researchers can easily make queries about different biological topics and link all this information together as all information is integrated. biocViews: Annotation, VariantAnnotation Author: Mohammed OE Abdallah Maintainer: Mohammed OE Abdallah URL: https://github.com/melsiddieg/cellbaseR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellbaseR git_branch: RELEASE_3_12 git_last_commit: 6ecfb00 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cellbaseR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cellbaseR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cellbaseR_1.14.0.tgz vignettes: vignettes/cellbaseR/inst/doc/cellbaseR.html vignetteTitles: "Simplifying Genomic Annotations in R" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellbaseR/inst/doc/cellbaseR.R dependencyCount: 64 Package: CellBench Version: 1.6.0 Depends: R (>= 3.6), SingleCellExperiment, magrittr, methods, stats, tibble, utils Imports: BiocFileCache, BiocParallel, dplyr, rlang, glue, memoise, purrr (>= 0.3.0), rappdirs, tidyr, tidyselect, lubridate Suggests: BiocStyle, covr, knitr, rmarkdown, testthat, limma, ggplot2 License: GPL-3 MD5sum: 36bd1054275e52e61b6980e0401eb850 NeedsCompilation: no Title: Construct Benchmarks for Single Cell Analysis Methods Description: This package contains infrastructure for benchmarking analysis methods and access to single cell mixture benchmarking data. It provides a framework for organising analysis methods and testing combinations of methods in a pipeline without explicitly laying out each combination. It also provides utilities for sampling and filtering SingleCellExperiment objects, constructing lists of functions with varying parameters, and multithreaded evaluation of analysis methods. biocViews: Software, Infrastructure Author: Shian Su [cre, aut], Saskia Freytag [aut], Luyi Tian [aut], Xueyi Dong [aut], Matthew Ritchie [aut], Peter Hickey [ctb], Stuart Lee [ctb] Maintainer: Shian Su URL: https://github.com/shians/cellbench VignetteBuilder: knitr BugReports: https://github.com/Shians/CellBench/issues git_url: https://git.bioconductor.org/packages/CellBench git_branch: RELEASE_3_12 git_last_commit: b830843 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CellBench_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CellBench_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CellBench_1.6.0.tgz vignettes: vignettes/CellBench/inst/doc/DataManipulation.pdf, vignettes/CellBench/inst/doc/TidyversePatterns.pdf, vignettes/CellBench/inst/doc/CellBenchCaseStudy.html, vignettes/CellBench/inst/doc/Introduction.html, vignettes/CellBench/inst/doc/Timing.html, vignettes/CellBench/inst/doc/WritingWrappers.html vignetteTitles: Data Manipulation, Tidyverse Patterns, CellBenchCaseStudy.html, Introduction, Timing, Writing Wrappers hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellBench/inst/doc/DataManipulation.R, vignettes/CellBench/inst/doc/Introduction.R, vignettes/CellBench/inst/doc/TidyversePatterns.R, vignettes/CellBench/inst/doc/Timing.R, vignettes/CellBench/inst/doc/WritingWrappers.R suggestsMe: corral dependencyCount: 77 Package: cellHTS2 Version: 2.54.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: prada, GSEABase, Category, stats4, BiocGenerics Suggests: ggplot2 License: Artistic-2.0 MD5sum: 36893689f9ad1ae609e9dae0b3a906b4 NeedsCompilation: no Title: Analysis of cell-based screens - revised version of cellHTS Description: This package provides tools for the analysis of high-throughput assays that were performed in microtitre plate formats (including but not limited to 384-well plates). The functionality includes data import and management, normalisation, quality assessment, replicate summarisation and statistical scoring. A webpage that provides a detailed graphical overview over the data and analysis results is produced. In our work, we have applied the package to RNAi screens on fly and human cells, and for screens of yeast libraries. See ?cellHTS2 for a brief introduction. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Visualization Author: Ligia Bras, Wolfgang Huber , Michael Boutros , Gregoire Pau , Florian Hahne Maintainer: Joseph Barry URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber git_url: https://git.bioconductor.org/packages/cellHTS2 git_branch: RELEASE_3_12 git_last_commit: c1dc5cf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/cellHTS2_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cellHTS2_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cellHTS2_2.54.0.tgz vignettes: vignettes/cellHTS2/inst/doc/cellhts2.pdf, vignettes/cellHTS2/inst/doc/cellhts2Complete.pdf, vignettes/cellHTS2/inst/doc/twoChannels.pdf, vignettes/cellHTS2/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Main vignette (complete version): End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: enhancer-suppressor screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS2/inst/doc/cellhts2.R, vignettes/cellHTS2/inst/doc/cellhts2Complete.R, vignettes/cellHTS2/inst/doc/twoChannels.R, vignettes/cellHTS2/inst/doc/twoWay.R dependsOnMe: imageHTS, staRank importsMe: gespeR, RNAinteract suggestsMe: bioassayR dependencyCount: 84 Package: cellity Version: 1.18.0 Depends: R (>= 3.3) Imports: AnnotationDbi, e1071, ggplot2, graphics, grDevices, grid, mvoutlier, org.Hs.eg.db, org.Mm.eg.db, robustbase, stats, topGO, utils Suggests: BiocStyle, caret, knitr, testthat, rmarkdown License: GPL (>= 2) MD5sum: b39fe754806238d7c00b401ffb122710 NeedsCompilation: no Title: Quality Control for Single-Cell RNA-seq Data Description: A support vector machine approach to identifying and filtering low quality cells from single-cell RNA-seq datasets. biocViews: ImmunoOncology, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellity git_branch: RELEASE_3_12 git_last_commit: 65b7ecd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cellity_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cellity_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cellity_1.18.0.tgz vignettes: vignettes/cellity/inst/doc/cellity_vignette.html vignetteTitles: An introduction to the cellity package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R dependencyCount: 166 Package: CellMapper Version: 1.16.0 Depends: S4Vectors, methods Imports: stats, utils Suggests: CellMapperData, Biobase, HumanAffyData, ALL, BiocStyle, ExperimentHub License: Artistic-2.0 MD5sum: 9ce3a4ac7153ca9f610c0ca29a18647d NeedsCompilation: no Title: Predict genes expressed selectively in specific cell types Description: Infers cell type-specific expression based on co-expression similarity with known cell type marker genes. Can make accurate predictions using publicly available expression data, even when a cell type has not been isolated before. biocViews: Microarray, Software, GeneExpression Author: Brad Nelms Maintainer: Brad Nelms git_url: https://git.bioconductor.org/packages/CellMapper git_branch: RELEASE_3_12 git_last_commit: c73a675 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CellMapper_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CellMapper_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CellMapper_1.16.0.tgz vignettes: vignettes/CellMapper/inst/doc/CellMapper.pdf vignetteTitles: CellMapper Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMapper/inst/doc/CellMapper.R dependsOnMe: CellMapperData dependencyCount: 8 Package: CellMixS Version: 1.6.1 Depends: kSamples, R (>= 4.0) Imports: BiocNeighbors, ggplot2, scater, viridis, cowplot, SummarizedExperiment, SingleCellExperiment, tidyr, magrittr, dplyr, ggridges, stats, purrr, methods, BiocParallel, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, testthat, limma, Rtsne License: GPL (>=2) MD5sum: c8d5582c20992605f6ce812be247356c NeedsCompilation: no Title: Evaluate Cellspecific Mixing Description: CellMixS provides metrics and functions to evaluate batch effects, data integration and batch effect correction in single cell trancriptome data with single cell resolution. Results can be visualized and summarised on different levels, e.g. on cell, celltype or dataset level. biocViews: SingleCell, Transcriptomics, GeneExpression, BatchEffect Author: Almut Lütge [aut, cre] Maintainer: Almut Lütge URL: https://github.com/almutlue/CellMixS VignetteBuilder: knitr BugReports: https://github.com/almutlue/CellMixS/issues git_url: https://git.bioconductor.org/packages/CellMixS git_branch: RELEASE_3_12 git_last_commit: 7f356d8 git_last_commit_date: 2020-12-17 Date/Publication: 2020-12-17 source.ver: src/contrib/CellMixS_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/CellMixS_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/CellMixS_1.6.1.tgz vignettes: vignettes/CellMixS/inst/doc/CellMixS.html vignetteTitles: Explore data integration and batch effects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellMixS/inst/doc/CellMixS.R dependencyCount: 95 Package: CellNOptR Version: 1.36.0 Depends: R (>= 3.5.0), RBGL, graph, methods, hash, RCurl, Rgraphviz, XML, ggplot2 Imports: igraph, stringi, stringr, Suggests: data.table, dplyr, tidyr, readr, RUnit, BiocGenerics, Enhances: doParallel License: GPL-3 Archs: i386, x64 MD5sum: b78bb5631e74d6061d545270f558f77a NeedsCompilation: yes Title: Training of boolean logic models of signalling networks using prior knowledge networks and perturbation data Description: This package does optimisation of boolean logic networks of signalling pathways based on a previous knowledge network and a set of data upon perturbation of the nodes in the network. biocViews: CellBasedAssays, CellBiology, Proteomics, Pathways, Network, TimeCourse, ImmunoOncology Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve, E.Gjerga, A.Gabor Maintainer: A.Gabor SystemRequirements: Graphviz version >= 2.2 git_url: https://git.bioconductor.org/packages/CellNOptR git_branch: RELEASE_3_12 git_last_commit: 0f9d117 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CellNOptR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CellNOptR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CellNOptR_1.36.0.tgz vignettes: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CellNOptR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellNOptR/inst/doc/CellNOptR-vignette.R dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode suggestsMe: MEIGOR dependencyCount: 53 Package: cellscape Version: 1.14.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), reshape2 (>= 1.4.1), stringr (>= 1.0.0), plyr (>= 1.8.3), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 06b0c8f054a2b970899f3f4a3db8f86a NeedsCompilation: no Title: Explores single cell copy number profiles in the context of a single cell tree Description: CellScape facilitates interactive browsing of single cell clonal evolution datasets. The tool requires two main inputs: (i) the genomic content of each single cell in the form of either copy number segments or targeted mutation values, and (ii) a single cell phylogeny. Phylogenetic formats can vary from dendrogram-like phylogenies with leaf nodes to evolutionary model-derived phylogenies with observed or latent internal nodes. The CellScape phylogeny is flexibly input as a table of source-target edges to support arbitrary representations, where each node may or may not have associated genomic data. The output of CellScape is an interactive interface displaying a single cell phylogeny and a cell-by-locus genomic heatmap representing the mutation status in each cell for each locus. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellscape git_branch: RELEASE_3_12 git_last_commit: 0ec1ab9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cellscape_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cellscape_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cellscape_1.14.0.tgz vignettes: vignettes/cellscape/inst/doc/cellscape_vignette.html vignetteTitles: CellScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellscape/inst/doc/cellscape_vignette.R dependencyCount: 35 Package: CellScore Version: 1.10.0 Depends: R (>= 3.5.0) Imports: Biobase (>= 2.39.1), graphics (>= 3.5.0), grDevices (>= 3.5.0), gplots (>= 3.0.1), lsa (>= 0.73.1), methods (>= 3.5.0), RColorBrewer(>= 1.1-2), squash (>= 1.0.8), stats (>= 3.5.0), utils(>= 3.5.0) Suggests: hgu133plus2CellScore, knitr License: GPL-3 MD5sum: 3175394c0a981985c8ad74b29177f50f NeedsCompilation: no Title: Tool for Evaluation of Cell Identity from Transcription Profiles Description: The CellScore package contains functions to evaluate the cell identity of a test sample, given a cell transition defined with a starting (donor) cell type and a desired target cell type. The evaluation is based upon a scoring system, which uses a set of standard samples of known cell types, as the reference set. The functions have been carried out on a large set of microarray data from one platform (Affymetrix Human Genome U133 Plus 2.0). In principle, the method could be applied to any expression dataset, provided that there are a sufficient number of standard samples and that the data are normalized. biocViews: GeneExpression, Transcription, Microarray, MultipleComparison, ReportWriting, DataImport, Visualization Author: Nancy Mah, Katerina Taskova Maintainer: Nancy Mah VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellScore git_branch: RELEASE_3_12 git_last_commit: a714c29 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CellScore_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CellScore_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CellScore_1.10.0.tgz vignettes: vignettes/CellScore/inst/doc/CellScoreVignette.pdf vignetteTitles: R packages: CellScore hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellScore/inst/doc/CellScoreVignette.R dependencyCount: 17 Package: CellTrails Version: 1.8.0 Depends: R (>= 3.5), SingleCellExperiment Imports: BiocGenerics, Biobase, cba, dendextend, dtw, EnvStats, ggplot2, ggrepel, grDevices, igraph, maptree, methods, mgcv, reshape2, Rtsne, stats, splines, SummarizedExperiment, utils Suggests: AnnotationDbi, destiny, RUnit, scater, scran, knitr, org.Mm.eg.db, rmarkdown License: Artistic-2.0 MD5sum: 29ae636313eeab464896f9bca6fec3cd NeedsCompilation: no Title: Reconstruction, visualization and analysis of branching trajectories Description: CellTrails is an unsupervised algorithm for the de novo chronological ordering, visualization and analysis of single-cell expression data. CellTrails makes use of a geometrically motivated concept of lower-dimensional manifold learning, which exhibits a multitude of virtues that counteract intrinsic noise of single cell data caused by drop-outs, technical variance, and redundancy of predictive variables. CellTrails enables the reconstruction of branching trajectories and provides an intuitive graphical representation of expression patterns along all branches simultaneously. It allows the user to define and infer the expression dynamics of individual and multiple pathways towards distinct phenotypes. biocViews: ImmunoOncology, Clustering, DataRepresentation, DifferentialExpression, DimensionReduction, GeneExpression, Sequencing, SingleCell, Software, TimeCourse Author: Daniel Ellwanger [aut, cre, cph] Maintainer: Daniel Ellwanger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CellTrails git_branch: RELEASE_3_12 git_last_commit: f0ce473 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CellTrails_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CellTrails_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CellTrails_1.8.0.tgz vignettes: vignettes/CellTrails/inst/doc/vignette.pdf vignetteTitles: CellTrails: Reconstruction,, visualization,, and analysis of branching trajectories from single-cell expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CellTrails/inst/doc/vignette.R dependencyCount: 77 Package: cellTree Version: 1.20.0 Depends: R (>= 3.3), topGO Imports: topicmodels, slam, maptpx, igraph, xtable, gplots Suggests: BiocStyle, knitr, HSMMSingleCell, biomaRt, org.Hs.eg.db, Biobase, tools License: Artistic-2.0 MD5sum: c1910d545f2d996cdedeeb71cab2dc26 NeedsCompilation: no Title: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure Description: This packages computes a Latent Dirichlet Allocation (LDA) model of single-cell RNA-seq data and builds a compact tree modelling the relationship between individual cells over time or space. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology, GO, TimeCourse, Microarray Author: David duVerle [aut, cre], Koji Tsuda [aut] Maintainer: David duVerle URL: http://tsudalab.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cellTree git_branch: RELEASE_3_12 git_last_commit: 666febd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/cellTree_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/cellTree_1.20.0.tgz vignettes: vignettes/cellTree/inst/doc/cellTree-vignette.pdf vignetteTitles: Inference and visualisation of Single-Cell RNA-seq Data data as a hierarchical tree structure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellTree/inst/doc/cellTree-vignette.R dependencyCount: 52 Package: CEMiTool Version: 1.14.1 Depends: R (>= 4.0) Imports: methods, scales, dplyr, data.table (>= 1.9.4), WGCNA, grid, ggplot2, ggpmisc, ggthemes, ggrepel, sna, clusterProfiler, fgsea, stringr, knitr, rmarkdown, igraph, DT, htmltools, pracma, intergraph, grDevices, utils, network, matrixStats, ggdendro, gridExtra, gtable, fastcluster Suggests: testthat, BiocManager License: GPL-3 MD5sum: f55a69ee6edacb3b44688e5da580c41a NeedsCompilation: no Title: Co-expression Modules identification Tool Description: The CEMiTool package unifies the discovery and the analysis of coexpression gene modules in a fully automatic manner, while providing a user-friendly html report with high quality graphs. Our tool evaluates if modules contain genes that are over-represented by specific pathways or that are altered in a specific sample group. Additionally, CEMiTool is able to integrate transcriptomic data with interactome information, identifying the potential hubs on each network. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology Author: Pedro Russo [aut], Gustavo Ferreira [aut], Matheus Bürger [aut], Lucas Cardozo [aut], Diogenes Lima [aut], Thiago Hirata [aut], Melissa Lever [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CEMiTool git_branch: RELEASE_3_12 git_last_commit: 51fc0c6 git_last_commit_date: 2021-03-10 Date/Publication: 2021-03-11 source.ver: src/contrib/CEMiTool_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/CEMiTool_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/CEMiTool_1.14.1.tgz vignettes: vignettes/CEMiTool/inst/doc/CEMiTool.html vignetteTitles: CEMiTool: Co-expression Modules Identification Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CEMiTool/inst/doc/CEMiTool.R dependencyCount: 158 Package: ceRNAnetsim Version: 1.2.1 Depends: R (>= 4.0.0), dplyr, tidygraph Imports: furrr, rlang, tibble, ggplot2, ggraph, igraph, purrr, tidyr, future, stats Suggests: knitr, png, rmarkdown, testthat, covr License: GPL (>= 3.0) MD5sum: cf329467a3fc17dedf0c765ade9d927b NeedsCompilation: no Title: Regulation Simulator of Interaction between miRNA and Competing RNAs (ceRNA) Description: This package simulates regulations of ceRNA (Competing Endogenous) expression levels after a expression level change in one or more miRNA/mRNAs. The methodolgy adopted by the package has potential to incorparate any ceRNA (circRNA, lincRNA, etc.) into miRNA:target interaction network. The package basically distributes miRNA expression over available ceRNAs where each ceRNA attracks miRNAs proportional to its amount. But, the package can utilize multiple parameters that modify miRNA effect on its target (seed type, binding energy, binding location, etc.). The functions handle the given dataset as graph object and the processes progress via edge and node variables. biocViews: NetworkInference, SystemsBiology, Network, GraphAndNetwork, Transcriptomics Author: Selcen Ari Yuka [aut, cre] (), Alper Yilmaz [aut] () Maintainer: Selcen Ari Yuka URL: https://github.com/selcenari/ceRNAnetsim VignetteBuilder: knitr BugReports: https://github.com/selcenari/ceRNAnetsim/issues git_url: https://git.bioconductor.org/packages/ceRNAnetsim git_branch: RELEASE_3_12 git_last_commit: fa9dfdf git_last_commit_date: 2020-11-26 Date/Publication: 2020-11-26 source.ver: src/contrib/ceRNAnetsim_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ceRNAnetsim_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ceRNAnetsim_1.2.1.tgz vignettes: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.html, vignettes/ceRNAnetsim/inst/doc/basic_usage.html, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.html, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.html vignetteTitles: auxiliary_commands, basic_usage, A Suggestion: How to Find the Appropriate Iteration for Simulation, An TCGA dataset application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ceRNAnetsim/inst/doc/auxiliary_commands.R, vignettes/ceRNAnetsim/inst/doc/basic_usage.R, vignettes/ceRNAnetsim/inst/doc/convenient_iteration.R, vignettes/ceRNAnetsim/inst/doc/mirtarbase_example.R dependencyCount: 65 Package: CeTF Version: 1.2.4 Depends: R (>= 4.0), methods Imports: circlize, ComplexHeatmap, clusterProfiler, DESeq2, GenomicTools, GenomicTools.fileHandler, ggnetwork, GGally, ggplot2, ggpubr, ggrepel, graphics, grid, igraph, Matrix, network, Rcpp, RCy3, S4Vectors, stats, SummarizedExperiment, utils, WebGestaltR LinkingTo: Rcpp, RcppArmadillo Suggests: airway, kableExtra, knitr, org.Hs.eg.db, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: a399858ecfc6cc0fc8825f4520e9efa5 NeedsCompilation: yes Title: Coexpression for Transcription Factors using Regulatory Impact Factors and Partial Correlation and Information Theory analysis Description: This package provides the necessary functions for performing the Partial Correlation coefficient with Information Theory (PCIT) (Reverter and Chan 2008) and Regulatory Impact Factors (RIF) (Reverter et al. 2010) algorithm. The PCIT algorithm identifies meaningful correlations to define edges in a weighted network and can be applied to any correlation-based network including but not limited to gene co-expression networks, while the RIF algorithm identify critical Transcription Factors (TF) from gene expression data. These two algorithms when combined provide a very relevant layer of information for gene expression studies (Microarray, RNA-seq and single-cell RNA-seq data). biocViews: Sequencing, RNASeq, Microarray, GeneExpression, Transcription, Normalization, DifferentialExpression, SingleCell, Network, Regression, ChIPSeq, ImmunoOncology, Coverage Author: Carlos Alberto Oliveira de Biagi Junior [aut, cre], Ricardo Perecin Nociti [aut], Breno Osvaldo Funicheli [aut], João Paulo Bianchi Ximenez [ctb], Patrícia de Cássia Ruy [ctb], Marcelo Gomes de Paula [ctb], Rafael dos Santos Bezerra [ctb], Wilson Araújo da Silva Junior [aut, ths] Maintainer: Carlos Alberto Oliveira de Biagi Junior VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CeTF git_branch: RELEASE_3_12 git_last_commit: 2896169 git_last_commit_date: 2020-11-23 Date/Publication: 2020-11-23 source.ver: src/contrib/CeTF_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/CeTF_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.0/CeTF_1.2.4.tgz vignettes: vignettes/CeTF/inst/doc/CeTF.html vignetteTitles: Analyzing Regulatory Impact Factors and Partial Correlation and Information Theory hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CeTF/inst/doc/CeTF.R dependencyCount: 213 Package: CexoR Version: 1.28.0 Depends: R (>= 2.10.0), S4Vectors, IRanges Imports: Rsamtools, GenomeInfoDb, GenomicRanges, rtracklayer, idr, RColorBrewer, genomation Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 | GPL-2 + file LICENSE MD5sum: 3125101b8d6935fb83428330776a9db9 NeedsCompilation: no Title: An R package to uncover high-resolution protein-DNA interactions in ChIP-exo replicates Description: Strand specific peak-pair calling in ChIP-exo replicates. The cumulative Skellam distribution function (package 'skellam') is used to detect significant normalised count differences of opposed sign at each DNA strand (peak-pairs). Irreproducible discovery rate (IDR) for overlapping peak-pairs across biological replicates is estimated using the package 'idr'. biocViews: Transcription, Genetics, Sequencing Author: Pedro Madrigal Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/CexoR git_branch: RELEASE_3_12 git_last_commit: 3641236 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CexoR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CexoR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CexoR_1.28.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CexoR/inst/doc/CexoR.R dependencyCount: 88 Package: CFAssay Version: 1.24.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: ba41f8a5ab2f7ed4fda8269e3ebf9bc0 NeedsCompilation: no Title: Statistical analysis for the Colony Formation Assay Description: The package provides functions for calculation of linear-quadratic cell survival curves and for ANOVA of experimental 2-way designs along with the colony formation assay. biocViews: CellBasedAssays, CellBiology, ImmunoOncology, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann git_url: https://git.bioconductor.org/packages/CFAssay git_branch: RELEASE_3_12 git_last_commit: a065d70 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CFAssay_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CFAssay_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CFAssay_1.24.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CFAssay/inst/doc/cfassay.R dependencyCount: 0 Package: cfDNAPro Version: 1.0.0 Depends: R (>= 4.0), magrittr (>= 1.5.0), Imports: stats, utils, dplyr (>= 0.8.3), stringr (>= 1.4.0), quantmod (>= 0.4), ggplot2 (>= 3.2.1), Rsamtools (>= 2.4.0), rlang (>= 0.4.0) Suggests: scales, ggpubr, knitr (>= 1.23), rmarkdown (>= 1.14), devtools (>= 2.3.0), BiocStyle, testthat License: GPL-3 MD5sum: 8da7d0455e4e4d7f409c617fe68cf5f3 NeedsCompilation: no Title: This Package Helps Characterise and Visualise Whole Genome Sequencing Data from Liquid Biopsy Description: cfDNA fragment size metrics are important features for utilizing liquid biopsy in tumor early detection, diagnosis, therapy personlization and monitoring. Analyzing and visualizing insert size metrics could be time intensive. This package intends to simplify this exploration process, and it offers two sets of functions for data characterization and data visualization. biocViews: Visualization, Sequencing, WholeGenome Author: Haichao Wang [aut, cre], Hui Zhao [ctb], Christopher Smith [ctb] Maintainer: Haichao Wang URL: https://github.com/hw538/cfDNAPro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cfDNAPro git_branch: RELEASE_3_12 git_last_commit: 8233658 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cfDNAPro_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cfDNAPro_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cfDNAPro_1.0.0.tgz vignettes: vignettes/cfDNAPro/inst/doc/cfDNAPro.html vignetteTitles: cfDNAPro Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cfDNAPro/inst/doc/cfDNAPro.R dependencyCount: 72 Package: CGHbase Version: 1.50.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: 695397f51ba07a33d5deee456a85898a NeedsCompilation: no Title: CGHbase: Base functions and classes for arrayCGH data analysis. Description: Contains functions and classes that are needed by arrayCGH packages. biocViews: Infrastructure, Microarray, CopyNumberVariation Author: Sjoerd Vosse, Mark van de Wiel Maintainer: Mark van de Wiel URL: https://github.com/tgac-vumc/CGHbase BugReports: https://github.com/tgac-vumc/CGHbase/issues git_url: https://git.bioconductor.org/packages/CGHbase git_branch: RELEASE_3_12 git_last_commit: c783181 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CGHbase_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CGHbase_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CGHbase_1.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak importsMe: CGHnormaliter, QDNAseq, ragt2ridges dependencyCount: 10 Package: CGHcall Version: 2.52.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: a4ff7eab39f131a215402f572e5197e5 NeedsCompilation: no Title: Calling aberrations for array CGH tumor profiles. Description: Calls aberrations for array CGH data using a six state mixture model as well as several biological concepts that are ignored by existing algorithms. Visualization of profiles is also provided. biocViews: Microarray,Preprocessing,Visualization Author: Mark van de Wiel, Sjoerd Vosse Maintainer: Mark van de Wiel git_url: https://git.bioconductor.org/packages/CGHcall git_branch: RELEASE_3_12 git_last_commit: f0b28b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CGHcall_2.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CGHcall_2.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CGHcall_2.52.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHcall/inst/doc/CGHcall.R dependsOnMe: CGHnormaliter, GeneBreak importsMe: CGHnormaliter, QDNAseq dependencyCount: 15 Package: cghMCR Version: 1.48.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 4db53cded4091ef8384787ffd8b46f4a NeedsCompilation: no Title: Find chromosome regions showing common gains/losses Description: This package provides functions to identify genomic regions of interests based on segmented copy number data from multiple samples. biocViews: Microarray, CopyNumberVariation Author: J. Zhang and B. Feng Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/cghMCR git_branch: RELEASE_3_12 git_last_commit: 5b6061f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cghMCR_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cghMCR_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cghMCR_1.48.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cghMCR/inst/doc/findMCR.R dependencyCount: 48 Package: CGHnormaliter Version: 1.44.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 53c1e9e4f980ec4e74dc09169ec44c36 NeedsCompilation: no Title: Normalization of array CGH data with imbalanced aberrations. Description: Normalization and centralization of array comparative genomic hybridization (aCGH) data. The algorithm uses an iterative procedure that effectively eliminates the influence of imbalanced copy numbers. This leads to a more reliable assessment of copy number alterations (CNAs). biocViews: Microarray, Preprocessing Author: Bart P.P. van Houte, Thomas W. Binsl, Hannes Hettling Maintainer: Bart P.P. van Houte git_url: https://git.bioconductor.org/packages/CGHnormaliter git_branch: RELEASE_3_12 git_last_commit: 8b920f6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CGHnormaliter_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CGHnormaliter_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CGHnormaliter_1.44.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.R dependencyCount: 16 Package: CGHregions Version: 1.48.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: b9d85567db602721aa73df5c64e296b7 NeedsCompilation: no Title: Dimension Reduction for Array CGH Data with Minimal Information Loss. Description: Dimension Reduction for Array CGH Data with Minimal Information Loss biocViews: Microarray, CopyNumberVariation, Visualization Author: Sjoerd Vosse & Mark van de Wiel Maintainer: Sjoerd Vosse git_url: https://git.bioconductor.org/packages/CGHregions git_branch: RELEASE_3_12 git_last_commit: 65606df git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CGHregions_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CGHregions_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CGHregions_1.48.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CGHregions/inst/doc/CGHregions.R suggestsMe: ADaCGH2 dependencyCount: 11 Package: ChAMP Version: 2.20.1 Depends: R (>= 3.3), minfi, ChAMPdata (>= 2.6.0),DMRcate, Illumina450ProbeVariants.db,IlluminaHumanMethylationEPICmanifest, DT, RPMM Imports: prettydoc,Hmisc,globaltest,sva,illuminaio,rmarkdown,IlluminaHumanMethylation450kmanifest,IlluminaHumanMethylationEPICanno.ilm10b4.hg19, limma, DNAcopy, preprocessCore,impute, marray, wateRmelon, plyr,goseq,missMethyl,kpmt,ggplot2, GenomicRanges,qvalue,isva,doParallel,bumphunter,quadprog,shiny,shinythemes,plotly (>= 4.5.6),RColorBrewer,dendextend, matrixStats,combinat Suggests: knitr,rmarkdown License: GPL-3 MD5sum: 324518772d9e38f9c719ba8df882d329 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 and EPIC Description: The package includes quality control metrics, a selection of normalization methods and novel methods to identify differentially methylated regions and to highlight copy number alterations. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Yuan Tian [cre,aut], Tiffany Morris [ctb], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChAMP git_branch: RELEASE_3_12 git_last_commit: 99ea046 git_last_commit_date: 2020-11-02 Date/Publication: 2020-11-03 source.ver: src/contrib/ChAMP_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChAMP_2.20.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ChAMP_2.20.1.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.html vignetteTitles: ChAMP: The Chip Analysis Methylation Pipeline hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R dependencyCount: 246 Package: ChemmineOB Version: 1.28.4 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitrBootstrap, BiocManager Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: i386, x64 MD5sum: c063c457e83be470473d3cbb326a3aad NeedsCompilation: yes Title: R interface to a subset of OpenBabel functionalities Description: ChemmineOB provides an R interface to a subset of cheminformatics functionalities implemented by the OpelBabel C++ project. OpenBabel is an open source cheminformatics toolbox that includes utilities for structure format interconversions, descriptor calculations, compound similarity searching and more. ChemineOB aims to make a subset of these utilities available from within R. For non-developers, ChemineOB is primarily intended to be used from ChemmineR as an add-on package rather than used directly. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 3.0.0) with headers (http://openbabel.org). Eigen3 with headers. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineOB git_branch: RELEASE_3_12 git_last_commit: 4b26dd5 git_last_commit_date: 2021-04-12 Date/Publication: 2021-04-12 source.ver: src/contrib/ChemmineOB_1.28.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChemmineOB_1.28.4.zip mac.binary.ver: bin/macosx/contrib/4.0/ChemmineOB_1.28.4.tgz vignettes: vignettes/ChemmineOB/inst/doc/ChemmineOB.html vignetteTitles: ChemmineOB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R dependencyCount: 9 Package: ChemmineR Version: 3.42.2 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2,grid,gridExtra, png,base64enc,DT,rsvg LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineDrugs, png,rmarkdown, BiocManager Enhances: ChemmineOB License: Artistic-2.0 Archs: i386, x64 MD5sum: 9b61d99f62d30f0e846520e841e7a9c5 NeedsCompilation: yes Title: Cheminformatics Toolkit for R Description: ChemmineR is a cheminformatics package for analyzing drug-like small molecule data in R. Its latest version contains functions for efficient processing of large numbers of molecules, physicochemical/structural property predictions, structural similarity searching, classification and clustering of compound libraries with a wide spectrum of algorithms. In addition, it offers visualization functions for compound clustering results and chemical structures. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics,Metabolomics Author: Y. Eddie Cao, Kevin Horan, Tyler Backman, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineR SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChemmineR git_branch: RELEASE_3_12 git_last_commit: d1536d5 git_last_commit_date: 2021-02-25 Date/Publication: 2021-02-26 source.ver: src/contrib/ChemmineR_3.42.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChemmineR_3.42.2.zip mac.binary.ver: bin/macosx/contrib/4.0/ChemmineR_3.42.2.tgz vignettes: vignettes/ChemmineR/inst/doc/ChemmineR.html vignetteTitles: ChemmineR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R dependsOnMe: eiR, fmcsR, ChemmineDrugs importsMe: bioassayR, customCMPdb, eiR, fmcsR, MetID, Rcpi, BioMedR, MetaDBparse, uCAREChemSuiteCLI suggestsMe: ChemmineOB, xnet dependencyCount: 59 Package: CHETAH Version: 1.6.0 Depends: R (>= 3.6), ggplot2, SingleCellExperiment Imports: gplots, shiny, plotly, pheatmap, bioDist, dendextend, cowplot, corrplot, grDevices, stats, graphics, reshape2, S4Vectors, SummarizedExperiment Suggests: knitr, rmarkdown, Matrix, testthat, vdiffr License: file LICENSE MD5sum: a381f6db72e95f4d72a94467788fadb8 NeedsCompilation: no Title: Fast and accurate scRNA-seq cell type identification Description: CHETAH (CHaracterization of cEll Types Aided by Hierarchical classification) is an accurate, selective and fast scRNA-seq classifier. Classification is guided by a reference dataset, preferentially also a scRNA-seq dataset. By hierarchical clustering of the reference data, CHETAH creates a classification tree that enables a step-wise, top-to-bottom classification. Using a novel stopping rule, CHETAH classifies the input cells to the cell types of the references and to "intermediate types": more general classifications that ended in an intermediate node of the tree. biocViews: Classification, RNASeq, SingleCell, Clustering Author: Jurrian de Kanter [aut, cre], Philip Lijnzaad [aut] Maintainer: Jurrian de Kanter URL: https://github.com/jdekanter/CHETAH VignetteBuilder: knitr BugReports: https://github.com/jdekanter/CHETAH git_url: https://git.bioconductor.org/packages/CHETAH git_branch: RELEASE_3_12 git_last_commit: cd3dd78 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CHETAH_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CHETAH_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CHETAH_1.6.0.tgz vignettes: vignettes/CHETAH/inst/doc/CHETAH_introduction.html vignetteTitles: Introduction to the CHETAH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CHETAH/inst/doc/CHETAH_introduction.R dependencyCount: 109 Package: ChIC Version: 1.10.0 Depends: spp, R (>= 3.6) Imports: ChIC.data (>= 1.7.1), caTools, methods,GenomicRanges, IRanges, parallel, progress, caret, grDevices, stats, utils, graphics, S4Vectors, BiocGenerics License: GPL-2 MD5sum: 10606363f2461d8c80db63ecdbd7ac6a NeedsCompilation: no Title: Quality Control Pipeline for ChIP-Seq Data Description: Quality control (QC) pipeline for ChIP-seq data using a comprehensive set of QC metrics, including previously proposed metrics as well as novel ones, based on local characteristics of the enrichment profile. The package provides functions to calculate a set of QC metrics, a compendium with reference values and machine learning models to score sample quality. biocViews: ChIPSeq, QualityControl Author: Carmen Maria Livi Maintainer: Carmen Maria Livi git_url: https://git.bioconductor.org/packages/ChIC git_branch: RELEASE_3_12 git_last_commit: 2676c3d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIC_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/ChIC_1.10.0.tgz vignettes: vignettes/ChIC/inst/doc/ChIC-Vignette.pdf vignetteTitles: ChIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIC/inst/doc/ChIC-Vignette.R dependencyCount: 99 Package: Chicago Version: 1.18.0 Depends: R (>= 3.2), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods, grDevices, graphics, stats, utils Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: 773127bc58667690d35d6b1cc1710f7a NeedsCompilation: no Title: CHiCAGO: Capture Hi-C Analysis of Genomic Organization Description: A pipeline for analysing Capture Hi-C data. biocViews: Epigenetics, HiC, Sequencing, Software Author: Jonathan Cairns, Paula Freire Pritchett, Steven Wingett, Mikhail Spivakov Maintainer: Mikhail Spivakov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Chicago git_branch: RELEASE_3_12 git_last_commit: 36b6668 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Chicago_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Chicago_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Chicago_1.18.0.tgz vignettes: vignettes/Chicago/inst/doc/Chicago.html vignetteTitles: CHiCAGO Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R dependsOnMe: PCHiCdata dependencyCount: 71 Package: chimera Version: 1.32.0 Depends: Biobase, GenomicRanges (>= 1.13.3), Rsamtools (>= 1.13.1), GenomicAlignments, methods, AnnotationDbi, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, Homo.sapiens Suggests: BiocParallel, geneplotter Enhances: Rsubread, BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, BSgenome.Mmusculus.UCSC.mm10, TxDb.Mmusculus.UCSC.mm10.knownGene, Mus.musculus, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: d7366dc1526e56af09d32a424aef7745 NeedsCompilation: yes Title: A package for secondary analysis of fusion products Description: This package facilitates the characterisation of fusion products events. It allows to import fusion data results from the following fusion finders: chimeraScan, bellerophontes, deFuse, FusionFinder, FusionHunter, mapSplice, tophat-fusion, FusionMap, STAR, Rsubread, fusionCatcher. biocViews: Infrastructure Author: Raffaele A Calogero, Matteo Carrara, Marco Beccuti, Francesca Cordero Maintainer: Raffaele A Calogero SystemRequirements: STAR, TopHat, bowtie and samtools are required for some functionalities PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/chimera git_branch: RELEASE_3_12 git_last_commit: a3c6b1f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chimera_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/chimera_1.32.0.tgz vignettes: vignettes/chimera/inst/doc/chimera.pdf vignetteTitles: chimera hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimera/inst/doc/chimera.R dependencyCount: 98 Package: chimeraviz Version: 1.16.1 Depends: Biostrings, GenomicRanges, IRanges, Gviz, S4Vectors, ensembldb, AnnotationFilter, data.table Imports: methods, grid, Rsamtools, GenomeInfoDb, GenomicAlignments, RColorBrewer, graphics, AnnotationDbi, RCircos, org.Hs.eg.db, org.Mm.eg.db, rmarkdown, graph, Rgraphviz, DT, plyr, dplyr, BiocStyle, ArgumentCheck, gtools, magick Suggests: testthat, roxygen2, devtools, knitr, lintr License: Artistic-2.0 MD5sum: 17ea3f979fd0235bb307fe02d327946a NeedsCompilation: no Title: Visualization tools for gene fusions Description: chimeraviz manages data from fusion gene finders and provides useful visualization tools. biocViews: Infrastructure, Alignment Author: Stian Lågstad [aut, cre], Sen Zhao [ctb], Andreas M. Hoff [ctb], Bjarne Johannessen [ctb], Ole Christian Lingjærde [ctb], Rolf Skotheim [ctb] Maintainer: Stian Lågstad URL: https://github.com/stianlagstad/chimeraviz SystemRequirements: bowtie, samtools, and egrep are required for some functionalities VignetteBuilder: knitr BugReports: https://github.com/stianlagstad/chimeraviz/issues git_url: https://git.bioconductor.org/packages/chimeraviz git_branch: RELEASE_3_12 git_last_commit: 66a60b3 git_last_commit_date: 2021-01-16 Date/Publication: 2021-01-17 source.ver: src/contrib/chimeraviz_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/chimeraviz_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.0/chimeraviz_1.16.1.tgz vignettes: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.html vignetteTitles: chimeraviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chimeraviz/inst/doc/chimeraviz-vignette.R dependencyCount: 156 Package: ChIPanalyser Version: 1.12.0 Depends: R (>= 3.5.0),GenomicRanges, Biostrings, BSgenome, RcppRoll, parallel Imports: methods, IRanges, S4Vectors,grDevices,graphics,stats,utils,rtracklayer,ROCR, BiocManager,GenomeInfoDb Suggests: BSgenome.Dmelanogaster.UCSC.dm3,knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 315da2b79745b833dd1b79d2524bd43e NeedsCompilation: no Title: ChIPanalyser: Predicting Transcription Factor Binding Sites Description: Based on a statistical thermodynamic framework, ChIPanalyser tries to produce ChIP-seq like profile. The model relies on four consideration: TF binding sites can be scored using a Position weight Matrix, DNA accessibility plays a role in Transcription Factor binding, binding profiles are dependant on the number of transcription factors bound to DNA and finally binding energy (another way of describing PWM's) or binding specificity should be modulated (hence the introduction of a binding specificity modulator). The end result of ChIPanalyser is to produce profiles simulating real ChIP-seq profile and provide accuracy measurements of these predicted profiles after being compared to real ChIP-seq data. The ultimate goal is to produce ChIP-seq like profiles predicting ChIP-seq like profile to circumvent the need to produce costly ChIP-seq experiments. biocViews: Software, BiologicalQuestion, WorkflowStep, Transcription, Sequencing, ChipOnChip, Coverage, Alignment, ChIPSeq, SequenceMatching, DataImport ,PeakDetection Author: Patrick C.N.Martin & Nicolae Radu Zabet Maintainer: Patrick C.N. Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPanalyser git_branch: RELEASE_3_12 git_last_commit: 126b7fa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIPanalyser_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPanalyser_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPanalyser_1.12.0.tgz vignettes: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.pdf vignetteTitles: ChIPanalyser User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPanalyser/inst/doc/ChIPanalyser.R dependencyCount: 49 Package: ChIPComp Version: 1.20.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,rtracklayer,GenomeInfoDb,S4Vectors Imports: Rsamtools,limma,BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm9,BiocGenerics Suggests: BiocStyle,RUnit License: GPL Archs: i386, x64 MD5sum: 4860385dc275ac9665fa224c2794a67a NeedsCompilation: yes Title: Quantitative comparison of multiple ChIP-seq datasets Description: ChIPComp detects differentially bound sharp binding sites across multiple conditions considering matching control. biocViews: ChIPSeq, Sequencing, Transcription, Genetics,Coverage, MultipleComparison, DataImport Author: Hao Wu, Li Chen, Zhaohui S.Qin, Chi Wang Maintainer: Li Chen git_url: https://git.bioconductor.org/packages/ChIPComp git_branch: RELEASE_3_12 git_last_commit: 9dbb9ed git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIPComp_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPComp_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPComp_1.20.0.tgz vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPComp/inst/doc/ChIPComp.R dependencyCount: 44 Package: chipenrich Version: 2.14.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, BiocGenerics, chipenrich.data, GenomeInfoDb, GenomicRanges, grDevices, grid, IRanges, lattice, latticeExtra, MASS, methods, mgcv, org.Dm.eg.db, org.Dr.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, parallel, plyr, rms, rtracklayer, S4Vectors (>= 0.23.10), stats, stringr, utils Suggests: BiocStyle, devtools, knitr, rmarkdown, roxygen2, testthat License: GPL-3 MD5sum: e12b09a5ae49bc0fee7750441adc36d5 NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich and Poly-Enrich perform gene set enrichment testing using peaks called from a ChIP-seq experiment. The method empirically corrects for confounding factors such as the length of genes, and the mappability of the sequence surrounding genes. biocViews: ImmunoOncology, ChIPSeq, Epigenetics, FunctionalGenomics, GeneSetEnrichment, HistoneModification, Regression Author: Ryan P. Welch [aut, cph], Chee Lee [aut], Raymond G. Cavalcante [aut, cre], Chris Lee [aut], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chipenrich git_branch: RELEASE_3_12 git_last_commit: 2e41855 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chipenrich_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chipenrich_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chipenrich_2.14.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich-vignette.html vignetteTitles: chipenrich_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich-vignette.R dependencyCount: 134 Package: ChIPexoQual Version: 1.14.0 Depends: R (>= 3.4.0), GenomicAlignments (>= 1.0.1) Imports: methods, utils, GenomeInfoDb, stats, BiocParallel, GenomicRanges (>= 1.14.4), ggplot2 (>= 1.0), data.table (>= 1.9.6), Rsamtools (>= 1.16.1), IRanges (>= 1.6), S4Vectors (>= 0.8), biovizBase (>= 1.18), broom (>= 0.4), RColorBrewer (>= 1.1), dplyr (>= 0.5), scales (>= 0.4.0), viridis (>= 0.3), hexbin (>= 1.27), rmarkdown Suggests: ChIPexoQualExample (>= 0.99.1), knitr (>= 1.10), BiocStyle, gridExtra (>= 2.2), testthat License: GPL (>=2) MD5sum: 42c299e6add8240786e9a38446228bf7 NeedsCompilation: no Title: ChIPexoQual Description: Package with a quality control pipeline for ChIP-exo/nexus data. biocViews: ChIPSeq, Sequencing, Transcription, Visualization, QualityControl, Coverage, Alignment Author: Rene Welch, Dongjun Chung, Sunduz Keles Maintainer: Rene Welch URL: https:github.com/keleslab/ChIPexoQual VignetteBuilder: knitr BugReports: https://github.com/welch16/ChIPexoQual/issues git_url: https://git.bioconductor.org/packages/ChIPexoQual git_branch: RELEASE_3_12 git_last_commit: c593ac2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIPexoQual_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPexoQual_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPexoQual_1.14.0.tgz vignettes: vignettes/ChIPexoQual/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPexoQual/inst/doc/vignette.R dependencyCount: 143 Package: ChIPpeakAnno Version: 3.24.2 Depends: R (>= 3.5), methods, IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), S4Vectors (>= 0.17.25) Imports: AnnotationDbi, BiocGenerics (>= 0.1.0), Biostrings (>= 2.47.6), DBI, dplyr, ensembldb, GenomeInfoDb, GenomicAlignments, GenomicFeatures, RBGL, Rsamtools, SummarizedExperiment, VennDiagram, biomaRt, ggplot2, grDevices, graph, graphics, grid, KEGGREST, matrixStats, multtest, regioneR, rtracklayer, stats, utils Suggests: BSgenome, limma, reactome.db, BiocManager, BiocStyle, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, BSgenome.Hsapiens.UCSC.hg38, DelayedArray, idr, seqinr, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, GO.db, gplots, UpSetR, knitr, rmarkdown, testthat, trackViewer, motifStack, OrganismDbi License: GPL (>= 2) MD5sum: d5f6c0b84a5445ea560be1e87ae47340 NeedsCompilation: no Title: Batch annotation of the peaks identified from either ChIP-seq, ChIP-chip experiments or any experiments resulted in large number of chromosome ranges Description: The package includes functions to retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms, find the nearest gene, exon, miRNA or custom features such as most conserved elements and other transcription factor binding sites supplied by users. Starting 2.0.5, new functions have been added for finding the peaks with bi-directional promoters with summary statistics (peaksNearBDP), for summarizing the occurrence of motifs in peaks (summarizePatternInPeaks) and for adding other IDs to annotated peaks or enrichedGO (addGeneIDs). This package leverages the biomaRt, IRanges, Biostrings, BSgenome, GO.db, multtest and stat packages. biocViews: Annotation, ChIPSeq, ChIPchip Author: Lihua Julie Zhu, Jianhong Ou, Jun Yu, Kai Hu, Haibo Liu, Hervé Pagès, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPpeakAnno git_branch: RELEASE_3_12 git_last_commit: 09d9fec git_last_commit_date: 2021-03-30 Date/Publication: 2021-03-31 source.ver: src/contrib/ChIPpeakAnno_3.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPpeakAnno_3.24.2.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPpeakAnno_3.24.2.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/FAQs.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html vignetteTitles: ChIPpeakAnno Vignette, ChIPpeakAnno FAQs, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/FAQs.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R dependsOnMe: REDseq importsMe: ATACseqQC, DEScan2, FunciSNP, GUIDEseq suggestsMe: R3CPET, seqsetvis, chipseqDB dependencyCount: 116 Package: ChIPQC Version: 1.26.0 Depends: R (>= 3.0.0), ggplot2, DiffBind, GenomicRanges (>= 1.17.19) Imports: BiocGenerics (>= 0.11.3), S4Vectors (>= 0.1.0), IRanges (>= 1.99.17), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), chipseq (>= 1.12.0), gtools, BiocParallel, methods, reshape2, Nozzle.R1, Biobase, grDevices, stats, utils, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene Suggests: BiocStyle License: GPL (>= 3) MD5sum: b89cf9ab16c6368137782dff1d9f3e55 NeedsCompilation: no Title: Quality metrics for ChIPseq data Description: Quality metrics for ChIPseq data. biocViews: Sequencing, ChIPSeq, QualityControl, ReportWriting Author: Tom Carroll, Wei Liu, Ines de Santiago, Rory Stark Maintainer: Tom Carroll , Rory Stark git_url: https://git.bioconductor.org/packages/ChIPQC git_branch: RELEASE_3_12 git_last_commit: 967dee8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIPQC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPQC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPQC_1.26.0.tgz vignettes: vignettes/ChIPQC/inst/doc/ChIPQC.pdf, vignettes/ChIPQC/inst/doc/ChIPQCSampleReport.pdf vignetteTitles: Assessing ChIP-seq sample quality with ChIPQC, ChIPQCSampleReport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPQC/inst/doc/ChIPQC.R dependencyCount: 177 Package: ChIPseeker Version: 1.26.2 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocGenerics, boot, enrichplot, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils Suggests: clusterProfiler (>= 3.15.4), ggimage, ggplotify, ggupset, ReactomePA, org.Hs.eg.db, knitr, rmarkdown, testthat, tibble License: Artistic-2.0 MD5sum: b8a7fdbf3f60ff9bfd725c788bf51a70 NeedsCompilation: no Title: ChIPseeker for ChIP peak Annotation, Comparison, and Visualization Description: This package implements functions to retrieve the nearest genes around the peak, annotate genomic region of the peak, statstical methods for estimate the significance of overlap among ChIP peak data sets, and incorporate GEO database for user to compare the own dataset with those deposited in database. The comparison can be used to infer cooperative regulation and thus can be used to generate hypotheses. Several visualization functions are implemented to summarize the coverage of the peak experiment, average profile and heatmap of peaks binding to TSS regions, genomic annotation, distance to TSS, and overlap of peaks or genes. biocViews: Annotation, ChIPSeq, Software, Visualization, MultipleComparison Author: Guangchuang Yu [aut, cre] (), Yun Yan [ctb], Hervé Pagès [ctb], Michael Kluge [ctb], Thomas Schwarzl [ctb], Zhougeng Xu [ctb] Maintainer: Guangchuang Yu URL: https://guangchuangyu.github.io/software/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ChIPseeker/issues git_url: https://git.bioconductor.org/packages/ChIPseeker git_branch: RELEASE_3_12 git_last_commit: 8142321 git_last_commit_date: 2021-03-03 Date/Publication: 2021-03-04 source.ver: src/contrib/ChIPseeker_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPseeker_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPseeker_1.26.2.tgz vignettes: vignettes/ChIPseeker/inst/doc/ChIPseeker.html vignetteTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R importsMe: ALPS, esATAC, TCGAWorkflow, cinaR suggestsMe: curatedAdipoChIP dependencyCount: 140 Package: chipseq Version: 1.40.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), ShortRead Imports: methods, stats, lattice, BiocGenerics, IRanges, GenomicRanges, ShortRead Suggests: BSgenome, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: 645577cca78a01942a962f8f50691c8f NeedsCompilation: yes Title: chipseq: A package for analyzing chipseq data Description: Tools for helping process short read data for chipseq experiments biocViews: ChIPSeq, Sequencing, Coverage, QualityControl, DataImport Author: Deepayan Sarkar, Robert Gentleman, Michael Lawrence, Zizhen Yao Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/chipseq git_branch: RELEASE_3_12 git_last_commit: 84bcbc0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chipseq_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chipseq_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chipseq_1.40.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipseq/inst/doc/Workflow.R importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR suggestsMe: GenoGAM dependencyCount: 44 Package: ChIPseqR Version: 1.44.0 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors (>= 0.9.25) Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.5.14), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 9916b48064f12da4453ca5d61fed63df NeedsCompilation: yes Title: Identifying Protein Binding Sites in High-Throughput Sequencing Data Description: ChIPseqR identifies protein binding sites from ChIP-seq and nucleosome positioning experiments. The model used to describe binding events was developed to locate nucleosomes but should flexible enough to handle other types of experiments as well. biocViews: ChIPSeq, Infrastructure Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPseqR git_branch: RELEASE_3_12 git_last_commit: 719fbb0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIPseqR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPseqR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPseqR_1.44.0.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseqR/inst/doc/Introduction.R dependencyCount: 53 Package: ChIPSeqSpike Version: 1.9.0 Depends: R (>= 3.5), rtracklayer (>= 1.37.6) Imports: tools, stringr, Rsamtools, GenomicRanges, IRanges, seqplots, ggplot2, LSD, corrplot, methods, stats, grDevices, graphics, utils, BiocGenerics, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: be8eca0382185f3c16247aa9b6cac90a NeedsCompilation: no Title: ChIP-Seq data scaling according to spike-in control Description: Chromatin Immuno-Precipitation followed by Sequencing (ChIP-Seq) is used to determine the binding sites of any protein of interest, such as transcription factors or histones with or without a specific modification, at a genome scale. The many steps of the protocol can introduce biases that make ChIP-Seq more qualitative than quantitative. For instance, it was shown that global histone modification differences are not caught by traditional downstream data normalization techniques. A case study reported no differences in histone H3 lysine-27 trimethyl (H3K27me3) upon Ezh2 inhibitor treatment. To tackle this problem, external spike-in control were used to keep track of technical biases between conditions. Exogenous DNA from a different non-closely related species was inserted during the protocol to infer scaling factors that enabled an accurate normalization, thus revealing the inhibitor effect. ChIPSeqSpike offers tools for ChIP-Seq spike-in normalization. Ready to use scaled bigwig files and scaling factors values are obtained as output. ChIPSeqSpike also provides tools for ChIP-Seq spike-in assessment and analysis through a versatile collection of graphical functions. biocViews: ImmunoOncology, ChIPSeq, Sequencing, Normalization, Transcription, Coverage, DifferentialMethylation, Epigenetics, DataImport, HistoneModification Author: Nicolas Descostes Maintainer: Nicolas Descostes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ChIPSeqSpike git_branch: master git_last_commit: 401147a git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/ChIPSeqSpike_1.9.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPSeqSpike_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPSeqSpike_1.9.0.tgz vignettes: vignettes/ChIPSeqSpike/inst/doc/ChIPSeqSpike.pdf vignetteTitles: ChIPSeqSpike hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPSeqSpike/inst/doc/ChIPSeqSpike.R dependencyCount: 117 Package: ChIPsim Version: 1.44.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 21c4c41788db4c4ceda804af4f8e44fa NeedsCompilation: no Title: Simulation of ChIP-seq experiments Description: A general framework for the simulation of ChIP-seq data. Although currently focused on nucleosome positioning the package is designed to support different types of experiments. biocViews: Infrastructure, ChIPSeq Author: Peter Humburg Maintainer: Peter Humburg git_url: https://git.bioconductor.org/packages/ChIPsim git_branch: RELEASE_3_12 git_last_commit: 916ec05 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIPsim_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChIPsim_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChIPsim_1.44.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPsim/inst/doc/ChIPsimIntro.R dependencyCount: 44 Package: ChIPXpress Version: 1.34.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 56bd1809026cbb3d5cffba1806434cda NeedsCompilation: no Title: ChIPXpress: enhanced transcription factor target gene identification from ChIP-seq and ChIP-chip data using publicly available gene expression profiles Description: ChIPXpress takes as input predicted TF bound genes from ChIPx data and uses a corresponding database of gene expression profiles downloaded from NCBI GEO to rank the TF bound targets in order of which gene is most likely to be functional TF target. biocViews: ChIPchip, ChIPSeq Author: George Wu Maintainer: George Wu git_url: https://git.bioconductor.org/packages/ChIPXpress git_branch: RELEASE_3_12 git_last_commit: a21b9a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChIPXpress_1.34.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/ChIPXpress_1.34.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPXpress/inst/doc/ChIPXpress.R dependencyCount: 90 Package: chopsticks Version: 1.56.0 Imports: graphics, stats, utils, methods, survival Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 514adfa3e59365b6848dbfecef696305 NeedsCompilation: yes Title: The 'snp.matrix' and 'X.snp.matrix' Classes Description: Implements classes and methods for large-scale SNP association studies biocViews: Microarray, SNPsAndGeneticVariability, SNP, GeneticVariability Author: Hin-Tak Leung Maintainer: Hin-Tak Leung URL: http://outmodedbonsai.sourceforge.net/ git_url: https://git.bioconductor.org/packages/chopsticks git_branch: RELEASE_3_12 git_last_commit: 2804e1a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chopsticks_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chopsticks_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chopsticks_1.56.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chopsticks/inst/doc/chopsticks-vignette.R importsMe: CrypticIBDcheck, rJPSGCS dependencyCount: 10 Package: chromDraw Version: 2.20.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 151f4588d379ea08228dde4a082d439e NeedsCompilation: yes Title: chromDraw is a R package for drawing the schemes of karyotypes in the linear and circular fashion. Description: ChromDraw is a R package for drawing the schemes of karyotype(s) in the linear and circular fashion. It is possible to visualized cytogenetic marsk on the chromosomes. This tool has own input data format. Input data can be imported from the GenomicRanges data structure. This package can visualized the data in the BED file format. Here is requirement on to the first nine fields of the BED format. Output files format are *.eps and *.svg. biocViews: Software Author: Jan Janecka, Ing., Mgr. CEITEC Masaryk University Maintainer: Jan Janecka URL: www.plantcytogenomics.org/chromDraw SystemRequirements: Rtools (>= 3.1) git_url: https://git.bioconductor.org/packages/chromDraw git_branch: RELEASE_3_12 git_last_commit: 381b737 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chromDraw_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chromDraw_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chromDraw_2.20.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromDraw/inst/doc/chromDraw.R dependencyCount: 18 Package: ChromHeatMap Version: 1.44.0 Depends: R (>= 2.9.0), BiocGenerics (>= 0.3.2), annotate (>= 1.20.0), AnnotationDbi (>= 1.4.0) Imports: Biobase (>= 2.17.8), graphics, grDevices, methods, stats, IRanges, rtracklayer, GenomicRanges Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: 3e7b285267a03a80d89140327d3303e6 NeedsCompilation: no Title: Heat map plotting by genome coordinate Description: The ChromHeatMap package can be used to plot genome-wide data (e.g. expression, CGH, SNP) along each strand of a given chromosome as a heat map. The generated heat map can be used to interactively identify probes and genes of interest. biocViews: Visualization Author: Tim F. Rayner Maintainer: Tim F. Rayner git_url: https://git.bioconductor.org/packages/ChromHeatMap git_branch: RELEASE_3_12 git_last_commit: ee1c258 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChromHeatMap_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChromHeatMap_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChromHeatMap_1.44.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.R dependencyCount: 66 Package: chromPlot Version: 1.18.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.1.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 01b6ec494a17b21edade6432810d72ee NeedsCompilation: no Title: Global visualization tool of genomic data Description: Package designed to visualize genomic data along the chromosomes, where the vertical chromosomes are sorted by number, with sex chromosomes at the end. biocViews: DataRepresentation, FunctionalGenomics, Genetics, Sequencing, Annotation, Visualization Author: Ricardo A. Verdugo and Karen Y. Orostica Maintainer: Karen Y. Orostica git_url: https://git.bioconductor.org/packages/chromPlot git_branch: RELEASE_3_12 git_last_commit: 69bd469 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chromPlot_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chromPlot_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chromPlot_1.18.0.tgz vignettes: vignettes/chromPlot/inst/doc/chromPlot.pdf vignetteTitles: General Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromPlot/inst/doc/chromPlot.R dependencyCount: 70 Package: ChromSCape Version: 1.0.0 Depends: R (>= 4.0) Imports: shiny, colourpicker, shinyjs, rtracklayer, shinyFiles, shinyhelper, shinycssloaders, Matrix, plotly, shinydashboard, colorRamps, kableExtra, viridis, batchelor, BiocParallel, parallel, Rsamtools, ggplot2, qualV, stringdist, fs, DT, scran, scater, ConsensusClusterPlus, Rtsne, dplyr, tidyr, GenomicRanges, IRanges, irlba, rlist, umap, tibble, methods, jsonlite, edgeR, stats, graphics, grDevices, utils, S4Vectors, SingleCellExperiment, SummarizedExperiment, msigdbr Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 13b5b3eeebcf74f82cd526d1a0e7780c NeedsCompilation: no Title: Analysis of single-cell epigenomics datasets with a Shiny App Description: ChromSCape - Chromatin landscape profiling for Single Cells - is a ready-to-launch user-friendly Shiny Application for the analysis of single-cell epigenomics datasets (scChIP-seq, scATAC-seq, scCUT&Tag, ...) from aligned data to differential analysis & gene set enrichment analysis. It is highly interactive, enables users to save their analysis and covers a wide range of analytical steps: QC, preprocessing, filtering, batch correction, dimensionality reduction, vizualisation, clustering, differential analysis and gene set analysis. biocViews: Software, SingleCell, ChIPSeq, ATACSeq, MethylSeq, Classification, Clustering, Epigenetics, PrincipalComponent, SingleCell, ATACSeq, ChIPSeq, Annotation, BatchEffect, MultipleComparison, Normalization, Pathways, Preprocessing, QualityControl, ReportWriting, Visualization, GeneSetEnrichment, DifferentialPeakCalling Author: Pacome Prompsy [aut, cre] (), Celine Vallot [aut] () Maintainer: Pacome Prompsy URL: https://github.com/vallotlab/ChromSCape VignetteBuilder: knitr BugReports: https://github.com/vallotlab/ChromSCape/issues git_url: https://git.bioconductor.org/packages/ChromSCape git_branch: RELEASE_3_12 git_last_commit: c84df0d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ChromSCape_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ChromSCape_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ChromSCape_1.0.0.tgz vignettes: vignettes/ChromSCape/inst/doc/vignette.html vignetteTitles: ChromSCape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChromSCape/inst/doc/vignette.R dependencyCount: 180 Package: chromstaR Version: 1.16.0 Depends: R (>= 3.3), GenomicRanges, ggplot2, chromstaRData Imports: methods, utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics (>= 0.31.6), S4Vectors, GenomeInfoDb, IRanges, reshape2, Rsamtools, GenomicAlignments, bamsignals, mvtnorm Suggests: knitr, BiocStyle, testthat, biomaRt License: Artistic-2.0 Archs: i386, x64 MD5sum: a22a9a8fa3d8b7eba3d4468b78a65622 NeedsCompilation: yes Title: Combinatorial and Differential Chromatin State Analysis for ChIP-Seq Data Description: This package implements functions for combinatorial and differential analysis of ChIP-seq data. It includes uni- and multivariate peak-calling, export to genome browser viewable files, and functions for enrichment analyses. biocViews: ImmunoOncology, Software, DifferentialPeakCalling, HiddenMarkovModel, ChIPSeq, HistoneModification, MultipleComparison, Sequencing, PeakDetection, ATACSeq Author: Aaron Taudt, Maria Colome Tatche, Matthias Heinig, Minh Anh Nguyen Maintainer: Aaron Taudt URL: https://github.com/ataudt/chromstaR VignetteBuilder: knitr BugReports: https://github.com/ataudt/chromstaR/issues git_url: https://git.bioconductor.org/packages/chromstaR git_branch: RELEASE_3_12 git_last_commit: b2bacfd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chromstaR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chromstaR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chromstaR_1.16.0.tgz vignettes: vignettes/chromstaR/inst/doc/chromstaR.pdf vignetteTitles: The chromstaR user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chromstaR/inst/doc/chromstaR.R dependencyCount: 79 Package: chromswitch Version: 1.12.0 Depends: R (>= 3.5.0), GenomicRanges (>= 1.26.4) Imports: cluster (>= 2.0.6), Biobase (>= 2.36.2), BiocParallel (>= 1.8.2), dplyr (>= 0.5.0), gplots(>= 3.0.1), graphics, grDevices, IRanges (>= 2.4.8), lazyeval (>= 0.2.0), matrixStats (>= 0.52), magrittr (>= 1.5), methods, NMF (>= 0.20.6), rtracklayer (>= 1.36.4), S4Vectors (>= 0.23.19), stats, tidyr (>= 0.6.3) Suggests: BiocStyle, DescTools (>= 0.99.19), devtools (>= 1.13.3), GenomeInfoDb (>= 1.16.0), knitr, rmarkdown, mclust (>= 5.3), testthat License: MIT + file LICENSE MD5sum: c3e45352d65d6e31448e2df7728802a2 NeedsCompilation: no Title: An R package to detect chromatin state switches from epigenomic data Description: Chromswitch implements a flexible method to detect chromatin state switches between samples in two biological conditions in a specific genomic region of interest given peaks or chromatin state calls from ChIP-seq data. biocViews: ImmunoOncology, MultipleComparison, Transcription, GeneExpression, DifferentialPeakCalling, HistoneModification, Epigenetics, FunctionalGenomics, Clustering Author: Selin Jessa [aut, cre], Claudia L. Kleinman [aut] Maintainer: Selin Jessa URL: https://github.com/sjessa/chromswitch VignetteBuilder: knitr BugReports: https://github.com/sjessa/chromswitch/issues git_url: https://git.bioconductor.org/packages/chromswitch git_branch: RELEASE_3_12 git_last_commit: 385a597 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chromswitch_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chromswitch_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chromswitch_1.12.0.tgz vignettes: vignettes/chromswitch/inst/doc/chromswitch_intro.html vignetteTitles: An introduction to `chromswitch` for detecting chromatin state switches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromswitch/inst/doc/chromswitch_intro.R dependencyCount: 98 Package: chromVAR Version: 1.12.0 Depends: R (>= 3.4) Imports: IRanges, GenomeInfoDb, GenomicRanges, ggplot2, nabor, BiocParallel, BiocGenerics, Biostrings, TFBSTools, Rsamtools, S4Vectors, methods, Rcpp, grid, plotly, shiny, miniUI, stats, utils, graphics, DT, Rtsne, Matrix, SummarizedExperiment, RColorBrewer, BSgenome LinkingTo: Rcpp, RcppArmadillo Suggests: JASPAR2016, BSgenome.Hsapiens.UCSC.hg19, readr, testthat, knitr, rmarkdown, pheatmap, motifmatchr License: MIT + file LICENSE Archs: i386, x64 MD5sum: 5b1b276df6bba8b0895558c19ed738eb NeedsCompilation: yes Title: Chromatin Variation Across Regions Description: Determine variation in chromatin accessibility across sets of annotations or peaks. Designed primarily for single-cell or sparse chromatin accessibility data, e.g. from scATAC-seq or sparse bulk ATAC or DNAse-seq experiments. biocViews: SingleCell, Sequencing, GeneRegulation, ImmunoOncology Author: Alicia Schep [aut, cre], Jason Buenrostro [ctb], Caleb Lareau [ctb], William Greenleaf [ths], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/chromVAR git_branch: RELEASE_3_12 git_last_commit: ea35241 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/chromVAR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/chromVAR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/chromVAR_1.12.0.tgz vignettes: vignettes/chromVAR/inst/doc/Introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/chromVAR/inst/doc/Introduction.R suggestsMe: Signac dependencyCount: 143 Package: CHRONOS Version: 1.18.0 Depends: R (>= 3.5) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, igraph, circlize, graph, stats, utils, grDevices, graphics, methods, biomaRt, rJava Suggests: RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 002818213ecdbd4f5858d48dd6a8720f NeedsCompilation: no Title: CHRONOS: A time-varying method for microRNA-mediated sub-pathway enrichment analysis Description: A package used for efficient unraveling of the inherent dynamic properties of pathways. MicroRNA-mediated subpathway topologies are extracted and evaluated by exploiting the temporal transition and the fold change activity of the linked genes/microRNAs. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG Author: Aristidis G. Vrahatis, Konstantina Dimitrakopoulou, Panos Balomenos Maintainer: Panos Balomenos SystemRequirements: Java version >= 1.7, Pandoc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CHRONOS git_branch: RELEASE_3_12 git_last_commit: 359ccf9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CHRONOS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CHRONOS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CHRONOS_1.18.0.tgz vignettes: vignettes/CHRONOS/inst/doc/CHRONOS.pdf vignetteTitles: CHRONOS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CHRONOS/inst/doc/CHRONOS.R dependencyCount: 81 Package: cicero Version: 1.8.1 Depends: R (>= 3.5.0), monocle, Gviz (>= 1.22.3) Imports: assertthat (>= 0.2.0), Biobase (>= 2.37.2), BiocGenerics (>= 0.23.0), data.table (>= 1.10.4), dplyr (>= 0.7.4), FNN (>= 1.1), GenomicRanges (>= 1.30.3), ggplot2 (>= 2.2.1), glasso (>= 1.8), grDevices, igraph (>= 1.1.0), IRanges (>= 2.10.5), Matrix (>= 1.2-12), methods, parallel, plyr (>= 1.8.4), reshape2 (>= 1.4.3), S4Vectors (>= 0.14.7), stats, stringi, stringr (>= 1.2.0), tibble (>= 1.4.2), tidyr, VGAM (>= 1.0-5), utils Suggests: AnnotationDbi (>= 1.38.2), knitr, rmarkdown, rtracklayer (>= 1.36.6), testthat, vdiffr (>= 0.2.3), covr License: MIT + file LICENSE MD5sum: f9d693f120b20d53b04e94315f773ee2 NeedsCompilation: no Title: Precict cis-co-accessibility from single-cell chromatin accessibility data Description: Cicero computes putative cis-regulatory maps from single-cell chromatin accessibility data. It also extends monocle 2 for use in chromatin accessibility data. biocViews: Sequencing, Clustering, CellBasedAssays, ImmunoOncology, GeneRegulation, GeneTarget, Epigenetics, ATACSeq, SingleCell Author: Hannah Pliner [aut, cre], Cole Trapnell [aut] Maintainer: Hannah Pliner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cicero git_branch: RELEASE_3_12 git_last_commit: ffa460e git_last_commit_date: 2020-12-08 Date/Publication: 2020-12-08 source.ver: src/contrib/cicero_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/cicero_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.0/cicero_1.8.1.tgz vignettes: vignettes/cicero/inst/doc/website.html vignetteTitles: Vignette from Cicero Website hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cicero/inst/doc/website.R dependencyCount: 169 Package: CINdex Version: 1.18.0 Depends: R (>= 3.3), GenomicRanges Imports: bitops,gplots,grDevices,som, dplyr,gridExtra,png,stringr,S4Vectors, IRanges, GenomeInfoDb,graphics, stats, utils Suggests: knitr, testthat, ReactomePA, RUnit, BiocGenerics, AnnotationHub, rtracklayer, pd.genomewidesnp.6, org.Hs.eg.db, biovizBase, TxDb.Hsapiens.UCSC.hg18.knownGene, methods, Biostrings,Homo.sapiens License: GPL (>= 2) MD5sum: 6cd5a26b31527cd7f8e7895508806e80 NeedsCompilation: no Title: Chromosome Instability Index Description: The CINdex package addresses important area of high-throughput genomic analysis. It allows the automated processing and analysis of the experimental DNA copy number data generated by Affymetrix SNP 6.0 arrays or similar high throughput technologies. It calculates the chromosome instability (CIN) index that allows to quantitatively characterize genome-wide DNA copy number alterations as a measure of chromosomal instability. This package calculates not only overall genomic instability, but also instability in terms of copy number gains and losses separately at the chromosome and cytoband level. biocViews: Software, CopyNumberVariation, GenomicVariation, aCGH, Microarray, Genetics, Sequencing Author: Lei Song, Krithika Bhuvaneshwar, Yue Wang, Yuanjian Feng, Ie-Ming Shih, Subha Madhavan, Yuriy Gusev Maintainer: Yuriy Gusev VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CINdex git_branch: RELEASE_3_12 git_last_commit: 80688e0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CINdex_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CINdex_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CINdex_1.18.0.tgz vignettes: vignettes/CINdex/inst/doc/CINdex.pdf, vignettes/CINdex/inst/doc/PrepareInputData.pdf vignetteTitles: CINdex Tutorial, Prepare input data for CINdex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CINdex/inst/doc/CINdex.R, vignettes/CINdex/inst/doc/PrepareInputData.R dependencyCount: 47 Package: circRNAprofiler Version: 1.4.2 Depends: R(>= 4.0.0) Imports: dplyr, magrittr, readr, rtracklayer, stringr, stringi, DESeq2, edgeR, GenomicRanges, IRanges, seqinr, R.utils, reshape2, ggplot2, utils, rlang, S4Vectors, stats, GenomeInfoDb, universalmotif, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, Biostrings, gwascat, BSgenome, Suggests: testthat, knitr, roxygen2, rmarkdown, devtools, gridExtra, ggpubr, VennDiagram, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BiocManager, License: GPL-3 MD5sum: c6f05232edbda10503c561dd84fd9792 NeedsCompilation: no Title: circRNAprofiler: An R-Based Computational Framework for the Downstream Analysis of Circular RNAs Description: R-based computational framework for a comprehensive in silico analysis of circRNAs. This computational framework allows to combine and analyze circRNAs previously detected by multiple publicly available annotation-based circRNA detection tools. It covers different aspects of circRNAs analysis from differential expression analysis, evolutionary conservation, biogenesis to functional analysis. biocViews: Annotation, StructuralPrediction, FunctionalPrediction, GenePrediction, GenomeAssembly, DifferentialExpression Author: Simona Aufiero Maintainer: Simona Aufiero URL: https://github.com/Aufiero/circRNAprofiler VignetteBuilder: knitr BugReports: https://github.com/Aufiero/circRNAprofiler/issues git_url: https://git.bioconductor.org/packages/circRNAprofiler git_branch: RELEASE_3_12 git_last_commit: d699b41 git_last_commit_date: 2021-03-04 Date/Publication: 2021-03-04 source.ver: src/contrib/circRNAprofiler_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/circRNAprofiler_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/circRNAprofiler_1.4.2.tgz vignettes: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.html vignetteTitles: circRNAprofiler hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/circRNAprofiler/inst/doc/circRNAprofiler.R dependencyCount: 157 Package: cisPath Version: 1.30.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: d96852ed2cacc109145a1dc5277d8e6a NeedsCompilation: yes Title: Visualization and management of the protein-protein interaction networks. Description: cisPath is an R package that uses web browsers to visualize and manage protein-protein interaction networks. biocViews: Proteomics Author: Likun Wang Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/cisPath git_branch: RELEASE_3_12 git_last_commit: 65807d6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cisPath_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cisPath_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cisPath_1.30.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cisPath/inst/doc/cisPath.R dependencyCount: 2 Package: CiteFuse Version: 1.2.1 Depends: R (>= 4.0) Imports: SingleCellExperiment (>= 1.8.0), SummarizedExperiment (>= 1.16.0), Matrix, mixtools, cowplot, ggplot2, gridExtra, grid, dbscan, propr, uwot, Rtsne, S4Vectors (>= 0.24.0), igraph, scales, scran (>= 1.14.6), graphics, methods, stats, utils, reshape2, ggridges, randomForest, pheatmap, ggraph, grDevices, rhdf5, rlang Suggests: knitr, rmarkdown, DT, mclust, scater, ExPosition, BiocStyle, pkgdown License: GPL-3 MD5sum: 885c6ceb04bf620729c05eb678d6bfed NeedsCompilation: no Title: CiteFuse: multi-modal analysis of CITE-seq data Description: CiteFuse pacakage implements a suite of methods and tools for CITE-seq data from pre-processing to integrative analytics, including doublet detection, network-based modality integration, cell type clustering, differential RNA and protein expression analysis, ADT evaluation, ligand-receptor interaction analysis, and interactive web-based visualisation of the analyses. biocViews: SingleCell, GeneExpression Author: Yingxin Lin [aut, cre], Hani Kim [aut] Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/CiteFuse/issues git_url: https://git.bioconductor.org/packages/CiteFuse git_branch: RELEASE_3_12 git_last_commit: ba50ed6 git_last_commit_date: 2021-04-12 Date/Publication: 2021-04-13 source.ver: src/contrib/CiteFuse_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/CiteFuse_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/CiteFuse_1.2.1.tgz vignettes: vignettes/CiteFuse/inst/doc/CiteFuse.html vignetteTitles: CiteFuse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CiteFuse/inst/doc/CiteFuse.R dependencyCount: 125 Package: ClassifyR Version: 2.10.0 Depends: R (>= 3.5.0), methods, S4Vectors (>= 0.18.0), MultiAssayExperiment (>= 1.6.0), BiocParallel Imports: locfit, grid, utils, plyr Suggests: limma, genefilter, edgeR, car, Rmixmod, ggplot2 (>= 3.0.0), gridExtra (>= 2.0.0), cowplot, BiocStyle, pamr, PoiClaClu, parathyroidSE, knitr, htmltools, gtable, scales, e1071, rmarkdown, IRanges, randomForest, robustbase, glmnet, class License: GPL-3 MD5sum: c3edb92e3c72576a2dec610919e42011 NeedsCompilation: no Title: A framework for cross-validated classification problems, with applications to differential variability and differential distribution testing Description: The software formalises a framework for classification in R. There are four stages; Data transformation, feature selection, classifier training, and prediction. The requirements of variable types and names are fixed, but specialised variables for functions can also be provided. The classification framework is wrapped in a driver loop, that reproducibly carries out a number of cross-validation schemes. Functions for differential expression, differential variability, and differential distribution are included. Additional functions may be developed by the user, by creating an interface to the framework. biocViews: Classification, Survival Author: Dario Strbenac, John Ormerod, Graham Mann, Jean Yang Maintainer: Dario Strbenac VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClassifyR git_branch: RELEASE_3_12 git_last_commit: 37bf4a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ClassifyR_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ClassifyR_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ClassifyR_2.10.0.tgz vignettes: vignettes/ClassifyR/inst/doc/ClassifyR.html, vignettes/ClassifyR/inst/doc/wrapper.html vignetteTitles: An Introduction to the ClassifyR Package, Example: Creating a Wrapper Function for the k-NN Classifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R, vignettes/ClassifyR/inst/doc/wrapper.R dependencyCount: 57 Package: cleanUpdTSeq Version: 1.28.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.1.0), methods, stats Imports: BSgenome, GenomicRanges, seqinr, e1071, GenomeInfoDb, IRanges, utils, BSgenome.Drerio.UCSC.danRer7 Suggests: BiocStyle, knitr, RUnit License: GPL-2 MD5sum: 43138649956a25a617ba2f4bfa6e043b NeedsCompilation: no Title: This package classifies putative polyadenylation sites as true or false/internally oligodT primed Description: This package implements a Naive Bayes classifier for accurate identification of polyadenylation sites (pA sites) from oligodT based 3 prime end sequencing such as PAS-Seq, PolyA-Seq and RNA-Seq. The classifer is highly accurate and outperforms heuristic methods. biocViews: Sequencing, SequenceMatching, Genetics, GeneRegulation Author: Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cleanUpdTSeq git_branch: RELEASE_3_12 git_last_commit: 0a193f2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cleanUpdTSeq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cleanUpdTSeq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cleanUpdTSeq_1.28.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html vignetteTitles: cleanUpdTSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R importsMe: InPAS dependencyCount: 61 Package: cleaver Version: 1.28.0 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), rmarkdown, BRAIN, UniProt.ws (>= 2.1.4) License: GPL (>= 3) MD5sum: f590decfde3564a98b5f7013fa711849 NeedsCompilation: no Title: Cleavage of Polypeptide Sequences Description: In-silico cleavage of polypeptide sequences. The cleavage rules are taken from: http://web.expasy.org/peptide_cutter/peptidecutter_enzymes.html biocViews: Proteomics Author: Sebastian Gibb [aut, cre] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/cleaver/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/cleaver/issues/ git_url: https://git.bioconductor.org/packages/cleaver git_branch: RELEASE_3_12 git_last_commit: 9a76dd6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cleaver_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cleaver_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cleaver_1.28.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.html vignetteTitles: In-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: synapter suggestsMe: RforProteomics dependencyCount: 15 Package: clippda Version: 1.40.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 2be651876f6fd84421cf8bd83a6bbd6b NeedsCompilation: no Title: A package for the clinical proteomic profiling data analysis Description: Methods for the nalysis of data from clinical proteomic profiling studies. The focus is on the studies of human subjects, which are often observational case-control by design and have technical replicates. A method for sample size determination for planning these studies is proposed. It incorporates routines for adjusting for the expected heterogeneities and imbalances in the data and the within-sample replicate correlations. biocViews: Proteomics, OneChannel, Preprocessing, DifferentialExpression, MultipleComparison Author: Stephen Nyangoma Maintainer: Stephen Nyangoma URL: http://www.cancerstudies.bham.ac.uk/crctu/CLIPPDA.shtml git_url: https://git.bioconductor.org/packages/clippda git_branch: RELEASE_3_12 git_last_commit: f220b1f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clippda_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clippda_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clippda_1.40.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clippda/inst/doc/clippda.R dependencyCount: 61 Package: clipper Version: 1.30.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, graphite, ALL, hgu95av2.db, MASS, BiocStyle Enhances: RCy3 License: AGPL-3 MD5sum: a386dc3e0933991f3e082f9bed720422 NeedsCompilation: no Title: Gene Set Analysis Exploiting Pathway Topology Description: Implements topological gene set analysis using a two-step empirical approach. It exploits graph decomposition theory to create a junction tree and reconstruct the most relevant signal path. In the first step clipper selects significant pathways according to statistical tests on the means and the concentration matrices of the graphs derived from pathway topologies. Then, it "clips" the whole pathway identifying the signal paths having the greatest association with a specific phenotype. Author: Paolo Martini , Gabriele Sales , Chiara Romualdi Maintainer: Paolo Martini git_url: https://git.bioconductor.org/packages/clipper git_branch: RELEASE_3_12 git_last_commit: 3a1d234 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clipper_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clipper_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clipper_1.30.0.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clipper/inst/doc/clipper.R suggestsMe: graphite, simPATHy dependencyCount: 102 Package: cliqueMS Version: 1.4.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 0.12.15), xcms(>= 3.0.0), MSnbase, igraph, qlcMatrix, matrixStats, methods LinkingTo: Rcpp, BH, RcppArmadillo Suggests: knitr, rmarkdown, testthat, CAMERA License: GPL (>= 2) Archs: i386, x64 MD5sum: 59bb221701c4afd8f0c3420594407179 NeedsCompilation: yes Title: Annotation of Isotopes, Adducts and Fragmentation Adducts for in-Source LC/MS Metabolomics Data Description: Annotates data from liquid chromatography coupled to mass spectrometry (LC/MS) metabolomics experiments. Based on a network algorithm (O.Senan, A. Aguilar- Mogas, M. Navarro, O. Yanes, R.Guimerà and M. Sales-Pardo, Bioinformatics, 35(20), 2019), 'CliqueMS' builds a weighted similarity network where nodes are features and edges are weighted according to the similarity of this features. Then it searches for the most plausible division of the similarity network into cliques (fully connected components). Finally it annotates metabolites within each clique, obtaining for each annotated metabolite the neutral mass and their features, corresponding to isotopes, ionization adducts and fragmentation adducts of that metabolite. biocViews: Metabolomics, MassSpectrometry, Network, NetworkInference Author: Oriol Senan Campos [aut, cre], Antoni Aguilar-Mogas [aut], Jordi Capellades [aut], Miriam Navarro [aut], Oscar Yanes [aut], Roger Guimera [aut], Marta Sales-Pardo [aut] Maintainer: Oriol Senan Campos URL: http://cliquems.seeslab.net SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/osenan/cliqueMS/issues git_url: https://git.bioconductor.org/packages/cliqueMS git_branch: RELEASE_3_12 git_last_commit: 89665c6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cliqueMS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cliqueMS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cliqueMS_1.4.0.tgz vignettes: vignettes/cliqueMS/inst/doc/annotate_features.html vignetteTitles: Annotating LC/MS data with cliqueMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cliqueMS/inst/doc/annotate_features.R dependencyCount: 98 Package: Clomial Version: 1.26.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 2cf9cb2ad8ae5ba03edae4d675942be1 NeedsCompilation: no Title: Infers clonal composition of a tumor Description: Clomial fits binomial distributions to counts obtained from Next Gen Sequencing data of multiple samples of the same tumor. The trained parameters can be interpreted to infer the clonal structure of the tumor. biocViews: Genetics, GeneticVariability, Sequencing, Clustering, MultipleComparison, Bayesian, DNASeq, ExomeSeq, TargetedResequencing, ImmunoOncology Author: Habil Zare and Alex Hu Maintainer: Habil Zare git_url: https://git.bioconductor.org/packages/Clomial git_branch: RELEASE_3_12 git_last_commit: 4038aa2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Clomial_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Clomial_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Clomial_1.26.0.tgz vignettes: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.pdf vignetteTitles: A likelihood maximization approach to infer the clonal structure of a cancer using multiple tumor samples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clomial/inst/doc/Clonal_decomposition_by_Clomial.R dependencyCount: 4 Package: Clonality Version: 1.38.0 Depends: R (>= 2.12.2), DNAcopy Imports: grDevices, graphics, stats, utils Suggests: gdata License: GPL-3 MD5sum: e70f9bfeb47c054384f8ec0c80760433 NeedsCompilation: no Title: Clonality testing Description: Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles biocViews: CopyNumber, Classification, aCGH, Mutations, Diagnosis, metastasis Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya git_url: https://git.bioconductor.org/packages/Clonality git_branch: RELEASE_3_12 git_last_commit: 705ef53 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Clonality_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Clonality_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Clonality_1.38.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Clonality/inst/doc/Clonality.R dependencyCount: 5 Package: clonotypeR Version: 1.28.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: e59011dee74f0f439361a3799cba2c3e NeedsCompilation: no Title: High throughput analysis of T cell antigen receptor sequences Description: High throughput analysis of T cell antigen receptor sequences The genes encoding T cell receptors are created by somatic recombination, generating an immense combination of V, (D) and J segments. Additional processes during the recombination create extra sequence diversity between the V an J segments. Collectively, this hyper-variable region is called the CDR3 loop. The purpose of this package is to process and quantitatively analyse millions of V-CDR3-J combination, called clonotypes, from multiple sequence libraries. biocViews: Sequencing Author: Charles Plessy Maintainer: Charles Plessy URL: http://clonotyper.branchable.com/ VignetteBuilder: knitr BugReports: http://clonotyper.branchable.com/Bugs/ git_url: https://git.bioconductor.org/packages/clonotypeR git_branch: RELEASE_3_12 git_last_commit: bae9b95 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clonotypeR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clonotypeR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clonotypeR_1.28.0.tgz vignettes: vignettes/clonotypeR/inst/doc/clonotypeR.html vignetteTitles: clonotypeR User's Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R dependencyCount: 1 Package: clst Version: 1.38.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 008f2f99dca1635a4ab996f4c7e20bbb NeedsCompilation: no Title: Classification by local similarity threshold Description: Package for modified nearest-neighbor classification based on calculation of a similarity threshold distinguishing within-group from between-group comparisons. biocViews: Classification Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clst git_branch: RELEASE_3_12 git_last_commit: 7b1fc21 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clst_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clst_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clst_1.38.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clst/inst/doc/clstDemo.R dependsOnMe: clstutils dependencyCount: 20 Package: clstutils Version: 1.38.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: 16245755e370eed4b9eb5068ca7a8344 NeedsCompilation: no Title: Tools for performing taxonomic assignment. Description: Tools for performing taxonomic assignment based on phylogeny using pplacer and clst. biocViews: Sequencing, Classification, Visualization, QualityControl Author: Noah Hoffman Maintainer: Noah Hoffman git_url: https://git.bioconductor.org/packages/clstutils git_branch: RELEASE_3_12 git_last_commit: 85b70a4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clstutils_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clstutils_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clstutils_1.38.0.tgz vignettes: vignettes/clstutils/inst/doc/pplacerDemo.pdf, vignettes/clstutils/inst/doc/refSet.pdf vignetteTitles: clst, clstutils hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clstutils/inst/doc/pplacerDemo.R, vignettes/clstutils/inst/doc/refSet.R dependencyCount: 39 Package: CluMSID Version: 1.6.0 Depends: R (>= 3.6) Imports: mzR, S4Vectors, dbscan, RColorBrewer, ape, network, GGally, ggplot2, plotly, methods, utils, stats, sna, grDevices, graphics, Biobase, gplots, MSnbase Suggests: knitr, rmarkdown, testthat, dplyr, readr, stringr, magrittr, CluMSIDdata, metaMS, metaMSdata, xcms License: MIT + file LICENSE MD5sum: ac264a083501578624081f866c3616e4 NeedsCompilation: no Title: Clustering of MS2 Spectra for Metabolite Identification Description: CluMSID is a tool that aids the identification of features in untargeted LC-MS/MS analysis by the use of MS2 spectra similarity and unsupervised statistical methods. It offers functions for a complete and customisable workflow from raw data to visualisations and is interfaceable with the xmcs family of preprocessing packages. biocViews: Metabolomics, Preprocessing, Clustering Author: Tobias Depke [aut, cre], Raimo Franke [ctb], Mark Broenstrup [ths] Maintainer: Tobias Depke URL: https://github.com/tdepke/CluMSID VignetteBuilder: knitr BugReports: https://github.com/tdepke/CluMSID/issues git_url: https://git.bioconductor.org/packages/CluMSID git_branch: RELEASE_3_12 git_last_commit: c825617 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CluMSID_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CluMSID_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CluMSID_1.6.0.tgz vignettes: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.html, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.html, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.html, vignettes/CluMSID/inst/doc/CluMSID_tutorial.html vignetteTitles: CluMSID DI-MS/MS Tutorial, CluMSID GC-EI-MS Tutorial, CluMSID LowRes Tutorial, CluMSID MTBLS Tutorial, CluMSID Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CluMSID/inst/doc/CluMSID_DI-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_GC-EI-MS.R, vignettes/CluMSID/inst/doc/CluMSID_lowres-LC-MSMS.R, vignettes/CluMSID/inst/doc/CluMSID_MTBLS.R, vignettes/CluMSID/inst/doc/CluMSID_tutorial.R dependencyCount: 116 Package: clustComp Version: 1.18.0 Depends: R (>= 3.3) Imports: sm, stats, graphics, grDevices Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 41203c769f857e0b1d7374cbff5c0d68 NeedsCompilation: no Title: Clustering Comparison Package Description: clustComp is a package that implements several techniques for the comparison and visualisation of relationships between different clustering results, either flat versus flat or hierarchical versus flat. These relationships among clusters are displayed using a weighted bi-graph, in which the nodes represent the clusters and the edges connect pairs of nodes with non-empty intersection; the weight of each edge is the number of elements in that intersection and is displayed through the edge thickness. The best layout of the bi-graph is provided by the barycentre algorithm, which minimises the weighted number of crossings. In the case of comparing a hierarchical and a non-hierarchical clustering, the dendrogram is pruned at different heights, selected by exploring the tree by depth-first search, starting at the root. Branches are decided to be split according to the value of a scoring function, that can be based either on the aesthetics of the bi-graph or on the mutual information between the hierarchical and the flat clusterings. A mapping between groups of clusters from each side is constructed with a greedy algorithm, and can be additionally visualised. biocViews: GeneExpression, Clustering, Visualization Author: Aurora Torrente and Alvis Brazma. Maintainer: Aurora Torrente git_url: https://git.bioconductor.org/packages/clustComp git_branch: RELEASE_3_12 git_last_commit: 60c34eb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clustComp_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clustComp_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clustComp_1.18.0.tgz vignettes: vignettes/clustComp/inst/doc/clustComp.pdf vignetteTitles: The clustComp Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clustComp/inst/doc/clustComp.R dependencyCount: 4 Package: clusterExperiment Version: 2.10.1 Depends: R (>= 3.6.0), SingleCellExperiment, SummarizedExperiment (>= 1.15.4), BiocGenerics Imports: methods, NMF, RColorBrewer, ape (>= 5.0), cluster, stats, limma, howmany, locfdr, matrixStats, graphics, parallel, RSpectra, kernlab, stringr, S4Vectors, grDevices, DelayedArray (>= 0.7.48), HDF5Array (>= 1.7.10), Matrix, Rcpp, edgeR, scales, zinbwave, phylobase, pracma, mbkmeans LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, MAST, Rtsne, scran, igraph License: Artistic-2.0 Archs: i386, x64 MD5sum: 0ae892e32c87a87a42312a8c533154e2 NeedsCompilation: yes Title: Compare Clusterings for Single-Cell Sequencing Description: Provides functionality for running and comparing many different clusterings of single-cell sequencing data or other large mRNA Expression data sets. biocViews: Clustering, RNASeq, Sequencing, Software, SingleCell Author: Elizabeth Purdom [aut, cre, cph], Davide Risso [aut] Maintainer: Elizabeth Purdom VignetteBuilder: knitr BugReports: https://github.com/epurdom/clusterExperiment/issues git_url: https://git.bioconductor.org/packages/clusterExperiment git_branch: RELEASE_3_12 git_last_commit: 499be02 git_last_commit_date: 2021-02-05 Date/Publication: 2021-02-09 source.ver: src/contrib/clusterExperiment_2.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/clusterExperiment_2.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/clusterExperiment_2.10.1.tgz vignettes: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.html, vignettes/clusterExperiment/inst/doc/largeDataSets.html vignetteTitles: clusterExperiment Vignette, Working with Large Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterExperiment/inst/doc/clusterExperimentTutorial.R, vignettes/clusterExperiment/inst/doc/largeDataSets.R dependsOnMe: netSmooth suggestsMe: slingshot, tradeSeq dependencyCount: 150 Package: ClusterJudge Version: 1.12.1 Depends: R (>= 3.6), stats, utils, graphics, infotheo, lattice, latticeExtra, httr, jsonlite Suggests: yeastExpData, knitr, rmarkdown, devtools, testthat, biomaRt License: Artistic-2.0 MD5sum: 6eba19eeda98a6447b6fc573165f67ec NeedsCompilation: no Title: Judging Quality of Clustering Methods using Mutual Information Description: ClusterJudge implements the functions, examples and other software published as an algorithm by Gibbons, FD and Roth FP. The article is called "Judging the Quality of Gene Expression-Based Clustering Methods Using Gene Annotation" and it appeared in Genome Research, vol. 12, pp1574-1581 (2002). See package?ClusterJudge for an overview. biocViews: Software, StatisticalMethod, Clustering, GeneExpression, GO Author: Adrian Pasculescu Maintainer: Adrian Pasculescu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ClusterJudge git_branch: RELEASE_3_12 git_last_commit: e74b31d git_last_commit_date: 2021-03-09 Date/Publication: 2021-03-09 source.ver: src/contrib/ClusterJudge_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ClusterJudge_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ClusterJudge_1.12.1.tgz vignettes: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterJudge/inst/doc/ClusterJudge-intro.R dependencyCount: 21 Package: clusterProfiler Version: 3.18.1 Depends: R (>= 3.4.0) Imports: AnnotationDbi, downloader, DOSE (>= 3.13.1), dplyr, enrichplot (>= 1.9.3), GO.db, GOSemSim, magrittr, methods, plyr, qvalue, rlang, rvcheck, stats, tidyr, utils Suggests: AnnotationHub, knitr, org.Hs.eg.db, prettydoc, ReactomePA, testthat License: Artistic-2.0 MD5sum: 9945b12958d6c8d56e8752d02f679037 NeedsCompilation: no Title: statistical analysis and visualization of functional profiles for genes and gene clusters Description: This package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters. biocViews: Annotation, Clustering, GeneSetEnrichment, GO, KEGG, MultipleComparison, Pathways, Reactome, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Li-Gen Wang [ctb], Giovanni Dall'Olio [ctb] (formula interface of compareCluster) Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues git_url: https://git.bioconductor.org/packages/clusterProfiler git_branch: RELEASE_3_12 git_last_commit: 2c0fca7 git_last_commit_date: 2021-02-05 Date/Publication: 2021-02-09 source.ver: src/contrib/clusterProfiler_3.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/clusterProfiler_3.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/clusterProfiler_3.18.1.tgz vignettes: vignettes/clusterProfiler/inst/doc/clusterProfiler.html vignetteTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R dependsOnMe: maEndToEnd importsMe: bioCancer, CEMiTool, CeTF, debrowser, eegc, enrichTF, esATAC, famat, fcoex, GDCRNATools, MAGeCKFlute, methylGSA, miRspongeR, MoonlightR, netboxr, RNASeqR, signatureSearch, TCGAbiolinksGUI, TimiRGeN, recountWorkflow, TCGAWorkflow, immcp, RVA suggestsMe: ChIPseeker, cola, DOSE, enrichplot, epihet, GeneTonic, GOSemSim, GSEAmining, MesKit, paxtoolsr, ReactomePA, rrvgo, scGPS, simplifyEnrichment, TCGAbiolinks, tidybulk, org.Mxanthus.db, cRegulome dependencyCount: 100 Package: clusterSeq Version: 1.14.0 Depends: R (>= 3.0.0), methods, BiocParallel, baySeq, graphics, stats, utils Imports: BiocGenerics Suggests: BiocStyle License: GPL-3 MD5sum: cf3e1a41c10b38b33dfde7f932eeb240 NeedsCompilation: no Title: Clustering of high-throughput sequencing data by identifying co-expression patterns Description: Identification of clusters of co-expressed genes based on their expression across multiple (replicated) biological samples. biocViews: Sequencing, DifferentialExpression, MultipleComparison, Clustering, GeneExpression Author: Thomas J. Hardcastle & Irene Papatheodorou Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/clusterSeq git_branch: RELEASE_3_12 git_last_commit: f1e1602 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clusterSeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clusterSeq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clusterSeq_1.14.0.tgz vignettes: vignettes/clusterSeq/inst/doc/clusterSeq.pdf vignetteTitles: Advanced baySeq analyses hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterSeq/inst/doc/clusterSeq.R dependencyCount: 33 Package: ClusterSignificance Version: 1.18.0 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve (>= 2.0.5), scatterplot3d, RColorBrewer, grDevices, graphics, utils, stats Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics, covr License: GPL-3 MD5sum: 769443ae3fef701feb7fb6dfcb268448 NeedsCompilation: no Title: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data Description: The ClusterSignificance package provides tools to assess if class clusters in dimensionality reduced data representations have a separation different from permuted data. The term class clusters here refers to, clusters of points representing known classes in the data. This is particularly useful to determine if a subset of the variables, e.g. genes in a specific pathway, alone can separate samples into these established classes. ClusterSignificance accomplishes this by, projecting all points onto a one dimensional line. Cluster separations are then scored and the probability of the seen separation being due to chance is evaluated using a permutation method. biocViews: Clustering, Classification, PrincipalComponent, StatisticalMethod Author: Jason T. Serviss [aut, cre], Jesper R. Gadin [aut] Maintainer: Jason T Serviss URL: https://github.com/jasonserviss/ClusterSignificance/ VignetteBuilder: knitr BugReports: https://github.com/jasonserviss/ClusterSignificance/issues git_url: https://git.bioconductor.org/packages/ClusterSignificance git_branch: RELEASE_3_12 git_last_commit: ccf5490 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ClusterSignificance_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ClusterSignificance_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ClusterSignificance_1.18.0.tgz vignettes: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html vignetteTitles: ClusterSignificance Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R dependencyCount: 10 Package: clusterStab Version: 1.62.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: a053f7aa4e26d02ccb321648b96268d7 NeedsCompilation: no Title: Compute cluster stability scores for microarray data Description: This package can be used to estimate the number of clusters in a set of microarray data, as well as test the stability of these clusters. biocViews: Clustering Author: James W. MacDonald, Debashis Ghosh, Mark Smolkin Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/clusterStab git_branch: RELEASE_3_12 git_last_commit: 58d85d6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clusterStab_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clusterStab_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clusterStab_1.62.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterStab/inst/doc/clusterStab.R dependencyCount: 7 Package: clustifyr Version: 1.2.0 Depends: R (>= 4.0) Imports: cowplot, dplyr, entropy, fgsea, ggplot2, Matrix, readr, rlang, scales, stringr, tibble, tidyr, stats, methods, SingleCellExperiment, SummarizedExperiment, matrixStats, S4Vectors, proxy, httr Suggests: ComplexHeatmap, covr, knitr, rmarkdown, testthat, ggrepel, BiocStyle License: MIT + file LICENSE MD5sum: 4a515b18abaa559fd590aa66ace149a9 NeedsCompilation: no Title: Classifier for Single-cell RNA-seq Using Cell Clusters Description: Package designed to aid in classifying cells from single-cell RNA sequencing data using external reference data (e.g., bulk RNA-seq, scRNA-seq, microarray, gene lists). A variety of correlation based methods and gene list enrichment methods are provided to assist cell type assignment. biocViews: SingleCell, Annotation, Sequencing, Microarray Author: Rui Fu [aut, cre], Kent Riemondy [aut], Austin Gillen [ctb], Chengzhe Tian [ctb], Jay Hesselberth [ctb], Yue Hao [ctb], Michelle Daya [ctb], Sidhant Puntambekar [ctb] Maintainer: Rui Fu URL: http://github.com/rnabioco/clustifyr#readme, https://rnabioco.github.io/clustifyr/ VignetteBuilder: knitr BugReports: https://github.com/rnabioco/clustifyr/issues git_url: https://git.bioconductor.org/packages/clustifyr git_branch: RELEASE_3_12 git_last_commit: 02cd411 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/clustifyr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/clustifyr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/clustifyr_1.2.0.tgz vignettes: vignettes/clustifyr/inst/doc/clustifyR.html, vignettes/clustifyr/inst/doc/geo-annotations.html vignetteTitles: Introduction to clustifyr, geo-annotations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clustifyr/inst/doc/clustifyR.R, vignettes/clustifyr/inst/doc/geo-annotations.R suggestsMe: clustifyrdatahub dependencyCount: 90 Package: CMA Version: 1.48.0 Depends: R (>= 2.10), methods, stats, Biobase Suggests: MASS, class, nnet, glmnet, e1071, randomForest, plsgenomics, gbm, mgcv, corpcor, limma, st, mvtnorm License: GPL (>= 2) MD5sum: 3ed15a5d79dd225a09ba33e20f73ea8a NeedsCompilation: no Title: Synthesis of microarray-based classification Description: This package provides a comprehensive collection of various microarray-based classification algorithms both from Machine Learning and Statistics. Variable Selection, Hyperparameter tuning, Evaluation and Comparison can be performed combined or stepwise in a user-friendly environment. biocViews: Classification, DecisionTree Author: Martin Slawski , Anne-Laure Boulesteix , Christoph Bernau . Maintainer: Roman Hornung git_url: https://git.bioconductor.org/packages/CMA git_branch: RELEASE_3_12 git_last_commit: 4f2008c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CMA_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CMA_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CMA_1.48.0.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CMA/inst/doc/CMA_vignette.R dependencyCount: 7 Package: cmapR Version: 1.2.1 Depends: R (>= 4.0) Imports: methods, rhdf5, data.table, flowCore, SummarizedExperiment, matrixStats Suggests: knitr, testthat, BiocStyle License: file LICENSE MD5sum: 8ff33596af0207e2d5c9a9cb88ea4023 NeedsCompilation: no Title: CMap Tools in R Description: The Connectivity Map (CMap) is a massive resource of perturbational gene expression profiles built by researchers at the Broad Institute and funded by the NIH Library of Integrated Network-Based Cellular Signatures (LINCS) program. Please visit https://clue.io for more information. The cmapR package implements methods to parse, manipulate, and write common CMap data objects, such as annotated matrices and collections of gene sets. biocViews: DataImport, DataRepresentation, GeneExpression Author: Ted Natoli [aut, cre] () Maintainer: Ted Natoli URL: https://github.com/cmap/cmapR VignetteBuilder: knitr BugReports: https://github.com/cmap/cmapR/issues git_url: https://git.bioconductor.org/packages/cmapR git_branch: RELEASE_3_12 git_last_commit: 5a9929a git_last_commit_date: 2020-12-09 Date/Publication: 2020-12-10 source.ver: src/contrib/cmapR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/cmapR_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/cmapR_1.2.1.tgz vignettes: vignettes/cmapR/inst/doc/tutorial.html vignetteTitles: cmapR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cmapR/inst/doc/tutorial.R dependencyCount: 37 Package: cn.farms Version: 1.38.0 Depends: R (>= 3.0), Biobase, methods, ff, oligoClasses, snow Imports: DBI, affxparser, oligo, DNAcopy, preprocessCore, lattice Suggests: pd.mapping250k.sty, pd.mapping250k.nsp, pd.genomewidesnp.5, pd.genomewidesnp.6 License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: b0398e2319991aaca12b48143c555655 NeedsCompilation: yes Title: cn.FARMS - factor analysis for copy number estimation Description: This package implements the cn.FARMS algorithm for copy number variation (CNV) analysis. cn.FARMS allows to analyze the most common Affymetrix (250K-SNP6.0) array types, supports high-performance computing using snow and ff. biocViews: Microarray, CopyNumberVariation Author: Andreas Mitterecker, Djork-Arne Clevert Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/cnfarms/cnfarms.html git_url: https://git.bioconductor.org/packages/cn.farms git_branch: RELEASE_3_12 git_last_commit: 4d1b4e0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cn.farms_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cn.farms_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cn.farms_1.38.0.tgz vignettes: vignettes/cn.farms/inst/doc/cn.farms.pdf vignetteTitles: cn.farms: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.farms/inst/doc/cn.farms.R dependencyCount: 56 Package: cn.mops Version: 1.36.0 Depends: R (>= 2.12), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors, exomeCopy Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 51b6a9180d94af303dffd8d22010ab11 NeedsCompilation: yes Title: cn.mops - Mixture of Poissons for CNV detection in NGS data Description: cn.mops (Copy Number estimation by a Mixture Of PoissonS) is a data processing pipeline for copy number variations and aberrations (CNVs and CNAs) from next generation sequencing (NGS) data. The package supplies functions to convert BAM files into read count matrices or genomic ranges objects, which are the input objects for cn.mops. cn.mops models the depths of coverage across samples at each genomic position. Therefore, it does not suffer from read count biases along chromosomes. Using a Bayesian approach, cn.mops decomposes read variations across samples into integer copy numbers and noise by its mixture components and Poisson distributions, respectively. cn.mops guarantees a low FDR because wrong detections are indicated by high noise and filtered out. cn.mops is very fast and written in C++. biocViews: Sequencing, CopyNumberVariation, Homo_sapiens, CellBiology, HapMap, Genetics Author: Guenter Klambauer Maintainer: Gundula Povysil URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html git_url: https://git.bioconductor.org/packages/cn.mops git_branch: RELEASE_3_12 git_last_commit: 94d0afc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cn.mops_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cn.mops_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cn.mops_1.36.0.tgz vignettes: vignettes/cn.mops/inst/doc/cn.mops.pdf vignetteTitles: cn.mops: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cn.mops/inst/doc/cn.mops.R dependsOnMe: panelcn.mops importsMe: CopyNumberPlots dependencyCount: 31 Package: CNAnorm Version: 1.36.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: b796bb0220f70f1e8f3cdef10c86ed6f NeedsCompilation: yes Title: A normalization method for Copy Number Aberration in cancer samples Description: Performs ratio, GC content correction and normalization of data obtained using low coverage (one read every 100-10,000 bp) high troughput sequencing. It performs a "discrete" normalization looking for the ploidy of the genome. It will also provide tumour content if at least two ploidy states can be found. biocViews: CopyNumberVariation, Sequencing, Coverage, Normalization, WholeGenome, DNASeq, GenomicVariation Author: Stefano Berri , Henry M. Wood , Arief Gusnanto Maintainer: Stefano Berri URL: http://www.r-project.org, git_url: https://git.bioconductor.org/packages/CNAnorm git_branch: RELEASE_3_12 git_last_commit: 9bfd2c4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNAnorm_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNAnorm_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNAnorm_1.36.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNAnorm/inst/doc/CNAnorm.R dependencyCount: 2 Package: CNEr Version: 1.26.0 Depends: R (>= 3.4) Imports: Biostrings (>= 2.33.4), DBI (>= 0.7), RSQLite (>= 0.11.4), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.23.16), rtracklayer (>= 1.25.5), XVector (>= 0.5.4), GenomicAlignments (>= 1.1.9), methods, S4Vectors (>= 0.13.13), IRanges (>= 2.5.27), readr (>= 0.2.2), BiocGenerics, tools, parallel, reshape2 (>= 1.4.1), ggplot2 (>= 2.1.0), poweRlaw (>= 0.60.3), annotate (>= 1.50.0), GO.db (>= 3.3.0), R.utils (>= 2.3.0), KEGGREST (>= 1.14.0) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz (>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, TxDb.Drerio.UCSC.danRer10.refGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Ggallus.UCSC.galGal3 License: GPL-2 | file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 64a7e4140de9c72c1896389fad2b6590 NeedsCompilation: yes Title: CNE Detection and Visualization Description: Large-scale identification and advanced visualization of sets of conserved noncoding elements. biocViews: GeneRegulation, Visualization, DataImport Author: Ge Tan Maintainer: Ge Tan URL: https://github.com/ge11232002/CNEr VignetteBuilder: knitr BugReports: https://github.com/ge11232002/CNEr/issues git_url: https://git.bioconductor.org/packages/CNEr git_branch: RELEASE_3_12 git_last_commit: e5e582d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNEr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNEr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNEr_1.26.0.tgz vignettes: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html vignetteTitles: CNE identification and visualisation, Pairwise whole genome alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R importsMe: TFBSTools dependencyCount: 105 Package: CNORdt Version: 1.32.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 3dae1dbe0d6e6282d627c70d746f0f22 NeedsCompilation: yes Title: Add-on to CellNOptR: Discretized time treatments Description: This add-on to the package CellNOptR handles time-course data, as opposed to steady state data in CellNOptR. It scales the simulation step to allow comparison and model fitting for time-course data. Future versions will optimize delays and strengths for each edge. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara git_url: https://git.bioconductor.org/packages/CNORdt git_branch: RELEASE_3_12 git_last_commit: 545e476 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNORdt_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNORdt_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNORdt_1.32.0.tgz vignettes: vignettes/CNORdt/inst/doc/CNORdt-vignette.pdf vignetteTitles: Using multiple time points to train logic models to data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORdt/inst/doc/CNORdt-vignette-example.R, vignettes/CNORdt/inst/doc/CNORdt-vignette.R dependencyCount: 55 Package: CNORfeeder Version: 1.30.0 Depends: R (>= 3.6.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph Enhances: MEIGOR License: GPL-3 MD5sum: c461c631864867a973ca6149d0cc192e NeedsCompilation: no Title: Integration of CellNOptR to add missing links Description: This package integrates literature-constrained and data-driven methods to infer signalling networks from perturbation experiments. It permits to extends a given network with links derived from the data via various inference methods and uses information on physical interactions of proteins to guide and validate the integration of links. biocViews: CellBasedAssays, CellBiology, Proteomics, NetworkInference Author: F.Eduati, E. Gjerga Maintainer: E.Gjerga git_url: https://git.bioconductor.org/packages/CNORfeeder git_branch: RELEASE_3_12 git_last_commit: 6b5c912 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNORfeeder_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNORfeeder_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNORfeeder_1.30.0.tgz vignettes: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfeeder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfeeder/inst/doc/CNORfeeder-vignette.R dependencyCount: 54 Package: CNORfuzzy Version: 1.32.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), nloptr (>= 0.8.5) Suggests: xtable, Rgraphviz, RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: c8f7fdb2433f6e781b7bb3e582b4f820 NeedsCompilation: yes Title: Addon to CellNOptR: Fuzzy Logic Description: This package is an extension to CellNOptR. It contains additional functionality needed to simulate and train a prior knowledge network to experimental data using constrained fuzzy logic (cFL, rather than Boolean logic as is the case in CellNOptR). Additionally, this package will contain functions to use for the compilation of multiple optimization results (either Boolean or cFL). biocViews: Network Author: M. Morris, T. Cokelaer Maintainer: T. Cokelaer git_url: https://git.bioconductor.org/packages/CNORfuzzy git_branch: RELEASE_3_12 git_last_commit: 5472280 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNORfuzzy_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNORfuzzy_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNORfuzzy_1.32.0.tgz vignettes: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORfuzzyl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORfuzzy/inst/doc/CNORfuzzy-vignette.R dependencyCount: 55 Package: CNORode Version: 1.32.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: 6552185364e8d5953a412e96df018b51 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: ODE add-on to CellNOptR biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer, Attila Gabor, Federica Eduati, Enio Gjerga Maintainer: Enio Gjerga git_url: https://git.bioconductor.org/packages/CNORode git_branch: RELEASE_3_12 git_last_commit: 8516b08 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNORode_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNORode_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNORode_1.32.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR dependencyCount: 55 Package: CNTools Version: 1.46.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: 2e41ba557d48fd8e0e8fdfd8f7aba1b7 NeedsCompilation: yes Title: Convert segment data into a region by sample matrix to allow for other high level computational analyses. Description: This package provides tools to convert the output of segmentation analysis using DNAcopy to a matrix structure with overlapping segments as rows and samples as columns so that other computational analyses can be applied to segmented data biocViews: Microarray, CopyNumberVariation Author: Jianhua Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/CNTools git_branch: RELEASE_3_12 git_last_commit: 3f5eb4a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNTools_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNTools_1.46.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNTools/inst/doc/HowTo.R dependsOnMe: cghMCR dependencyCount: 45 Package: CNVfilteR Version: 1.4.2 Depends: R (>= 4.0) Imports: IRanges, GenomicRanges, SummarizedExperiment, pracma, regioneR, assertthat, karyoploteR, CopyNumberPlots, graphics, utils, VariantAnnotation, Rsamtools, GenomeInfoDb, Biostrings, methods Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, rmarkdown License: Artistic-2.0 MD5sum: 3847a8d674aee6f28fe3e6d21b1bc99b NeedsCompilation: no Title: Identifies false positives of CNV calling tools by using SNV calls Description: CNVfilteR identifies false positives produced by germline NGS copy number variant detection tools by using single nucleotide variants. biocViews: CopyNumberVariation, Sequencing, DNASeq, Visualization, DataImport Author: Jose Marcos Moreno-Cabrera [aut, cre] (), Bernat Gel [aut] Maintainer: Jose Marcos Moreno-Cabrera URL: https://github.com/jpuntomarcos/CNVfilteR VignetteBuilder: knitr BugReports: https://github.com/jpuntomarcos/CNVfilteR/issues git_url: https://git.bioconductor.org/packages/CNVfilteR git_branch: RELEASE_3_12 git_last_commit: 0cb32a5 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/CNVfilteR_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNVfilteR_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/CNVfilteR_1.4.2.tgz vignettes: vignettes/CNVfilteR/inst/doc/CNVfilteR.html vignetteTitles: CNVfilteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVfilteR/inst/doc/CNVfilteR.R dependencyCount: 148 Package: cnvGSA Version: 1.34.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: 3395eafaace86d184a30ba426d064273 NeedsCompilation: no Title: Gene Set Analysis of (Rare) Copy Number Variants Description: This package is intended to facilitate gene-set association with rare CNVs in case-control studies. biocViews: MultipleComparison Author: Daniele Merico , Robert Ziman ; packaged by Joseph Lugo Maintainer: Joseph Lugo git_url: https://git.bioconductor.org/packages/cnvGSA git_branch: RELEASE_3_12 git_last_commit: 9dc0c93 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cnvGSA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cnvGSA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cnvGSA_1.34.0.tgz vignettes: vignettes/cnvGSA/inst/doc/cnvGSA-vignette.pdf, vignettes/cnvGSA/inst/doc/cnvGSAUsersGuide.pdf vignetteTitles: cnvGSA - Gene-Set Analysis of Rare Copy Number Variants, cnvGSAUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cnvGSAdata dependencyCount: 25 Package: CNVPanelizer Version: 1.22.0 Depends: R (>= 3.2.0), GenomicRanges Imports: BiocGenerics, S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, GenomeInfoDb, gplots, reshape2, stringr, testthat, graphics, methods, shiny, shinyFiles, shinyjs, grid, openxlsx Suggests: knitr, RUnit License: GPL-3 MD5sum: 66ed5d0c7757bee8b75c3420236bc36f NeedsCompilation: no Title: Reliable CNV detection in targeted sequencing applications Description: A method that allows for the use of a collection of non-matched normal tissue samples. Our approach uses a non-parametric bootstrap subsampling of the available reference samples to estimate the distribution of read counts from targeted sequencing. As inspired by random forest, this is combined with a procedure that subsamples the amplicons associated with each of the targeted genes. The obtained information allows us to reliably classify the copy number aberrations on the gene level. biocViews: Classification, Sequencing, Normalization, CopyNumberVariation, Coverage Author: Cristiano Oliveira [aut], Thomas Wolf [aut, cre], Albrecht Stenzinger [ctb], Volker Endris [ctb], Nicole Pfarr [ctb], Benedikt Brors [ths], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVPanelizer git_branch: RELEASE_3_12 git_last_commit: e43f2fb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNVPanelizer_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNVPanelizer_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNVPanelizer_1.22.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.R dependencyCount: 112 Package: CNVRanger Version: 1.6.1 Depends: GenomicRanges, RaggedExperiment Imports: BiocGenerics, BiocParallel, GDSArray, GenomeInfoDb, IRanges, S4Vectors, SNPRelate, SummarizedExperiment, data.table, edgeR, gdsfmt, grDevices, lattice, limma, methods, plyr, qqman, rappdirs, reshape2, stats, utils Suggests: AnnotationHub, BSgenome.Btaurus.UCSC.bosTau6.masked, BiocStyle, ComplexHeatmap, Gviz, MultiAssayExperiment, TCGAutils, curatedTCGAData, ensembldb, grid, knitr, regioneR, rmarkdown License: Artistic-2.0 MD5sum: 1de0caf2dfbb01352a8b61a8c0debfa4 NeedsCompilation: no Title: Summarization and expression/phenotype association of CNV ranges Description: The CNVRanger package implements a comprehensive tool suite for CNV analysis. This includes functionality for summarizing individual CNV calls across a population, assessing overlap with functional genomic regions, and association analysis with gene expression and quantitative phenotypes. biocViews: CopyNumberVariation, DifferentialExpression, GeneExpression, GenomeWideAssociation, GenomicVariation, Microarray, RNASeq, SNP Author: Ludwig Geistlinger [aut, cre], Vinicius Henrique da Silva [aut], Marcel Ramos [ctb], Levi Waldron [ctb] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/waldronlab/CNVRanger/issues git_url: https://git.bioconductor.org/packages/CNVRanger git_branch: RELEASE_3_12 git_last_commit: a995fa8 git_last_commit_date: 2020-12-09 Date/Publication: 2020-12-10 source.ver: src/contrib/CNVRanger_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNVRanger_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/CNVRanger_1.6.1.tgz vignettes: vignettes/CNVRanger/inst/doc/CNVRanger.html vignetteTitles: Summarization and quantitative trait analysis of CNV ranges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVRanger/inst/doc/CNVRanger.R dependencyCount: 55 Package: CNVrd2 Version: 1.28.0 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 4e9db22cedf74b6c4dd511653acc4749 NeedsCompilation: no Title: CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data. Description: CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions. biocViews: CopyNumberVariation, SNP, Sequencing, Software, Coverage, LinkageDisequilibrium, Clustering. Author: Hoang Tan Nguyen, Tony R Merriman and Mik Black Maintainer: Hoang Tan Nguyen URL: https://github.com/hoangtn/CNVrd2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CNVrd2 git_branch: RELEASE_3_12 git_last_commit: 84628b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CNVrd2_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CNVrd2_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CNVrd2_1.28.0.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVrd2/inst/doc/CNVrd2.R dependencyCount: 109 Package: CoCiteStats Version: 1.62.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 89eeb89dbab6819ee949c47db3142a39 NeedsCompilation: no Title: Different test statistics based on co-citation. Description: A collection of software tools for dealing with co-citation data. biocViews: Software Author: B. Ding and R. Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/CoCiteStats git_branch: RELEASE_3_12 git_last_commit: a075751 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CoCiteStats_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CoCiteStats_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CoCiteStats_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 27 Package: COCOA Version: 2.4.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocGenerics, S4Vectors, IRanges, data.table, ggplot2, Biobase, stats, methods, ComplexHeatmap, MIRA, tidyr, grid, grDevices, simpleCache, fitdistrplus Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 57027d4c4e683424ebb5bb11a1500f5b NeedsCompilation: no Title: Coordinate Covariation Analysis Description: COCOA is a method for understanding epigenetic variation among samples. COCOA can be used with epigenetic data that includes genomic coordinates and an epigenetic signal, such as DNA methylation and chromatin accessibility data. To describe the method on a high level, COCOA quantifies inter-sample variation with either a supervised or unsupervised technique then uses a database of "region sets" to annotate the variation among samples. A region set is a set of genomic regions that share a biological annotation, for instance transcription factor (TF) binding regions, histone modification regions, or open chromatin regions. COCOA can identify region sets that are associated with epigenetic variation between samples and increase understanding of variation in your data. biocViews: Epigenetics, DNAMethylation, ATACSeq, DNaseSeq, MethylSeq, MethylationArray, PrincipalComponent, GenomicVariation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, Sequencing, ImmunoOncology Author: John Lawson [aut, cre], Nathan Sheffield [aut] (http://www.databio.org), Jason Smith [ctb] Maintainer: John Lawson URL: http://code.databio.org/COCOA/ VignetteBuilder: knitr BugReports: https://github.com/databio/COCOA git_url: https://git.bioconductor.org/packages/COCOA git_branch: RELEASE_3_12 git_last_commit: 9a75d3f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/COCOA_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/COCOA_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/COCOA_2.4.0.tgz vignettes: vignettes/COCOA/inst/doc/IntroToCOCOA.html vignetteTitles: Introduction to Coordinate Covariation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COCOA/inst/doc/IntroToCOCOA.R dependencyCount: 107 Package: codelink Version: 1.58.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: c139fd6cbc181f59a89b604b76eb88ce NeedsCompilation: no Title: Manipulation of Codelink microarray data Description: This package facilitates reading, preprocessing and manipulating Codelink microarray data. The raw data must be exported as text file using the Codelink software. biocViews: Microarray, OneChannel, DataImport, Preprocessing Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/codelink VignetteBuilder: knitr BugReports: https://github.com/ddiez/codelink/issues git_url: https://git.bioconductor.org/packages/codelink git_branch: RELEASE_3_12 git_last_commit: 0db11e7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/codelink_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/codelink_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/codelink_1.58.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/codelink/inst/doc/Codelink_Introduction.R, vignettes/codelink/inst/doc/Codelink_Legacy.R suggestsMe: MAQCsubset dependencyCount: 40 Package: CODEX Version: 1.22.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings, S4Vectors Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: 8fdf8daa4d12154cd75690331453c37f NeedsCompilation: no Title: A Normalization and Copy Number Variation Detection Method for Whole Exome Sequencing Description: A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data. biocViews: ImmunoOncology, ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang git_url: https://git.bioconductor.org/packages/CODEX git_branch: RELEASE_3_12 git_last_commit: aa0ee42 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CODEX_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CODEX_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CODEX_1.22.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CODEX/inst/doc/CODEX_vignettes.R dependsOnMe: iCNV dependencyCount: 42 Package: coexnet Version: 1.12.0 Depends: R (>= 3.6) Imports: affy, siggenes, GEOquery, vsn, igraph, acde, Biobase, limma, graphics, stats, utils, STRINGdb, SummarizedExperiment, minet, rmarkdown Suggests: RUnit, BiocGenerics, knitr License: LGPL MD5sum: 782d343dea0a952ba9856de91c16a420 NeedsCompilation: no Title: coexnet: An R package to build CO-EXpression NETworks from Microarray Data Description: Extracts the gene expression matrix from GEO DataSets (.CEL files) as a AffyBatch object. Additionally, can make the normalization process using two different methods (vsn and rma). The summarization (pass from multi-probe to one gene) uses two different criteria (Maximum value and Median of the samples expression data) and the process of gene differentially expressed analisys using two methods (sam and acde). The construction of the co-expression network can be conduced using two different methods, Pearson Correlation Coefficient (PCC) or Mutual Information (MI) and choosing a threshold value using a graph theory approach. biocViews: GeneExpression, Microarray, DifferentialExpression, GraphAndNetwork, NetworkInference, SystemsBiology, Normalization, Network Author: Juan David Henao [aut,cre], Liliana Lopez-Kleine [aut], Andres Pinzon-Velasco [aut] Maintainer: Juan David Henao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coexnet git_branch: RELEASE_3_12 git_last_commit: 10225db git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/coexnet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/coexnet_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/coexnet_1.12.0.tgz vignettes: vignettes/coexnet/inst/doc/coexnet.pdf vignetteTitles: The title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coexnet/inst/doc/coexnet.R dependencyCount: 125 Package: CoGAPS Version: 3.10.0 Depends: R (>= 3.5.0) Imports: BiocParallel, cluster, methods, gplots, graphics, grDevices, RColorBrewer, Rcpp, S4Vectors, SingleCellExperiment, stats, SummarizedExperiment, tools, utils, rhdf5 LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: 771f6182ddb44454f40aa5a306c65028 NeedsCompilation: yes Title: Coordinated Gene Activity in Pattern Sets Description: Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis. biocViews: GeneExpression, Transcription, GeneSetEnrichment, DifferentialExpression, Bayesian, Clustering, TimeCourse, RNASeq, Microarray, MultipleComparison, DimensionReduction, ImmunoOncology Author: Thomas Sherman, Wai-shing Lee, Conor Kelton, Ondrej Maxian, Jacob Carey, Genevieve Stein-O'Brien, Michael Considine, Maggie Wodicka, John Stansfield, Shawn Sivy, Carlo Colantuoni, Alexander Favorov, Mike Ochs, Elana Fertig Maintainer: Elana J. Fertig , Thomas D. Sherman , Melanie L. Loth VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoGAPS git_branch: RELEASE_3_12 git_last_commit: 395fcd3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CoGAPS_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CoGAPS_3.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CoGAPS_3.10.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPS.html vignetteTitles: CoGAPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPS.R importsMe: projectR dependencyCount: 44 Package: cogena Version: 1.24.0 Depends: R (>= 3.6), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, stringr, tibble, tidyr, dplyr, devtools Suggests: knitr, rmarkdown (>= 2.1) License: LGPL-3 MD5sum: 93060ef220dde3bfa8ac930c6654bfd8 NeedsCompilation: no Title: co-expressed gene-set enrichment analysis Description: cogena is a workflow for co-expressed gene-set enrichment analysis. It aims to discovery smaller scale, but highly correlated cellular events that may be of great biological relevance. A novel pipeline for drug discovery and drug repositioning based on the cogena workflow is proposed. Particularly, candidate drugs can be predicted based on the gene expression of disease-related data, or other similar drugs can be identified based on the gene expression of drug-related data. Moreover, the drug mode of action can be disclosed by the associated pathway analysis. In summary, cogena is a flexible workflow for various gene set enrichment analysis for co-expressed genes, with a focus on pathway/GO analysis and drug repositioning. biocViews: Clustering, GeneSetEnrichment, GeneExpression, Visualization, Pathways, KEGG, GO, Microarray, Sequencing, SystemsBiology, DataRepresentation, DataImport Author: Zhilong Jia [aut, cre], Michael Barnes [aut] Maintainer: Zhilong Jia URL: https://github.com/zhilongjia/cogena VignetteBuilder: knitr BugReports: https://github.com/zhilongjia/cogena/issues git_url: https://git.bioconductor.org/packages/cogena git_branch: RELEASE_3_12 git_last_commit: d5339b0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cogena_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cogena_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cogena_1.24.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf, vignettes/cogena/inst/doc/cogena-vignette_html.html vignetteTitles: a workflow of cogena, cogena,, a workflow for gene set enrichment analysis of co-expressed genes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cogena/inst/doc/cogena-vignette_html.R, vignettes/cogena/inst/doc/cogena-vignette_pdf.R dependencyCount: 129 Package: coGPS Version: 1.34.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: a07ef82a83a219f311c710cab97867d8 NeedsCompilation: no Title: cancer outlier Gene Profile Sets Description: Gene Set Enrichment Analysis of P-value based statistics for outlier gene detection in dataset merged from multiple studies biocViews: Microarray, DifferentialExpression Author: Yingying Wei, Michael Ochs Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/coGPS git_branch: RELEASE_3_12 git_last_commit: dcc4e8f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/coGPS_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/coGPS_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/coGPS_1.34.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coGPS/inst/doc/coGPS.R dependencyCount: 2 Package: COHCAP Version: 1.36.0 Depends: WriteXLS, COHCAPanno, RColorBrewer, gplots Imports: Rcpp, RcppArmadillo, BH LinkingTo: Rcpp, BH License: GPL-3 Archs: i386, x64 MD5sum: 156812898360950046a7a6d3335a1096 NeedsCompilation: yes Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: COHCAP (pronounced "co-cap") provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k/EPIC methylation array, targeted BS-Seq, etc.). It provides differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, with visualizaton options. Discussion Group: https://sourceforge.net/p/cohcap/discussion/bioconductor/ biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden , Yate-Ching Yuan , Xiwei Wu Maintainer: Charles Warden SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/COHCAP git_branch: RELEASE_3_12 git_last_commit: 43cc6de git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/COHCAP_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/COHCAP_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/COHCAP_1.36.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COHCAP/inst/doc/COHCAP.R dependencyCount: 14 Package: cola Version: 1.6.0 Depends: R (>= 3.6.0) Imports: grDevices, graphics, grid, stats, utils, ComplexHeatmap (>= 2.5.4), matrixStats, GetoptLong, circlize (>= 0.4.7), GlobalOptions (>= 0.1.0), clue, parallel, RColorBrewer, cluster, skmeans, png, mclust, crayon, methods, xml2, microbenchmark, httr, knitr, markdown, digest, impute, brew, Rcpp (>= 0.11.0), BiocGenerics, eulerr LinkingTo: Rcpp Suggests: genefilter, mvtnorm, testthat (>= 0.3), samr, pamr, kohonen, NMF, WGCNA, Rtsne, umap, clusterProfiler, ReactomePA, DOSE, AnnotationDbi, gplots, hu6800.db, BiocManager, data.tree, dendextend License: MIT + file LICENSE Archs: i386, x64 MD5sum: 5a734c73a68243530f041be82341f8cc NeedsCompilation: yes Title: A Framework for Consensus Partitioning Description: Subgroup classification is a basic task in genomic data analysis, especially for gene expression and DNA methylation data analysis. It can also be used to test the agreement to known clinical annotations, or to test whether there exist significant batch effects. The cola package provides a general framework for subgroup classification by consensus partitioning. It has the following features: 1. It modularizes the consensus partitioning processes that various methods can be easily integrated. 2. It provides rich visualizations for interpreting the results. 3. It allows running multiple methods at the same time and provides functionalities to straightforward compare results. 4. It provides a new method to extract features which are more efficient to separate subgroups. 5. It automatically generates detailed reports for the complete analysis. biocViews: Clustering, GeneExpression, Classification, Software Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/cola, https://jokergoo.github.io/cola_collection/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cola git_branch: RELEASE_3_12 git_last_commit: 2008ea7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cola_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cola_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cola_1.6.0.tgz vignettes: vignettes/cola/inst/doc/cola_quick.html, vignettes/cola/inst/doc/cola.html, vignettes/cola/inst/doc/functional_enrichment.html, vignettes/cola/inst/doc/predict.html, vignettes/cola/inst/doc/work_with_big_datasets.html vignetteTitles: 1. A Quick Start of cola Package, 2. cola: A Framework for Consensus Partitioning, 3. Automatic Functional Enrichment on Signature Genes, 4. Predict Classes for New Samples, 5. Work with Big Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cola/inst/doc/cola_quick.R, vignettes/cola/inst/doc/cola.R, vignettes/cola/inst/doc/functional_enrichment.R, vignettes/cola/inst/doc/predict.R, vignettes/cola/inst/doc/work_with_big_datasets.R dependencyCount: 58 Package: combi Version: 1.2.0 Depends: R (>= 3.5.0) Imports: ggplot2, nleqslv, phyloseq, tensor, stats, limma, Matrix, BB, reshape2, alabama, cobs, Biobase, vegan, grDevices, graphics, methods, SummarizedExperiment Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 8b3fe74615dc33170eff76484b309b51 NeedsCompilation: no Title: Compositional omics model based visual integration Description: Combine quasi-likelihood estimation, compositional regression models and latent variable models for integrative visualization of several omics datasets. Both unconstrained and constrained integration is available, the results are shown as interpretable multiplots. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization, Metabolomics Author: Stijn Hawinkel Maintainer: Joris Meys VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/combi/issues git_url: https://git.bioconductor.org/packages/combi git_branch: RELEASE_3_12 git_last_commit: 9a2a1d2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/combi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/combi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/combi_1.2.0.tgz vignettes: vignettes/combi/inst/doc/combi.html vignetteTitles: Manual for the combi pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/combi/inst/doc/combi.R dependencyCount: 98 Package: coMET Version: 1.22.0 Depends: R (>= 3.6.0), grid, utils, biomaRt, Gviz, psych Imports: colortools, hash,grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, stats, corrplot Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: a4f62cbaf0b8056c49dda5fc97ebd290 NeedsCompilation: no Title: coMET: visualisation of regional epigenome-wide association scan (EWAS) results and DNA co-methylation patterns Description: Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as long as the data can be translated to genomic level and for any species. biocViews: Software, DifferentialMethylation, Visualization, Sequencing, Genetics, FunctionalGenomics, Microarray, MethylationArray, MethylSeq, ChIPSeq, DNASeq, RiboSeq, RNASeq, ExomeSeq, DNAMethylation, GenomeWideAssociation, MotifAnnotation Author: Tiphaine C. Martin [aut,cre], Thomas Hardiman [aut], Idil Yet [aut], Pei-Chien Tsai [aut], Jordana T. Bell [aut] Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coMET git_branch: RELEASE_3_12 git_last_commit: a5dc4f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/coMET_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/coMET_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/coMET_1.22.0.tgz vignettes: vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/coMET/inst/doc/coMET.R dependencyCount: 144 Package: compartmap Version: 1.8.0 Depends: R (>= 3.5.0), minfi, Homo.sapiens, mixOmics Imports: SummarizedExperiment, GenomicRanges, gtools, parallel Suggests: covr, testthat, knitr License: GPL-3 + file LICENSE MD5sum: 0519bc5efa6d391783a6bcafcb43b824 NeedsCompilation: no Title: A/B compartment inference from ATAC-seq and methylation array data Description: Compartmap performs shrunken A/B compartment inference from ATAC-seq and methylation arrays. biocViews: ImmunoOncology, Genetics, Epigenetics, ATACSeq, MethylSeq, MethylationArray Author: Benjamin Johnson [aut, cre], Tim Triche [aut], Kasper Hansen [aut], Jean-Philippe Fortin [aut] Maintainer: Benjamin Johnson URL: https://github.com/biobenkj/compartmap VignetteBuilder: knitr BugReports: https://github.com/biobenkj/compartmap/issues git_url: https://git.bioconductor.org/packages/compartmap git_branch: RELEASE_3_12 git_last_commit: 7f08ce9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/compartmap_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/compartmap_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/compartmap_1.8.0.tgz vignettes: vignettes/compartmap/inst/doc/compartmap_vignette.html vignetteTitles: A/B compartment inference with compartmap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/compartmap/inst/doc/compartmap_vignette.R dependencyCount: 159 Package: COMPASS Version: 1.28.0 Depends: R (>= 3.0.3) Imports: methods, Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils, pdist, magrittr, reshape2, dplyr, tidyr, rlang, BiocStyle, rmarkdown, foreach, coda LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.33.1), flowCore, ncdfFlow, shiny, testthat, devtools, flowWorkspaceData, ggplot2, doMC, progress License: Artistic-2.0 Archs: i386, x64 MD5sum: 5306304e489e790bf4167c78f0945609 NeedsCompilation: yes Title: Combinatorial Polyfunctionality Analysis of Single Cells Description: COMPASS is a statistical framework that enables unbiased analysis of antigen-specific T-cell subsets. COMPASS uses a Bayesian hierarchical framework to model all observed cell-subsets and select the most likely to be antigen-specific while regularizing the small cell counts that often arise in multi-parameter space. The model provides a posterior probability of specificity for each cell subset and each sample, which can be used to profile a subject's immune response to external stimuli such as infection or vaccination. biocViews: ImmunoOncology, FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues git_url: https://git.bioconductor.org/packages/COMPASS git_branch: RELEASE_3_12 git_last_commit: f1557e7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/COMPASS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/COMPASS_1.27.0.zip mac.binary.ver: bin/macosx/contrib/4.0/COMPASS_1.28.0.tgz vignettes: vignettes/COMPASS/inst/doc/SimpleCOMPASS.pdf, vignettes/COMPASS/inst/doc/COMPASS.html vignetteTitles: SimpleCOMPASS, COMPASS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R, vignettes/COMPASS/inst/doc/SimpleCOMPASS.R dependencyCount: 65 Package: compcodeR Version: 1.26.1 Depends: sm Imports: tcltk, knitr (>= 1.2), markdown, ROCR, lattice (>= 0.16), gplots, gtools, gdata, caTools, grid, KernSmooth, MASS, ggplot2, stringr, modeest, edgeR, limma, vioplot, methods, utils, stats, grDevices, graphics Suggests: BiocStyle, EBSeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, NBPSeq (>= 0.3.0), rmarkdown, testthat Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: 9cd628f4a036250e5113b0a20bc0c9ad NeedsCompilation: no Title: RNAseq data simulation, differential expression analysis and performance comparison of differential expression methods Description: This package provides extensive functionality for comparing results obtained by different methods for differential expression analysis of RNAseq data. It also contains functions for simulating count data. Finally, it provides convenient interfaces to several packages for performing the differential expression analysis. These can also be used as templates for setting up and running a user-defined differential analysis workflow within the framework of the package. biocViews: ImmunoOncology, RNASeq, DifferentialExpression Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/compcodeR VignetteBuilder: knitr BugReports: https://github.com/csoneson/compcodeR/issues git_url: https://git.bioconductor.org/packages/compcodeR git_branch: RELEASE_3_12 git_last_commit: b692f6a git_last_commit_date: 2020-11-08 Date/Publication: 2020-11-08 source.ver: src/contrib/compcodeR_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/compcodeR_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.0/compcodeR_1.26.1.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.html vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R dependencyCount: 76 Package: compEpiTools Version: 1.24.0 Depends: R (>= 3.1.1), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 5b8749e058d6e21a8caa82741a933f10 NeedsCompilation: no Title: Tools for computational epigenomics Description: Tools for computational epigenomics developed for the analysis, integration and simultaneous visualization of various (epi)genomics data types across multiple genomic regions in multiple samples. biocViews: GeneExpression, Sequencing, Visualization, GenomeAnnotation, Coverage Author: Mattia Pelizzola [aut], Kamal Kishore [aut, cre] Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/compEpiTools git_branch: RELEASE_3_12 git_last_commit: 9acaf26 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/compEpiTools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/compEpiTools_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/compEpiTools_1.24.0.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compEpiTools/inst/doc/compEpiTools.R dependencyCount: 149 Package: CompGO Version: 1.26.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: 61f2bc3e307a2bb47284929cfe52a5b9 NeedsCompilation: no Title: An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation Description: This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg git_url: https://git.bioconductor.org/packages/CompGO git_branch: RELEASE_3_12 git_last_commit: f5a9442 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CompGO_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CompGO_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CompGO_1.26.0.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R dependencyCount: 126 Package: ComplexHeatmap Version: 2.6.2 Depends: R (>= 3.1.2), methods, grid, graphics, stats, grDevices Imports: circlize (>= 0.4.5), GetoptLong, colorspace, clue, RColorBrewer, GlobalOptions (>= 0.1.0), parallel, png, Cairo, digest, S4Vectors (>= 0.26.1), IRanges, matrixStats Suggests: testthat (>= 1.0.0), knitr, markdown, dendsort, jpeg, tiff, fastcluster, dendextend (>= 1.0.1), grImport, grImport2, glue, GenomicRanges, gridtext, pheatmap (>= 1.0.12), shiny License: MIT + file LICENSE MD5sum: e6632656411e8c085cb1dfbc4b85ed9c NeedsCompilation: no Title: Make Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential patterns. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports various annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap, https://jokergoo.github.io/ComplexHeatmap-reference/book/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ComplexHeatmap git_branch: RELEASE_3_12 git_last_commit: 0383bad git_last_commit_date: 2020-11-11 Date/Publication: 2020-11-12 source.ver: src/contrib/ComplexHeatmap_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/ComplexHeatmap_2.6.2.zip mac.binary.ver: bin/macosx/contrib/4.0/ComplexHeatmap_2.6.2.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/complex_heatmap.html, vignettes/ComplexHeatmap/inst/doc/interactive.html, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.html vignetteTitles: complex_heatmap.html, Interactive ComplexHeatmap, Most probably asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ComplexHeatmap/inst/doc/interactive.R, vignettes/ComplexHeatmap/inst/doc/most_probably_asked_questions.R dependsOnMe: AMARETTO, EnrichedHeatmap, recoup, countToFPKM importsMe: artMS, BiocOncoTK, blacksheepr, CATALYST, celda, CeTF, COCOA, cola, DEComplexDisease, DEGreport, DEP, diffcyt, ELMER, fCCAC, GeneTonic, gmoviz, iSEE, LineagePulse, MesKit, MOMA, muscat, MWASTools, PathoStat, PeacoQC, pipeComp, POMA, profileplyr, SEtools, simplifyEnrichment, singleCellTK, TBSignatureProfiler, Xeva, YAPSA, TCGAWorkflow, armada, conos, MAFDash, pkgndep, rKOMICS, RVA, sigQC, tidyHeatmap, wilson suggestsMe: ALPS, bambu, BrainSABER, clustifyr, CNVRanger, dittoSeq, EnrichmentBrowser, gtrellis, HilbertCurve, msImpute, projectR, TCGAbiolinks, TCGAutils, TimeSeriesExperiment, weitrix, NanoporeRNASeq, circlize, eclust, i2dash, MOSS, multipanelfigure dependencyCount: 25 Package: CONFESS Version: 1.18.0 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,data.table(>= 1.9.7),ecp,EBImage,flexmix,flowCore,flowClust,flowMeans,flowMerge,flowPeaks,foreach,ggplot2,grid,limma,MASS,moments,outliers,parallel,plotrix,raster,readbitmap,reshape2,SamSPECTRAL,waveslim,wavethresh,zoo Suggests: BiocStyle, knitr, rmarkdown, CONFESSdata License: GPL-2 MD5sum: 3202c5815550732c0f5e574c790367bd NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: ImmunoOncology, GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CONFESS git_branch: RELEASE_3_12 git_last_commit: a8b55b3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CONFESS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CONFESS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CONFESS_1.18.0.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf, vignettes/CONFESS/inst/doc/vignette.html vignetteTitles: CONFESS, CONFESS Walkthrough hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R dependencyCount: 151 Package: consensus Version: 1.8.0 Depends: R (>= 3.5), RColorBrewer Imports: matrixStats, gplots, grDevices, methods, graphics, stats, utils Suggests: knitr, RUnit, rmarkdown, BiocGenerics License: BSD_3_clause + file LICENSE MD5sum: 85549a4d69e9390789f42cf0db13c54a NeedsCompilation: no Title: Cross-platform consensus analysis of genomic measurements via interlaboratory testing method Description: An implementation of the American Society for Testing and Materials (ASTM) Standard E691 for interlaboratory testing procedures, designed for cross-platform genomic measurements. Given three (3) or more genomic platforms or laboratory protocols, this package provides interlaboratory testing procedures giving per-locus comparisons for sensitivity and precision between platforms. biocViews: QualityControl, Regression, DataRepresentation, GeneExpression, Microarray, RNASeq Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensus git_branch: RELEASE_3_12 git_last_commit: 2e6fb99 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/consensus_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/consensus_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/consensus_1.8.0.tgz vignettes: vignettes/consensus/inst/doc/consensus.pdf vignetteTitles: Fitting and visualising row-linear models with \texttt{consensus} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/consensus/inst/doc/consensus.R dependencyCount: 12 Package: ConsensusClusterPlus Version: 1.54.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: f7f5266d34b8504cd0d7796df9fae1bd NeedsCompilation: no Title: ConsensusClusterPlus Description: algorithm for determining cluster count and membership by stability evidence in unsupervised analysis biocViews: Software, Clustering Author: Matt Wilkerson , Peter Waltman Maintainer: Matt Wilkerson git_url: https://git.bioconductor.org/packages/ConsensusClusterPlus git_branch: RELEASE_3_12 git_last_commit: 4826e07 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ConsensusClusterPlus_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ConsensusClusterPlus_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ConsensusClusterPlus_1.54.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.R importsMe: CancerSubtypes, CATALYST, ChromSCape, DEGreport, FlowSOM, DeSousa2013, iSubGen, neatmaps, scRNAtools suggestsMe: TCGAbiolinks dependencyCount: 10 Package: consensusDE Version: 1.8.0 Depends: R (>= 3.5), BiocGenerics Imports: airway, AnnotationDbi, BiocParallel, Biobase, Biostrings, data.table, dendextend, DESeq2 (>= 1.20.0), EDASeq, ensembldb, edgeR, EnsDb.Hsapiens.v86, GenomicAlignments, GenomicFeatures, limma, org.Hs.eg.db, pcaMethods, RColorBrewer, Rsamtools, RUVSeq, S4Vectors, stats, SummarizedExperiment, TxDb.Dmelanogaster.UCSC.dm3.ensGene, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: b2bf2cac4486d1f6b457b9e6af7ba8bf NeedsCompilation: no Title: RNA-seq analysis using multiple algorithms Description: This package allows users to perform DE analysis using multiple algorithms. It seeks consensus from multiple methods. Currently it supports "Voom", "EdgeR" and "DESeq". It uses RUV-seq (optional) to remove unwanted sources of variation. biocViews: Transcriptomics, MultipleComparison, Clustering, Sequencing, Software Author: Ashley J. Waardenberg [aut, cre], Martha M. Cooper [ctb] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/consensusDE git_branch: RELEASE_3_12 git_last_commit: 0ade359 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/consensusDE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/consensusDE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/consensusDE_1.8.0.tgz vignettes: vignettes/consensusDE/inst/doc/consensusDE.html vignetteTitles: consensusDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusDE/inst/doc/consensusDE.R dependencyCount: 139 Package: consensusOV Version: 1.12.0 Depends: R (>= 3.6) Imports: Biobase, GSVA, gdata, genefu, limma, matrixStats, randomForest, stats, utils, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown License: Artistic-2.0 MD5sum: 7e5696ffc486ab79c08252a5465260de NeedsCompilation: no Title: Gene expression-based subtype classification for high-grade serous ovarian cancer Description: This package implements four major subtype classifiers for high-grade serous (HGS) ovarian cancer as described by Helland et al. (PLoS One, 2011), Bentink et al. (PLoS One, 2012), Verhaak et al. (J Clin Invest, 2013), and Konecny et al. (J Natl Cancer Inst, 2014). In addition, the package implements a consensus classifier, which consolidates and improves on the robustness of the proposed subtype classifiers, thereby providing reliable stratification of patients with HGS ovarian tumors of clearly defined subtype. biocViews: Classification, Clustering, DifferentialExpression, GeneExpression, Microarray, Transcriptomics Author: Gregory M Chen, Lavanya Kannan, Ludwig Geistlinger, Victor Kofia, Levi Waldron, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/consensusOV VignetteBuilder: knitr BugReports: https://github.com/bhklab/consensusOV/issues git_url: https://git.bioconductor.org/packages/consensusOV git_branch: RELEASE_3_12 git_last_commit: 4f06161 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/consensusOV_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/consensusOV_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/consensusOV_1.12.0.tgz vignettes: vignettes/consensusOV/inst/doc/consensusOV.html vignetteTitles: Molecular subtyping for ovarian cancer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusOV/inst/doc/consensusOV.R dependencyCount: 119 Package: consensusSeekeR Version: 1.18.0 Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors, methods Suggests: BiocStyle, ggplot2, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 5707bc3030c21b26b29afbdb64ac72f0 NeedsCompilation: no Title: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Description: This package compares genomic positions and genomic ranges from multiple experiments to extract common regions. The size of the analyzed region is adjustable as well as the number of experiences in which a feature must be present in a potential region to tag this region as a consensus region. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, Transcription, PeakDetection, Sequencing, Coverage Author: Astrid Deschenes [cre, aut], Fabien Claude Lamaze [ctb], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues git_url: https://git.bioconductor.org/packages/consensusSeekeR git_branch: RELEASE_3_12 git_last_commit: 2ef95ae git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/consensusSeekeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/consensusSeekeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/consensusSeekeR_1.18.0.tgz vignettes: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html vignetteTitles: Detection of consensus regions inside a group of experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R importsMe: RJMCMCNucleosomes dependencyCount: 44 Package: contiBAIT Version: 1.18.0 Depends: BH (>= 1.51.0-3), Rsamtools (>= 1.21) Imports: data.table, grDevices, clue, cluster, gplots, BiocGenerics (>= 0.31.6), S4Vectors, IRanges, GenomicRanges, Rcpp, TSP, GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy, colorspace, reshape2, ggplot2, methods, exomeCopy, GenomicAlignments, diagram LinkingTo: Rcpp, BH Suggests: BiocStyle License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: cac5115674835751e5929d3b2e89f925 NeedsCompilation: yes Title: Improves Early Build Genome Assemblies using Strand-Seq Data Description: Using strand inheritance data from multiple single cells from the organism whose genome is to be assembled, contiBAIT can cluster unbridged contigs together into putative chromosomes, and order the contigs within those chromosomes. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, WholeGenome, Genetics, GenomeAssembly Author: Kieran O'Neill, Mark Hills, Mike Gottlieb Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/contiBAIT git_branch: RELEASE_3_12 git_last_commit: d50ef02 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/contiBAIT_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/contiBAIT_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/contiBAIT_1.18.0.tgz vignettes: vignettes/contiBAIT/inst/doc/contiBAIT.pdf vignetteTitles: flowBi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/contiBAIT/inst/doc/contiBAIT.R dependencyCount: 123 Package: conumee Version: 1.24.0 Depends: R (>= 3.0), minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, RCurl License: GPL (>= 2) MD5sum: 1dc6e685a66dd04773ff119347d1bbfb NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina DNA methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k or EPIC methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/conumee git_branch: RELEASE_3_12 git_last_commit: 0be6400 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/conumee_1.24.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/conumee_1.24.0.tgz vignettes: vignettes/conumee/inst/doc/conumee.html vignetteTitles: conumee hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R dependencyCount: 135 Package: convert Version: 1.66.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: 6be7d06026b3e31a741091a667716098 NeedsCompilation: no Title: Convert Microarray Data Objects Description: Define coerce methods for microarray data objects. biocViews: Infrastructure, Microarray, TwoChannel Author: Gordon Smyth , James Wettenhall , Yee Hwa (Jean Yang) , Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html git_url: https://git.bioconductor.org/packages/convert git_branch: RELEASE_3_12 git_last_commit: 73a5da9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/convert_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/convert_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/convert_1.66.0.tgz vignettes: vignettes/convert/inst/doc/convert.pdf vignetteTitles: Converting Between Microarray Data Classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maigesPack, TurboNorm suggestsMe: BiocCaseStudies, dyebias, OLIN, dyebiasexamples, maGUI dependencyCount: 10 Package: copa Version: 1.58.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: f50e0168a1484e094b2c21e26081e31d NeedsCompilation: yes Title: Functions to perform cancer outlier profile analysis. Description: COPA is a method to find genes that undergo recurrent fusion in a given cancer type by finding pairs of genes that have mutually exclusive outlier profiles. biocViews: OneChannel, TwoChannel, DifferentialExpression, Visualization Author: James W. MacDonald Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/copa git_branch: RELEASE_3_12 git_last_commit: 0c12c59 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/copa_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/copa_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/copa_1.58.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copa/inst/doc/copa.R dependencyCount: 7 Package: copynumber Version: 1.30.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 443ffeb5dbc715bde17b7ec7267a76db NeedsCompilation: no Title: Segmentation of single- and multi-track copy number data by penalized least squares regression. Description: Penalized least squares regression is applied to fit piecewise constant curves to copy number data to locate genomic regions of constant copy number. Procedures are available for individual segmentation of each sample, joint segmentation of several samples and joint segmentation of the two data tracks from SNP-arrays. Several plotting functions are available for visualization of the data and the segmentation results. biocViews: aCGH, SNP, CopyNumberVariation, Genetics, Visualization Author: Gro Nilsen, Knut Liestoel and Ole Christian Lingjaerde. Maintainer: Gro Nilsen git_url: https://git.bioconductor.org/packages/copynumber git_branch: RELEASE_3_12 git_last_commit: 0671fc2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/copynumber_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/copynumber_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/copynumber_1.30.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/copynumber/inst/doc/copynumber.R importsMe: sequenza suggestsMe: PureCN, sigminer dependencyCount: 17 Package: CopyNumberPlots Version: 1.6.0 Depends: R (>= 3.6), karyoploteR Imports: regioneR, IRanges, Rsamtools, SummarizedExperiment, VariantAnnotation, methods, stats, GenomeInfoDb, GenomicRanges, cn.mops, rhdf5, utils Suggests: BiocStyle, knitr, panelcn.mops, BSgenome.Hsapiens.UCSC.hg19.masked, DNAcopy, testthat License: Artistic-2.0 MD5sum: d673f63d95efcc978f3ae47a1194f4be NeedsCompilation: no Title: Create Copy-Number Plots using karyoploteR functionality Description: CopyNumberPlots have a set of functions extending karyoploteRs functionality to create beautiful, customizable and flexible plots of copy-number related data. biocViews: Visualization, CopyNumberVariation, Coverage, OneChannel, DataImport, Sequencing, DNASeq Author: Bernat Gel and Miriam Magallon Maintainer: Bernat Gel URL: https://github.com/bernatgel/CopyNumberPlots VignetteBuilder: knitr BugReports: https://github.com/bernatgel/CopyNumberPlots/issues git_url: https://git.bioconductor.org/packages/CopyNumberPlots git_branch: RELEASE_3_12 git_last_commit: bb544ff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CopyNumberPlots_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CopyNumberPlots_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CopyNumberPlots_1.6.0.tgz vignettes: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.html vignetteTitles: CopyNumberPlots vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumberPlots/inst/doc/CopyNumberPlots.R importsMe: CNVfilteR dependencyCount: 146 Package: CopywriteR Version: 2.22.0 Depends: R(>= 3.2), BiocParallel Imports: matrixStats, gtools, data.table, S4Vectors, chipseq, IRanges, Rsamtools, DNAcopy, GenomicAlignments, GenomicRanges, CopyhelpeR, GenomeInfoDb, futile.logger Suggests: BiocStyle, SCLCBam, snow License: GPL-2 MD5sum: ecbfb87fd273d203038417350e10df6a NeedsCompilation: no Title: Copy number information from targeted sequencing using off-target reads Description: CopywriteR extracts DNA copy number information from targeted sequencing by utiizing off-target reads. It allows for extracting uniformly distributed copy number information, can be used without reference, and can be applied to sequencing data obtained from various techniques including chromatin immunoprecipitation and target enrichment on small gene panels. Thereby, CopywriteR constitutes a widely applicable alternative to available copy number detection tools. biocViews: ImmunoOncology, TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Oscar Krijgsman URL: https://github.com/PeeperLab/CopywriteR git_url: https://git.bioconductor.org/packages/CopywriteR git_branch: RELEASE_3_12 git_last_commit: fadf9a9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CopywriteR_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CopywriteR_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CopywriteR_2.22.0.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopywriteR/inst/doc/CopywriteR.R dependencyCount: 49 Package: coRdon Version: 1.8.0 Depends: R (>= 3.5) Imports: methods, stats, utils, Biostrings, Biobase, dplyr, stringr, purrr, ggplot2, data.table Suggests: BiocStyle, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: eff2b37f2bf281933e27fb4c3a38e86d NeedsCompilation: no Title: Codon Usage Analysis and Prediction of Gene Expressivity Description: Tool for analysis of codon usage in various unannotated or KEGG/COG annotated DNA sequences. Calculates different measures of CU bias and CU-based predictors of gene expressivity, and performs gene set enrichment analysis for annotated sequences. Implements several methods for visualization of CU and enrichment analysis results. biocViews: Software, Metagenomics, GeneExpression, GeneSetEnrichment, GenePrediction, Visualization, KEGG, Pathways, Genetics CellBiology, BiomedicalInformatics, ImmunoOncology Author: Anamaria Elek [cre, aut], Maja Kuzman [aut], Kristian Vlahovicek [aut] Maintainer: Anamaria Elek URL: https://github.com/BioinfoHR/coRdon VignetteBuilder: knitr BugReports: https://github.com/BioinfoHR/coRdon/issues git_url: https://git.bioconductor.org/packages/coRdon git_branch: RELEASE_3_12 git_last_commit: 4209a51 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/coRdon_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/coRdon_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/coRdon_1.8.0.tgz vignettes: vignettes/coRdon/inst/doc/coRdon.html vignetteTitles: coRdon hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRdon/inst/doc/coRdon.R importsMe: vhcub dependencyCount: 55 Package: CoRegFlux Version: 1.6.0 Depends: R (>= 3.6) Imports: CoRegNet, sybil Suggests: glpkAPI, testthat, knitr, rmarkdown, digest, R.cache, ggplot2, plyr, igraph, methods, latex2exp, rBayesianOptimization License: GPL-3 MD5sum: 4a2c177397aa453856b64e0eb010ae20 NeedsCompilation: no Title: CoRegFlux Description: CoRegFlux aims at providing tools to integrate reverse engineered gene regulatory networks and gene-expression into metabolic models to improve prediction of phenotypes, both for metabolic engineering, through transcription factor or gene (TF) knock-out or overexpression in various conditions as well as to improve our understanding of the interactions and cell inner-working. biocViews: GeneRegulation,Network,SystemsBiology,GeneExpression,Transcription,GenePrediction Author: Pauline Trébulle, Daniel Trejo-Banos, Mohamed Elati Maintainer: Pauline Trébulle and Mohamed Elati SystemRequirements: GLPK (>= 4.42) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoRegFlux git_branch: RELEASE_3_12 git_last_commit: 6e77093 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CoRegFlux_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CoRegFlux_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CoRegFlux_1.6.0.tgz vignettes: vignettes/CoRegFlux/inst/doc/coregflux.html vignetteTitles: CoRegFlux hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegFlux/inst/doc/coregflux.R dependencyCount: 42 Package: CoRegNet Version: 1.28.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: 6c94bd60e371c0e7f3493b4c26feb974 NeedsCompilation: yes Title: CoRegNet : reconstruction and integrated analysis of co-regulatory networks Description: This package provides methods to identify active transcriptional programs. Methods and classes are provided to import or infer large scale co-regulatory network from transcriptomic data. The specificity of the encoded networks is to model Transcription Factor cooperation. External regulation evidences (TFBS, ChIP,...) can be integrated to assess the inferred network and refine it if necessary. Transcriptional activity of the regulators in the network can be estimated using an measure of their influence in a given sample. Finally, an interactive UI can be used to navigate through the network of cooperative regulators and to visualize their activity in a specific sample or subgroup sample. The proposed visualization tool can be used to integrate gene expression, transcriptional activity, copy number status, sample classification and a transcriptional network including co-regulation information. biocViews: NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork,SystemsBiology, Network, Visualization, Transcription Author: Remy Nicolle, Thibault Venzac and Mohamed Elati Maintainer: Remy Nicolle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoRegNet git_branch: RELEASE_3_12 git_last_commit: 9af6fb4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CoRegNet_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CoRegNet_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CoRegNet_1.28.0.tgz vignettes: vignettes/CoRegNet/inst/doc/CoRegNet.html vignetteTitles: Custom Print Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R importsMe: CoRegFlux dependencyCount: 40 Package: CoreGx Version: 1.2.0 Depends: R (>= 4.0) Imports: Biobase, S4Vectors, SummarizedExperiment, piano, BiocParallel, BiocGenerics, methods, stats, utils, graphics, grDevices, lsa, data.table, crayon Suggests: pander, BiocStyle, rmarkdown, knitr, formatR, testthat License: GPL-3 MD5sum: 817bde5dc86257b89310a1c0aafd51bb NeedsCompilation: no Title: Classes and Functions to Serve as the Basis for Other 'Gx' Packages Description: A collection of functions and classes which serve as the foundation for our lab's suite of R packages, such as 'PharmacoGx' and 'RadioGx'. This package was created to abstract shared functionality from other lab package releases to increase ease of maintainability and reduce code repetition in current and future 'Gx' suite programs. Major features include a 'CoreSet' class, from which 'RadioSet' and 'PharmacoSet' are derived, along with get and set methods for each respective slot. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, as well as: Smirnov, P., Safikhani, Z., El-Hachem, N., Wang, D., She, A., Olsen, C., Freeman, M., Selby, H., Gendoo, D., Grossman, P., Beck, A., Aerts, H., Lupien, M., Goldenberg, A. (2015) . Manem, V., Labie, M., Smirnov, P., Kofia, V., Freeman, M., Koritzinksy, M., Abazeed, M., Haibe-Kains, B., Bratman, S. (2018) . biocViews: Software, Pharmacogenomics, Classification, Survival Author: Petr Smirnov [aut], Ian Smith [aut], Christopher Eeles [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CoreGx git_branch: RELEASE_3_12 git_last_commit: e2021c4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CoreGx_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CoreGx_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CoreGx_1.2.0.tgz vignettes: vignettes/CoreGx/inst/doc/coreGx.html, vignettes/CoreGx/inst/doc/LongTable.html vignetteTitles: CoreGx: Class and Function Abstractions, The LongTable Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoreGx/inst/doc/coreGx.R, vignettes/CoreGx/inst/doc/LongTable.R dependsOnMe: PharmacoGx, RadioGx, ToxicoGx dependencyCount: 108 Package: Cormotif Version: 1.36.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: 8ab4ba957019b43348468fa7611e7968 NeedsCompilation: no Title: Correlation Motif Fit Description: It fits correlation motif model to multiple studies to detect study specific differential expression patterns. biocViews: Microarray, DifferentialExpression Author: Hongkai Ji, Yingying Wei Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/Cormotif git_branch: RELEASE_3_12 git_last_commit: 24907d1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Cormotif_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Cormotif_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Cormotif_1.36.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cormotif/inst/doc/CormotifVignette.R dependencyCount: 14 Package: corral Version: 1.0.0 Imports: ggplot2, ggthemes, grDevices, gridExtra, irlba, Matrix, methods, MultiAssayExperiment, pals, SingleCellExperiment, SummarizedExperiment, transport Suggests: ade4, BiocStyle, CellBench, DuoClustering2018, knitr, testthat License: GPL-2 MD5sum: fdc8ae8948bd134e0cbc04f79caa8c40 NeedsCompilation: no Title: Correspondence Analysis for Single Cell Data Description: Correspondence analysis (CA) is a matrix factorization method, and is similar to principal components analysis (PCA). Whereas PCA is designed for application to continuous, approximately normally distributed data, CA is appropriate for non-negative, count-based data that are in the same additive scale. The corral package implements CA for dimensionality reduction of a single matrix of single-cell data, as well as a multi-table adaptation of CA that leverages data-optimized scaling to align data generated from different sequencing platforms by projecting into a shared latent space. corral utilizes sparse matrices and a fast implementation of SVD, and can be called directly on Bioconductor objects (e.g., SingleCellExperiment) for easy pipeline integration. The package also includes the option to apply CA-style processing to continuous data (e.g., proteomic TOF intensities) with the Hellinger distance adaptation of CA. biocViews: BatchEffect, DimensionReduction, Preprocessing, PrincipalComponent, Sequencing, SingleCell, Software, Visualization Author: Lauren Hsu [aut, cre] (), Aedin Culhane [aut] () Maintainer: Lauren Hsu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/corral git_branch: RELEASE_3_12 git_last_commit: 806b978 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/corral_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/corral_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/corral_1.0.0.tgz vignettes: vignettes/corral/inst/doc/corral_dimred.html, vignettes/corral/inst/doc/corralm_alignment.html vignetteTitles: dim reduction with corral, alignment with corralm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/corral/inst/doc/corral_dimred.R, vignettes/corral/inst/doc/corralm_alignment.R dependencyCount: 77 Package: CORREP Version: 1.56.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 23fdba88f3c8c6655748ff881e9a1235 NeedsCompilation: no Title: Multivariate Correlation Estimator and Statistical Inference Procedures. Description: Multivariate correlation estimation and statistical inference. See package vignette. biocViews: Microarray, Clustering, GraphAndNetwork Author: Dongxiao Zhu and Youjuan Li Maintainer: Dongxiao Zhu git_url: https://git.bioconductor.org/packages/CORREP git_branch: RELEASE_3_12 git_last_commit: dea3834 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CORREP_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CORREP_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CORREP_1.56.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CORREP/inst/doc/CORREP.R dependencyCount: 9 Package: coseq Version: 1.14.0 Depends: R (>= 3.4.0), SummarizedExperiment, S4Vectors Imports: edgeR, DESeq2, capushe, Rmixmod, e1071, BiocParallel, ggplot2 (>= 2.1.0), scales, HTSFilter, corrplot, HTSCluster (>= 2.0.8), grDevices, graphics, stats, methods, compositions, mvtnorm Suggests: Biobase, knitr, rmarkdown, testthat License: GPL (>=3) MD5sum: a977cf445a6eb040d1e1c6c1280676bc NeedsCompilation: no Title: Co-Expression Analysis of Sequencing Data Description: Co-expression analysis for expression profiles arising from high-throughput sequencing data. Feature (e.g., gene) profiles are clustered using adapted transformations and mixture models or a K-means algorithm, and model selection criteria (to choose an appropriate number of clusters) are provided. biocViews: GeneExpression, RNASeq, Sequencing, Software, ImmunoOncology Author: Andrea Rau, Cathy Maugis-Rabusseau, Antoine Godichon-Baggioni Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/coseq git_branch: RELEASE_3_12 git_last_commit: 25e5aa6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/coseq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/coseq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/coseq_1.14.0.tgz vignettes: vignettes/coseq/inst/doc/coseq.html vignetteTitles: coseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coseq/inst/doc/coseq.R dependencyCount: 108 Package: cosmiq Version: 1.24.0 Depends: R (>= 3.6), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 35b5d06e20ecfb644a829c45b3f49144 NeedsCompilation: yes Title: cosmiq - COmbining Single Masses Into Quantities Description: cosmiq is a tool for the preprocessing of liquid- or gas - chromatography mass spectrometry (LCMS/GCMS) data with a focus on metabolomics or lipidomics applications. To improve the detection of low abundant signals, cosmiq generates master maps of the mZ/RT space from all acquired runs before a peak detection algorithm is applied. The result is a more robust identification and quantification of low-intensity MS signals compared to conventional approaches where peak picking is performed in each LCMS/GCMS file separately. The cosmiq package builds on the xcmsSet object structure and can be therefore integrated well with the package xcms as an alternative preprocessing step. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: David Fischer [aut, cre], Christian Panse [aut] (), Endre Laczko [ctb] Maintainer: David Fischer URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html git_url: https://git.bioconductor.org/packages/cosmiq git_branch: RELEASE_3_12 git_last_commit: e04ae98 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cosmiq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cosmiq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cosmiq_1.24.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cosmiq/inst/doc/cosmiq.R dependencyCount: 94 Package: COSNet Version: 1.24.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: e872e81838bcf6ef325db4b01e8d2056 NeedsCompilation: yes Title: Cost Sensitive Network for node label prediction on graphs with highly unbalanced labelings Description: Package that implements the COSNet classification algorithm. The algorithm predicts node labels in partially labeled graphs where few positives are available for the class being predicted. biocViews: GraphAndNetwork, Classification,Network, NeuralNetwork Author: Marco Frasca and Giorgio Valentini -- Universita' degli Studi di Milano Maintainer: Marco Frasca URL: https://github.com/m1frasca/COSNet_GitHub git_url: https://git.bioconductor.org/packages/COSNet git_branch: RELEASE_3_12 git_last_commit: 29b7959 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/COSNet_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/COSNet_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/COSNet_1.24.0.tgz vignettes: vignettes/COSNet/inst/doc/COSNet_v.pdf vignetteTitles: An R Package for Predicting Binary Labels in Partially-Labeled Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COSNet/inst/doc/COSNet_v.R dependencyCount: 0 Package: CountClust Version: 1.18.0 Depends: R (>= 3.4), ggplot2 (>= 2.1.0) Imports: SQUAREM, slam, maptpx, plyr(>= 1.7.1), cowplot, gtools, flexmix, picante, limma, parallel, reshape2, stats, utils, graphics, grDevices Suggests: knitr, kableExtra, BiocStyle, Biobase, roxygen2, RColorBrewer, devtools, xtable License: GPL (>= 2) MD5sum: b6d9395d275a577271fec0bd5873ec22 NeedsCompilation: no Title: Clustering and Visualizing RNA-Seq Expression Data using Grade of Membership Models Description: Fits grade of membership models (GoM, also known as admixture models) to cluster RNA-seq gene expression count data, identifies characteristic genes driving cluster memberships, and provides a visual summary of the cluster memberships. biocViews: ImmunoOncology, RNASeq, GeneExpression, Clustering, Sequencing, StatisticalMethod, Software, Visualization Author: Kushal Dey [aut, cre], Joyce Hsiao [aut], Matthew Stephens [aut] Maintainer: Kushal Dey URL: https://github.com/kkdey/CountClust VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CountClust git_branch: RELEASE_3_12 git_last_commit: 5173174 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CountClust_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CountClust_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CountClust_1.18.0.tgz vignettes: vignettes/CountClust/inst/doc/count-clust.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CountClust/inst/doc/count-clust.R dependencyCount: 60 Package: countsimQC Version: 1.8.1 Depends: R (>= 3.5) Imports: rmarkdown (>= 2.5), edgeR, DESeq2 (>= 1.16.0), dplyr, tidyr, ggplot2, grDevices, tools, SummarizedExperiment, genefilter, DT, GenomeInfoDbData, caTools, randtests, stats, utils, methods Suggests: knitr, testthat License: GPL (>=2) MD5sum: 1b4d38298738db6813a8f834303de67b NeedsCompilation: no Title: Compare Characteristic Features of Count Data Sets Description: countsimQC provides functionality to create a comprehensive report comparing a broad range of characteristics across a collection of count matrices. One important use case is the comparison of one or more synthetic count matrices to a real count matrix, possibly the one underlying the simulations. However, any collection of count matrices can be compared. biocViews: Microbiome, RNASeq, SingleCell, ExperimentalDesign, QualityControl, ReportWriting, Visualization, ImmunoOncology Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/countsimQC VignetteBuilder: knitr BugReports: https://github.com/csoneson/countsimQC/issues git_url: https://git.bioconductor.org/packages/countsimQC git_branch: RELEASE_3_12 git_last_commit: 9ad1241 git_last_commit_date: 2021-02-02 Date/Publication: 2021-02-03 source.ver: src/contrib/countsimQC_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/countsimQC_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.0/countsimQC_1.8.1.tgz vignettes: vignettes/countsimQC/inst/doc/countsimQC.html vignetteTitles: countsimQC User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/countsimQC/inst/doc/countsimQC.R suggestsMe: muscat dependencyCount: 118 Package: covEB Version: 1.16.0 Depends: R (>= 3.3), mvtnorm, igraph, gsl, Biobase, stats, LaplacesDemon, Matrix Suggests: curatedBladderData License: GPL-3 MD5sum: b7c9bc7f3fcd6e9faff1e55492ca9800 NeedsCompilation: no Title: Empirical Bayes estimate of block diagonal covariance matrices Description: Using bayesian methods to estimate correlation matrices assuming that they can be written and estimated as block diagonal matrices. These block diagonal matrices are determined using shrinkage parameters that values below this parameter to zero. biocViews: ImmunoOncology, Bayesian, Microarray, RNASeq, Preprocessing, Software, GeneExpression, StatisticalMethod Author: C. Pacini Maintainer: C. Pacini git_url: https://git.bioconductor.org/packages/covEB git_branch: RELEASE_3_12 git_last_commit: 0468257 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/covEB_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/covEB_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/covEB_1.16.0.tgz vignettes: vignettes/covEB/inst/doc/covEB.pdf vignetteTitles: covEB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covEB/inst/doc/covEB.R dependencyCount: 17 Package: CoverageView Version: 1.28.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: c8ce0d245b34c98d8256e48437d80151 NeedsCompilation: no Title: Coverage visualization package for R Description: This package provides a framework for the visualization of genome coverage profiles. It can be used for ChIP-seq experiments, but it can be also used for genome-wide nucleosome positioning experiments or other experiment types where it is important to have a framework in order to inspect how the coverage distributed across the genome biocViews: ImmunoOncology, Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy git_url: https://git.bioconductor.org/packages/CoverageView git_branch: RELEASE_3_12 git_last_commit: e1e05ed git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CoverageView_1.28.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/CoverageView_1.28.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoverageView/inst/doc/CoverageView.R dependencyCount: 40 Package: covRNA Version: 1.16.0 Depends: ade4, Biobase Imports: parallel, genefilter, grDevices, stats, graphics Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: f47fac640956efe0098aa8239c28b25f NeedsCompilation: no Title: Multivariate Analysis of Transcriptomic Data Description: This package provides the analysis methods fourthcorner and RLQ analysis for large-scale transcriptomic data. biocViews: GeneExpression, Transcription Author: Lara Urban Maintainer: Lara Urban VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/covRNA git_branch: RELEASE_3_12 git_last_commit: 39b00d1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/covRNA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/covRNA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/covRNA_1.16.0.tgz vignettes: vignettes/covRNA/inst/doc/covRNA.html vignetteTitles: An Introduction to covRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/covRNA/inst/doc/covRNA.R dependencyCount: 54 Package: cpvSNP Version: 1.22.0 Depends: R (>= 2.10), GenomicFeatures, GSEABase (>= 1.24.0) Imports: methods, corpcor, BiocParallel, ggplot2, plyr Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 4f4f59d1fd5529bdfcdd6a7a49451446 NeedsCompilation: no Title: Gene set analysis methods for SNP association p-values that lie in genes in given gene sets Description: Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results. biocViews: Genetics, StatisticalMethod, Pathways, GeneSetEnrichment, GenomicVariation Author: Caitlin McHugh, Jessica Larson, and Jason Hackney Maintainer: Caitlin McHugh git_url: https://git.bioconductor.org/packages/cpvSNP git_branch: RELEASE_3_12 git_last_commit: 61798d0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cpvSNP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cpvSNP_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cpvSNP_1.22.0.tgz vignettes: vignettes/cpvSNP/inst/doc/cpvSNP.pdf vignetteTitles: Running gene set analyses with the "cpvSNP" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cpvSNP/inst/doc/cpvSNP.R dependencyCount: 109 Package: cqn Version: 1.36.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: 3bd176b78ba8d05f1a0bcdf93336be39 NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: ImmunoOncology, RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen git_url: https://git.bioconductor.org/packages/cqn git_branch: RELEASE_3_12 git_last_commit: 8fe0753 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cqn_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cqn_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cqn_1.36.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cqn/inst/doc/cqn.R dependsOnMe: KnowSeq importsMe: tweeDEseq dependencyCount: 19 Package: CRImage Version: 1.38.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 16c32d281907b55243b0752ced3b3967 NeedsCompilation: no Title: CRImage a package to classify cells and calculate tumour cellularity Description: CRImage provides functionality to process and analyze images, in particular to classify cells in biological images. Furthermore, in the context of tumor images, it provides functionality to calculate tumour cellularity. biocViews: CellBiology, Classification Author: Henrik Failmezger , Yinyin Yuan , Oscar Rueda , Florian Markowetz Maintainer: Henrik Failmezger , Yinyin Yuan git_url: https://git.bioconductor.org/packages/CRImage git_branch: RELEASE_3_12 git_last_commit: a469669 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CRImage_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CRImage_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CRImage_1.38.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRImage/inst/doc/CRImage.R dependencyCount: 42 Package: CRISPRseek Version: 1.30.1 Depends: R (>= 3.0.1), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, BiocParallel, hash, methods,reticulate,rhdf5 Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 8a95af2d0100c4340756953cef13bd30 NeedsCompilation: no Title: Design of target-specific guide RNAs in CRISPR-Cas9, genome-editing systems Description: The package includes functions to find potential guide RNAs for the CRISPR editing system including Base Editors and the Prime Editor for input target sequences, optionally filter guide RNAs without restriction enzyme cut site, or without paired guide RNAs, genome-wide search for off-targets, score, rank, fetch flank sequence and indicate whether the target and off-targets are located in exon region or not. Potential guide RNAs are annotated with total score of the top5 and topN off-targets, detailed topN mismatch sites, restriction enzyme cut sites, and paired guide RNAs. The package also output indels and their frequencies for Cas9 targeted sites. biocViews: ImmunoOncology, GeneRegulation, SequenceMatching, CRISPR Author: Lihua Julie Zhu, Benjamin R. Holmes, Hervé Pagès, Hui Mao, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/CRISPRseek git_branch: RELEASE_3_12 git_last_commit: 3b0270d git_last_commit_date: 2021-01-11 Date/Publication: 2021-01-12 source.ver: src/contrib/CRISPRseek_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/CRISPRseek_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.0/CRISPRseek_1.30.1.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CRISPRseek/inst/doc/CRISPRseek.R dependsOnMe: crisprseekplus importsMe: GUIDEseq, multicrispr dependencyCount: 68 Package: crisprseekplus Version: 1.16.0 Depends: R (>= 3.3.0), shiny, shinyjs, CRISPRseek Imports: DT, utils, GUIDEseq, GenomicRanges, GenomicFeatures, BiocManager, BSgenome, AnnotationDbi, hash Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: 7290a5c46a94b10a678e6e98d0d28da9 NeedsCompilation: no Title: crisprseekplus Description: Bioinformatics platform containing interface to work with offTargetAnalysis and compare2Sequences in the CRISPRseek package, and GUIDEseqAnalysis. biocViews: GeneRegulation, SequenceMatching, Software Author: Sophie Wigmore , Alper Kucukural , Lihua Julie Zhu , Michael Brodsky , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/crisprseekplus VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/crisprseekplus/issues/new git_url: https://git.bioconductor.org/packages/crisprseekplus git_branch: RELEASE_3_12 git_last_commit: cfd3641 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/crisprseekplus_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/crisprseekplus_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/crisprseekplus_1.16.0.tgz vignettes: vignettes/crisprseekplus/inst/doc/crisprseekplus.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crisprseekplus/inst/doc/crisprseekplus.R dependencyCount: 150 Package: CrispRVariants Version: 1.18.0 Depends: R (>= 3.5), ggplot2 (>= 2.2.0) Imports: AnnotationDbi, BiocParallel, Biostrings, methods, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, gridExtra, IRanges, reshape2, Rsamtools, S4Vectors (>= 0.9.38), utils Suggests: BiocStyle, gdata, GenomicFeatures, knitr, rmarkdown, rtracklayer, sangerseqR, testthat, VariantAnnotation License: GPL-2 MD5sum: c850e7296104b907bc830beefb028b60 NeedsCompilation: no Title: Tools for counting and visualising mutations in a target location Description: CrispRVariants provides tools for analysing the results of a CRISPR-Cas9 mutagenesis sequencing experiment, or other sequencing experiments where variants within a given region are of interest. These tools allow users to localize variant allele combinations with respect to any genomic location (e.g. the Cas9 cut site), plot allele combinations and calculate mutation rates with flexible filtering of unrelated variants. biocViews: ImmunoOncology, CRISPR, GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CrispRVariants git_branch: RELEASE_3_12 git_last_commit: 4bbba2e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CrispRVariants_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CrispRVariants_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CrispRVariants_1.18.0.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf vignetteTitles: CrispRVariants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R dependencyCount: 83 Package: crlmm Version: 1.48.0 Depends: R (>= 2.14.0), oligoClasses (>= 1.21.12), preprocessCore (>= 1.17.7) Imports: methods, Biobase (>= 2.15.4), BiocGenerics, affyio (>= 1.23.2), illuminaio, ellipse, mvtnorm, splines, stats, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel, graphics, limma, beanplot LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), GGdata, snpStats, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 73b71e9c0da352554d064f36ba5858ae NeedsCompilation: yes Title: Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays Description: Faster implementation of CRLMM specific to SNP 5.0 and 6.0 arrays, as well as a copy number tool specific to 5.0, 6.0, and Illumina platforms. biocViews: Microarray, Preprocessing, SNP, CopyNumberVariation Author: Benilton S Carvalho, Robert Scharpf, Matt Ritchie, Ingo Ruczinski, Rafael A Irizarry Maintainer: Benilton S Carvalho , Robert Scharpf , Matt Ritchie git_url: https://git.bioconductor.org/packages/crlmm git_branch: RELEASE_3_12 git_last_commit: bb8c119 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/crlmm_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/crlmm_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/crlmm_1.48.0.tgz vignettes: vignettes/crlmm/inst/doc/AffyGW.pdf, vignettes/crlmm/inst/doc/CopyNumberOverview.pdf, vignettes/crlmm/inst/doc/genotyping.pdf, vignettes/crlmm/inst/doc/gtypeDownstream.pdf, vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf, vignettes/crlmm/inst/doc/Infrastructure.pdf vignetteTitles: Copy number estimation, Overview of copy number vignettes, crlmm Vignette - Genotyping, crlmm Vignette - Downstream Analysis, Preprocessing and genotyping Illumina arrays for copy number analysis, Infrastructure for copy number analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/crlmm/inst/doc/genotyping.R importsMe: VanillaICE suggestsMe: oligoClasses, hapmap370k dependencyCount: 63 Package: crossmeta Version: 1.16.1 Depends: R (>= 4.0) Imports: affy (>= 1.52.0), affxparser (>= 1.46.0), AnnotationDbi (>= 1.36.2), Biobase (>= 2.34.0), BiocGenerics (>= 0.20.0), BiocManager (>= 1.30.4), DT (>= 0.2), DBI (>= 1.0.0), data.table (>= 1.10.4), fdrtool (>= 1.2.15), GEOquery (>= 2.40.0), limma (>= 3.30.13), matrixStats (>= 0.51.0), metaMA (>= 3.1.2), miniUI (>= 0.1.1), oligo (>= 1.38.0), reader(>= 1.0.6), RColorBrewer (>= 1.1.2), RCurl (>= 1.95.4.11), RSQLite (>= 2.1.1), randomcoloR (>= 1.1.0.1), stringr (>= 1.2.0), sva (>= 3.22.0), shiny (>= 1.0.0), shinyjs (>= 2.0.0), shinyBS (>= 0.61), shinyWidgets (>= 0.5.3), shinypanel (>= 0.1.0), statmod (>= 1.4.34), XML (>= 3.98.1.17), readxl (>= 1.3.1) Suggests: knitr, rmarkdown, lydata, org.Hs.eg.db, testthat License: MIT + file LICENSE MD5sum: 4115168188b277b69d414f309c1006f9 NeedsCompilation: no Title: Cross Platform Meta-Analysis of Microarray Data Description: Implements cross-platform and cross-species meta-analyses of Affymentrix, Illumina, and Agilent microarray data. This package automates common tasks such as downloading, normalizing, and annotating raw GEO data. The user then selects control and treatment samples in order to perform differential expression analyses for all comparisons. After analysing each contrast seperately, the user can select tissue sources for each contrast and specify any tissue sources that should be grouped for the subsequent meta-analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, TissueMicroarray, OneChannel, Annotation, BatchEffect, Preprocessing, GUI Author: Alex Pickering Maintainer: Alex Pickering SystemRequirements: libxml2: libxml2-dev (deb), libxml2-devel (rpm) libcurl: libcurl4-openssl-dev (deb), libcurl-devel (rpm) openssl: libssl-dev (deb), openssl-devel (rpm), libssl_dev (csw), openssl@1.1 (brew) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/crossmeta git_branch: RELEASE_3_12 git_last_commit: 02d63d6 git_last_commit_date: 2020-11-02 Date/Publication: 2020-11-02 source.ver: src/contrib/crossmeta_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/crossmeta_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.0/crossmeta_1.16.1.tgz vignettes: vignettes/crossmeta/inst/doc/crossmeta-vignette.html vignetteTitles: crossmeta vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/crossmeta/inst/doc/crossmeta-vignette.R suggestsMe: ccmap dependencyCount: 151 Package: CSAR Version: 1.42.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: 88cb267d8a67b34b56f7c05d88cc403e NeedsCompilation: yes Title: Statistical tools for the analysis of ChIP-seq data Description: Statistical tools for ChIP-seq data analysis. The package includes the statistical method described in Kaufmann et al. (2009) PLoS Biology: 7(4):e1000090. Briefly, Taking the average DNA fragment size subjected to sequencing into account, the software calculates genomic single-nucleotide read-enrichment values. After normalization, sample and control are compared using a test based on the Poisson distribution. Test statistic thresholds to control the false discovery rate are obtained through random permutation. biocViews: ChIPSeq, Transcription, Genetics Author: Jose M Muino Maintainer: Jose M Muino git_url: https://git.bioconductor.org/packages/CSAR git_branch: RELEASE_3_12 git_last_commit: 5e29252 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CSAR_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CSAR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CSAR_1.42.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSAR/inst/doc/CSAR.R dependencyCount: 17 Package: csaw Version: 1.24.3 Depends: GenomicRanges, SummarizedExperiment Imports: Rcpp, Matrix, BiocGenerics, Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, S4Vectors, IRanges, GenomeInfoDb, stats, BiocParallel, utils LinkingTo: Rhtslib, zlibbioc, Rcpp Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene, testthat, GenomicAlignments, knitr, BiocStyle, rmarkdown, BiocManager License: GPL-3 Archs: i386, x64 MD5sum: 0eb4552869baa667225b0aa64af8b0fa NeedsCompilation: yes Title: ChIP-Seq Analysis with Windows Description: Detection of differentially bound regions in ChIP-seq data with sliding windows, with methods for normalization and proper FDR control. biocViews: MultipleComparison, ChIPSeq, Normalization, Sequencing, Coverage, Genetics, Annotation, DifferentialPeakCalling Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/csaw git_branch: RELEASE_3_12 git_last_commit: 9bad47b git_last_commit_date: 2020-11-09 Date/Publication: 2020-11-10 source.ver: src/contrib/csaw_1.24.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/csaw_1.24.3.zip mac.binary.ver: bin/macosx/contrib/4.0/csaw_1.24.3.tgz vignettes: vignettes/csaw/inst/doc/csaw.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/csaw/inst/doc/csaw.R importsMe: diffHic, icetea, NADfinder, vulcan, BinQuasi suggestsMe: tximport, chipseqDB, csawUsersGuide dependencyCount: 91 Package: CSSP Version: 1.28.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: 6b6ef2676803286e8e08a15f9cce841d NeedsCompilation: yes Title: ChIP-Seq Statistical Power Description: Power computation for ChIP-Seq data based on Bayesian estimation for local poisson counting process. biocViews: ChIPSeq, Sequencing, QualityControl, Bayesian Author: Chandler Zuo, Sunduz Keles Maintainer: Chandler Zuo git_url: https://git.bioconductor.org/packages/CSSP git_branch: RELEASE_3_12 git_last_commit: 7603e08 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CSSP_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CSSP_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CSSP_1.28.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSP/inst/doc/cssp.R dependencyCount: 4 Package: CSSQ Version: 1.2.0 Depends: SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, rtracklayer Imports: GenomicAlignments, GenomicFeatures, Rsamtools, ggplot2, grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 7d1b1b7f30ea21a8b9b01663f5846f71 NeedsCompilation: no Title: Chip-seq Signal Quantifier Pipeline Description: This package is desgined to perform statistical analysis to identify statistically significant differentially bound regions between multiple groups of ChIP-seq dataset. biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Normalization Author: Ashwath Kumar [aut], Yajun Mei [aut], Yuhong Fan [aut] Maintainer: Fan Lab at Georgia Institute of Technology VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CSSQ git_branch: RELEASE_3_12 git_last_commit: 1e9ee24 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CSSQ_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CSSQ_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CSSQ_1.2.0.tgz vignettes: vignettes/CSSQ/inst/doc/CSSQ.html vignetteTitles: Introduction to CSSQ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CSSQ/inst/doc/CSSQ.R dependencyCount: 103 Package: ctc Version: 1.64.0 Depends: amap License: GPL-2 MD5sum: 99fb0ec18123dbfdb6c3f324495a8a27 NeedsCompilation: no Title: Cluster and Tree Conversion. Description: Tools for export and import classification trees and clusters to other programs biocViews: Microarray, Clustering, Classification, DataImport, Visualization Author: Antoine Lucas , Laurent Gautier Maintainer: Antoine Lucas URL: http://antoinelucas.free.fr/ctc git_url: https://git.bioconductor.org/packages/ctc git_branch: RELEASE_3_12 git_last_commit: 35dbe62 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ctc_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ctc_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ctc_1.64.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctc/inst/doc/ctc.R importsMe: miRLAB, multiClust dependencyCount: 1 Package: ctgGEM Version: 1.2.0 Depends: monocle, SummarizedExperiment, Imports: Biobase, BiocGenerics, graphics, grDevices, igraph, methods, utils, sincell, TSCAN, destiny, HSMMSingleCell Suggests: BiocStyle, biomaRt, irlba, knitr, VGAM License: GPL(>=2) MD5sum: f9b3de941ffc577de8b2f3179aad840b NeedsCompilation: no Title: Generating Tree Hierarchy Visualizations from Gene Expression Data Description: Cell Tree Generator for Gene Expression Matrices (ctgGEM) streamlines the building of cell-state hierarchies from single-cell gene expression data across multiple existing tools for improved comparability and reproducibility. It supports pseudotemporal ordering algorithms and visualization tools from monocle, cellTree, TSCAN, sincell, and destiny, and provides a unified output format for integration with downstream data analysis workflows and Cytoscape. biocViews: GeneExpression, Visualization, Sequencing, SingleCell, Clustering, RNASeq, ImmunoOncology, DifferentialExpression, MultipleComparison, QualityControl, DataImport Author: Mark Block and Carrie Minette Maintainer: USD Biomedical Engineering VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ctgGEM git_branch: RELEASE_3_12 git_last_commit: f889bcf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ctgGEM_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ctgGEM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ctgGEM_1.2.0.tgz vignettes: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.html vignetteTitles: ctgGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctgGEM/inst/doc/ctgGEM-Vignette.R dependencyCount: 197 Package: cTRAP Version: 1.8.0 Depends: R (>= 3.6.0) Imports: biomaRt, binr, cowplot, data.table, dplyr, DT, fgsea, ggplot2, ggrepel, graphics, highcharter, httr, limma, methods, pbapply, R.utils, readxl, reshape2, rhdf5, scales, shiny, stats, tools, utils Suggests: testthat, knitr, covr, rmarkdown, spelling License: MIT + file LICENSE MD5sum: 0f43af697bc7e411e1976e9765f0af1d NeedsCompilation: no Title: Identification of candidate causal perturbations from differential gene expression data Description: Compare differential gene expression results with those from known cellular perturbations (such as gene knock-down, overexpression or small molecules) derived from the Connectivity Map. Such analyses allow not only to infer the molecular causes of the observed difference in gene expression but also to identify small molecules that could drive or revert specific transcriptomic alterations. biocViews: DifferentialExpression, GeneExpression, RNASeq, Transcriptomics, Pathways, ImmunoOncology, GeneSetEnrichment Author: Bernardo P. de Almeida [aut], Nuno Saraiva-Agostinho [aut, cre], Nuno L. Barbosa-Morais [aut, led] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/cTRAP, https://github.com/nuno-agostinho/cTRAP VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/cTRAP/issues git_url: https://git.bioconductor.org/packages/cTRAP git_branch: RELEASE_3_12 git_last_commit: 1bd4716 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cTRAP_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cTRAP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cTRAP_1.8.0.tgz vignettes: vignettes/cTRAP/inst/doc/cTRAP.html vignetteTitles: cTRAP: identifying candidate causal perturbations from differential gene expression data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cTRAP/inst/doc/cTRAP.R dependencyCount: 137 Package: ctsGE Version: 1.16.0 Depends: R (>= 3.2) Imports: ccaPP, ggplot2, limma, reshape2, shiny, stats, stringr, utils Suggests: BiocStyle, dplyr, DT, GEOquery, knitr, pander, rmarkdown, testthat License: GPL-2 MD5sum: 93cdb7bd4f6b50c370e1cb9e11454d6d NeedsCompilation: no Title: Clustering of Time Series Gene Expression data Description: Methodology for supervised clustering of potentially many predictor variables, such as genes etc., in time series datasets Provides functions that help the user assigning genes to predefined set of model profiles. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Bayesian, Clustering, TimeCourse, Sequencing, RNASeq Author: Michal Sharabi-Schwager [aut, cre], Ron Ophir [aut] Maintainer: Michal Sharabi-Schwager URL: https://github.com/michalsharabi/ctsGE VignetteBuilder: knitr BugReports: https://github.com/michalsharabi/ctsGE/issues git_url: https://git.bioconductor.org/packages/ctsGE git_branch: RELEASE_3_12 git_last_commit: 51ee446 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ctsGE_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ctsGE_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ctsGE_1.16.0.tgz vignettes: vignettes/ctsGE/inst/doc/ctsGE.html vignetteTitles: ctsGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ctsGE/inst/doc/ctsGE.R dependencyCount: 70 Package: cummeRbund Version: 2.32.0 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, S4Vectors (>= 0.9.25), Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: 4f70751a082ab25cb059a884845886b3 NeedsCompilation: no Title: Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Description: Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations. biocViews: HighThroughputSequencing, HighThroughputSequencingData, RNAseq, RNAseqData, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Bioinformatics, Clustering, MultipleComparisons, QualityControl Author: L. Goff, C. Trapnell, D. Kelley Maintainer: Loyal A. Goff git_url: https://git.bioconductor.org/packages/cummeRbund git_branch: RELEASE_3_12 git_last_commit: 045dd83 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cummeRbund_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cummeRbund_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cummeRbund_2.32.0.tgz vignettes: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.pdf, vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf vignetteTitles: Sample cummeRbund workflow, CummeRbund User Guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cummeRbund/inst/doc/cummeRbund-example-workflow.R, vignettes/cummeRbund/inst/doc/cummeRbund-manual.R importsMe: meshr suggestsMe: IsoformSwitchAnalyzeR dependencyCount: 141 Package: customCMPdb Version: 1.0.0 Depends: R (>= 4.0) Imports: AnnotationHub, RSQLite, XML, utils, ChemmineR, methods, stats, rappdirs, BiocFileCache Suggests: knitr, rmarkdown, testthat, BiocStyle License: Artistic-2.0 MD5sum: 7b15c949c2b469c83bc39903071438bb NeedsCompilation: no Title: Customize and Query Compound Annotation Database Description: This package serves as a query interface for important community collections of small molecules, while also allowing users to include custom compound collections. biocViews: Software, Cheminformatics Author: Yuzhu Duan [aut, cre], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/customCMPdb/ VignetteBuilder: knitr BugReports: https://github.com/yduan004/customCMPdb/issues git_url: https://git.bioconductor.org/packages/customCMPdb git_branch: RELEASE_3_12 git_last_commit: caace26 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/customCMPdb_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/customCMPdb_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/customCMPdb_1.0.0.tgz vignettes: vignettes/customCMPdb/inst/doc/customCMPdb.html vignetteTitles: customCMPdb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customCMPdb/inst/doc/customCMPdb.R dependencyCount: 102 Package: customProDB Version: 1.30.1 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt(>= 2.17.1) Imports: S4Vectors (>= 0.9.25), DBI, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.32.0), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AhoCorasickTrie, methods Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: a60f66433d48b5cd52d05aea0443bff1 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search Description: Database search is the most widely used approach for peptide and protein identification in mass spectrometry-based proteomics studies. Our previous study showed that sample-specific protein databases derived from RNA-Seq data can better approximate the real protein pools in the samples and thus improve protein identification. More importantly, single nucleotide variations, short insertion and deletions and novel junctions identified from RNA-Seq data make protein database more complete and sample-specific. Here, we report an R package customProDB that enables the easy generation of customized databases from RNA-Seq data for proteomics search. This work bridges genomics and proteomics studies and facilitates cross-omics data integration. biocViews: ImmunoOncology, Sequencing, MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing, FunctionalGenomics Author: Xiaojing Wang Maintainer: Xiaojing Wang Bo Wen git_url: https://git.bioconductor.org/packages/customProDB git_branch: RELEASE_3_12 git_last_commit: 2c66727 git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-26 source.ver: src/contrib/customProDB_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/customProDB_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.0/customProDB_1.30.1.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/customProDB/inst/doc/customProDB.R importsMe: PGA dependencyCount: 92 Package: cycle Version: 1.44.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: d34f40b6747db487e4e6f33808a3218f NeedsCompilation: no Title: Significance of periodic expression pattern in time-series data Description: Package for assessing the statistical significance of periodic expression based on Fourier analysis and comparison with data generated by different background models biocViews: Microarray, TimeCourse Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://cycle.sysbiolab.eu git_url: https://git.bioconductor.org/packages/cycle git_branch: RELEASE_3_12 git_last_commit: 83e153e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cycle_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cycle_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cycle_1.44.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cycle/inst/doc/cycle.R dependencyCount: 18 Package: cydar Version: 1.14.1 Depends: SingleCellExperiment Imports: viridis, methods, shiny, graphics, stats, grDevices, utils, BiocGenerics, S4Vectors, BiocParallel, SummarizedExperiment, flowCore, Biobase, Rcpp, BiocNeighbors LinkingTo: Rcpp Suggests: ncdfFlow, testthat, rmarkdown, knitr, edgeR, limma, glmnet, BiocStyle, flowStats License: GPL-3 Archs: i386, x64 MD5sum: 4faff8ca77d2930946c0419acfa58f4f NeedsCompilation: yes Title: Using Mass Cytometry for Differential Abundance Analyses Description: Identifies differentially abundant populations between samples and groups in mass cytometry data. Provides methods for counting cells into hyperspheres, controlling the spatial false discovery rate, and visualizing changes in abundance in the high-dimensional marker space. biocViews: ImmunoOncology, FlowCytometry, MultipleComparison, Proteomics, SingleCell Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cydar git_branch: RELEASE_3_12 git_last_commit: 8225626 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/cydar_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/cydar_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/cydar_1.14.1.tgz vignettes: vignettes/cydar/inst/doc/cydar.html vignetteTitles: Detecting differential abundance hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cydar/inst/doc/cydar.R dependencyCount: 93 Package: CytoDx Version: 1.10.0 Depends: R (>= 3.5) Imports: doParallel, dplyr, glmnet, rpart, rpart.plot, stats, flowCore,grDevices, graphics, utils Suggests: knitr License: GPL-2 MD5sum: 467152aec4772a32b8bd8bb847376dad NeedsCompilation: no Title: Robust prediction of clinical outcomes using cytometry data without cell gating Description: This package provides functions that predict clinical outcomes using single cell data (such as flow cytometry data, RNA single cell sequencing data) without the requirement of cell gating or clustering. biocViews: ImmunoOncology, CellBiology, FlowCytometry, StatisticalMethod, Software, CellBasedAssays, Regression, Classification, Survival Author: Zicheng Hu Maintainer: Zicheng Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/CytoDx git_branch: RELEASE_3_12 git_last_commit: 7f2330a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/CytoDx_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/CytoDx_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/CytoDx_1.10.0.tgz vignettes: vignettes/CytoDx/inst/doc/CytoDx_Vignette.pdf vignetteTitles: Introduction to CytoDx hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoDx/inst/doc/CytoDx_Vignette.R dependencyCount: 50 Package: cytofast Version: 1.6.0 Depends: R (>= 3.6.0) Imports: flowCore, ggplot2, ggridges, RColorBrewer, reshape2, stats, grDevices, Rdpack, methods, grid, FlowSOM Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 6ac0a15cc689d5f1cd0be744a7e82640 NeedsCompilation: no Title: cytofast - A quick visualization and analysis tool for CyTOF data Description: Multi-parametric flow and mass cytometry allows exceptional high-resolution exploration of the cellular composition of the immune system. Together with tools like FlowSOM and Cytosplore it is possible to identify novel cell types. By introducing cytofast we hope to offer a workflow for visualization and quantification of cell clusters for an efficient discovery of cell populations associated with diseases or other clinical outcomes. biocViews: FlowCytometry, Visualization, Clustering Author: K.A. Stam [aut, cre], G. Beyrend [aut] Maintainer: K.A. Stam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytofast git_branch: RELEASE_3_12 git_last_commit: c9be79d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/cytofast_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/cytofast_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/cytofast_1.6.0.tgz vignettes: vignettes/cytofast/inst/doc/spitzer.html vignetteTitles: Spitzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytofast/inst/doc/spitzer.R dependencyCount: 134 Package: cytolib Version: 2.2.1 Depends: R (>= 3.4) Imports: RcppParallel, RProtoBufLib LinkingTo: Rcpp, BH(>= 1.72.0-2), RProtoBufLib(>= 1.99.8),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1) Suggests: knitr License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 682b1e18a84a306f6fec19ea0c1b381e NeedsCompilation: yes Title: C++ infrastructure for representing and interacting with the gated cytometry data Description: This package provides the core data structure and API to represent and interact with the gated cytometry data. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang Maintainer: Mike Jiang , Jake Wagner SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/cytolib git_branch: RELEASE_3_12 git_last_commit: fb08852 git_last_commit_date: 2021-01-16 Date/Publication: 2021-01-17 source.ver: src/contrib/cytolib_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/cytolib_2.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/cytolib_2.2.1.tgz vignettes: vignettes/cytolib/inst/doc/cytolib.html vignetteTitles: Using cytolib hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/cytolib/inst/doc/cytolib.R importsMe: CytoML, flowCore, flowWorkspace linksToMe: CytoML, flowCore, flowWorkspace dependencyCount: 9 Package: cytomapper Version: 1.2.1 Depends: R (>= 4.0), EBImage, SingleCellExperiment, methods Imports: S4Vectors, RColorBrewer, viridis, utils, SummarizedExperiment, tools, graphics, raster, grDevices, stats, ggplot2, ggbeeswarm, svgPanZoom, svglite, shiny, shinydashboard, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 2) MD5sum: d60627dd1e8095f901ab01eb2d4a6334 NeedsCompilation: no Title: Visualization of highly multiplexed imaging data in R Description: Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells. biocViews: ImmunoOncology, Software, SingleCell, OneChannel, TwoChannel, MultipleComparison, Normalization, DataImport Author: Nils Eling [aut, cre] (), Nicolas Damond [aut] (), Tobias Hoch [ctb] Maintainer: Nils Eling URL: https://github.com/BodenmillerGroup/cytomapper VignetteBuilder: knitr BugReports: https://github.com/BodenmillerGroup/cytomapper/issues git_url: https://git.bioconductor.org/packages/cytomapper git_branch: RELEASE_3_12 git_last_commit: cdddecf git_last_commit_date: 2021-01-28 Date/Publication: 2021-01-28 source.ver: src/contrib/cytomapper_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/cytomapper_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/cytomapper_1.2.1.tgz vignettes: vignettes/cytomapper/inst/doc/cytomapper.html vignetteTitles: "Visualization of imaging cytometry data in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytomapper/inst/doc/cytomapper.R dependencyCount: 97 Package: CytoML Version: 2.2.2 Depends: R (>= 3.5.0) Imports: cytolib(>= 2.1.18), flowCore (>= 1.99.10), flowWorkspace (>= 4.1.8), openCyto (>= 1.99.2), XML, data.table, jsonlite, RBGL, Rgraphviz, Biobase, methods, graph, graphics, utils, base64enc, plyr, dplyr, grDevices, methods, ggcyto (>= 1.11.4), yaml, lattice, stats, corpcor, RUnit, tibble, RcppParallel, xml2 LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib, cytolib ( >= 1.99.26),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1), flowWorkspace Suggests: testthat, flowWorkspaceData , knitr, parallel License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: a8ce052d9a06af3a600890f644dcd266 NeedsCompilation: yes Title: A GatingML Interface for Cross Platform Cytometry Data Sharing Description: Uses platform-specific implemenations of the GatingML2.0 standard to exchange gated cytometry data with other software platforms. biocViews: ImmunoOncology, FlowCytometry, DataImport, DataRepresentation Author: Mike Jiang, Jake Wagner Maintainer: Mike Jiang , Jake Wagner URL: https://github.com/RGLab/CytoML SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/RGLab/CytoML/issues git_url: https://git.bioconductor.org/packages/CytoML git_branch: RELEASE_3_12 git_last_commit: 66a7c54 git_last_commit_date: 2021-03-09 Date/Publication: 2021-03-10 source.ver: src/contrib/CytoML_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/CytoML_2.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/CytoML_2.2.2.tgz vignettes: vignettes/CytoML/inst/doc/cytobank2GatingSet.html, vignettes/CytoML/inst/doc/flowjo_to_gatingset.html, vignettes/CytoML/inst/doc/HowToExportGatingSet.html vignetteTitles: How to import Cytobank into a GatingSet, flowJo parser, How to export a GatingSet to GatingML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CytoML/inst/doc/cytobank2GatingSet.R, vignettes/CytoML/inst/doc/flowjo_to_gatingset.R, vignettes/CytoML/inst/doc/HowToExportGatingSet.R importsMe: FlowSOM suggestsMe: flowWorkspace, openCyto dependencyCount: 122 Package: CytoTree Version: 1.0.3 Depends: R (>= 4.0), igraph Imports: FlowSOM, Rtsne, ggplot2, destiny, gmodels, flowUtils, Biobase, Matrix, flowCore, sva, matrixStats, methods, mclust, prettydoc, RANN(>= 2.5), Rcpp (>= 0.12.0), BiocNeighbors, cluster, pheatmap, scatterpie, umap, scatterplot3d, limma, stringr, grDevices, grid, stats LinkingTo: Rcpp Suggests: BiocGenerics, knitr, RColorBrewer, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 5f5c264b11fa159315fc9e00141cd2a3 NeedsCompilation: yes Title: A Toolkit for Flow And Mass Cytometry Data Description: A trajectory inference toolkit for flow and mass cytometry data. CytoTree is a valuable tool to build a tree-shaped trajectory using flow and mass cytometry data. The application of CytoTree ranges from clustering and dimensionality reduction to trajectory reconstruction and pseudotime estimation. It offers complete analyzing workflow for flow and mass cytometry data. biocViews: CellBiology, Clustering, Visualization, Software, CellBasedAssays, FlowCytometry, NetworkInference, Network Author: Yuting Dai [aut, cre] Maintainer: Yuting Dai URL: http://www.r-project.org, https://github.com/JhuangLab/CytoTree VignetteBuilder: knitr BugReports: https://github.com/JhuangLab/CytoTree/issues git_url: https://git.bioconductor.org/packages/CytoTree git_branch: RELEASE_3_12 git_last_commit: 929282c git_last_commit_date: 2020-11-08 Date/Publication: 2020-11-08 source.ver: src/contrib/CytoTree_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/CytoTree_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.0/CytoTree_1.0.3.tgz vignettes: vignettes/CytoTree/inst/doc/Tutorial.html vignetteTitles: Quick_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CytoTree/inst/doc/Tutorial.R dependencyCount: 244 Package: dada2 Version: 1.18.0 Depends: R (>= 3.4.0), Rcpp (>= 0.12.0), methods (>= 3.4.0) Imports: Biostrings (>= 2.42.1), ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), ShortRead (>= 1.32.0), RcppParallel (>= 4.3.0), parallel (>= 3.2.0), IRanges (>= 2.6.0), XVector (>= 0.16.0), BiocGenerics (>= 0.22.0) LinkingTo: Rcpp, RcppParallel Suggests: BiocStyle, knitr, rmarkdown License: LGPL-3 Archs: i386, x64 MD5sum: 28f673816cb39aaa92c7e6d959016921 NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package infers exact amplicon sequence variants (ASVs) from high-throughput amplicon sequencing data, replacing the coarser and less accurate OTU clustering approach. The dada2 pipeline takes as input demultiplexed fastq files, and outputs the sequence variants and their sample-wise abundances after removing substitution and chimera errors. Taxonomic classification is available via a native implementation of the RDP naive Bayesian classifier, and species-level assignment to 16S rRNA gene fragments by exact matching. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues git_url: https://git.bioconductor.org/packages/dada2 git_branch: RELEASE_3_12 git_last_commit: a20a676 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dada2_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dada2_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dada2_1.18.0.tgz vignettes: vignettes/dada2/inst/doc/dada2-intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R importsMe: microbial dependencyCount: 78 Package: dagLogo Version: 1.28.1 Depends: R (>= 3.0.1), methods, grid Imports: pheatmap, Biostrings, UniProt.ws, BiocGenerics, utils, biomaRt, motifStack Suggests: XML, grImport, grImport2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 4e89998edd8f4dde8c1b8d66cec228f4 NeedsCompilation: no Title: dagLogo: a Bioconductor package for visualizing conserved amino acid sequence pattern in groups based on probability theory Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Haibo Liu, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dagLogo git_branch: RELEASE_3_12 git_last_commit: 28e6088 git_last_commit_date: 2020-12-03 Date/Publication: 2020-12-03 source.ver: src/contrib/dagLogo_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/dagLogo_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.0/dagLogo_1.28.1.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.html vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R dependencyCount: 94 Package: daMA Version: 1.62.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: 00c07d01d2bb0ec5a1914eadaefd0472 NeedsCompilation: no Title: Efficient design and analysis of factorial two-colour microarray data Description: This package contains functions for the efficient design of factorial two-colour microarray experiments and for the statistical analysis of factorial microarray data. Statistical details are described in Bretz et al. (2003, submitted) biocViews: Microarray, TwoChannel, DifferentialExpression Author: Jobst Landgrebe and Frank Bretz Maintainer: Jobst Landgrebe URL: http://www.microarrays.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/daMA git_branch: RELEASE_3_12 git_last_commit: 4a4fa9d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/daMA_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/daMA_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/daMA_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: DAMEfinder Version: 1.2.0 Depends: R (>= 4.0) Imports: stats, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, readr, SummarizedExperiment, GenomicAlignments, stringr, plyr, VariantAnnotation, parallel, ggplot2, Rsamtools, BiocGenerics, methods, limma, bumphunter, Biostrings, reshape2, cowplot, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE MD5sum: 96f16b103bdff4a79ad9985d6887bbe0 NeedsCompilation: no Title: Finds DAMEs - Differential Allelicly MEthylated regions Description: 'DAMEfinder' offers functionality for taking methtuple or bismark outputs to calculate ASM scores and compute DAMEs. It also offers nice visualization of methyl-circle plots. biocViews: DNAMethylation, DifferentialMethylation, Coverage Author: Stephany Orjuela [aut, cre] (), Dania Machlab [aut], Mark Robinson [aut] Maintainer: Stephany Orjuela VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/DAMEfinder/issues git_url: https://git.bioconductor.org/packages/DAMEfinder git_branch: RELEASE_3_12 git_last_commit: 99d17c3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DAMEfinder_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DAMEfinder_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DAMEfinder_1.2.0.tgz vignettes: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.html vignetteTitles: DAMEfinder Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DAMEfinder/inst/doc/DAMEfinder_workflow.R dependencyCount: 119 Package: DaMiRseq Version: 2.2.0 Depends: R (>= 3.4), SummarizedExperiment, ggplot2 Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap, FactoMineR, corrplot, randomForest, e1071, caret, MASS, lubridate, plsVarSel, kknn, FSelector, methods, stats, utils, graphics, grDevices, reshape2, ineq, arm, pls, RSNNS, edgeR, plyr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: ebba28861c611330965152ecf6dd935b NeedsCompilation: no Title: Data Mining for RNA-seq data: normalization, feature selection and classification Description: The DaMiRseq package offers a tidy pipeline of data mining procedures to identify transcriptional biomarkers and exploit them for both binary and multi-class classification purposes. The package accepts any kind of data presented as a table of raw counts and allows including both continous and factorial variables that occur with the experimental setting. A series of functions enable the user to clean up the data by filtering genomic features and samples, to adjust data by identifying and removing the unwanted source of variation (i.e. batches and confounding factors) and to select the best predictors for modeling. Finally, a "stacking" ensemble learning technique is applied to build a robust classification model. Every step includes a checkpoint that the user may exploit to assess the effects of data management by looking at diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plot. biocViews: Sequencing, RNASeq, Classification, ImmunoOncology Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DaMiRseq git_branch: RELEASE_3_12 git_last_commit: 2d32a85 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DaMiRseq_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DaMiRseq_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DaMiRseq_2.2.0.tgz vignettes: vignettes/DaMiRseq/inst/doc/DaMiRseq.pdf vignetteTitles: Data Mining for RNA-seq data: normalization,, features selection and classification - DaMiRseq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DaMiRseq/inst/doc/DaMiRseq.R importsMe: GARS dependencyCount: 244 Package: DAPAR Version: 1.22.9 Depends: R (>= 4.0.3) Imports: Biobase, MSnbase, tibble, RColorBrewer,stats,preprocessCore, Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2, limma,knitr,tmvtnorm,norm,impute, stringr, grDevices, graphics, openxlsx, utils, cp4p (>= 0.3.5), scales, Matrix, vioplot, imp4p (>= 0.8), forcats, methods, highcharter, DAPARdata (>= 1.18.0), siggenes, graph, lme4, readxl, clusterProfiler, dplyr, tidyr,AnnotationDbi, tidyverse, vsn, FactoMineR, factoextra, multcomp, purrr, visNetwork, foreach, parallel, doParallel, igraph, dendextend, Mfuzz, apcluster, diptest, cluster Suggests: BiocGenerics, testthat, BiocStyle License: Artistic-2.0 MD5sum: 4b045b597e9582f5ded10e732d2dc305 NeedsCompilation: no Title: Tools for the Differential Analysis of Proteins Abundance with R Description: This package contains a collection of functions for the visualisation and the statistical analysis of proteomic data. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, GO, DataImport Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut], Cosmin Lazar [ctb], Alexia Dorffer [ctb], Anais Courtier [ctb], Helene Borges [ctb], Enora Fremy [ctb] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ VignetteBuilder: knitr BugReports: https://github.com/samWieczorek/DAPAR/issues git_url: https://git.bioconductor.org/packages/DAPAR git_branch: RELEASE_3_12 git_last_commit: 0e5ad9b git_last_commit_date: 2021-04-30 Date/Publication: 2021-04-30 source.ver: src/contrib/DAPAR_1.22.9.tar.gz win.binary.ver: bin/windows/contrib/4.0/DAPAR_1.22.9.zip mac.binary.ver: bin/macosx/contrib/4.0/DAPAR_1.22.9.tgz vignettes: vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar user manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R Package: DART Version: 1.38.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 37c999b981fc177693e6a2ee94263aed NeedsCompilation: no Title: Denoising Algorithm based on Relevance network Topology Description: Denoising Algorithm based on Relevance network Topology (DART) is an algorithm designed to evaluate the consistency of prior information molecular signatures (e.g in-vitro perturbation expression signatures) in independent molecular data (e.g gene expression data sets). If consistent, a pruning network strategy is then used to infer the activation status of the molecular signature in individual samples. biocViews: GeneExpression, DifferentialExpression, GraphAndNetwork, Pathways Author: Yan Jiao, Katherine Lawler, Andrew E Teschendorff, Charles Shijie Zheng Maintainer: Charles Shijie Zheng git_url: https://git.bioconductor.org/packages/DART git_branch: RELEASE_3_12 git_last_commit: bfb1c62 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DART_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DART_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DART_1.38.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DART/inst/doc/DART.R dependencyCount: 11 Package: dasper Version: 1.0.0 Depends: R (>= 4.0) Imports: basilisk, BiocFileCache, BiocParallel, data.table, dplyr, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, magrittr, megadepth, methods, plyranges, readr, reticulate, S4Vectors, stringr, SummarizedExperiment, tidyr Suggests: BiocStyle, covr, testthat, GenomicState, ggplot2, ggpubr, ggrepel, grid, knitcitations, knitr, recount, rmarkdown, sessioninfo, rtracklayer, tibble License: Artistic-2.0 MD5sum: 95e244b9fc53c2589f48253072c41cdc NeedsCompilation: no Title: Detecting abberant splicing events from RNA-sequencing data Description: The aim of dasper is to detect aberrant splicing events from RNA-seq data. dasper will use as input both junction and coverage data from RNA-seq to calculate the deviation of each splicing event in a patient from a set of user-defined controls. dasper uses an unsupervised outlier detection algorithm to score each splicing event in the patient with an outlier score representing the degree to which that splicing event looks abnormal. biocViews: Software, RNASeq, Transcriptomics, AlternativeSplicing, Coverage, Sequencing Author: David Zhang [aut, cre] (), Leonardo Collado-Torres [ctb] () Maintainer: David Zhang URL: https://github.com/dzhang32/dasper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/dasper git_url: https://git.bioconductor.org/packages/dasper git_branch: RELEASE_3_12 git_last_commit: 395b887 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dasper_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dasper_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dasper_1.0.0.tgz vignettes: vignettes/dasper/inst/doc/dasper.html vignetteTitles: Introduction to dasper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dasper/inst/doc/dasper.R dependencyCount: 130 Package: DBChIP Version: 1.34.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: baf9d92ba43a3ec41d4491a7edf87d3d NeedsCompilation: no Title: Differential Binding of Transcription Factor with ChIP-seq Description: DBChIP detects differentially bound sharp binding sites across multiple conditions, with or without matching control samples. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Kun Liang Maintainer: Kun Liang git_url: https://git.bioconductor.org/packages/DBChIP git_branch: RELEASE_3_12 git_last_commit: a165505 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DBChIP_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DBChIP_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DBChIP_1.34.0.tgz vignettes: vignettes/DBChIP/inst/doc/DBChIP.pdf vignetteTitles: DBChIP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DBChIP/inst/doc/DBChIP.R importsMe: metagene dependencyCount: 12 Package: dcanr Version: 1.6.0 Depends: R (>= 3.6.0) Imports: igraph, foreach, plyr, stringr, reshape2, methods, Matrix, graphics, stats, RColorBrewer, circlize, doRNG Suggests: EBcoexpress, testthat, EBarrays, GeneNet, COSINE, mclust, minqa, SummarizedExperiment, Biobase, knitr, rmarkdown, BiocStyle, edgeR Enhances: parallel, doSNOW, doParallel License: GPL-3 MD5sum: 68dcfdbef97b23156139188eca7ed2ec NeedsCompilation: no Title: Differential co-expression/association network analysis Description: Methods and an evaluation framework for the inference of differential co-expression/association networks. biocViews: NetworkInference, GraphAndNetwork, DifferentialExpression, Network Author: Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/dcanr/, https://github.com/DavisLaboratory/dcanr VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/dcanr/issues git_url: https://git.bioconductor.org/packages/dcanr git_branch: RELEASE_3_12 git_last_commit: 39a8446 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dcanr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dcanr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dcanr_1.6.0.tgz vignettes: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.html, vignettes/dcanr/inst/doc/dcanr_vignette.html vignetteTitles: 2. DC method evaluation, 1. Differential co-expression analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dcanr/inst/doc/dcanr_evaluation_vignette.R, vignettes/dcanr/inst/doc/dcanr_vignette.R importsMe: SingscoreAMLMutations dependencyCount: 30 Package: dcGSA Version: 1.18.0 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: c20ff7789a5cff21448da9c1679c47c6 NeedsCompilation: no Title: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles Description: Distance-correlation based Gene Set Analysis for longitudinal gene expression profiles. In longitudinal studies, the gene expression profiles were collected at each visit from each subject and hence there are multiple measurements of the gene expression profiles for each subject. The dcGSA package could be used to assess the associations between gene sets and clinical outcomes of interest by fully taking advantage of the longitudinal nature of both the gene expression profiles and clinical outcomes. biocViews: ImmunoOncology, GeneSetEnrichment,Microarray, StatisticalMethod, Sequencing, RNASeq, GeneExpression Author: Jiehuan Sun [aut, cre], Jose Herazo-Maya [aut], Xiu Huang [aut], Naftali Kaminski [aut], and Hongyu Zhao [aut] Maintainer: Jiehuan sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dcGSA git_branch: RELEASE_3_12 git_last_commit: a0a7d07 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dcGSA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dcGSA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dcGSA_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 16 Package: ddCt Version: 1.46.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: RUnit License: LGPL-3 MD5sum: 2367b6279a1b336056fceae0214690c2 NeedsCompilation: no Title: The ddCt Algorithm for the Analysis of Quantitative Real-Time PCR (qRT-PCR) Description: The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions. biocViews: GeneExpression, DifferentialExpression, MicrotitrePlateAssay, qPCR Author: Jitao David Zhang, Rudolf Biczok, and Markus Ruschhaupt Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/ddCt git_branch: RELEASE_3_12 git_last_commit: 8cf072d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ddCt_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ddCt_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ddCt_1.46.0.tgz vignettes: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.pdf, vignettes/ddCt/inst/doc/rtPCR-usage.pdf, vignettes/ddCt/inst/doc/rtPCR.pdf vignetteTitles: How to apply the ddCt method, Analyse RT-PCR data with the end-to-end script in ddCt package, Introduction to the ddCt method for qRT-PCR data analysis: background,, algorithm and example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddCt/inst/doc/RT-PCR-Script-ddCt.R, vignettes/ddCt/inst/doc/rtPCR-usage.R, vignettes/ddCt/inst/doc/rtPCR.R dependencyCount: 12 Package: ddPCRclust Version: 1.10.0 Depends: R (>= 3.5) Imports: plotrix, clue, parallel, ggplot2, openxlsx, R.utils, flowCore, flowDensity (>= 1.13.3), SamSPECTRAL, flowPeaks Suggests: BiocStyle License: Artistic-2.0 MD5sum: f44e0b951caccc1efaeed761367ed13d NeedsCompilation: no Title: Clustering algorithm for ddPCR data Description: The ddPCRclust algorithm can automatically quantify the CPDs of non-orthogonal ddPCR reactions with up to four targets. In order to determine the correct droplet count for each target, it is crucial to both identify all clusters and label them correctly based on their position. For more information on what data can be analyzed and how a template needs to be formatted, please check the vignette. biocViews: ddPCR, Clustering Author: Benedikt G. Brink [aut, cre], Justin Meskas [ctb], Ryan R. Brinkman [ctb] Maintainer: Benedikt G. Brink URL: https://github.com/bgbrink/ddPCRclust BugReports: https://github.com/bgbrink/ddPCRclust/issues git_url: https://git.bioconductor.org/packages/ddPCRclust git_branch: RELEASE_3_12 git_last_commit: 4f8f519 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ddPCRclust_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ddPCRclust_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ddPCRclust_1.10.0.tgz vignettes: vignettes/ddPCRclust/inst/doc/ddPCRclust.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddPCRclust/inst/doc/ddPCRclust.R dependencyCount: 152 Package: dearseq Version: 1.2.0 Depends: R (>= 3.6.0) Imports: CompQuadForm, ggplot2, KernSmooth, matrixStats, methods, parallel, pbapply, stats, statmod Suggests: Biobase, BiocManager, BiocSet, edgeR, DESeq2, GEOquery, GSA, knitr, limma, readxl, rmarkdown, S4Vectors, SummarizedExperiment, testthat, covr License: GPL-2 | file LICENSE MD5sum: bd1fc407ae7f45121aed216184bf7862 NeedsCompilation: no Title: Differential Expression Analysis for RNA-seq data through a robust variance component test Description: Differential Expression Analysis RNA-seq data with variance component score test accounting for data heteroscedasticity through precision weights. Perform both gene-wise and gene set analyses, and can deal with repeated or longitudinal data. Methods are detailed in: Agniel D & Hejblum BP (2017) Variance component score test for time-course gene set analysis of longitudinal RNA-seq data, Biostatistics, 18(4):589-604. and Gauthier M, Agniel D, Thiébaut R & Hejblum BP (2019). dearseq: a variance component score test for RNA-Seq differential analysis that effectively controls the false discovery rate, *bioRxiv* 635714. biocViews: BiomedicalInformatics, CellBiology, DifferentialExpression, DNASeq, GeneExpression, Genetics, GeneSetEnrichment, ImmunoOncology, KEGG, Regression, RNASeq, Sequencing, SystemsBiology, TimeCourse, Transcription, Transcriptomics Author: Denis Agniel [aut], Boris P. Hejblum [aut, cre], Marine Gauthier [aut] Maintainer: Boris P. Hejblum VignetteBuilder: knitr BugReports: https://github.com/borishejblum/dearseq/issues git_url: https://git.bioconductor.org/packages/dearseq git_branch: RELEASE_3_12 git_last_commit: baec756 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dearseq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dearseq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dearseq_1.2.0.tgz vignettes: vignettes/dearseq/inst/doc/dearseqUserguide.html vignetteTitles: dearseqUserguide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dearseq/inst/doc/dearseqUserguide.R dependencyCount: 44 Package: debCAM Version: 1.8.0 Depends: R (>= 3.5) Imports: methods, rJava, BiocParallel, stats, Biobase, SummarizedExperiment, corpcor, geometry, NMF, nnls, DMwR, pcaPP, apcluster, graphics Suggests: knitr, rmarkdown, BiocStyle, testthat, GEOquery, rgl License: GPL-2 MD5sum: d2974e7fb378be7ff5dafcd34978246f NeedsCompilation: no Title: Deconvolution by Convex Analysis of Mixtures Description: An R package for fully unsupervised deconvolution of complex tissues. It provides basic functions to perform unsupervised deconvolution on mixture expression profiles by Convex Analysis of Mixtures (CAM) and some auxiliary functions to help understand the subpopulation-specific results. It also implements functions to perform supervised deconvolution based on prior knowledge of molecular markers, S matrix or A matrix. Combining molecular markers from CAM and from prior knowledge can achieve semi-supervised deconvolution of mixtures. biocViews: Software, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/Lululuella/debCAM/issues git_url: https://git.bioconductor.org/packages/debCAM git_branch: RELEASE_3_12 git_last_commit: 19e8e5a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/debCAM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/debCAM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/debCAM_1.8.0.tgz vignettes: vignettes/debCAM/inst/doc/debcam.html vignetteTitles: debCAM User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/debCAM/inst/doc/debcam.R dependencyCount: 94 Package: debrowser Version: 1.18.3 Depends: R (>= 3.5.0), Imports: shiny, jsonlite, shinyjs, shinydashboard, shinyBS, gplots, DT, ggplot2, RColorBrewer, annotate, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, org.Hs.eg.db, org.Mm.eg.db, limma, edgeR, clusterProfiler, methods, sva, RCurl, enrichplot, colourpicker, plotly, heatmaply, Harman, pathview, apeglm, ashr Suggests: testthat, rmarkdown, BiocStyle, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: 38f85a2a2356f98c43489c298e120599 NeedsCompilation: no Title: Interactive Differential Expresion Analysis Browser Description: Bioinformatics platform containing interactive plots and tables for differential gene and region expression studies. Allows visualizing expression data much more deeply in an interactive and faster way. By changing the parameters, users can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With DEBrowser users can prepare plots without writing any code. Differential expression, PCA and clustering analysis are made on site and the results are shown in various plots such as scatter, bar, box, volcano, ma plots and Heatmaps. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Clustering, ImmunoOncology Author: Alper Kucukural , Onur Yukselen , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, BiocStyle, rmarkdown, R.rsp BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new git_url: https://git.bioconductor.org/packages/debrowser git_branch: RELEASE_3_12 git_last_commit: 8dbbe8b git_last_commit_date: 2021-04-23 Date/Publication: 2021-04-23 source.ver: src/contrib/debrowser_1.18.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/debrowser_1.18.3.zip mac.binary.ver: bin/macosx/contrib/4.0/debrowser_1.18.3.tgz vignettes: vignettes/debrowser/inst/doc/DEBrowser.html vignetteTitles: DEBrowser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R dependencyCount: 201 Package: DECIPHER Version: 2.18.1 Depends: R (>= 3.5.0), Biostrings (>= 2.35.12), RSQLite (>= 1.1), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, S4Vectors, IRanges, XVector License: GPL-3 Archs: i386, x64 MD5sum: 4cfa154ff5b66988e79b65ed828a922c NeedsCompilation: yes Title: Tools for curating, analyzing, and manipulating biological sequences Description: A toolset for deciphering and managing biological sequences. biocViews: Clustering, Genetics, Sequencing, DataImport, Visualization, Microarray, QualityControl, qPCR, Alignment, WholeGenome, Microbiome, ImmunoOncology, GenePrediction Author: Erik Wright Maintainer: Erik Wright git_url: https://git.bioconductor.org/packages/DECIPHER git_branch: RELEASE_3_12 git_last_commit: 6a70842 git_last_commit_date: 2020-10-28 Date/Publication: 2020-10-29 source.ver: src/contrib/DECIPHER_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/DECIPHER_2.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/DECIPHER_2.18.1.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.pdf, vignettes/DECIPHER/inst/doc/ClassifySequences.pdf, vignettes/DECIPHER/inst/doc/DECIPHERing.pdf, vignettes/DECIPHER/inst/doc/DesignMicroarray.pdf, vignettes/DECIPHER/inst/doc/DesignPrimers.pdf, vignettes/DECIPHER/inst/doc/DesignProbes.pdf, vignettes/DECIPHER/inst/doc/DesignSignatures.pdf, vignettes/DECIPHER/inst/doc/FindChimeras.pdf, vignettes/DECIPHER/inst/doc/FindingGenes.pdf vignetteTitles: The Art of Multiple Sequence Alignment in R, Classify Sequences, Getting Started DECIPHERing, Design Microarray Probes, Design Group-Specific Primers, Design Group-Specific FISH Probes, Design Primers That Yield Group-Specific Signatures, Finding Chimeric Sequences, The Magic of Gene Finding hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.R, vignettes/DECIPHER/inst/doc/ClassifySequences.R, vignettes/DECIPHER/inst/doc/DECIPHERing.R, vignettes/DECIPHER/inst/doc/DesignMicroarray.R, vignettes/DECIPHER/inst/doc/DesignPrimers.R, vignettes/DECIPHER/inst/doc/DesignProbes.R, vignettes/DECIPHER/inst/doc/DesignSignatures.R, vignettes/DECIPHER/inst/doc/FindChimeras.R, vignettes/DECIPHER/inst/doc/FindingGenes.R dependsOnMe: AssessORF, sangeranalyseR, SynExtend importsMe: metagenomeFeatures, openPrimeR, AssessORFData, ensembleTax, microbial suggestsMe: MicrobiotaProcess, pagoo dependencyCount: 30 Package: deco Version: 1.6.0 Depends: R (>= 3.5.0), AnnotationDbi, BiocParallel, SummarizedExperiment, limma Imports: stats, methods, ggplot2, foreign, graphics, BiocStyle, Biobase, cluster, gplots, RColorBrewer, locfit, made4, ade4, sfsmisc, scatterplot3d, gdata, grDevices, utils, reshape2, gridExtra Suggests: knitr, curatedTCGAData, MultiAssayExperiment, Homo.sapiens License: GPL (>=3) MD5sum: 0aa843af87c91e69a96bb4f38ab5e60d NeedsCompilation: no Title: Decomposing Heterogeneous Cohorts using Omic Data Profiling Description: This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels. biocViews: Software, FeatureExtraction, Clustering, MultipleComparison, DifferentialExpression, Transcriptomics, BiomedicalInformatics, Proteomics, Bayesian, GeneExpression, Transcription, Sequencing, Microarray, ExonArray, RNASeq, MicroRNAArray, mRNAMicroarray Author: Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Francisco Jose Campos Laborie URL: https://github.com/fjcamlab/deco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deco git_branch: RELEASE_3_12 git_last_commit: 910e12a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/deco_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/deco_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/deco_1.6.0.tgz vignettes: vignettes/deco/inst/doc/DECO.html vignetteTitles: deco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deco/inst/doc/DECO.R dependencyCount: 112 Package: DEComplexDisease Version: 1.10.0 Depends: R (>= 3.3.3) Imports: Rcpp (>= 0.12.7), DESeq2, edgeR, SummarizedExperiment, ComplexHeatmap, grid, parallel, BiocParallel, grDevices, graphics, stats, methods, utils LinkingTo: Rcpp Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 4d8a6d31801b6ed09a67a350591bb77b NeedsCompilation: yes Title: A tool for differential expression analysis and DEGs based investigation to complex diseases by bi-clustering analysis Description: It is designed to find the differential expressed genes (DEGs) for complex disease, which is characterized by the heterogeneous genomic expression profiles. Different from the established DEG analysis tools, it does not assume the patients of complex diseases to share the common DEGs. By applying a bi-clustering algorithm, DECD finds the DEGs shared by as many patients. In this way, DECD describes the DEGs of complex disease in a novel syntax, e.g. a gene list composed of 200 genes are differentially expressed in 30% percent of studied complex disease. Applying the DECD analysis results, users are possible to find the patients affected by the same mechanism based on the shared signatures. biocViews: DNASeq, WholeGenome, FunctionalGenomics, DifferentialExpression,GeneExpression, Clustering Author: Guofeng Meng Maintainer: Guofeng Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEComplexDisease git_branch: RELEASE_3_12 git_last_commit: 0ebebc3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEComplexDisease_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEComplexDisease_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEComplexDisease_1.10.0.tgz vignettes: vignettes/DEComplexDisease/inst/doc/vignettes.pdf, vignettes/DEComplexDisease/inst/doc/decd.html vignetteTitles: DEComplexDisease: a R package for DE analysis, DEComplexDisease: a R package for DE analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEComplexDisease/inst/doc/decd.R dependencyCount: 102 Package: decompTumor2Sig Version: 2.6.0 Depends: R(>= 3.6), ggplot2 Imports: methods, Matrix, quadprog(>= 1.5-5), GenomicRanges, stats, GenomicFeatures, Biostrings, BiocGenerics, S4Vectors, plyr, utils, graphics, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, VariantAnnotation, SummarizedExperiment, ggseqlogo, gridExtra, data.table, GenomeInfoDb Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: de216f3ebfae39dd811bcfc3ee242a58 NeedsCompilation: no Title: Decomposition of individual tumors into mutational signatures by signature refitting Description: Uses quadratic programming for signature refitting, i.e., to decompose the mutation catalog from an individual tumor sample into a set of given mutational signatures (either Alexandrov-model signatures or Shiraishi-model signatures), computing weights that reflect the contributions of the signatures to the mutation load of the tumor. biocViews: Software, SNP, Sequencing, DNASeq, GenomicVariation, SomaticMutation, BiomedicalInformatics, Genetics, BiologicalQuestion, StatisticalMethod Author: Rosario M. Piro [aut, cre], Sandra Krueger [ctb] Maintainer: Rosario M. Piro URL: http://rmpiro.net/decompTumor2Sig/, https://github.com/rmpiro/decompTumor2Sig VignetteBuilder: knitr BugReports: https://github.com/rmpiro/decompTumor2Sig/issues git_url: https://git.bioconductor.org/packages/decompTumor2Sig git_branch: RELEASE_3_12 git_last_commit: f498786 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/decompTumor2Sig_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/decompTumor2Sig_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/decompTumor2Sig_2.6.0.tgz vignettes: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.html vignetteTitles: A brief introduction to decompTumor2Sig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decompTumor2Sig/inst/doc/decompTumor2Sig.R importsMe: musicatk dependencyCount: 112 Package: DeconRNASeq Version: 1.32.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: a284da987315e3ec24fc7b30c3dcd70e NeedsCompilation: no Title: Deconvolution of Heterogeneous Tissue Samples for mRNA-Seq data Description: DeconSeq is an R package for deconvolution of heterogeneous tissues based on mRNA-Seq data. It modeled expression levels from heterogeneous cell populations in mRNA-Seq as the weighted average of expression from different constituting cell types and predicted cell type proportions of single expression profiles. biocViews: DifferentialExpression Author: Ting Gong Joseph D. Szustakowski Maintainer: Ting Gong git_url: https://git.bioconductor.org/packages/DeconRNASeq git_branch: RELEASE_3_12 git_last_commit: d5bc25f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DeconRNASeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DeconRNASeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DeconRNASeq_1.32.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.R suggestsMe: ADAPTS dependencyCount: 46 Package: decontam Version: 1.10.0 Depends: R (>= 3.4.1), methods (>= 3.4.1) Imports: ggplot2 (>= 2.1.0), reshape2 (>= 1.4.1), stats Suggests: BiocStyle, knitr, rmarkdown, phyloseq License: Artistic-2.0 MD5sum: 64d6323212294ae77e59acaf4e40cf3e NeedsCompilation: no Title: Identify Contaminants in Marker-gene and Metagenomics Sequencing Data Description: Simple statistical identification of contaminating sequence features in marker-gene or metagenomics data. Works on any kind of feature derived from environmental sequencing data (e.g. ASVs, OTUs, taxonomic groups, MAGs,...). Requires DNA quantitation data or sequenced negative control samples. biocViews: ImmunoOncology, Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Nicole Marie Davis Maintainer: Benjamin Callahan URL: https://github.com/benjjneb/decontam VignetteBuilder: knitr BugReports: https://github.com/benjjneb/decontam/issues git_url: https://git.bioconductor.org/packages/decontam git_branch: RELEASE_3_12 git_last_commit: f1d82ae git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/decontam_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/decontam_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/decontam_1.10.0.tgz vignettes: vignettes/decontam/inst/doc/decontam_intro.html vignetteTitles: Introduction to dada2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/decontam/inst/doc/decontam_intro.R dependencyCount: 44 Package: DeepBlueR Version: 1.16.0 Depends: R (>= 3.3), XML, RCurl Imports: GenomicRanges, data.table, stringr, diffr, dplyr, methods, rjson, utils, R.utils, foreach, withr, rtracklayer, GenomeInfoDb, settings, filehash Suggests: knitr, rmarkdown, LOLA, Gviz, gplots, ggplot2, tidyr, RColorBrewer, matrixStats License: GPL (>=2.0) MD5sum: 23f56ef8d0b6085cf7f252578b6af280 NeedsCompilation: no Title: DeepBlueR Description: Accessing the DeepBlue Epigenetics Data Server through R. biocViews: DataImport, DataRepresentation, ThirdPartyClient, GeneRegulation, GenomeAnnotation, CpGIsland, DNAMethylation, Epigenetics, Annotation, Preprocessing, ImmunoOncology Author: Felipe Albrecht, Markus List Maintainer: Felipe Albrecht , Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeepBlueR git_branch: RELEASE_3_12 git_last_commit: 9130898 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DeepBlueR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DeepBlueR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DeepBlueR_1.16.0.tgz vignettes: vignettes/DeepBlueR/inst/doc/DeepBlueR.html vignetteTitles: The DeepBlue epigenomic data server - R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeepBlueR/inst/doc/DeepBlueR.R dependencyCount: 77 Package: deepSNV Version: 1.36.0 Depends: R (>= 2.13.0), methods, graphics, parallel, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib (>= 1.13.1) Suggests: RColorBrewer, knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 7c40b8877e8b60b56fbbfd7a33cf7818 NeedsCompilation: yes Title: Detection of subclonal SNVs in deep sequencing data. Description: This package provides provides quantitative variant callers for detecting subclonal mutations in ultra-deep (>=100x coverage) sequencing experiments. The deepSNV algorithm is used for a comparative setup with a control experiment of the same loci and uses a beta-binomial model and a likelihood ratio test to discriminate sequencing errors and subclonal SNVs. The shearwater algorithm computes a Bayes classifier based on a beta-binomial model for variant calling with multiple samples for precisely estimating model parameters - such as local error rates and dispersion - and prior knowledge, e.g. from variation data bases such as COSMIC. biocViews: GeneticVariability, SNP, Sequencing, Genetics, DataImport Author: Niko Beerenwinkel [ths], Raul Alcantara [ctb], David Jones [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deepSNV git_branch: RELEASE_3_12 git_last_commit: 20969f8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/deepSNV_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/deepSNV_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/deepSNV_1.36.0.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf, vignettes/deepSNV/inst/doc/shearwaterML.html vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater, Shearwater ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deepSNV/inst/doc/deepSNV.R, vignettes/deepSNV/inst/doc/shearwater.R, vignettes/deepSNV/inst/doc/shearwaterML.R suggestsMe: GenomicFiles dependencyCount: 92 Package: DEFormats Version: 1.18.0 Imports: checkmate, data.table, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: 978cfd2ebf4cc244733bdcf33c202154 NeedsCompilation: no Title: Differential gene expression data formats converter Description: Convert between different data formats used by differential gene expression analysis tools. biocViews: ImmunoOncology, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Transcription Author: Andrzej Oleś Maintainer: Andrzej Oleś URL: https://github.com/aoles/DEFormats VignetteBuilder: knitr BugReports: https://github.com/aoles/DEFormats/issues git_url: https://git.bioconductor.org/packages/DEFormats git_branch: RELEASE_3_12 git_last_commit: 5297c56 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEFormats_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEFormats_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEFormats_1.18.0.tgz vignettes: vignettes/DEFormats/inst/doc/DEFormats.html vignetteTitles: Differential gene expression data formats converter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R importsMe: regionReport suggestsMe: ideal dependencyCount: 95 Package: DegNorm Version: 1.0.0 Depends: R (>= 4.0.0), methods Imports: Rcpp (>= 1.0.2),GenomicFeatures, parallel, foreach, S4Vectors, doParallel, Rsamtools (>= 1.31.2), GenomicAlignments, heatmaply, data.table, stats, ggplot2, GenomicRanges, IRanges, plyr, plotly, utils,viridis LinkingTo: Rcpp, RcppArmadillo,S4Vectors,IRanges Suggests: knitr,rmarkdown,formatR License: LGPL (>= 3) Archs: i386, x64 MD5sum: 1060c727ca42d4b5baf8270bee2fecce NeedsCompilation: yes Title: DegNorm: degradation normalization for RNA-seq data Description: This package performs degradation normalization in bulk RNA-seq data to improve differential expression analysis accuracy. biocViews: RNASeq, Normalization, GeneExpression, Alignment,Coverage, DifferentialExpression, BatchEffect,Software,Sequencing, ImmunoOncology, QualityControl, DataImport Author: Bin Xiong and Ji-Ping Wang Maintainer: Ji-Ping Wang VignetteBuilder: knitr BugReports: https://github.com/jipingw/DegNorm/issues git_url: https://git.bioconductor.org/packages/DegNorm git_branch: RELEASE_3_12 git_last_commit: 5331464 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DegNorm_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DegNorm_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DegNorm_1.0.0.tgz vignettes: vignettes/DegNorm/inst/doc/DegNorm.html vignetteTitles: DegNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DegNorm/inst/doc/DegNorm.R dependencyCount: 142 Package: DEGraph Version: 1.42.0 Depends: R (>= 2.10.0), R.utils Imports: graph, KEGGgraph, lattice, mvtnorm, R.methodsS3, RBGL, Rgraphviz, rrcov, NCIgraph Suggests: corpcor, fields, graph, KEGGgraph, lattice, marray, RBGL, rrcov, Rgraphviz, NCIgraph License: GPL-3 MD5sum: 68c52a19f8b515d30d97bbcaee300993 NeedsCompilation: no Title: Two-sample tests on a graph Description: DEGraph implements recent hypothesis testing methods which directly assess whether a particular gene network is differentially expressed between two conditions. This is to be contrasted with the more classical two-step approaches which first test individual genes, then test gene sets for enrichment in differentially expressed genes. These recent methods take into account the topology of the network to yield more powerful detection procedures. DEGraph provides methods to easily test all KEGG pathways for differential expression on any gene expression data set and tools to visualize the results. biocViews: Microarray, DifferentialExpression, GraphAndNetwork, Network, NetworkEnrichment, DecisionTree Author: Laurent Jacob, Pierre Neuvial and Sandrine Dudoit Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/DEGraph git_branch: RELEASE_3_12 git_last_commit: 7c7919a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEGraph_1.42.0.tar.gz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGraph/inst/doc/DEGraph.R dependencyCount: 42 Package: DEGreport Version: 1.26.0 Depends: R (>= 3.6.0) Imports: utils, methods, Biobase, BiocGenerics, broom, circlize, ComplexHeatmap, cowplot, ConsensusClusterPlus, cluster, DESeq2, dplyr, edgeR, ggplot2, ggdendro, grid, ggrepel, grDevices, knitr, logging, lasso2, magrittr, Nozzle.R1, psych, RColorBrewer, reshape, rlang, scales, stats, stringr, S4Vectors, SummarizedExperiment, tidyr, tibble Suggests: BiocStyle, AnnotationDbi, limma, pheatmap, rmarkdown, statmod, testthat License: MIT + file LICENSE MD5sum: f6588a36498425416a61ee994390292c NeedsCompilation: no Title: Report of DEG analysis Description: Creation of a HTML report of differential expression analyses of count data. It integrates some of the code mentioned in DESeq2 and edgeR vignettes, and report a ranked list of genes according to the fold changes mean and variability for each selected gene. biocViews: DifferentialExpression, Visualization, RNASeq, ReportWriting, GeneExpression, ImmunoOncology Author: Lorena Pantano [aut, cre], John Hutchinson [ctb], Victor Barrera [ctb], Mary Piper [ctb], Radhika Khetani [ctb], Kenneth Daily [ctb], Thanneer Malai Perumal [ctb], Rory Kirchner [ctb], Michael Steinbaugh [ctb] Maintainer: Lorena Pantano URL: http://lpantano.github.io/DEGreport/ VignetteBuilder: knitr BugReports: https://github.com/lpantano/DEGreport/issues git_url: https://git.bioconductor.org/packages/DEGreport git_branch: RELEASE_3_12 git_last_commit: 18aeb4f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEGreport_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEGreport_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEGreport_1.26.0.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.html vignetteTitles: QC and downstream analysis for differential expression RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R importsMe: isomiRs dependencyCount: 131 Package: DEGseq Version: 1.44.0 Depends: R (>= 2.8.0), qvalue, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: 1302f87f87a461235a64fce344354f0d NeedsCompilation: yes Title: Identify Differentially Expressed Genes from RNA-seq data Description: DEGseq is an R package to identify differentially expressed genes from RNA-Seq data. biocViews: RNASeq, Preprocessing, GeneExpression, DifferentialExpression, ImmunoOncology Author: Likun Wang and Xi Wang . Maintainer: Likun Wang git_url: https://git.bioconductor.org/packages/DEGseq git_branch: RELEASE_3_12 git_last_commit: 44a377b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEGseq_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEGseq_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEGseq_1.44.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGseq/inst/doc/DEGseq.R dependencyCount: 45 Package: DelayedArray Version: 0.16.3 Depends: R (>= 3.4), methods, stats4, Matrix, BiocGenerics (>= 0.31.5), MatrixGenerics (>= 1.1.3), S4Vectors (>= 0.27.2), IRanges (>= 2.17.3) Imports: stats LinkingTo: S4Vectors Suggests: BiocParallel, HDF5Array (>= 1.17.12), genefilter, SummarizedExperiment, airway, pryr, DelayedMatrixStats, knitr, BiocStyle, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 24e8e5cf8a88943e7002ef77fc6b1c95 NeedsCompilation: yes Title: A unified framework for working transparently with on-disk and in-memory array-like datasets Description: Wrapping an array-like object (typically an on-disk object) in a DelayedArray object allows one to perform common array operations on it without loading the object in memory. In order to reduce memory usage and optimize performance, operations on the object are either delayed or executed using a block processing mechanism. Note that this also works on in-memory array-like objects like DataFrame objects (typically with Rle columns), Matrix objects, ordinary arrays and, data frames. biocViews: Infrastructure, DataRepresentation, Annotation, GenomeAnnotation Author: Hervé Pagès , with contributions from Peter Hickey and Aaron Lun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/DelayedArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedArray/issues git_url: https://git.bioconductor.org/packages/DelayedArray git_branch: RELEASE_3_12 git_last_commit: 42c3398 git_last_commit_date: 2021-03-23 Date/Publication: 2021-03-24 source.ver: src/contrib/DelayedArray_0.16.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/DelayedArray_0.16.3.zip mac.binary.ver: bin/macosx/contrib/4.0/DelayedArray_0.16.3.tgz vignettes: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.pdf, vignettes/DelayedArray/inst/doc/02-Implementing_a_backend.html vignetteTitles: Working with large arrays in R, Implementing A DelayedArray Backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedArray/inst/doc/01-Working_with_large_arrays.R dependsOnMe: DelayedDataFrame, DelayedMatrixStats, GDSArray, HDF5Array, rhdf5client, singleCellTK, TileDBArray, VCFArray importsMe: batchelor, beachmat, bigPint, BiocSingular, bsseq, CAGEr, celaref, celda, clusterExperiment, DEScan2, DropletUtils, DSS, ELMER, FRASER, GenoGAM, GenomicScores, glmGamPoi, hipathia, LoomExperiment, mbkmeans, MethReg, methrix, methylSig, minfi, MOFA2, netSmooth, NewWave, PCAtools, ResidualMatrix, RTCGAToolbox, scater, scDblFinder, scMerge, scmeth, scPCA, scran, scry, scuttle, signatureSearch, SingleR, SummarizedExperiment, TSCAN, VariantExperiment, velociraptor, weitrix, zellkonverter, celldex suggestsMe: BiocGenerics, ChIPpeakAnno, gwascat, iSEE, MAST, S4Vectors, SQLDataFrame dependencyCount: 15 Package: DelayedDataFrame Version: 1.6.0 Depends: R (>= 3.6), S4Vectors (>= 0.23.19), DelayedArray (>= 0.7.5) Imports: methods, stats, BiocGenerics Suggests: testthat, knitr, rmarkdown, SeqArray, GDSArray License: GPL-3 MD5sum: bec6c8c8c40396b2de85656e9cf54a1f NeedsCompilation: no Title: Delayed operation on DataFrame using standard DataFrame metaphor Description: Based on the standard DataFrame metaphor, we are trying to implement the feature of delayed operation on the DelayedDataFrame, with a slot of lazyIndex, which saves the mapping indexes for each column of DelayedDataFrame. Methods like show, validity check, [/[[ subsetting, rbind/cbind are implemented for DelayedDataFrame to be operated around lazyIndex. The listData slot stays untouched until a realization call e.g., DataFrame constructor OR as.list() is invoked. biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/DelayedDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/DelayedDataFrame/issues git_url: https://git.bioconductor.org/packages/DelayedDataFrame git_branch: RELEASE_3_12 git_last_commit: 7bffba7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DelayedDataFrame_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DelayedDataFrame_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DelayedDataFrame_1.6.0.tgz vignettes: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.html vignetteTitles: DelayedDataFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DelayedDataFrame/inst/doc/DelayedDataFrame.R dependsOnMe: VariantExperiment dependencyCount: 16 Package: DelayedMatrixStats Version: 1.12.3 Depends: MatrixGenerics, DelayedArray (>= 0.15.3) Imports: methods, matrixStats (>= 0.56.0), sparseMatrixStats, Matrix, S4Vectors (>= 0.17.5), IRanges, HDF5Array (>= 1.17.2), BiocParallel Suggests: testthat, knitr, rmarkdown, covr, BiocStyle, microbenchmark, profmem License: MIT + file LICENSE MD5sum: 3c50b3baa9f0e4a4f6b40b1e71d852e5 NeedsCompilation: no Title: Functions that Apply to Rows and Columns of 'DelayedMatrix' Objects Description: A port of the 'matrixStats' API for use with DelayedMatrix objects from the 'DelayedArray' package. High-performing functions operating on rows and columns of DelayedMatrix objects, e.g. col / rowMedians(), col / rowRanks(), and col / rowSds(). Functions optimized per data type and for subsetted calculations such that both memory usage and processing time is minimized. biocViews: Infrastructure, DataRepresentation, Software Author: Peter Hickey [aut, cre], Hervé Pagès [ctb], Aaron Lun [ctb] Maintainer: Peter Hickey URL: https://github.com/PeteHaitch/DelayedMatrixStats VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/DelayedMatrixStats/issues git_url: https://git.bioconductor.org/packages/DelayedMatrixStats git_branch: RELEASE_3_12 git_last_commit: 81b5218 git_last_commit_date: 2021-02-02 Date/Publication: 2021-02-03 source.ver: src/contrib/DelayedMatrixStats_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/DelayedMatrixStats_1.12.3.zip mac.binary.ver: bin/macosx/contrib/4.0/DelayedMatrixStats_1.12.3.tgz vignettes: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.html vignetteTitles: Overview of DelayedMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DelayedMatrixStats/inst/doc/DelayedMatrixStatsOverview.R importsMe: batchelor, biscuiteer, bsseq, dmrseq, FRASER, glmGamPoi, methrix, methylSig, minfi, PCAtools, scater, scMerge, scran, scuttle, singleCellTK, SingleR, weitrix, celldex suggestsMe: DelayedArray, MatrixGenerics, mbkmeans, scPCA dependencyCount: 30 Package: deltaCaptureC Version: 1.4.0 Depends: R (>= 3.6) Imports: IRanges, GenomicRanges, SummarizedExperiment, ggplot2, DESeq2 Suggests: knitr, rmarkdown License: MIT + file LICENSE MD5sum: 48814e06e56feff9d7270bd51c3a75ed NeedsCompilation: no Title: This Package Discovers Meso-scale Chromatin Remodeling from 3C Data Description: This package discovers meso-scale chromatin remodelling from 3C data. 3C data is local in nature. It givens interaction counts between restriction enzyme digestion fragments and a preferred 'viewpoint' region. By binning this data and using permutation testing, this package can test whether there are statistically significant changes in the interaction counts between the data from two cell types or two treatments. biocViews: BiologicalQuestion, StatisticalMethod Author: Michael Shapiro [aut, cre] () Maintainer: Michael Shapiro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaCaptureC git_branch: RELEASE_3_12 git_last_commit: 19f2c77 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/deltaCaptureC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/deltaCaptureC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/deltaCaptureC_1.4.0.tgz vignettes: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.html vignetteTitles: Delta Capture-C hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/deltaCaptureC/inst/doc/deltaCaptureC.R dependencyCount: 90 Package: deltaGseg Version: 1.30.0 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 8843bf3cc89ff55a8a025c0fd62dcb3b NeedsCompilation: no Title: deltaGseg Description: Identifying distinct subpopulations through multiscale time series analysis biocViews: Proteomics, TimeCourse, Visualization, Clustering Author: Diana Low, Efthymios Motakis Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/deltaGseg git_branch: RELEASE_3_12 git_last_commit: 89e7554 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/deltaGseg_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/deltaGseg_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/deltaGseg_1.30.0.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/deltaGseg/inst/doc/deltaGseg.R dependencyCount: 57 Package: DeMAND Version: 1.20.0 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 95f7f3e0408e9c3d938fc915f116608c NeedsCompilation: no Title: DeMAND Description: DEMAND predicts Drug MoA by interrogating a cell context specific regulatory network with a small number (N >= 6) of compound-induced gene expression signatures, to elucidate specific proteins whose interactions in the network is dysregulated by the compound. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, StatisticalMethod, Network Author: Jung Hoon Woo , Yishai Shimoni Maintainer: Jung Hoon Woo , Mariano Alvarez git_url: https://git.bioconductor.org/packages/DeMAND git_branch: RELEASE_3_12 git_last_commit: d3b8f91 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DeMAND_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DeMAND_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DeMAND_1.20.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DeMAND/inst/doc/DeMAND.R dependencyCount: 3 Package: DeMixT Version: 1.6.0 Depends: R (>= 3.6.0), parallel, Rcpp (>= 1.0.0), SummarizedExperiment, knitr, KernSmooth, matrixcalc Imports: matrixStats, stats, truncdist, base64enc, ggplot2 LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 3f2c40f85d48d793a7ec29bda407a84f NeedsCompilation: yes Title: Cell type-specific deconvolution of heterogeneous tumor samples with two or three components using expression data from RNAseq or microarray platforms Description: DeMixT is a software package that performs deconvolution on transcriptome data from a mixture of two or three components. biocViews: Software, StatisticalMethod, Classification, GeneExpression, Sequencing, Microarray, TissueMicroarray, Coverage Author: Zeya Wang , Shaolong Cao, Wenyi Wang Maintainer: Shaolong Cao, Peng Yang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DeMixT git_branch: RELEASE_3_12 git_last_commit: f3a7555 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DeMixT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DeMixT_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DeMixT_1.6.0.tgz vignettes: vignettes/DeMixT/inst/doc/demixt.html vignetteTitles: DeMixT.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DeMixT/inst/doc/demixt.R dependencyCount: 71 Package: densvis Version: 1.00.6 Imports: Rcpp, basilisk, assertthat, reticulate LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, ggplot2, Rtsne, uwot, testthat License: MIT + file LICENSE Archs: x64 MD5sum: fe9b72ccc2465efa05ed153aea49ffcc NeedsCompilation: yes Title: Density-Preserving Data Visualization via Non-Linear Dimensionality Reduction Description: Implements the density-preserving modification to t-SNE and UMAP described by Narayan et al. (2020) . The non-linear dimensionality reduction techniques t-SNE and UMAP enable users to summarise complex high-dimensional sequencing data such as single cell RNAseq using lower dimensional representations. These lower dimensional representations enable the visualisation of discrete transcriptional states, as well as continuous trajectory (for example, in early development). However, these methods focus on the local neighbourhood structure of the data. In some cases, this results in misleading visualisations, where the density of cells in the low-dimensional embedding does not represent the transcriptional heterogeneity of data in the original high-dimensional space. den-SNE and densMAP aim to enable more accurate visual interpretation of high-dimensional datasets by producing lower-dimensional embeddings that accurately represent the heterogeneity of the original high-dimensional space, enabling the identification of homogeneous and heterogeneous cell states. This accuracy is accomplished by including in the optimisation process a term which considers the local density of points in the original high-dimensional space. This can help to create visualisations that are more representative of heterogeneity in the original high-dimensional space. biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Ashwinn Narayan [aut], Hyunghoon Cho [aut] Maintainer: Alan O'Callaghan VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/densvis/issues git_url: https://git.bioconductor.org/packages/densvis git_branch: RELEASE_3_12 git_last_commit: 2001229 git_last_commit_date: 2021-01-26 Date/Publication: 2021-01-26 source.ver: src/contrib/densvis_1.00.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/densvis_1.00.6.zip mac.binary.ver: bin/macosx/contrib/4.0/densvis_1.00.6.tgz vignettes: vignettes/densvis/inst/doc/densvis.html vignetteTitles: Introduction to densvis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/densvis/inst/doc/densvis.R dependencyCount: 20 Package: DEP Version: 1.12.0 Depends: R (>= 3.5) Imports: ggplot2, dplyr, purrr, readr, tibble, tidyr, SummarizedExperiment (>= 1.11.5), MSnbase, limma, vsn, fdrtool, ggrepel, ComplexHeatmap, RColorBrewer, circlize, shiny, shinydashboard, DT, rmarkdown, assertthat, gridExtra, grid, stats, imputeLCMD, cluster Suggests: testthat, enrichR, knitr, BiocStyle License: Artistic-2.0 MD5sum: 0d72188f2bff733bf690bba3470c947f NeedsCompilation: no Title: Differential Enrichment analysis of Proteomics data Description: This package provides an integrated analysis workflow for robust and reproducible analysis of mass spectrometry proteomics data for differential protein expression or differential enrichment. It requires tabular input (e.g. txt files) as generated by quantitative analysis softwares of raw mass spectrometry data, such as MaxQuant or IsobarQuant. Functions are provided for data preparation, filtering, variance normalization and imputation of missing values, as well as statistical testing of differentially enriched / expressed proteins. It also includes tools to check intermediate steps in the workflow, such as normalization and missing values imputation. Finally, visualization tools are provided to explore the results, including heatmap, volcano plot and barplot representations. For scientists with limited experience in R, the package also contains wrapper functions that entail the complete analysis workflow and generate a report. Even easier to use are the interactive Shiny apps that are provided by the package. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DifferentialExpression, DataRepresentation Author: Arne Smits [cre, aut], Wolfgang Huber [aut] Maintainer: Arne Smits VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEP git_branch: RELEASE_3_12 git_last_commit: d9de56f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEP_1.12.0.tgz vignettes: vignettes/DEP/inst/doc/DEP.html, vignettes/DEP/inst/doc/MissingValues.html vignetteTitles: DEP: Introduction, DEP: Missing value handling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEP/inst/doc/DEP.R, vignettes/DEP/inst/doc/MissingValues.R suggestsMe: proDA, RforProteomics dependencyCount: 148 Package: DepecheR Version: 1.6.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), MASS (>= 7.3.51), Rcpp (>= 1.0.0), dplyr (>= 0.7.8), gplots (>= 3.0.1), viridis (>= 0.5.1), foreach (>= 1.4.4), doSNOW (>= 1.0.16), matrixStats (>= 0.54.0), mixOmics (>= 6.6.1), moments (>= 0.14), grDevices (>= 3.5.2), graphics (>= 3.5.2), stats (>= 3.5.2), utils (>= 3.5), methods (>= 3.5), parallel (>= 3.5.2), reshape2 (>= 1.4.3), beanplot (>= 1.2), FNN (>= 1.1.3), robustbase (>= 0.93.5), gmodels (>= 2.18.1) LinkingTo: Rcpp, RcppEigen Suggests: uwot, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: 3548d14171c9abeec005354a53e4c77c NeedsCompilation: yes Title: Determination of essential phenotypic elements of clusters in high-dimensional entities Description: The purpose of this package is to identify traits in a dataset that can separate groups. This is done on two levels. First, clustering is performed, using an implementation of sparse K-means. Secondly, the generated clusters are used to predict outcomes of groups of individuals based on their distribution of observations in the different clusters. As certain clusters with separating information will be identified, and these clusters are defined by a sparse number of variables, this method can reduce the complexity of data, to only emphasize the data that actually matters. biocViews: Software,CellBasedAssays,Transcription,DifferentialExpression, DataRepresentation,ImmunoOncology,Transcriptomics,Classification,Clustering, DimensionReduction,FeatureExtraction,FlowCytometry,RNASeq,SingleCell, Visualization Author: Jakob Theorell [aut, cre], Axel Theorell [aut] Maintainer: Jakob Theorell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DepecheR git_branch: RELEASE_3_12 git_last_commit: bbc2e36 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DepecheR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DepecheR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DepecheR_1.6.0.tgz vignettes: vignettes/DepecheR/inst/doc/DepecheR_test.html, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.html vignetteTitles: Example of a cytometry data analysis with DepecheR, Using the groupProbPlot plot function for single-cell probability display hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DepecheR/inst/doc/DepecheR_test.R, vignettes/DepecheR/inst/doc/GroupProbPlot_usage.R suggestsMe: flowSpecs dependencyCount: 85 Package: DEqMS Version: 1.8.0 Depends: R(>= 3.5),graphics,stats,ggplot2,limma(>= 3.34) Suggests: BiocStyle,knitr,rmarkdown,plyr,matrixStats,reshape2,farms,utils,ggrepel,ExperimentHub,LSD License: LGPL MD5sum: 46716e590039544b253873ce4656cca7 NeedsCompilation: no Title: a tool to perform statistical analysis of differential protein expression for quantitative proteomics data. Description: DEqMS is developped on top of Limma. However, Limma assumes same prior variance for all genes. In proteomics, the accuracy of protein abundance estimates varies by the number of peptides/PSMs quantified in both label-free and labelled data. Proteins quantification by multiple peptides or PSMs are more accurate. DEqMS package is able to estimate different prior variances for proteins quantified by different number of PSMs/peptides, therefore acchieving better accuracy. The package can be applied to analyze both label-free and labelled proteomics data. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Preprocessing, DifferentialExpression, MultipleComparison,Normalization,Bayesian Author: Yafeng Zhu Maintainer: Yafeng Zhu VignetteBuilder: knitr BugReports: https://github.com/yafeng/DEqMS/issues git_url: https://git.bioconductor.org/packages/DEqMS git_branch: RELEASE_3_12 git_last_commit: 39cbf3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEqMS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEqMS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEqMS_1.8.0.tgz vignettes: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.html vignetteTitles: DEqMS R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEqMS/inst/doc/DEqMS-package-vignette.R dependencyCount: 39 Package: derfinder Version: 1.24.2 Depends: R (>= 3.5.0) Imports: BiocGenerics (>= 0.25.1), AnnotationDbi (>= 1.27.9), BiocParallel (>= 1.15.15), bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 2.3.23), methods, qvalue (>= 1.99.0), Rsamtools (>= 1.25.0), rtracklayer, S4Vectors (>= 0.23.19), stats, utils Suggests: BiocStyle (>= 2.5.19), sessioninfo, derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitr (>= 1.6), limma, RefManageR, rmarkdown (>= 0.3.3), testthat (>= 2.1.0), TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: 1f753a888b2e11dec4ad0c4090570f51 NeedsCompilation: no Title: Annotation-agnostic differential expression analysis of RNA-seq data at base-pair resolution via the DER Finder approach Description: This package provides functions for annotation-agnostic differential expression analysis of RNA-seq data. Two implementations of the DER Finder approach are included in this package: (1) single base-level F-statistics and (2) DER identification at the expressed regions-level. The DER Finder approach can also be used to identify differentially bounded ChIP-seq peaks. biocViews: DifferentialExpression, Sequencing, RNASeq, ChIPSeq, DifferentialPeakCalling, Software, ImmunoOncology, Coverage Author: Leonardo Collado-Torres [aut, cre] (), Alyssa C. Frazee [ctb], Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinder/ git_url: https://git.bioconductor.org/packages/derfinder git_branch: RELEASE_3_12 git_last_commit: 20f2596 git_last_commit_date: 2020-12-18 Date/Publication: 2020-12-18 source.ver: src/contrib/derfinder_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/derfinder_1.24.2.zip mac.binary.ver: bin/macosx/contrib/4.0/derfinder_1.24.2.tgz vignettes: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html vignetteTitles: derfinder quick start guide, derfinder users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R importsMe: brainflowprobes, derfinderPlot, recount, regionReport, GenomicState, recountWorkflow suggestsMe: megadepth dependencyCount: 144 Package: derfinderHelper Version: 1.24.1 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, methods, S4Vectors (>= 0.2.2) Suggests: sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), RefManageR, rmarkdown (>= 0.3.3), testthat, covr License: Artistic-2.0 MD5sum: 073a5d360a2ac859e9f745b5164f1e70 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderHelper VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderHelper git_url: https://git.bioconductor.org/packages/derfinderHelper git_branch: RELEASE_3_12 git_last_commit: de88f99 git_last_commit_date: 2020-12-17 Date/Publication: 2020-12-18 source.ver: src/contrib/derfinderHelper_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/derfinderHelper_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.0/derfinderHelper_1.24.1.tgz vignettes: vignettes/derfinderHelper/inst/doc/derfinderHelper.html vignetteTitles: Introduction to derfinderHelper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R importsMe: derfinder dependencyCount: 13 Package: derfinderPlot Version: 1.24.1 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, graphics, grDevices, IRanges (>= 1.99.28), limma, methods, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales, utils Suggests: biovizBase (>= 1.27.2), bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), sessioninfo, knitr (>= 1.6), BiocStyle (>= 2.5.19), org.Hs.eg.db, RefManageR, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, covr License: Artistic-2.0 MD5sum: fd5718d63606669d2bd3362bc2a0ed99 NeedsCompilation: no Title: Plotting functions for derfinder Description: This package provides plotting functions for results from the derfinder package. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/derfinderPlot VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/derfinderPlot git_url: https://git.bioconductor.org/packages/derfinderPlot git_branch: RELEASE_3_12 git_last_commit: 8c36901 git_last_commit_date: 2020-12-17 Date/Publication: 2020-12-18 source.ver: src/contrib/derfinderPlot_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/derfinderPlot_1.24.1.zip mac.binary.ver: bin/macosx/contrib/4.0/derfinderPlot_1.24.1.tgz vignettes: vignettes/derfinderPlot/inst/doc/derfinderPlot.html vignetteTitles: Introduction to derfinderPlot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R importsMe: brainflowprobes, recountWorkflow suggestsMe: derfinder, regionReport, GenomicState dependencyCount: 161 Package: DEScan2 Version: 1.10.0 Depends: R (>= 3.5), GenomicRanges Imports: BiocParallel, BiocGenerics, ChIPpeakAnno, data.table, DelayedArray, GenomeInfoDb, GenomicAlignments, glue, IRanges, plyr, Rcpp (>= 0.12.13), rtracklayer, S4Vectors (>= 0.23.19), SummarizedExperiment, tools, utils LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, edgeR, limma, EDASeq, RUVSeq, RColorBrewer, statmod License: Artistic-2.0 Archs: i386, x64 MD5sum: 6e7bdbfc52668cb7215a9f71fccd489c NeedsCompilation: yes Title: Differential Enrichment Scan 2 Description: Integrated peak and differential caller, specifically designed for broad epigenomic signals. biocViews: ImmunoOncology, PeakDetection, Epigenetics, Software, Sequencing, Coverage Author: Dario Righelli [aut, cre], John Koberstein [aut], Bruce Gomes [aut], Nancy Zhang [aut], Claudia Angelini [aut], Lucia Peixoto [aut], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEScan2 git_branch: RELEASE_3_12 git_last_commit: 7a7d1be git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEScan2_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEScan2_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEScan2_1.10.0.tgz vignettes: vignettes/DEScan2/inst/doc/DEScan2.html vignetteTitles: DEScan2 Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEScan2/inst/doc/DEScan2.R dependencyCount: 120 Package: DESeq2 Version: 1.30.1 Depends: S4Vectors (>= 0.23.18), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, stats4, locfit, geneplotter, ggplot2, Rcpp (>= 0.11.0) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, vsn, pheatmap, RColorBrewer, apeglm, ashr, tximport, tximeta, tximportData, readr, pbapply, airway, pasilla (>= 0.2.10), glmGamPoi, BiocManager License: LGPL (>= 3) Archs: i386, x64 MD5sum: 7b22e80a95125b4d377afa854e0edc0a NeedsCompilation: yes Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution. biocViews: Sequencing, RNASeq, ChIPSeq, GeneExpression, Transcription, Normalization, DifferentialExpression, Bayesian, Regression, PrincipalComponent, Clustering, ImmunoOncology Author: Michael Love [aut, cre], Constantin Ahlmann-Eltze [ctb], Kwame Forbes [ctb], Simon Anders [aut, ctb], Wolfgang Huber [aut, ctb] Maintainer: Michael Love URL: https://github.com/mikelove/DESeq2 VignetteBuilder: knitr, rmarkdown git_url: https://git.bioconductor.org/packages/DESeq2 git_branch: RELEASE_3_12 git_last_commit: 340e674 git_last_commit_date: 2021-02-19 Date/Publication: 2021-02-19 source.ver: src/contrib/DESeq2_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/DESeq2_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.0/DESeq2_1.30.1.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.html vignetteTitles: Analyzing RNA-seq data with DESeq2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DEWSeq, DEXSeq, FourCSeq, metaseqR2, rgsepd, TCC, XBSeq, rnaseqDTU, rnaseqGene, Brundle, DRomics importsMe: Anaquin, animalcules, BRGenomics, CeTF, circRNAprofiler, consensusDE, coseq, countsimQC, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, deltaCaptureC, DEsubs, DiffBind, EBSEA, eegc, ERSSA, GDCRNATools, GeneTonic, GenoGAM, Glimma, HTSFilter, icetea, ideal, INSPEcT, IntEREst, isomiRs, kissDE, microbiomeExplorer, MLSeq, muscat, NBAMSeq, ORFik, OUTRIDER, PathoStat, pcaExplorer, phantasus, proActiv, RegEnrich, regionReport, ReportingTools, Rmmquant, RNASeqR, scBFA, singleCellTK, SNPhood, spatialHeatmap, srnadiff, systemPipeR, TBSignatureProfiler, TimeSeriesExperiment, UMI4Cats, vidger, BloodCancerMultiOmics2017, FieldEffectCrc, IHWpaper, recountWorkflow, cinaR, HeritSeq, HTSSIP, MetaLonDA, microbial, rmRNAseq, wilson suggestsMe: aggregateBioVar, apeglm, bambu, biobroom, BiocGenerics, BioCor, BiocSet, CAGEr, compcodeR, dearseq, derfinder, diffloop, dittoSeq, EDASeq, EnhancedVolcano, EnrichmentBrowser, fishpond, gage, GenomicAlignments, GenomicRanges, glmGamPoi, IHW, miRmine, OPWeight, phyloseq, progeny, recount, RUVSeq, scran, subSeq, SummarizedBenchmark, systemPipeShiny, TFEA.ChIP, tidybulk, ToPASeq, topconfects, tximeta, tximport, variancePartition, Wrench, zinbwave, curatedAdipoChIP, curatedAdipoRNA, JctSeqData, RegParallel, Single.mTEC.Transcriptomes, CAGEWorkflow, fluentGenomics, conos, FateID, GeoTcgaData, metaRNASeq, RaceID, seqgendiff, Seurat, tcgsaseq dependencyCount: 89 Package: DEsingle Version: 1.10.0 Depends: R (>= 3.4.0) Imports: stats, Matrix (>= 1.2-14), MASS (>= 7.3-45), VGAM (>= 1.0-2), bbmle (>= 1.0.18), gamlss (>= 4.4-0), maxLik (>= 1.3-4), pscl (>= 1.4.9), BiocParallel (>= 1.12.0), Suggests: knitr, rmarkdown, SingleCellExperiment License: GPL-2 MD5sum: a01f068379f3bc34f63bf0d823c23cee NeedsCompilation: no Title: DEsingle for detecting three types of differential expression in single-cell RNA-seq data Description: DEsingle is an R package for differential expression (DE) analysis of single-cell RNA-seq (scRNA-seq) data. It defines and detects 3 types of differentially expressed genes between two groups of single cells, with regard to different expression status (DEs), differential expression abundance (DEa), and general differential expression (DEg). DEsingle employs Zero-Inflated Negative Binomial model to estimate the proportion of real and dropout zeros and to define and detect the 3 types of DE genes. Results showed that DEsingle outperforms existing methods for scRNA-seq DE analysis, and can reveal different types of DE genes that are enriched in different biological functions. biocViews: DifferentialExpression, GeneExpression, SingleCell, ImmunoOncology, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao Maintainer: Zhun Miao URL: https://miaozhun.github.io/DEsingle/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsingle git_branch: RELEASE_3_12 git_last_commit: 2b58523 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEsingle_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEsingle_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEsingle_1.10.0.tgz vignettes: vignettes/DEsingle/inst/doc/DEsingle.html vignetteTitles: DEsingle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsingle/inst/doc/DEsingle.R dependencyCount: 36 Package: destiny Version: 3.4.0 Depends: R (>= 3.4.0) Imports: methods, graphics, grDevices, utils, stats, Matrix, Rcpp (>= 0.10.3), RcppEigen, RSpectra (>= 0.14-0), irlba, pcaMethods, Biobase, BiocGenerics, SummarizedExperiment, SingleCellExperiment, ggplot2, ggplot.multistats, tidyr, tidyselect, ggthemes, VIM, knn.covertree, proxy, RcppHNSW, smoother, scales, scatterplot3d LinkingTo: Rcpp, RcppEigen, grDevices Suggests: nbconvertR (>= 1.3.2), igraph, testthat, FNN, tidyr Enhances: rgl, SingleCellExperiment License: GPL Archs: i386, x64 MD5sum: 885fa89bd647770e4e44ca84a047916e NeedsCompilation: yes Title: Creates diffusion maps Description: Create and plot diffusion maps. biocViews: CellBiology, CellBasedAssays, Clustering, Software, Visualization Author: Philipp Angerer [cre, aut] (), Laleh Haghverdi [ctb], Maren Büttner [ctb] (), Fabian Theis [ctb] (), Carsten Marr [ctb] (), Florian Büttner [ctb] () Maintainer: Philipp Angerer URL: https://theislab.github.io/destiny/, https://github.com/theislab/destiny/, https://www.helmholtz-muenchen.de/icb/destiny, https://bioconductor.org/packages/destiny, https://doi.org/10.1093/bioinformatics/btv715 SystemRequirements: C++11, jupyter nbconvert (see nbconvertR’s INSTALL file) VignetteBuilder: nbconvertR BugReports: https://github.com/theislab/destiny/issues git_url: https://git.bioconductor.org/packages/destiny git_branch: RELEASE_3_12 git_last_commit: a7323c8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/destiny_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/destiny_3.4.0.zip vignettes: vignettes/destiny/inst/doc/Diffusion-Map-recap.pdf, vignettes/destiny/inst/doc/Diffusion-Maps.pdf, vignettes/destiny/inst/doc/DPT.pdf, vignettes/destiny/inst/doc/Gene-Relevance.pdf, vignettes/destiny/inst/doc/Global-Sigma.pdf, vignettes/destiny/inst/doc/tidyverse.pdf vignetteTitles: Diffusion-Map-recap.pdf, Diffusion-Maps.pdf, DPT.pdf, Gene-Relevance.pdf, Global-Sigma.pdf, tidyverse.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: ctgGEM, CytoTree, flowSpy, phemd suggestsMe: CellTrails, monocle, scater dependencyCount: 134 Package: DEsubs Version: 1.16.0 Depends: R (>= 3.3), locfit Imports: graph, igraph, RBGL, circlize, limma, edgeR, EBSeq, NBPSeq, DESeq, stats, grDevices, graphics, pheatmap, utils, ggplot2, Matrix, jsonlite, tools, DESeq2, methods Suggests: RUnit, BiocGenerics, knitr License: GPL-3 MD5sum: 57b57e89981a95287f8424ca0ae2fde9 NeedsCompilation: no Title: DEsubs: an R package for flexible identification of differentially expressed subpathways using RNA-seq expression experiments Description: DEsubs is a network-based systems biology package that extracts disease-perturbed subpathways within a pathway network as recorded by RNA-seq experiments. It contains an extensive and customizable framework covering a broad range of operation modes at all stages of the subpathway analysis, enabling a case-specific approach. The operation modes refer to the pathway network construction and processing, the subpathway extraction, visualization and enrichment analysis with regard to various biological and pharmacological features. Its capabilities render it a tool-guide for both the modeler and experimentalist for the identification of more robust systems-level biomarkers for complex diseases. biocViews: SystemsBiology, GraphAndNetwork, Pathways, KEGG, GeneExpression, NetworkEnrichment, Network, RNASeq, DifferentialExpression, Normalization, ImmunoOncology Author: Aristidis G. Vrahatis and Panos Balomenos Maintainer: Aristidis G. Vrahatis , Panos Balomenos VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEsubs git_branch: RELEASE_3_12 git_last_commit: f47d89e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEsubs_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEsubs_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEsubs_1.16.0.tgz vignettes: vignettes/DEsubs/inst/doc/DEsubs.pdf vignetteTitles: DEsubs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEsubs/inst/doc/DEsubs.R dependencyCount: 126 Package: DEWSeq Version: 1.4.4 Depends: R(>= 4.0.0), R.utils, DESeq2, BiocParallel Imports: BiocGenerics, data.table(>= 1.11.8), GenomeInfoDb, GenomicRanges, methods, S4Vectors, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, IHW License: LGPL (>= 3) MD5sum: 1511313df222dc308fc120ab69057f96 NeedsCompilation: no Title: Differential Expressed Windows Based on Negative Binomial Distribution Description: DEWSeq is a sliding window approach for the analysis of differentially enriched binding regions eCLIP or iCLIP next generation sequencing data. biocViews: Sequencing, GeneRegulation, FunctionalGenomics, DifferentialExpression Author: Sudeep Sahadevan [aut], Thomas Schwarzl [aut], bioinformatics team Hentze [aut, cre] Maintainer: bioinformatics team Hentze URL: https://github.com/EMBL-Hentze-group/DEWSeq/ VignetteBuilder: knitr BugReports: https://github.com/EMBL-Hentze-group/DEWSeq/issues git_url: https://git.bioconductor.org/packages/DEWSeq git_branch: RELEASE_3_12 git_last_commit: 18bf75d git_last_commit_date: 2020-11-26 Date/Publication: 2020-11-26 source.ver: src/contrib/DEWSeq_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEWSeq_1.4.4.zip mac.binary.ver: bin/macosx/contrib/4.0/DEWSeq_1.4.4.tgz vignettes: vignettes/DEWSeq/inst/doc/DEWSeq.html vignetteTitles: Analyzing eCLIP/iCLIP data with DEWSeq hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEWSeq/inst/doc/DEWSeq.R dependencyCount: 94 Package: DEXSeq Version: 1.36.0 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors (>= 0.23.18) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr, rmarkdown, testthat License: GPL (>= 3) MD5sum: e125ed3fdeea239bb095bae87f1d1b9a NeedsCompilation: no Title: Inference of differential exon usage in RNA-Seq Description: The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression, AlternativeSplicing, DifferentialSplicing, GeneExpression, Visualization Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DEXSeq git_branch: RELEASE_3_12 git_last_commit: f0a361a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DEXSeq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DEXSeq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DEXSeq_1.36.0.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.html vignetteTitles: Inferring differential exon usage in RNA-Seq data with the DEXSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R dependsOnMe: IsoformSwitchAnalyzeR, rnaseqDTU importsMe: IntEREst suggestsMe: bambu, GenomicRanges, proActiv, stageR, subSeq, JctSeqData, pasilla dependencyCount: 110 Package: dexus Version: 1.30.0 Depends: R (>= 2.15), methods, BiocGenerics Imports: stats Suggests: parallel, statmod, DESeq, RColorBrewer License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 1bed37ef6c81f1abf2b88a070de2dd56 NeedsCompilation: yes Title: DEXUS - Identifying Differential Expression in RNA-Seq Studies with Unknown Conditions or without Replicates Description: DEXUS identifies differentially expressed genes in RNA-Seq data under all possible study designs such as studies without replicates, without sample groups, and with unknown conditions. DEXUS works also for known conditions, for example for RNA-Seq data with two or multiple conditions. RNA-Seq read count data can be provided both by the S4 class Count Data Set and by read count matrices. Differentially expressed transcripts can be visualized by heatmaps, in which unknown conditions, replicates, and samples groups are also indicated. This software is fast since the core algorithm is written in C. For very large data sets, a parallel version of DEXUS is provided in this package. DEXUS is a statistical model that is selected in a Bayesian framework by an EM algorithm. DEXUS does not need replicates to detect differentially expressed transcripts, since the replicates (or conditions) are estimated by the EM method for each transcript. The method provides an informative/non-informative value to extract differentially expressed transcripts at a desired significance level or power. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, CellBiology, Classification, QualityControl Author: Guenter Klambauer Maintainer: Guenter Klambauer git_url: https://git.bioconductor.org/packages/dexus git_branch: RELEASE_3_12 git_last_commit: 6735357 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dexus_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dexus_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dexus_1.30.0.tgz vignettes: vignettes/dexus/inst/doc/dexus.pdf vignetteTitles: dexus: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dexus/inst/doc/dexus.R dependencyCount: 6 Package: DFP Version: 1.48.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 50bb372e38aaa59a854c4cd258036bd7 NeedsCompilation: no Title: Gene Selection Description: This package provides a supervised technique able to identify differentially expressed genes, based on the construction of \emph{Fuzzy Patterns} (FPs). The Fuzzy Patterns are built by means of applying 3 Membership Functions to discretized gene expression values. biocViews: Microarray, DifferentialExpression Author: R. Alvarez-Gonzalez, D. Glez-Pena, F. Diaz, F. Fdez-Riverola Maintainer: Rodrigo Alvarez-Glez git_url: https://git.bioconductor.org/packages/DFP git_branch: RELEASE_3_12 git_last_commit: 4813c59 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DFP_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DFP_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DFP_1.48.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DFP/inst/doc/DFP.R dependencyCount: 7 Package: DIAlignR Version: 1.2.0 Depends: methods, stats Imports: zoo (>= 1.8-3), dplyr, tidyr, rlang, mzR (>= 2.18), signal, ggplot2, scales, gridExtra, RSQLite, DBI, Rcpp LinkingTo: Rcpp Suggests: knitr, lattice, latticeExtra, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 Archs: i386, x64 MD5sum: 98a95d9016edf124fe837ccbec5b2b1a NeedsCompilation: yes Title: Dynamic Programming Based Alignment of MS2 Chromatograms Description: To obtain unbiased proteome coverage from a biological sample, mass-spectrometer is operated in Data Independent Acquisition (DIA) mode. Alignment of these DIA runs establishes consistency and less missing values in complete data-matrix. This package implements dynamic programming with affine gap penalty based approach for pair-wise alignment of analytes. A hybrid approach of global alignment (through MS2 features) and local alignment (with MS2 chromatograms) is implemented in this tool. biocViews: MassSpectrometry, Metabolomics, Proteomics, Alignment, Software Author: Shubham Gupta , Hannes Rost Maintainer: Shubham Gupta SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/shubham1637/DIAlignR/issues git_url: https://git.bioconductor.org/packages/DIAlignR git_branch: RELEASE_3_12 git_last_commit: 25b9e1a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DIAlignR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DIAlignR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DIAlignR_1.2.0.tgz vignettes: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.html vignetteTitles: MS2 chromatograms based alignment of targeted mass-spectrometry runs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DIAlignR/inst/doc/DIAlignR-vignette.R dependencyCount: 65 Package: DiffBind Version: 3.0.15 Depends: R (>= 4.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, lattice, systemPipeR, tools, Rcpp, dplyr, ggplot2, BiocParallel, parallel, S4Vectors, Rsamtools (>= 2.0), DESeq2, methods, graphics, ggrepel, apeglm, ashr, GreyListChIP LinkingTo: Rhtslib (>= 1.15.3), Rcpp Suggests: BiocStyle, testthat, xtable Enhances: rgl, XLConnect, edgeR, csaw, BSgenome, GenomeInfoDb License: Artistic-2.0 Archs: i386, x64 MD5sum: acdbfbd3e4f10c0af349fd16a8cf1abe NeedsCompilation: yes Title: Differential Binding Analysis of ChIP-Seq Peak Data Description: Compute differentially bound sites from multiple ChIP-seq experiments using affinity (quantitative) data. Also enables occupancy (overlap) analysis and plotting functions. biocViews: Sequencing, ChIPSeq, DifferentialPeakCalling, ATACSeq, Epigenetics, FunctionalGenomics Author: Rory Stark [aut, cre], Gord Brown [aut] Maintainer: Rory Stark URL: https://www.cruk.cam.ac.uk/core-facilities/bioinformatics-core/software/diffbind SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/DiffBind git_branch: RELEASE_3_12 git_last_commit: 9c4e998 git_last_commit_date: 2021-03-22 Date/Publication: 2021-03-23 source.ver: src/contrib/DiffBind_3.0.15.tar.gz win.binary.ver: bin/windows/contrib/4.0/DiffBind_3.0.15.zip mac.binary.ver: bin/macosx/contrib/4.0/DiffBind_3.0.15.tgz vignettes: vignettes/DiffBind/inst/doc/DiffBind.pdf vignetteTitles: DiffBind: Differential binding analysis of ChIP-Seq peak data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffBind/inst/doc/DiffBind.R dependsOnMe: ChIPQC, vulcan, Brundle dependencyCount: 166 Package: diffcoexp Version: 1.10.0 Depends: R (>= 3.5), WGCNA, SummarizedExperiment Imports: stats, DiffCorr, psych, igraph, BiocGenerics Suggests: GEOquery License: GPL (>2) MD5sum: 48cad304920ef94b066c4ed0c360f4b1 NeedsCompilation: no Title: Differential Co-expression Analysis Description: A tool for the identification of differentially coexpressed links (DCLs) and differentially coexpressed genes (DCGs). DCLs are gene pairs with significantly different correlation coefficients under two conditions. DCGs are genes with significantly more DCLs than by chance. biocViews: GeneExpression, DifferentialExpression, Transcription, Microarray, OneChannel, TwoChannel, RNASeq, Sequencing, Coverage, ImmunoOncology Author: Wenbin Wei, Sandeep Amberkar, Winston Hide Maintainer: Wenbin Wei URL: https://github.com/hidelab/diffcoexp git_url: https://git.bioconductor.org/packages/diffcoexp git_branch: RELEASE_3_12 git_last_commit: 71abe86 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/diffcoexp_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/diffcoexp_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/diffcoexp_1.10.0.tgz vignettes: vignettes/diffcoexp/inst/doc/diffcoexp.pdf vignetteTitles: About diffcoexp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffcoexp/inst/doc/diffcoexp.R dependencyCount: 115 Package: diffcyt Version: 1.10.0 Depends: R (>= 3.4.0) Imports: flowCore, FlowSOM, SummarizedExperiment, S4Vectors, limma, edgeR, lme4, multcomp, dplyr, tidyr, reshape2, magrittr, stats, methods, utils, grDevices, graphics, ComplexHeatmap, circlize, grid Suggests: BiocStyle, knitr, rmarkdown, testthat, HDCytoData, CATALYST License: MIT + file LICENSE MD5sum: bfeff8108516e5997771d62c82cf0a99 NeedsCompilation: no Title: Differential discovery in high-dimensional cytometry via high-resolution clustering Description: Statistical methods for differential discovery analyses in high-dimensional cytometry data (including flow cytometry, mass cytometry or CyTOF, and oligonucleotide-tagged cytometry), based on a combination of high-resolution clustering and empirical Bayes moderated tests adapted from transcriptomics. biocViews: ImmunoOncology, FlowCytometry, Proteomics, SingleCell, CellBasedAssays, CellBiology, Clustering, FeatureExtraction, Software Author: Lukas M. Weber [aut, cre] () Maintainer: Lukas M. Weber URL: https://github.com/lmweber/diffcyt VignetteBuilder: knitr BugReports: https://github.com/lmweber/diffcyt/issues git_url: https://git.bioconductor.org/packages/diffcyt git_branch: RELEASE_3_12 git_last_commit: d186f82 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/diffcyt_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/diffcyt_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/diffcyt_1.10.0.tgz vignettes: vignettes/diffcyt/inst/doc/diffcyt_workflow.html vignetteTitles: diffcyt workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffcyt/inst/doc/diffcyt_workflow.R dependsOnMe: cytofWorkflow suggestsMe: CATALYST, HDCytoData dependencyCount: 162 Package: diffGeneAnalysis Version: 1.72.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: 74e38d29dbf9e9f32ab0563dd462542e NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi git_url: https://git.bioconductor.org/packages/diffGeneAnalysis git_branch: RELEASE_3_12 git_last_commit: d4a3792 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/diffGeneAnalysis_1.72.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/diffGeneAnalysis_1.72.0.zip mac.binary.ver: bin/macosx/contrib/4.0/diffGeneAnalysis_1.72.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.R dependencyCount: 5 Package: diffHic Version: 1.22.0 Depends: R (>= 3.5), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils, Rcpp, rtracklayer LinkingTo: Rhtslib (>= 1.13.1), zlibbioc, Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix, testthat License: GPL-3 Archs: i386, x64 MD5sum: a3787827073592f4c2d486050518ff9f NeedsCompilation: yes Title: Differential Analyis of Hi-C Data Description: Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available. biocViews: MultipleComparison, Preprocessing, Sequencing, Coverage, Alignment, Normalization, Clustering, HiC Author: Aaron Lun [aut, cre], Gordon Smyth [aut] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make git_url: https://git.bioconductor.org/packages/diffHic git_branch: RELEASE_3_12 git_last_commit: db4e4f8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/diffHic_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/diffHic_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/diffHic_1.22.0.tgz vignettes: vignettes/diffHic/inst/doc/diffHic.pdf, vignettes/diffHic/inst/doc/diffHicUsersGuide.pdf vignetteTitles: diffHic Vignette, diffHicUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 97 Package: DiffLogo Version: 2.14.0 Depends: R (>= 3.4), stats, cba Imports: grDevices, graphics, utils, tools Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: f25bda2d5fe88b3325073df8afd154df NeedsCompilation: no Title: DiffLogo: A comparative visualisation of biooligomer motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization, Alignment Author: c( person("Martin", "Nettling", role = c("aut", "cre"), email = "martin.nettling@informatik.uni-halle.de"), person("Hendrik", "Treutler", role = c("aut", "cre"), email = "hendrik.treutler@ipb-halle.de"), person("Jan", "Grau", role = c("aut", "ctb"), email = "grau@informatik.uni-halle.de"), person("Andrey", "Lando", role = c("aut", "ctb"), email = "dronte@autosome.ru"), person("Jens", "Keilwagen", role = c("aut", "ctb"), email = "jens.keilwagen@julius-kuehn.de"), person("Stefan", "Posch", role = "aut", email = "posch@informatik.uni-halle.de"), person("Ivo", "Grosse", role = "aut", email = "grosse@informatik.uni-halle.de")) Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ BugReports: https://github.com/mgledi/DiffLogo/issues git_url: https://git.bioconductor.org/packages/DiffLogo git_branch: RELEASE_3_12 git_last_commit: 7edeada git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DiffLogo_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DiffLogo_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DiffLogo_2.14.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiffLogo/inst/doc/DiffLogoBasics.R dependencyCount: 9 Package: diffloop Version: 1.18.0 Imports: methods, GenomicRanges, foreach, plyr, dplyr, reshape2, ggplot2, matrixStats, Sushi, edgeR, locfit, statmod, biomaRt, GenomeInfoDb, S4Vectors, IRanges, grDevices, graphics, stats, utils, Biobase, readr, data.table, rtracklayer, pbapply, limma Suggests: DESeq2, diffloopdata, ggrepel, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 2a1b097070c6dc4d82b434f2bf82ff47 NeedsCompilation: no Title: Identifying differential DNA loops from chromatin topology data Description: A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments or other assays that infer chromatin loops. biocViews: Preprocessing, QualityControl, Visualization, DataImport, DataRepresentation, GO Author: Caleb Lareau [aut, cre], Martin Aryee [aut] Maintainer: Caleb Lareau URL: https://github.com/aryeelab/diffloop VignetteBuilder: knitr BugReports: https://github.com/aryeelab/diffloop/issues git_url: https://git.bioconductor.org/packages/diffloop git_branch: RELEASE_3_12 git_last_commit: df423a8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/diffloop_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/diffloop_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/diffloop_1.18.0.tgz vignettes: vignettes/diffloop/inst/doc/diffloop.html vignetteTitles: diffloop: Identifying differential DNA loops from chromatin topology data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffloop/inst/doc/diffloop.R dependencyCount: 118 Package: diffuStats Version: 1.10.2 Depends: R (>= 3.4) Imports: grDevices, stats, methods, Matrix, MASS, checkmate, expm, igraph, Rcpp, RcppArmadillo, RcppParallel, plyr, precrec LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: testthat, knitr, rmarkdown, ggplot2, ggsci, igraphdata, BiocStyle, reshape2, utils License: GPL-3 Archs: i386, x64 MD5sum: ef70264d162fd10d9ad818609e4633a1 NeedsCompilation: yes Title: Diffusion scores on biological networks Description: Label propagation approaches are a widely used procedure in computational biology for giving context to molecular entities using network data. Node labels, which can derive from gene expression, genome-wide association studies, protein domains or metabolomics profiling, are propagated to their neighbours in the network, effectively smoothing the scores through prior annotated knowledge and prioritising novel candidates. The R package diffuStats contains a collection of diffusion kernels and scoring approaches that facilitates their computation, characterisation and benchmarking. biocViews: Network, GeneExpression, GraphAndNetwork, Metabolomics, Transcriptomics, Proteomics, Genetics, GenomeWideAssociation, Normalization Author: Sergio Picart-Armada [aut, cre], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/diffuStats git_branch: RELEASE_3_12 git_last_commit: b578ed0 git_last_commit_date: 2021-02-20 Date/Publication: 2021-02-20 source.ver: src/contrib/diffuStats_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/diffuStats_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.0/diffuStats_1.10.2.tgz vignettes: vignettes/diffuStats/inst/doc/diffuStats.pdf, vignettes/diffuStats/inst/doc/intro.html vignetteTitles: Case study: predicting protein function, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/diffuStats/inst/doc/diffuStats.R, vignettes/diffuStats/inst/doc/intro.R dependencyCount: 51 Package: diggit Version: 1.22.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: 3a29fd044dc2ebd2094059ba00c5a850 NeedsCompilation: no Title: Inference of Genetic Variants Driving Cellular Phenotypes Description: Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/diggit git_branch: RELEASE_3_12 git_last_commit: 60b3344 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/diggit_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/diggit_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/diggit_1.22.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diggit/inst/doc/diggit.R dependencyCount: 33 Package: Director Version: 1.16.0 Depends: R (>= 4.0) Imports: htmltools, utils, grDevices License: GPL-3 + file LICENSE MD5sum: b301c90a7b64ec5d133fc8bb0f65926b NeedsCompilation: no Title: A dynamic visualization tool of multi-level data Description: Director is an R package designed to streamline the visualization of molecular effects in regulatory cascades. It utilizes the R package htmltools and a modified Sankey plugin of the JavaScript library D3 to provide a fast and easy, browser-enabled solution to discovering potentially interesting downstream effects of regulatory and/or co-expressed molecules. The diagrams are robust, interactive, and packaged as highly-portable HTML files that eliminate the need for third-party software to view. This enables a straightforward approach for scientists to interpret the data produced, and bioinformatics developers an alternative means to present relevant data. biocViews: Visualization Author: Katherine Icay [aut, cre] Maintainer: Katherine Icay URL: https://github.com/kzouchka/Director BugReports: https://github.com/kzouchka/Director/issues git_url: https://git.bioconductor.org/packages/Director git_branch: RELEASE_3_12 git_last_commit: 8ac811e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Director_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/Director_1.16.0.tgz vignettes: vignettes/Director/inst/doc/vignette.pdf vignetteTitles: Using Director hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Director/inst/doc/vignette.R dependencyCount: 6 Package: DirichletMultinomial Version: 1.32.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: 4ac5864b12a4ece2e4a2af8f96137848 NeedsCompilation: yes Title: Dirichlet-Multinomial Mixture Model Machine Learning for Microbiome Data Description: Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial. biocViews: ImmunoOncology, Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/DirichletMultinomial git_branch: RELEASE_3_12 git_last_commit: 6949aba git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DirichletMultinomial_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DirichletMultinomial_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DirichletMultinomial_1.32.0.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.R importsMe: TFBSTools dependencyCount: 9 Package: discordant Version: 1.14.0 Depends: R (>= 3.4) Imports: Biobase, stats, biwt, gtools, MASS, tools Suggests: BiocStyle, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 6b7a4ba7c9ee8456ce83e1049504771e NeedsCompilation: yes Title: The Discordant Method: A Novel Approach for Differential Correlation Description: Discordant is a method to determine differential correlation of molecular feature pairs from -omics data using mixture models. Algorithm is explained further in Siska et al. biocViews: ImmunoOncology, BiologicalQuestion, StatisticalMethod, mRNAMicroarray, Microarray, Genetics, RNASeq Author: Charlotte Siska [cre,aut], Katerina Kechris [aut] Maintainer: Charlotte Siska URL: https://github.com/siskac/discordant VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/discordant git_branch: RELEASE_3_12 git_last_commit: 33f13a5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/discordant_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/discordant_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/discordant_1.14.0.tgz vignettes: vignettes/discordant/inst/doc/Discordant_vignette.pdf vignetteTitles: Discordant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/discordant/inst/doc/Discordant_vignette.R dependencyCount: 20 Package: DiscoRhythm Version: 1.6.0 Depends: R (>= 3.6.0) Imports: matrixTests, matrixStats, MetaCycle (>= 1.2.0), data.table, ggplot2, ggExtra, dplyr, broom, shiny, shinyBS, shinycssloaders, shinydashboard, shinyjs, BiocStyle, rmarkdown, knitr, kableExtra, magick, VennDiagram, UpSetR, heatmaply, viridis, plotly, DT, gridExtra, methods, stats, SummarizedExperiment, BiocGenerics, S4Vectors, zip, reshape2 Suggests: testthat License: GPL-3 MD5sum: ad0a2f8932582c1963bc4f05e8b2243c NeedsCompilation: no Title: Interactive Workflow for Discovering Rhythmicity in Biological Data Description: Set of functions for estimation of cyclical characteristics, such as period, phase, amplitude, and statistical significance in large temporal datasets. Supporting functions are available for quality control, dimensionality reduction, spectral analysis, and analysis of experimental replicates. Contains a R Shiny web interface to execute all workflow steps. biocViews: Software, TimeCourse, QualityControl, Visualization, GUI, PrincipalComponent Author: Matthew Carlucci [aut, cre], Algimantas Kriščiūnas [aut], Haohan Li [aut], Povilas Gibas [aut], Karolis Koncevičius [aut], Art Petronis [aut], Gabriel Oh [aut] Maintainer: Matthew Carlucci URL: https://github.com/matthewcarlucci/DiscoRhythm SystemRequirements: To generate html reports pandoc (http://pandoc.org/installing.html) is required. VignetteBuilder: knitr BugReports: https://github.com/matthewcarlucci/DiscoRhythm/issues git_url: https://git.bioconductor.org/packages/DiscoRhythm git_branch: RELEASE_3_12 git_last_commit: 59e8f79 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DiscoRhythm_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DiscoRhythm_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DiscoRhythm_1.6.0.tgz vignettes: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.html vignetteTitles: Introduction to DiscoRhythm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DiscoRhythm/inst/doc/disco_workflow_vignette.R dependencyCount: 161 Package: distinct Version: 1.2.0 Depends: R (>= 4.0) Imports: Rcpp, stats, SummarizedExperiment, SingleCellExperiment, methods, Matrix, foreach, parallel, doParallel, doRNG, ggplot2, limma, scater LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, testthat, UpSetR License: GPL (>= 3) Archs: i386, x64 MD5sum: 4da0017615d179be8d9e3669a3aa4381 NeedsCompilation: yes Title: distinct: a method for differential analyses via hierarchical permutation tests Description: distinct is a statistical method to perform differential testing between two or more groups of distributions; differential testing is performed via hierarchical non-parametric permutation tests on the cumulative distribution functions (cdfs) of each sample. While most methods for differential expression target differences in the mean abundance between conditions, distinct, by comparing full cdfs, identifies, both, differential patterns involving changes in the mean, as well as more subtle variations that do not involve the mean (e.g., unimodal vs. bi-modal distributions with the same mean). distinct is a general and flexible tool: due to its fully non-parametric nature, which makes no assumptions on how the data was generated, it can be applied to a variety of datasets. It is particularly suitable to perform differential state analyses on single cell data (i.e., differential analyses within sub-populations of cells), such as single cell RNA sequencing (scRNA-seq) and high-dimensional flow or mass cytometry (HDCyto) data. To use distinct one needs data from two or more groups of samples (i.e., experimental conditions), with at least 2 samples (i.e., biological replicates) per group. biocViews: Genetics, RNASeq, Sequencing, DifferentialExpression, GeneExpression, MultipleComparison, Software, Transcription, StatisticalMethod, Visualization, SingleCell, FlowCytometry, GeneTarget Author: Simone Tiberi [aut, cre], Mark D. Robinson [aut]. Maintainer: Simone Tiberi URL: https://github.com/SimoneTiberi/distinct SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/SimoneTiberi/distinct/issues git_url: https://git.bioconductor.org/packages/distinct git_branch: RELEASE_3_12 git_last_commit: 3952abd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/distinct_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/distinct_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/distinct_1.2.0.tgz vignettes: vignettes/distinct/inst/doc/distinct.html vignetteTitles: distinct: a method for differential analyses via hierarchical permutation tests hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/distinct/inst/doc/distinct.R dependencyCount: 92 Package: dittoSeq Version: 1.2.6 Depends: ggplot2 Imports: methods, colorspace (>= 1.4), gridExtra, cowplot, reshape2, pheatmap, grDevices, ggrepel, ggridges, stats, utils, SummarizedExperiment, SingleCellExperiment, edgeR, S4Vectors Suggests: plotly, testthat, Seurat (>= 2.2), DESeq2, ggplot.multistats, knitr, rmarkdown, BiocStyle, scRNAseq, ggrastr (>= 0.2.0), ComplexHeatmap License: MIT + file LICENSE MD5sum: 27c74e3524cf09130c3499e3da12c6bf NeedsCompilation: no Title: User Friendly Single-Cell and Bulk RNA Sequencing Visualization Description: A universal, user friendly, single-cell and bulk RNA sequencing visualization toolkit that allows highly customizable creation of color blindness friendly, publication-quality figures. dittoSeq accepts both SingleCellExperiment (SCE) and Seurat objects, as well as the import and usage, via conversion to an SCE, of SummarizedExperiment or DGEList bulk data. Visualizations include dimensionality reduction plots, heatmaps, scatterplots, percent composition or expression across groups, and more. Customizations range from size and title adjustments to automatic generation of annotations for heatmaps, overlay of trajectory analysis onto any dimensionality reduciton plot, hidden data overlay upon cursor hovering via ggplotly conversion, and many more. All with simple, discrete inputs. Color blindness friendliness is powered by legend adjustments (enlarged keys), and by allowing the use of shapes or letter-overlay in addition to the carefully selected dittoColors(). biocViews: Software, Visualization, RNASeq, SingleCell, GeneExpression, Transcriptomics, DataImport Author: Daniel Bunis [aut, cre], Jared Andrews [aut, ctb] Maintainer: Daniel Bunis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dittoSeq git_branch: RELEASE_3_12 git_last_commit: cbf2618 git_last_commit_date: 2021-04-15 Date/Publication: 2021-04-16 source.ver: src/contrib/dittoSeq_1.2.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/dittoSeq_1.2.6.zip mac.binary.ver: bin/macosx/contrib/4.0/dittoSeq_1.2.6.tgz vignettes: vignettes/dittoSeq/inst/doc/dittoSeq.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dittoSeq/inst/doc/dittoSeq.R suggestsMe: escape, tidySingleCellExperiment, magmaR dependencyCount: 70 Package: divergence Version: 1.6.0 Depends: R (>= 3.6), SummarizedExperiment Suggests: knitr, rmarkdown License: GPL-2 MD5sum: d7814e9de8b066ae5c2283d2b888cf31 NeedsCompilation: no Title: Divergence: Functionality for assessing omics data by divergence with respect to a baseline Description: This package provides functionality for performing divergence analysis as presented in Dinalankara et al, "Digitizing omics profiles by divergence from a baseline", PANS 2018. This allows the user to simplify high dimensional omics data into a binary or ternary format which encapsulates how the data is divergent from a specified baseline group with the same univariate or multivariate features. biocViews: Software, StatisticalMethod Author: Wikum Dinalankara , Luigi Marchionni , Qian Ke Maintainer: Wikum Dinalankara , Luigi Marchionni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/divergence git_branch: RELEASE_3_12 git_last_commit: b4e5249 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/divergence_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/divergence_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/divergence_1.6.0.tgz vignettes: vignettes/divergence/inst/doc/divergence.html vignetteTitles: Performing Divergence Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/divergence/inst/doc/divergence.R dependencyCount: 26 Package: dks Version: 1.36.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 3749c32f47e85425c0528e289aa67695 NeedsCompilation: no Title: The double Kolmogorov-Smirnov package for evaluating multiple testing procedures. Description: The dks package consists of a set of diagnostic functions for multiple testing methods. The functions can be used to determine if the p-values produced by a multiple testing procedure are correct. These functions are designed to be applied to simulated data. The functions require the entire set of p-values from multiple simulated studies, so that the joint distribution can be evaluated. biocViews: MultipleComparison, QualityControl Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/dks git_branch: RELEASE_3_12 git_last_commit: b4db683 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dks_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dks_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dks_1.36.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dks/inst/doc/dks.R dependencyCount: 4 Package: DMCFB Version: 1.4.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges Imports: utils, stats, speedglm, MASS, data.table, splines, arm, rtracklayer, benchmarkme, tibble, matrixStats, fastDummies, graphics Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 8d5ce4a5d57ccb8316f93c8cff3e91cc NeedsCompilation: no Title: Differentially Methylated Cytosines via a Bayesian Functional Approach Description: DMCFB is a pipeline for identifying differentially methylated cytosines using a Bayesian functional regression model in bisulfite sequencing data. By using a functional regression data model, it tries to capture position-specific, group-specific and other covariates-specific methylation patterns as well as spatial correlation patterns and unknown underlying models of methylation data. It is robust and flexible with respect to the true underlying models and inclusion of any covariates, and the missing values are imputed using spatial correlation between positions and samples. A Bayesian approach is adopted for estimation and inference in the proposed method. biocViews: DifferentialMethylation, Sequencing, Coverage, Bayesian, Regression Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCFB/issues git_url: https://git.bioconductor.org/packages/DMCFB git_branch: RELEASE_3_12 git_last_commit: 2e5e757 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DMCFB_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DMCFB_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DMCFB_1.4.0.tgz vignettes: vignettes/DMCFB/inst/doc/DMCFB.html vignetteTitles: Identifying DMCs using Bayesian functional regressions in BS-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCFB/inst/doc/DMCFB.R dependencyCount: 127 Package: DMCHMM Version: 1.12.0 Depends: R (>= 4.0.0), SummarizedExperiment, methods, S4Vectors, BiocParallel, GenomicRanges, IRanges, fdrtool Imports: utils, stats, grDevices, rtracklayer, multcomp, calibrate, graphics Suggests: testthat, knitr License: GPL-3 MD5sum: adb926216dd48503a7bc325932b68992 NeedsCompilation: no Title: Differentially Methylated CpG using Hidden Markov Model Description: A pipeline for identifying differentially methylated CpG sites using Hidden Markov Model in bisulfite sequencing data. biocViews: DifferentialMethylation, Sequencing, HiddenMarkovModel, Coverage Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/DMCHMM/issues git_url: https://git.bioconductor.org/packages/DMCHMM git_branch: RELEASE_3_12 git_last_commit: c44e65d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DMCHMM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DMCHMM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DMCHMM_1.12.0.tgz vignettes: vignettes/DMCHMM/inst/doc/DMCHMM.html vignetteTitles: Sending Messages With Gmailr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMCHMM/inst/doc/DMCHMM.R dependencyCount: 51 Package: DMRcaller Version: 1.22.0 Depends: R (>= 3.5), GenomicRanges, IRanges, S4Vectors (>= 0.23.10) Imports: parallel, Rcpp, RcppRoll, betareg, grDevices, graphics, methods, stats, utils Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: f9a9f86c6a2d3a651acef3a7fa982452 NeedsCompilation: no Title: Differentially Methylated Regions caller Description: Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects. biocViews: DifferentialMethylation, DNAMethylation, Software, Sequencing, Coverage Author: Nicolae Radu Zabet , Jonathan Michael Foonlan Tsang , Alessandro Pio Greco and Ryan Merritt Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcaller git_branch: RELEASE_3_12 git_last_commit: 6b19597 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DMRcaller_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DMRcaller_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DMRcaller_1.22.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRcaller/inst/doc/DMRcaller.R dependencyCount: 30 Package: DMRcate Version: 2.4.1 Depends: R (>= 3.6.0), minfi, SummarizedExperiment Imports: ExperimentHub, bsseq, GenomeInfoDb, limma, edgeR, DSS, missMethyl, GenomicRanges, methods, graphics, plyr, Gviz, IRanges, stats, utils, S4Vectors Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 License: file LICENSE MD5sum: c27cea5223dfaf9fc06030a4a0787663 NeedsCompilation: no Title: Methylation array and sequencing spatial analysis methods Description: De novo identification and extraction of differentially methylated regions (DMRs) from the human genome using Whole Genome Bisulfite Sequencing (WGBS) and Illumina Infinium Array (450K and EPIC) data. Provides functionality for filtering probes possibly confounded by SNPs and cross-hybridisation. Includes GRanges generation and plotting functions. biocViews: DifferentialMethylation, GeneExpression, Microarray, MethylationArray, Genetics, DifferentialExpression, GenomeAnnotation, DNAMethylation, OneChannel, TwoChannel, MultipleComparison, QualityControl, TimeCourse, Sequencing, WholeGenome, Epigenetics, Coverage, Preprocessing, DataImport Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DMRcate git_branch: RELEASE_3_12 git_last_commit: bc6242a git_last_commit_date: 2021-01-13 Date/Publication: 2021-01-15 source.ver: src/contrib/DMRcate_2.4.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/DMRcate_2.4.1.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DMRcate/inst/doc/DMRcate.R dependsOnMe: methylationArrayAnalysis suggestsMe: missMethyl dependencyCount: 213 Package: DMRforPairs Version: 1.26.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 81160130c43845f0555ffa6c01f2a2cb NeedsCompilation: no Title: DMRforPairs: identifying Differentially Methylated Regions between unique samples using array based methylation profiles Description: DMRforPairs (formerly DMR2+) allows researchers to compare n>=2 unique samples with regard to their methylation profile. The (pairwise) comparison of n unique single samples distinguishes DMRforPairs from other existing pipelines as these often compare groups of samples in either single CpG locus or region based analysis. DMRforPairs defines regions of interest as genomic ranges with sufficient probes located in close proximity to each other. Probes in one region are optionally annotated to the same functional class(es). Differential methylation is evaluated by comparing the methylation values within each region between individual samples and (if the difference is sufficiently large), testing this difference formally for statistical significance. biocViews: Microarray, DNAMethylation, DifferentialMethylation, ReportWriting, Visualization, Annotation Author: Martin Rijlaarsdam [aut, cre], Yvonne vd Zwan [aut], Lambert Dorssers [aut], Leendert Looijenga [aut] Maintainer: Martin Rijlaarsdam URL: http://www.martinrijlaarsdam.nl, http://www.erasmusmc.nl/pathologie/research/lepo/3898639/ git_url: https://git.bioconductor.org/packages/DMRforPairs git_branch: RELEASE_3_12 git_last_commit: bf7108b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DMRforPairs_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DMRforPairs_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DMRforPairs_1.26.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.R dependencyCount: 139 Package: DMRScan Version: 1.12.0 Depends: R (>= 3.6.0) Imports: Matrix, MASS, RcppRoll,GenomicRanges, IRanges, GenomeInfoDb, methods, mvtnorm, stats, parallel Suggests: knitr, rmarkdown, BiocStyle, BiocManager License: GPL-3 MD5sum: 6fbb71cd152e636a87e75a3d1de97b77 NeedsCompilation: no Title: Detection of Differentially Methylated Regions Description: This package detects significant differentially methylated regions (for both qualitative and quantitative traits), using a scan statistic with underlying Poisson heuristics. The scan statistic will depend on a sequence of window sizes (# of CpGs within each window) and on a threshold for each window size. This threshold can be calculated by three different means: i) analytically using Siegmund et.al (2012) solution (preferred), ii) an important sampling as suggested by Zhang (2008), and a iii) full MCMC modeling of the data, choosing between a number of different options for modeling the dependency between each CpG. biocViews: Software, Technology, Sequencing, WholeGenome Author: Christian M Page [aut, cre], Linda Vos [aut], Trine B Rounge [ctb, dtc], Hanne F Harbo [ths], Bettina K Andreassen [aut] Maintainer: Christian M Page URL: https://github.com/christpa/DMRScan VignetteBuilder: knitr BugReports: https://github.com/christpa/DMRScan/issues PackageStatus: Active git_url: https://git.bioconductor.org/packages/DMRScan git_branch: RELEASE_3_12 git_last_commit: 25a84f2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DMRScan_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DMRScan_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DMRScan_1.12.0.tgz vignettes: vignettes/DMRScan/inst/doc/DMRScan_vignette.html vignetteTitles: DMR Scan Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DMRScan/inst/doc/DMRScan_vignette.R dependencyCount: 25 Package: dmrseq Version: 1.10.1 Depends: R (>= 3.5), bsseq Imports: GenomicRanges, nlme, ggplot2, S4Vectors, RColorBrewer, bumphunter, DelayedMatrixStats (>= 1.1.13), matrixStats, BiocParallel, outliers, methods, locfit, IRanges, grDevices, graphics, stats, utils, annotatr, AnnotationHub, rtracklayer, GenomeInfoDb, splines Suggests: knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 5920545bf9ac6c64a642b10064dfe217 NeedsCompilation: no Title: Detection and inference of differentially methylated regions from Whole Genome Bisulfite Sequencing Description: This package implements an approach for scanning the genome to detect and perform accurate inference on differentially methylated regions from Whole Genome Bisulfite Sequencing data. The method is based on comparing detected regions to a pooled null distribution, that can be implemented even when as few as two samples per population are available. Region-level statistics are obtained by fitting a generalized least squares (GLS) regression model with a nested autoregressive correlated error structure for the effect of interest on transformed methylation proportions. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, MultipleComparison, Software, Sequencing, DifferentialMethylation, WholeGenome, Regression, FunctionalGenomics Author: Keegan Korthauer [cre, aut] (), Rafael Irizarry [aut] (), Yuval Benjamini [aut], Sutirtha Chakraborty [aut] Maintainer: Keegan Korthauer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dmrseq git_branch: RELEASE_3_12 git_last_commit: 9fc35ee git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-17 source.ver: src/contrib/dmrseq_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/dmrseq_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/dmrseq_1.10.1.tgz vignettes: vignettes/dmrseq/inst/doc/dmrseq.html vignetteTitles: Analyzing Bisulfite-seq data with dmrseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dmrseq/inst/doc/dmrseq.R importsMe: biscuiteer dependencyCount: 156 Package: DNABarcodeCompatibility Version: 1.6.0 Depends: R (>= 3.6.0) Imports: dplyr, tidyr, numbers, purrr, stringr, DNABarcodes, stats, utils, methods Suggests: knitr, rmarkdown, BiocStyle, testthat License: file LICENSE MD5sum: 7c75624fb370db463828f2fc7d592fcc NeedsCompilation: no Title: A Tool for Optimizing Combinations of DNA Barcodes Used in Multiplexed Experiments on Next Generation Sequencing Platforms Description: The package allows one to obtain optimised combinations of DNA barcodes to be used for multiplex sequencing. In each barcode combination, barcodes are pooled with respect to Illumina chemistry constraints. Combinations can be filtered to keep those that are robust against substitution and insertion/deletion errors thereby facilitating the demultiplexing step. In addition, the package provides an optimiser function to further favor the selection of barcode combinations with least heterogeneity in barcode usage. biocViews: Preprocessing, Sequencing Author: Céline Trébeau [cre] (), Jacques Boutet de Monvel [aut] (), Fabienne Wong Jun Tai [ctb], Raphaël Etournay [aut] () Maintainer: Céline Trébeau VignetteBuilder: knitr BugReports: https://github.com/comoto-pasteur-fr/DNABarcodeCompatibility/issues git_url: https://git.bioconductor.org/packages/DNABarcodeCompatibility git_branch: RELEASE_3_12 git_last_commit: 5766b58 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DNABarcodeCompatibility_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DNABarcodeCompatibility_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DNABarcodeCompatibility_1.6.0.tgz vignettes: vignettes/DNABarcodeCompatibility/inst/doc/introduction.html vignetteTitles: Introduction to DNABarcodeCompatibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/DNABarcodeCompatibility/inst/doc/introduction.R dependencyCount: 36 Package: DNABarcodes Version: 1.20.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 7f0298fc2297e5af5d6deffddc99eedf NeedsCompilation: yes Title: A tool for creating and analysing DNA barcodes used in Next Generation Sequencing multiplexing experiments Description: The package offers a function to create DNA barcode sets capable of correcting insertion, deletion, and substitution errors. Existing barcodes can be analysed regarding their minimal, maximal and average distances between barcodes. Finally, reads that start with a (possibly mutated) barcode can be demultiplexed, i.e., assigned to their original reference barcode. biocViews: Preprocessing, Sequencing Author: Tilo Buschmann Maintainer: Tilo Buschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNABarcodes git_branch: RELEASE_3_12 git_last_commit: 54f0a01 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DNABarcodes_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DNABarcodes_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DNABarcodes_1.20.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/DNABarcodes.html vignetteTitles: DNABarcodes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R importsMe: DNABarcodeCompatibility dependencyCount: 11 Package: DNAcopy Version: 1.64.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: a2512d7144f4c2d95232920f5db1471b NeedsCompilation: yes Title: DNA copy number data analysis Description: Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number. biocViews: Microarray, CopyNumberVariation Author: Venkatraman E. Seshan, Adam Olshen Maintainer: Venkatraman E. Seshan git_url: https://git.bioconductor.org/packages/DNAcopy git_branch: RELEASE_3_12 git_last_commit: 0165026 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DNAcopy_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DNAcopy_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DNAcopy_1.64.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAcopy/inst/doc/DNAcopy.R dependsOnMe: CGHcall, cghMCR, Clonality, CRImage, PureCN, CSclone, ParDNAcopy, saasCNV importsMe: ADaCGH2, AneuFinder, ChAMP, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, MDTS, MEDIPS, MethCP, MinimumDistance, QDNAseq, Repitools, SCOPE, sesame, snapCGH, cghRA, jointseg, PSCBS suggestsMe: beadarraySNP, cn.mops, CopyNumberPlots, fastseg, genoset, ACNE, aroma.cn, aroma.core, bcp, calmate dependencyCount: 0 Package: DNAshapeR Version: 1.18.0 Depends: R (>= 3.4), GenomicRanges Imports: Rcpp (>= 0.12.1), Biostrings, fields LinkingTo: Rcpp Suggests: AnnotationHub, knitr, rmarkdown, testthat, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Hsapiens.UCSC.hg19, caret License: GPL-2 Archs: i386, x64 MD5sum: b046361c8faec17ce8de3aa13c32c975 NeedsCompilation: yes Title: High-throughput prediction of DNA shape features Description: DNAhapeR is an R/BioConductor package for ultra-fast, high-throughput predictions of DNA shape features. The package allows to predict, visualize and encode DNA shape features for statistical learning. biocViews: StructuralPrediction, DNA3DStructure, Software Author: Tsu-Pei Chiu and Federico Comoglio Maintainer: Tsu-Pei Chiu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DNAshapeR git_branch: RELEASE_3_12 git_last_commit: 383b9b2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DNAshapeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DNAshapeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DNAshapeR_1.18.0.tgz vignettes: vignettes/DNAshapeR/inst/doc/DNAshapeR.html vignetteTitles: DNAshapeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R dependencyCount: 26 Package: DominoEffect Version: 1.10.1 Depends: R(>= 3.5) Imports: biomaRt, data.table, utils, stats, Biostrings, SummarizedExperiment, VariantAnnotation, AnnotationDbi, GenomeInfoDb, IRanges, GenomicRanges, methods Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 0c3c7ec7d289c1788d5f6293aa1bf874 NeedsCompilation: no Title: Identification and Annotation of Protein Hotspot Residues Description: The functions support identification and annotation of hotspot residues in proteins. These are individual amino acids that accumulate mutations at a much higher rate than their surrounding regions. biocViews: Software, SomaticMutation, Proteomics, SequenceMatching, Alignment Author: Marija Buljan and Peter Blattmann Maintainer: Marija Buljan , Peter Blattmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DominoEffect git_branch: RELEASE_3_12 git_last_commit: 4fcce39 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-22 source.ver: src/contrib/DominoEffect_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/DominoEffect_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/DominoEffect_1.10.1.tgz vignettes: vignettes/DominoEffect/inst/doc/Vignette.html vignetteTitles: Vignette for DominoEffect package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DominoEffect/inst/doc/Vignette.R dependencyCount: 91 Package: doppelgangR Version: 1.18.0 Depends: R (>= 3.5.0), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, testthat License: GPL (>=2.0) MD5sum: 8245d57c2439dc098017c0992da41249 NeedsCompilation: no Title: Identify likely duplicate samples from genomic or meta-data Description: The main function is doppelgangR(), which takes as minimal input a list of ExpressionSet object, and searches all list pairs for duplicated samples. The search is based on the genomic data (exprs(eset)), phenotype/clinical data (pData(eset)), and "smoking guns" - supposedly unique identifiers found in pData(eset). biocViews: ImmunoOncology, RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron [aut, cre], Markus Reister [aut, ctb], Marcel Ramos [ctb] Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues git_url: https://git.bioconductor.org/packages/doppelgangR git_branch: RELEASE_3_12 git_last_commit: a14c896 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/doppelgangR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/doppelgangR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/doppelgangR_1.18.0.tgz vignettes: vignettes/doppelgangR/inst/doc/doppelgangR.html vignetteTitles: doppelgangR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R dependencyCount: 73 Package: Doscheda Version: 1.12.0 Depends: R (>= 3.4) Imports: methods, drc, stats, httr, jsonlite, reshape2 , vsn, affy, limma, stringr, ggplot2, graphics, grDevices, calibrate, corrgram, gridExtra, DT, shiny, shinydashboard, readxl, prodlim, matrixStats Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: e60241cd66ce7f32350ca90879582028 NeedsCompilation: no Title: A DownStream Chemo-Proteomics Analysis Pipeline Description: Doscheda focuses on quantitative chemoproteomics used to determine protein interaction profiles of small molecules from whole cell or tissue lysates using Mass Spectrometry data. The package provides a shiny application to run the pipeline, several visualisations and a downloadable report of an experiment. biocViews: Proteomics, Normalization, Preprocessing, MassSpectrometry, QualityControl, DataImport, Regression Author: Bruno Contrino, Piero Ricchiuto Maintainer: Bruno Contrino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Doscheda git_branch: RELEASE_3_12 git_last_commit: cac45a7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Doscheda_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Doscheda_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Doscheda_1.12.0.tgz vignettes: vignettes/Doscheda/inst/doc/Doscheda.html vignetteTitles: Doscheda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Doscheda/inst/doc/Doscheda.R dependencyCount: 149 Package: DOSE Version: 3.16.0 Depends: R (>= 3.5.0) Imports: AnnotationDbi, BiocParallel, DO.db, fgsea, ggplot2, GOSemSim (>= 2.0.0), methods, qvalue, reshape2, stats, utils Suggests: prettydoc, clusterProfiler, knitr, org.Hs.eg.db, testthat License: Artistic-2.0 MD5sum: fa4f7caceb5a92bc2f7769f0ae37f0f3 NeedsCompilation: no Title: Disease Ontology Semantic and Enrichment analysis Description: This package implements five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for measuring semantic similarities among DO terms and gene products. Enrichment analyses including hypergeometric model and gene set enrichment analysis are also implemented for discovering disease associations of high-throughput biological data. biocViews: Annotation, Visualization, MultipleComparison, GeneSetEnrichment, Pathways, Software Author: Guangchuang Yu [aut, cre], Li-Gen Wang [ctb], Vladislav Petyuk [ctb], Giovanni Dall'Olio [ctb], Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues git_url: https://git.bioconductor.org/packages/DOSE git_branch: RELEASE_3_12 git_last_commit: a534a4f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DOSE_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DOSE_3.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DOSE_3.16.0.tgz vignettes: vignettes/DOSE/inst/doc/DOSE.html vignetteTitles: DOSE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R importsMe: bioCancer, clusterProfiler, debrowser, eegc, enrichplot, GDCRNATools, meshes, miRspongeR, MoonlightR, ReactomePA, RegEnrich, RNASeqR, scTensor, signatureSearch suggestsMe: cola, GOSemSim, MAGeCKFlute, rrvgo, scGPS, simplifyEnrichment dependencyCount: 75 Package: doseR Version: 1.6.0 Depends: R (>= 3.6) Imports: edgeR, methods, stats, graphics, matrixStats, mclust, lme4, RUnit, SummarizedExperiment, digest, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 3d9ce1a7bb823c3d3e85b28ac70e3aa2 NeedsCompilation: no Title: doseR Description: doseR package is a next generation sequencing package for sex chromosome dosage compensation which can be applied broadly to detect shifts in gene expression among an arbitrary number of pre-defined groups of loci. doseR is a differential gene expression package for count data, that detects directional shifts in expression for multiple, specific subsets of genes, broad utility in systems biology research. doseR has been prepared to manage the nature of the data and the desired set of inferences. doseR uses S4 classes to store count data from sequencing experiment. It contains functions to normalize and filter count data, as well as to plot and calculate statistics of count data. It contains a framework for linear modeling of count data. The package has been tested using real and simulated data. biocViews: Infrastructure, Software, DataRepresentation, Sequencing, GeneExpression, SystemsBiology, DifferentialExpression Author: AJ Vaestermark, JR Walters. Maintainer: ake.vastermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/doseR git_branch: RELEASE_3_12 git_last_commit: 9c21c34 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/doseR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/doseR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/doseR_1.6.0.tgz vignettes: vignettes/doseR/inst/doc/doseR.html vignetteTitles: "doseR" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doseR/inst/doc/doseR.R dependencyCount: 42 Package: dpeak Version: 1.2.0 Depends: R (>= 4.0.0), methods, stats, utils, graphics, Rcpp Imports: MASS, IRanges, BSgenome, grDevices, parallel LinkingTo: Rcpp Suggests: BSgenome.Ecoli.NCBI.20080805 License: GPL (>= 2) Archs: i386, x64 MD5sum: c73ffc4ad2adf646a7ae1cec2788efc6 NeedsCompilation: yes Title: dPeak (Deconvolution of Peaks in ChIP-seq Analysis) Description: dPeak is a statistical framework for the high resolution identification of protein-DNA interaction sites using PET and SET ChIP-Seq and ChIP-exo data. It provides computationally efficient and user friendly interface to process ChIP-seq and ChIP-exo data, implement exploratory analysis, fit dPeak model, and export list of predicted binding sites for downstream analysis. biocViews: ChIPSeq, Genetics, Sequencing, Software, Transcription Author: Dongjun Chung, Carter Allen Maintainer: Dongjun Chung SystemRequirements: GNU make, meme, fimo BugReports: https://github.com/dongjunchung/dpeak/issues git_url: https://git.bioconductor.org/packages/dpeak git_branch: RELEASE_3_12 git_last_commit: bd5fad4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dpeak_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dpeak_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dpeak_1.2.0.tgz vignettes: vignettes/dpeak/inst/doc/dpeak-example.pdf vignetteTitles: dPeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dpeak/inst/doc/dpeak-example.R dependencyCount: 43 Package: drawProteins Version: 1.10.0 Depends: R (>= 4.0) Imports: ggplot2, httr, dplyr, readr, tidyr Suggests: covr, testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 13bfe7983a97888e6ee9be70e7b53c7c NeedsCompilation: no Title: Package to Draw Protein Schematics from Uniprot API output Description: This package draws protein schematics from Uniprot API output. From the JSON returned by the GET command, it creates a dataframe from the Uniprot Features API. This dataframe can then be used by geoms based on ggplot2 and base R to draw protein schematics. biocViews: Visualization, FunctionalPrediction, Proteomics Author: Paul Brennan [aut, cre] Maintainer: Paul Brennan URL: https://github.com/brennanpincardiff/drawProteins VignetteBuilder: knitr BugReports: https://github.com/brennanpincardiff/drawProteins/issues/new git_url: https://git.bioconductor.org/packages/drawProteins git_branch: RELEASE_3_12 git_last_commit: ae19e21 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/drawProteins_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/drawProteins_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/drawProteins_1.10.0.tgz vignettes: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.html, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.html vignetteTitles: Using drawProteins, Using extract_transcripts in drawProteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/drawProteins/inst/doc/drawProteins_BiocStyle.R, vignettes/drawProteins/inst/doc/drawProteins_extract_transcripts_BiocStyle.R dependencyCount: 56 Package: DRIMSeq Version: 1.18.0 Depends: R (>= 3.4.0) Imports: utils, stats, MASS, GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, limma, edgeR, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: c2df0fc7eddf54254c142aad87a5fbe9 NeedsCompilation: no Title: Differential transcript usage and tuQTL analyses with Dirichlet-multinomial model in RNA-seq Description: The package provides two frameworks. One for the differential transcript usage analysis between different conditions and one for the tuQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts) with the Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: ImmunoOncology, SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DRIMSeq git_branch: RELEASE_3_12 git_last_commit: 3b9c0e8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DRIMSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DRIMSeq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DRIMSeq_1.18.0.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential transcript usage and transcript usage QTL analyses in RNA-seq with the DRIMSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R dependsOnMe: rnaseqDTU importsMe: BANDITS, IsoformSwitchAnalyzeR dependencyCount: 66 Package: DriverNet Version: 1.30.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: af8a5621bed91563e090a1194ad76fd7 NeedsCompilation: no Title: Drivernet: uncovering somatic driver mutations modulating transcriptional networks in cancer Description: DriverNet is a package to predict functional important driver genes in cancer by integrating genome data (mutation and copy number variation data) and transcriptome data (gene expression data). The different kinds of data are combined by an influence graph, which is a gene-gene interaction network deduced from pathway data. A greedy algorithm is used to find the possible driver genes, which may mutated in a larger number of patients and these mutations will push the gene expression values of the connected genes to some extreme values. biocViews: Network Author: Ali Bashashati, Reza Haffari, Jiarui Ding, Gavin Ha, Kenneth Liu, Jamie Rosner and Sohrab Shah Maintainer: Jiarui Ding git_url: https://git.bioconductor.org/packages/DriverNet git_branch: RELEASE_3_12 git_last_commit: 73cbab4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DriverNet_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DriverNet_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DriverNet_1.30.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DriverNet/inst/doc/DriverNet-Overview.R dependencyCount: 1 Package: DropletUtils Version: 1.10.3 Depends: SingleCellExperiment Imports: utils, stats, methods, Matrix, Rcpp, BiocGenerics, S4Vectors, SummarizedExperiment, BiocParallel, DelayedArray, HDF5Array, rhdf5, edgeR, R.utils, dqrng, beachmat, scuttle LinkingTo: Rcpp, beachmat, Rhdf5lib, BH, dqrng, scuttle Suggests: testthat, knitr, BiocStyle, rmarkdown, jsonlite, DropletTestFiles License: GPL-3 Archs: i386, x64 MD5sum: 534efeea83f6a7fcd078d4cddec62702 NeedsCompilation: yes Title: Utilities for Handling Single-Cell Droplet Data Description: Provides a number of utility functions for handling single-cell (RNA-seq) data from droplet technologies such as 10X Genomics. This includes data loading from count matrices or molecule information files, identification of cells from empty droplets, removal of barcode-swapped pseudo-cells, and downsampling of the count matrix. biocViews: ImmunoOncology, SingleCell, Sequencing, RNASeq, GeneExpression, Transcriptomics, DataImport, Coverage Author: Aaron Lun [aut, cre], Jonathan Griffiths [ctb], Davis McCarthy [ctb] Maintainer: Aaron Lun SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DropletUtils git_branch: RELEASE_3_12 git_last_commit: 4bdf932 git_last_commit_date: 2021-02-01 Date/Publication: 2021-02-02 source.ver: src/contrib/DropletUtils_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/DropletUtils_1.10.3.zip mac.binary.ver: bin/macosx/contrib/4.0/DropletUtils_1.10.3.tgz vignettes: vignettes/DropletUtils/inst/doc/DropletUtils.html vignetteTitles: Utilities for handling droplet-based single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DropletUtils/inst/doc/DropletUtils.R importsMe: scCB2, singleCellTK suggestsMe: Nebulosa, DropletTestFiles, muscData dependencyCount: 51 Package: DrugVsDisease Version: 2.32.0 Depends: R (>= 2.10), affy, limma, biomaRt, ArrayExpress, GEOquery, DrugVsDiseasedata, cMap2data, qvalue Imports: annotate, hgu133a.db, hgu133a2.db, hgu133plus2.db, RUnit, BiocGenerics, xtable License: GPL-3 MD5sum: 5d8aee1ab48bc55ccf6fdac3ba356e40 NeedsCompilation: no Title: Comparison of disease and drug profiles using Gene set Enrichment Analysis Description: This package generates ranked lists of differential gene expression for either disease or drug profiles. Input data can be downloaded from Array Express or GEO, or from local CEL files. Ranked lists of differential expression and associated p-values are calculated using Limma. Enrichment scores (Subramanian et al. PNAS 2005) are calculated to a reference set of default drug or disease profiles, or a set of custom data supplied by the user. Network visualisation of significant scores are output in Cytoscape format. biocViews: Microarray, GeneExpression, Clustering Author: C. Pacini Maintainer: j. Saez-Rodriguez git_url: https://git.bioconductor.org/packages/DrugVsDisease git_branch: RELEASE_3_12 git_last_commit: c8ef84c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DrugVsDisease_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DrugVsDisease_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DrugVsDisease_2.32.0.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.R dependencyCount: 122 Package: DSS Version: 2.38.0 Depends: R (>= 3.3), methods, Biobase, BiocParallel, bsseq Imports: utils, graphics, stats, splines, DelayedArray Suggests: BiocStyle, knitr, rmarkdown License: GPL Archs: i386, x64 MD5sum: 8c70a5557bcb4c31a360031dbdc05cc8 NeedsCompilation: yes Title: Dispersion shrinkage for sequencing data Description: DSS is an R library performing differntial analysis for count-based sequencing data. It detectes differentially expressed genes (DEGs) from RNA-seq, and differentially methylated loci or regions (DML/DMRs) from bisulfite sequencing (BS-seq). The core of DSS is a new dispersion shrinkage method for estimating the dispersion parameter from Gamma-Poisson or Beta-Binomial distributions. biocViews: Sequencing, RNASeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu, Hao Feng Maintainer: Hao Wu , Hao Feng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/DSS git_branch: RELEASE_3_12 git_last_commit: 82e65b9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DSS_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DSS_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DSS_2.38.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.html vignetteTitles: The DSS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R importsMe: DMRcate, kissDE, metaseqR2, MethCP, methylSig suggestsMe: biscuiteer, methrix, NanoMethViz dependencyCount: 70 Package: DTA Version: 2.36.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 9cdfd00981661fc10d714238482b64ec NeedsCompilation: no Title: Dynamic Transcriptome Analysis Description: Dynamic Transcriptome Analysis (DTA) can monitor the cellular response to perturbations with higher sensitivity and temporal resolution than standard transcriptomics. The package implements the underlying kinetic modeling approach capable of the precise determination of synthesis- and decay rates from individual microarray or RNAseq measurements. biocViews: Microarray, DifferentialExpression, GeneExpression, Transcription Author: Bjoern Schwalb, Benedikt Zacher, Sebastian Duemcke, Achim Tresch Maintainer: Bjoern Schwalb git_url: https://git.bioconductor.org/packages/DTA git_branch: RELEASE_3_12 git_last_commit: 9654dcd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DTA_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DTA_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DTA_2.36.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DTA/inst/doc/DTA.R dependencyCount: 5 Package: dualKS Version: 1.50.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: dcdcff0ecf6deb79387ccd27a21ba0d5 NeedsCompilation: no Title: Dual KS Discriminant Analysis and Classification Description: This package implements a Kolmogorov Smirnov rank-sum based algorithm for training (i.e. discriminant analysis--identification of genes that discriminate between classes) and classification of gene expression data sets. One of the chief strengths of this approach is that it is amenable to the "multiclass" problem. That is, it can discriminate between more than 2 classes. biocViews: Microarray, Classification Author: Eric J. Kort, Yarong Yang Maintainer: Eric J. Kort , Yarong Yang git_url: https://git.bioconductor.org/packages/dualKS git_branch: RELEASE_3_12 git_last_commit: 5eb36a6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dualKS_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dualKS_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dualKS_1.50.0.tgz vignettes: vignettes/dualKS/inst/doc/dualKS.pdf vignetteTitles: dualKS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dualKS/inst/doc/dualKS.R dependencyCount: 13 Package: Dune Version: 1.2.0 Depends: R (>= 4.0) Imports: BiocParallel, SummarizedExperiment, mclust, utils, ggplot2, dplyr, tidyr, RColorBrewer, magrittr, gganimate, purrr Suggests: knitr, rmarkdown, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 5c475658f72c76ef843db892abd38b94 NeedsCompilation: no Title: Improving replicability in single-cell RNA-Seq cell type discovery Description: Given a set of clustering labels, Dune merges pairs of clusters to increase mean ARI between labels, improving replicability. biocViews: Clustering, GeneExpression, RNASeq, Software, SingleCell, Transcriptomics, Visualization Author: Hector Roux de Bezieux [aut, cre] (), Kelly Street [aut] Maintainer: Hector Roux de Bezieux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Dune git_branch: RELEASE_3_12 git_last_commit: a60b7f3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Dune_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Dune_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Dune_1.2.0.tgz vignettes: vignettes/Dune/inst/doc/Dune.html vignetteTitles: Dune Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Dune/inst/doc/Dune.R dependencyCount: 78 Package: dupRadar Version: 1.20.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 36526071010bad8705d77056a121b7d2 NeedsCompilation: no Title: Assessment of duplication rates in RNA-Seq datasets Description: Duplication rate quality control for RNA-Seq datasets. biocViews: Technology, Sequencing, RNASeq, QualityControl, ImmunoOncology Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/dupRadar git_branch: RELEASE_3_12 git_last_commit: d9b9321 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dupRadar_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/dupRadar_1.20.0.tgz vignettes: vignettes/dupRadar/inst/doc/dupRadar.html vignetteTitles: Using dupRadar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R dependencyCount: 9 Package: dyebias Version: 1.50.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 5b04fef716381bf1b872e06f34c71242 NeedsCompilation: no Title: The GASSCO method for correcting for slide-dependent gene-specific dye bias Description: Many two-colour hybridizations suffer from a dye bias that is both gene-specific and slide-specific. The former depends on the content of the nucleotide used for labeling; the latter depends on the labeling percentage. The slide-dependency was hitherto not recognized, and made addressing the artefact impossible. Given a reasonable number of dye-swapped pairs of hybridizations, or of same vs. same hybridizations, both the gene- and slide-biases can be estimated and corrected using the GASSCO method (Margaritis et al., Mol. Sys. Biol. 5:266 (2009), doi:10.1038/msb.2009.21) biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Philip Lijnzaad and Thanasis Margaritis Maintainer: Philip Lijnzaad URL: http://www.holstegelab.nl/publications/margaritis_lijnzaad git_url: https://git.bioconductor.org/packages/dyebias git_branch: RELEASE_3_12 git_last_commit: b11dbbc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/dyebias_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/dyebias_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/dyebias_1.50.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/dyebias/inst/doc/dyebias-vignette.R suggestsMe: dyebiasexamples dependencyCount: 10 Package: DynDoc Version: 1.68.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 290b54314401afb81222a0685c0ba9a5 NeedsCompilation: no Title: Dynamic document tools Description: A set of functions to create and interact with dynamic documents and vignettes. biocViews: ReportWriting, Infrastructure Author: R. Gentleman, Jeff Gentry Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/DynDoc git_branch: RELEASE_3_12 git_last_commit: 0e1cd51 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/DynDoc_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/DynDoc_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/DynDoc_1.68.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets dependencyCount: 2 Package: EasyqpcR Version: 1.31.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: b71deb12ec3b05c8c940f2753f1dcbc3 NeedsCompilation: no Title: EasyqpcR for low-throughput real-time quantitative PCR data analysis Description: This package is based on the qBase algorithms published by Hellemans et al. in 2007. The EasyqpcR package allows you to import easily qPCR data files as described in the vignette. Thereafter, you can calculate amplification efficiencies, relative quantities and their standard errors, normalization factors based on the best reference genes choosen (using the SLqPCR package), and then the normalized relative quantities, the NRQs scaled to your control and their standard errors. This package has been created for low-throughput qPCR data analysis. biocViews: qPCR, GeneExpression Author: Le Pape Sylvain Maintainer: Le Pape Sylvain git_url: https://git.bioconductor.org/packages/EasyqpcR git_branch: master git_last_commit: 74f0b16 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/EasyqpcR_1.31.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EasyqpcR_1.31.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EasyqpcR_1.31.0.tgz vignettes: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.pdf vignetteTitles: EasyqpcR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.R dependencyCount: 10 Package: easyreporting Version: 1.2.0 Imports: rmarkdown, methods, tools Suggests: BiocStyle, knitr, readxl, edgeR, limma, EDASeq, statmod License: Artistic-2.0 MD5sum: fd9d5726b09188a4923e3d0f3562f653 NeedsCompilation: no Title: Helps creating report for improving Reproducible computational Research Description: An S4 class for facilitating the automated creation of rmarkdown files inside other packages/software, even without knowing rmarkdown language. Best if implemented in functions as "recursive" style programming. biocViews: ReportWriting Author: Dario Righelli [cre, aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/easyreporting/issues git_url: https://git.bioconductor.org/packages/easyreporting git_branch: RELEASE_3_12 git_last_commit: 34555b4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/easyreporting_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/easyreporting_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/easyreporting_1.2.0.tgz vignettes: vignettes/easyreporting/inst/doc/bio_usage.html, vignettes/easyreporting/inst/doc/standard_usage.html vignetteTitles: bio_usage.html, standard_usage.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyreporting/inst/doc/bio_usage.R, vignettes/easyreporting/inst/doc/standard_usage.R dependencyCount: 23 Package: EBarrays Version: 2.54.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: 7f5974b09776bd9de80e9a3a6a1afd81 NeedsCompilation: yes Title: Unified Approach for Simultaneous Gene Clustering and Differential Expression Identification Description: EBarrays provides tools for the analysis of replicated/unreplicated microarray data. biocViews: Clustering, DifferentialExpression Author: Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski Maintainer: Ming Yuan git_url: https://git.bioconductor.org/packages/EBarrays git_branch: RELEASE_3_12 git_last_commit: 6ec7d68 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EBarrays_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EBarrays_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EBarrays_2.54.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBarrays/inst/doc/vignette.R dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category, dcanr dependencyCount: 11 Package: EBcoexpress Version: 1.34.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: e1b0c1a16d6384c89713246309020bf9 NeedsCompilation: yes Title: EBcoexpress for Differential Co-Expression Analysis Description: An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level biocViews: Bayesian Author: John A. Dawson Maintainer: John A. Dawson git_url: https://git.bioconductor.org/packages/EBcoexpress git_branch: RELEASE_3_12 git_last_commit: 1485e38 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EBcoexpress_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EBcoexpress_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EBcoexpress_1.34.0.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R dependsOnMe: SRGnet suggestsMe: dcanr dependencyCount: 15 Package: EBImage Version: 4.32.0 Depends: methods Imports: BiocGenerics (>= 0.7.1), graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils, htmltools, htmlwidgets, RCurl Suggests: BiocStyle, digest, knitr, rmarkdown, shiny License: LGPL Archs: i386, x64 MD5sum: 09f51964aff372d63db743fc0415cff8 NeedsCompilation: yes Title: Image processing and analysis toolbox for R Description: EBImage provides general purpose functionality for image processing and analysis. In the context of (high-throughput) microscopy-based cellular assays, EBImage offers tools to segment cells and extract quantitative cellular descriptors. This allows the automation of such tasks using the R programming language and facilitates the use of other tools in the R environment for signal processing, statistical modeling, machine learning and visualization with image data. biocViews: Visualization Author: Andrzej Oleś, Gregoire Pau, Mike Smith, Oleg Sklyar, Wolfgang Huber, with contributions from Joseph Barry and Philip A. Marais Maintainer: Andrzej Oleś URL: https://github.com/aoles/EBImage VignetteBuilder: knitr BugReports: https://github.com/aoles/EBImage/issues git_url: https://git.bioconductor.org/packages/EBImage git_branch: RELEASE_3_12 git_last_commit: de9ff23 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EBImage_4.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EBImage_4.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EBImage_4.32.0.tgz vignettes: vignettes/EBImage/inst/doc/EBImage-introduction.html vignetteTitles: Introduction to EBImage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R dependsOnMe: Cardinal, CRImage, cytomapper, flowcatchR, imageHTS, DonaPLLP2013, furrowSeg, GiNA, nucim, ShinyImage importsMe: bnbc, flowCHIC, heatmaps, yamss, bioimagetools, CropDetectR, RockFab, SAFARI, trackter suggestsMe: HilbertVis, tofsims, DmelSGI, aroma.core, ijtiff, juicr, lidR, metagear, ProFound dependencyCount: 24 Package: EBSEA Version: 1.18.0 Depends: R (>= 4.0.0) Imports: DESeq2, graphics, stats, EmpiricalBrownsMethod Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 9117a85ca9bf7322a85dc61379469a9b NeedsCompilation: no Title: Exon Based Strategy for Expression Analysis of genes Description: Calculates differential expression of genes based on exon counts of genes obtained from RNA-seq sequencing data. biocViews: Software, DifferentialExpression, GeneExpression, Sequencing Author: Arfa Mehmood, Asta Laiho, Laura L. Elo Maintainer: Arfa Mehmood VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EBSEA git_branch: RELEASE_3_12 git_last_commit: ee7b37b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EBSEA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EBSEA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EBSEA_1.18.0.tgz vignettes: vignettes/EBSEA/inst/doc/EBSEA.html vignetteTitles: EBSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R dependencyCount: 91 Package: EBSeq Version: 1.30.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 8d3e26993abd88adad935b5071f4460b NeedsCompilation: no Title: An R package for gene and isoform differential expression analysis of RNA-seq data Description: Differential Expression analysis at both gene and isoform level using RNA-seq data biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeq git_branch: RELEASE_3_12 git_last_commit: 73bd6a4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EBSeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EBSeq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EBSeq_1.30.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeq/inst/doc/EBSeq_Vignette.R dependsOnMe: EBSeqHMM, Oscope importsMe: DEsubs, scDD suggestsMe: compcodeR dependencyCount: 47 Package: EBSeqHMM Version: 1.24.0 Depends: EBSeq License: Artistic-2.0 MD5sum: 61f59aabd7d79e46c1bf364be494ecfe NeedsCompilation: no Title: Bayesian analysis for identifying gene or isoform expression changes in ordered RNA-seq experiments Description: The EBSeqHMM package implements an auto-regressive hidden Markov model for statistical analysis in ordered RNA-seq experiments (e.g. time course or spatial course data). The EBSeqHMM package provides functions to identify genes and isoforms that have non-constant expression profile over the time points/positions, and cluster them into expression paths. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/EBSeqHMM git_branch: RELEASE_3_12 git_last_commit: 3413b0e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EBSeqHMM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EBSeqHMM_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EBSeqHMM_1.24.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.R dependencyCount: 48 Package: ecolitk Version: 1.62.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 27d03b2d6d217aa54a6fb8a5b0e59251 NeedsCompilation: no Title: Meta-data and tools for E. coli Description: Meta-data and tools to work with E. coli. The tools are mostly plotting functions to work with circular genomes. They can used with other genomes/plasmids. biocViews: Annotation, Visualization Author: Laurent Gautier Maintainer: Laurent Gautier git_url: https://git.bioconductor.org/packages/ecolitk git_branch: RELEASE_3_12 git_last_commit: 7e4a633 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ecolitk_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ecolitk_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ecolitk_1.62.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ecolitk/inst/doc/ecolitk.R dependencyCount: 7 Package: EDASeq Version: 2.24.0 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges, BiocManager Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth, testthat, DESeq2 License: Artistic-2.0 MD5sum: 6266fd0f92bd97bea9a69b8e12a34379 NeedsCompilation: no Title: Exploratory Data Analysis and Normalization for RNA-Seq Description: Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010). biocViews: ImmunoOncology, Sequencing, RNASeq, Preprocessing, QualityControl, DifferentialExpression Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Ludwig Geistlinger [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/EDASeq VignetteBuilder: knitr BugReports: https://github.com/drisso/EDASeq/issues git_url: https://git.bioconductor.org/packages/EDASeq git_branch: RELEASE_3_12 git_last_commit: c7c4531 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EDASeq_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EDASeq_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EDASeq_2.24.0.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.html vignetteTitles: EDASeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: metaseqR, RUVSeq importsMe: consensusDE, DaMiRseq, metaseqR2, ribosomeProfilingQC suggestsMe: bigPint, DEScan2, easyreporting, HTSFilter, TCGAbiolinks dependencyCount: 99 Package: EDDA Version: 1.28.0 Depends: Rcpp (>= 0.10.4),parallel,methods,ROCR,DESeq,baySeq,snow,edgeR Imports: graphics, stats, utils, parallel, methods, ROCR, DESeq, baySeq, snow, edgeR LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: 2d5e3a73b2405105de2aeba51ee0f5cc NeedsCompilation: yes Title: Experimental Design in Differential Abundance analysis Description: EDDA can aid in the design of a range of common experiments such as RNA-seq, Nanostring assays, RIP-seq and Metagenomic sequencing, and enables researchers to comprehensively investigate the impact of experimental decisions on the ability to detect differential abundance. This work was published on 3 December 2014 at Genome Biology under the title "The importance of study design for detecting differentially abundant features in high-throughput experiments" (http://genomebiology.com/2014/15/12/527). biocViews: ImmunoOncology, Sequencing, ExperimentalDesign, Normalization, RNASeq, ChIPSeq Author: Li Juntao, Luo Huaien, Chia Kuan Hui Burton, Niranjan Nagarajan Maintainer: Chia Kuan Hui Burton , Niranjan Nagarajan URL: http://edda.gis.a-star.edu.sg/, http://genomebiology.com/2014/15/12/527 git_url: https://git.bioconductor.org/packages/EDDA git_branch: RELEASE_3_12 git_last_commit: 8f36bfe git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EDDA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EDDA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EDDA_1.28.0.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 33 Package: edge Version: 2.22.0 Depends: R(>= 3.1.0), Biobase Imports: methods, splines, sva, snm, jackstraw, qvalue(>= 1.99.0), MASS Suggests: testthat, knitr, ggplot2, reshape2 License: MIT + file LICENSE Archs: i386, x64 MD5sum: a1ca2805a3116dd53fb6e67a4fc435af NeedsCompilation: yes Title: Extraction of Differential Gene Expression Description: The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis. biocViews: MultipleComparison, DifferentialExpression, TimeCourse, Regression, GeneExpression, DataImport Author: John D. Storey, Jeffrey T. Leek and Andrew J. Bass Maintainer: John D. Storey , Andrew J. Bass URL: https://github.com/jdstorey/edge VignetteBuilder: knitr BugReports: https://github.com/jdstorey/edge/issues git_url: https://git.bioconductor.org/packages/edge git_branch: RELEASE_3_12 git_last_commit: 040ce62 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/edge_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/edge_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/edge_2.22.0.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/edge/inst/doc/edge.R dependencyCount: 102 Package: edgeR Version: 3.32.1 Depends: R (>= 3.6.0), limma (>= 3.41.5) Imports: methods, graphics, stats, utils, locfit, Rcpp LinkingTo: Rcpp Suggests: jsonlite, readr, rhdf5, splines, Biobase, AnnotationDbi, SummarizedExperiment, org.Hs.eg.db License: GPL (>=2) Archs: i386, x64 MD5sum: 4fc0983d0a4515bb206c5d88a2c60c1b NeedsCompilation: yes Title: Empirical Analysis of Digital Gene Expression Data in R Description: Differential expression analysis of RNA-seq expression profiles with biological replication. Implements a range of statistical methodology based on the negative binomial distributions, including empirical Bayes estimation, exact tests, generalized linear models and quasi-likelihood tests. As well as RNA-seq, it be applied to differential signal analysis of other types of genomic data that produce read counts, including ChIP-seq, ATAC-seq, Bisulfite-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, DifferentialMethylation, GeneSetEnrichment, Pathways, Genetics, DNAMethylation, Bayesian, Clustering, ChIPSeq, Regression, TimeCourse, Sequencing, RNASeq, BatchEffect, SAGE, Normalization, QualityControl, MultipleComparison, BiomedicalInformatics, CellBiology, FunctionalGenomics, Epigenetics, Genetics, ImmunoOncology, SystemsBiology, Transcriptomics Author: Yunshun Chen, Aaron TL Lun, Davis J McCarthy, Matthew E Ritchie, Belinda Phipson, Yifang Hu, Xiaobei Zhou, Mark D Robinson, Gordon K Smyth Maintainer: Yunshun Chen , Gordon Smyth , Aaron Lun , Mark Robinson URL: http://bioinf.wehi.edu.au/edgeR, https://bioconductor.org/packages/edgeR SystemRequirements: C++11 git_url: https://git.bioconductor.org/packages/edgeR git_branch: RELEASE_3_12 git_last_commit: b881d80 git_last_commit_date: 2021-01-14 Date/Publication: 2021-01-14 source.ver: src/contrib/edgeR_3.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/edgeR_3.32.1.zip mac.binary.ver: bin/macosx/contrib/4.0/edgeR_3.32.1.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ASpli, DBChIP, IntEREst, methylMnM, RNASeqR, RUVSeq, TCC, tRanslatome, ReactomeGSA.data, EGSEA123, RNAseq123, rnaseqDTU, RnaSeqGeneEdgeRQL, babel, BALLI, BioInsight, edgeRun, GSAgm importsMe: affycoretools, ArrayExpressHTS, ATACseqQC, AWFisher, baySeq, BioQC, ChromSCape, circRNAprofiler, clusterExperiment, CNVRanger, compcodeR, consensusDE, coseq, countsimQC, csaw, DaMiRseq, debrowser, DEComplexDisease, DEFormats, DEGreport, DEsubs, diffcyt, diffHic, diffloop, dittoSeq, DMRcate, doseR, DRIMSeq, DropletUtils, EDDA, eegc, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, ERSSA, GDCRNATools, Glimma, GSEABenchmarkeR, HTSFilter, icetea, infercnv, IsoformSwitchAnalyzeR, KnowSeq, Maaslin2, MEDIPS, metaseqR, metaseqR2, MIGSA, MLSeq, msgbsR, msmsTests, multiHiCcompare, muscat, NBSplice, PathoStat, PROPER, psichomics, RCM, regsplice, Repitools, rnaSeqMap, ROSeq, scCB2, scde, scone, scran, SEtools, SIMD, SingleCellSignalR, singscore, spatialHeatmap, splatter, SPsimSeq, STATegRa, sva, systemPipeR, TBSignatureProfiler, TCseq, TimeSeriesExperiment, tradeSeq, tweeDEseq, vidger, yarn, zinbwave, recountWorkflow, SingscoreAMLMutations, BinQuasi, cinaR, DGEobj.utils, HTSCluster, MetaLonDA, microbial, myTAI, QuasiSeq, rmRNAseq, RVA, scRNAtools, SPUTNIK, ssizeRNA suggestsMe: ABSSeq, bigPint, biobroom, BitSeq, ClassifyR, clonotypeR, cqn, cydar, dcanr, dearseq, DEScan2, easyreporting, EDASeq, gage, gCrisprTools, GenomicAlignments, GenomicRanges, glmGamPoi, goseq, groHMM, GSAR, GSVA, ideal, iSEEu, missMethyl, multiMiR, regionReport, ribosomeProfilingQC, SeqGate, SSPA, stageR, subSeq, SummarizedBenchmark, TCGAbiolinks, tidybulk, ToPASeq, topconfects, tximeta, tximport, variancePartition, weitrix, Wrench, zFPKM, JctSeqData, leeBamViews, CAGEWorkflow, chipseqDB, csawUsersGuide, DGEobj, DiPALM, glmmSeq, seqgendiff, SIBERG, tcgsaseq dependencyCount: 10 Package: eegc Version: 1.16.0 Depends: R (>= 3.4.0) Imports: R.utils, gplots, sna, wordcloud, igraph, pheatmap, edgeR, DESeq2, clusterProfiler, S4Vectors, ggplot2, org.Hs.eg.db, org.Mm.eg.db, limma, DOSE, AnnotationDbi Suggests: knitr License: GPL-2 MD5sum: 46fb5be3151a72c6fdcb4ecec9378e9f NeedsCompilation: no Title: Engineering Evaluation by Gene Categorization (eegc) Description: This package has been developed to evaluate cellular engineering processes for direct differentiation of stem cells or conversion (transdifferentiation) of somatic cells to primary cells based on high throughput gene expression data screened either by DNA microarray or RNA sequencing. The package takes gene expression profiles as inputs from three types of samples: (i) somatic or stem cells to be (trans)differentiated (input of the engineering process), (ii) induced cells to be evaluated (output of the engineering process) and (iii) target primary cells (reference for the output). The package performs differential gene expression analysis for each pair-wise sample comparison to identify and evaluate the transcriptional differences among the 3 types of samples (input, output, reference). The ideal goal is to have induced and primary reference cell showing overlapping profiles, both very different from the original cells. biocViews: ImmunoOncology, Microarray, Sequencing, RNASeq, DifferentialExpression, GeneRegulation, GeneSetEnrichment, GeneExpression, GeneTarget Author: Xiaoyuan Zhou, Guofeng Meng, Christine Nardini, Hongkang Mei Maintainer: Xiaoyuan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eegc git_branch: RELEASE_3_12 git_last_commit: 8e13c5e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/eegc_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/eegc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/eegc_1.16.0.tgz vignettes: vignettes/eegc/inst/doc/eegc.pdf vignetteTitles: Engineering Evaluation by Gene Categorization (eegc) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eegc/inst/doc/eegc.R dependencyCount: 145 Package: EGAD Version: 1.18.0 Depends: R (>= 3.5.0) Imports: gplots, Biobase, GEOquery, limma, arrayQualityMetrics, impute, RColorBrewer, zoo, igraph, plyr, MASS, RCurl, methods Suggests: knitr, testthat License: GPL-2 MD5sum: 02b94b3ebf9a40a8b2ba20e2c358b7fc NeedsCompilation: no Title: Extending guilt by association by degree Description: The package implements a series of highly efficient tools to calculate functional properties of networks based on guilt by association methods. biocViews: Software, FunctionalGenomics, SystemsBiology, GenePrediction, FunctionalPrediction, NetworkEnrichment, GraphAndNetwork, Network Author: Sara Ballouz [aut, cre], Melanie Weber [aut, ctb], Paul Pavlidis [aut], Jesse Gillis [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGAD git_branch: RELEASE_3_12 git_last_commit: 4034d12 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EGAD_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EGAD_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EGAD_1.18.0.tgz vignettes: vignettes/EGAD/inst/doc/EGAD.html vignetteTitles: EGAD user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGAD/inst/doc/EGAD.R dependencyCount: 143 Package: EGSEA Version: 1.18.1 Depends: R (>= 3.5), Biobase, gage (>= 2.14.4), AnnotationDbi, topGO (>= 2.16.0), pathview (>= 1.4.2) Imports: PADOG (>= 1.6.0), GSVA (>= 1.12.0), globaltest (>= 5.18.0), limma (>= 3.20.9), edgeR (>= 3.6.8), HTMLUtils (>= 0.1.5), hwriter (>= 1.2.2), gplots (>= 2.14.2), ggplot2 (>= 1.0.0), safe (>= 3.4.0), stringi (>= 0.5.0), parallel, stats, metap, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, RColorBrewer, methods, EGSEAdata (>= 1.3.1), Glimma (>= 1.4.0), htmlwidgets, plotly, DT Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: 2fd5604c1e99520a1591dd0972417252 NeedsCompilation: no Title: Ensemble of Gene Set Enrichment Analyses Description: This package implements the Ensemble of Gene Set Enrichment Analyses (EGSEA) method for gene set testing. biocViews: ImmunoOncology, DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork, GeneSignaling, GeneTarget, NetworkEnrichment, Network, Classification Author: Monther Alhamdoosh, Luyi Tian, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EGSEA git_branch: RELEASE_3_12 git_last_commit: f1be510 git_last_commit_date: 2021-01-27 Date/Publication: 2021-01-28 source.ver: src/contrib/EGSEA_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/EGSEA_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/EGSEA_1.18.1.tgz vignettes: vignettes/EGSEA/inst/doc/EGSEA.pdf vignetteTitles: EGSEA vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGSEA/inst/doc/EGSEA.R dependsOnMe: EGSEA123 suggestsMe: EGSEAdata dependencyCount: 167 Package: eiR Version: 1.30.0 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics, gespeR,RcppAnnoy (>= 0.0.9) Suggests: BiocStyle, knitcitations, knitr, knitrBootstrap License: Artistic-2.0 Archs: x64 MD5sum: 9b349515308bc1c9596dfe77ba9cc6b4 NeedsCompilation: yes Title: Accelerated similarity searching of small molecules Description: The eiR package provides utilities for accelerated structure similarity searching of very large small molecule data sets using an embedding and indexing approach. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/eiR git_branch: RELEASE_3_12 git_last_commit: ac54c4a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/eiR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/eiR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/eiR_1.30.0.tgz vignettes: vignettes/eiR/inst/doc/eiR.html vignetteTitles: eiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R dependencyCount: 129 Package: eisa Version: 1.42.0 Depends: isa2, Biobase (>= 2.17.8), AnnotationDbi, methods Imports: BiocGenerics, Category, genefilter, DBI Suggests: igraph (>= 0.6), Matrix, GOstats, GO.db, KEGG.db, biclust, MASS, xtable, ALL, hgu95av2.db, targetscan.Hs.eg.db, org.Hs.eg.db License: GPL (>= 2) MD5sum: b5206e4b0e04691c48c65c0dfe6cb7d8 NeedsCompilation: no Title: Expression data analysis via the Iterative Signature Algorithm Description: The Iterative Signature Algorithm (ISA) is a biclustering method; it finds correlated blocks (transcription modules) in gene expression (or other tabular) data. The ISA is capable of finding overlapping modules and it is resilient to noise. This package provides a convenient interface to the ISA, using standard BioConductor data structures; and also contains various visualization tools that can be used with other biclustering algorithms. biocViews: Classification, Visualization, Microarray, GeneExpression Author: Gabor Csardi Maintainer: Gabor Csardi git_url: https://git.bioconductor.org/packages/eisa git_branch: RELEASE_3_12 git_last_commit: 1feb134 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/eisa_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/eisa_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/eisa_1.42.0.tgz vignettes: vignettes/eisa/inst/doc/EISA_tutorial.pdf vignetteTitles: The Iterative Signature Algorithm for Gene Expression Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisa/inst/doc/EISA_tutorial.R dependsOnMe: ExpressionView importsMe: ExpressionView dependencyCount: 51 Package: eisaR Version: 1.2.0 Depends: R (>= 4.0.0) Imports: graphics, stats, GenomicRanges, GenomicFeatures, S4Vectors, IRanges, AnnotationDbi, limma, edgeR, methods, SummarizedExperiment, BiocGenerics, rtracklayer, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, QuasR, Rbowtie, Biostrings, BSgenome, BSgenome.Hsapiens.UCSC.hg38, ensembldb License: GPL-3 MD5sum: a33251db2860f9c2d3ef89f0a75f8b05 NeedsCompilation: no Title: Exon-Intron Split Analysis (EISA) in R Description: Exon-intron split analysis (EISA) uses ordinary RNA-seq data to measure changes in mature RNA and pre-mRNA reads across different experimental conditions to quantify transcriptional and post-transcriptional regulation of gene expression. For details see Gaidatzis et al., Nat Biotechnol 2015. doi: 10.1038/nbt.3269. eisaR implements the major steps of EISA in R. biocViews: Transcription, GeneExpression, GeneRegulation, FunctionalGenomics, Transcriptomics, Regression, RNASeq Author: Michael Stadler [aut, cre], Dimos Gaidatzis [aut], Lukas Burger [aut], Charlotte Soneson [aut] Maintainer: Michael Stadler URL: https://github.com/fmicompbio/eisaR VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/eisaR/issues git_url: https://git.bioconductor.org/packages/eisaR git_branch: RELEASE_3_12 git_last_commit: e93caba git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/eisaR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/eisaR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/eisaR_1.2.0.tgz vignettes: vignettes/eisaR/inst/doc/eisaR.html, vignettes/eisaR/inst/doc/rna-velocity.html vignetteTitles: Using eisaR for Exon-Intron Split Analysis (EISA), Generating reference files for spliced and unspliced abundance estimation with alignment-free methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eisaR/inst/doc/eisaR.R, vignettes/eisaR/inst/doc/rna-velocity.R dependencyCount: 91 Package: ELBOW Version: 1.26.0 Depends: R (>= 2.15.0) Imports: graphics, stats, utils Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer, hgu133plus2cdf, hgu133plus2probe License: file LICENSE License_is_FOSS: yes License_restricts_use: no MD5sum: a9a1d4864f16dad8f88eee5aa02cc4ba NeedsCompilation: no Title: ELBOW - Evaluating foLd change By the lOgit Way Description: Elbow an improved fold change test that uses cluster analysis and pattern recognition to set cut off limits that are derived directly from intrareplicate variance without assuming a normal distribution for as few as 2 biological replicates. Elbow also provides the same consistency as fold testing in cross platform analysis. Elbow has lower false positive and false negative rates than standard fold testing when both are evaluated using T testing and Statistical Analysis of Microarray using 12 replicates (six replicates each for initial and final conditions). Elbow provides a null value based on initial condition replicates and gives error bounds for results to allow better evaluation of significance. biocViews: ImmunoOncology, Technology, Microarray, RNASeq, Sequencing, Sequencing, Software, MultiChannel, OneChannel, TwoChannel, GeneExpression Author: Xiangli Zhang, Natalie Bjorklund, Graham Alvare, Tom Ryzdak, Richard Sparling, Brian Fristensky Maintainer: Graham Alvare , Xiangli Zhang git_url: https://git.bioconductor.org/packages/ELBOW git_branch: RELEASE_3_12 git_last_commit: 40d6a67 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ELBOW_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ELBOW_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ELBOW_1.26.0.tgz vignettes: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.pdf vignetteTitles: Using ELBOW --- the definitive ELBOW tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ELBOW/inst/doc/Elbow_tutorial_vignette.R dependencyCount: 3 Package: ELMER Version: 2.14.0 Depends: R (>= 3.4.0), ELMER.data (>= 2.9.3) Imports: GenomicRanges, ggplot2, reshape, grid, grDevices, graphics, methods, parallel, stats, utils, IRanges, GenomeInfoDb, S4Vectors, GenomicFeatures, TCGAbiolinks (>= 2.9.2), plyr, Matrix, dplyr, Gviz, ComplexHeatmap, circlize, MultiAssayExperiment, SummarizedExperiment, biomaRt, doParallel, downloader, ggrepel, lattice, magrittr, readr, scales, rvest, xml2, plotly, gridExtra, rmarkdown, stringr, tibble, tidyr, progress, purrr, reshape2, ggpubr, rtracklayer, DelayedArray Suggests: BiocStyle, knitr, testthat, data.table, DT, GenomicInteractions, webshot, R.utils, covr, sesameData License: GPL-3 MD5sum: b7bea4e11b491d9855aff10c3979ce4e NeedsCompilation: no Title: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Cancer Methylomes Description: ELMER is designed to use DNA methylation and gene expression from a large number of samples to infere regulatory element landscape and transcription factor network in primary tissue. biocViews: DNAMethylation, GeneExpression, MotifAnnotation, Software, GeneRegulation, Transcription, Network Author: Tiago Chedraoui Silva [aut, cre], Lijing Yao [aut], Simon Coetzee [aut], Nicole Gull [ctb], Hui Shen [ctb], Peter Laird [ctb], Peggy Farnham [aut], Dechen Li [ctb], Benjamin Berman [aut] Maintainer: Tiago Chedraoui Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ELMER git_branch: RELEASE_3_12 git_last_commit: 1a74024 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ELMER_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ELMER_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ELMER_2.14.0.tgz vignettes: vignettes/ELMER/inst/doc/analysis_data_input.html, vignettes/ELMER/inst/doc/analysis_diff_meth.html, vignettes/ELMER/inst/doc/analysis_get_pair.html, vignettes/ELMER/inst/doc/analysis_gui.html, vignettes/ELMER/inst/doc/analysis_motif_enrichment.html, vignettes/ELMER/inst/doc/analysis_regulatory_tf.html, vignettes/ELMER/inst/doc/index.html, vignettes/ELMER/inst/doc/input.html, vignettes/ELMER/inst/doc/pipe.html, vignettes/ELMER/inst/doc/plots_heatmap.html, vignettes/ELMER/inst/doc/plots_motif_enrichment.html, vignettes/ELMER/inst/doc/plots_scatter.html, vignettes/ELMER/inst/doc/plots_schematic.html, vignettes/ELMER/inst/doc/plots_TF.html, vignettes/ELMER/inst/doc/usecase.html vignetteTitles: "3.1 - Data input - Creating MAE object", "3.2 - Identifying differentially methylated probes", "3.3 - Identifying putative probe-gene pairs", 5 - Integrative analysis workshop with TCGAbiolinks and ELMER - Analysis GUI, "3.4 - Motif enrichment analysis on the selected probes", "3.5 - Identifying regulatory TFs", "1 - ELMER v.2: An R/Bioconductor package to reconstruct gene regulatory networks from DNA methylation and transcriptome profiles", "2 - Introduction: Input data", "3.6 - TCGA.pipe: Running ELMER for TCGA data in a compact way", "4.5 - Heatmap plots", "4.3 - Motif enrichment plots", "4.1 - Scatter plots", "4.2 - Schematic plots", "4.4 - Regulatory TF plots", "11 - ELMER: Use case" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/analysis_data_input.R, vignettes/ELMER/inst/doc/analysis_diff_meth.R, vignettes/ELMER/inst/doc/analysis_get_pair.R, vignettes/ELMER/inst/doc/analysis_gui.R, vignettes/ELMER/inst/doc/analysis_motif_enrichment.R, vignettes/ELMER/inst/doc/analysis_regulatory_tf.R, vignettes/ELMER/inst/doc/index.R, vignettes/ELMER/inst/doc/input.R, vignettes/ELMER/inst/doc/pipe.R, vignettes/ELMER/inst/doc/plots_heatmap.R, vignettes/ELMER/inst/doc/plots_motif_enrichment.R, vignettes/ELMER/inst/doc/plots_scatter.R, vignettes/ELMER/inst/doc/plots_schematic.R, vignettes/ELMER/inst/doc/plots_TF.R, vignettes/ELMER/inst/doc/usecase.R importsMe: TCGAbiolinksGUI, TCGAWorkflow dependencyCount: 208 Package: EMDomics Version: 2.20.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 7ce839c5d77e23ea8fa6af8e8c8f8aa5 NeedsCompilation: no Title: Earth Mover's Distance for Differential Analysis of Genomics Data Description: The EMDomics algorithm is used to perform a supervised multi-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover's Distance (EMD) algorithm instead computes the "work" needed to transform one distribution into another, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores. This package also incorporates the Komolgorov-Smirnov (K-S) test and the Cramer von Mises test (CVM), which are both common distribution comparison tests. biocViews: Software, DifferentialExpression, GeneExpression, Microarray Author: Sadhika Malladi [aut, cre], Daniel Schmolze [aut, cre], Andrew Beck [aut], Sheida Nabavi [aut] Maintainer: Sadhika Malladi and Daniel Schmolze VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EMDomics git_branch: RELEASE_3_12 git_last_commit: 59aa6de git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EMDomics_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EMDomics_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EMDomics_2.20.0.tgz vignettes: vignettes/EMDomics/inst/doc/EMDomics.html vignetteTitles: EMDomics Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R dependencyCount: 50 Package: EmpiricalBrownsMethod Version: 1.18.0 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ceae159b0da55748af3492477a8794f4 NeedsCompilation: no Title: Uses Brown's method to combine p-values from dependent tests Description: Combining P-values from multiple statistical tests is common in bioinformatics. However, this procedure is non-trivial for dependent P-values. This package implements an empirical adaptation of Brown’s Method (an extension of Fisher’s Method) for combining dependent P-values which is appropriate for highly correlated data sets found in high-throughput biological experiments. biocViews: StatisticalMethod, GeneExpression, Pathways Author: William Poole Maintainer: David Gibbs URL: https://github.com/IlyaLab/CombiningDependentPvaluesUsingEBM.git VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EmpiricalBrownsMethod git_branch: RELEASE_3_12 git_last_commit: 23dd11c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EmpiricalBrownsMethod_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EmpiricalBrownsMethod_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EmpiricalBrownsMethod_1.18.0.tgz vignettes: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html vignetteTitles: Empirical Browns Method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R dependsOnMe: poolVIM importsMe: EBSEA suggestsMe: ActivePathways dependencyCount: 0 Package: ENCODExplorer Version: 2.16.0 Depends: R (>= 3.6) Imports: methods, tools, jsonlite, RCurl, tidyr, data.table, dplyr, stringr, stringi, utils, AnnotationHub, GenomicRanges, rtracklayer, S4Vectors, GenomeInfoDb, ENCODExplorerData Suggests: RUnit,BiocGenerics,knitr, curl, httr, shiny, shinythemes, DT License: Artistic-2.0 MD5sum: 5c15bca686e3bb19519357e15fc6c38f NeedsCompilation: no Title: A compilation of ENCODE metadata Description: This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format. biocViews: Infrastructure, DataImport Author: Charles Joly Beauparlant [aut, cre], Audrey Lemacon [aut], Eric Fournier [aut], Louis Gendron [ctb], Astrid-Louise Deschenes [ctb], Arnaud Droit [aut] Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues git_url: https://git.bioconductor.org/packages/ENCODExplorer git_branch: RELEASE_3_12 git_last_commit: cd5a8bf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ENCODExplorer_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ENCODExplorer_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ENCODExplorer_2.16.0.tgz vignettes: vignettes/ENCODExplorer/inst/doc/ENCODExplorer.html vignetteTitles: ENCODExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENCODExplorer/inst/doc/ENCODExplorer.R suggestsMe: TSRchitect dependencyCount: 109 Package: EnhancedVolcano Version: 1.8.0 Depends: ggplot2, ggrepel Imports: ggalt, ggrastr Suggests: RUnit, BiocGenerics, knitr, DESeq2, pasilla, airway, org.Hs.eg.db, gridExtra, magrittr License: GPL-3 MD5sum: 3e44150793c060da988b9b0f8c82ea3c NeedsCompilation: no Title: Publication-ready volcano plots with enhanced colouring and labeling Description: Volcano plots represent a useful way to visualise the results of differential expression analyses. Here, we present a highly-configurable function that produces publication-ready volcano plots. EnhancedVolcano will attempt to fit as many point labels in the plot window as possible, thus avoiding 'clogging' up the plot with labels that could not otherwise have been read. Other functionality allows the user to identify up to 4 different types of attributes in the same plot space via colour, shape, size, and shade parameter configurations. biocViews: RNASeq, GeneExpression, Transcription, DifferentialExpression, ImmunoOncology Author: Kevin Blighe [aut, cre], Sharmila Rana [aut], Emir Turkes [ctb], Benjamin Ostendorf [ctb], Myles Lewis [aut] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/EnhancedVolcano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnhancedVolcano git_branch: RELEASE_3_12 git_last_commit: 8eefba4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EnhancedVolcano_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EnhancedVolcano_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EnhancedVolcano_1.8.0.tgz vignettes: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.html vignetteTitles: Publication-ready volcano plots with enhanced colouring and labeling hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnhancedVolcano/inst/doc/EnhancedVolcano.R dependencyCount: 81 Package: EnMCB Version: 1.2.2 Depends: R (>= 4.0) Imports: foreach, doParallel, parallel, stats, survivalROC, glmnet, rms, survivalsvm, ggplot2, minfi, IlluminaHumanMethylation450kanno.ilmn12.hg19, survival, utils Suggests: SummarizedExperiment, testthat, Biobase, survminer, affycoretools, knitr, plotROC, prognosticROC License: GPL-2 MD5sum: 75466d5a94dbecbbba2dfc48af8dcb74 NeedsCompilation: no Title: Predicting Disease Progression Based on Methylation Correlated Blocks using Ensemble Models Description: Creation of the correlated blocks using DNA methylation profiles. A stacked ensemble of machine learning models, which combined the support vector machine and elastic-net regression model, can be constructed to predict disease progression. biocViews: Normalization, DNAMethylation, MethylationArray, SupportVectorMachine Author: Xin Yu Maintainer: Xin Yu VignetteBuilder: knitr BugReports: https://github.com/whirlsyu/EnMCB/issues git_url: https://git.bioconductor.org/packages/EnMCB git_branch: RELEASE_3_12 git_last_commit: 79cdea7 git_last_commit_date: 2020-12-20 Date/Publication: 2020-12-20 source.ver: src/contrib/EnMCB_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/EnMCB_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/EnMCB_1.2.2.tgz vignettes: vignettes/EnMCB/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnMCB/inst/doc/vignette.R dependencyCount: 184 Package: ENmix Version: 1.26.10 Depends: parallel,doParallel,foreach,SummarizedExperiment,stats Imports: grDevices,graphics,preprocessCore,matrixStats,methods,utils,irr, geneplotter,impute,minfi,RPMM,illuminaio,dynamicTreeCut,IRanges,gtools, Biobase,ExperimentHub,AnnotationHub,genefilter,gplots,quadprog,S4Vectors Suggests: minfiData, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: fad7ff49f9d2daab59c8faa3f8ec2350 NeedsCompilation: no Title: Quality control and analysis tools for Illumina DNA methylation BeadChip Description: Tool kits for quanlity control, analysis and visulization of Illumina DNA methylation arrays. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent,Epigenetics, MultiChannel, DifferentialMethylation, ImmunoOncology Author: Zongli Xu [cre, aut], Liang Niu [aut], Jack Taylor [ctb] Maintainer: Zongli Xu git_url: https://git.bioconductor.org/packages/ENmix git_branch: RELEASE_3_12 git_last_commit: c027267 git_last_commit_date: 2021-05-01 Date/Publication: 2021-05-02 source.ver: src/contrib/ENmix_1.26.10.tar.gz win.binary.ver: bin/windows/contrib/4.0/ENmix_1.26.10.zip mac.binary.ver: bin/macosx/contrib/4.0/ENmix_1.26.10.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.pdf vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENmix/inst/doc/ENmix.R dependencyCount: 164 Package: EnrichedHeatmap Version: 1.20.0 Depends: R (>= 3.1.2), methods, grid, ComplexHeatmap (>= 2.5.1), GenomicRanges Imports: matrixStats, stats, GetoptLong, Rcpp, utils, locfit, circlize (>= 0.4.5), IRanges LinkingTo: Rcpp Suggests: testthat (>= 0.3), knitr, markdown, genefilter, RColorBrewer License: MIT + file LICENSE Archs: i386, x64 MD5sum: 00384a8a22f88df559ea49e1e846a973 NeedsCompilation: yes Title: Making Enriched Heatmaps Description: Enriched heatmap is a special type of heatmap which visualizes the enrichment of genomic signals on specific target regions. Here we implement enriched heatmap by ComplexHeatmap package. Since this type of heatmap is just a normal heatmap but with some special settings, with the functionality of ComplexHeatmap, it would be much easier to customize the heatmap as well as concatenating to a list of heatmaps to show correspondance between different data sources. biocViews: Software, Visualization, Sequencing, GenomeAnnotation, Coverage Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/EnrichedHeatmap VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/EnrichedHeatmap git_branch: RELEASE_3_12 git_last_commit: 3bcdeac git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EnrichedHeatmap_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EnrichedHeatmap_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EnrichedHeatmap_1.20.0.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html, vignettes/EnrichedHeatmap/inst/doc/roadmap.html, vignettes/EnrichedHeatmap/inst/doc/row_odering.html, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.html vignetteTitles: 1. Make Enriched Heatmaps, 4. Visualize Comprehensive Associations in Roadmap dataset, 3. Compare row ordering methods, 2. Visualize Categorical Signals hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R, vignettes/EnrichedHeatmap/inst/doc/roadmap.R, vignettes/EnrichedHeatmap/inst/doc/row_odering.R, vignettes/EnrichedHeatmap/inst/doc/visualize_categorical_signals_wrapper.R importsMe: profileplyr dependencyCount: 37 Package: EnrichmentBrowser Version: 2.20.7 Depends: SummarizedExperiment, graph Imports: AnnotationDbi, BiocFileCache, BiocManager, GSEABase, GO.db, KEGGREST, KEGGgraph, Rgraphviz, S4Vectors, SPIA, edgeR, graphite, hwriter, limma, methods, pathview, safe Suggests: ALL, BiocStyle, ComplexHeatmap, DESeq2, ReportingTools, airway, biocGraph, hgu95av2.db, geneplotter, knitr, msigdbr, rmarkdown License: Artistic-2.0 MD5sum: ee71e3f4d74d8dadc7c60f8a8c33e6e5 NeedsCompilation: no Title: Seamless navigation through combined results of set-based and network-based enrichment analysis Description: The EnrichmentBrowser package implements essential functionality for the enrichment analysis of gene expression data. The analysis combines the advantages of set-based and network-based enrichment analysis in order to derive high-confidence gene sets and biological pathways that are differentially regulated in the expression data under investigation. Besides, the package facilitates the visualization and exploration of such sets and pathways. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Mirko Signorelli [ctb], Marcel Ramos [ctb], Levi Waldron [ctb], Ralf Zimmer [aut] Maintainer: Ludwig Geistlinger VignetteBuilder: knitr BugReports: https://github.com/lgeistlinger/EnrichmentBrowser/issues git_url: https://git.bioconductor.org/packages/EnrichmentBrowser git_branch: RELEASE_3_12 git_last_commit: 4e4444e git_last_commit_date: 2020-12-09 Date/Publication: 2020-12-10 source.ver: src/contrib/EnrichmentBrowser_2.20.7.tar.gz win.binary.ver: bin/windows/contrib/4.0/EnrichmentBrowser_2.20.7.zip mac.binary.ver: bin/macosx/contrib/4.0/EnrichmentBrowser_2.20.7.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.R importsMe: GSEABenchmarkeR suggestsMe: ToPASeq dependencyCount: 91 Package: enrichplot Version: 1.10.2 Depends: R (>= 3.5.0) Imports: cowplot, DOSE, ggplot2, ggraph, graphics, grid, igraph, methods, plyr, purrr, RColorBrewer, reshape2, stats, utils, scatterpie, shadowtext, GOSemSim, magrittr Suggests: clusterProfiler, dplyr, europepmc, ggupset, knitr, org.Hs.eg.db, prettydoc, tibble, tidyr, ggforce, AnnotationDbi, ggplotify, ggridges, grDevices, gridExtra, ggnewscale, ggrepel License: Artistic-2.0 MD5sum: 0aee32fdace3026ccaf0b4d626f219f8 NeedsCompilation: no Title: Visualization of Functional Enrichment Result Description: The 'enrichplot' package implements several visualization methods for interpreting functional enrichment results obtained from ORA or GSEA analysis. All the visualization methods are developed based on 'ggplot2' graphics. biocViews: Annotation, GeneSetEnrichment, GO, KEGG, Pathways, Software, Visualization Author: Guangchuang Yu [aut, cre] (), Erqiang Hu [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/enrichplot/issues git_url: https://git.bioconductor.org/packages/enrichplot git_branch: RELEASE_3_12 git_last_commit: 77ee04f git_last_commit_date: 2021-01-28 Date/Publication: 2021-01-28 source.ver: src/contrib/enrichplot_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/enrichplot_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.0/enrichplot_1.10.2.tgz vignettes: vignettes/enrichplot/inst/doc/enrichplot.html vignetteTitles: enrichplot hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: maEndToEnd importsMe: ChIPseeker, clusterProfiler, debrowser, MAGeCKFlute, meshes, ReactomePA suggestsMe: methylGSA dependencyCount: 98 Package: enrichTF Version: 1.6.0 Depends: pipeFrame Imports: BSgenome, rtracklayer, motifmatchr, TFBSTools, R.utils, methods, JASPAR2018, GenomeInfoDb, GenomicRanges, IRanges, BiocGenerics, S4Vectors, utils, parallel, stats, ggpubr, heatmap3, ggplot2, clusterProfiler, rmarkdown, grDevices, magrittr Suggests: knitr, testthat, webshot License: GPL-3 MD5sum: 4e70b343f4b759fd198f610d3b30a625 NeedsCompilation: no Title: Transcription Factors Enrichment Analysis Description: As transcription factors (TFs) play a crucial role in regulating the transcription process through binding on the genome alone or in a combinatorial manner, TF enrichment analysis is an efficient and important procedure to locate the candidate functional TFs from a set of experimentally defined regulatory regions. While it is commonly accepted that structurally related TFs may have similar binding preference to sequences (i.e. motifs) and one TF may have multiple motifs, TF enrichment analysis is much more challenging than motif enrichment analysis. Here we present a R package for TF enrichment analysis which combine motif enrichment with the PECA model. biocViews: Software, GeneTarget, MotifAnnotation, GraphAndNetwork, Transcription Author: Zheng Wei, Zhana Duren, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/enrichTF VignetteBuilder: knitr BugReports: https://github.com/wzthu/enrichTF/issues git_url: https://git.bioconductor.org/packages/enrichTF git_branch: RELEASE_3_12 git_last_commit: 1e2abb2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/enrichTF_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/enrichTF_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/enrichTF_1.6.0.tgz vignettes: vignettes/enrichTF/inst/doc/enrichTF.html vignetteTitles: An Introduction to enrichTF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/enrichTF/inst/doc/enrichTF.R dependencyCount: 198 Package: ensembldb Version: 2.14.1 Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.31.18), GenomicFeatures (>= 1.29.10), AnnotationFilter (>= 1.5.2) Imports: methods, RSQLite (>= 1.1), DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors (>= 0.23.10), Rsamtools, IRanges (>= 2.13.24), ProtGenerics, Biostrings (>= 2.47.9), curl Suggests: BiocStyle, knitr, EnsDb.Hsapiens.v86 (>= 0.99.8), testthat, BSgenome.Hsapiens.NCBI.GRCh38, ggbio (>= 1.24.0), Gviz (>= 1.20.0), magrittr, rmarkdown, AnnotationHub Enhances: RMariaDB, shiny License: LGPL MD5sum: b7bdf36cb0b9eb2a97a6c0f99c7eab20 NeedsCompilation: no Title: Utilities to create and use Ensembl-based annotation databases Description: The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, ensembldb provides a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. EnsDb databases built with ensembldb contain also protein annotations and mappings between proteins and their encoding transcripts. Finally, ensembldb provides functions to map between genomic, transcript and protein coordinates. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer with contributions from Tim Triche, Sebastian Gibb, Laurent Gatto and Christian Weichenberger. Maintainer: Johannes Rainer URL: https://github.com/jorainer/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jorainer/ensembldb/issues git_url: https://git.bioconductor.org/packages/ensembldb git_branch: RELEASE_3_12 git_last_commit: c8945b3 git_last_commit_date: 2021-04-19 Date/Publication: 2021-04-19 source.ver: src/contrib/ensembldb_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ensembldb_2.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ensembldb_2.14.1.tgz vignettes: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.html, vignettes/ensembldb/inst/doc/coordinate-mapping.html, vignettes/ensembldb/inst/doc/ensembldb.html, vignettes/ensembldb/inst/doc/MySQL-backend.html, vignettes/ensembldb/inst/doc/proteins.html vignetteTitles: Use cases for coordinate mapping with ensembldb, Mapping between genome,, transcript and protein coordinates, Generating an using Ensembl based annotation packages, Using a MariaDB/MySQL server backend, Querying protein features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/coordinate-mapping-use-cases.R, vignettes/ensembldb/inst/doc/coordinate-mapping.R, vignettes/ensembldb/inst/doc/ensembldb.R, vignettes/ensembldb/inst/doc/MySQL-backend.R, vignettes/ensembldb/inst/doc/proteins.R dependsOnMe: chimeraviz, AHEnsDbs, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, EnsDb.Hsapiens.v86, EnsDb.Mmusculus.v75, EnsDb.Mmusculus.v79, EnsDb.Rnorvegicus.v75, EnsDb.Rnorvegicus.v79 importsMe: APAlyzer, biovizBase, BUSpaRse, ChIPpeakAnno, consensusDE, epivizrData, ggbio, Gviz, ldblock, metagene, TVTB, tximeta, scRNAseq suggestsMe: alpine, CNVRanger, eisaR, EpiTxDb, GenomicFeatures, multicrispr, TxRegInfra, wiggleplotr dependencyCount: 91 Package: ensemblVEP Version: 1.32.1 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment, GenomeInfoDb, stats Suggests: RUnit License: Artistic-2.0 MD5sum: c43e8c6c35c92ad0091b25ebbb290f6c NeedsCompilation: no Title: R Interface to Ensembl Variant Effect Predictor Description: Query the Ensembl Variant Effect Predictor via the perl API. biocViews: Annotation, VariantAnnotation, SNP Author: Valerie Obenchain and Lori Shepherd Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 103) and the Perl modules DBI and DBD::mysql must be installed. See the package README and Ensembl installation instructions: http://www.ensembl.org/info/docs/tools/vep/script/vep_download.html#installer git_url: https://git.bioconductor.org/packages/ensemblVEP git_branch: RELEASE_3_12 git_last_commit: 5095beb git_last_commit_date: 2021-03-08 Date/Publication: 2021-03-15 source.ver: src/contrib/ensemblVEP_1.32.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/ensemblVEP_1.32.1.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.pdf vignetteTitles: ensemblVEP, PreV90EnsemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R, vignettes/ensemblVEP/inst/doc/PreV90EnsemblVEP.R importsMe: MMAPPR2, TVTB dependencyCount: 90 Package: ENVISIONQuery Version: 1.38.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: 3db16ef064aa25cd0d42a73183c709e5 NeedsCompilation: no Title: Retrieval from the ENVISION bioinformatics data portal into R Description: Tools to retrieve data from ENVISION, the Database for Annotation, Visualization and Integrated Discovery portal biocViews: Annotation Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day git_url: https://git.bioconductor.org/packages/ENVISIONQuery git_branch: RELEASE_3_12 git_last_commit: ff2bb74 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ENVISIONQuery_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ENVISIONQuery_1.38.0.zip vignettes: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.pdf vignetteTitles: An R Package for retrieving data from EnVision into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENVISIONQuery/inst/doc/ENVISIONQuery.R dependencyCount: 4 Package: EpiDISH Version: 2.6.1 Depends: R (>= 4.0) Imports: MASS, e1071, quadprog, parallel, stats, matrixStats, stringr, locfdr, Matrix Suggests: roxygen2, GEOquery, BiocStyle, knitr, rmarkdown, Biobase, testthat License: GPL-2 MD5sum: f4fd5e525e4059799c001815e4854925 NeedsCompilation: no Title: Epigenetic Dissection of Intra-Sample-Heterogeneity Description: EpiDISH is a R package to infer the proportions of a priori known cell-types present in a sample representing a mixture of such cell-types. Right now, the package can be used on DNAm data of whole blood, generic epithelial tissue and breast tissue. Besides, the package provides a function that allows the identification of differentially methylated cell-types and their directionality of change in Epigenome-Wide Association Studies. biocViews: DNAMethylation, MethylationArray, Epigenetics, DifferentialMethylation, ImmunoOncology Author: Andrew E. Teschendorff [aut], Shijie C. Zheng [aut, cre] Maintainer: Shijie C. Zheng URL: https://github.com/sjczheng/EpiDISH VignetteBuilder: knitr BugReports: https://github.com/sjczheng/EpiDISH/issues git_url: https://git.bioconductor.org/packages/EpiDISH git_branch: RELEASE_3_12 git_last_commit: c27e9e1 git_last_commit_date: 2021-04-27 Date/Publication: 2021-04-27 source.ver: src/contrib/EpiDISH_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/EpiDISH_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/EpiDISH_2.6.1.tgz vignettes: vignettes/EpiDISH/inst/doc/EpiDISH.html vignetteTitles: Epigenetic Dissection of Intra-Sample-Heterogeneity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiDISH/inst/doc/EpiDISH.R dependsOnMe: TOAST suggestsMe: FlowSorted.Blood.EPIC dependencyCount: 22 Package: epigenomix Version: 1.30.0 Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 46a7c8868ccdaa5a119a01497607b857 NeedsCompilation: no Title: Epigenetic and gene transcription data normalization and integration with mixture models Description: A package for the integrative analysis of RNA-seq or microarray based gene transcription and histone modification data obtained by ChIP-seq. The package provides methods for data preprocessing and matching as well as methods for fitting bayesian mixture models in order to detect genes with differences in both data types. biocViews: ChIPSeq, GeneExpression, DifferentialExpression, Classification Author: Hans-Ulrich Klein, Martin Schaefer Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/epigenomix git_branch: RELEASE_3_12 git_last_commit: 56b3595 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/epigenomix_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/epigenomix_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/epigenomix_1.30.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epigenomix/inst/doc/epigenomix.R dependencyCount: 98 Package: epihet Version: 1.6.1 Depends: R(>= 3.6), GenomicRanges, IRanges, S4Vectors, ggplot2, foreach, Rtsne, igraph Imports: data.table, doParallel, EntropyExplorer, graphics, stats, grDevices, pheatmap, utils, qvalue, WGCNA, ReactomePA Suggests: knitr, clusterProfiler, ggfortify, org.Hs.eg.db, rmarkdown License: Artistic-2.0 MD5sum: d1f661ebc354a8b4d27733e915427c4c NeedsCompilation: no Title: Determining Epigenetic Heterogeneity from Bisulfite Sequencing Data Description: epihet is an R-package that calculates the epigenetic heterogeneity between cancer cells and/or normal cells. The functions establish a pipeline that take in bisulfite sequencing data from multiple samples and use the data to track similarities and differences in epipolymorphism,proportion of discordantly methylated sequencing reads (PDR),and Shannon entropy values at epialleles that are shared between the samples.epihet can be used to perform analysis on the data by creating pheatmaps, box plots, PCA plots, and t-SNE plots. MA plots can also be created by calculating the differential heterogeneity of the samples. And we construct co-epihet network and perform network analysis. biocViews: DNAMethylation, Epigenetics, MethylSeq, Sequencing, Software Author: Xiaowen Chen [aut, cre], Haitham Ashoor [aut], Ryan Musich [aut], Mingsheng Zhang [aut], Jiahui Wang [aut], Sheng Li [aut] Maintainer: Xiaowen Chen URL: https://github.com/TheJacksonLaboratory/epihet VignetteBuilder: knitr BugReports: https://github.com/TheJacksonLaboratory/epihet/issues git_url: https://git.bioconductor.org/packages/epihet git_branch: RELEASE_3_12 git_last_commit: 9cff2f9 git_last_commit_date: 2020-12-21 Date/Publication: 2020-12-29 source.ver: src/contrib/epihet_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/epihet_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/epihet_1.6.1.tgz vignettes: vignettes/epihet/inst/doc/epihet.pdf vignetteTitles: epihet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epihet/inst/doc/epihet.R dependencyCount: 154 Package: epiNEM Version: 1.14.1 Depends: R (>= 3.4) Imports: BoolNet, e1071, gtools, stats, igraph, utils, lattice, latticeExtra, RColorBrewer, pcalg, minet, grDevices, graph, mnem Suggests: knitr, RUnit, BiocGenerics, STRINGdb, devtools, rmarkdown, GOSemSim, AnnotationHub, org.Sc.sgd.db License: GPL-3 MD5sum: 58f9b3696c41a383fa578ecf1bec5e13 NeedsCompilation: no Title: epiNEM Description: epiNEM is an extension of the original Nested Effects Models (NEM). EpiNEM is able to take into account double knockouts and infer more complex network signalling pathways. biocViews: Pathways, SystemsBiology, NetworkInference, Network Author: Madeline Diekmann & Martin Pirkl Maintainer: Martin Pirkl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epiNEM git_branch: RELEASE_3_12 git_last_commit: a2d4ce7 git_last_commit_date: 2020-10-29 Date/Publication: 2020-10-29 source.ver: src/contrib/epiNEM_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/epiNEM_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/epiNEM_1.14.1.tgz vignettes: vignettes/epiNEM/inst/doc/epiNEM.html vignetteTitles: epiNEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epiNEM/inst/doc/epiNEM.R suggestsMe: mnem dependencyCount: 104 Package: EpiTxDb Version: 1.2.1 Depends: R (>= 4.0), AnnotationDbi, Modstrings Imports: methods, utils, httr, xml2, curl, GenomicFeatures, GenomicRanges, GenomeInfoDb, BiocGenerics, BiocFileCache, S4Vectors, IRanges, RSQLite, DBI, Biostrings, tRNAdbImport Suggests: BiocStyle, knitr, rmarkdown, testthat, httptest, AnnotationHub, ensembldb, ggplot2, EpiTxDb.Hs.hg38, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Scerevisiae.UCSC.sacCer3, TxDb.Hsapiens.UCSC.hg38.knownGene License: Artistic-2.0 MD5sum: 9e4b9ccec3149e739e6f2875fe45de0e NeedsCompilation: no Title: Storing and accessing epitranscriptomic information using the AnnotationDbi interface Description: EpiTxDb facilitates the storage of epitranscriptomic information. More specifically, it can keep track of modification identity, position, the enzyme for introducing it on the RNA, a specifier which determines the position on the RNA to be modified and the literature references each modification is associated with. biocViews: Software, Epitranscriptomics Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/EpiTxDb VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/EpiTxDb/issues git_url: https://git.bioconductor.org/packages/EpiTxDb git_branch: RELEASE_3_12 git_last_commit: 359e443 git_last_commit_date: 2021-03-25 Date/Publication: 2021-03-25 source.ver: src/contrib/EpiTxDb_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/EpiTxDb_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/EpiTxDb_1.2.1.tgz vignettes: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.html, vignettes/EpiTxDb/inst/doc/EpiTxDb.html vignetteTitles: EpiTxDb-creation, EpiTxDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EpiTxDb/inst/doc/EpiTxDb-creation.R, vignettes/EpiTxDb/inst/doc/EpiTxDb.R dependsOnMe: EpiTxDb.Hs.hg38, EpiTxDb.Mm.mm10, EpiTxDb.Sc.sacCer3 dependencyCount: 107 Package: epivizr Version: 2.20.0 Depends: R (>= 3.0), methods, Imports: epivizrServer (>= 1.1.1), epivizrData (>= 1.3.4), GenomicRanges, S4Vectors, IRanges, bumphunter, GenomeInfoDb Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, minfi License: Artistic-2.0 MD5sum: 3d55056e94606245365823a11d1cdfd0 NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides connections to the epiviz web app (http://epiviz.cbcb.umd.edu) for interactive visualization of genomic data. Objects in R/bioc interactive sessions can be displayed in genome browser tracks or plots to be explored by navigation through genomic regions. Fundamental Bioconductor data structures are supported (e.g., GenomicRanges and RangedSummarizedExperiment objects), while providing an easy mechanism to support other data structures (through package epivizrData). Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Florin Chelaru, Llewellyn Smith, Naomi Goldstein, Jayaram Kancherla, Morgan Walter, Brian Gottfried Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA git_url: https://git.bioconductor.org/packages/epivizr git_branch: RELEASE_3_12 git_last_commit: 5b93e6c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/epivizr_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/epivizr_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/epivizr_2.20.0.tgz vignettes: vignettes/epivizr/inst/doc/IntroToEpivizr.html vignetteTitles: Introduction to epivizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R dependsOnMe: epivizrStandalone importsMe: metavizr dependencyCount: 111 Package: epivizrChart Version: 1.12.0 Depends: R (>= 3.4.0) Imports: epivizrData (>= 1.5.1), epivizrServer, htmltools, rjson, methods, BiocGenerics Suggests: testthat, roxygen2, knitr, Biobase, GenomicRanges, S4Vectors, IRanges, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus, BiocStyle, Homo.sapiens, shiny, minfi, Rsamtools, rtracklayer, RColorBrewer, magrittr, AnnotationHub License: Artistic-2.0 MD5sum: 8281d12f380fabf440968414e819dbdd NeedsCompilation: no Title: R interface to epiviz web components Description: This package provides an API for interactive visualization of genomic data using epiviz web components. Objects in R/BioConductor can be used to generate interactive R markdown/notebook documents or can be visualized in the R Studio's default viewer. biocViews: Visualization, GUI Author: Brian Gottfried [aut], Jayaram Kancherla [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrChart git_branch: RELEASE_3_12 git_last_commit: ca6387d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/epivizrChart_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/epivizrChart_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/epivizrChart_1.12.0.tgz vignettes: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.html, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.html, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.html, vignettes/epivizrChart/inst/doc/VisualizeSumExp.html vignetteTitles: Visualizing Files with epivizrChart, Visualizing genomic data in Shiny Apps using epivizrChart, Introduction to epivizrChart, Visualizing `RangeSummarizedExperiment` objects Shiny Apps using epivizrChart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizrChart/inst/doc/IntegrationWithIGVjs.R, vignettes/epivizrChart/inst/doc/IntegrationWithShiny.R, vignettes/epivizrChart/inst/doc/IntroToEpivizrChart.R, vignettes/epivizrChart/inst/doc/VisualizeSumExp.R dependencyCount: 105 Package: epivizrData Version: 1.18.0 Depends: R (>= 3.4), methods, epivizrServer (>= 1.1.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges, ensembldb Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown, BiocStyle, EnsDb.Mmusculus.v79, AnnotationHub, rtracklayer, utils, RMySQL, DBI License: MIT + file LICENSE MD5sum: 3150a74739893698fb1dbba1f8655a68 NeedsCompilation: no Title: Data Management API for epiviz interactive visualization app Description: Serve data from Bioconductor Objects through a WebSocket connection. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre], Florin Chelaru [aut] Maintainer: Hector Corrada Bravo URL: http://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrData/issues git_url: https://git.bioconductor.org/packages/epivizrData git_branch: RELEASE_3_12 git_last_commit: 57c859f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/epivizrData_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/epivizrData_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/epivizrData_1.18.0.tgz vignettes: vignettes/epivizrData/inst/doc/epivizrData.html vignetteTitles: epivizrData Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R importsMe: epivizr, epivizrChart, metavizr dependencyCount: 101 Package: epivizrServer Version: 1.18.0 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: c7029e738aae634302927cff0ec93049 NeedsCompilation: no Title: WebSocket server infrastructure for epivizr apps and packages Description: This package provides objects to manage WebSocket connections to epiviz apps. Other epivizr package use this infrastructure. biocViews: Infrastructure, Visualization Author: Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://epiviz.github.io VignetteBuilder: knitr BugReports: https://github.com/epiviz/epivizrServer git_url: https://git.bioconductor.org/packages/epivizrServer git_branch: RELEASE_3_12 git_last_commit: a315bd4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/epivizrServer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/epivizrServer_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/epivizrServer_1.18.0.tgz vignettes: vignettes/epivizrServer/inst/doc/epivizrServer.html vignetteTitles: epivizrServer Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: epivizrData importsMe: epivizr, epivizrChart, epivizrStandalone, metavizr dependencyCount: 13 Package: epivizrStandalone Version: 1.18.0 Depends: R (>= 3.2.3), epivizr (>= 2.3.6), methods Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: 6e6e2690d6244e4159094e898ce69c3d NeedsCompilation: no Title: Run Epiviz Interactive Genomic Data Visualization App within R Description: This package imports the epiviz visualization JavaScript app for genomic data interactive visualization. The 'epivizrServer' package is used to provide a web server running completely within R. This standalone version allows to browse arbitrary genomes through genome annotations provided by Bioconductor packages. biocViews: Visualization, Infrastructure, GUI Author: Hector Corrada Bravo, Jayaram Kancherla Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/epivizrStandalone git_branch: RELEASE_3_12 git_last_commit: 3178098 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/epivizrStandalone_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/epivizrStandalone_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/epivizrStandalone_1.18.0.tgz vignettes: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html vignetteTitles: Introduction to epivizrStandalone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: metavizr dependencyCount: 113 Package: erccdashboard Version: 1.24.0 Depends: R (>= 3.2), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: e73d75426dfef03d1a624ecccfbe0cd6 NeedsCompilation: no Title: Assess Differential Gene Expression Experiments with ERCC Controls Description: Technical performance metrics for differential gene expression experiments using External RNA Controls Consortium (ERCC) spike-in ratio mixtures. biocViews: ImmunoOncology, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: https://github.com/munrosa/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/munrosa/erccdashboard/issues git_url: https://git.bioconductor.org/packages/erccdashboard git_branch: RELEASE_3_12 git_last_commit: b13c8b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/erccdashboard_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/erccdashboard_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/erccdashboard_1.24.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erccdashboard/inst/doc/erccdashboard.R dependencyCount: 55 Package: erma Version: 1.6.0 Depends: R (>= 3.1), methods, Homo.sapiens, GenomicFiles (>= 1.5.2) Imports: rtracklayer (>= 1.38.1), S4Vectors (>= 0.23.18), BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, GenomeInfoDb, Biobase, shiny, BiocParallel, IRanges, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, png, DT, doParallel License: Artistic-2.0 MD5sum: 0c5bbe22502ebcac7ca75dc7e87eac2e NeedsCompilation: no Title: epigenomic road map adventures Description: Software and data to support epigenomic road map adventures. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/erma git_branch: RELEASE_3_12 git_last_commit: 2b7cca5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/erma_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/erma_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/erma_1.6.0.tgz vignettes: vignettes/erma/inst/doc/erma.html vignetteTitles: ermaInteractive hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R importsMe: gQTLstats suggestsMe: gQTLBase, yriMulti dependencyCount: 127 Package: ERSSA Version: 1.8.0 Depends: R (>= 4.0.0) Imports: edgeR (>= 3.23.3), DESeq2 (>= 1.21.16), ggplot2 (>= 3.0.0), RColorBrewer (>= 1.1-2), plyr (>= 1.8.4), BiocParallel (>= 1.15.8), grDevices, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 46bd05b8da31bb859f7eded0eb2a5ed1 NeedsCompilation: no Title: Empirical RNA-seq Sample Size Analysis Description: The ERSSA package takes user supplied RNA-seq differential expression dataset and calculates the number of differentially expressed genes at varying biological replicate levels. This allows the user to determine, without relying on any a priori assumptions, whether sufficient differential detection has been acheived with their RNA-seq dataset. biocViews: ImmunoOncology, GeneExpression, Transcription, DifferentialExpression, RNASeq, MultipleComparison, QualityControl Author: Zixuan Shao [aut, cre] Maintainer: Zixuan Shao URL: https://github.com/zshao1/ERSSA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ERSSA git_branch: RELEASE_3_12 git_last_commit: 88db202 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ERSSA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ERSSA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ERSSA_1.8.0.tgz vignettes: vignettes/ERSSA/inst/doc/ERSSA.html vignetteTitles: ERSSA Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ERSSA/inst/doc/ERSSA.R dependencyCount: 93 Package: esATAC Version: 1.12.0 Depends: R (>= 3.5), Rsamtools, GenomicRanges, ShortRead, pipeFrame Imports: Rcpp (>= 0.12.11), methods, knitr, Rbowtie2, rtracklayer, ggplot2, Biostrings, ChIPseeker, clusterProfiler, igraph, rJava, magrittr, digest, BSgenome, AnnotationDbi, GenomicFeatures, R.utils, GenomeInfoDb, BiocGenerics, S4Vectors, IRanges, rmarkdown, tools, VennDiagram, grid, JASPAR2018, TFBSTools, grDevices, graphics, stats, utils, parallel, corrplot, BiocManager, motifmatchr LinkingTo: Rcpp Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, testthat, webshot License: GPL-3 | file LICENSE Archs: x64 MD5sum: 753baa1a6132fa4913789f3762721e7b NeedsCompilation: yes Title: An Easy-to-use Systematic pipeline for ATACseq data analysis Description: This package provides a framework and complete preset pipeline for quantification and analysis of ATAC-seq Reads. It covers raw sequencing reads preprocessing (FASTQ files), reads alignment (Rbowtie2), aligned reads file operations (SAM, BAM, and BED files), peak calling (F-seq), genome annotations (Motif, GO, SNP analysis) and quality control report. The package is managed by dataflow graph. It is easy for user to pass variables seamlessly between processes and understand the workflow. Users can process FASTQ files through end-to-end preset pipeline which produces a pretty HTML report for quality control and preliminary statistical results, or customize workflow starting from any intermediate stages with esATAC functions easily and flexibly. biocViews: ImmunoOncology, Sequencing, DNASeq, QualityControl, Alignment, Preprocessing, Coverage, ATACSeq, DNaseSeq Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei URL: https://github.com/wzthu/esATAC SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/wzthu/esATAC/issues git_url: https://git.bioconductor.org/packages/esATAC git_branch: RELEASE_3_12 git_last_commit: 7a68538 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/esATAC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/esATAC_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/esATAC_1.12.0.tgz vignettes: vignettes/esATAC/inst/doc/esATAC-Introduction.html vignetteTitles: esATAC: an Easy-to-use Systematic pipeline for ATAC-seq data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/esATAC/inst/doc/esATAC-Introduction.R dependencyCount: 183 Package: escape Version: 1.0.1 Depends: R (>= 4.0) Imports: grDevices, dplyr, ggplot2, GSEABase, GSVA, SingleCellExperiment, limma, ggridges, msigdbr, stats, BiocParallel, Matrix Suggests: Seurat, SeuratObject, knitr, rmarkdown, BiocStyle, testthat, dittoSeq (>= 1.1.2) License: Apache License 2.0 MD5sum: dedd3dac40952fb42496a34493c2946e NeedsCompilation: no Title: Easy single cell analysis platform for enrichment Description: A bridging R package to facilitate gene set enrichment analysis (GSEA) in the context of single-cell RNA sequencing. Using raw count information, Seurat objects, or SingleCellExperiment format, users can perform and visualize GSEA across individual cells. biocViews: Software, SingleCell, Classification, Annotation, GeneSetEnrichment, Sequencing, GeneSignaling, Pathways Author: Nick Borcherding [aut, cre], Jared Andrews [aut] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/escape git_branch: RELEASE_3_12 git_last_commit: d8e6dce git_last_commit_date: 2021-04-22 Date/Publication: 2021-04-22 source.ver: src/contrib/escape_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/escape_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/escape_1.0.1.tgz vignettes: vignettes/escape/inst/doc/vignette.html vignetteTitles: Escape-ingToWork hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/escape/inst/doc/vignette.R dependencyCount: 97 Package: esetVis Version: 1.16.0 Imports: mpm, hexbin, Rtsne, MLP, grid, Biobase, MASS, stats, utils, grDevices, methods Suggests: ggplot2, ggvis, rbokeh, ggrepel, knitr, rmarkdown, ALL, hgu95av2.db, AnnotationDbi, pander, SummarizedExperiment License: GPL-3 MD5sum: bed50d90b32702b48b1484286e567049 NeedsCompilation: no Title: Visualizations of expressionSet Bioconductor object Description: Utility functions for visualization of expressionSet (or SummarizedExperiment) Bioconductor object, including spectral map, tsne and linear discriminant analysis. Static plot via the ggplot2 package or interactive via the ggvis or rbokeh packages are available. biocViews: Visualization, DataRepresentation, DimensionReduction, PrincipalComponent, Pathways Author: Laure Cougnaud Maintainer: Laure Cougnaud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/esetVis git_branch: RELEASE_3_12 git_last_commit: b9feb65 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/esetVis_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/esetVis_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/esetVis_1.16.0.tgz vignettes: vignettes/esetVis/inst/doc/esetVis-vignette.html vignetteTitles: esetVis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/esetVis/inst/doc/esetVis-vignette.R dependencyCount: 47 Package: eudysbiome Version: 1.20.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: c34c1dded690d4e3b016337e986604f0 NeedsCompilation: no Title: Cartesian plot and contingency test on 16S Microbial data Description: eudysbiome a package that permits to annotate the differential genera as harmful/harmless based on their ability to contribute to host diseases (as indicated in literature) or unknown based on their ambiguous genus classification. Further, the package statistically measures the eubiotic (harmless genera increase or harmful genera decrease) or dysbiotic(harmless genera decrease or harmful genera increase) impact of a given treatment or environmental change on the (gut-intestinal, GI) microbiome in comparison to the microbiome of the reference condition. Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou git_url: https://git.bioconductor.org/packages/eudysbiome git_branch: RELEASE_3_12 git_last_commit: f5a2821 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/eudysbiome_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/eudysbiome_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/eudysbiome_1.20.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/eudysbiome/inst/doc/eudysbiome.R dependencyCount: 34 Package: evaluomeR Version: 1.6.6 Depends: R (>= 3.6), SummarizedExperiment, MultiAssayExperiment, cluster (>= 2.0.9), fpc (>= 2.2-3), randomForest (>= 4.6.14), flexmix (>= 2.3.15) Imports: corrplot (>= 0.84), grDevices, graphics, reshape2, ggplot2, ggdendro, plotrix, stats, matrixStats, Rdpack, MASS, class, prabclus, mclust, kableExtra Suggests: BiocStyle, knitr, rmarkdown, magrittr License: GPL-3 MD5sum: 88913545f75a7bc7314fdebef619d307 NeedsCompilation: no Title: Evaluation of Bioinformatics Metrics Description: Evaluating the reliability of your own metrics and the measurements done on your own datasets by analysing the stability and goodness of the classifications of such metrics. biocViews: Clustering, Classification, FeatureExtraction Author: José Antonio Bernabé-Díaz [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut], Manuel Quesada-Martínez [aut], Astrid Duque-Ramos [aut], Jesualdo Tomás Fernández-Breis [aut] Maintainer: José Antonio Bernabé-Díaz URL: https://github.com/neobernad/evaluomeR VignetteBuilder: knitr BugReports: https://github.com/neobernad/evaluomeR/issues git_url: https://git.bioconductor.org/packages/evaluomeR git_branch: RELEASE_3_12 git_last_commit: 55e0b3e git_last_commit_date: 2021-04-28 Date/Publication: 2021-04-28 source.ver: src/contrib/evaluomeR_1.6.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/evaluomeR_1.6.6.zip mac.binary.ver: bin/macosx/contrib/4.0/evaluomeR_1.6.6.tgz vignettes: vignettes/evaluomeR/inst/doc/manual.html vignetteTitles: Evaluation of Bioinformatics Metrics with evaluomeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/evaluomeR/inst/doc/manual.R dependencyCount: 114 Package: EventPointer Version: 2.8.0 Depends: R (>= 3.5.0), SGSeq, Matrix, SummarizedExperiment Imports: GenomicFeatures, stringr, GenomeInfoDb, igraph, MASS, nnls, limma, matrixStats, RBGL, prodlim, graph, methods, utils, stats, doParallel, foreach, affxparser, GenomicRanges, S4Vectors, IRanges, qvalue, cobs, rhdf5, BSgenome, BSgenome.Hsapiens.UCSC.hg38, Biostrings Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, dplyr, kableExtra License: Artistic-2.0 MD5sum: f61576293f43491cf1046cc818412960 NeedsCompilation: no Title: An effective identification of alternative splicing events using junction arrays and RNA-Seq data Description: EventPointer is an R package to identify alternative splicing events that involve either simple (case-control experiment) or complex experimental designs such as time course experiments and studies including paired-samples. The algorithm can be used to analyze data from either junction arrays (Affymetrix Arrays) or sequencing data (RNA-Seq). The software returns a data.frame with the detected alternative splicing events: gene name, type of event (cassette, alternative 3',...,etc), genomic position, statistical significance and increment of the percent spliced in (Delta PSI) for all the events. The algorithm can generate a series of files to visualize the detected alternative splicing events in IGV. This eases the interpretation of results and the design of primers for standard PCR validation. biocViews: AlternativeSplicing, DifferentialSplicing, mRNAMicroarray, RNASeq, Transcription, Sequencing, TimeCourse, ImmunoOncology Author: Juan Pablo Romero, Juan A. Ferrer-Bonsoms, Pablo Sacristan, Ander Muniategui, Fernando Carazo, Ander Aramburu, Angel Rubio Maintainer: Juan Pablo Romero VignetteBuilder: knitr BugReports: https://github.com/jpromeror/EventPointer/issues git_url: https://git.bioconductor.org/packages/EventPointer git_branch: RELEASE_3_12 git_last_commit: bde3577 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/EventPointer_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/EventPointer_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/EventPointer_2.8.0.tgz vignettes: vignettes/EventPointer/inst/doc/EventPointer.html vignetteTitles: EventPointer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EventPointer/inst/doc/EventPointer.R dependencyCount: 135 Package: ExCluster Version: 1.8.0 Depends: Rsubread, GenomicRanges, rtracklayer, matrixStats, IRanges Imports: stats, methods, grDevices, graphics, utils License: GPL-3 MD5sum: 6afb8bb20b928bfc6a3f3b0339d906d2 NeedsCompilation: no Title: ExCluster robustly detects differentially expressed exons between two conditions of RNA-seq data, requiring at least two independent biological replicates per condition Description: ExCluster flattens Ensembl and GENCODE GTF files into GFF files, which are used to count reads per non-overlapping exon bin from BAM files. This read counting is done using the function featureCounts from the package Rsubread. Library sizes are normalized across all biological replicates, and ExCluster then compares two different conditions to detect signifcantly differentially spliced genes. This process requires at least two independent biological repliates per condition, and ExCluster accepts only exactly two conditions at a time. ExCluster ultimately produces false discovery rates (FDRs) per gene, which are used to detect significance. Exon log2 fold change (log2FC) means and variances may be plotted for each significantly differentially spliced gene, which helps scientists develop hypothesis and target differential splicing events for RT-qPCR validation in the wet lab. biocViews: ImmunoOncology, DifferentialSplicing, RNASeq, Software Author: R. Matthew Tanner, William L. Stanford, and Theodore J. Perkins Maintainer: R. Matthew Tanner git_url: https://git.bioconductor.org/packages/ExCluster git_branch: RELEASE_3_12 git_last_commit: 392eb82 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ExCluster_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/ExCluster_1.8.0.tgz vignettes: vignettes/ExCluster/inst/doc/ExCluster.pdf vignetteTitles: ExCluster Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExCluster/inst/doc/ExCluster.R dependencyCount: 41 Package: ExiMiR Version: 2.32.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), affy (>= 1.26.1), limma Imports: affyio(>= 1.13.3), Biobase(>= 2.5.5), preprocessCore(>= 1.10.0) Suggests: mirna10cdf License: GPL-2 MD5sum: 0dfb626a392f37a557962b9389768d8e NeedsCompilation: no Title: R functions for the normalization of Exiqon miRNA array data Description: This package contains functions for reading raw data in ImaGene TXT format obtained from Exiqon miRCURY LNA arrays, annotating them with appropriate GAL files, and normalizing them using a spike-in probe-based method. Other platforms and data formats are also supported. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, GeneExpression, Transcription Author: Sylvain Gubian , Alain Sewer , PMP SA Maintainer: Sylvain Gubian git_url: https://git.bioconductor.org/packages/ExiMiR git_branch: RELEASE_3_12 git_last_commit: 67d3866 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ExiMiR_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ExiMiR_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ExiMiR_2.32.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.R dependencyCount: 14 Package: exomeCopy Version: 1.36.0 Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) Archs: i386, x64 MD5sum: f58d196d03b6fed9d55c1ac2d5e42edc NeedsCompilation: yes Title: Copy number variant detection from exome sequencing read depth Description: Detection of copy number variants (CNV) from exome sequencing samples, including unpaired samples. The package implements a hidden Markov model which uses positional covariates, such as background read depth and GC-content, to simultaneously normalize and segment the samples into regions of constant copy count. biocViews: CopyNumberVariation, Sequencing, Genetics Author: Michael Love Maintainer: Michael Love git_url: https://git.bioconductor.org/packages/exomeCopy git_branch: RELEASE_3_12 git_last_commit: cbe3134 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/exomeCopy_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/exomeCopy_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/exomeCopy_1.36.0.tgz vignettes: vignettes/exomeCopy/inst/doc/exomeCopy.pdf vignetteTitles: Copy number variant detection in exome sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomeCopy/inst/doc/exomeCopy.R importsMe: cn.mops, CNVPanelizer, contiBAIT, Rariant, SomaticCancerAlterations dependencyCount: 29 Package: exomePeak2 Version: 1.2.0 Depends: SummarizedExperiment,cqn Imports: Rsamtools,GenomicAlignments,GenomicRanges,GenomicFeatures,DESeq2,ggplot2,mclust,genefilter,Biostrings,BSgenome,BiocParallel,IRanges,S4Vectors,reshape2,rtracklayer,apeglm,methods,stats,utils,Biobase,GenomeInfoDb Suggests: knitr, rmarkdown, RMariaDB License: GPL (>= 2) MD5sum: 57359fe680f111bf4dd7c04c4e700593 NeedsCompilation: no Title: Bias Awared Peak Calling and Quantification for MeRIP-Seq Description: exomePeak2 provides bias awared quantification and peak detection on Methylated RNA immunoprecipitation sequencing data (MeRIP-Seq). MeRIP-Seq is a commonly applied sequencing technology to measure the transcriptome-wide location and abundance of RNA modification sites under a given cellular condition. However, the quantification and peak calling in MeRIP-Seq are sensitive to PCR amplification bias which is prevalent in next generation sequencing (NGS) techniques. In addition, the RNA-Seq based count data exhibits biological variation in small reads count. exomePeak2 collectively address these challanges by introducing a rich set of robust data science models tailored for MeRIP-Seq. With exomePeak2, users can perform peak calling, modification site quantification, and differential analysis with a straightforward one-step function. Alternatively, users could define personalized methods for their own analysis through multi-step functions and diagnostic plots. biocViews: Sequencing, MethylSeq, RNASeq, ExomeSeq, Coverage, Normalization, Preprocessing, ImmunoOncology, DifferentialExpression Author: Zhen Wei Maintainer: Zhen Wei VignetteBuilder: knitr BugReports: https://github.com/ZW-xjtlu/exomePeak2/issues git_url: https://git.bioconductor.org/packages/exomePeak2 git_branch: RELEASE_3_12 git_last_commit: a214469 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/exomePeak2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/exomePeak2_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/exomePeak2_1.2.0.tgz vignettes: vignettes/exomePeak2/inst/doc/Vignette_V_0.99.html vignetteTitles: The exomePeak2 user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak2/inst/doc/Vignette_V_0.99.R dependencyCount: 131 Package: ExperimentHub Version: 1.16.1 Depends: methods, BiocGenerics (>= 0.15.10), AnnotationHub (>= 2.19.3), BiocFileCache (>= 1.5.1) Imports: utils, S4Vectors, BiocManager, curl, rappdirs Suggests: knitr, BiocStyle, rmarkdown Enhances: ExperimentHubData License: Artistic-2.0 MD5sum: c5050b2a38b4f256cd61ee5ffe6706dd NeedsCompilation: no Title: Client to access ExperimentHub resources Description: This package provides a client for the Bioconductor ExperimentHub web resource. ExperimentHub provides a central location where curated data from experiments, publications or training courses can be accessed. Each resource has associated metadata, tags and date of modification. The client creates and manages a local cache of files retrieved enabling quick and reproducible access. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb], Valerie Oberchain [ctb], Kayla Morrell [ctb], Lori Shepherd [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/ExperimentHub/issues git_url: https://git.bioconductor.org/packages/ExperimentHub git_branch: RELEASE_3_12 git_last_commit: 61d51b7 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/ExperimentHub_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ExperimentHub_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ExperimentHub_1.16.1.tgz vignettes: vignettes/ExperimentHub/inst/doc/CreateAnExperimentHubPackage.html, vignettes/ExperimentHub/inst/doc/ExperimentHub.html vignetteTitles: Creating An ExperimentHub Package, ExperimentHub: Access the ExperimentHub Web Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHub/inst/doc/CreateAnExperimentHubPackage.R, vignettes/ExperimentHub/inst/doc/ExperimentHub.R dependsOnMe: adductomicsR, SeqSQC, alpineData, benchmarkfdrData2019, biscuiteerData, bodymapRat, brainImageRdata, CellMapperData, clustifyrdatahub, curatedAdipoChIP, curatedMetagenomicData, DMRcatedata, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, HDCytoData, HighlyReplicatedRNASeq, HumanAffyData, mcsurvdata, MetaGxBreast, MetaGxOvarian, MetaGxPancreas, muscData, NanoporeRNASeq, NestLink, restfulSEData, RNAmodR.Data, SCATEData, sesameData, tartare, tcgaWGBSData.hg19 importsMe: DMRcate, ExperimentHubData, GSEABenchmarkeR, PhyloProfile, restfulSE, signatureSearch, singleCellTK, adductData, celldex, chipseqDBData, CLLmethylation, curatedTCGAData, depmap, DropletTestFiles, DuoClustering2018, FieldEffectCrc, HarmonizedTCGAData, HCAData, HMP16SData, HMP2Data, MethylSeqData, MouseGastrulationData, PhyloProfileData, pwrEWAS.data, scRNAseq, signatureSearchData, SingleCellMultiModal, spatialLIBD, TabulaMurisData, TENxBrainData, TENxBUSData, TENxPBMCData suggestsMe: ANF, AnnotationHub, bambu, celaref, CellMapper, HDF5Array, missMethyl, muscat, recountmethylation, celarefData, curatedAdipoArray, GSE62944, tissueTreg dependencyCount: 76 Package: ExperimentHubData Version: 1.16.1 Depends: utils, BiocGenerics (>= 0.15.10), S4Vectors, AnnotationHubData (>= 1.17.3) Imports: methods, ExperimentHub, BiocManager, DBI, BiocCheck, httr, curl, biocViews, graph Suggests: GenomeInfoDb, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: fc290898631ef1cfbde0f8be2ceefb68 NeedsCompilation: no Title: Add resources to ExperimentHub Description: Functions to add metadata to ExperimentHub db and resource files to AWS S3 buckets. biocViews: Infrastructure, DataImport, GUI, ThirdPartyClient Author: Bioconductor Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentHubData git_branch: RELEASE_3_12 git_last_commit: 51ef8b3 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/ExperimentHubData_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ExperimentHubData_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ExperimentHubData_1.16.1.tgz vignettes: vignettes/ExperimentHubData/inst/doc/CreateAnExperimentHubPackage.html, vignettes/ExperimentHubData/inst/doc/ExperimentHubData.html vignetteTitles: Creating An ExperimentHub Package, Introduction to ExperimentHubData hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExperimentHubData/inst/doc/CreateAnExperimentHubPackage.R dependsOnMe: RNAmodR.Data dependencyCount: 129 Package: ExperimentSubset Version: 1.0.0 Depends: R (>= 4.0.0), SummarizedExperiment, SingleCellExperiment Imports: methods, Matrix Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, stats, scran, scater, scds, TENxPBMCData License: MIT + file LICENSE MD5sum: 12a1cb1189ca54a1c9f3417b6142f624 NeedsCompilation: no Title: Manages subsets of data with Bioconductor Experiment objects Description: Experiment objects such as the SummarizedExperiment or SingleCellExperiment are data containers for one or more matrix-like assays along with the associated row and column data. Often only a subset of the original data is needed for down-stream analysis. For example, filtering out poor quality samples will require excluding some columns before analysis. The ExperimentSubset object is a container to efficiently manage different subsets of the same data without having to make separate objects for each new subset. biocViews: Infrastructure, Software, DataImport, DataRepresentation Author: Irzam Sarfraz [aut, cre] (), Muhammad Asif [aut, ths] (), Joshua D. Campbell [aut] () Maintainer: Irzam Sarfraz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExperimentSubset git_branch: RELEASE_3_12 git_last_commit: ae54a23 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ExperimentSubset_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ExperimentSubset_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ExperimentSubset_1.0.0.tgz vignettes: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.html vignetteTitles: An introduction to ExperimentSubset class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExperimentSubset/inst/doc/ExperimentSubset.R dependencyCount: 27 Package: explorase Version: 1.53.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: ad0940e0c5eeed33030b3d8c8e96bc9c NeedsCompilation: no Title: GUI for exploratory data analysis of systems biology data Description: explore and analyze *omics data with R and GGobi biocViews: Visualization,Microarray,GUI Author: Michael Lawrence, Eun-kyung Lee, Dianne Cook, Jihong Kim, Hogeun An, and Dongshin Kim Maintainer: Michael Lawrence URL: http://www.metnetdb.org/MetNet_exploRase.htm git_url: https://git.bioconductor.org/packages/explorase git_branch: master git_last_commit: 704870c git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/explorase_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/explorase_1.53.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: ExploreModelMatrix Version: 1.2.0 Imports: shiny (>= 1.5.0), shinydashboard, DT, cowplot, utils, dplyr, magrittr, tidyr, ggplot2, stats, methods, rintrojs, scales, tibble, MASS, limma, S4Vectors, shinyjs Suggests: testthat (>= 2.1.0), knitr, rmarkdown, htmltools, BiocStyle License: MIT + file LICENSE MD5sum: 29bdf9724420c043c87a0734df8e0758 NeedsCompilation: no Title: Graphical Exploration of Design Matrices Description: Given a sample data table and a design formula, ExploreModelMatrix generates an interactive application for exploration of the resulting design matrix. This can be helpful for interpreting model coefficients and constructing appropriate contrasts in (generalized) linear models. Static visualizations can also be generated. biocViews: ExperimentalDesign, Regression, DifferentialExpression Author: Charlotte Soneson [aut, cre] (), Federico Marini [aut] (), Michael Love [aut] (), Florian Geier [aut] (), Michael Stadler [aut] () Maintainer: Charlotte Soneson URL: https://github.com/csoneson/ExploreModelMatrix VignetteBuilder: knitr BugReports: https://github.com/csoneson/ExploreModelMatrix/issues git_url: https://git.bioconductor.org/packages/ExploreModelMatrix git_branch: RELEASE_3_12 git_last_commit: bc10d9b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ExploreModelMatrix_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ExploreModelMatrix_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ExploreModelMatrix_1.2.0.tgz vignettes: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.html, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.html vignetteTitles: ExploreModelMatrix-deploy, ExploreModelMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ExploreModelMatrix/inst/doc/EMMdeploy.R, vignettes/ExploreModelMatrix/inst/doc/ExploreModelMatrix.R dependencyCount: 78 Package: ExpressionAtlas Version: 1.18.0 Depends: R (>= 3.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: d5581d73a06df18b94a2577bc0313d5a NeedsCompilation: no Title: Download datasets from EMBL-EBI Expression Atlas Description: This package is for searching for datasets in EMBL-EBI Expression Atlas, and downloading them into R for further analysis. Each Expression Atlas dataset is represented as a SimpleList object with one element per platform. Sequencing data is contained in a SummarizedExperiment object, while microarray data is contained in an ExpressionSet or MAList object. biocViews: ExpressionData, ExperimentData, SequencingData, MicroarrayData, ArrayExpress Author: Maria Keays Maintainer: Suhaib Mohammed VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ExpressionAtlas git_branch: RELEASE_3_12 git_last_commit: cdb85d6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ExpressionAtlas_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ExpressionAtlas_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ExpressionAtlas_1.18.0.tgz vignettes: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html vignetteTitles: ExpressionAtlas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R suggestsMe: spatialHeatmap dependencyCount: 37 Package: ExpressionView Version: 1.42.0 Depends: caTools, bitops, methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Imports: methods, isa2, eisa, GO.db, KEGG.db, AnnotationDbi Suggests: ALL, hgu95av2.db, biclust, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: 106d347cb83cd1887f0d928931cbde89 NeedsCompilation: yes Title: Visualize biclusters identified in gene expression data Description: ExpressionView visualizes possibly overlapping biclusters in a gene expression matrix. It can use the result of the ISA method (eisa package) or the algorithms in the biclust package or others. The viewer itself was developed using Adobe Flex and runs in a flash-enabled web browser. biocViews: Classification, Visualization, Microarray, GeneExpression, GO, KEGG Author: Andreas Luscher Maintainer: Gabor Csardi git_url: https://git.bioconductor.org/packages/ExpressionView git_branch: RELEASE_3_12 git_last_commit: 5f05898 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ExpressionView_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ExpressionView_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ExpressionView_1.42.0.tgz vignettes: vignettes/ExpressionView/inst/doc/ExpressionView.format.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.ordering.pdf, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.pdf vignetteTitles: ExpressionView file format, How the ordering algorithm works, ExpressionView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionView/inst/doc/ExpressionView.ordering.R, vignettes/ExpressionView/inst/doc/ExpressionView.tutorial.R dependencyCount: 56 Package: fabia Version: 2.36.0 Depends: R (>= 3.6.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 1153b154a88d4ff92d842e900dc8d48c NeedsCompilation: yes Title: FABIA: Factor Analysis for Bicluster Acquisition Description: Biclustering by "Factor Analysis for Bicluster Acquisition" (FABIA). FABIA is a model-based technique for biclustering, that is clustering rows and columns simultaneously. Biclusters are found by factor analysis where both the factors and the loading matrix are sparse. FABIA is a multiplicative model that extracts linear dependencies between samples and feature patterns. It captures realistic non-Gaussian data distributions with heavy tails as observed in gene expression measurements. FABIA utilizes well understood model selection techniques like the EM algorithm and variational approaches and is embedded into a Bayesian framework. FABIA ranks biclusters according to their information content and separates spurious biclusters from true biclusters. The code is written in C. biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/fabia/fabia.html git_url: https://git.bioconductor.org/packages/fabia git_branch: RELEASE_3_12 git_last_commit: 881c213 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fabia_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fabia_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fabia_2.36.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fabia/inst/doc/fabia.R dependsOnMe: hapFabia, RcmdrPlugin.BiclustGUI, superbiclust importsMe: miRSM, BcDiag, CSFA suggestsMe: fabiaData dependencyCount: 8 Package: factDesign Version: 1.66.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: ddf16eda4c34a1fce344f0cb657bf819 NeedsCompilation: no Title: Factorial designed microarray experiment analysis Description: This package provides a set of tools for analyzing data from a factorial designed microarray experiment, or any microarray experiment for which a linear model is appropriate. The functions can be used to evaluate tests of contrast of biological interest and perform single outlier detection. biocViews: Microarray, DifferentialExpression Author: Denise Scholtens Maintainer: Denise Scholtens git_url: https://git.bioconductor.org/packages/factDesign git_branch: RELEASE_3_12 git_last_commit: 212f8ab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/factDesign_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/factDesign_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/factDesign_1.66.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/factDesign/inst/doc/factDesign.R dependencyCount: 7 Package: FamAgg Version: 1.18.0 Depends: methods, kinship2, igraph Imports: gap (>= 1.1-17), Matrix, BiocGenerics, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 0420ea8b1c00e0c0bf27e3bb11f7b4b8 NeedsCompilation: no Title: Pedigree Analysis and Familial Aggregation Description: Framework providing basic pedigree analysis and plotting utilities as well as a variety of methods to evaluate familial aggregation of traits in large pedigrees. biocViews: Genetics Author: J. Rainer, D. Taliun, C.X. Weichenberger Maintainer: Johannes Rainer URL: https://github.com/EuracBiomedicalResearch/FamAgg VignetteBuilder: knitr BugReports: https://github.com/EuracBiomedicalResearch/FamAgg/issues git_url: https://git.bioconductor.org/packages/FamAgg git_branch: RELEASE_3_12 git_last_commit: 6c433c2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FamAgg_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FamAgg_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FamAgg_1.18.0.tgz vignettes: vignettes/FamAgg/inst/doc/FamAgg.html vignetteTitles: Pedigree Analysis and Familial Aggregation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R dependencyCount: 24 Package: famat Version: 1.0.0 Depends: R (>= 4.0) Imports: KEGGREST, MPINet, dplyr, gprofiler2, rWikiPathways, reactome.db, stringr, GO.db, ontologyIndex, tidyr, shiny, shinydashboard, shinyBS, plotly, magrittr, DT, clusterProfiler, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat, BiocManager License: GPL-3 MD5sum: 6b5bdf2264eadd9c8b84a553dcaee083 NeedsCompilation: no Title: Functional analysis of metabolic and transcriptomic data Description: Famat is made to collect data about lists of genes and metabolites provided by user, and to visualize it through a Shiny app. Information collected is: - Pathways containing some of the user's genes and metabolites (obtained using a pathway enrichment analysis). - Direct interactions between user's elements inside pathways. - Information about elements (their identifiers and descriptions). - Go terms enrichment analysis performed on user's genes. The Shiny app is composed of: - information about genes, metabolites, and direct interactions between them inside pathways. - an heatmap showing which elements from the list are in pathways (pathways are structured in hierarchies). - hierarchies of enriched go terms using Molecular Function and Biological Process. biocViews: FunctionalPrediction, GeneSetEnrichment, Pathways, GO, Reactome, KEGG Author: Emilie Secherre [aut, cre] () Maintainer: Emilie Secherre URL: https://github.com/emiliesecherre/famat VignetteBuilder: knitr BugReports: https://github.com/emiliesecherre/famat/issues git_url: https://git.bioconductor.org/packages/famat git_branch: RELEASE_3_12 git_last_commit: 6f56a8b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/famat_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/famat_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/famat_1.0.0.tgz vignettes: vignettes/famat/inst/doc/famat.html vignetteTitles: famat hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/famat/inst/doc/famat.R dependencyCount: 146 Package: farms Version: 1.42.0 Depends: R (>= 2.8), affy (>= 1.20.0), MASS, methods Imports: affy, MASS, Biobase (>= 1.13.41), methods, graphics Suggests: affydata, Biobase, utils License: LGPL (>= 2.1) MD5sum: b6dff9bb62a3536824c034904572eb4f NeedsCompilation: no Title: FARMS - Factor Analysis for Robust Microarray Summarization Description: The package provides the summarization algorithm called Factor Analysis for Robust Microarray Summarization (FARMS) and a novel unsupervised feature selection criterion called "I/NI-calls" biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Djork-Arne Clevert Maintainer: Djork-Arne Clevert URL: http://www.bioinf.jku.at/software/farms/farms.html git_url: https://git.bioconductor.org/packages/farms git_branch: RELEASE_3_12 git_last_commit: 0f2f0aa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/farms_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/farms_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/farms_1.42.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/farms/inst/doc/farms.R dependencyCount: 14 Package: fastLiquidAssociation Version: 1.26.0 Depends: methods, LiquidAssociation, parallel, doParallel, stats, Hmisc, utils Imports: WGCNA, impute, preprocessCore Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 257438770cd7d0395103791dacde7380 NeedsCompilation: no Title: functions for genome-wide application of Liquid Association Description: This package extends the function of the LiquidAssociation package for genome-wide application. It integrates a screening method into the LA analysis to reduce the number of triplets to be examined for a high LA value and provides code for use in subsequent significance analyses. biocViews: Software, GeneExpression, Genetics, Pathways, CellBiology Author: Tina Gunderson Maintainer: Tina Gunderson git_url: https://git.bioconductor.org/packages/fastLiquidAssociation git_branch: RELEASE_3_12 git_last_commit: b96ec34 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fastLiquidAssociation_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fastLiquidAssociation_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fastLiquidAssociation_1.26.0.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.R dependencyCount: 108 Package: FastqCleaner Version: 1.8.0 Imports: methods, shiny, stats, IRanges, Biostrings, ShortRead, DT, S4Vectors, graphics, htmltools, shinyBS, Rcpp (>= 0.12.12) LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: a8c7550a1e7addbcf507202aa0de7277 NeedsCompilation: yes Title: A Shiny Application for Quality Control, Filtering and Trimming of FASTQ Files Description: An interactive web application for quality control, filtering and trimming of FASTQ files. This user-friendly tool combines a pipeline for data processing based on Biostrings and ShortRead infrastructure, with a cutting-edge visual environment. Single-Read and Paired-End files can be locally processed. Diagnostic interactive plots (CG content, per-base sequence quality, etc.) are provided for both the input and output files. biocViews: QualityControl,Sequencing,Software,SangerSeq,SequenceMatching Author: Leandro Roser [aut, cre], Fernán Agüero [aut], Daniel Sánchez [aut] Maintainer: Leandro Roser VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FastqCleaner git_branch: RELEASE_3_12 git_last_commit: a2a6837 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FastqCleaner_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FastqCleaner_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FastqCleaner_1.8.0.tgz vignettes: vignettes/FastqCleaner/inst/doc/Overview.pdf vignetteTitles: An Introduction to FastqCleaner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FastqCleaner/inst/doc/Overview.R dependencyCount: 77 Package: fastseg Version: 1.36.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 630e2b8d9eccdc97bea0c84581c2fdba NeedsCompilation: yes Title: fastseg - a fast segmentation algorithm Description: fastseg implements a very fast and efficient segmentation algorithm. It has similar functionality as DNACopy (Olshen and Venkatraman 2004), but is considerably faster and more flexible. fastseg can segment data from DNA microarrays and data from next generation sequencing for example to detect copy number segments. Further it can segment data from RNA microarrays like tiling arrays to identify transcripts. Most generally, it can segment data given as a matrix or as a vector. Various data formats can be used as input to fastseg like expression set objects for microarrays or GRanges for sequencing data. The segmentation criterion of fastseg is based on a statistical test in a Bayesian framework, namely the cyber t-test (Baldi 2001). The speed-up arises from the facts, that sampling is not necessary in for fastseg and that a dynamic programming approach is used for calculation of the segments' first and higher order moments. biocViews: Classification, CopyNumberVariation Author: Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/fastseg/fastseg.html git_url: https://git.bioconductor.org/packages/fastseg git_branch: RELEASE_3_12 git_last_commit: 851ff86 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fastseg_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fastseg_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fastseg_1.36.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fastseg/inst/doc/fastseg.R importsMe: methylKit dependencyCount: 18 Package: FCBF Version: 1.8.0 Depends: R (>= 3.6) Imports: ggplot2, gridExtra, pbapply, parallel, SummarizedExperiment, stats, mclust Suggests: caret, mlbench, SingleCellExperiment, knitr, rmarkdown, testthat, BiocManager License: MIT + file LICENSE MD5sum: 348d7d3a4ff9f9e6177d9e69924d8104 NeedsCompilation: no Title: Fast Correlation Based Filter for Feature Selection Description: This package provides a simple R implementation for the Fast Correlation Based Filter described in Yu, L. and Liu, H.; Feature Selection for High-Dimensional Data: A Fast Correlation Based Filter Solution,Proc. 20th Intl. Conf. Mach. Learn. (ICML-2003), Washington DC, 2003 The current package is an intent to make easier for bioinformaticians to use FCBF for feature selection, especially regarding transcriptomic data.This implies discretizing expression (function discretize_exprs) before calculating the features that explain the class, but are not predictable by other features. The functions are implemented based on the algorithm of Yu and Liu, 2003 and Rajarshi Guha's implementation from 13/05/2005 available (as of 26/08/2018) at http://www.rguha.net/code/R/fcbf.R . biocViews: GeneTarget, FeatureExtraction, Classification, GeneExpression, SingleCell, ImmunoOncology Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FCBF git_branch: RELEASE_3_12 git_last_commit: 9f3d43d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FCBF_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FCBF_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FCBF_1.8.0.tgz vignettes: vignettes/FCBF/inst/doc/FCBF-Vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FCBF/inst/doc/FCBF-Vignette.R importsMe: fcoex suggestsMe: PubScore dependencyCount: 59 Package: fCCAC Version: 1.16.0 Depends: R (>= 3.3.0), S4Vectors, IRanges, GenomicRanges, grid Imports: fda, RColorBrewer, genomation, ggplot2, ComplexHeatmap, grDevices, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: b710db65632b6af2034fdbb8422b6efa NeedsCompilation: no Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets Description: An application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq). The package can be used as well with other types of sequencing data such as neMeRIP-seq (see PMID: 29489750). biocViews: Transcription, Genetics, Sequencing, Coverage Author: Pedro Madrigal Maintainer: Pedro Madrigal git_url: https://git.bioconductor.org/packages/fCCAC git_branch: RELEASE_3_12 git_last_commit: 34a8c76 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fCCAC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fCCAC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fCCAC_1.16.0.tgz vignettes: vignettes/fCCAC/inst/doc/fCCAC.pdf vignetteTitles: fCCAC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCCAC/inst/doc/fCCAC.R dependencyCount: 113 Package: fCI Version: 1.20.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: b3ec0fae81f69876a5eef9e89d6f0add NeedsCompilation: no Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics Description: (f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods. biocViews: Proteomics Author: Shaojun Tang Maintainer: Shaojun Tang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fCI git_branch: RELEASE_3_12 git_last_commit: 537dfba git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fCI_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fCI_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fCI_1.20.0.tgz vignettes: vignettes/fCI/inst/doc/fCI.html vignetteTitles: fCI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R dependencyCount: 68 Package: fcoex Version: 1.4.0 Depends: R (>= 3.5.0) Imports: FCBF, parallel, progress, dplyr, ggplot2, ggrepel, igraph, grid, intergraph, stringr, clusterProfiler, data.table, grDevices, methods, network, scales, sna, utils, stats, SingleCellExperiment, pathwayPCA Suggests: testthat (>= 2.1.0), devtools, BiocManager, TENxPBMCData, scater, gridExtra, scran, Seurat, knitr License: GPL-3 MD5sum: 90bc3d58f540134b94d878c814f58df8 NeedsCompilation: no Title: FCBF-based Co-Expression Networks for Single Cells Description: The fcoex package implements an easy-to use interface to co-expression analysis based on the FCBF (Fast Correlation-Based Filter) algorithm. it was implemented especifically to deal with single-cell data. The modules found can be used to redefine cell populations, unrevel novel gene associations and predict gene function by guilt-by-association. The package structure is adapted from the CEMiTool package, relying on visualizations and code designed and written by CEMiTool's authors. biocViews: GeneExpression, Transcriptomics, GraphAndNetwork, mRNAMicroarray, RNASeq, Network, NetworkEnrichment, Pathways, ImmunoOncology, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcoex git_branch: RELEASE_3_12 git_last_commit: 4e80420 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fcoex_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fcoex_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fcoex_1.4.0.tgz vignettes: vignettes/fcoex/inst/doc/fcoex.html vignetteTitles: fcoex: co-expression for single-cell data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcoex/inst/doc/fcoex.R dependencyCount: 128 Package: fcScan Version: 1.4.0 Imports: stats, plyr, VariantAnnotation, SummarizedExperiment, rtracklayer, GenomicRanges, methods, IRanges Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9a687f5ecdd858d4465ab1627efd59ca NeedsCompilation: no Title: fcScan for detecting clusters of coordinates with user defined options Description: This package is used to detect combination of genomic coordinates falling within a user defined window size along with user defined overlap between identified neighboring clusters. It can be used for genomic data where the clusters are built on a specific chromosome or specific strand. Clustering can be performed with a "greedy" option allowing thus the presence of additional sites within the allowed window size. biocViews: GenomeAnnotation, Clustering Author: Abdullah El-Kurdi Ghiwa khalil Georges Khazen Pierre Khoueiry Maintainer: Pierre Khoueiry Abdullah El-Kurdi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fcScan git_branch: RELEASE_3_12 git_last_commit: ec9c90d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fcScan_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fcScan_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fcScan_1.4.0.tgz vignettes: vignettes/fcScan/inst/doc/fcScan_vignette.html vignetteTitles: fcScan hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fcScan/inst/doc/fcScan_vignette.R dependencyCount: 91 Package: fdrame Version: 1.62.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 442ca3613b591a87e560a9a3fc4990cd NeedsCompilation: yes Title: FDR adjustments of Microarray Experiments (FDR-AME) Description: This package contains two main functions. The first is fdr.ma which takes normalized expression data array, experimental design and computes adjusted p-values It returns the fdr adjusted p-values and plots, according to the methods described in (Reiner, Yekutieli and Benjamini 2002). The second, is fdr.gui() which creates a simple graphic user interface to access fdr.ma biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli Maintainer: Effi Kenigsberg git_url: https://git.bioconductor.org/packages/fdrame git_branch: RELEASE_3_12 git_last_commit: 9559fe6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fdrame_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fdrame_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fdrame_1.62.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 5 Package: FELLA Version: 1.10.0 Depends: R (>= 3.5.0) Imports: methods, igraph, Matrix, KEGGREST, plyr, stats, graphics, utils Suggests: shiny, DT, magrittr, visNetwork, knitr, BiocStyle, rmarkdown, testthat, biomaRt, org.Hs.eg.db, org.Mm.eg.db, AnnotationDbi, GOSemSim License: GPL-3 MD5sum: 9094088585a685b00abf2de444ea3a59 NeedsCompilation: no Title: Interpretation and enrichment for metabolomics data Description: Enrichment of metabolomics data using KEGG entries. Given a set of affected compounds, FELLA suggests affected reactions, enzymes, modules and pathways using label propagation in a knowledge model network. The resulting subnetwork can be visualised and exported. biocViews: Software, Metabolomics, GraphAndNetwork, KEGG, GO, Pathways, Network, NetworkEnrichment Author: Sergio Picart-Armada [aut, cre], Francesc Fernandez-Albert [aut], Alexandre Perera-Lluna [aut] Maintainer: Sergio Picart-Armada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FELLA git_branch: RELEASE_3_12 git_last_commit: a3d4453 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FELLA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FELLA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FELLA_1.10.0.tgz vignettes: vignettes/FELLA/inst/doc/FELLA.pdf, vignettes/FELLA/inst/doc/musmusculus.pdf, vignettes/FELLA/inst/doc/zebrafish.pdf, vignettes/FELLA/inst/doc/quickstart.html vignetteTitles: FELLA, Example: a fatty liver study on Mus musculus, Example: oxybenzone exposition in gilt-head bream, Quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FELLA/inst/doc/FELLA.R, vignettes/FELLA/inst/doc/musmusculus.R, vignettes/FELLA/inst/doc/quickstart.R, vignettes/FELLA/inst/doc/zebrafish.R dependencyCount: 33 Package: ffpe Version: 1.34.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 6d0c89bbee4bcfef854cc40f5c54c880 NeedsCompilation: no Title: Quality assessment and control for FFPE microarray expression data Description: Identify low-quality data using metrics developed for expression data derived from Formalin-Fixed, Paraffin-Embedded (FFPE) data. Also a function for making Concordance at the Top plots (CAT-plots). biocViews: Microarray, GeneExpression, QualityControl Author: Levi Waldron Maintainer: Levi Waldron git_url: https://git.bioconductor.org/packages/ffpe git_branch: RELEASE_3_12 git_last_commit: 97fc3b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ffpe_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ffpe_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ffpe_1.34.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ffpe/inst/doc/ffpe.R dependencyCount: 155 Package: FGNet Version: 3.24.0 Depends: R (>= 2.15) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png, methods, stats, utils, graphics, grDevices Suggests: RCurl, RDAVIDWebService, gage, topGO, GO.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi, RGtk2, BiocManager License: GPL (>= 2) MD5sum: 3aeca70cb3ddaeed513379bcf6bce5a8 NeedsCompilation: no Title: Functional Gene Networks derived from biological enrichment analyses Description: Build and visualize functional gene and term networks from clustering of enrichment analyses in multiple annotation spaces. The package includes a graphical user interface (GUI) and functions to perform the functional enrichment analysis through DAVID, GeneTerm Linker, gage (GSEA) and topGO. biocViews: Annotation, GO, Pathways, GeneSetEnrichment, Network, Visualization, FunctionalGenomics, NetworkEnrichment, Clustering Author: Sara Aibar, Celia Fontanillo, Conrad Droste and Javier De Las Rivas. Maintainer: Sara Aibar URL: http://www.cicancer.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FGNet git_branch: RELEASE_3_12 git_last_commit: 92c6ba3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/FGNet_3.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FGNet_3.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FGNet_3.24.0.tgz vignettes: vignettes/FGNet/inst/doc/FGNet.html vignetteTitles: FGNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R importsMe: IntramiRExploreR dependencyCount: 26 Package: fgsea Version: 1.16.0 Depends: R (>= 3.3) Imports: Rcpp, data.table, BiocParallel, stats, ggplot2 (>= 2.2.0), gridExtra, grid, fastmatch, Matrix, utils LinkingTo: Rcpp, BH Suggests: testthat, knitr, rmarkdown, reactome.db, AnnotationDbi, parallel, org.Mm.eg.db, limma, GEOquery License: MIT + file LICENCE Archs: i386, x64 MD5sum: cdac3d98d88e76356916457c055fb5a0 NeedsCompilation: yes Title: Fast Gene Set Enrichment Analysis Description: The package implements an algorithm for fast gene set enrichment analysis. Using the fast algorithm allows to make more permutations and get more fine grained p-values, which allows to use accurate stantard approaches to multiple hypothesis correction. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Pathways Author: Gennady Korotkevich [aut], Vladimir Sukhov [aut], Nikolay Budin [ctb], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://github.com/ctlab/fgsea/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/ctlab/fgsea/issues git_url: https://git.bioconductor.org/packages/fgsea git_branch: RELEASE_3_12 git_last_commit: 9d9df59 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fgsea_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fgsea_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fgsea_1.16.0.tgz vignettes: vignettes/fgsea/inst/doc/fgsea-tutorial.html vignetteTitles: Using fgsea package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fgsea/inst/doc/fgsea-tutorial.R dependsOnMe: gsean, PPInfer importsMe: ASpediaFI, CEMiTool, clustifyr, cTRAP, DOSE, lipidr, mCSEA, multiGSEA, phantasus, piano, RegEnrich, signatureSearch, ViSEAGO, cinaR suggestsMe: mdp, Pi, rliger dependencyCount: 50 Package: FilterFFPE Version: 1.0.0 Imports: foreach, doParallel, GenomicRanges, IRanges, Rsamtools, parallel, S4Vectors Suggests: BiocStyle License: LGPL-3 MD5sum: eea68669bf85d7da5845fd2aac640ec3 NeedsCompilation: no Title: FFPE Artificial Chimeric Read Filter for NGS data Description: This package finds and filters artificial chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissues. These artificial chimeric reads can lead to a large number of false positive structural variation (SV) calls. The required input is an indexed BAM file of a FFPE sample. biocViews: StructuralVariation, Sequencing, Alignment, QualityControl, Preprocessing Author: Lanying Wei [aut, cre] () Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/FilterFFPE git_branch: RELEASE_3_12 git_last_commit: 8838232 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FilterFFPE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FilterFFPE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FilterFFPE_1.0.0.tgz vignettes: vignettes/FilterFFPE/inst/doc/FilterFFPE.pdf vignetteTitles: An introduction to FilterFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FilterFFPE/inst/doc/FilterFFPE.R dependencyCount: 33 Package: FindMyFriends Version: 1.20.0 Imports: methods, BiocGenerics, Biobase, tools, dplyr, IRanges, Biostrings, S4Vectors, kebabs, igraph, Matrix, digest, filehash, Rcpp, ggplot2, gtable, grid, reshape2, ggdendro, BiocParallel, utils, stats LinkingTo: Rcpp Suggests: BiocStyle, testthat, knitr, rmarkdown, reutils License: GPL (>=2) Archs: i386, x64 MD5sum: 22d02d3ccba2c64115e135659fdce5a4 NeedsCompilation: yes Title: Microbial Comparative Genomics in R Description: A framework for doing microbial comparative genomics in R. The main purpose of the package is assisting in the creation of pangenome matrices where genes from related organisms are grouped by similarity, as well as the analysis of these data. FindMyFriends provides many novel approaches to doing pangenome analysis and supports a gene grouping algorithm that scales linearly, thus making the creation of huge pangenomes feasible. biocViews: ComparativeGenomics, Clustering, DataRepresentation, GenomicVariation, SequenceMatching, GraphAndNetwork Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/FindMyFriends VignetteBuilder: knitr BugReports: https://github.com/thomasp85/FindMyFriends/issues git_url: https://git.bioconductor.org/packages/FindMyFriends git_branch: RELEASE_3_12 git_last_commit: 43d1515 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FindMyFriends_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FindMyFriends_1.19.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FindMyFriends_1.20.0.tgz vignettes: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html vignetteTitles: Creating pangenomes using FindMyFriends hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.R importsMe: PanVizGenerator dependencyCount: 74 Package: FISHalyseR Version: 1.24.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: 1d45338fb4c71cf36dbdedf9c9695b4a NeedsCompilation: no Title: FISHalyseR a package for automated FISH quantification Description: FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis. biocViews: CellBiology Author: Karesh Arunakirinathan , Andreas Heindl Maintainer: Karesh Arunakirinathan , Andreas Heindl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FISHalyseR git_branch: RELEASE_3_12 git_last_commit: 59239bd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FISHalyseR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FISHalyseR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FISHalyseR_1.24.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FISHalyseR/inst/doc/FISHalyseR.R dependencyCount: 25 Package: fishpond Version: 1.6.0 Imports: graphics, stats, utils, methods, abind, gtools, qvalue, S4Vectors, SummarizedExperiment, matrixStats, svMisc, Rcpp, Matrix LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, macrophage, tximeta, org.Hs.eg.db, samr, DESeq2, apeglm, tximportData, SingleCellExperiment License: GPL-2 Archs: i386, x64 MD5sum: 8d350ed7e2e7ebe7c5b3eda074b53763 NeedsCompilation: yes Title: Fishpond: differential transcript and gene expression with inferential replicates Description: Fishpond contains methods for differential transcript and gene expression analysis of RNA-seq data using inferential replicates for uncertainty of abundance quantification, as generated by Gibbs sampling or bootstrap sampling. Also the package contains utilities for working with Salmon and Alevin quantification files. biocViews: Sequencing, RNASeq, GeneExpression, Transcription, Normalization, Regression, MultipleComparison, BatchEffect, Visualization, DifferentialExpression, DifferentialSplicing, AlternativeSplicing, SingleCell Author: Anzi Zhu [aut, ctb], Michael Love [aut, cre], Avi Srivastava [aut, ctb], Rob Patro [aut, ctb], Joseph Ibrahim [aut, ctb], Hirak Sarkar [ctb], Scott Van Buren [ctb] Maintainer: Michael Love URL: https://github.com/mikelove/fishpond SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fishpond git_branch: RELEASE_3_12 git_last_commit: c35e803 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fishpond_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fishpond_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fishpond_1.6.0.tgz vignettes: vignettes/fishpond/inst/doc/swish.html vignetteTitles: DTE and DGE with inferential replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fishpond/inst/doc/swish.R suggestsMe: tximport dependencyCount: 65 Package: FitHiC Version: 1.16.0 Imports: data.table, fdrtool, grDevices, graphics, Rcpp, stats, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>= 2) Archs: i386, x64 MD5sum: 5f214e30adfe06efcd4d0b44c9f6a122 NeedsCompilation: yes Title: Confidence estimation for intra-chromosomal contact maps Description: Fit-Hi-C is a tool for assigning statistical confidence estimates to intra-chromosomal contact maps produced by genome-wide genome architecture assays such as Hi-C. biocViews: DNA3DStructure, Software Author: Ferhat Ay [aut] (Python original, https://noble.gs.washington.edu/proj/fit-hi-c/), Timothy L. Bailey [aut], William S. Noble [aut], Ruyu Tan [aut, cre, trl] (R port) Maintainer: Ruyu Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FitHiC git_branch: RELEASE_3_12 git_last_commit: 14c0247 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FitHiC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FitHiC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FitHiC_1.16.0.tgz vignettes: vignettes/FitHiC/inst/doc/fithic.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FitHiC/inst/doc/fithic.R dependencyCount: 8 Package: flagme Version: 1.46.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: f60459b6783089d63bfe9c57299f5ee1 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: ImmunoOncology, DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli git_url: https://git.bioconductor.org/packages/flagme git_branch: RELEASE_3_12 git_last_commit: 469f775 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flagme_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flagme_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flagme_1.46.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GC-MS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flagme/inst/doc/flagme.R dependencyCount: 132 Package: flowAI Version: 1.20.1 Depends: R (>= 3.6) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales, methods, graphics, stats, utils, rmarkdown Suggests: testthat, shiny License: GPL (>= 2) MD5sum: b0243bc56a6fd2463ebc0d8a9e9a9e2c NeedsCompilation: no Title: Automatic and interactive quality control for flow cytometry data Description: The package is able to perform an automatic or interactive quality control on FCS data acquired using flow cytometry instruments. By evaluating three different properties: 1) flow rate, 2) signal acquisition, 3) dynamic range, the quality control enables the detection and removal of anomalies. biocViews: FlowCytometry, QualityControl, BiomedicalInformatics, ImmunoOncology Author: Gianni Monaco, Hao Chen Maintainer: Gianni Monaco VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowAI git_branch: RELEASE_3_12 git_last_commit: a47af13 git_last_commit_date: 2020-11-01 Date/Publication: 2020-11-01 source.ver: src/contrib/flowAI_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowAI_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.0/flowAI_1.20.1.tgz vignettes: vignettes/flowAI/inst/doc/flowAI.html vignetteTitles: Automatic and GUI methods to do quality control on Flow cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R dependencyCount: 71 Package: flowBeads Version: 1.28.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 95008bc0160b556797f1adc247000145 NeedsCompilation: no Title: flowBeads: Analysis of flow bead data Description: This package extends flowCore to provide functionality specific to bead data. One of the goals of this package is to automate analysis of bead data for the purpose of normalisation. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos git_url: https://git.bioconductor.org/packages/flowBeads git_branch: RELEASE_3_12 git_last_commit: 3d3a3ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowBeads_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowBeads_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowBeads_1.28.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBeads/inst/doc/HowTo-flowBeads.R dependencyCount: 39 Package: flowBin Version: 1.26.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: 0cfee03b09426881b39630fb09a1610c NeedsCompilation: no Title: Combining multitube flow cytometry data by binning Description: Software to combine flow cytometry data that has been multiplexed into multiple tubes with common markers between them, by establishing common bins across tubes in terms of the common markers, then determining expression within each tube for each bin in terms of the tube-specific markers. biocViews: ImmunoOncology, CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill git_url: https://git.bioconductor.org/packages/flowBin git_branch: RELEASE_3_12 git_last_commit: 9c939b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowBin_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowBin_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowBin_1.26.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowBin/inst/doc/flowBin.R dependencyCount: 34 Package: flowcatchR Version: 1.24.0 Depends: R (>= 2.10), methods, EBImage Imports: colorRamps, abind, BiocParallel, graphics, stats, utils, plotly, shiny Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: befb0610130a54807f9ec7163c06bca9 NeedsCompilation: no Title: Tools to analyze in vivo microscopy imaging data focused on tracking flowing blood cells Description: flowcatchR is a set of tools to analyze in vivo microscopy imaging data, focused on tracking flowing blood cells. It guides the steps from segmentation to calculation of features, filtering out particles not of interest, providing also a set of utilities to help checking the quality of the performed operations (e.g. how good the segmentation was). It allows investigating the issue of tracking flowing cells such as in blood vessels, to categorize the particles in flowing, rolling and adherent. This classification is applied in the study of phenomena such as hemostasis and study of thrombosis development. Moreover, flowcatchR presents an integrated workflow solution, based on the integration with a Shiny App and Jupyter notebooks, which is delivered alongside the package, and can enable fully reproducible bioimage analysis in the R environment. biocViews: Software, Visualization, CellBiology, Classification, Infrastructure, GUI Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/flowcatchR, https://federicomarini.github.io/flowcatchR/ SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues git_url: https://git.bioconductor.org/packages/flowcatchR git_branch: RELEASE_3_12 git_last_commit: 2749cf4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowcatchR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowcatchR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowcatchR_1.24.0.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.html vignetteTitles: flowcatchR: tracking and analyzing cells in time lapse microscopy images hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowcatchR/inst/doc/flowcatchr_vignette.R dependencyCount: 94 Package: flowCHIC Version: 1.24.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 94d9149dc8a28db701c25f34d6d9cc64 NeedsCompilation: no Title: Analyze flow cytometric data using histogram information Description: A package to analyze flow cytometric data of complex microbial communities based on histogram images biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Ingo Fetzer , Susann Müller Maintainer: Author: Joachim Schumann URL: http://www.ufz.de/index.php?en=16773 git_url: https://git.bioconductor.org/packages/flowCHIC git_branch: RELEASE_3_12 git_last_commit: a75c1d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowCHIC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowCHIC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowCHIC_1.24.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCHIC/inst/doc/flowCHICmanual.R dependencyCount: 70 Package: flowCL Version: 1.28.1 Depends: R (>= 3.4), Rgraphviz, SPARQL Imports: methods, grDevices, utils, graph Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 541d3a26b31f101ed83ad76fb03a5454 NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry, ImmunoOncology Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas git_url: https://git.bioconductor.org/packages/flowCL git_branch: RELEASE_3_12 git_last_commit: 03d9747 git_last_commit_date: 2020-12-01 Date/Publication: 2020-12-01 source.ver: src/contrib/flowCL_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowCL_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.0/flowCL_1.28.1.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 15 Package: flowClean Version: 1.28.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 58ea4b103c75d8616d1332767f525c07 NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl, ImmunoOncology Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant git_url: https://git.bioconductor.org/packages/flowClean git_branch: RELEASE_3_12 git_last_commit: 49ca7e8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowClean_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowClean_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowClean_1.28.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClean/inst/doc/flowClean.R dependencyCount: 26 Package: flowClust Version: 3.28.0 Depends: R(>= 2.5.0) Imports: BiocGenerics, methods, Biobase, graph, ellipse, flowViz, flowCore, clue, corpcor, mnormt, parallel Suggests: testthat, flowWorkspace, flowWorkspaceData, knitr, rmarkdown, openCyto License: Artistic-2.0 Archs: i386, x64 MD5sum: 2d3dcaa969333d4349d27ac632842c98 NeedsCompilation: yes Title: Clustering for Flow Cytometry Description: Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. biocViews: ImmunoOncology, Clustering, Visualization, FlowCytometry Author: Raphael Gottardo , Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang , Jake Wagner SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowClust git_branch: RELEASE_3_12 git_last_commit: 12da966 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowClust_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowClust_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowClust_3.28.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.html vignetteTitles: Robust Model-based Clustering of Flow Cytometry Data\\ The flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: flowTrans suggestsMe: BiocGenerics, flowTime, segmenTier dependencyCount: 37 Package: flowCore Version: 2.2.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics (>= 0.29.2), grDevices, graphics, methods, stats, utils, stats4, Rcpp, matrixStats, cytolib (>= 1.9.15), S4Vectors LinkingTo: Rcpp, RcppArmadillo, BH(>= 1.65.0.1), cytolib(>= 1.7.2), RProtoBufLib Suggests: Rgraphviz, flowViz, flowStats (>= 3.43.4), testthat, flowWorkspace, flowWorkspaceData, openCyto, knitr, ggcyto, gridExtra License: Artistic-2.0 Archs: i386, x64 MD5sum: 91e684d2d8b4dce1b428623af4f0e5a6 NeedsCompilation: yes Title: flowCore: Basic structures for flow cytometry data Description: Provides S4 data structures and basic functions to deal with flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays Author: B Ellis [aut], Perry Haaland [aut], Florian Hahne [aut], Nolwenn Le Meur [aut], Nishant Gopalakrishnan [aut], Josef Spidlen [aut], Mike Jiang [aut, cre], Greg Finak [aut], Samuel Granjeaud [ctb] Maintainer: Mike Jiang , Jake Wagner SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCore git_branch: RELEASE_3_12 git_last_commit: 86a9aca git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowCore_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowCore_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowCore_2.2.0.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf, vignettes/flowCore/inst/doc/fcs3.html, vignettes/flowCore/inst/doc/hyperlog.notice.html vignetteTitles: Basic Functions for Flow Cytometry Data, fcs3.html, hyperlog.notice.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowCut, flowFP, flowMatch, flowTime, flowTrans, flowViz, flowVS, ggcyto, immunoClust, infinityFlow, ncdfFlow, flowFitExampleData, HDCytoData, healthyFlowData, highthroughputassays importsMe: CATALYST, cmapR, cydar, cytofast, CytoML, CytoTree, ddPCRclust, diffcyt, flowAI, flowBeads, flowCHIC, flowClust, flowDensity, flowMeans, flowPloidy, FlowSOM, flowSpecs, flowSpy, flowStats, flowTrans, flowUtils, flowViz, flowWorkspace, GateFinder, ImmuneSpaceR, MetaCyto, oneSENSE, PeacoQC, scDataviz, Sconify suggestsMe: COMPASS, FlowRepositoryR, RchyOptimyx, flowPloidyData, hypergate, segmenTier dependencyCount: 18 Package: flowCut Version: 1.0.0 Depends: R (>= 3.4), flowCore Imports: flowDensity (>= 1.13.1), Cairo, e1071, grDevices, graphics, stats,methods Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 5e0d6188c8950f10588f999a0358e6f2 NeedsCompilation: no Title: Precise and Accurate Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis Description: Common techinical complications such as clogging can result in spurious events and fluorescence intensity shifting, flowCut is designed to detect and remove technical artifacts from your data by removing segments that show statistical differences from other segments. biocViews: FlowCytometry, Preprocessing, QualityControl, CellBasedAssays Author: Justin Meskas [cre, aut], Sherrie Wang [aut] Maintainer: Justin Meskas VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowCut git_branch: RELEASE_3_12 git_last_commit: 7ca3027 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowCut_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowCut_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowCut_1.0.0.tgz vignettes: vignettes/flowCut/inst/doc/flowCut.html vignetteTitles: _**flowCut**_: Precise and Accurate Automated Removal of Outlier Events and Flagging of Files Based on Time Versus Fluorescence Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCut/inst/doc/flowCut.R dependencyCount: 148 Package: flowCyBar Version: 1.26.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 2cbfa5b5a402c801571b7f6c26d56305 NeedsCompilation: no Title: Analyze flow cytometric data using gate information Description: A package to analyze flow cytometric data using gate information to follow population/community dynamics biocViews: ImmunoOncology, CellBasedAssays, Clustering, FlowCytometry, Software, Visualization Author: Joachim Schumann , Christin Koch , Susanne Günther , Ingo Fetzer , Susann Müller Maintainer: Joachim Schumann URL: http://www.ufz.de/index.php?de=16773 git_url: https://git.bioconductor.org/packages/flowCyBar git_branch: RELEASE_3_12 git_last_commit: b771c10 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowCyBar_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowCyBar_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowCyBar_1.26.0.tgz vignettes: vignettes/flowCyBar/inst/doc/flowCyBar-manual.pdf vignetteTitles: Analyze flow cytometric data using gate information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCyBar/inst/doc/flowCyBar-manual.R dependencyCount: 20 Package: flowDensity Version: 1.24.0 Imports: flowCore, graphics, flowViz (>= 1.46.1), car, sp, rgeos, gplots, RFOC, flowWorkspace (>= 3.33.1), methods, stats, grDevices Suggests: knitr License: Artistic-2.0 MD5sum: dbd16042d126cd4dac36418e8d82b4ff NeedsCompilation: no Title: Sequential Flow Cytometry Data Gating Description: This package provides tools for automated sequential gating analogous to the manual gating strategy based on the density of the data. biocViews: Bioinformatics, FlowCytometry, CellBiology, Clustering, Cancer, FlowCytData, DataRepresentation, StemCell, DensityGating Author: Mehrnoush Malek,M. Jafar Taghiyar Maintainer: Mehrnoush Malek SystemRequirements: xml2, GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowDensity git_branch: RELEASE_3_12 git_last_commit: 36553c0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowDensity_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowDensity_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowDensity_1.24.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensity.html vignetteTitles: Introduction to automated gating hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensity.R importsMe: ddPCRclust, flowCut dependencyCount: 143 Package: flowFP Version: 1.48.0 Depends: R (>= 2.10), flowCore, flowViz Imports: Biobase, BiocGenerics (>= 0.1.6), graphics, grDevices, methods, stats, stats4 Suggests: RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: d531813828d4146460b0ad96480b1154 NeedsCompilation: yes Title: Fingerprinting for Flow Cytometry Description: Fingerprint generation of flow cytometry data, used to facilitate the application of machine learning and datamining tools for flow cytometry. biocViews: FlowCytometry, CellBasedAssays, Clustering, Visualization Author: Herb Holyst , Wade Rogers Maintainer: Herb Holyst git_url: https://git.bioconductor.org/packages/flowFP git_branch: RELEASE_3_12 git_last_commit: 362d44f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowFP_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowFP_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowFP_1.48.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFP/inst/doc/flowFP_HowTo.R dependsOnMe: flowBin importsMe: GateFinder dependencyCount: 30 Package: flowMap Version: 1.28.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: 259acd8d149d2c44de13211a3d9bc5d5 NeedsCompilation: no Title: Mapping cell populations in flow cytometry data for cross-sample comparisons using the Friedman-Rafsky Test Description: flowMap quantifies the similarity of cell populations across multiple flow cytometry samples using a nonparametric multivariate statistical test. The method is able to map cell populations of different size, shape, and proportion across multiple flow cytometry samples. The algorithm can be incorporate in any flow cytometry work flow that requires accurat quantification of similarity between cell populations. biocViews: ImmunoOncology, MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMap git_branch: RELEASE_3_12 git_last_commit: c8cbd16 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowMap_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowMap_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowMap_1.28.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMap/inst/doc/flowMap.R dependencyCount: 43 Package: flowMatch Version: 1.26.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.0), methods, flowCore Imports: Biobase LinkingTo: Rcpp Suggests: healthyFlowData License: Artistic-2.0 Archs: i386, x64 MD5sum: 50d8d33e231da92626959bac1f9097b4 NeedsCompilation: yes Title: Matching and meta-clustering in flow cytometry Description: Matching cell populations and building meta-clusters and templates from a collection of FC samples. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad git_url: https://git.bioconductor.org/packages/flowMatch git_branch: RELEASE_3_12 git_last_commit: f77f3dd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowMatch_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowMatch_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowMatch_1.26.0.tgz vignettes: vignettes/flowMatch/inst/doc/flowMatch.pdf vignetteTitles: flowMatch: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMatch/inst/doc/flowMatch.R dependencyCount: 19 Package: flowMeans Version: 1.50.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: e0e7c291389b2953a88378c76ec1f88e NeedsCompilation: no Title: Non-parametric Flow Cytometry Data Gating Description: Identifies cell populations in Flow Cytometry data using non-parametric clustering and segmented-regression-based change point detection. Note: R 2.11.0 or newer is required. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/flowMeans git_branch: RELEASE_3_12 git_last_commit: 933e306 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowMeans_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowMeans_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowMeans_1.50.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMeans/inst/doc/flowMeans.R importsMe: optimalFlow dependencyCount: 40 Package: flowMerge Version: 2.38.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Suggests: knitr, rmarkdown Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 1469c86d6e7a12c64d18d3ae18ed1899 NeedsCompilation: no Title: Cluster Merging for Flow Cytometry Data Description: Merging of mixture components for model-based automated gating of flow cytometry data using the flowClust framework. Note: users should have a working copy of flowClust 2.0 installed. biocViews: ImmunoOncology, Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowMerge git_branch: RELEASE_3_12 git_last_commit: 7881c9f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowMerge_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowMerge_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowMerge_2.38.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowmerge.html vignetteTitles: Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge Package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowmerge.R suggestsMe: segmenTier dependencyCount: 61 Package: flowPeaks Version: 1.36.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: 93ebdf8e023a3c54d218dbf45c52a082 NeedsCompilation: yes Title: An R package for flow data clustering Description: A fast and automatic clustering to classify the cells into subpopulations based on finding the peaks from the overall density function generated by K-means. biocViews: ImmunoOncology, FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge SystemRequirements: gsl git_url: https://git.bioconductor.org/packages/flowPeaks git_branch: RELEASE_3_12 git_last_commit: e53b039 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowPeaks_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowPeaks_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowPeaks_1.36.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPeaks/inst/doc/flowPeaks-guide.R importsMe: ddPCRclust dependencyCount: 0 Package: flowPloidy Version: 1.16.0 Imports: flowCore, car, caTools, knitr, rmarkdown, minpack.lm, shiny, methods, graphics, stats, utils Suggests: flowPloidyData, testthat License: GPL-3 MD5sum: f65011a56d50e70e3cc913c57ba886d4 NeedsCompilation: no Title: Analyze flow cytometer data to determine sample ploidy Description: Determine sample ploidy via flow cytometry histogram analysis. Reads Flow Cytometry Standard (FCS) files via the flowCore bioconductor package, and provides functions for determining the DNA ploidy of samples based on internal standards. biocViews: FlowCytometry, GUI, Regression, Visualization Author: Tyler Smith Maintainer: Tyler Smith URL: https://github.com/plantarum/flowPloidy VignetteBuilder: knitr BugReports: https://github.com/plantarum/flowPloidy/issues git_url: https://git.bioconductor.org/packages/flowPloidy git_branch: RELEASE_3_12 git_last_commit: ccf3b7c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowPloidy_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowPloidy_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowPloidy_1.16.0.tgz vignettes: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.pdf, vignettes/flowPloidy/inst/doc/histogram-tour.pdf vignetteTitles: flowPloidy: Getting Started, flowPloidy: FCM Histograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPloidy/inst/doc/flowPloidy-gettingStarted.R, vignettes/flowPloidy/inst/doc/histogram-tour.R dependencyCount: 116 Package: flowPlots Version: 1.38.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 1dfa69664c2980690e08bb3a2ac0fc17 NeedsCompilation: no Title: flowPlots: analysis plots and data class for gated flow cytometry data Description: Graphical displays with embedded statistical tests for gated ICS flow cytometry data, and a data class which stores "stacked" data and has methods for computing summary measures on stacked data, such as marginal and polyfunctional degree data. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins git_url: https://git.bioconductor.org/packages/flowPlots git_branch: RELEASE_3_12 git_last_commit: 52a3878 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowPlots_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowPlots_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowPlots_1.38.0.tgz vignettes: vignettes/flowPlots/inst/doc/flowPlots.pdf vignetteTitles: Plots with Embedded Tests for Gated Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowPlots/inst/doc/flowPlots.R dependencyCount: 1 Package: FlowRepositoryR Version: 1.22.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools, utils, jsonlite Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 MD5sum: 866dcc6443d30c53ed66fe143bcecde2 NeedsCompilation: no Title: FlowRepository R Interface Description: This package provides an interface to search and download data and annotations from FlowRepository (flowrepository.org). It uses the FlowRepository programming interface to communicate with a FlowRepository server. biocViews: ImmunoOncology, Infrastructure, FlowCytometry Author: Josef Spidlen [aut, cre] Maintainer: Josef Spidlen git_url: https://git.bioconductor.org/packages/FlowRepositoryR git_branch: RELEASE_3_12 git_last_commit: be013a0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FlowRepositoryR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FlowRepositoryR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FlowRepositoryR_1.22.0.tgz vignettes: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.pdf vignetteTitles: FlowRepository R Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.R dependencyCount: 7 Package: FlowSOM Version: 1.22.0 Depends: R (>= 3.2), igraph Imports: stats, utils, flowCore, ConsensusClusterPlus, BiocGenerics, tsne, CytoML, flowWorkspace, XML, RColorBrewer Suggests: BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 0b3ed517ffa60ece0dc570cff28f3102 NeedsCompilation: yes Title: Using self-organizing maps for visualization and interpretation of cytometry data Description: FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees. biocViews: CellBiology, FlowCytometry, Clustering, Visualization, Software, CellBasedAssays Author: Sofie Van Gassen, Britt Callebaut and Yvan Saeys Maintainer: Sofie Van Gassen URL: http://www.r-project.org, http://dambi.ugent.be git_url: https://git.bioconductor.org/packages/FlowSOM git_branch: RELEASE_3_12 git_last_commit: 89cd5ba git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FlowSOM_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FlowSOM_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FlowSOM_1.22.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: FlowSOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: CATALYST, cytofast, CytoTree, diffcyt, flowSpy suggestsMe: HDCytoData dependencyCount: 127 Package: flowSpecs Version: 1.4.0 Depends: R (>= 4.0) Imports: ggplot2 (>= 3.1.0), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), Biobase (>= 2.48.0), reshape2 (>= 1.4.3), flowCore (>= 1.50.0), zoo (>= 1.8.6), stats (>= 3.6.0), methods (>= 3.6.0) Suggests: testthat, knitr, rmarkdown, BiocStyle, DepecheR License: MIT + file LICENSE MD5sum: 77ba2ca4df776fb9db65ec039b38c458 NeedsCompilation: no Title: Tools for processing of high-dimensional cytometry data Description: This package is intended to fill the role of conventional cytometry pre-processing software, for spectral decomposition, transformation, visualization and cleanup, and to aid further downstream analyses, such as with DepecheR, by enabling transformation of flowFrames and flowSets to dataframes. Functions for flowCore-compliant automatic 1D-gating/filtering are in the pipe line. The package name has been chosen both as it will deal with spectral cytometry and as it will hopefully give the user a nice pair of spectacles through which to view their data. biocViews: Software,CellBasedAssays,DataRepresentation,ImmunoOncology, FlowCytometry,SingleCell,Visualization,Normalization,DataImport Author: Jakob Theorell [aut, cre] Maintainer: Jakob Theorell VignetteBuilder: knitr BugReports: https://github.com/jtheorell/flowSpecs/issues git_url: https://git.bioconductor.org/packages/flowSpecs git_branch: RELEASE_3_12 git_last_commit: 8ad2220 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowSpecs_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowSpecs_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowSpecs_1.4.0.tgz vignettes: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.html vignetteTitles: Example workflow for processing of raw spectral cytometry files hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/flowSpecs/inst/doc/flowSpecs_vinjette.R dependencyCount: 64 Package: flowSpy Version: 1.4.0 Depends: R (>= 3.6), igraph Imports: FlowSOM, Rtsne, ggplot2, destiny, gmodels, flowUtils, Biobase, Matrix, flowCore, sva, matrixStats, methods, mclust, prettydoc, RANN(>= 2.5), Rcpp (>= 0.12.0), BiocNeighbors, cluster, pheatmap, scatterpie, umap, scatterplot3d, limma, stringr, grDevices, grid, stats LinkingTo: Rcpp Suggests: BiocGenerics, knitr, RColorBrewer, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 830236ad7171c51a75182ec5e79a60e2 NeedsCompilation: yes Title: A Toolkit for Flow And Mass Cytometry Data Description: A trajectory inference and visualization toolkit for flow and mass cytometry data. flowSpy offers complete analyzing workflow for flow and mass cytometry data. flowSpy can be a valuable tool for application ranging from clustering and dimensionality reduction to trajectory reconstruction and pseudotime estimation for flow and mass cytometry data. biocViews: CellBiology, Clustering, Visualization, Software, CellBasedAssays, FlowCytometry, NetworkInference, Network Author: Yuting Dai [aut, cre] Maintainer: Yuting Dai URL: http://www.r-project.org, https://github.com/JhuangLab/flowSpy VignetteBuilder: knitr BugReports: https://github.com/JhuangLab/flowSpy/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/flowSpy git_branch: RELEASE_3_12 git_last_commit: e8ba381 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowSpy_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowSpy_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowSpy_1.4.0.tgz vignettes: vignettes/flowSpy/inst/doc/Quick_start_of_flowSpy.html, vignettes/flowSpy/inst/doc/Quick_start.html vignetteTitles: Quick_start_of_flowSpy.html, Quick_start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowSpy/inst/doc/Quick_start.R dependencyCount: 244 Package: flowStats Version: 4.2.0 Depends: R (>= 3.0.2) Imports: BiocGenerics, MASS, flowCore (>= 1.99.6), flowWorkspace, ncdfFlow(>= 2.19.5), flowViz, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, cluster, utils, KernSmooth, lattice, ks, RColorBrewer, rrcov Suggests: xtable, testthat, openCyto Enhances: RBGL,graph License: Artistic-2.0 MD5sum: 394cc31c92390ac4e0e150ca44a902d7 NeedsCompilation: no Title: Statistical methods for the analysis of flow cytometry data Description: Methods and functionality to analyse flow data that is beyond the basic infrastructure provided by the flowCore package. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak , Mike Jiang , Jake Wagner URL: http://www.github.com/RGLab/flowStats BugReports: http://www.github.com/RGLab/flowStats/issues git_url: https://git.bioconductor.org/packages/flowStats git_branch: RELEASE_3_12 git_last_commit: dac2093 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowStats_4.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowStats_4.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowStats_4.2.0.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.R dependsOnMe: flowVS, highthroughputassays suggestsMe: cydar, flowCore, flowTime, flowViz, ggcyto dependencyCount: 105 Package: flowTime Version: 1.14.0 Depends: R (>= 3.4), flowCore Imports: utils, dplyr (>= 1.0.0), tibble, magrittr, plyr, rlang Suggests: knitr, rmarkdown, flowViz, ggplot2, BiocGenerics, stats, flowClust, openCyto, flowStats, ggcyto License: Artistic-2.0 MD5sum: f49ba800179f1eeb784f5ce3c75569bd NeedsCompilation: no Title: Annotation and analysis of biological dynamical systems using flow cytometry Description: This package facilitates analysis of both timecourse and steady state flow cytometry experiments. This package was originially developed for quantifying the function of gene regulatory networks in yeast (strain W303) expressing fluorescent reporter proteins using BD Accuri C6 and SORP cytometers. However, the functions are for the most part general and may be adapted for analysis of other organisms using other flow cytometers. Functions in this package facilitate the annotation of flow cytometry data with experimental metadata, as often required for publication and general ease-of-reuse. Functions for creating, saving and loading gate sets are also included. In the past, we have typically generated summary statistics for each flowset for each timepoint and then annotated and analyzed these summary statistics. This method loses a great deal of the power that comes from the large amounts of individual cell data generated in flow cytometry, by essentially collapsing this data into a bulk measurement after subsetting. In addition to these summary functions, this package also contains functions to facilitate annotation and analysis of steady-state or time-lapse data utilizing all of the data collected from the thousands of individual cells in each sample. biocViews: FlowCytometry, TimeCourse, Visualization, DataImport, CellBasedAssays, ImmunoOncology Author: R. Clay Wright [aut, cre], Nick Bolten [aut], Edith Pierre-Jerome [aut] Maintainer: R. Clay Wright VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowTime git_branch: RELEASE_3_12 git_last_commit: 9398c74 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowTime_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowTime_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowTime_1.14.0.tgz vignettes: vignettes/flowTime/inst/doc/gating-vignette.html, vignettes/flowTime/inst/doc/steady-state-vignette.html, vignettes/flowTime/inst/doc/time-course-vignette.html vignetteTitles: Yeast gating, Steady-state analysis of flow cytometry data, Time course analysis of flow cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTime/inst/doc/gating-vignette.R, vignettes/flowTime/inst/doc/steady-state-vignette.R, vignettes/flowTime/inst/doc/time-course-vignette.R dependencyCount: 38 Package: flowTrans Version: 1.42.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 5d9c32829e775e0c204db5a59ba9c520 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: ImmunoOncology, FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak git_url: https://git.bioconductor.org/packages/flowTrans git_branch: RELEASE_3_12 git_last_commit: b7efe1a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowTrans_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowTrans_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowTrans_1.42.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowTrans/inst/doc/flowTrans.R dependencyCount: 38 Package: flowUtils Version: 1.54.0 Depends: R (>= 2.2.0) Imports: Biobase, graph, methods, stats, utils, corpcor, RUnit, XML, flowCore (>= 1.32.0) Suggests: gatingMLData License: Artistic-2.0 MD5sum: bff0626e8dba8bbf66cb4f483f32a1be NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, DecisionTree Author: J. Spidlen., N. Gopalakrishnan, F. Hahne, B. Ellis, R. Gentleman, M. Dalphin, N. Le Meur, B. Purcell, W. Jiang Maintainer: Josef Spidlen URL: https://github.com/jspidlen/flowUtils BugReports: https://github.com/jspidlen/flowUtils/issues git_url: https://git.bioconductor.org/packages/flowUtils git_branch: RELEASE_3_12 git_last_commit: accbdbb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowUtils_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowUtils_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowUtils_1.54.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowUtils/inst/doc/HowTo-flowUtils.R importsMe: CytoTree, flowSpy suggestsMe: gatingMLData dependencyCount: 23 Package: flowViz Version: 1.54.0 Depends: R (>= 2.7.0), flowCore(>= 1.41.9), lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats, knitr, testthat License: Artistic-2.0 MD5sum: 83affe578979039075bae25ce76b4878 NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: ImmunoOncology, Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang , Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowViz git_branch: RELEASE_3_12 git_last_commit: f80fd24 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowViz_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowViz_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowViz_1.54.0.tgz vignettes: vignettes/flowViz/inst/doc/filters.html vignetteTitles: Visualizing Gates with Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R dependsOnMe: flowFP, flowVS importsMe: flowClust, flowDensity, flowStats, flowTrans suggestsMe: flowBeads, flowClean, flowCore, flowTime, ggcyto dependencyCount: 29 Package: flowVS Version: 1.22.4 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: a73224182d96e1356b2605c36a53d277 NeedsCompilation: no Title: Variance stabilization in flow cytometry (and microarrays) Description: Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowVS git_branch: RELEASE_3_12 git_last_commit: a7adc64 git_last_commit_date: 2021-05-03 Date/Publication: 2021-05-03 source.ver: src/contrib/flowVS_1.22.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowVS_1.22.4.zip mac.binary.ver: bin/macosx/contrib/4.0/flowVS_1.22.4.tgz vignettes: vignettes/flowVS/inst/doc/flowVS.pdf vignetteTitles: flowVS: Cell population matching and meta-clustering in Flow Cytometry hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowVS/inst/doc/flowVS.R dependencyCount: 106 Package: flowWorkspace Version: 4.2.0 Depends: R (>= 3.5.0) Imports: Biobase, BiocGenerics, cytolib (>= 2.1.20), lattice, latticeExtra, XML, ggplot2, graph, graphics, grDevices, methods, stats, stats4, utils, RBGL, tools, Rgraphviz, data.table, dplyr, Rcpp, scales, matrixStats, RcppParallel, RProtoBufLib, digest, aws.s3, aws.signature, flowCore(>= 2.1.1), ncdfFlow(>= 2.25.4) LinkingTo: Rcpp, BH(>= 1.62.0-1), RProtoBufLib(>= 1.99.4), cytolib (>= 2.1.15),Rhdf5lib, RcppArmadillo, RcppParallel(>= 4.4.2-1) Suggests: testthat, flowWorkspaceData (>= 2.23.2), knitr, ggcyto, parallel, CytoML, openCyto License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: be33b717762a7ec832b28fefad7c671a NeedsCompilation: yes Title: Infrastructure for representing and interacting with gated and ungated cytometry data sets. Description: This package is designed to facilitate comparison of automated gating methods against manual gating done in flowJo. This package allows you to import basic flowJo workspaces into BioConductor and replicate the gating from flowJo using the flowCore functionality. Gating hierarchies, groups of samples, compensation, and transformation are performed so that the output matches the flowJo analysis. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak ,Mike Jiang ,Jake Wagner SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/flowWorkspace git_branch: RELEASE_3_12 git_last_commit: 6af477a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/flowWorkspace_4.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/flowWorkspace_4.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/flowWorkspace_4.2.0.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.html, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html vignetteTitles: flowWorkspace Introduction: A Package to store and maninpulate gated flow data, How to merge GatingSets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace-Introduction.R, vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R dependsOnMe: ggcyto, highthroughputassays importsMe: CytoML, flowDensity, FlowSOM, flowStats, ImmuneSpaceR, PeacoQC suggestsMe: CATALYST, COMPASS, flowClust, flowCore linksToMe: CytoML dependencyCount: 78 Package: fmcsR Version: 1.32.0 Depends: R (>= 2.10.0), ChemmineR, methods Imports: RUnit, methods, ChemmineR, BiocGenerics, parallel Suggests: BiocStyle, knitr, knitcitations, knitrBootstrap License: Artistic-2.0 Archs: i386, x64 MD5sum: 90102f4c859c761a4033f89cf19ece45 NeedsCompilation: yes Title: Mismatch Tolerant Maximum Common Substructure Searching Description: The fmcsR package introduces an efficient maximum common substructure (MCS) algorithms combined with a novel matching strategy that allows for atom and/or bond mismatches in the substructures shared among two small molecules. The resulting flexible MCSs (FMCSs) are often larger than strict MCSs, resulting in the identification of more common features in their source structures, as well as a higher sensitivity in finding compounds with weak structural similarities. The fmcsR package provides several utilities to use the FMCS algorithm for pairwise compound comparisons, structure similarity searching and clustering. biocViews: Cheminformatics, BiomedicalInformatics, Pharmacogenetics, Pharmacogenomics, MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Clustering, Proteomics, Metabolomics Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/fmcsR git_branch: RELEASE_3_12 git_last_commit: 91d402d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fmcsR_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fmcsR_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fmcsR_1.32.0.tgz vignettes: vignettes/fmcsR/inst/doc/fmcsR.html vignetteTitles: fmcsR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R importsMe: Rcpi, BioMedR suggestsMe: ChemmineR, xnet dependencyCount: 61 Package: fmrs Version: 1.0.0 Depends: R (>= 4.0.0) Imports: methods, survival, stats Suggests: BiocGenerics, testthat, knitr, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: b3e3bcd35726c41889e5948c8cfb0a14 NeedsCompilation: yes Title: Variable Selection in Finite Mixture of AFT Regression and FMR Description: Provides parameter estimation as well as variable selection in Finite Mixture of Accelerated Failure Time Regression and Finite Mixture of Regression Models. Furthermore, this package provides Ridge Regression and Elastic Net. biocViews: Survival, Regression, DimensionReduction Author: Farhad Shokoohi [aut, cre] () Maintainer: Farhad Shokoohi VignetteBuilder: knitr BugReports: https://github.com/shokoohi/fmrs/issues git_url: https://git.bioconductor.org/packages/fmrs git_branch: RELEASE_3_12 git_last_commit: 3e2efc3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/fmrs_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/fmrs_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/fmrs_1.0.0.tgz vignettes: vignettes/fmrs/inst/doc/usingfmrs.html vignetteTitles: Using fmrs package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmrs/inst/doc/usingfmrs.R dependencyCount: 10 Package: FoldGO Version: 1.8.0 Depends: R (>= 4.0) Imports: topGO (>= 2.30.1), ggplot2 (>= 2.2.1), tidyr (>= 0.8.0), stats, methods Suggests: knitr, rmarkdown, devtools, kableExtra License: GPL-3 MD5sum: c5d8ef0a8ebfa9be4086b98e85dcc453 NeedsCompilation: no Title: Package for Fold-specific GO Terms Recognition Description: FoldGO is a package designed to annotate gene sets derived from expression experiments and identify fold-change-specific GO terms. biocViews: DifferentialExpression, GeneExpression, GO, Software Author: Daniil Wiebe [aut, cre] Maintainer: Daniil Wiebe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FoldGO git_branch: RELEASE_3_12 git_last_commit: 79fa689 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/FoldGO_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FoldGO_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FoldGO_1.8.0.tgz vignettes: vignettes/FoldGO/inst/doc/vignette.html vignetteTitles: FoldGO: a tool for fold-change-specific functional enrichment analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FoldGO/inst/doc/vignette.R dependencyCount: 66 Package: FourCSeq Version: 1.24.0 Depends: R (>= 3.0), splines, LSD, DESeq2 (>= 1.9.11), ggplot2 Imports: Biobase, Biostrings, GenomicRanges, SummarizedExperiment, Rsamtools, ggbio, reshape2, rtracklayer, fda, GenomicAlignments, gtools, Matrix, methods Suggests: BiocStyle, knitr, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) MD5sum: 5492bd20dfc4921e9f47acca28983f09 NeedsCompilation: no Title: Package analyse 4C sequencing data Description: FourCSeq is an R package dedicated to the analysis of (multiplexed) 4C sequencing data. The package provides a pipeline to detect specific interactions between DNA elements and identify differential interactions between conditions. The statistical analysis in R starts with individual bam files for each sample as inputs. To obtain these files, the package contains a python script (extdata/python/demultiplex.py) to demultiplex libraries and trim off primer sequences. With a standard alignment software the required bam files can be then be generated. biocViews: Software, Preprocessing, Sequencing Author: Felix A. Klein [aut], Mike Smith [cre] Maintainer: Mike Smith VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/FourCSeq git_branch: RELEASE_3_12 git_last_commit: 5575105 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FourCSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FourCSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FourCSeq_1.24.0.tgz vignettes: vignettes/FourCSeq/inst/doc/FourCSeq.pdf vignetteTitles: FourCSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FourCSeq/inst/doc/FourCSeq.R dependencyCount: 174 Package: FRASER Version: 1.2.1 Depends: BiocParallel, data.table, Rsamtools, SummarizedExperiment Imports: AnnotationDbi, BBmisc, Biobase, BiocGenerics, biomaRt, BSgenome, cowplot, DelayedArray (>= 0.5.11), DelayedMatrixStats, extraDistr, generics, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, IRanges, grDevices, ggplot2, ggrepel, HDF5Array, matrixStats, methods, OUTRIDER, pcaMethods, pheatmap, plotly, PRROC, RColorBrewer, rhdf5, Rsubread, R.utils, S4Vectors, stats, tibble, tools, utils, VGAM LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, testthat, covr, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, License: MIT + file LICENSE Archs: i386, x64 MD5sum: 32b2a81cd139970819354d8597923516 NeedsCompilation: yes Title: Find RAre Splicing Events in RNA-Seq Data Description: Detection of rare aberrant splicing events in transcriptome profiles. The workflow aims to assist the diagnostics in the field of rare diseases where RNA-seq is performed to identify aberrant splicing defects. biocViews: RNASeq, AlternativeSplicing, Sequencing, Software, Genetics, Coverage Author: Christian Mertes [aut, cre], Ines Scheller [aut], Vicente Yepez [ctb], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/FRASER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/FRASER git_branch: RELEASE_3_12 git_last_commit: 9160638 git_last_commit_date: 2021-01-31 Date/Publication: 2021-02-01 source.ver: src/contrib/FRASER_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/FRASER_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/FRASER_1.2.1.tgz vignettes: vignettes/FRASER/inst/doc/FRASER.pdf vignetteTitles: FRASER: Find RAre Splicing Evens in RNA-seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FRASER/inst/doc/FRASER.R dependencyCount: 170 Package: frenchFISH Version: 1.2.0 Imports: utils, MCMCpack, NHPoisson Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 2ec382232e7be13a1578d07482ff99db NeedsCompilation: no Title: Poisson Models for Quantifying DNA Copy-number from FISH Images of Tissue Sections Description: FrenchFISH comprises a nuclear volume correction method coupled with two types of Poisson models: either a Poisson model for improved manual spot counting without the need for control probes; or a homogenous Poisson Point Process model for automated spot counting. biocViews: Software, BiomedicalInformatics, CellBiology, Genetics, HiddenMarkovModel, Preprocessing Author: Adam Berman, Geoff Macintyre Maintainer: Adam Berman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/frenchFISH git_branch: RELEASE_3_12 git_last_commit: 9a75ff7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/frenchFISH_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/frenchFISH_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/frenchFISH_1.2.0.tgz vignettes: vignettes/frenchFISH/inst/doc/frenchFISH.html vignetteTitles: Correcting FISH probe counts with frenchFISH hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frenchFISH/inst/doc/frenchFISH.R dependencyCount: 88 Package: FRGEpistasis Version: 1.26.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 6820892ff7d8249f6ff4d8e0b38e8781 NeedsCompilation: no Title: Epistasis Analysis for Quantitative Traits by Functional Regression Model Description: A Tool for Epistasis Analysis Based on Functional Regression Model biocViews: Genetics, NetworkInference, GeneticVariability, Software Author: Futao Zhang Maintainer: Futao Zhang git_url: https://git.bioconductor.org/packages/FRGEpistasis git_branch: RELEASE_3_12 git_last_commit: 78212d5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FRGEpistasis_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FRGEpistasis_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FRGEpistasis_1.26.0.tgz vignettes: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.pdf vignetteTitles: FRGEpistasis: A Tool for Epistasis Analysis Based on Functional Regression Model hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FRGEpistasis/inst/doc/FRGEpistasis.R dependencyCount: 59 Package: frma Version: 1.42.0 Depends: R (>= 2.10.0), Biobase (>= 2.6.0) Imports: Biobase, MASS, DBI, affy, methods, oligo, oligoClasses, preprocessCore, utils, BiocGenerics Suggests: hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: eaced8f5f131c10e1e54909725803c58 NeedsCompilation: no Title: Frozen RMA and Barcode Description: Preprocessing and analysis for single microarrays and microarray batches. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry , with contributions from Terry Therneau Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frma git_branch: RELEASE_3_12 git_last_commit: 4a819d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/frma_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/frma_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/frma_1.42.0.tgz vignettes: vignettes/frma/inst/doc/frma.pdf vignetteTitles: frma: Preprocessing for single arrays and array batches hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frma/inst/doc/frma.R importsMe: ChIPXpress, rat2302frmavecs, DeSousa2013 suggestsMe: frmaTools, antiProfilesData dependencyCount: 56 Package: frmaTools Version: 1.42.0 Depends: R (>= 2.10.0), affy Imports: Biobase, DBI, methods, preprocessCore, stats, utils Suggests: oligo, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, frma, affyPLM, hgu133aprobe, hgu133atagprobe, hgu133plus2probe, hgu133acdf, hgu133atagcdf, hgu133plus2cdf, hgu133afrmavecs, frmaExampleData License: GPL (>= 2) MD5sum: eca8db3f76c92b3f999638518c93e938 NeedsCompilation: no Title: Frozen RMA Tools Description: Tools for advanced use of the frma package. biocViews: Software, Microarray, Preprocessing Author: Matthew N. McCall , Rafael A. Irizarry Maintainer: Matthew N. McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/frmaTools git_branch: RELEASE_3_12 git_last_commit: 07b8c0b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/frmaTools_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/frmaTools_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/frmaTools_1.42.0.tgz vignettes: vignettes/frmaTools/inst/doc/frmaTools.pdf vignetteTitles: frmaTools: Create packages containing the vectors used by frma. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/frmaTools/inst/doc/frmaTools.R importsMe: DeSousa2013 dependencyCount: 14 Package: FScanR Version: 1.0.0 Depends: R (>= 4.0) Imports: stats Suggests: knitr, rmarkdown License: Artistic-2.0 MD5sum: 8c77b3cad22017d725596f9d8fb72140 NeedsCompilation: no Title: Detect Programmed Ribosomal Frameshifting Events from mRNA/cDNA BLASTX Output Description: 'FScanR' identifies Programmed Ribosomal Frameshifting (PRF) events from BLASTX homolog sequence alignment between targeted genomic/cDNA/mRNA sequences against the peptide library of the same species or a close relative. The output by BLASTX or diamond BLASTX will be used as input of 'FScanR' and should be in a tabular format with 14 columns. For BLASTX, the output parameter should be: -outfmt '6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe sframe'. For diamond BLASTX, the output parameter should be: -outfmt 6 qseqid sseqid pident length mismatch gapopen qstart qend sstart send evalue bitscore qframe qframe. biocViews: Alignment, Annotation, Software Author: Xiao Chen [aut, cre] () Maintainer: Xiao Chen VignetteBuilder: knitr BugReports: https://github.com/seanchen607/FScanR/issues git_url: https://git.bioconductor.org/packages/FScanR git_branch: RELEASE_3_12 git_last_commit: 200ef80 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FScanR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FScanR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FScanR_1.0.0.tgz vignettes: vignettes/FScanR/inst/doc/FScanR.html vignetteTitles: FScanR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FScanR/inst/doc/FScanR.R dependencyCount: 1 Package: FunChIP Version: 1.16.0 Depends: R (>= 3.2), GenomicRanges Imports: shiny, fda, doParallel, GenomicAlignments, Rcpp, methods, foreach, parallel, GenomeInfoDb, Rsamtools, grDevices, graphics, stats, RColorBrewer LinkingTo: Rcpp License: Artistic-2.0 Archs: i386, x64 MD5sum: 06414423147913abaf1f66848651f58b NeedsCompilation: yes Title: Clustering and Alignment of ChIP-Seq peaks based on their shapes Description: Preprocessing and smoothing of ChIP-Seq peaks and efficient implementation of the k-mean alignment algorithm to classify them. biocViews: StatisticalMethod, Clustering, ChIPSeq Author: Alice Parodi [aut, cre], Marco Morelli [aut, cre], Laura M. Sangalli [aut], Piercesare Secchi [aut], Simone Vantini [aut] Maintainer: Alice Parodi git_url: https://git.bioconductor.org/packages/FunChIP git_branch: RELEASE_3_12 git_last_commit: 477b137 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FunChIP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/FunChIP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/FunChIP_1.16.0.tgz vignettes: vignettes/FunChIP/inst/doc/FunChIP.pdf vignetteTitles: An introduction to FunChIP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunChIP/inst/doc/FunChIP.R dependencyCount: 108 Package: FunciSNP Version: 1.34.0 Depends: R (>= 2.14.0), ggplot2, TxDb.Hsapiens.UCSC.hg19.knownGene, FunciSNP.data Imports: methods, BiocGenerics, Biobase, S4Vectors, IRanges, GenomicRanges, Rsamtools (>= 1.6.1), rtracklayer (>= 1.14.1), ChIPpeakAnno (>= 2.2.0), VariantAnnotation, plyr, snpStats, ggplot2 (>= 0.9.0), reshape (>= 0.8.4), scales Suggests: org.Hs.eg.db Enhances: parallel License: GPL-3 MD5sum: a92933f233ac7c60c9685c23889803a1 NeedsCompilation: no Title: Integrating Functional Non-coding Datasets with Genetic Association Studies to Identify Candidate Regulatory SNPs Description: FunciSNP integrates information from GWAS, 1000genomes and chromatin feature to identify functional SNP in coding or non-coding regions. biocViews: Infrastructure, DataRepresentation, DataImport, SequenceMatching, Annotation Author: Simon G. Coetzee and Houtan Noushmehr, PhD Maintainer: Simon G. Coetzee URL: http://coetzeeseq.usc.edu/publication/Coetzee_SG_et_al_2012/ PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/FunciSNP git_branch: RELEASE_3_12 git_last_commit: f3222c1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/FunciSNP_1.34.0.tar.gz vignettes: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.pdf vignetteTitles: FunciSNP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.R dependencyCount: 123 Package: funtooNorm Version: 1.14.0 Depends: R(>= 3.4) Imports: pls, matrixStats, minfi, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, GenomeInfoDb, grDevices, graphics, stats Suggests: prettydoc, minfiData, knitr, rmarkdown License: GPL-3 MD5sum: 5910f7ed08b524cfe60649838949c8d5 NeedsCompilation: no Title: Normalization Procedure for Infinium HumanMethylation450 BeadChip Kit Description: Provides a function to normalize Illumina Infinium Human Methylation 450 BeadChip (Illumina 450K), correcting for tissue and/or cell type. biocViews: DNAMethylation, Preprocessing, Normalization Author: Celia Greenwood ,Stepan Grinek , Maxime Turgeon , Kathleen Klein Maintainer: Kathleen Klein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/funtooNorm git_branch: RELEASE_3_12 git_last_commit: 66254f5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/funtooNorm_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/funtooNorm_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/funtooNorm_1.14.0.tgz vignettes: vignettes/funtooNorm/inst/doc/funtooNorm.pdf vignetteTitles: Normalizing Illumina Infinium Human Methylation 450k for multiple cell types with funtooNorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/funtooNorm/inst/doc/funtooNorm.R dependencyCount: 133 Package: GA4GHclient Version: 1.14.0 Depends: S4Vectors Imports: BiocGenerics, Biostrings, dplyr, GenomeInfoDb, GenomicRanges, httr, IRanges, jsonlite, methods, VariantAnnotation Suggests: AnnotationDbi, BiocStyle, DT, knitr, org.Hs.eg.db, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: 740ecf0af30dbf1903d1198e20ee4b63 NeedsCompilation: no Title: A Bioconductor package for accessing GA4GH API data servers Description: GA4GHclient provides an easy way to access public data servers through Global Alliance for Genomics and Health (GA4GH) genomics API. It provides low-level access to GA4GH API and translates response data into Bioconductor-based class objects. biocViews: DataRepresentation, ThirdPartyClient Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHclient VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHclient/issues git_url: https://git.bioconductor.org/packages/GA4GHclient git_branch: RELEASE_3_12 git_last_commit: e012515 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GA4GHclient_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GA4GHclient_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GA4GHclient_1.14.0.tgz vignettes: vignettes/GA4GHclient/inst/doc/GA4GHclient.html vignetteTitles: GA4GHclient hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHclient/inst/doc/GA4GHclient.R dependsOnMe: GA4GHshiny dependencyCount: 90 Package: GA4GHshiny Version: 1.12.0 Depends: GA4GHclient Imports: AnnotationDbi, BiocGenerics, dplyr, DT, GenomeInfoDb, openxlsx, GenomicFeatures, methods, purrr, S4Vectors, shiny, shinyjs, tidyr, shinythemes Suggests: BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 47501914a03a433061a41b755fe42fec NeedsCompilation: no Title: Shiny application for interacting with GA4GH-based data servers Description: GA4GHshiny package provides an easy way to interact with data servers based on Global Alliance for Genomics and Health (GA4GH) genomics API through a Shiny application. It also integrates with Beacon Network. biocViews: GUI Author: Welliton Souza [aut, cre], Benilton Carvalho [ctb], Cristiane Rocha [ctb], Elizabeth Borgognoni [ctb] Maintainer: Welliton Souza URL: https://github.com/labbcb/GA4GHshiny VignetteBuilder: knitr BugReports: https://github.com/labbcb/GA4GHshiny/issues git_url: https://git.bioconductor.org/packages/GA4GHshiny git_branch: RELEASE_3_12 git_last_commit: e1efc4e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GA4GHshiny_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GA4GHshiny_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GA4GHshiny_1.12.0.tgz vignettes: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.html vignetteTitles: GA4GHshiny hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GA4GHshiny/inst/doc/GA4GHshiny.R dependencyCount: 116 Package: gaga Version: 2.36.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: c0b4538abe8abc08a433b7dcab029ef3 NeedsCompilation: yes Title: GaGa hierarchical model for high-throughput data analysis Description: Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). biocViews: ImmunoOncology, OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell git_url: https://git.bioconductor.org/packages/gaga git_branch: RELEASE_3_12 git_last_commit: a81789d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gaga_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gaga_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gaga_2.36.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaga/inst/doc/gagamanual.R importsMe: casper dependencyCount: 17 Package: gage Version: 2.40.2 Depends: R (>= 3.5.0) Imports: graph, KEGGREST, AnnotationDbi, GO.db Suggests: pathview, gageData, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: 0281c966169258b695df8b9678e4af32 NeedsCompilation: no Title: Generally Applicable Gene-set Enrichment for Pathway Analysis Description: GAGE is a published method for gene set (enrichment or GSEA) or pathway analysis. GAGE is generally applicable independent of microarray or RNA-Seq data attributes including sample sizes, experimental designs, assay platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods. biocViews: Pathways, GO, DifferentialExpression, Microarray, OneChannel, TwoChannel, RNASeq, Genetics, MultipleComparison, GeneSetEnrichment, GeneExpression, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/gage, http://www.biomedcentral.com/1471-2105/10/161 git_url: https://git.bioconductor.org/packages/gage git_branch: RELEASE_3_12 git_last_commit: c9a5cac git_last_commit_date: 2021-04-29 Date/Publication: 2021-04-30 source.ver: src/contrib/gage_2.40.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/gage_2.40.2.zip mac.binary.ver: bin/macosx/contrib/4.0/gage_2.40.2.tgz vignettes: vignettes/gage/inst/doc/dataPrep.pdf, vignettes/gage/inst/doc/gage.pdf, vignettes/gage/inst/doc/RNA-seqWorkflow.pdf vignetteTitles: Gene set and data preparation, Generally Applicable Gene-set/Pathway Analysis, RNA-Seq Data Pathway and Gene-set Analysis Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gage/inst/doc/dataPrep.R, vignettes/gage/inst/doc/gage.R, vignettes/gage/inst/doc/RNA-seqWorkflow.R dependsOnMe: EGSEA importsMe: exp2flux suggestsMe: FGNet, pathview, SBGNview, gageData dependencyCount: 44 Package: gaggle Version: 1.58.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: c2e2ed2edbf687c0bdc80eddbbdd4574 NeedsCompilation: no Title: Broadcast data between R and Gaggle Description: This package contains functions enabling data exchange between R and Gaggle enabled bioinformatics software, including Cytoscape, Firegoose and Gaggle Genome Browser. biocViews: ThirdPartyClient, Visualization, Annotation, GraphAndNetwork, DataImport Author: Paul Shannon Maintainer: Christopher Bare URL: http://gaggle.systemsbiology.net/docs/geese/r/ git_url: https://git.bioconductor.org/packages/gaggle git_branch: RELEASE_3_12 git_last_commit: 6f6cb16 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gaggle_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gaggle_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gaggle_1.58.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaggle/inst/doc/gaggle.R dependencyCount: 10 Package: gaia Version: 2.34.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: f378fcd885a35e90aa0afa2674c0ce52 NeedsCompilation: no Title: GAIA: An R package for genomic analysis of significant chromosomal aberrations. Description: This package allows to assess the statistical significance of chromosomal aberrations. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella et al. Maintainer: S. Morganella git_url: https://git.bioconductor.org/packages/gaia git_branch: RELEASE_3_12 git_last_commit: 161a74e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gaia_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gaia_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gaia_2.34.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaia/inst/doc/gaia.R importsMe: TCGAWorkflow dependencyCount: 0 Package: GAPGOM Version: 1.6.0 Depends: R (>= 4.0) Imports: stats, utils, methods, Matrix, fastmatch, plyr, dplyr, magrittr, data.table, igraph, graph, RBGL, GO.db, org.Hs.eg.db, org.Mm.eg.db, GOSemSim, GEOquery, AnnotationDbi, Biobase, BiocFileCache, matrixStats Suggests: org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Dr.eg.db, org.Ce.eg.db, org.At.tair.db, org.EcK12.eg.db, org.Bt.eg.db, org.Cf.eg.db, org.Ag.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Pt.eg.db, org.Pf.plasmo.db, org.Mmu.eg.db, org.Ss.eg.db, org.Xl.eg.db, testthat, pryr, knitr, rmarkdown, prettydoc, ggplot2, kableExtra, profvis, reshape2 License: MIT + file LICENSE MD5sum: 770ebdd2fdbf5e8fd8fed6b9b5cbdaa4 NeedsCompilation: no Title: GAPGOM (novel Gene Annotation Prediction and other GO Metrics) Description: Collection of various measures and tools for lncRNA annotation prediction put inside a redistributable R package. The package contains two main algorithms; lncRNA2GOA and TopoICSim. lncRNA2GOA tries to annotate novel genes (in this specific case lncRNAs) by using various correlation/geometric scoring methods on correlated expression data. After correlating/scoring, the results are annotated and enriched. TopoICSim is a topologically based method, that compares gene similarity based on the topology of the GO DAG by information content (IC) between GO terms. biocViews: GO, GeneExpression, GenePrediction Author: Rezvan Ehsani [aut, cre], Casper van Mourik [aut], Finn Drabløs [aut] Maintainer: Rezvan Ehsani URL: https://github.com/Berghopper/GAPGOM/ VignetteBuilder: knitr BugReports: https://github.com/Berghopper/GAPGOM/issues/ git_url: https://git.bioconductor.org/packages/GAPGOM git_branch: RELEASE_3_12 git_last_commit: 48b8298 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GAPGOM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GAPGOM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GAPGOM_1.6.0.tgz vignettes: vignettes/GAPGOM/inst/doc/benchmarks.html, vignettes/GAPGOM/inst/doc/GAPGOM.html vignetteTitles: Benchmarks and other GO similarity methods, An Introduction to GAPGOM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GAPGOM/inst/doc/benchmarks.R, vignettes/GAPGOM/inst/doc/GAPGOM.R dependencyCount: 76 Package: GAprediction Version: 1.16.0 Depends: R (>= 3.3) Imports: glmnet, stats, utils, Matrix Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: d1f8a0acfe53581a1bd7e7e897ebbe7a NeedsCompilation: no Title: Prediction of gestational age with Illumina HumanMethylation450 data Description: [GAprediction] predicts gestational age using Illumina HumanMethylation450 CpG data. biocViews: ImmunoOncology, DNAMethylation, Epigenetics, Regression, BiomedicalInformatics Author: Jon Bohlin Maintainer: Jon Bohlin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GAprediction git_branch: RELEASE_3_12 git_last_commit: c063013 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GAprediction_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GAprediction_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GAprediction_1.16.0.tgz vignettes: vignettes/GAprediction/inst/doc/GAprediction.html vignetteTitles: GAprediction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GAprediction/inst/doc/GAprediction.R dependencyCount: 15 Package: garfield Version: 1.18.0 Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 13c5a91c34a83f29da87bb54cd2ab0aa NeedsCompilation: yes Title: GWAS Analysis of Regulatory or Functional Information Enrichment with LD correction Description: GARFIELD is a non-parametric functional enrichment analysis approach described in the paper GARFIELD: GWAS analysis of regulatory or functional information enrichment with LD correction. Briefly, it is a method that leverages GWAS findings with regulatory or functional annotations (primarily from ENCODE and Roadmap epigenomics data) to find features relevant to a phenotype of interest. It performs greedy pruning of GWAS SNPs (LD r2 > 0.1) and then annotates them based on functional information overlap. Next, it quantifies Fold Enrichment (FE) at various GWAS significance cutoffs and assesses them by permutation testing, while matching for minor allele frequency, distance to nearest transcription start site and number of LD proxies (r2 > 0.8). biocViews: Software, StatisticalMethod, Annotation, FunctionalPrediction, GenomeAnnotation Author: Sandro Morganella Maintainer: Valentina Iotchkova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/garfield git_branch: RELEASE_3_12 git_last_commit: 2f32716 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/garfield_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/garfield_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/garfield_1.18.0.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 0 Package: GARS Version: 1.10.0 Depends: R (>= 3.5), ggplot2, cluster Imports: DaMiRseq, MLSeq, stats, methods, SummarizedExperiment Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: 03a7202a386e29633d01b5b3be6912ce NeedsCompilation: no Title: GARS: Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets Description: Feature selection aims to identify and remove redundant, irrelevant and noisy variables from high-dimensional datasets. Selecting informative features affects the subsequent classification and regression analyses by improving their overall performances. Several methods have been proposed to perform feature selection: most of them relies on univariate statistics, correlation, entropy measurements or the usage of backward/forward regressions. Herein, we propose an efficient, robust and fast method that adopts stochastic optimization approaches for high-dimensional. GARS is an innovative implementation of a genetic algorithm that selects robust features in high-dimensional and challenging datasets. biocViews: Classification, FeatureExtraction, Clustering Author: Mattia Chiesa , Luca Piacentini Maintainer: Mattia Chiesa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GARS git_branch: RELEASE_3_12 git_last_commit: 81e74b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GARS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GARS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GARS_1.10.0.tgz vignettes: vignettes/GARS/inst/doc/GARS.pdf vignetteTitles: GARS: a Genetic Algorithm for the identification of Robust Subsets of variables in high-dimensional and challenging datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GARS/inst/doc/GARS.R dependencyCount: 248 Package: GateFinder Version: 1.10.0 Imports: splancs, mvoutlier, methods, stats, diptest, flowCore, flowFP Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 0cfff442a0d642f38043c15b7ae656d9 NeedsCompilation: no Title: Projection-based Gating Strategy Optimization for Flow and Mass Cytometry Description: Given a vector of cluster memberships for a cell population, identifies a sequence of gates (polygon filters on 2D scatter plots) for isolation of that cell type. biocViews: ImmunoOncology, FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour , Erin F. Simonds Maintainer: Nima Aghaeepour git_url: https://git.bioconductor.org/packages/GateFinder git_branch: RELEASE_3_12 git_last_commit: c870840 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GateFinder_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GateFinder_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GateFinder_1.10.0.tgz vignettes: vignettes/GateFinder/inst/doc/GateFinder.pdf vignetteTitles: GateFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GateFinder/inst/doc/GateFinder.R dependencyCount: 163 Package: gcapc Version: 1.14.0 Depends: R (>= 3.4) Imports: BiocGenerics, GenomeInfoDb, S4Vectors, IRanges, Biostrings, BSgenome, GenomicRanges, Rsamtools, GenomicAlignments, matrixStats, MASS, splines, grDevices, graphics, stats, methods Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: GPL-3 MD5sum: 0c8372ccd4c8e3c4d259c9345f0e4ab3 NeedsCompilation: no Title: GC Aware Peak Caller Description: Peak calling for ChIP-seq data with consideration of potential GC bias in sequencing reads. GC bias is first estimated with generalized linear mixture models using effective GC strategy, then applied into peak significance estimation. biocViews: Sequencing, ChIPSeq, BatchEffect, PeakDetection Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/gcapc VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gcapc git_branch: RELEASE_3_12 git_last_commit: 6c7d83d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gcapc_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gcapc_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gcapc_1.14.0.tgz vignettes: vignettes/gcapc/inst/doc/gcapc.html vignetteTitles: The gcapc user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcapc/inst/doc/gcapc.R dependencyCount: 43 Package: gcatest Version: 1.20.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 94680e6cd0cb4483be408cfbac991b87 NeedsCompilation: yes Title: Genotype Conditional Association TEST Description: GCAT is an association test for genome wide association studies that controls for population structure under a general class of trait. models. biocViews: SNP, DimensionReduction, PrincipalComponent, GenomeWideAssociation Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/gcatest VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/gcatest/issues git_url: https://git.bioconductor.org/packages/gcatest git_branch: RELEASE_3_12 git_last_commit: b459b97 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gcatest_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gcatest_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gcatest_1.20.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gcatest/inst/doc/gcatest.R dependencyCount: 3 Package: gCrisprTools Version: 1.18.0 Depends: R (>= 3.6) Imports: Biobase, limma, RobustRankAggreg, ggplot2, PANTHER.db, rmarkdown, grDevices, graphics, stats, utils, parallel, SummarizedExperiment Suggests: edgeR, knitr, grid, AnnotationDbi, org.Mm.eg.db, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 83bf84e056d4806aead6e73fbc01b924 NeedsCompilation: no Title: Suite of Functions for Pooled Crispr Screen QC and Analysis Description: Set of tools for evaluating pooled high-throughput screening experiments, typically employing CRISPR/Cas9 or shRNA expression cassettes. Contains methods for interrogating library and cassette behavior within an experiment, identifying differentially abundant cassettes, aggregating signals to identify candidate targets for empirical validation, hypothesis testing, and comprehensive reporting. biocViews: ImmunoOncology, CRISPR, PooledScreens, ExperimentalDesign, BiomedicalInformatics, CellBiology, FunctionalGenomics, Pharmacogenomics, Pharmacogenetics, SystemsBiology, DifferentialExpression, GeneSetEnrichment, Genetics, MultipleComparison, Normalization, Preprocessing, QualityControl, RNASeq, Regression, Software, Visualization Author: Russell Bainer, Dariusz Ratman, Steve Lianoglou, Peter Haverty Maintainer: Russell Bainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gCrisprTools git_branch: RELEASE_3_12 git_last_commit: e6168b2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gCrisprTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gCrisprTools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gCrisprTools_1.18.0.tgz vignettes: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.html, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.html vignetteTitles: Example_Workflow_gCrisprTools, gCrisprTools_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCrisprTools/inst/doc/Crispr_example_workflow.R, vignettes/gCrisprTools/inst/doc/gCrisprTools_Vignette.R dependencyCount: 116 Package: gcrma Version: 2.62.0 Depends: R (>= 2.6.0), affy (>= 1.23.2), graphics, methods, stats, utils Imports: Biobase, affy (>= 1.23.2), affyio (>= 1.13.3), XVector, Biostrings (>= 2.11.32), splines, BiocManager Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL Archs: i386, x64 MD5sum: 85fec1f1891e6571669d15b4bda031b6 NeedsCompilation: yes Title: Background Adjustment Using Sequence Information Description: Background adjustment using sequence information biocViews: Microarray, OneChannel, Preprocessing Author: Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald Jeff Gentry Maintainer: Z. Wu git_url: https://git.bioconductor.org/packages/gcrma git_branch: RELEASE_3_12 git_last_commit: b91bdf5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gcrma_2.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gcrma_2.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gcrma_2.62.0.tgz vignettes: vignettes/gcrma/inst/doc/gcrma2.0.pdf vignetteTitles: gcrma1.2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyILM, affyPLM, bgx, maskBAD, simpleaffy, webbioc importsMe: affycoretools, affylmGUI, simpleaffy suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, panp, aroma.affymetrix dependencyCount: 21 Package: GCSConnection Version: 1.2.0 Depends: R (>= 4.0.0) Imports: Rcpp (>= 1.0.2), httr, googleAuthR, googleCloudStorageR, methods, jsonlite, utils LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 762c6258e3191a9dd364fc92a5705de1 NeedsCompilation: yes Title: Creating R Connection with Google Cloud Storage Description: Create R 'connection' objects to google cloud storage buckets using the Google REST interface. Both read and write connections are supported. The package also provides functions to view and manage files on Google Cloud. biocViews: Infrastructure Author: Jiefei Wang [cre] Maintainer: Jiefei Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GCSConnection git_branch: RELEASE_3_12 git_last_commit: 46a0fb4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GCSConnection_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GCSConnection_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GCSConnection_1.2.0.tgz vignettes: vignettes/GCSConnection/inst/doc/Introduction.html vignetteTitles: quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSConnection/inst/doc/Introduction.R suggestsMe: GCSFilesystem dependencyCount: 32 Package: GCSFilesystem Version: 1.0.0 Depends: R (>= 4.0.0) Imports: stats Suggests: testthat, knitr, rmarkdown, BiocStyle, GCSConnection License: GPL (>= 2) MD5sum: d24edcfd6479e58aafd111a51f9dc895 NeedsCompilation: no Title: Mounting a Google Cloud bucket to a local directory Description: Mounting a Google Cloud bucket to a local directory. The files in the bucket can be viewed and read as if they are locally stored. For using the package, you need to install GCSDokan on Windows or gcsfuse on Linux and MacOs. biocViews: Infrastructure Author: Jiefei Wang [aut, cre] Maintainer: Jiefei Wang SystemRequirements: GCSDokan for Windows, gcsfuse for Linux and macOs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GCSFilesystem git_branch: RELEASE_3_12 git_last_commit: 565da25 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GCSFilesystem_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GCSFilesystem_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GCSFilesystem_1.0.0.tgz vignettes: vignettes/GCSFilesystem/inst/doc/Quick-Start-Guide.html vignetteTitles: Quick-Start-Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 1 Package: GCSscore Version: 1.4.0 Depends: R (>= 3.6) Imports: BiocManager, Biobase, utils, methods, RSQLite, devtools, dplR, stringr, graphics, stats, affxparser, data.table Suggests: siggenes, GEOquery, R.utils License: GPL (>=3) MD5sum: 338fedeeb2078b04903bc6c454d9fd55 NeedsCompilation: no Title: GCSscore: an R package for microarray analysis for Affymetrix/Thermo Fisher arrays Description: For differential expression analysis of 3'IVT and WT-style microarrays from Affymetrix/Thermo-Fisher. Based on S-score algorithm originally described by Zhang et al 2002. biocViews: DifferentialExpression, Microarray, OneChannel, ProprietaryPlatforms, DataImport Author: Guy M. Harris & Shahroze Abbas & Michael F. Miles Maintainer: Guy M. Harris git_url: https://git.bioconductor.org/packages/GCSscore git_branch: RELEASE_3_12 git_last_commit: e8a5fb0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GCSscore_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GCSscore_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GCSscore_1.4.0.tgz vignettes: vignettes/GCSscore/inst/doc/GCSscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GCSscore/inst/doc/GCSscore.R dependencyCount: 103 Package: GDCRNATools Version: 1.10.1 Depends: R (>= 3.5.0) Imports: shiny, jsonlite, rjson, XML, limma, edgeR, DESeq2, clusterProfiler, DOSE, org.Hs.eg.db, biomaRt, survival, survminer, pathview, ggplot2, gplots, DT, GenomicDataCommons, BiocParallel Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 64dcc888ddc6ee1db58e8c2ca42224da NeedsCompilation: no Title: GDCRNATools: an R/Bioconductor package for integrative analysis of lncRNA, mRNA, and miRNA data in GDC Description: This is an easy-to-use package for downloading, organizing, and integrative analyzing RNA expression data in GDC with an emphasis on deciphering the lncRNA-mRNA related ceRNA regulatory network in cancer. Three databases of lncRNA-miRNA interactions including spongeScan, starBase, and miRcode, as well as three databases of mRNA-miRNA interactions including miRTarBase, starBase, and miRcode are incorporated into the package for ceRNAs network construction. limma, edgeR, and DESeq2 can be used to identify differentially expressed genes/miRNAs. Functional enrichment analyses including GO, KEGG, and DO can be performed based on the clusterProfiler and DO packages. Both univariate CoxPH and KM survival analyses of multiple genes can be implemented in the package. Besides some routine visualization functions such as volcano plot, bar plot, and KM plot, a few simply shiny apps are developed to facilitate visualization of results on a local webpage. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, GeneRegulation, GeneTarget, NetworkInference, Survival, Visualization, GeneSetEnrichment, NetworkEnrichment, Network, RNASeq, GO, KEGG Author: Ruidong Li, Han Qu, Shibo Wang, Julong Wei, Le Zhang, Renyuan Ma, Jianming Lu, Jianguo Zhu, Wei-De Zhong, Zhenyu Jia Maintainer: Ruidong Li , Han Qu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GDCRNATools git_branch: RELEASE_3_12 git_last_commit: 0827796 git_last_commit_date: 2020-11-25 Date/Publication: 2020-11-25 source.ver: src/contrib/GDCRNATools_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GDCRNATools_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GDCRNATools_1.10.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GDCRNATools/inst/doc/GDCRNATools.R dependencyCount: 221 Package: GDSArray Version: 1.10.1 Depends: R (>= 3.5), gdsfmt, methods, BiocGenerics, DelayedArray (>= 0.5.32) Imports: tools, S4Vectors (>= 0.17.34), SNPRelate, SeqArray Suggests: testthat, knitr, BiocStyle, BiocManager License: GPL-3 MD5sum: f1a1fae569353157b3353a31d74d8b57 NeedsCompilation: no Title: Representing GDS files as array-like objects Description: GDS files are widely used to represent genotyping or sequence data. The GDSArray package implements the `GDSArray` class to represent nodes in GDS files in a matrix-like representation that allows easy manipulation (e.g., subsetting, mathematical transformation) in _R_. The data remains on disk until needed, so that very large files can be processed. biocViews: Infrastructure, DataRepresentation, Sequencing, GenotypingArray Author: Qian Liu [aut, cre], Martin Morgan [aut], Hervé Pagès [aut], Xiuwen Zheng [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/GDSArray VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GDSArray/issues git_url: https://git.bioconductor.org/packages/GDSArray git_branch: RELEASE_3_12 git_last_commit: 839d7ac git_last_commit_date: 2021-04-01 Date/Publication: 2021-04-01 source.ver: src/contrib/GDSArray_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GDSArray_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GDSArray_1.10.1.tgz vignettes: vignettes/GDSArray/inst/doc/GDSArray.html vignetteTitles: GDSArray: Representing GDS files as array-like objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GDSArray/inst/doc/GDSArray.R dependsOnMe: VariantExperiment importsMe: CNVRanger suggestsMe: DelayedDataFrame dependencyCount: 29 Package: gdsfmt Version: 1.26.1 Depends: R (>= 2.15.0), methods Suggests: parallel, digest, Matrix, RUnit, knitr, crayon, BiocGenerics License: LGPL-3 Archs: i386, x64 MD5sum: 7298b94276856b198364b2817184ba6b NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: Provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files. GDS is portable across platforms with hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers of less than 8 bits, since a diploid genotype, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are available with relatively efficient random access. It is also allowed to read a GDS file in parallel with multiple R processes supported by the package parallel. biocViews: Infrastructure, DataImport Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [ctb], Jean-loup Gailly and Mark Adler [ctb] (for the included zlib sources), Yann Collet [ctb] (for the included LZ4 sources), xz contributors [ctb] (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/gdsfmt/issues git_url: https://git.bioconductor.org/packages/gdsfmt git_branch: RELEASE_3_12 git_last_commit: bd180b2 git_last_commit_date: 2020-12-21 Date/Publication: 2020-12-22 source.ver: src/contrib/gdsfmt_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/gdsfmt_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.0/gdsfmt_1.26.1.tgz vignettes: vignettes/gdsfmt/inst/doc/gdsfmt.html vignetteTitles: Introduction to GDS Format hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt.R dependsOnMe: bigmelon, GDSArray, SAIGEgds, SeqArray, SNPRelate importsMe: CNVRanger, GENESIS, GWASTools, SeqSQC, SeqVarTools, VariantExperiment, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: AnnotationHub, HIBAG, coxmeg linksToMe: SeqArray, SNPRelate dependencyCount: 1 Package: GEM Version: 1.16.0 Depends: R (>= 3.3) Imports: tcltk, ggplot2, methods, stats, grDevices, graphics, utils Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 MD5sum: 4ca6af1b4ecc9c23ef06f03a6c98cb62 NeedsCompilation: no Title: GEM: fast association study for the interplay of Gene, Environment and Methylation Description: Tools for analyzing EWAS, methQTL and GxE genome widely. biocViews: MethylSeq, MethylationArray, GenomeWideAssociation, Regression, DNAMethylation, SNP, GeneExpression, GUI Author: Hong Pan, Joanna D Holbrook, Neerja Karnani, Chee-Keong Kwoh Maintainer: Hong Pan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEM git_branch: RELEASE_3_12 git_last_commit: 4085197 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GEM_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GEM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GEM_1.16.0.tgz vignettes: vignettes/GEM/inst/doc/user_guide.html vignetteTitles: The GEM User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEM/inst/doc/user_guide.R dependencyCount: 39 Package: gemini Version: 1.4.0 Depends: R (>= 3.6.0) Imports: dplyr, grDevices, ggplot2, magrittr, mixtools, scales, pbmcapply, parallel, stats, utils Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: b4c7f9abf9cf968e0248a5d7bd966c93 NeedsCompilation: no Title: GEMINI: Variational inference approach to infer genetic interactions from pairwise CRISPR screens Description: GEMINI uses log-fold changes to model sample-dependent and independent effects, and uses a variational Bayes approach to infer these effects. The inferred effects are used to score and identify genetic interactions, such as lethality and recovery. More details can be found in Zamanighomi et al. 2019 (in press). biocViews: Software, CRISPR, Bayesian, DataImport Author: Mahdi Zamanighomi [aut], Sidharth Jain [aut, cre] Maintainer: Sidharth Jain VignetteBuilder: knitr BugReports: https://github.com/sellerslab/gemini/issues git_url: https://git.bioconductor.org/packages/gemini git_branch: RELEASE_3_12 git_last_commit: ee4db06 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gemini_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gemini_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gemini_1.4.0.tgz vignettes: vignettes/gemini/inst/doc/gemini-quickstart.html vignetteTitles: QuickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gemini/inst/doc/gemini-quickstart.R dependencyCount: 48 Package: genArise Version: 1.66.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 158decd3887c2249f288ca113943e780 NeedsCompilation: no Title: Microarray Analysis tool Description: genArise is an easy to use tool for dual color microarray data. Its GUI-Tk based environment let any non-experienced user performs a basic, but not simple, data analysis just following a wizard. In addition it provides some tools for the developer. biocViews: Microarray, TwoChannel, Preprocessing Author: Ana Patricia Gomez Mayen ,\\ Gustavo Corral Guille , \\ Lina Riego Ruiz ,\\ Gerardo Coello Coutino Maintainer: IFC Development Team URL: http://www.ifc.unam.mx/genarise git_url: https://git.bioconductor.org/packages/genArise git_branch: RELEASE_3_12 git_last_commit: d3d46d5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genArise_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genArise_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genArise_1.66.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genArise/inst/doc/genArise.R dependencyCount: 11 Package: genbankr Version: 1.18.0 Depends: methods Imports: BiocGenerics, IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), GenomicFeatures (>= 1.31.5), Biostrings, VariantAnnotation, rtracklayer, S4Vectors (>= 0.17.28), GenomeInfoDb, Biobase Suggests: RUnit, rentrez, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1c961c48fc499ab946b019fd3e9fa86d NeedsCompilation: no Title: Parsing GenBank files into semantically useful objects Description: Reads Genbank files. biocViews: Infrastructure, DataImport Author: Gabriel Becker [aut, cre], Michael Lawrence [aut] Maintainer: Gabriel Becker VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genbankr git_branch: RELEASE_3_12 git_last_commit: d3c2004 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genbankr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genbankr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genbankr_1.18.0.tgz vignettes: vignettes/genbankr/inst/doc/genbankr.html vignetteTitles: An introduction to genbankr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R importsMe: PACVr dependencyCount: 90 Package: GeneAccord Version: 1.8.0 Depends: R (>= 3.5) Imports: biomaRt, caTools, dplyr, ggplot2, graphics, grDevices, gtools, ggpubr, magrittr, maxLik, RColorBrewer, reshape2, stats, tibble, utils Suggests: assertthat, BiocStyle, devtools, knitr, rmarkdown, testthat License: file LICENSE MD5sum: 17923a6e00f0668a18def047572993dd NeedsCompilation: no Title: Detection of clonally exclusive gene or pathway pairs in a cohort of cancer patients Description: A statistical framework to examine the combinations of clones that co-exist in tumors. More precisely, the algorithm finds pairs of genes that are mutated in the same tumor but in different clones, i.e. their subclonal mutation profiles are mutually exclusive. We refer to this as clonally exclusive. It means that the mutations occurred in different branches of the tumor phylogeny, indicating parallel evolution of the clones. Our statistical framework assesses whether a pattern of clonal exclusivity occurs more often than expected by chance alone across a cohort of patients. The required input data are the mutated gene-to-clone assignments from a cohort of cancer patients, which were obtained by running phylogenetic tree inference methods. Reconstructing the evolutionary history of a tumor and detecting the clones is challenging. For nondeterministic algorithms, repeated tree inference runs may lead to slightly different mutation-to-clone assignments. Therefore, our algorithm was designed to allow the input of multiple gene-to-clone assignments per patient. They may have been generated by repeatedly performing the tree inference, or by sampling from the posterior distribution of trees. The tree inference methods designate the mutations to individual clones. The mutations can then be mapped to genes or pathways. Hence our statistical framework can be applied on the gene level, or on the pathway level to detect clonally exclusive pairs of pathways. If a pair is significantly clonally exclusive, it points towards the fact that this specific clone configuration confers a selective advantage, possibly through synergies between the clones with these mutations. biocViews: BiomedicalInformatics, GeneticVariability, GenomicVariation, SomaticMutation, FunctionalGenomics, Genetics, MathematicalBiology, SystemsBiology, FeatureExtraction, PatternLogic, Pathways Author: Ariane L. Moore, Jack Kuipers and Niko Beerenwinkel Maintainer: Ariane L. Moore URL: https://github.com/cbg-ethz/GeneAccord VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneAccord git_branch: RELEASE_3_12 git_last_commit: 0413942 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneAccord_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneAccord_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneAccord_1.8.0.tgz vignettes: vignettes/GeneAccord/inst/doc/GeneAccord.html vignetteTitles: GeneAccord hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneAccord/inst/doc/GeneAccord.R dependencyCount: 140 Package: GeneAnswers Version: 2.32.0 Depends: R (>= 3.0.0), igraph, RCurl, annotate, Biobase (>= 1.12.0), methods, XML, RSQLite, MASS, Heatplus, RColorBrewer Imports: RBGL, annotate, downloader Suggests: GO.db, KEGG.db, reactome.db, biomaRt, AnnotationDbi, org.Hs.eg.db, org.Rn.eg.db, org.Mm.eg.db, org.Dm.eg.db, graph License: LGPL (>= 2) MD5sum: 054fbcb91a6dfb49e7860927712fc162 NeedsCompilation: no Title: Integrated Interpretation of Genes Description: GeneAnswers provides an integrated tool for biological or medical interpretation of the given one or more groups of genes by means of statistical test. biocViews: Infrastructure, DataRepresentation, Visualization, GraphsAndNetworks Author: Lei Huang, Gang Feng, Pan Du, Tian Xia, Xishu Wang, Jing, Wen, Warren Kibbe and Simon Lin Maintainer: Lei Huang and Gang Feng git_url: https://git.bioconductor.org/packages/GeneAnswers git_branch: RELEASE_3_12 git_last_commit: a310951 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneAnswers_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneAnswers_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneAnswers_2.32.0.tgz vignettes: vignettes/GeneAnswers/inst/doc/geneAnswers.pdf, vignettes/GeneAnswers/inst/doc/getListGIF.pdf vignetteTitles: GeneAnswers, GeneAnswers web-based visualization module hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneAnswers/inst/doc/geneAnswers.R, vignettes/GeneAnswers/inst/doc/getListGIF.R suggestsMe: InterMineR dependencyCount: 54 Package: geneAttribution Version: 1.16.0 Imports: utils, GenomicRanges, org.Hs.eg.db, BiocGenerics, GenomeInfoDb, GenomicFeatures, IRanges, rtracklayer Suggests: TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: b71e2b9bf302c4ff4cff200971734951 NeedsCompilation: no Title: Identification of candidate genes associated with genetic variation Description: Identification of the most likely gene or genes through which variation at a given genomic locus in the human genome acts. The most basic functionality assumes that the closer gene is to the input locus, the more likely the gene is to be causative. Additionally, any empirical data that links genomic regions to genes (e.g. eQTL or genome conformation data) can be used if it is supplied in the UCSC .BED file format. biocViews: SNP, GenePrediction, GenomeWideAssociation, VariantAnnotation, GenomicVariation Author: Arthur Wuster Maintainer: Arthur Wuster VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneAttribution git_branch: RELEASE_3_12 git_last_commit: 4f9fa9a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geneAttribution_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geneAttribution_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geneAttribution_1.16.0.tgz vignettes: vignettes/geneAttribution/inst/doc/geneAttribution.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 89 Package: GeneBreak Version: 1.20.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: 29d73467d2a2796b517f5dee95ce0829 NeedsCompilation: no Title: Gene Break Detection Description: Recurrent breakpoint gene detection on copy number aberration profiles. biocViews: aCGH, CopyNumberVariation, DNASeq, Genetics, Sequencing, WholeGenome, Visualization Author: Evert van den Broek, Stef van Lieshout Maintainer: Evert van den Broek URL: https://github.com/stefvanlieshout/GeneBreak git_url: https://git.bioconductor.org/packages/GeneBreak git_branch: RELEASE_3_12 git_last_commit: 59ce27a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneBreak_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneBreak_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneBreak_1.20.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneBreak/inst/doc/GeneBreak.R dependencyCount: 49 Package: geneClassifiers Version: 1.14.0 Depends: R (>= 3.6.0) Imports: utils, methods, stats, Biobase, BiocGenerics Suggests: testthat License: GPL-2 MD5sum: 72f9baa250308b07610b640178cea39e NeedsCompilation: no Title: Application of gene classifiers Description: This packages aims for easy accessible application of classifiers which have been published in literature using an ExpressionSet as input. biocViews: GeneExpression, BiomedicalInformatics, Classification, Survival, Microarray Author: R Kuiper [cre, aut] () Maintainer: R Kuiper URL: https://doi.org/doi:10.18129/B9.bioc.geneClassifiers BugReports: https://github.com/rkuiper/geneClassifiers/issues git_url: https://git.bioconductor.org/packages/geneClassifiers git_branch: RELEASE_3_12 git_last_commit: 1267ca3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geneClassifiers_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geneClassifiers_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geneClassifiers_1.14.0.tgz vignettes: vignettes/geneClassifiers/inst/doc/geneClassifiers.pdf, vignettes/geneClassifiers/inst/doc/MissingCovariates.pdf vignetteTitles: geneClassifiers introduction, geneClassifiers and missing probesets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneClassifiers/inst/doc/geneClassifiers.R dependencyCount: 7 Package: GeneExpressionSignature Version: 1.36.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: 361ff3ccd392e771fcabf7976380c3e3 NeedsCompilation: no Title: Gene Expression Signature based Similarity Metric Description: This package gives the implementations of the gene expression signature and its distance to each. Gene expression signature is represented as a list of genes whose expression is correlated with a biological state of interest. And its distance is defined using a nonparametric, rank-based pattern-matching strategy based on the Kolmogorov-Smirnov statistic. Gene expression signature and its distance can be used to detect similarities among the signatures of drugs, diseases, and biological states of interest. biocViews: GeneExpression Author: Yang Cao Maintainer: Yang Cao , Fei Li ,Lu Han git_url: https://git.bioconductor.org/packages/GeneExpressionSignature git_branch: RELEASE_3_12 git_last_commit: b183615 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneExpressionSignature_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneExpressionSignature_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneExpressionSignature_1.36.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.pdf vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.R dependencyCount: 8 Package: genefilter Version: 1.72.1 Imports: BiocGenerics (>= 0.31.2), AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, RColorBrewer, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: ebf02b933c3f4e09ed52cdf46f65cb1e NeedsCompilation: yes Title: genefilter: methods for filtering genes from high-throughput experiments Description: Some basic functions for filtering genes. biocViews: Microarray Author: R. Gentleman, V. Carey, W. Huber, F. Hahne Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefilter git_branch: RELEASE_3_12 git_last_commit: b01b00a git_last_commit_date: 2021-01-21 Date/Publication: 2021-01-21 source.ver: src/contrib/genefilter_1.72.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/genefilter_1.72.1.zip mac.binary.ver: bin/macosx/contrib/4.0/genefilter_1.72.1.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf vignetteTitles: Using the genefilter function to filter genes from a microarray dataset, How to find genes whose expression profile is similar to that of specified genes, Additional plots for: Independent filtering increases power for detecting differentially expressed genes,, Bourgon et al.,, PNAS (2010) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefilter/inst/doc/howtogenefilter.R, vignettes/genefilter/inst/doc/howtogenefinder.R, vignettes/genefilter/inst/doc/independent_filtering_plots.R dependsOnMe: cellHTS2, CNTools, GeneMeta, simpleaffy, sva, FlowSorted.Blood.EPIC, Hiiragi2013, maEndToEnd, rnaseqGene, lmQCM, orQA importsMe: a4Base, affyQCReport, ALPS, annmap, arrayQualityMetrics, Category, cbaf, countsimQC, covRNA, DESeq2, DEXSeq, eisa, GGBase, GISPA, GSRI, metaseqR2, methyAnalysis, methylCC, methylumi, minfi, MLInterfaces, mogsa, NBAMSeq, pcaExplorer, PECA, phenoTest, pwrEWAS, Ringo, simpleaffy, spatialHeatmap, tilingArray, XDE, zinbwave, IHWpaper, RNAinteractMAPK, dGAselID, INCATome, MiDA, specmine suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, categoryCompare, ClassifyR, clusterStab, codelink, cola, compcodeR, DelayedArray, EnrichedHeatmap, factDesign, ffpe, GenoGAM, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MMUPHin, npGSEA, oligo, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, TCGAbiolinks, topGO, BloodCancerMultiOmics2017, curatedBladderData, curatedCRCData, curatedOvarianData, ffpeExampleData, gageData, MAQCsubset, RforProteomics, rheumaticConditionWOLLBOLD, Single.mTEC.Transcriptomes, maGUI, rknn, SuperLearner dependencyCount: 44 Package: genefu Version: 2.22.1 Depends: survcomp, mclust, limma, biomaRt, iC10, AIMS, R (>= 2.10) Imports: amap, impute Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival License: Artistic-2.0 MD5sum: 9872ab9e3aece8c66bb30fb9c5f5a08e NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo, Natchar Ratanasirigulchai, Markus S. Schroeder, Laia Pare, Joel S. Parker, Aleix Prat, and Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genefu git_branch: RELEASE_3_12 git_last_commit: a5a0480 git_last_commit_date: 2021-01-25 Date/Publication: 2021-01-26 source.ver: src/contrib/genefu_2.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/genefu_2.22.1.zip mac.binary.ver: bin/macosx/contrib/4.0/genefu_2.22.1.tgz vignettes: vignettes/genefu/inst/doc/genefu.pdf vignetteTitles: genefu An Introduction (HowTo) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R importsMe: consensusOV suggestsMe: GSgalgoR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 92 Package: GeneGA Version: 1.40.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: 6074e3f2dff160650a6460b3796a41da NeedsCompilation: no Title: Design gene based on both mRNA secondary structure and codon usage bias using Genetic algorithm Description: R based Genetic algorithm for gene expression optimization by considering both mRNA secondary structure and codon usage bias, GeneGA includes the information of highly expressed genes of almost 200 genomes. Meanwhile, Vienna RNA Package is needed to ensure GeneGA to function properly. biocViews: GeneExpression Author: Zhenpeng Li and Haixiu Huang Maintainer: Zhenpeng Li URL: http://www.tbi.univie.ac.at/~ivo/RNA/ git_url: https://git.bioconductor.org/packages/GeneGA git_branch: RELEASE_3_12 git_last_commit: c11c129 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneGA_1.40.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/GeneGA_1.40.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGA/inst/doc/GeneGA.R dependencyCount: 25 Package: GeneGeneInteR Version: 1.16.0 Depends: R (>= 4.0) Imports: snpStats, mvtnorm, Rsamtools, igraph, kernlab, FactoMineR, IRanges, GenomicRanges, data.table,grDevices, graphics,stats, utils, methods, GGtools License: GPL (>= 2) Archs: i386, x64 MD5sum: 4797f4fe092dbbc67deaf57ac40f43b9 NeedsCompilation: yes Title: Tools for Testing Gene-Gene Interaction at the Gene Level Description: The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. . biocViews: GenomeWideAssociation, SNP, Genetics, GeneticVariability Author: Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell [ctb], Magalie Houee-Bigot [aut] Maintainer: Mathieu Emily git_url: https://git.bioconductor.org/packages/GeneGeneInteR git_branch: RELEASE_3_12 git_last_commit: b8a4049 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneGeneInteR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneGeneInteR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneGeneInteR_1.16.0.tgz vignettes: vignettes/GeneGeneInteR/inst/doc/GenePair.pdf, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.pdf vignetteTitles: Pairwise interaction tests, GeneGeneInteR Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneGeneInteR/inst/doc/GenePair.R, vignettes/GeneGeneInteR/inst/doc/VignetteGeneGeneInteR_Introduction.R dependencyCount: 208 Package: GeneMeta Version: 1.62.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: 8fa051b6392c078a712fba13fc72fdeb NeedsCompilation: no Title: MetaAnalysis for High Throughput Experiments Description: A collection of meta-analysis tools for analysing high throughput experimental data biocViews: Sequencing, GeneExpression, Microarray Author: Lara Lusa , R. Gentleman, M. Ruschhaupt Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GeneMeta git_branch: RELEASE_3_12 git_last_commit: eb4273f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneMeta_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneMeta_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneMeta_1.62.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneMeta/inst/doc/GeneMeta.R importsMe: XDE suggestsMe: genefu dependencyCount: 45 Package: GeneNetworkBuilder Version: 1.32.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph, htmlwidgets, Rgraphviz, rjson, XML, methods, grDevices, stats, graphics LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, RBGL, knitr, simpIntLists, shiny, STRINGdb, BiocStyle, magick License: GPL (>= 2) Archs: i386, x64 MD5sum: dee74491d886e3cd67bc5015e696fa2d NeedsCompilation: yes Title: GeneNetworkBuilder: a bioconductor package for building regulatory network using ChIP-chip/ChIP-seq data and Gene Expression Data Description: Appliation for discovering direct or indirect targets of transcription factors using ChIP-chip or ChIP-seq, and microarray or RNA-seq gene expression data. Inputting a list of genes of potential targets of one TF from ChIP-chip or ChIP-seq, and the gene expression results, GeneNetworkBuilder generates a regulatory network of the TF. biocViews: Sequencing, Microarray, GraphAndNetwork Author: Jianhong Ou, Haibo Liu, Heidi A Tissenbaum and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneNetworkBuilder git_branch: RELEASE_3_12 git_last_commit: 308e65c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneNetworkBuilder_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneNetworkBuilder_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneNetworkBuilder_1.32.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.html, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.html, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.html vignetteTitles: GeneNetworkBuilder Vignette, Generate Network from a list of gene, Working with BioGRID,, STRING hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder_vignettes.R, vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkFromGenes.R, vignettes/GeneNetworkBuilder/inst/doc/with.BioGRID.STRING.R dependencyCount: 22 Package: GeneOverlap Version: 1.26.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 0d000b167520af90956f4ddd02a5c37f NeedsCompilation: no Title: Test and visualize gene overlaps Description: Test two sets of gene lists and visualize the results. biocViews: MultipleComparison, Visualization Author: Li Shen, Icahn School of Medicine at Mount Sinai Maintainer: Antnio Miguel de Jesus Domingues, Max-Planck Institute for Cell Biology and Genetics URL: http://shenlab-sinai.github.io/shenlab-sinai/ git_url: https://git.bioconductor.org/packages/GeneOverlap git_branch: RELEASE_3_12 git_last_commit: 9569ec4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneOverlap_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneOverlap_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneOverlap_1.26.0.tgz vignettes: vignettes/GeneOverlap/inst/doc/GeneOverlap.pdf vignetteTitles: Testing and visualizing gene overlaps with the "GeneOverlap" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneOverlap/inst/doc/GeneOverlap.R dependencyCount: 9 Package: geneplast Version: 1.16.0 Depends: R (>= 3.3), methods Imports: igraph, snow, ape, grDevices, graphics, stats, utils, data.table Suggests: RTN, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown, Fletcher2013b, geneplast.data.string.v91, ggplot2, ggpubr, plyr License: GPL (>= 2) MD5sum: f8ce18045c174d1c9dcae71099477a0e NeedsCompilation: no Title: Evolutionary and plasticity analysis of orthologous groups Description: Geneplast is designed for evolutionary and plasticity analysis based on orthologous groups distribution in a given species tree. It uses Shannon information theory and orthologs abundance to estimate the Evolutionary Plasticity Index. Additionally, it implements the Bridge algorithm to determine the evolutionary root of a given gene based on its orthologs distribution. biocViews: Genetics, GeneRegulation, SystemsBiology Author: Rodrigo Dalmolin, Mauro Castro Maintainer: Mauro Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/geneplast git_branch: RELEASE_3_12 git_last_commit: 78af113 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geneplast_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geneplast_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geneplast_1.16.0.tgz vignettes: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.html, vignettes/geneplast/inst/doc/geneplast.html vignetteTitles: "Supporting Material for Trefflich2019.", "Geneplast: evolutionary rooting and plasticity analysis." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplast/inst/doc/geneplast_Trefflich2019.R, vignettes/geneplast/inst/doc/geneplast.R suggestsMe: TreeAndLeaf dependencyCount: 18 Package: geneplotter Version: 1.68.0 Depends: R (>= 2.10), methods, Biobase, BiocGenerics, lattice, annotate Imports: AnnotationDbi, graphics, grDevices, grid, RColorBrewer, stats, utils Suggests: Rgraphviz, fibroEset, hgu95av2.db, hu6800.db, hgu133a.db License: Artistic-2.0 MD5sum: b56c0040c88b2a934fab8a2959feb587 NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/geneplotter git_branch: RELEASE_3_12 git_last_commit: f1fea7e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geneplotter_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geneplotter_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geneplotter_1.68.0.tgz vignettes: vignettes/geneplotter/inst/doc/byChroms.pdf, vignettes/geneplotter/inst/doc/visualize.pdf vignetteTitles: How to assemble a chromLocation object, Visualization of Microarray Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneplotter/inst/doc/byChroms.R, vignettes/geneplotter/inst/doc/visualize.R dependsOnMe: HD2013SGI, Hiiragi2013, maEndToEnd importsMe: biocGraph, DESeq2, DEXSeq, IsoGeneGUI, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, EnrichmentBrowser, GOstats, Single.mTEC.Transcriptomes dependencyCount: 42 Package: geneRecommender Version: 1.62.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 5a60c9b9990c633938070a7e982f373c NeedsCompilation: no Title: A gene recommender algorithm to identify genes coexpressed with a query set of genes Description: This package contains a targeted clustering algorithm for the analysis of microarray data. The algorithm can aid in the discovery of new genes with similar functions to a given list of genes already known to have closely related functions. biocViews: Microarray, Clustering Author: Gregory J. Hather , with contributions from Art B. Owen and Terence P. Speed Maintainer: Greg Hather git_url: https://git.bioconductor.org/packages/geneRecommender git_branch: RELEASE_3_12 git_last_commit: 0123f86 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geneRecommender_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geneRecommender_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geneRecommender_1.62.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRecommender/inst/doc/geneRecommender.R dependencyCount: 7 Package: GeneRegionScan Version: 1.46.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: S4Vectors (>= 0.9.25), Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 57c514788d51bd544948a86bdffae2fe NeedsCompilation: no Title: GeneRegionScan Description: A package with focus on analysis of discrete regions of the genome. This package is useful for investigation of one or a few genes using Affymetrix data, since it will extract probe level data using the Affymetrix Power Tools application and wrap these data into a ProbeLevelSet. A ProbeLevelSet directly extends the expressionSet, but includes additional information about the sequence of each probe and the probe set it is derived from. The package includes a number of functions used for plotting these probe level data as a function of location along sequences of mRNA-strands. This can be used for analysis of variable splicing, and is especially well suited for use with exon-array data. biocViews: Microarray, DataImport, SNP, OneChannel, Visualization Author: Lasse Folkersen, Diego Diez Maintainer: Lasse Folkersen git_url: https://git.bioconductor.org/packages/GeneRegionScan git_branch: RELEASE_3_12 git_last_commit: 43d8744 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneRegionScan_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneRegionScan_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneRegionScan_1.46.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.R dependencyCount: 18 Package: geneRxCluster Version: 1.26.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: f44c83d5838e9785f3c001870d3fbea3 NeedsCompilation: yes Title: gRx Differential Clustering Description: Detect Differential Clustering of Genomic Sites such as gene therapy integrations. The package provides some functions for exploring genomic insertion sites originating from two different sources. Possibly, the two sources are two different gene therapy vectors. Vectors are preferred that target sensitive regions less frequently, motivating the search for localized clusters of insertions and comparison of the clusters formed by integration of different vectors. Scan statistics allow the discovery of spatial differences in clustering and calculation of False Discovery Rates (FDRs) providing statistical methods for comparing retroviral vectors. A scan statistic for comparing two vectors using multiple window widths to detect clustering differentials and compute FDRs is implemented here. biocViews: Sequencing, Clustering, Genetics Author: Charles Berry Maintainer: Charles Berry git_url: https://git.bioconductor.org/packages/geneRxCluster git_branch: RELEASE_3_12 git_last_commit: cf69aa4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geneRxCluster_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geneRxCluster_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geneRxCluster_1.26.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geneRxCluster/inst/doc/tutorial.R dependencyCount: 17 Package: GeneSelectMMD Version: 2.34.0 Depends: R (>= 2.13.2), Biobase Imports: MASS, graphics, stats, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: 9ffbbeae12b0e508f030f9a0b137cd0c NeedsCompilation: yes Title: Gene selection based on the marginal distributions of gene profiles that characterized by a mixture of three-component multivariate distributions Description: Gene selection based on a mixture of marginal distributions. biocViews: DifferentialExpression Author: Jarrett Morrow , Weiliang Qiu , Wenqing He , Xiaogang Wang , Ross Lazarus . Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/GeneSelectMMD git_branch: RELEASE_3_12 git_last_commit: d1f684c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneSelectMMD_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneSelectMMD_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneSelectMMD_2.34.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck dependencyCount: 10 Package: GENESIS Version: 2.20.1 Imports: Biobase, BiocGenerics, GWASTools, gdsfmt, GenomicRanges, IRanges, S4Vectors, SeqArray, SeqVarTools, SNPRelate, data.table, foreach, graphics, grDevices, igraph, Matrix, methods, reshape2, stats, utils Suggests: CompQuadForm, COMPoissonReg, poibin, SPAtest, survey, testthat, BiocStyle, knitr, rmarkdown, GWASdata, dplyr, ggplot2, GGally, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: e3f5272a4c43cfa0334ddaafe3a40cdf NeedsCompilation: yes Title: GENetic EStimation and Inference in Structured samples (GENESIS): Statistical methods for analyzing genetic data from samples with population structure and/or relatedness Description: The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015, Gen Epi) and PC-Relate (Conomos et al., 2016, AJHG). PC-AiR performs a Principal Components Analysis on genome-wide SNP data for the detection of population structure in a sample that may contain known or cryptic relatedness. Unlike standard PCA, PC-AiR accounts for relatedness in the sample to provide accurate ancestry inference that is not confounded by family structure. PC-Relate uses ancestry representative principal components to adjust for population structure/ancestry and accurately estimate measures of recent genetic relatedness such as kinship coefficients, IBD sharing probabilities, and inbreeding coefficients. Additionally, functions are provided to perform efficient variance component estimation and mixed model association testing for both quantitative and binary phenotypes. biocViews: SNP, GeneticVariability, Genetics, StatisticalMethod, DimensionReduction, PrincipalComponent, GenomeWideAssociation, QualityControl, BiocViews Author: Matthew P. Conomos, Stephanie M. Gogarten, Lisa Brown, Han Chen, Thomas Lumley, Ken Rice, Tamar Sofer, Adrienne Stilp, Timothy Thornton, Chaoyu Yu Maintainer: Stephanie M. Gogarten URL: https://github.com/UW-GAC/GENESIS VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENESIS git_branch: RELEASE_3_12 git_last_commit: d7ecf7b git_last_commit_date: 2021-01-27 Date/Publication: 2021-01-28 source.ver: src/contrib/GENESIS_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GENESIS_2.20.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GENESIS_2.20.1.tgz vignettes: vignettes/GENESIS/inst/doc/assoc_test_seq.html, vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html vignetteTitles: Analyzing Sequence Data using the GENESIS Package, Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test_seq.R, vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R suggestsMe: coxmeg dependencyCount: 89 Package: GeneStructureTools Version: 1.10.0 Imports: Biostrings,GenomicRanges,IRanges,data.table,plyr,stringdist,stringr,S4Vectors,BSgenome.Mmusculus.UCSC.mm10,stats,utils,Gviz,rtracklayer,methods Suggests: BiocStyle, knitr, rmarkdown License: BSD_3_clause + file LICENSE MD5sum: 9fd7a37ea74f1b3bb5596c854b00a594 NeedsCompilation: no Title: Tools for spliced gene structure manipulation and analysis Description: GeneStructureTools can be used to create in silico alternative splicing events, and analyse potential effects this has on functional gene products. biocViews: ImmunoOncology, Software, DifferentialSplicing, FunctionalPrediction, Transcriptomics, AlternativeSplicing, RNASeq Author: Beth Signal Maintainer: Beth Signal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GeneStructureTools git_branch: RELEASE_3_12 git_last_commit: 61bb2b5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneStructureTools_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneStructureTools_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneStructureTools_1.10.0.tgz vignettes: vignettes/GeneStructureTools/inst/doc/Vignette.html vignetteTitles: Introduction to GeneStructureTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneStructureTools/inst/doc/Vignette.R dependencyCount: 141 Package: geNetClassifier Version: 1.30.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics, grDevices Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: 7659433c6d526283048e838b3c0d5974 NeedsCompilation: no Title: Classify diseases and build associated gene networks using gene expression profiles Description: Comprehensive package to automatically train and validate a multi-class SVM classifier based on gene expression data. Provides transparent selection of gene markers, their coexpression networks, and an interface to query the classifier. biocViews: Classification, DifferentialExpression, Microarray Author: Sara Aibar, Celia Fontanillo and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain. Maintainer: Sara Aibar URL: http://www.cicancer.org git_url: https://git.bioconductor.org/packages/geNetClassifier git_branch: RELEASE_3_12 git_last_commit: 47d0c61 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geNetClassifier_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geNetClassifier_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geNetClassifier_1.30.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.R importsMe: bioCancer, canceR dependencyCount: 18 Package: GeneticsPed Version: 1.52.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: 6372eaac3e2d4e0aadba561d7cb35599 NeedsCompilation: yes Title: Pedigree and genetic relationship functions Description: Classes and methods for handling pedigree data. It also includes functions to calculate genetic relationship measures as relationship and inbreeding coefficients and other utilities. Note that package is not yet stable. Use it with care! biocViews: Genetics Author: Gregor Gorjanc and David A. Henderson , with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING) Maintainer: David Henderson URL: http://rgenetics.org git_url: https://git.bioconductor.org/packages/GeneticsPed git_branch: RELEASE_3_12 git_last_commit: d883a2f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneticsPed_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneticsPed_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneticsPed_1.52.0.tgz vignettes: vignettes/GeneticsPed/inst/doc/geneticRelatedness.pdf, vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf vignetteTitles: Calculation of genetic relatedness/relationship between individuals in the pedigree, Pedigree handling, Quantitative genetic (animal) model example in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneticsPed/inst/doc/geneticRelatedness.R, vignettes/GeneticsPed/inst/doc/pedigreeHandling.R, vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.R importsMe: LRQMM dependencyCount: 11 Package: GeneTonic Version: 1.2.0 Depends: R (>= 4.0.0) Imports: AnnotationDbi, bs4Dash, colorspace, ComplexHeatmap, dendextend, DESeq2, dplyr, DT, dynamicTreeCut, expm, ggforce, ggplot2, ggrepel, GO.db, graphics, grDevices, grid, igraph, matrixStats, methods, plotly, RColorBrewer, rintrojs, rlang, rmarkdown, S4Vectors, scales, shiny, shinycssloaders, shinyWidgets, stats, SummarizedExperiment, tidyr, tools, utils, viridis, visNetwork Suggests: knitr, BiocStyle, htmltools, clusterProfiler, macrophage, org.Hs.eg.db, magrittr, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 0007edeb2858ab9b078889973d94b9b4 NeedsCompilation: no Title: Enjoy Analyzing And Integrating The Results From Differential Expression Analysis And Functional Enrichment Analysis Description: This package provides a Shiny application that aims to combine at different levels the existing pieces of the transcriptome data and results, in a way that makes it easier to generate insightful observations and hypothesis - combining the benefits of interactivity and reproducibility, e.g. by capturing the features and gene sets of interest highlighted during the live session, and creating an HTML report as an artifact where text, code, and output coexist. biocViews: GUI, GeneExpression, Software, Transcription, Transcriptomics, Visualization, DifferentialExpression, Pathways, ReportWriting, GeneSetEnrichment, Annotation, Pathways, GO Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/GeneTonic VignetteBuilder: knitr BugReports: https://github.com/federicomarini/GeneTonic/issues git_url: https://git.bioconductor.org/packages/GeneTonic git_branch: RELEASE_3_12 git_last_commit: 6b41744 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GeneTonic_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GeneTonic_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GeneTonic_1.2.0.tgz vignettes: vignettes/GeneTonic/inst/doc/GeneTonic_manual.html vignetteTitles: The GeneTonic User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GeneTonic/inst/doc/GeneTonic_manual.R dependencyCount: 153 Package: geneXtendeR Version: 1.16.0 Depends: rtracklayer, GO.db, R (>= 3.5.0) Imports: data.table, dplyr, graphics, networkD3, RColorBrewer, SnowballC, tm, utils, wordcloud, AnnotationDbi, BiocStyle, org.Rn.eg.db Suggests: knitr, rmarkdown, testthat, org.Ag.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Pt.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db, rtracklayer License: GPL (>= 3) Archs: i386, x64 MD5sum: e04e6be0f4007586b8e99434d1a6bd17 NeedsCompilation: yes Title: Optimized Functional Annotation Of ChIP-seq Data Description: geneXtendeR optimizes the functional annotation of ChIP-seq peaks by exploring relative differences in annotating ChIP-seq peak sets to variable-length gene bodies. In contrast to prior techniques, geneXtendeR considers peak annotations beyond just the closest gene, allowing users to see peak summary statistics for the first-closest gene, second-closest gene, ..., n-closest gene whilst ranking the output according to biologically relevant events and iteratively comparing the fidelity of peak-to-gene overlap across a user-defined range of upstream and downstream extensions on the original boundaries of each gene's coordinates. Since different ChIP-seq peak callers produce different differentially enriched peaks with a large variance in peak length distribution and total peak count, annotating peak lists with their nearest genes can often be a noisy process. As such, the goal of geneXtendeR is to robustly link differentially enriched peaks with their respective genes, thereby aiding experimental follow-up and validation in designing primers for a set of prospective gene candidates during qPCR. biocViews: ChIPSeq, Genetics, Annotation, GenomeAnnotation, DifferentialPeakCalling, Coverage, PeakDetection, ChipOnChip, HistoneModification, DataImport, NaturalLanguageProcessing, Visualization, GO, Software Author: Bohdan Khomtchouk [aut, cre], William Koehler [aut] Maintainer: Bohdan Khomtchouk URL: https://github.com/Bohdan-Khomtchouk/geneXtendeR VignetteBuilder: knitr BugReports: https://github.com/Bohdan-Khomtchouk/geneXtendeR/issues git_url: https://git.bioconductor.org/packages/geneXtendeR git_branch: RELEASE_3_12 git_last_commit: 30dad28 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/geneXtendeR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/geneXtendeR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/geneXtendeR_1.16.0.tgz vignettes: vignettes/geneXtendeR/inst/doc/geneXtendeR.pdf vignetteTitles: geneXtendeR.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 99 Package: GENIE3 Version: 1.12.0 Imports: stats, reshape2 Suggests: knitr, rmarkdown, foreach, doRNG, doParallel, Biobase, SummarizedExperiment, testthat, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 833398ca4048551777471f8c22453cce NeedsCompilation: yes Title: GEne Network Inference with Ensemble of trees Description: This package implements the GENIE3 algorithm for inferring gene regulatory networks from expression data. biocViews: NetworkInference, SystemsBiology, DecisionTree, Regression, Network, GraphAndNetwork, GeneExpression Author: Van Anh Huynh-Thu, Sara Aibar, Pierre Geurts Maintainer: Van Anh Huynh-Thu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GENIE3 git_branch: RELEASE_3_12 git_last_commit: 14289ce git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GENIE3_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GENIE3_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GENIE3_1.12.0.tgz vignettes: vignettes/GENIE3/inst/doc/GENIE3.html vignetteTitles: GENIE3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENIE3/inst/doc/GENIE3.R importsMe: MetNet dependencyCount: 11 Package: genoCN Version: 1.42.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: d5d0e64e1221fa6296684698b6f00873 NeedsCompilation: yes Title: genotyping and copy number study tools Description: Simultaneous identification of copy number states and genotype calls for regions of either copy number variations or copy number aberrations biocViews: Microarray, Genetics Author: Wei Sun and ZhengZheng Tang Maintainer: Wei Sun git_url: https://git.bioconductor.org/packages/genoCN git_branch: RELEASE_3_12 git_last_commit: ce233a6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genoCN_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genoCN_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genoCN_1.42.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoCN/inst/doc/genoCN.R dependencyCount: 3 Package: GenoGAM Version: 2.8.0 Depends: R (>= 3.5), SummarizedExperiment (>= 1.1.19), HDF5Array (>= 1.8.0), rhdf5 (>= 2.21.6), S4Vectors (>= 0.23.18), Matrix (>= 1.2-8), data.table (>= 1.9.4) Imports: Rcpp (>= 0.12.14), sparseinv (>= 0.1.1), Rsamtools (>= 1.18.2), GenomicRanges (>= 1.23.16), BiocParallel (>= 1.5.17), DESeq2 (>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb (>= 1.7.6), GenomicAlignments (>= 1.7.17), IRanges (>= 2.5.30), Biostrings (>= 2.39.14), DelayedArray (>= 0.3.19), methods, stats LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, chipseq (>= 1.21.2), LSD (>= 3.0.0), genefilter (>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 2fac12f4d64fbb6c32c5f1161d70fa15 NeedsCompilation: yes Title: A GAM based framework for analysis of ChIP-Seq data Description: This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals. biocViews: Regression, DifferentialPeakCalling, ChIPSeq, DifferentialExpression, Genetics, Epigenetics, WholeGenome, ChipOnChip, ImmunoOncology Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut] Maintainer: Georg Stricker URL: https://github.com/gstricker/GenoGAM VignetteBuilder: knitr BugReports: https://github.com/gstricker/GenoGAM/issues git_url: https://git.bioconductor.org/packages/GenoGAM git_branch: RELEASE_3_12 git_last_commit: 7698827 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenoGAM_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenoGAM_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenoGAM_2.8.0.tgz vignettes: vignettes/GenoGAM/inst/doc/GenoGAM.html vignetteTitles: "Modeling ChIP-Seq data with GenoGAM 2.0: A Genome-wide generalized additive model" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenoGAM/inst/doc/GenoGAM.R dependencyCount: 102 Package: genomation Version: 1.22.0 Depends: R (>= 3.0.0),grid Imports: Biostrings (>= 2.47.6), BSgenome (>= 1.47.3), data.table, GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), S4Vectors (>= 0.17.25), ggplot2, gridBase, impute, IRanges (>= 2.13.12), matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools (>= 1.31.2), seqPattern, rtracklayer (>= 1.39.7), RUnit, Rcpp (>= 0.12.14) LinkingTo: Rcpp Suggests: BiocGenerics, genomationData, knitr, RColorBrewer, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: c0ecfd2c734ffdb9ae0b22edf51ddad1 NeedsCompilation: yes Title: Summary, annotation and visualization of genomic data Description: A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input. biocViews: Annotation, Sequencing, Visualization, CpGIsland Author: Altuna Akalin [aut, cre], Vedran Franke [aut, cre], Katarzyna Wreczycka [aut], Alexander Gosdschan [ctb], Liz Ing-Simmons [ctb], Bozena Mika-Gospodorz [ctb] Maintainer: Altuna Akalin , Vedran Franke , Katarzyna Wreczycka URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues git_url: https://git.bioconductor.org/packages/genomation git_branch: RELEASE_3_12 git_last_commit: 4ab56f3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genomation_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genomation_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genomation_1.22.0.tgz vignettes: vignettes/genomation/inst/doc/GenomationManual.html vignetteTitles: genomation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual.R importsMe: CexoR, fCCAC, RCAS suggestsMe: methylKit dependencyCount: 86 Package: GenomeInfoDb Version: 1.26.7 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.25.12), IRanges (>= 2.13.12) Imports: stats, stats4, utils, RCurl, GenomeInfoDbData Suggests: GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 17bb177838890de8702414f7c6f69c85 NeedsCompilation: no Title: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style Description: Contains data and functions that define and allow translation between different chromosome sequence naming conventions (e.g., "chr1" versus "1"), including a function that attempts to place sequence names in their natural, rather than lexicographic, order. biocViews: Genetics, DataRepresentation, Annotation, GenomeAnnotation Author: Sonali Arora, Martin Morgan, Marc Carlson, H. Pagès Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomeInfoDb VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w BugReports: https://github.com/Bioconductor/GenomeInfoDb/issues git_url: https://git.bioconductor.org/packages/GenomeInfoDb git_branch: RELEASE_3_12 git_last_commit: 6bef593 git_last_commit_date: 2021-04-08 Date/Publication: 2021-04-08 source.ver: src/contrib/GenomeInfoDb_1.26.7.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomeInfoDb_1.26.7.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomeInfoDb_1.26.7.tgz vignettes: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.pdf, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.pdf vignetteTitles: GenomeInfoDb: Submitting your organism to GenomeInfoDb, GenomeInfoDb: Introduction to GenomeInfoDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeInfoDb/inst/doc/Accept-organism-for-GenomeInfoDb.R, vignettes/GenomeInfoDb/inst/doc/GenomeInfoDb.R dependsOnMe: BRGenomics, BSgenome, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, HelloRanges, methyAnalysis, Rsamtools, SCOPE, VariantAnnotation, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, eQTL, RTIGER importsMe: AllelicImbalance, alpine, amplican, AneuFinder, AnnotationHubData, annotatr, ASpediaFI, ATACseqQC, BaalChIP, ballgown, bambu, biovizBase, biscuiteer, BiSeq, bnbc, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, CAGEr, casper, cBioPortalData, CexoR, chimeraviz, chipenrich, ChIPexoQual, ChIPpeakAnno, ChIPseeker, chromstaR, chromVAR, circRNAprofiler, cleanUpdTSeq, cn.mops, CNEr, CNVfilteR, CNVPanelizer, CNVRanger, compEpiTools, consensusSeekeR, conumee, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, customProDB, DAMEfinder, dasper, decompTumor2Sig, DeepBlueR, derfinder, derfinderPlot, DEScan2, DEWSeq, diffHic, diffloop, DMRcate, DMRScan, dmrseq, DominoEffect, ELMER, ENCODExplorer, enrichTF, ensembldb, ensemblVEP, epigenomix, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, exomeCopy, FRASER, FunChIP, funtooNorm, GA4GHclient, GA4GHshiny, gcapc, genbankr, geneAttribution, GenoGAM, genomation, genomeIntervals, GenomicFiles, GenomicInteractions, GenomicOZone, GenomicScores, genoset, genotypeeval, GenVisR, ggbio, GGtools, gmoviz, GOTHiC, gQTLstats, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, heatmaps, HiCBricks, HiTC, HTSeqGenie, idr2d, IMAS, InPAS, INSPEcT, InteractionSet, IsoformSwitchAnalyzeR, IVAS, karyoploteR, ldblock, MACPET, MADSEQ, maser, metagene, metagene2, metaseqR2, metavizr, MethCP, methimpute, methInheritSim, methylKit, methylPipe, methylSig, methylumi, methyvim, minfi, MinimumDistance, MMAPPR2, mosaics, motifbreakR, motifmatchr, MouseFM, msgbsR, multicrispr, multiHiCcompare, musicatk, MutationalPatterns, myvariant, NADfinder, nearBynding, normr, nucleR, OMICsPCA, ORFik, Organism.dplyr, panelcn.mops, periodicDNA, Pi, pipeFrame, plyranges, podkat, pram, prebs, proActiv, profileplyr, ProteomicsAnnotationHubData, PureCN, qpgraph, qsea, QuasR, R3CPET, r3Cseq, RaggedExperiment, RareVariantVis, Rariant, Rcade, RCAS, recount, recoup, regioneR, regionReport, REMP, Repitools, RiboProfiling, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, rnaEditr, RNAmodR, roar, RTCGAToolbox, rtracklayer, scmeth, scruff, segmentSeq, SeqArray, seqCAT, seqplots, seqsetvis, sevenC, SGSeq, ShortRead, signeR, SigsPack, SNPhood, soGGi, SomaticSignatures, SparseSignatures, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, TAPseq, TarSeqQC, TCGAutils, TFBSTools, TitanCNA, TnT, trackViewer, transcriptR, tRNAscanImport, TSRchitect, TVTB, tximeta, TxRegInfra, Ularcirc, UMI4Cats, VanillaICE, VariantFiltering, VariantTools, vasp, VaSP, VplotR, wiggleplotr, YAPSA, fitCons.UCSC.hg19, GenomicState, grasp2db, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, chipenrich.data, MethylSeqData, yriMulti, TCGAWorkflow, ActiveDriverWGS, crispRdesignR, deconstructSigs, driveR, HiCfeat, ICAMS, MicroSEC, Signac, simMP suggestsMe: AnnotationForge, AnnotationHub, BiocOncoTK, chromswitch, ExperimentHubData, gQTLBase, megadepth, methrix, parglms, QDNAseq, splatter, StructuralVariantAnnotation, TFutils, gkmSVM, LDheatmap, polyRAD, Seurat dependencyCount: 12 Package: genomeIntervals Version: 1.46.0 Depends: R (>= 2.15.0), methods, intervals (>= 0.14.0), BiocGenerics (>= 0.15.2) Imports: GenomeInfoDb (>= 1.5.8), GenomicRanges (>= 1.21.16), IRanges(>= 2.3.14), S4Vectors (>= 0.7.10) License: Artistic-2.0 MD5sum: b7c4556ac74d9f35cf0c64dfd8614688 NeedsCompilation: no Title: Operations on genomic intervals Description: This package defines classes for representing genomic intervals and provides functions and methods for working with these. Note: The package provides the basic infrastructure for and is enhanced by the package 'girafe'. biocViews: DataImport, Infrastructure, Genetics Author: Julien Gagneur , Joern Toedling, Richard Bourgon, Nicolas Delhomme Maintainer: Julien Gagneur git_url: https://git.bioconductor.org/packages/genomeIntervals git_branch: RELEASE_3_12 git_last_commit: 519c416 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genomeIntervals_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genomeIntervals_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genomeIntervals_1.46.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomeIntervals/inst/doc/genomeIntervals.R dependsOnMe: girafe dependencyCount: 18 Package: genomes Version: 3.20.0 Depends: readr, curl License: GPL-3 MD5sum: 4fcdffd6f6c70603ad8b036f6f36c380 NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben git_url: https://git.bioconductor.org/packages/genomes git_branch: RELEASE_3_12 git_last_commit: ee7daac git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genomes_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genomes_3.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genomes_3.20.0.tgz vignettes: vignettes/genomes/inst/doc/genomes.pdf vignetteTitles: Genome metadata hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomes/inst/doc/genomes.R dependencyCount: 23 Package: GenomicAlignments Version: 1.26.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.9.13), Biostrings (>= 2.55.7), Rsamtools (>= 1.31.2) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings, Rsamtools, BiocParallel LinkingTo: S4Vectors, IRanges Suggests: ShortRead, rtracklayer, BSgenome, GenomicFeatures, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Hsapiens.UCSC.hg19, DESeq2, edgeR, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 3adfdee731d1f065d8e70420abc6e331 NeedsCompilation: yes Title: Representation and manipulation of short genomic alignments Description: Provides efficient containers for storing and manipulating short genomic alignments (typically obtained by aligning short reads to a reference genome). This includes read counting, computing the coverage, junction detection, and working with the nucleotide content of the alignments. biocViews: Infrastructure, DataImport, Genetics, Sequencing, RNASeq, SNP, Coverage, Alignment, ImmunoOncology Author: Hervé Pagès, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomicAlignments Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs BugReports: https://github.com/Bioconductor/GenomicAlignments/issues git_url: https://git.bioconductor.org/packages/GenomicAlignments git_branch: RELEASE_3_12 git_last_commit: 6c74c74 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenomicAlignments_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicAlignments_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicAlignments_1.26.0.tgz vignettes: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.pdf, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: An Introduction to the GenomicAlignments Package, Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicAlignments/inst/doc/GenomicAlignmentsIntroduction.R, vignettes/GenomicAlignments/inst/doc/OverlapEncodings.R, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.R, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.R dependsOnMe: AllelicImbalance, Basic4Cseq, chimera, ChIPexoQual, groHMM, HelloRanges, hiReadsProcessor, igvR, ORFik, prebs, recoup, rnaSeqMap, ShortRead, SplicingGraphs, alpineData, SCATEData, sequencing importsMe: alpine, AneuFinder, APAlyzer, ASpediaFI, ASpli, ATACseqQC, BaalChIP, bambu, biovizBase, breakpointR, BRGenomics, CAGEfightR, CAGEr, chimeraviz, ChIPpeakAnno, ChIPQC, chromstaR, CNEr, consensusDE, contiBAIT, CopywriteR, CoverageView, CrispRVariants, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEScan2, DiffBind, FourCSeq, FRASER, FunChIP, gcapc, GenoGAM, genomation, GenomicFiles, ggbio, gmapR, gmoviz, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, icetea, IMAS, INSPEcT, IntEREst, MACPET, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylPipe, mosaics, msgbsR, NADfinder, PICS, plyranges, pram, proActiv, QuasR, ramwas, Rcade, Repitools, RiboProfiling, ribosomeProfilingQC, RNAmodR, RNAprobR, roar, Rqc, rtracklayer, SCATE, scruff, seqplots, seqsetvis, SGSeq, soGGi, SplicingGraphs, SPLINTER, srnadiff, strandCheckR, TAPseq, TarSeqQC, TCseq, trackViewer, transcriptR, TSRchitect, Ularcirc, UMI4Cats, vasp, VaSP, VplotR, leeBamViews, alakazam, BinQuasi, ExomeDepth, MicroSEC, PACVr, pulseTD, RAPIDR, VALERIE suggestsMe: amplican, BiocParallel, csaw, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, IRanges, Rsamtools, similaRpeak, Streamer, systemPipeR, alpineData, NanoporeRNASeq, parathyroidSE, RNAseqData.HNRNPC.bam.chr14, seqmagick dependencyCount: 37 Package: GenomicDataCommons Version: 1.14.0 Depends: R (>= 3.4.0), magrittr Imports: stats, httr, xml2, jsonlite, utils, rlang, readr, GenomicRanges, IRanges, dplyr, rappdirs, SummarizedExperiment, S4Vectors, tibble Suggests: BiocStyle, knitr, rmarkdown, DT, testthat, listviewer, ggplot2, GenomicAlignments, Rsamtools License: Artistic-2.0 MD5sum: 9ce9d9241aac4a18a052df4938bd500a NeedsCompilation: no Title: NIH / NCI Genomic Data Commons Access Description: Programmatically access the NIH / NCI Genomic Data Commons RESTful service. biocViews: DataImport, Sequencing Author: Martin Morgan [aut], Sean Davis [aut, cre] Maintainer: Sean Davis URL: https://bioconductor.org/packages/GenomicDataCommons, http://github.com/Bioconductor/GenomicDataCommons VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicDataCommons/issues/new git_url: https://git.bioconductor.org/packages/GenomicDataCommons git_branch: RELEASE_3_12 git_last_commit: c08477c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenomicDataCommons_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicDataCommons_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicDataCommons_1.14.0.tgz vignettes: vignettes/GenomicDataCommons/inst/doc/overview.html vignetteTitles: Introduction to Accessing the NCI Genomic Data Commons hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicDataCommons/inst/doc/overview.R importsMe: GDCRNATools, TCGAutils dependencyCount: 58 Package: GenomicFeatures Version: 1.42.3 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.17.29), IRanges (>= 2.13.23), GenomeInfoDb (>= 1.25.7), GenomicRanges (>= 1.31.17), AnnotationDbi (>= 1.41.4) Imports: methods, utils, stats, tools, DBI, RSQLite (>= 2.0), RCurl, XVector (>= 0.19.7), Biostrings (>= 2.47.6), rtracklayer (>= 1.39.7), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: RMariaDB, org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene (>= 3.4.7), TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene (>= 3.4.6), SNPlocs.Hsapiens.dbSNP144.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments (>= 1.15.7), ensembldb, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: 4b7e53ca9ed7848d8fc7d93d00aa3d50 NeedsCompilation: no Title: Conveniently import and query gene models Description: A set of tools and methods for making and manipulating transcript centric annotations. With these tools the user can easily download the genomic locations of the transcripts, exons and cds of a given organism, from either the UCSC Genome Browser or a BioMart database (more sources will be supported in the future). This information is then stored in a local database that keeps track of the relationship between transcripts, exons, cds and genes. Flexible methods are provided for extracting the desired features in a convenient format. biocViews: Genetics, Infrastructure, Annotation, Sequencing, GenomeAnnotation Author: M. Carlson, H. Pagès, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence, V. Obenchain Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomicFeatures VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicFeatures/issues git_url: https://git.bioconductor.org/packages/GenomicFeatures git_branch: RELEASE_3_12 git_last_commit: 9ce5435 git_last_commit_date: 2021-03-31 Date/Publication: 2021-04-01 source.ver: src/contrib/GenomicFeatures_1.42.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicFeatures_1.42.3.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicFeatures_1.42.3.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.R dependsOnMe: cpvSNP, ensembldb, GSReg, Guitar, HelloRanges, InPAS, OrganismDbi, OUTRIDER, RareVariantVis, RNAprobR, SplicingGraphs, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Athaliana.BioMart.plantsmart28, TxDb.Btaurus.UCSC.bosTau8.refGene, TxDb.Btaurus.UCSC.bosTau9.refGene, TxDb.Celegans.UCSC.ce11.ensGene, TxDb.Celegans.UCSC.ce11.refGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Cfamiliaris.UCSC.canFam3.refGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Dmelanogaster.UCSC.dm6.ensGene, TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Drerio.UCSC.danRer11.refGene, TxDb.Ggallus.UCSC.galGal4.refGene, TxDb.Ggallus.UCSC.galGal5.refGene, TxDb.Ggallus.UCSC.galGal6.refGene, TxDb.Hsapiens.BioMart.igis, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmulatta.UCSC.rheMac10.refGene, TxDb.Mmulatta.UCSC.rheMac3.refGene, TxDb.Mmulatta.UCSC.rheMac8.refGene, TxDb.Mmusculus.UCSC.mm10.ensGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm39.refGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Ptroglodytes.UCSC.panTro4.refGene, TxDb.Ptroglodytes.UCSC.panTro5.refGene, TxDb.Ptroglodytes.UCSC.panTro6.refGene, TxDb.Rnorvegicus.BioMart.igis, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Rnorvegicus.UCSC.rn5.refGene, TxDb.Rnorvegicus.UCSC.rn6.ncbiRefSeq, TxDb.Rnorvegicus.UCSC.rn6.refGene, TxDb.Scerevisiae.UCSC.sacCer2.sgdGene, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, TxDb.Sscrofa.UCSC.susScr11.refGene, TxDb.Sscrofa.UCSC.susScr3.refGene, generegulation importsMe: AllelicImbalance, alpine, AnnotationHubData, annotatr, APAlyzer, appreci8R, ASpediaFI, ASpli, bambu, BgeeCall, BiocOncoTK, biovizBase, bumphunter, BUSpaRse, CAGEfightR, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, CompGO, consensusDE, crisprseekplus, csaw, CSSQ, customProDB, dasper, decompTumor2Sig, derfinder, derfinderPlot, EDASeq, eisaR, ELMER, EpiTxDb, epivizrData, epivizrStandalone, esATAC, EventPointer, FRASER, GA4GHshiny, genbankr, geneAttribution, GenVisR, ggbio, gmapR, gmoviz, gQTLstats, Gviz, gwascat, HiLDA, HTSeqGenie, icetea, INSPEcT, IntEREst, karyoploteR, lumi, mCSEA, metagene, metaseqR2, msgbsR, multicrispr, musicatk, ORFik, Organism.dplyr, PGA, proActiv, proBAMr, PureCN, qpgraph, QuasR, RCAS, recoup, Rhisat2, RiboProfiling, ribosomeProfilingQC, RNAmodR, scruff, SGSeq, SplicingGraphs, SPLINTER, srnadiff, systemPipeR, TAPseq, TCGAutils, TFEA.ChIP, trackViewer, transcriptR, tximeta, Ularcirc, UMI4Cats, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR, FDb.FANTOM4.promoters.hg19, FDb.InfiniumMethylation.hg18, FDb.InfiniumMethylation.hg19, FDb.UCSC.snp135common.hg19, FDb.UCSC.snp137common.hg19, FDb.UCSC.tRNAs, GenomicState, Homo.sapiens, Mus.musculus, Rattus.norvegicus, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Athaliana.BioMart.plantsmart25, TxDb.Hsapiens.BioMart.igis, TxDb.Rnorvegicus.BioMart.igis, DMRcatedata, geneLenDataBase, scRNAseq, driveR, pulseTD suggestsMe: AnnotationHub, BANDITS, biomvRCNS, Biostrings, chipseq, chromPlot, CrispRVariants, cummeRbund, DEXSeq, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, HDF5Array, IRanges, MiRaGE, MutationalPatterns, pageRank, recount, RNAmodR.ML, Rsamtools, rtracklayer, ShortRead, SummarizedExperiment, TFutils, TnT, VplotR, wiggleplotr, BSgenome.Btaurus.UCSC.bosTau3, BSgenome.Btaurus.UCSC.bosTau4, BSgenome.Btaurus.UCSC.bosTau6, BSgenome.Btaurus.UCSC.bosTau8, BSgenome.Btaurus.UCSC.bosTau9, BSgenome.Celegans.UCSC.ce10, BSgenome.Celegans.UCSC.ce11, BSgenome.Celegans.UCSC.ce2, BSgenome.Cfamiliaris.UCSC.canFam2, BSgenome.Cfamiliaris.UCSC.canFam3, BSgenome.Dmelanogaster.UCSC.dm2, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Drerio.UCSC.danRer11, BSgenome.Drerio.UCSC.danRer5, BSgenome.Drerio.UCSC.danRer6, BSgenome.Drerio.UCSC.danRer7, BSgenome.Gaculeatus.UCSC.gasAcu1, BSgenome.Ggallus.UCSC.galGal3, BSgenome.Ggallus.UCSC.galGal4, BSgenome.Hsapiens.UCSC.hg17, BSgenome.Mmulatta.UCSC.rheMac2, BSgenome.Mmulatta.UCSC.rheMac3, BSgenome.Mmusculus.UCSC.mm8, BSgenome.Ptroglodytes.UCSC.panTro2, BSgenome.Ptroglodytes.UCSC.panTro3, BSgenome.Rnorvegicus.UCSC.rn6, curatedAdipoChIP, parathyroidSE, Single.mTEC.Transcriptomes, CAGEWorkflow, polyRAD dependencyCount: 87 Package: GenomicFiles Version: 1.26.0 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), MatrixGenerics, GenomicRanges (>= 1.31.16), SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments (>= 1.7.7), IRanges, S4Vectors (>= 0.9.25), VariantAnnotation (>= 1.27.9), GenomeInfoDb Suggests: BiocStyle, RUnit, genefilter, deepSNV, snpStats, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 MD5sum: b05dfa75a2f7548568674dd36c3edeb5 NeedsCompilation: no Title: Distributed computing by file or by range Description: This package provides infrastructure for parallel computations distributed 'by file' or 'by range'. User defined MAPPER and REDUCER functions provide added flexibility for data combination and manipulation. biocViews: Genetics, Infrastructure, DataImport, Sequencing, Coverage Author: Bioconductor Package Maintainer [aut, cre], Valerie Obenchain [aut], Michael Love [aut], Lori Shepherd [aut], Martin Morgan [aut] Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs git_url: https://git.bioconductor.org/packages/GenomicFiles git_branch: RELEASE_3_12 git_last_commit: 8c9e00d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenomicFiles_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicFiles_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicFiles_1.26.0.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicFiles/inst/doc/GenomicFiles.R dependsOnMe: erma importsMe: CAGEfightR, contiBAIT, derfinder, gQTLBase, gQTLstats, ldblock, QuasR, Rqc, VCFArray, yriMulti suggestsMe: TFutils, TxRegInfra dependencyCount: 90 Package: GenomicInteractions Version: 1.24.0 Depends: R (>= 3.5), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges (>= 1.29.6), IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors (>= 0.13.13), dplyr, Gviz, Biobase, graphics, stats, utils, grDevices Suggests: knitr, BiocStyle, testthat License: GPL-3 MD5sum: e6fbd53b07559a44d52a5da81f2455ad NeedsCompilation: no Title: Utilities for handling genomic interaction data Description: Utilities for handling genomic interaction data such as ChIA-PET or Hi-C, annotating genomic features with interaction information, and producing plots and summary statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicInteractions git_branch: RELEASE_3_12 git_last_commit: 014f226 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenomicInteractions_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicInteractions_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicInteractions_1.24.0.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html vignetteTitles: chiapet_vignette.html, GenomicInteractions-HiC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R importsMe: CAGEfightR suggestsMe: Chicago, ELMER, sevenC, chicane dependencyCount: 140 Package: GenomicOZone Version: 1.4.1 Depends: R (>= 4.0.0), Ckmeans.1d.dp (>= 4.3.0), GenomicRanges, biomaRt, ggplot2 Imports: grDevices, stats, utils, plyr, gridExtra, lsr, parallel, ggbio, S4Vectors, IRanges, GenomeInfoDb, Rdpack Suggests: readxl, knitr, rmarkdown License: LGPL (>=3) MD5sum: cdbac8bb39ff13fe7557c08d01c6bb2e NeedsCompilation: no Title: Delineate outstanding genomic zones of differential gene activity Description: The package clusters gene activity along chromosome into zones, detects differential zones as outstanding, and visualizes maps of outstanding zones across the genome. It enables characterization of effects on multiple genes within adaptive genomic neighborhoods, which could arise from genome reorganization, structural variation, or epigenome alteration. It guarantees cluster optimality, linear runtime to sample size, and reproducibility. One can apply it on genome-wide activity measurements such as copy number, transcriptomic, proteomic, and methylation data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, FunctionalPrediction, GeneRegulation, BiomedicalInformatics, CellBiology, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Clustering, Regression, RNASeq, Annotation, Visualization, Sequencing, Coverage, DifferentialMethylation, GenomicVariation, StructuralVariation, CopyNumberVariation Author: Hua Zhong [aut], Mingzhou Song [aut, cre] Maintainer: Mingzhou Song VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GenomicOZone git_branch: RELEASE_3_12 git_last_commit: 4bc5a11 git_last_commit_date: 2021-04-27 Date/Publication: 2021-04-27 source.ver: src/contrib/GenomicOZone_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicOZone_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicOZone_1.4.1.tgz vignettes: vignettes/GenomicOZone/inst/doc/GenomicOZone.html vignetteTitles: GenomicOZone hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicOZone/inst/doc/GenomicOZone.R dependencyCount: 153 Package: GenomicRanges Version: 1.42.0 Depends: R (>= 2.10), methods, stats4, BiocGenerics (>= 0.25.3), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.15.2) Imports: utils, stats, XVector (>= 0.29.2) LinkingTo: S4Vectors, IRanges Suggests: Matrix, Biobase, AnnotationDbi, annotate, Biostrings (>= 2.25.3), SummarizedExperiment (>= 0.1.5), Rsamtools (>= 1.13.53), GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, RNAseqData.HNRNPC.bam.chr14, pasillaBamSubset, KEGG.db, hgu95av2.db, hgu95av2probe, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, RUnit, digest, knitr, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 03bc0867ceaa7a1b2dbee00f9723f230 NeedsCompilation: yes Title: Representation and manipulation of genomic intervals Description: The ability to efficiently represent and manipulate genomic annotations and alignments is playing a central role when it comes to analyzing high-throughput sequencing data (a.k.a. NGS data). The GenomicRanges package defines general purpose containers for storing and manipulating genomic intervals and variables defined along a genome. More specialized containers for representing and manipulating short alignments against a reference genome, or a matrix-like summarization of an experiment, are defined in the GenomicAlignments and SummarizedExperiment packages, respectively. Both packages build on top of the GenomicRanges infrastructure. biocViews: Genetics, Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, Coverage Author: P. Aboyoun, H. Pagès, and M. Lawrence Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/GenomicRanges VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GenomicRanges/issues git_url: https://git.bioconductor.org/packages/GenomicRanges git_branch: RELEASE_3_12 git_last_commit: 32baca7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenomicRanges_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicRanges_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicRanges_1.42.0.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf, vignettes/GenomicRanges/inst/doc/Ten_things_slides.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.html vignetteTitles: 5. Extending GenomicRanges, 2. GenomicRanges HOWTOs, 3. A quick introduction to GRanges and GRangesList objects (slides), 4. Ten Things You Didn't Know (slides from BioC 2016), 1. An Introduction to the GenomicRanges Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.R, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.R, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.R, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.R, vignettes/GenomicRanges/inst/doc/Ten_things_slides.R dependsOnMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData, BaalChIP, Basic4Cseq, baySeq, biomvRCNS, BiSeq, bnbc, BPRMeth, breakpointR, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, CAGEfightR, casper, chimera, chimeraviz, ChIPpeakAnno, ChIPQC, chipseq, chromPlot, chromstaR, chromswitch, CINdex, cn.mops, cnvGSA, CNVPanelizer, CNVRanger, COCOA, compEpiTools, consensusSeekeR, CSAR, csaw, CSSQ, deepSNV, DEScan2, DESeq2, DEXSeq, DiffBind, diffHic, DMCFB, DMCHMM, DMRcaller, DMRforPairs, DNAshapeR, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, epihet, esATAC, ExCluster, exomeCopy, fastseg, fCCAC, FunChIP, GeneBreak, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicOZone, GenomicScores, GenomicTuples, genoset, gmapR, gmoviz, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, HelloRanges, hiAnnotator, HiTC, IdeoViz, igvR, InPAS, InTAD, intansv, InteractionSet, IntEREst, IWTomics, karyoploteR, maser, MBASED, Melissa, metagene, metagene2, methimpute, methyAnalysis, methylKit, methylPipe, minfi, MotifDb, msgbsR, MutationalPatterns, NADfinder, ORFik, periodicDNA, PGA, plyranges, podkat, QuasR, r3Cseq, RaggedExperiment, Rariant, Rcade, recoup, regioneR, RepViz, rfPred, rGREAT, riboSeqR, ribosomeProfilingQC, RJMCMCNucleosomes, RNAmodR, RnBeads, Rsamtools, RSVSim, rtracklayer, Scale4C, SCOPE, segmentSeq, seqbias, seqCAT, SeqGate, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomaticSignatures, StructuralVariantAnnotation, SummarizedExperiment, TarSeqQC, TnT, trackViewer, TransView, tRNA, tRNAdbImport, tRNAscanImport, VanillaICE, VariantAnnotation, VariantExperiment, VariantTools, VplotR, vtpnet, vulcan, wavClusteR, YAPSA, EuPathDB, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, ChAMPdata, EatonEtAlChIPseq, geuvStore2, RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5, SCATEData, WGSmapp, liftOver, sequencing, HiCfeat, PlasmaMutationDetector, RTIGER importsMe: ACE, ALDEx2, alpine, ALPS, amplican, AnnotationFilter, annotatr, APAlyzer, apeglm, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, AssessORF, ATACseqQC, BadRegionFinder, ballgown, bambu, bamsignals, BBCAnalyzer, beadarray, BEAT, BiFET, BiocIO, BiocOncoTK, BioTIP, biovizBase, biscuiteer, BiSeq, brainflowprobes, branchpointer, BRGenomics, BSgenome, BUSpaRse, CAGEr, cBioPortalData, CexoR, chipenrich, ChIPexoQual, ChIPseeker, chipseq, ChIPseqR, ChIPSeqSpike, chromDraw, ChromHeatMap, ChromSCape, chromVAR, cicero, circRNAprofiler, cleanUpdTSeq, CNEr, CNVfilteR, coMET, compartmap, contiBAIT, conumee, copynumber, CopyNumberPlots, CopywriteR, CoverageView, crisprseekplus, CrispRVariants, customProDB, DAMEfinder, dasper, debrowser, decompTumor2Sig, DeepBlueR, DEFormats, DegNorm, deltaCaptureC, derfinder, derfinderPlot, DEWSeq, diffloop, DMRcate, dmrseq, DominoEffect, DRIMSeq, EDASeq, eisaR, ELMER, ENCODExplorer, enrichTF, EpiTxDb, epivizr, epivizrData, erma, EventPointer, fcScan, FilterFFPE, FourCSeq, FRASER, FunciSNP, GA4GHclient, gcapc, genbankr, geneAttribution, GeneGeneInteR, GENESIS, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicInteractions, genotypeeval, GenVisR, GGBase, ggbio, GGtools, GOfuncR, gpart, gQTLBase, gQTLstats, gwascat, h5vc, heatmaps, HiCBricks, HiCcompare, HilbertCurve, HiLDA, hiReadsProcessor, HTSeqGenie, hummingbird, icetea, ideal, idr2d, IMAS, INSPEcT, InterMineR, ipdDb, IsoformSwitchAnalyzeR, isomiRs, iteremoval, IVAS, karyoploteR, loci2path, LOLA, LoomExperiment, lumi, MACPET, MADSEQ, mCSEA, MDTS, MEAL, MEDIPS, megadepth, metaseqR2, MethCP, methInheritSim, MethReg, methrix, methyAnalysis, methylCC, methylInheritance, MethylSeekR, methylSig, methylumi, MinimumDistance, MIRA, missMethyl, MMAPPR2, MMDiff2, Modstrings, mosaics, motifbreakR, motifmatchr, MouseFM, MultiAssayExperiment, multicrispr, MultiDataSet, multiHiCcompare, musicatk, NanoMethViz, ncRNAtools, nearBynding, normr, nucleR, oligoClasses, OmaDB, openPrimeR, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, pageRank, panelcn.mops, PAST, pcaExplorer, pepStat, Pi, PICS, PING, pqsfinder, pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv, proBAMr, profileplyr, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, qsea, Qtlizer, R3CPET, R453Plus1Toolbox, RareVariantVis, RCAS, recount, recount3, regioneR, regionReport, regutools, REMP, Repitools, rGADEM, RGMQL, Rhisat2, RiboProfiling, RIPAT, Rmmquant, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAprobR, rnaSeqMap, roar, RTCGAToolbox, SCATE, scmeth, scoreInvHap, scPipe, scruff, scuttle, seq2pathway, SeqArray, seqPattern, seqplots, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, SigsPack, SimFFPE, simulatorZ, snapcount, soGGi, SparseSignatures, SpectralTAD, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, systemPipeR, TAPseq, target, TCGAbiolinks, TCGAutils, TCseq, TFARM, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, tracktables, transcriptR, transite, trena, triplex, tscR, TSRchitect, TVTB, tximeta, TxRegInfra, Ularcirc, UMI4Cats, uncoverappLib, Uniquorn, VariantFiltering, vasp, VaSP, VCFArray, wiggleplotr, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, cgdv17, chipenrich.data, COSMIC.67, ELMER.data, leeBamViews, MethylSeqData, pepDat, scRNAseq, SomaticCancerAlterations, VariantToolsData, yriMulti, recountWorkflow, TCGAWorkflow, ActiveDriverWGS, BinQuasi, cinaR, crispRdesignR, DGEobj, driveR, ExomeDepth, geno2proteo, hoardeR, ICAMS, LoopRig, MAFDash, noisyr, PACVr, pagoo, RapidoPGS, RAPIDR, Signac, simMP, VALERIE suggestsMe: AnnotationHub, biobroom, BiocGenerics, BiocParallel, Chicago, ComplexHeatmap, cummeRbund, epivizrChart, GenomeInfoDb, Glimma, GSReg, GWASTools, HDF5Array, interactiveDisplay, IRanges, metaseqR, MiRaGE, omicsPrint, Onassis, parglms, recountmethylation, RTCGA, S4Vectors, SeqGSEA, splatter, TFutils, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, GenomicState, BeadArrayUseCases, GeuvadisTranscriptExpr, nanotubes, RNAmodR.Data, sesameData, Single.mTEC.Transcriptomes, CAGEWorkflow, cancerTiming, chicane, gkmSVM, LDheatmap, polyRAD, rliger, seqmagick, Seurat, sigminer, valr dependencyCount: 16 Package: GenomicScores Version: 2.2.0 Depends: R (>= 3.5), S4Vectors (>= 0.7.21), GenomicRanges, methods, BiocGenerics (>= 0.13.8) Imports: stats, utils, XML, Biobase, BiocManager, BiocFileCache, IRanges (>= 2.3.23), Biostrings, GenomeInfoDb, AnnotationHub, rhdf5, DelayedArray, HDF5Array Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19, phastCons100way.UCSC.hg19, MafDb.1Kgenomes.phase1.hs37d5, SNPlocs.Hsapiens.dbSNP144.GRCh37, VariantAnnotation, TxDb.Hsapiens.UCSC.hg19.knownGene, gwascat, RColorBrewer, shiny, shinythemes, shinyjs, shinycustomloader, data.table, DT License: Artistic-2.0 MD5sum: 109dd61271eaabc81c2bf14e0b429dbd NeedsCompilation: no Title: Infrastructure to work with genomewide position-specific scores Description: Provide infrastructure to store and access genomewide position-specific scores within R and Bioconductor. biocViews: Infrastructure, Genetics, Annotation, Sequencing, Coverage Author: Robert Castelo [aut, cre], Pau Puigdevall [ctb], Pablo Rodríguez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/GenomicScores VignetteBuilder: knitr BugReports: https://github.com/rcastelo/GenomicScores/issues git_url: https://git.bioconductor.org/packages/GenomicScores git_branch: RELEASE_3_12 git_last_commit: 1a877e1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenomicScores_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicScores_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicScores_2.2.0.tgz vignettes: vignettes/GenomicScores/inst/doc/GenomicScores.html vignetteTitles: An introduction to the GenomicScores package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicScores/inst/doc/GenomicScores.R dependsOnMe: fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons30way.UCSC.hg38, phastCons7way.UCSC.hg38 importsMe: appreci8R, ATACseqQC, primirTSS, RareVariantVis, VariantFiltering suggestsMe: methrix dependencyCount: 95 Package: GenomicTuples Version: 1.24.0 Depends: R (>= 4.0), GenomicRanges (>= 1.37.4), GenomeInfoDb (>= 1.15.2), S4Vectors (>= 0.17.25) Imports: methods, BiocGenerics (>= 0.21.2), Rcpp (>= 0.11.2), IRanges (>= 2.19.13), data.table, stats4, stats, utils LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: fe49824c537a9ea2ed19392d45a6d6de NeedsCompilation: yes Title: Representation and Manipulation of Genomic Tuples Description: GenomicTuples defines general purpose containers for storing genomic tuples. It aims to provide functionality for tuples of genomic co-ordinates that are analogous to those available for genomic ranges in the GenomicRanges Bioconductor package. biocViews: Infrastructure, DataRepresentation, Sequencing Author: Peter Hickey [aut, cre], Marcin Cieslik [ctb], Hervé Pagès [ctb] Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues git_url: https://git.bioconductor.org/packages/GenomicTuples git_branch: RELEASE_3_12 git_last_commit: c458252 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GenomicTuples_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenomicTuples_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GenomicTuples_1.24.0.tgz vignettes: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html vignetteTitles: GenomicTuplesIntroduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R dependencyCount: 19 Package: genoset Version: 1.45.1 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), GenomicRanges (>= 1.17.19), SummarizedExperiment (>= 1.1.6) Imports: S4Vectors (>= 0.27.3), GenomeInfoDb (>= 1.1.3), IRanges (>= 2.5.12), methods, graphics Suggests: testthat, knitr, BiocStyle, rmarkdown, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: ad0a5b2587a1c089242fc97f870c14f1 NeedsCompilation: yes Title: A RangedSummarizedExperiment with methods for copy number analysis Description: GenoSet provides an extension of the RangedSummarizedExperiment class with additional API features. This class provides convenient and fast methods for working with segmented genomic data. Additionally, GenoSet provides the class RleDataFrame which stores runs of data along the genome for multiple samples and provides very fast summaries of arbitrary row sets (regions of the genome). biocViews: Infrastructure, DataRepresentation, Microarray, SNP, CopyNumberVariation Author: Peter M. Haverty Maintainer: Peter M. Haverty URL: https://github.com/phaverty/genoset VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/genoset git_branch: master git_last_commit: 16bbf75 git_last_commit_date: 2020-05-02 Date/Publication: 2020-05-02 source.ver: src/contrib/genoset_1.45.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/genoset_1.45.1.zip mac.binary.ver: bin/macosx/contrib/4.0/genoset_1.45.1.tgz vignettes: vignettes/genoset/inst/doc/genoset.html vignetteTitles: genoset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoset/inst/doc/genoset.R importsMe: methyAnalysis, VegaMC dependencyCount: 26 Package: genotypeeval Version: 1.22.0 Depends: R (>= 3.4.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel, graphics, stats Suggests: rmarkdown, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: 1af8b4c3e8096947348caaf4e2247a89 NeedsCompilation: no Title: QA/QC of a gVCF or VCF file Description: Takes in a gVCF or VCF and reports metrics to assess quality of calls. biocViews: Genetics, BatchEffect, Sequencing, SNP, VariantAnnotation, DataImport Author: Jennifer Tom [aut, cre] Maintainer: Jennifer Tom VignetteBuilder: rmarkdown git_url: https://git.bioconductor.org/packages/genotypeeval git_branch: RELEASE_3_12 git_last_commit: fdb3376 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genotypeeval_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genotypeeval_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genotypeeval_1.22.0.tgz vignettes: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html vignetteTitles: genotypeeval_vignette.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 105 Package: genphen Version: 1.18.0 Depends: R (>= 3.5.0), Rcpp (>= 0.12.17), methods, stats, graphics Imports: rstan (>= 2.17.3), ranger, parallel, foreach, doParallel, e1071, Biostrings, rPref Suggests: testthat, ggplot2, gridExtra, ape, ggrepel, knitr, reshape, xtable License: GPL (>= 2) MD5sum: 6b3c66592313dcf60fa40c630faa73ed NeedsCompilation: no Title: A tool for quantification of associations between genotypes and phenotypes in genome wide association studies (GWAS) with Bayesian inference and statistical learning Description: Genetic association studies are an essential tool for studying the relationship between genotypes and phenotypes. With genphen we can jointly study multiple phenotypes of different types, by quantifying the association between different genotypes and each phenotype using a hybrid method which uses statistical learning techniques such as random forest and support vector machines, and Bayesian inference using hierarchical models. biocViews: GenomeWideAssociation, Regression, Classification, SupportVectorMachine, Genetics, SequenceMatching, Bayesian, FeatureExtraction, Sequencing Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski BugReports: https://github.com/snaketron/genphen/issues git_url: https://git.bioconductor.org/packages/genphen git_branch: RELEASE_3_12 git_last_commit: 7474de2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/genphen_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/genphen_1.17.0.zip mac.binary.ver: bin/macosx/contrib/4.0/genphen_1.18.0.tgz vignettes: vignettes/genphen/inst/doc/genphenManual.pdf vignetteTitles: genphen overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genphen/inst/doc/genphenManual.R dependencyCount: 84 Package: GenVisR Version: 1.22.1 Depends: R (>= 3.3.0), methods Imports: AnnotationDbi, biomaRt (>= 2.45.8), BiocGenerics, Biostrings, DBI, FField, GenomicFeatures, GenomicRanges (>= 1.25.4), ggplot2 (>= 2.1.0), gridExtra (>= 2.0.0), gtable, gtools, IRanges (>= 2.7.5), plyr (>= 1.8.3), reshape2, Rsamtools, scales, viridis, data.table, BSgenome, GenomeInfoDb, VariantAnnotation Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene, rmarkdown, vdiffr, formatR, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg38 License: GPL-3 + file LICENSE MD5sum: dbbec931c15071c63c7c8746357c6865 NeedsCompilation: no Title: Genomic Visualizations in R Description: Produce highly customizable publication quality graphics for genomic data primarily at the cohort level. biocViews: Infrastructure, DataRepresentation, Classification, DNASeq Author: Zachary Skidmore [aut, cre], Alex Wagner [aut], Robert Lesurf [aut], Katie Campbell [aut], Jason Kunisaki [aut], Obi Griffith [aut], Malachi Griffith [aut] Maintainer: Zachary Skidmore VignetteBuilder: knitr BugReports: https://github.com/griffithlab/GenVisR/issues git_url: https://git.bioconductor.org/packages/GenVisR git_branch: RELEASE_3_12 git_last_commit: 2e30ead git_last_commit_date: 2020-12-25 Date/Publication: 2020-12-26 source.ver: src/contrib/GenVisR_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GenVisR_1.22.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GenVisR_1.22.1.tgz vignettes: vignettes/GenVisR/inst/doc/Intro.html, vignettes/GenVisR/inst/doc/Upcoming_Features.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html vignetteTitles: GenVisR: An introduction, Visualizing Small Variants, waterfall: function introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/Intro.R, vignettes/GenVisR/inst/doc/Upcoming_Features.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R dependencyCount: 112 Package: GEOmetadb Version: 1.52.0 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 1993276ed5110862b2ab6002918f293f NeedsCompilation: no Title: A compilation of metadata from NCBI GEO Description: The NCBI Gene Expression Omnibus (GEO) represents the largest public repository of microarray data. However, finding data of interest can be challenging using current tools. GEOmetadb is an attempt to make access to the metadata associated with samples, platforms, and datasets much more feasible. This is accomplished by parsing all the NCBI GEO metadata into a SQLite database that can be stored and queried locally. GEOmetadb is simply a thin wrapper around the SQLite database along with associated documentation. Finally, the SQLite database is updated regularly as new data is added to GEO and can be downloaded at will for the most up-to-date metadata. GEOmetadb paper: http://bioinformatics.oxfordjournals.org/cgi/content/short/24/23/2798 . biocViews: Infrastructure Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/geo/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GEOmetadb git_branch: RELEASE_3_12 git_last_commit: 88673b4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GEOmetadb_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GEOmetadb_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GEOmetadb_1.52.0.tgz vignettes: vignettes/GEOmetadb/inst/doc/GEOmetadb.html vignetteTitles: GEOmetadb hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R importsMe: Onassis, MetaIntegrator suggestsMe: antiProfilesData, maGUI dependencyCount: 53 Package: GEOquery Version: 2.58.0 Depends: methods, Biobase Imports: httr, readr (>= 1.3.1), xml2, dplyr, tidyr, magrittr, limma Suggests: knitr, rmarkdown, BiocGenerics, testthat, covr License: GPL-2 MD5sum: 9e041d946e0331210e96b28ecc74c2cb NeedsCompilation: no Title: Get data from NCBI Gene Expression Omnibus (GEO) Description: The NCBI Gene Expression Omnibus (GEO) is a public repository of microarray data. Given the rich and varied nature of this resource, it is only natural to want to apply BioConductor tools to these data. GEOquery is the bridge between GEO and BioConductor. biocViews: Microarray, DataImport, OneChannel, TwoChannel, SAGE Author: Sean Davis Maintainer: Sean Davis URL: https://github.com/seandavi/GEOquery VignetteBuilder: knitr BugReports: https://github.com/seandavi/GEOquery/issues/new git_url: https://git.bioconductor.org/packages/GEOquery git_branch: RELEASE_3_12 git_last_commit: 6332ca3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GEOquery_2.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GEOquery_2.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GEOquery_2.58.0.tgz vignettes: vignettes/GEOquery/inst/doc/GEOquery.html vignetteTitles: Using GEOquery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R dependsOnMe: DrugVsDisease, SCAN.UPC, dyebiasexamples, GSE62944 importsMe: bigmelon, ChIPXpress, coexnet, crossmeta, EGAD, GAPGOM, MACPET, minfi, MoonlightR, phantasus, recount, SRAdb, BeadArrayUseCases, geneExpressionFromGEO, MetaIntegrator suggestsMe: AUCell, ctsGE, dearseq, debCAM, diffcoexp, dyebias, ELBOW, EpiDISH, fgsea, GCSscore, multiClust, MultiDataSet, omicsPrint, PCAtools, RegEnrich, RGSEA, Rnits, runibic, skewr, spatialHeatmap, TargetScore, zFPKM, airway, antiProfilesData, muscData, parathyroidSE, prostateCancerCamcap, prostateCancerGrasso, prostateCancerStockholm, prostateCancerTaylor, prostateCancerVarambally, RegParallel, BED, maGUI, metaMA, MLML2R, NACHO, robustSingleCell, TcGSA dependencyCount: 42 Package: GEOsubmission Version: 1.42.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: d5a1748dd91d2d9f99784af998befd6d NeedsCompilation: no Title: Prepares microarray data for submission to GEO Description: Helps to easily submit a microarray dataset and the associated sample information to GEO by preparing a single file for upload (direct deposit). biocViews: Microarray Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/GEOsubmission git_branch: RELEASE_3_12 git_last_commit: 00c6b88 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GEOsubmission_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GEOsubmission_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GEOsubmission_1.42.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsubmission/inst/doc/GEOsubmission.R dependencyCount: 13 Package: gep2pep Version: 1.10.0 Imports: repo (>= 2.1.1), foreach, stats, utils, GSEABase, methods, Biobase, XML, rhdf5, digest, iterators Suggests: WriteXLS, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 8acba2c272a82610ed7491feadedcace NeedsCompilation: no Title: Creation and Analysis of Pathway Expression Profiles (PEPs) Description: Pathway Expression Profiles (PEPs) are based on the expression of pathways (defined as sets of genes) as opposed to individual genes. This package converts gene expression profiles to PEPs and performs enrichment analysis of both pathways and experimental conditions, such as "drug set enrichment analysis" and "gene2drug" drug discovery analysis respectively. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DimensionReduction, Pathways, GO Author: Francesco Napolitano Maintainer: Francesco Napolitano VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gep2pep git_branch: RELEASE_3_12 git_last_commit: 801e702 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gep2pep_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gep2pep_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gep2pep_1.10.0.tgz vignettes: vignettes/gep2pep/inst/doc/vignette.html vignetteTitles: Introduction to gep2pep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gep2pep/inst/doc/vignette.R dependencyCount: 48 Package: gespeR Version: 1.22.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: c0709df23a3e6c022d2ffd9d53ab8b11 NeedsCompilation: no Title: Gene-Specific Phenotype EstimatoR Description: Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gespeR git_branch: RELEASE_3_12 git_last_commit: b3af830 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gespeR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gespeR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gespeR_1.22.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gespeR/inst/doc/gespeR.R dependencyCount: 108 Package: getDEE2 Version: 1.0.0 Depends: R (>= 4.0) Imports: stats, utils, SummarizedExperiment, htm2txt Suggests: knitr, testthat License: GPL-3 MD5sum: 4fb7bb9f2596d3f4016641e983fa78d1 NeedsCompilation: no Title: Programmatic access to the DEE2 RNA expression dataset Description: Digital Expression Explorer 2 (or DEE2 for short) is a repository of processed RNA-seq data in the form of counts. It was designed so that researchers could undertake re-analysis and meta-analysis of published RNA-seq studies quickly and easily. As of April 2020, over 1 million SRA datasets have been processed. This package provides an R interface to access these expression data. More information about the DEE2 project can be found at the project homepage (http://dee2.io) and main publication (https://doi.org/10.1093/gigascience/giz022). biocViews: GeneExpression, Transcriptomics, Sequencing Author: Mark Ziemann [aut, cre], Antony Kaspi [aut] Maintainer: Mark Ziemann URL: https://github.com/markziemann/getDEE2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/getDEE2 git_branch: RELEASE_3_12 git_last_commit: ed39494 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-29 source.ver: src/contrib/getDEE2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/getDEE2_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/getDEE2_1.0.0.tgz vignettes: vignettes/getDEE2/inst/doc/getDEE2.html vignetteTitles: getDEE2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/getDEE2/inst/doc/getDEE2.R dependencyCount: 27 Package: GEWIST Version: 1.34.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 82dc3b0caa1383597a2c6a35c29e543b NeedsCompilation: no Title: Gene Environment Wide Interaction Search Threshold Description: This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. biocViews: MultipleComparison, Genetics Author: Wei Q. Deng, Guillaume Pare Maintainer: Wei Q. Deng git_url: https://git.bioconductor.org/packages/GEWIST git_branch: RELEASE_3_12 git_last_commit: e747b44 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GEWIST_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GEWIST_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GEWIST_1.34.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEWIST/inst/doc/GEWIST.R dependencyCount: 83 Package: GGBase Version: 3.52.0 Depends: R (>= 2.14), methods, snpStats Imports: limma, genefilter, Biobase, BiocGenerics (>= 0.35.3), S4Vectors, IRanges, Matrix, AnnotationDbi, digest, GenomicRanges, SummarizedExperiment Suggests: GGtools, illuminaHumanv1.db, knitr License: Artistic-2.0 MD5sum: 6fc62e931457f378d65d503e7734bb02 NeedsCompilation: no Title: GGBase infrastructure for genetics of gene expression package GGtools Description: infrastructure biocViews: Genetics, Infrastructure Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/GGBase git_branch: RELEASE_3_12 git_last_commit: 61410a4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GGBase_3.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GGBase_3.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GGBase_3.52.0.tgz vignettes: vignettes/GGBase/inst/doc/ggbase.html vignetteTitles: GGBase -- infrastructure for GGtools,, genetics of gene expression hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGBase/inst/doc/ggbase.R dependsOnMe: GGtools, ceu1kg, ceu1kgv, ceuhm3, dsQTL, GGdata, hmyriB36, yri1kgv dependencyCount: 59 Package: ggbio Version: 1.38.0 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.29.2), Biobase, S4Vectors (>= 0.13.13), IRanges (>= 2.11.16), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.29.14), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.29.11), OrganismDbi, GGally, ensembldb (>= 1.99.13), AnnotationDbi, AnnotationFilter, rlang Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75, tinytex License: Artistic-2.0 MD5sum: 96e6a557917feb186b3ba00ef7b49d47 NeedsCompilation: no Title: Visualization tools for genomic data Description: The ggbio package extends and specializes the grammar of graphics for biological data. The graphics are designed to answer common scientific questions, in particular those often asked of high throughput genomics data. All core Bioconductor data structures are supported, where appropriate. The package supports detailed views of particular genomic regions, as well as genome-wide overviews. Supported overviews include ideograms and grand linear views. High-level plots include sequence fragment length, edge-linked interval to data view, mismatch pileup, and several splicing summaries. biocViews: Infrastructure, Visualization Author: Tengfei Yin [aut], Michael Lawrence [aut, ths, cre], Dianne Cook [aut, ths], Johannes Rainer [ctb] Maintainer: Michael Lawrence URL: http://tengfei.github.com/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/tengfei/ggbio/issues git_url: https://git.bioconductor.org/packages/ggbio git_branch: RELEASE_3_12 git_last_commit: c39c519 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ggbio_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ggbio_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ggbio_1.38.0.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: Part 0: Introduction and quick start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: derfinderPlot, FourCSeq, GenomicOZone, msgbsR, Pi, R3CPET, Rariant, ReportingTools, RiboProfiling, scruff, SomaticSignatures suggestsMe: bambu, beadarray, ensembldb, gQTLstats, gwascat, interactiveDisplay, regionReport, RnBeads, StructuralVariantAnnotation, IHWpaper, NanoporeRNASeq, Single.mTEC.Transcriptomes, SomaticCancerAlterations dependencyCount: 148 Package: ggcyto Version: 1.18.0 Depends: methods, ggplot2(>= 3.3.0), flowCore(>= 1.41.5), ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.33.1) Imports: plyr, scales, hexbin, data.table, RColorBrewer, gridExtra, rlang Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz, ggridges, vdiffr License: Artistic-2.0 MD5sum: 0c47d99641bcd192a0f0b7081569bfa3 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fortify method implemented for flowSet, ncdfFlowSet and GatingSet classes, both raw and gated flow cytometry data can be plotted directly with ggplot. ggcyto wrapper and some customed layers also make it easy to add gates and population statistics to the plot. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang ,Jake Wagner URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ggcyto git_branch: RELEASE_3_12 git_last_commit: 659fc4e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ggcyto_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ggcyto_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ggcyto_1.18.0.tgz vignettes: vignettes/ggcyto/inst/doc/autoplot.html, vignettes/ggcyto/inst/doc/ggcyto.flowSet.html, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.html, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.html vignetteTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggcyto/inst/doc/autoplot.R, vignettes/ggcyto/inst/doc/ggcyto.flowSet.R, vignettes/ggcyto/inst/doc/ggcyto.GatingSet.R, vignettes/ggcyto/inst/doc/Top_features_of_ggcyto.R importsMe: CytoML suggestsMe: CATALYST, flowCore, flowTime, flowWorkspace, openCyto dependencyCount: 82 Package: GGPA Version: 1.2.0 Depends: R (>= 4.0.0), stats, methods, graphics, GGally, network, sna, scales, matrixStats Imports: Rcpp (>= 0.11.3) LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 2f85f7bc1e93b69a9de316eb5344d7be NeedsCompilation: yes Title: graph-GPA: A graphical model for prioritizing GWAS results and investigating pleiotropic architecture Description: Genome-wide association studies (GWAS) is a widely used tool for identification of genetic variants associated with phenotypes and diseases, though complex diseases featuring many genetic variants with small effects present difficulties for traditional these studies. By leveraging pleiotropy, the statistical power of a single GWAS can be increased. This package provides functions for fitting graph-GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy. 'GGPA' package provides user-friendly interface to fit graph-GPA models, implement association mapping, and generate a phenotype graph. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Hang J. Kim, Carter Allen Maintainer: Dongjun Chung URL: https://github.com/dongjunchung/GGPA/ SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/GGPA git_branch: RELEASE_3_12 git_last_commit: 3dc4987 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GGPA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GGPA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GGPA_1.2.0.tgz vignettes: vignettes/GGPA/inst/doc/GGPA-example.pdf vignetteTitles: GGPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGPA/inst/doc/GGPA-example.R dependencyCount: 61 Package: GGtools Version: 5.25.3 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel, Homo.sapiens Imports: methods, utils, stats, BiocGenerics (>= 0.25.1), snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), iterators, Biostrings, ROCR, biglm, ggplot2, reshape2 Suggests: GGdata, illuminaHumanv1.db, SNPlocs.Hsapiens.dbSNP144.GRCh37, multtest, aod, rmeta Enhances: MatrixEQTL, foreach, doParallel, gwascat License: Artistic-2.0 MD5sum: 7fca8396df9e29321d6663c04d51cb04 NeedsCompilation: no Title: software and data for analyses in genetics of gene expression Description: software and data for analyses in genetics of gene expression and/or DNA methylation biocViews: Genetics, GeneExpression, GeneticVariability, SNP Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/GGtools git_branch: master git_last_commit: 4425f3d git_last_commit_date: 2020-07-02 Date/Publication: 2020-07-02 source.ver: src/contrib/GGtools_5.25.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/GGtools_5.25.3.zip mac.binary.ver: bin/macosx/contrib/4.0/GGtools_5.25.3.tgz vignettes: vignettes/GGtools/inst/doc/GGtools.pdf vignetteTitles: GGtools: software for eQTL identification hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GGtools/inst/doc/GGtools.R dependsOnMe: ceu1kg, eQTL importsMe: GeneGeneInteR, ceuhm3 suggestsMe: GGBase, cgdv17, dsQTL, hmyriB36 dependencyCount: 163 Package: ggtree Version: 2.4.2 Depends: R (>= 3.5.0) Imports: ape, aplot (>= 0.0.4), dplyr, ggplot2 (>= 3.0.0), grid, magrittr, methods, purrr, rlang, rvcheck, tidyr, tidytree (>= 0.2.6), treeio (>= 1.8.0), utils, scales Suggests: emojifont, ggimage, ggplotify, grDevices, knitr, prettydoc, rmarkdown, stats, testthat, tibble License: Artistic-2.0 MD5sum: 5a67ec7f12e854b9a12ee842de725195 NeedsCompilation: no Title: an R package for visualization of tree and annotation data Description: 'ggtree' extends the 'ggplot2' plotting system which implemented the grammar of graphics. 'ggtree' is designed for visualization and annotation of phylogenetic trees and other tree-like structures with their annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, Phylogenetics, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu [aut, cre, cph] (), Tommy Tsan-Yuk Lam [aut, ths], Shuangbin Xu [aut] (), Yonghe Xia [ctb], Justin Silverman [ctb], Bradley Jones [ctb], Watal M. Iwasaki [ctb], Ruizhu Huang [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/treedata-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtree/issues git_url: https://git.bioconductor.org/packages/ggtree git_branch: RELEASE_3_12 git_last_commit: a9f9cb3 git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-26 source.ver: src/contrib/ggtree_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/ggtree_2.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/ggtree_2.4.2.tgz vignettes: vignettes/ggtree/inst/doc/ggtree.html vignetteTitles: ggtree hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/ggtree.R importsMe: LymphoSeq, MicrobiotaProcess, philr, singleCellTK, sitePath, genBaRcode, harrietr, RAINBOWR, STraTUS suggestsMe: ggtreeExtra, metagenomeFeatures, systemPipeShiny, treeio, TreeSummarizedExperiment, universalmotif, aplot, CoOL, DAISIE, ggimage, ggmsa, idiogramFISH, microeco, nosoi, oppr, PCMBase, rhierbaps, tidytree, yatah dependencyCount: 56 Package: ggtreeExtra Version: 1.0.4 Imports: ggplot2, utils, rlang, ggnewscale, stats Suggests: ggtree, treeio, ggstar, patchwork, knitr, rmarkdown, prettydoc, markdown License: GPL-3 MD5sum: 7fdf428bc61a50d905c4aa1b4a7bedac NeedsCompilation: no Title: An R Package To Add Geometric Layers On Circular Or Other Layout Tree Of "ggtree" Description: 'ggtreeExtra' extends the method for mapping and visualizing associated data on phylogenetic tree using 'ggtree'. These associated data can be presented on the external panels to circular layout, fan layout, or other rectangular layout tree built by 'ggtree' with the grammar of 'ggplot2'. biocViews: Software, Visualization, Phylogenetics, Annotation Author: Shuangbin Xu [aut, cre] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/ggtreeExtra/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/ggtreeExtra/issues git_url: https://git.bioconductor.org/packages/ggtreeExtra git_branch: RELEASE_3_12 git_last_commit: 921bdf2 git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-26 source.ver: src/contrib/ggtreeExtra_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/ggtreeExtra_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.0/ggtreeExtra_1.0.4.tgz vignettes: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.html vignetteTitles: ggtreeExtra hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtreeExtra/inst/doc/ggtreeExtra.R dependencyCount: 39 Package: GIGSEA Version: 1.8.0 Depends: R (>= 3.5), Matrix, MASS, locfdr, stats, utils Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: 3cfde1aaf4c263d5f4fbbbc3526a4a94 NeedsCompilation: no Title: Genotype Imputed Gene Set Enrichment Analysis Description: We presented the Genotype-imputed Gene Set Enrichment Analysis (GIGSEA), a novel method that uses GWAS-and-eQTL-imputed trait-associated differential gene expression to interrogate gene set enrichment for the trait-associated SNPs. By incorporating eQTL from large gene expression studies, e.g. GTEx, GIGSEA appropriately addresses such challenges for SNP enrichment as gene size, gene boundary, SNP distal regulation, and multiple-marker regulation. The weighted linear regression model, taking as weights both imputation accuracy and model completeness, was used to perform the enrichment test, properly adjusting the bias due to redundancy in different gene sets. The permutation test, furthermore, is used to evaluate the significance of enrichment, whose efficiency can be largely elevated by expressing the computational intensive part in terms of large matrix operation. We have shown the appropriate type I error rates for GIGSEA (<5%), and the preliminary results also demonstrate its good performance to uncover the real signal. biocViews: GeneSetEnrichment,SNP,VariantAnnotation,GeneExpression,GeneRegulation,Regression,DifferentialExpression Author: Shijia Zhu Maintainer: Shijia Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GIGSEA git_branch: RELEASE_3_12 git_last_commit: 3a7ac0b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GIGSEA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GIGSEA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GIGSEA_1.8.0.tgz vignettes: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.pdf vignetteTitles: GIGSEA: Genotype Imputed Gene Set Enrichment Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GIGSEA/inst/doc/GIGSEA_tutorial.R suggestsMe: GIGSEAdata dependencyCount: 11 Package: girafe Version: 1.42.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.17.25), Rsamtools (>= 1.31.2), intervals (>= 0.13.1), ShortRead (>= 1.37.1), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings (>= 2.47.6), graphics, grDevices, stats, utils, IRanges (>= 2.13.12) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 Archs: i386, x64 MD5sum: 7e1ef25bf291900dce8dca2fa0f094ef NeedsCompilation: yes Title: Genome Intervals and Read Alignments for Functional Exploration Description: The package 'girafe' deals with the genome-level representation of aligned reads from next-generation sequencing data. It contains an object class for enabling a detailed description of genome intervals with aligned reads and functions for comparing, visualising, exporting and working with such intervals and the aligned reads. As such, the package interacts with and provides a link between the packages ShortRead, IRanges and genomeIntervals. biocViews: Sequencing Author: Joern Toedling, with contributions from Constance Ciaudo, Olivier Voinnet, Edith Heard, Emmanuel Barillot, and Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/girafe git_branch: RELEASE_3_12 git_last_commit: 983e1ff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/girafe_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/girafe_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/girafe_1.42.0.tgz vignettes: vignettes/girafe/inst/doc/girafe.pdf vignetteTitles: Genome intervals and read alignments for functional exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/girafe/inst/doc/girafe.R dependencyCount: 46 Package: GISPA Version: 1.14.0 Depends: R (>= 3.5) Imports: Biobase, changepoint, data.table, genefilter, graphics, GSEABase, HH, lattice, latticeExtra, plyr, scatterplot3d, stats Suggests: knitr License: GPL-2 MD5sum: 0221c89bd5f8307a5b3c44c033d45fb9 NeedsCompilation: no Title: GISPA: Method for Gene Integrated Set Profile Analysis Description: GISPA is a method intended for the researchers who are interested in defining gene sets with similar, a priori specified molecular profile. GISPA method has been previously published in Nucleic Acid Research (Kowalski et al., 2016; PMID: 26826710). biocViews: StatisticalMethod,GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GISPA git_branch: RELEASE_3_12 git_last_commit: 7d5d53d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GISPA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GISPA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GISPA_1.14.0.tgz vignettes: vignettes/GISPA/inst/doc/GISPA_manual.html vignetteTitles: GISPA:Method for Gene Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GISPA/inst/doc/GISPA_manual.R dependencyCount: 125 Package: GLAD Version: 2.54.0 Depends: R (>= 2.10) Imports: aws License: GPL-2 Archs: i386, x64 MD5sum: ad8276f497ace2541097050c187deb4a NeedsCompilation: yes Title: Gain and Loss Analysis of DNA Description: Analysis of array CGH data : detection of breakpoints in genomic profiles and assignment of a status (gain, normal or loss) to each chromosomal regions identified. biocViews: Microarray, CopyNumberVariation Author: Philippe Hupe Maintainer: Philippe Hupe URL: http://bioinfo.curie.fr SystemRequirements: gsl. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. git_url: https://git.bioconductor.org/packages/GLAD git_branch: RELEASE_3_12 git_last_commit: 135a44f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GLAD_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GLAD_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GLAD_2.54.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GLAD/inst/doc/GLAD.R dependsOnMe: ADaCGH2, ITALICS, seqCNA importsMe: ITALICS, MANOR, snapCGH suggestsMe: RnBeads, aroma.cn, aroma.core, cghRA dependencyCount: 4 Package: GladiaTOX Version: 1.6.1 Depends: R (>= 3.6.0), data.table (>= 1.9.4) Imports: DBI, RMySQL, RSQLite, numDeriv, RColorBrewer, parallel, stats, methods, graphics, grDevices, xtable, tools, brew, stringr, RJSONIO, ggplot2, ggrepel, tidyr, utils, RCurl, XML Suggests: roxygen2, knitr, rmarkdown, testthat, BiocStyle License: GPL-2 MD5sum: a05ffb29b7a2bdd2dee2fd3ec07a437e NeedsCompilation: no Title: R Package for Processing High Content Screening data Description: GladiaTOX R package is an open-source, flexible solution to high-content screening data processing and reporting in biomedical research. GladiaTOX takes advantage of the tcpl core functionalities and provides a number of extensions: it provides a web-service solution to fetch raw data; it computes severity scores and exports ToxPi formatted files; furthermore it contains a suite of functionalities to generate pdf reports for quality control and data processing. biocViews: Software, WorkflowStep, Normalization, Preprocessing, QualityControl Author: Vincenzo Belcastro [aut, cre], Dayne L Filer [aut], Stephane Cano [aut] Maintainer: PMP S.A. R Support VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GladiaTOX git_branch: RELEASE_3_12 git_last_commit: 364144b git_last_commit_date: 2020-11-13 Date/Publication: 2020-11-13 source.ver: src/contrib/GladiaTOX_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GladiaTOX_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GladiaTOX_1.6.1.tgz vignettes: vignettes/GladiaTOX/inst/doc/GladiaTOX.html vignetteTitles: GladiaTOX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GladiaTOX/inst/doc/GladiaTOX.R dependencyCount: 68 Package: Glimma Version: 2.0.0 Depends: R (>= 4.0.0) Imports: htmlwidgets, edgeR, DESeq2, limma, SummarizedExperiment, stats, jsonlite, methods, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle, IRanges, GenomicRanges, pryr License: GPL-3 MD5sum: b701109ba91bdfd16edc19bf47d02e51 NeedsCompilation: no Title: Interactive HTML graphics Description: This package generates interactive visualisations for analysis of RNA-sequencing data using output from limma, edgeR or DESeq2 packages in an HTML page. The interactions are built on top of the popular static representations of analysis results in order to provide additional information. biocViews: DifferentialExpression, GeneExpression, Microarray, ReportWriting, RNASeq, Sequencing, Visualization Author: Shian Su [aut, cre], Hasaru Kariyawasam [aut], Oliver Voogd [aut], Matthew Ritchie [aut], Charity Law [aut], Stuart Lee [ctb], Isaac Virshup [ctb] Maintainer: Shian Su URL: https://github.com/hasaru-k/GlimmaV2 VignetteBuilder: knitr BugReports: https://github.com/hasaru-k/GlimmaV2/issues git_url: https://git.bioconductor.org/packages/Glimma git_branch: RELEASE_3_12 git_last_commit: 40bebaa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Glimma_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Glimma_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Glimma_2.0.0.tgz vignettes: vignettes/Glimma/inst/doc/DESeq2.html, vignettes/Glimma/inst/doc/limma_edger.html vignetteTitles: DESeq2, limma hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/DESeq2.R, vignettes/Glimma/inst/doc/limma_edger.R dependsOnMe: RNAseq123 importsMe: affycoretools, EGSEA dependencyCount: 96 Package: glmGamPoi Version: 1.2.0 Imports: Rcpp, DelayedMatrixStats, matrixStats, DelayedArray, HDF5Array, SummarizedExperiment, methods, stats, utils, splines LinkingTo: Rcpp, RcppArmadillo, beachmat Suggests: testthat (>= 2.1.0), zoo, DESeq2, edgeR, limma, beachmat, MASS, statmod, ggplot2, bench, BiocParallel, knitr, rmarkdown, BiocStyle, TENxPBMCData, scran License: GPL-3 Archs: i386, x64 MD5sum: 962cbb19d7fb346c0d2d595e922995c5 NeedsCompilation: yes Title: Fit a Gamma-Poisson Generalized Linear Model Description: Fit linear models to overdispersed count data. The package can estimate the overdispersion and fit repeated models for matrix input. It is designed to handle large input datasets as they typically occur in single cell RNA-seq experiments. biocViews: Regression, RNASeq, Software, SingleCell Author: Constantin Ahlmann-Eltze [aut, cre] (), Michael Love [ctb] Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/glmGamPoi SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/const-ae/glmGamPoi/issues git_url: https://git.bioconductor.org/packages/glmGamPoi git_branch: RELEASE_3_12 git_last_commit: a11d461 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/glmGamPoi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/glmGamPoi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/glmGamPoi_1.2.0.tgz vignettes: vignettes/glmGamPoi/inst/doc/glmGamPoi.html vignetteTitles: glmGamPoi Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmGamPoi/inst/doc/glmGamPoi.R suggestsMe: DESeq2 dependencyCount: 43 Package: glmSparseNet Version: 1.8.1 Depends: R (>= 3.5), Matrix, MultiAssayExperiment, glmnet Imports: SummarizedExperiment, STRINGdb, biomaRt, futile.logger, sparsebn, sparsebnUtils, forcats, dplyr, readr, ggplot2, survminer, reshape2, stats, stringr, rlang, parallel, methods, loose.rock (>= 1.0.12) Suggests: testthat, knitr, rmarkdown, survival, survcomp, pROC, VennDiagram, BiocStyle, curatedTCGAData, TCGAutils License: GPL (>=3) MD5sum: 9c97f462da742afc1cc8189fcf138a8b NeedsCompilation: no Title: Network Centrality Metrics for Elastic-Net Regularized Models Description: glmSparseNet is an R-package that generalizes sparse regression models when the features (e.g. genes) have a graph structure (e.g. protein-protein interactions), by including network-based regularizers. glmSparseNet uses the glmnet R-package, by including centrality measures of the network as penalty weights in the regularization. The current version implements regularization based on node degree, i.e. the strength and/or number of its associated edges, either by promoting hubs in the solution or orphan genes in the solution. All the glmnet distribution families are supported, namely "gaussian", "poisson", "binomial", "multinomial", "cox", and "mgaussian". biocViews: Software, StatisticalMethod, DimensionReduction, Regression, Classification, Survival, Network, GraphAndNetwork Author: André Veríssimo [aut, cre], Susana Vinga [aut], Eunice Carrasquinha [ctb], Marta Lopes [ctb] Maintainer: André Veríssimo URL: https://www.github.com/sysbiomed/glmSparseNet VignetteBuilder: knitr BugReports: https://www.github.com/sysbiomed/glmSparseNet/issues git_url: https://git.bioconductor.org/packages/glmSparseNet git_branch: RELEASE_3_12 git_last_commit: 90162a3 git_last_commit_date: 2021-04-13 Date/Publication: 2021-04-13 source.ver: src/contrib/glmSparseNet_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/glmSparseNet_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.0/glmSparseNet_1.8.1.tgz vignettes: vignettes/glmSparseNet/inst/doc/example_brca_logistic.html, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.html, vignettes/glmSparseNet/inst/doc/example_brca_survival.html, vignettes/glmSparseNet/inst/doc/example_prad_survival.html, vignettes/glmSparseNet/inst/doc/example_skcm_survival.html vignetteTitles: Example for Classification -- Breast Invasive Carcinoma, Breast survival dataset using network from STRING DB, Example for Survival Data -- Breast Invasive Carcinoma, Example for Survival Data -- Prostate Adenocarcinoma, Example for Survival Data -- Skin Melanoma hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/glmSparseNet/inst/doc/example_brca_logistic.R, vignettes/glmSparseNet/inst/doc/example_brca_protein-protein-interactions_survival.R, vignettes/glmSparseNet/inst/doc/example_brca_survival.R, vignettes/glmSparseNet/inst/doc/example_prad_survival.R, vignettes/glmSparseNet/inst/doc/example_skcm_survival.R dependencyCount: 184 Package: GlobalAncova Version: 4.8.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi, Biobase, dendextend, GSEABase, VGAM Suggests: GO.db, KEGG.db, golubEsets, hu6800.db, vsn, Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: 221c300f8d0321b232aa1b96aff4743f NeedsCompilation: yes Title: Global test for groups of variables via model comparisons Description: The association between a variable of interest (e.g. two groups) and the global pattern of a group of variables (e.g. a gene set) is tested via a global F-test. We give the following arguments in support of the GlobalAncova approach: After appropriate normalisation, gene-expression-data appear rather symmetrical and outliers are no real problem, so least squares should be rather robust. ANCOVA with interaction yields saturated data modelling e.g. different means per group and gene. Covariate adjustment can help to correct for possible selection bias. Variance homogeneity and uncorrelated residuals cannot be expected. Application of ordinary least squares gives unbiased, but no longer optimal estimates (Gauss-Markov-Aitken). Therefore, using the classical F-test is inappropriate, due to correlation. The test statistic however mirrors deviations from the null hypothesis. In combination with a permutation approach, empirical significance levels can be approximated. Alternatively, an approximation yields asymptotic p-values. The framework is generalized to groups of categorical variables or even mixed data by a likelihood ratio approach. Closed and hierarchical testing procedures are supported. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany and BMBF grant 01ZX1309B, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways, Regression Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/GlobalAncova git_branch: RELEASE_3_12 git_last_commit: fffc416 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GlobalAncova_4.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GlobalAncova_4.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GlobalAncova_4.8.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R importsMe: miRtest dependencyCount: 76 Package: globalSeq Version: 1.18.0 Depends: R (>= 3.0.0) Suggests: knitr, testthat, SummarizedExperiment, S4Vectors License: GPL-3 MD5sum: 554c0aca36bdc355dcca9a905e90e460 NeedsCompilation: no Title: Global Test for Counts Description: The method may be conceptualised as a test of overall significance in regression analysis, where the response variable is overdispersed and the number of explanatory variables exceeds the sample size. Useful for testing for association between RNA-Seq and high-dimensional data. biocViews: GeneExpression, ExonArray, DifferentialExpression, GenomeWideAssociation, Transcriptomics, DimensionReduction, Regression, Sequencing, WholeGenome, RNASeq, ExomeSeq, miRNA, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/globalSeq VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/globalSeq/issues git_url: https://git.bioconductor.org/packages/globalSeq git_branch: RELEASE_3_12 git_last_commit: 3f93493 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/globalSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/globalSeq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/globalSeq_1.18.0.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf, vignettes/globalSeq/inst/doc/article.html, vignettes/globalSeq/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R dependencyCount: 0 Package: globaltest Version: 5.44.0 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGGREST, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart, mstate License: GPL (>= 2) MD5sum: d34f68048d6c10e6a327b72d4f6918df NeedsCompilation: no Title: Testing Groups of Covariates/Features for Association with a Response Variable, with Applications to Gene Set Testing Description: The global test tests groups of covariates (or features) for association with a response variable. This package implements the test with diagnostic plots and multiple testing utilities, along with several functions to facilitate the use of this test for gene set testing of GO and KEGG terms. biocViews: Microarray, OneChannel, Bioinformatics, DifferentialExpression, GO, Pathways Author: Jelle Goeman and Jan Oosting, with contributions by Livio Finos, Aldo Solari, Dominic Edelmann Maintainer: Jelle Goeman git_url: https://git.bioconductor.org/packages/globaltest git_branch: RELEASE_3_12 git_last_commit: 571933d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/globaltest_5.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/globaltest_5.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/globaltest_5.44.0.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globaltest/inst/doc/GlobalTest.R dependsOnMe: GlobalAncova importsMe: BiSeq, EGSEA, SIM, miRtest, SlaPMEG suggestsMe: topGO, maGUI, penalized dependencyCount: 44 Package: gmapR Version: 1.32.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Rsamtools (>= 1.31.2) Imports: S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), BiocGenerics (>= 0.25.1), rtracklayer (>= 1.39.7), GenomicFeatures (>= 1.31.3), Biostrings, VariantAnnotation (>= 1.25.11), tools, Biobase, BSgenome, GenomicAlignments (>= 1.15.6), BiocParallel Suggests: RUnit, BSgenome.Dmelanogaster.UCSC.dm3, BSgenome.Scerevisiae.UCSC.sacCer3, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines License: Artistic-2.0 MD5sum: d0c0244da3538780ab07c6b88b431db2 NeedsCompilation: yes Title: An R interface to the GMAP/GSNAP/GSTRUCT suite Description: GSNAP and GMAP are a pair of tools to align short-read data written by Tom Wu. This package provides convenience methods to work with GMAP and GSNAP from within R. In addition, it provides methods to tally alignment results on a per-nucleotide basis using the bam_tally tool. biocViews: Alignment Author: Cory Barr, Thomas Wu, Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/gmapR git_branch: RELEASE_3_12 git_last_commit: 47ad3c2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gmapR_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/gmapR_1.32.0.tgz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmapR/inst/doc/gmapR.R dependsOnMe: HTSeqGenie importsMe: MMAPPR2 suggestsMe: VariantTools, VariantToolsData dependencyCount: 90 Package: GmicR Version: 1.4.0 Imports: AnnotationDbi, ape, bnlearn, Category, DT, doParallel, foreach, gRbase, GSEABase, gRain, GOstats, org.Hs.eg.db, org.Mm.eg.db, shiny, WGCNA, data.table, grDevices, graphics, reshape2, stats, utils Suggests: knitr, rmarkdown, testthat License: GPL-2 + file LICENSE MD5sum: f53948a1de2261760fee7b77229fe85f NeedsCompilation: no Title: Combines WGCNA and xCell readouts with bayesian network learrning to generate a Gene-Module Immune-Cell network (GMIC) Description: This package uses bayesian network learning to detect relationships between Gene Modules detected by WGCNA and immune cell signatures defined by xCell. It is a hypothesis generating tool. biocViews: Software, SystemsBiology, GraphAndNetwork, Network, NetworkInference, GUI, ImmunoOncology, GeneExpression, QualityControl, Bayesian, Clustering Author: Richard Virgen-Slane Maintainer: Richard Virgen-Slane VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GmicR git_branch: RELEASE_3_12 git_last_commit: dcdd04b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GmicR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GmicR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GmicR_1.4.0.tgz vignettes: vignettes/GmicR/inst/doc/GmicR_vignette.html vignetteTitles: GmicR_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GmicR/inst/doc/GmicR_vignette.R dependencyCount: 141 Package: gmoviz Version: 1.2.0 Depends: circlize, GenomicRanges, graphics, R (>= 4.0) Imports: grid, gridBase, Rsamtools, ComplexHeatmap, BiocGenerics, Biostrings, GenomeInfoDb, methods, GenomicAlignments, GenomicFeatures, IRanges, rtracklayer, pracma, colorspace, S4Vectors Suggests: testthat, knitr, rmarkdown, pasillaBamSubset, BiocStyle, BiocManager License: GPL-3 MD5sum: c304746cc76c7d68edee621a5e09a8dd NeedsCompilation: no Title: Seamless visualization of complex genomic variations in GMOs and edited cell lines Description: Genetically modified organisms (GMOs) and cell lines are widely used models in all kinds of biological research. As part of characterising these models, DNA sequencing technology and bioinformatics analyses are used systematically to study their genomes. Therefore, large volumes of data are generated and various algorithms are applied to analyse this data, which introduces a challenge on representing all findings in an informative and concise manner. `gmoviz` provides users with an easy way to visualise and facilitate the explanation of complex genomic editing events on a larger, biologically-relevant scale. biocViews: Visualization, Sequencing, GeneticVariability, GenomicVariation, Coverage Author: Kathleen Zeglinski [cre, aut], Arthur Hsu [aut], Monther Alhamdoosh [aut] (), Constantinos Koutsakis [aut] Maintainer: Kathleen Zeglinski VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gmoviz git_branch: RELEASE_3_12 git_last_commit: 67f6302 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gmoviz_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gmoviz_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gmoviz_1.2.0.tgz vignettes: vignettes/gmoviz/inst/doc/gmoviz_advanced.html, vignettes/gmoviz/inst/doc/gmoviz_overview.html vignetteTitles: Advanced usage of gmoviz, Introduction to gmoviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gmoviz/inst/doc/gmoviz_advanced.R, vignettes/gmoviz/inst/doc/gmoviz_overview.R dependencyCount: 103 Package: GMRP Version: 1.18.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics License: GPL (>= 2) MD5sum: 1f4acc214ee4652307add6c125a28557 NeedsCompilation: no Title: GWAS-based Mendelian Randomization and Path Analyses Description: Perform Mendelian randomization analysis of multiple SNPs to determine risk factors causing disease of study and to exclude confounding variabels and perform path analysis to construct path of risk factors to the disease. biocViews: Sequencing, Regression, SNP Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/GMRP git_branch: RELEASE_3_12 git_last_commit: 3cf56a1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GMRP_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GMRP_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GMRP_1.18.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP-manual.pdf, vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: GMRP-manual.pdf, Causal Effect Analysis of Risk Factors for Disease with the "GMRP" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GMRP/inst/doc/GMRP.R dependencyCount: 22 Package: GNET2 Version: 1.6.0 Depends: R (>= 3.6) Imports: ggplot2,xgboost,Rcpp,reshape2,grid,DiagrammeR,methods,stats,matrixStats,graphics,SummarizedExperiment,dplyr,igraph, grDevices, utils LinkingTo: Rcpp Suggests: knitr, rmarkdown License: Apache License 2.0 Archs: i386, x64 MD5sum: 9aa2cb5a7bbe98c4cfac2a52feecc3b6 NeedsCompilation: yes Title: Constructing gene regulatory networks from expression data through functional module inference Description: Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached. biocViews: GeneExpression, Regression, Network, NetworkInference, Software Author: Chen Chen, Jie Hou and Jianlin Cheng Maintainer: Chen Chen URL: https://github.com/chrischen1/GNET2 VignetteBuilder: knitr BugReports: https://github.com/chrischen1/GNET2/issues git_url: https://git.bioconductor.org/packages/GNET2 git_branch: RELEASE_3_12 git_last_commit: 271a59f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GNET2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GNET2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GNET2_1.6.0.tgz vignettes: vignettes/GNET2/inst/doc/run_gnet2.html vignetteTitles: Build functional gene modules with GNET2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GNET2/inst/doc/run_gnet2.R dependencyCount: 86 Package: GOexpress Version: 1.24.0 Depends: R (>= 3.4), grid, stats, graphics, Biobase (>= 2.22.0) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6), RCurl (>= 1.95) Suggests: BiocStyle License: GPL (>= 3) MD5sum: c22c8894c04227404e32a0afba6b2fdd NeedsCompilation: no Title: Visualise microarray and RNAseq data using gene ontology annotations Description: The package contains methods to visualise the expression profile of genes from a microarray or RNA-seq experiment, and offers a supervised clustering approach to identify GO terms containing genes with expression levels that best classify two or more predefined groups of samples. Annotations for the genes present in the expression dataset may be obtained from Ensembl through the biomaRt package, if not provided by the user. The default random forest framework is used to evaluate the capacity of each gene to cluster samples according to the factor of interest. Finally, GO terms are scored by averaging the rank (alternatively, score) of their respective gene sets to cluster the samples. P-values may be computed to assess the significance of GO term ranking. Visualisation function include gene expression profile, gene ontology-based heatmaps, and hierarchical clustering of experimental samples using gene expression data. biocViews: Software, GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Clustering, TimeCourse, Microarray, Sequencing, RNASeq, Annotation, MultipleComparison, Pathways, GO, Visualization, ImmunoOncology Author: Kevin Rue-Albrecht [aut, cre], Tharvesh M.L. Ali [ctb], Paul A. McGettigan [ctb], Belinda Hernandez [ctb], David A. Magee [ctb], Nicolas C. Nalpas [ctb], Andrew Parnell [ctb], Stephen V. Gordon [ths], David E. MacHugh [ths] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/GOexpress git_url: https://git.bioconductor.org/packages/GOexpress git_branch: RELEASE_3_12 git_last_commit: 6c7a0d5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GOexpress_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOexpress_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GOexpress_1.24.0.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.R dependencyCount: 86 Package: GOfuncR Version: 1.10.0 Depends: R (>= 3.4), vioplot (>= 0.2), Imports: Rcpp (>= 0.11.5), mapplots (>= 1.5), gtools (>= 3.5.0), GenomicRanges (>= 1.28.4), IRanges, AnnotationDbi, utils, grDevices, graphics, stats, LinkingTo: Rcpp Suggests: Homo.sapiens, BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 0fb3482cadabbf0b0850ff8ccc16c841 NeedsCompilation: yes Title: Gene ontology enrichment using FUNC Description: GOfuncR performs a gene ontology enrichment analysis based on the ontology enrichment software FUNC. GO-annotations are obtained from OrganismDb or OrgDb packages ('Homo.sapiens' by default); the GO-graph is included in the package and updated regularly (23-Mar-2020). GOfuncR provides the standard candidate vs. background enrichment analysis using the hypergeometric test, as well as three additional tests: (i) the Wilcoxon rank-sum test that is used when genes are ranked, (ii) a binomial test that is used when genes are associated with two counts and (iii) a Chi-square or Fisher's exact test that is used in cases when genes are associated with four counts. To correct for multiple testing and interdependency of the tests, family-wise error rates are computed based on random permutations of the gene-associated variables. GOfuncR also provides tools for exploring the ontology graph and the annotations, and options to take gene-length or spatial clustering of genes into account. It is also possible to provide custom gene coordinates, annotations and ontologies. biocViews: GeneSetEnrichment, GO Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GOfuncR git_branch: RELEASE_3_12 git_last_commit: 51b01a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GOfuncR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOfuncR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GOfuncR_1.10.0.tgz vignettes: vignettes/GOfuncR/inst/doc/GOfuncR.html vignetteTitles: Introduction to GOfuncR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOfuncR/inst/doc/GOfuncR.R importsMe: ABAEnrichment dependencyCount: 42 Package: GOpro Version: 1.16.0 Depends: R (>= 3.4) Imports: AnnotationDbi, dendextend, doParallel, foreach, parallel, org.Hs.eg.db, GO.db, Rcpp, stats, graphics, MultiAssayExperiment, IRanges, S4Vectors LinkingTo: Rcpp, BH Suggests: knitr, rmarkdown, RTCGA.PANCAN12, BiocStyle, testthat License: GPL-3 Archs: i386, x64 MD5sum: 72253a4ef7a4881a8856fab6651ff0d5 NeedsCompilation: yes Title: Find the most characteristic gene ontology terms for groups of human genes Description: Find the most characteristic gene ontology terms for groups of human genes. This package was created as a part of the thesis which was developed under the auspices of MI^2 Group (http://mi2.mini.pw.edu.pl/, https://github.com/geneticsMiNIng). biocViews: Annotation, Clustering, GO, GeneExpression, GeneSetEnrichment, MultipleComparison Author: Lidia Chrabaszcz Maintainer: Lidia Chrabaszcz URL: https://github.com/mi2-warsaw/GOpro VignetteBuilder: knitr BugReports: https://github.com/mi2-warsaw/GOpro/issues git_url: https://git.bioconductor.org/packages/GOpro git_branch: RELEASE_3_12 git_last_commit: 54b3be7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GOpro_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOpro_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GOpro_1.16.0.tgz vignettes: vignettes/GOpro/inst/doc/GOpro_vignette.html vignetteTitles: GOpro: Determine groups of genes and find their characteristic GO term hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOpro/inst/doc/GOpro_vignette.R dependencyCount: 85 Package: goProfiles Version: 1.52.0 Depends: Biobase, AnnotationDbi, GO.db, CompQuadForm, stringr Suggests: org.Hs.eg.db License: GPL-2 MD5sum: 48653845672738ab4ad2da6670a0f102 NeedsCompilation: no Title: goProfiles: an R package for the statistical analysis of functional profiles Description: The package implements methods to compare lists of genes based on comparing the corresponding 'functional profiles'. biocViews: Annotation, GO, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Microarray, MultipleComparison, Pathways, Software Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez git_url: https://git.bioconductor.org/packages/goProfiles git_branch: RELEASE_3_12 git_last_commit: 21823c2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/goProfiles_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/goProfiles_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/goProfiles_1.52.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles-comparevisual.pdf, vignettes/goProfiles/inst/doc/goProfiles-plotProfileMF.pdf, vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles-comparevisual.pdf, goProfiles-plotProfileMF.pdf, goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R dependencyCount: 32 Package: GOSemSim Version: 2.16.1 Depends: R (>= 3.5.0) Imports: AnnotationDbi, GO.db, methods, utils LinkingTo: Rcpp Suggests: AnnotationHub, BiocManager, clusterProfiler, DOSE, knitr, org.Hs.eg.db, prettydoc, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 87cc24c6773fb9b9778a6f4337dfbe7c NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: The semantic comparisons of Gene Ontology (GO) annotations provide quantitative ways to compute similarities between genes and gene groups, and have became important basis for many bioinformatics analysis approaches. GOSemSim is an R package for semantic similarity computation among GO terms, sets of GO terms, gene products and gene clusters. GOSemSim implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu [aut, cre], Alexey Stukalov [ctb], Chuanle Xiao [ctb], Lluís Revilla Sancho [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/GOSemSim/issues git_url: https://git.bioconductor.org/packages/GOSemSim git_branch: RELEASE_3_12 git_last_commit: 92f1d56 git_last_commit_date: 2020-10-29 Date/Publication: 2020-10-29 source.ver: src/contrib/GOSemSim_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOSemSim_2.16.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GOSemSim_2.16.1.tgz vignettes: vignettes/GOSemSim/inst/doc/GOSemSim.html vignetteTitles: GOSemSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R dependsOnMe: tRanslatome, BiSEp importsMe: clusterProfiler, DOSE, enrichplot, GAPGOM, meshes, Rcpi, rrvgo, simplifyEnrichment, ViSEAGO, BioMedR, LANDD suggestsMe: BioCor, epiNEM, FELLA, SemDist, protr, rDNAse dependencyCount: 27 Package: goseq Version: 1.42.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase (>= 1.9.2) Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) MD5sum: bdc4fb8d47589efbc39cd146901cfee9 NeedsCompilation: no Title: Gene Ontology analyser for RNA-seq and other length biased data Description: Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data biocViews: ImmunoOncology, Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Matthew Young , Nadia Davidson git_url: https://git.bioconductor.org/packages/goseq git_branch: RELEASE_3_12 git_last_commit: 8164b90 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/goseq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/goseq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/goseq_1.42.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goseq/inst/doc/goseq.R dependsOnMe: rgsepd importsMe: ideal, SMITE dependencyCount: 94 Package: GOSim Version: 1.28.0 Depends: GO.db, annotate Imports: org.Hs.eg.db, AnnotationDbi, topGO, cluster, flexmix, RBGL, graph, Matrix, corpcor, Rcpp LinkingTo: Rcpp Enhances: igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: 8b45d945d771385cd5813804e64ea5c9 NeedsCompilation: yes Title: Computation of functional similarities between GO terms and gene products; GO enrichment analysis Description: This package implements several functions useful for computing similarities between GO terms and gene products based on their GO annotation. Moreover it allows for computing a GO enrichment analysis biocViews: GO, Clustering, Software, Pathways Author: Holger Froehlich Maintainer: Holger Froehlich git_url: https://git.bioconductor.org/packages/GOSim git_branch: RELEASE_3_12 git_last_commit: 75911ff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/GOSim_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOSim_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GOSim_1.28.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSim/inst/doc/GOSim.R dependencyCount: 55 Package: goSTAG Version: 1.14.2 Depends: R (>= 3.4) Imports: AnnotationDbi, biomaRt, GO.db, graphics, memoise, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 93aa6d282a4a8a907e5d632c3415e73e NeedsCompilation: no Title: A tool to use GO Subtrees to Tag and Annotate Genes within a set Description: Gene lists derived from the results of genomic analyses are rich in biological information. For instance, differentially expressed genes (DEGs) from a microarray or RNA-Seq analysis are related functionally in terms of their response to a treatment or condition. Gene lists can vary in size, up to several thousand genes, depending on the robustness of the perturbations or how widely different the conditions are biologically. Having a way to associate biological relatedness between hundreds and thousands of genes systematically is impractical by manually curating the annotation and function of each gene. Over-representation analysis (ORA) of genes was developed to identify biological themes. Given a Gene Ontology (GO) and an annotation of genes that indicate the categories each one fits into, significance of the over-representation of the genes within the ontological categories is determined by a Fisher's exact test or modeling according to a hypergeometric distribution. Comparing a small number of enriched biological categories for a few samples is manageable using Venn diagrams or other means for assessing overlaps. However, with hundreds of enriched categories and many samples, the comparisons are laborious. Furthermore, if there are enriched categories that are shared between samples, trying to represent a common theme across them is highly subjective. goSTAG uses GO subtrees to tag and annotate genes within a set. goSTAG visualizes the similarities between the over-representation of DEGs by clustering the p-values from the enrichment statistical tests and labels clusters with the GO term that has the most paths to the root within the subtree generated from all the GO terms in the cluster. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, Clustering, Microarray, mRNAMicroarray, RNASeq, Visualization, GO, ImmunoOncology Author: Brian D. Bennett and Pierre R. Bushel Maintainer: Brian D. Bennett VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/goSTAG git_branch: RELEASE_3_12 git_last_commit: 33319d2 git_last_commit_date: 2020-11-05 Date/Publication: 2020-11-05 source.ver: src/contrib/goSTAG_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/goSTAG_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.0/goSTAG_1.14.2.tgz vignettes: vignettes/goSTAG/inst/doc/goSTAG.html vignetteTitles: The goSTAG User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goSTAG/inst/doc/goSTAG.R dependencyCount: 62 Package: GOstats Version: 2.56.0 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.43.2), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), AnnotationForge, Rgraphviz Suggests: hgu95av2.db (>= 1.13.0), ALL, multtest, genefilter, RColorBrewer, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: db8a41a811f2f820d3702a4fac45b5f3 NeedsCompilation: no Title: Tools for manipulating GO and microarrays Description: A set of tools for interacting with GO and microarray data. A variety of basic manipulation tools for graphs, hypothesis testing and other simple calculations. biocViews: Annotation, GO, MultipleComparison, GeneExpression, Microarray, Pathways, GeneSetEnrichment, GraphAndNetwork Author: Robert Gentleman [aut], Seth Falcon [ctb], Robert Castelo [ctb], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/GOstats git_branch: RELEASE_3_12 git_last_commit: 8f988c3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GOstats_2.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOstats_2.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GOstats_2.56.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.R, vignettes/GOstats/inst/doc/GOstatsHyperG.R, vignettes/GOstats/inst/doc/GOvis.R dependsOnMe: MineICA, PloGO2, RDAVIDWebService importsMe: affycoretools, attract, categoryCompare, GmicR, ideal, MIGSA, miRLAB, pcaExplorer, scTensor, systemPipeR, DNLC, LANDD, MARVEL suggestsMe: a4, BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, interactiveDisplay, MineICA, MLP, qpgraph, RnBeads, safe, DGCA, maGUI, sand dependencyCount: 55 Package: GOsummaries Version: 2.26.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: 5bdd6d751c14962407516b1a71324155 NeedsCompilation: yes Title: Word cloud summaries of GO enrichment analysis Description: A package to visualise Gene Ontology (GO) enrichment analysis results on gene lists arising from different analyses such clustering or PCA. The significant GO categories are visualised as word clouds that can be combined with different plots summarising the underlying data. biocViews: GeneExpression, Clustering, GO, Visualization Author: Raivo Kolde Maintainer: Raivo Kolde URL: https://github.com/raivokolde/GOsummaries git_url: https://git.bioconductor.org/packages/GOsummaries git_branch: RELEASE_3_12 git_last_commit: f9d57af git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GOsummaries_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOsummaries_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GOsummaries_2.26.0.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOsummaries/inst/doc/GOsummaries-basics.R dependencyCount: 48 Package: GOTHiC Version: 1.26.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2, BiocManager, grDevices, utils, stats, GenomeInfoDb Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: 58da5756ed06167dd42655eb9383571f NeedsCompilation: no Title: Binomial test for Hi-C data analysis Description: This is a Hi-C analysis package using a cumulative binomial test to detect interactions between distal genomic loci that have significantly more reads than expected by chance in Hi-C experiments. It takes mapped paired NGS reads as input and gives back the list of significant interactions for a given bin size in the genome. biocViews: ImmunoOncology, Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud git_url: https://git.bioconductor.org/packages/GOTHiC git_branch: RELEASE_3_12 git_last_commit: 49fc755 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GOTHiC_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GOTHiC_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GOTHiC_1.26.0.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOTHiC/inst/doc/package_vignettes.R dependencyCount: 77 Package: goTools Version: 1.64.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: cbc82f55753f71077f1d42433e78c275 NeedsCompilation: no Title: Functions for Gene Ontology database Description: Wraper functions for description/comparison of oligo ID list using Gene Ontology database biocViews: Microarray,GO,Visualization Author: Yee Hwa (Jean) Yang , Agnes Paquet Maintainer: Agnes Paquet git_url: https://git.bioconductor.org/packages/goTools git_branch: RELEASE_3_12 git_last_commit: b1cbb37 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/goTools_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/goTools_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/goTools_1.64.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/goTools/inst/doc/goTools.R dependencyCount: 28 Package: GPA Version: 1.2.0 Depends: R (>= 4.0.0), methods, graphics, Rcpp Imports: parallel, ggplot2, ggrepel, plyr, vegan, DT, shiny, shinyBS, stats, utils, grDevices LinkingTo: Rcpp Suggests: gpaExample License: GPL (>= 2) Archs: i386, x64 MD5sum: 295b7b77949e47a3528962bc01ae5ee2 NeedsCompilation: yes Title: GPA (Genetic analysis incorporating Pleiotropy and Annotation) Description: This package provides functions for fitting GPA, a statistical framework to prioritize GWAS results by integrating pleiotropy information and annotation data. In addition, it also includes ShinyGPA, an interactive visualization toolkit to investigate pleiotropic architecture. biocViews: Software, StatisticalMethod, Classification, GenomeWideAssociation, SNP, Genetics, Clustering, MultipleComparison, Preprocessing, GeneExpression, DifferentialExpression Author: Dongjun Chung, Emma Kortemeier, Carter Allen Maintainer: Dongjun Chung URL: http://dongjunchung.github.io/GPA/ SystemRequirements: GNU make BugReports: https://github.com/dongjunchung/GPA/issues git_url: https://git.bioconductor.org/packages/GPA git_branch: RELEASE_3_12 git_last_commit: 1f67c24 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GPA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GPA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GPA_1.2.0.tgz vignettes: vignettes/GPA/inst/doc/GPA-example.pdf vignetteTitles: GPA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GPA/inst/doc/GPA-example.R dependencyCount: 70 Package: gpart Version: 1.8.0 Depends: R (>= 3.5.0), grid, Homo.sapiens, TxDb.Hsapiens.UCSC.hg38.knownGene, Imports: igraph, biomaRt, Rcpp, data.table, OrganismDbi, AnnotationDbi, grDevices, stats, utils, GenomicRanges, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 7caeb0c3123e8fb764a3b0c03f883233 NeedsCompilation: yes Title: Human genome partitioning of dense sequencing data by identifying haplotype blocks Description: we provide a new SNP sequence partitioning method which partitions the whole SNP sequence based on not only LD block structures but also gene location information. The LD block construction for GPART is performed using Big-LD algorithm, with additional improvement from previous version reported in Kim et al.(2017). We also add a visualization tool to show the LD heatmap with the information of LD block boundaries and gene locations in the package. biocViews: Software, Clustering Author: Sun Ah Kim [aut, cre, cph], Yun Joo Yoo [aut, cph] Maintainer: Sun Ah Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gpart git_branch: RELEASE_3_12 git_last_commit: 54a9514 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gpart_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gpart_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gpart_1.8.0.tgz vignettes: vignettes/gpart/inst/doc/gpart.html vignetteTitles: Your Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gpart/inst/doc/gpart.R dependencyCount: 99 Package: gpls Version: 1.62.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: 3daf1860a32ddf01f778deca1950d5d3 NeedsCompilation: no Title: Classification using generalized partial least squares Description: Classification using generalized partial least squares for two-group and multi-group (more than 2 group) classification. biocViews: Classification, Microarray, Regression Author: Beiying Ding Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/gpls git_branch: RELEASE_3_12 git_last_commit: cdcc082 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gpls_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gpls_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gpls_1.62.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpls/inst/doc/gpls.R suggestsMe: MLInterfaces dependencyCount: 1 Package: gprege Version: 1.34.0 Depends: R (>= 2.10), gptk Suggests: spam License: AGPL-3 MD5sum: c336ef7ac815cbfed70a9502a39f5a36 NeedsCompilation: no Title: Gaussian Process Ranking and Estimation of Gene Expression time-series Description: The gprege package implements the methodology described in Kalaitzis & Lawrence (2011) "A simple approach to ranking differentially expressed gene expression time-courses through Gaussian process regression". The software fits two GPs with the an RBF (+ noise diagonal) kernel on each profile. One GP kernel is initialised wih a short lengthscale hyperparameter, signal variance as the observed variance and a zero noise variance. It is optimised via scaled conjugate gradients (netlab). A second GP has fixed hyperparameters: zero inverse-width, zero signal variance and noise variance as the observed variance. The log-ratio of marginal likelihoods of the two hypotheses acts as a score of differential expression for the profile. Comparison via ROC curves is performed against BATS (Angelini et.al, 2007). A detailed discussion of the ranking approach and dataset used can be found in the paper (http://www.biomedcentral.com/1471-2105/12/180). biocViews: Microarray, Preprocessing, Bioinformatics, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis BugReports: alkalait@gmail.com git_url: https://git.bioconductor.org/packages/gprege git_branch: RELEASE_3_12 git_last_commit: 120a130 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gprege_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gprege_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gprege_1.34.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gprege/inst/doc/gprege_quick.R dependsOnMe: robin dependencyCount: 13 Package: gpuMagic Version: 1.6.0 Depends: R (>= 3.6.0), methods, utils Imports: Deriv, DescTools, digest, pryr, stringr, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 06433da21ab128a2d71d7c1a8facfec1 NeedsCompilation: yes Title: An openCL compiler with the capacity to compile R functions and run the code on GPU Description: The package aims to help users write openCL code with little or no effort. It is able to compile an user-defined R function and run it on a device such as a CPU or a GPU. The user can also write and run their openCL code directly by calling .kernel function. biocViews: Infrastructure Author: Jiefei Wang Maintainer: Jiefei Wang SystemRequirements: 1. C++11, 2. a graphic driver or a CPU SDK. 3. ICD loader For Windows user, an ICD loader is required at C:/windows/system32/OpenCL.dll (Usually it is installed by the graphic driver). For Linux user (Except mac): ocl-icd-opencl-dev package is required. For Mac user, no action is needed for the system has installed the dependency. 4. GNU make VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/gpuMagic/issues git_url: https://git.bioconductor.org/packages/gpuMagic git_branch: RELEASE_3_12 git_last_commit: 45cd18e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gpuMagic_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gpuMagic_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gpuMagic_1.6.0.tgz vignettes: vignettes/gpuMagic/inst/doc/Customized-openCL-code.html, vignettes/gpuMagic/inst/doc/Quick_start_guide.html vignetteTitles: Customized_opencl_code, quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gpuMagic/inst/doc/Customized-openCL-code.R, vignettes/gpuMagic/inst/doc/Quick_start_guide.R dependencyCount: 36 Package: gQTLBase Version: 1.21.1 Imports: GenomicRanges, methods, BatchJobs, BBmisc, S4Vectors, BiocGenerics, foreach, doParallel, bit, ff, rtracklayer, ffbase, GenomicFiles, SummarizedExperiment Suggests: geuvStore2, knitr, rmarkdown, BiocStyle, RUnit, Homo.sapiens, IRanges, erma, GenomeInfoDb, gwascat, geuvPack License: Artistic-2.0 MD5sum: 2e5e749f3cea0b27eaeca82827f0d556 NeedsCompilation: no Title: gQTLBase: infrastructure for eQTL, mQTL and similar studies Description: Infrastructure for eQTL, mQTL and similar studies. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gQTLBase git_branch: master git_last_commit: 7eb24a5 git_last_commit_date: 2020-06-30 Date/Publication: 2020-06-30 source.ver: src/contrib/gQTLBase_1.21.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/gQTLBase_1.21.1.zip mac.binary.ver: bin/macosx/contrib/4.0/gQTLBase_1.21.1.tgz vignettes: vignettes/gQTLBase/inst/doc/gQTLBase.html vignetteTitles: gQTLBase infrastructure for eQTL archives hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLBase/inst/doc/gQTLBase.R importsMe: gQTLstats, geuvStore2, yriMulti dependencyCount: 107 Package: gQTLstats Version: 1.21.3 Depends: R (>= 3.5.0), Homo.sapiens Imports: methods, snpStats, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicFiles, GenomicRanges, SummarizedExperiment, VariantAnnotation, Biobase, BatchJobs, gQTLBase, limma, mgcv, dplyr, AnnotationDbi, GenomicFeatures, ggplot2, reshape2, doParallel, foreach, ffbase, BBmisc, beeswarm, HardyWeinberg, graphics, stats, utils, shiny, plotly, erma, ggbeeswarm Suggests: geuvPack, geuvStore2, Rsamtools, knitr, rmarkdown, ggbio, BiocStyle, RUnit, multtest, gwascat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, ldblock License: Artistic-2.0 MD5sum: a9ffb074c659e8ecca898ffe492cd551 NeedsCompilation: no Title: gQTLstats: computationally efficient analysis for eQTL and allied studies Description: computationally efficient analysis of eQTL, mQTL, dsQTL, etc. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gQTLstats git_branch: master git_last_commit: ece6975 git_last_commit_date: 2020-07-08 Date/Publication: 2020-07-08 source.ver: src/contrib/gQTLstats_1.21.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/gQTLstats_1.21.3.zip mac.binary.ver: bin/macosx/contrib/4.0/gQTLstats_1.21.3.tgz vignettes: vignettes/gQTLstats/inst/doc/gQTLstats.html vignetteTitles: gQTLstats: statistics for genetics of genomic features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLstats/inst/doc/gQTLstats.R suggestsMe: yriMulti dependencyCount: 163 Package: gramm4R Version: 1.4.0 Depends: R (>= 3.6.0) Imports: basicTrendline,investr,minerva,psych,grDevices, graphics, stats,DelayedArray,SummarizedExperiment,DMwR,phyloseq Suggests: knitr, rmarkdown License: GPL-2 MD5sum: afa23a612c88781ee3d4974d3553121d NeedsCompilation: no Title: Generalized correlation analysis and model construction strategy for metabolome and microbiome Description: Generalized Correlation Analysis for Metabolome and Microbiome (GRaMM), for inter-correlation pairs discovery among metabolome and microbiome. biocViews: GraphAndNetwork,Microbiome Author: Mengci Li, Dandan Liang, Tianlu Chen and Wei Jia Maintainer: Tianlu Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gramm4R git_branch: RELEASE_3_12 git_last_commit: a617642 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gramm4R_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gramm4R_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gramm4R_1.4.0.tgz vignettes: vignettes/gramm4R/inst/doc/gramm4R.html vignetteTitles: gramm4R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gramm4R/inst/doc/gramm4R.R dependencyCount: 94 Package: graper Version: 1.6.0 Depends: R (>= 3.6) Imports: Matrix, Rcpp, stats, ggplot2, methods, cowplot, matrixStats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: a1e0e1c4531e47f92ddc29c3dfe7947f NeedsCompilation: yes Title: Adaptive penalization in high-dimensional regression and classification with external covariates using variational Bayes Description: This package enables regression and classification on high-dimensional data with different relative strengths of penalization for different feature groups, such as different assays or omic types. The optimal relative strengths are chosen adaptively. Optimisation is performed using a variational Bayes approach. biocViews: Regression, Bayesian, Classification Author: Britta Velten [aut, cre], Wolfgang Huber [aut] Maintainer: Britta Velten VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/graper git_branch: RELEASE_3_12 git_last_commit: f9d8f22 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/graper_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/graper_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/graper_1.6.0.tgz vignettes: vignettes/graper/inst/doc/example_linear.html, vignettes/graper/inst/doc/example_logistic.html vignetteTitles: example_linear, example_logistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graper/inst/doc/example_linear.R, vignettes/graper/inst/doc/example_logistic.R dependencyCount: 43 Package: graph Version: 1.68.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.11) Imports: stats, stats4, utils Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: ea86a5c8f2abff7d293ff02db2283b20 NeedsCompilation: yes Title: graph: A package to handle graph data structures Description: A package that implements some simple graph handling capabilities. biocViews: GraphAndNetwork Author: R. Gentleman, Elizabeth Whalen, W. Huber, S. Falcon Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/graph git_branch: RELEASE_3_12 git_last_commit: 03ad9ed git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/graph_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/graph_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/graph_1.68.0.tgz vignettes: vignettes/graph/inst/doc/clusterGraph.pdf, vignettes/graph/inst/doc/graph.pdf, vignettes/graph/inst/doc/graphAttributes.pdf, vignettes/graph/inst/doc/GraphClass.pdf, vignettes/graph/inst/doc/MultiGraphClass.pdf vignetteTitles: clusterGraph and distGraph, Graph, Attributes for Graph Objects, Graph Design, graphBAM and MultiGraph classes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graph/inst/doc/clusterGraph.R, vignettes/graph/inst/doc/graph.R, vignettes/graph/inst/doc/graphAttributes.R, vignettes/graph/inst/doc/GraphClass.R, vignettes/graph/inst/doc/MultiGraphClass.R dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, BLMA, CellNOptR, clipper, CNORfeeder, EnrichmentBrowser, gaggle, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, NetSAM, pathRender, Pigengene, pkgDepTools, PoTRA, RbcBook1, RBGL, RBioinf, RCyjs, RDAVIDWebService, Rgraphviz, ROntoTools, RpsiXML, SRAdb, topGO, vtpnet, ppiData, SNAData, yeastExpData, dlsem, geneNetBP, gridGraphviz, GUIProfiler, hasseDiagram, msSurv, NFP, PairViz, PerfMeas, QuACN, RSeed, SubpathwayLNCE importsMe: alpine, BgeeDB, BiocCheck, biocGraph, BiocOncoTK, BiocPkgTools, biocViews, CAMERA, Category, categoryCompare, chimeraviz, ChIPpeakAnno, CHRONOS, CytoML, DEGraph, DEsubs, epiNEM, EventPointer, ExperimentHubData, flowCL, flowClust, flowUtils, flowWorkspace, gage, GAPGOM, GeneNetworkBuilder, GOSim, GraphAT, graphite, hyperdraw, KEGGgraph, keggorthology, MIGSA, mnem, NCIgraph, NeighborNet, netresponse, OncoSimulR, ontoProc, OrganismDbi, pathview, PCpheno, PhenStat, pkgDepTools, ppiStats, pwOmics, qpgraph, RchyOptimyx, RCy3, RGraph2js, rsbml, Rtreemix, SplicingGraphs, Streamer, ToPASeq, trackViewer, VariantFiltering, BayesNetBP, BiDAG, BNrich, ceg, CePa, classGraph, CodeDepends, cogmapr, dnet, eulerian, ggm, GGRidge, gRain, gRbase, gridDebug, gRim, HEMDAG, hmma, HydeNet, IMaGES, kpcalg, MetaClean, NetPreProc, pcalg, pcgen, rags2ridges, RANKS, rsolr, simPATHy, SourceSet, stablespec, topologyGSA, unifDAG, wiseR, zenplots suggestsMe: AnnotationDbi, BiocCaseStudies, DEGraph, EBcoexpress, ecolitk, GeneAnswers, gwascat, KEGGlincs, NetPathMiner, rBiopaxParser, rTRM, S4Vectors, SPIA, TxRegInfra, VariantTools, arulesViz, bnclassify, bnlearn, bnstruct, bsub, ccdrAlgorithm, ChoR, epoc, gbutils, GeneNet, gMCP, igraph, lava, loon, maGUI, psych, rEMM, rPref, sisal, sparsebn, sparsebnUtils, textplot, tidygraph dependencyCount: 7 Package: GraphAlignment Version: 1.54.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: cec39e7ee6bf9f880e2ccea8d5e80300 NeedsCompilation: yes Title: GraphAlignment Description: Graph alignment is an extension package for the R programming environment which provides functions for finding an alignment between two networks based on link and node similarity scores. (J. Berg and M. Laessig, "Cross-species analysis of biological networks by Bayesian alignment", PNAS 103 (29), 10967-10972 (2006)) biocViews: GraphAndNetwork, Network Author: Joern P. Meier , Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg. Maintainer: Joern P. Meier URL: http://www.thp.uni-koeln.de/~berg/GraphAlignment/ git_url: https://git.bioconductor.org/packages/GraphAlignment git_branch: RELEASE_3_12 git_last_commit: d44854e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GraphAlignment_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GraphAlignment_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GraphAlignment_1.54.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GraphAlignment/inst/doc/GraphAlignment.R dependencyCount: 0 Package: GraphAT Version: 1.62.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: d7b6d20c0543274d9be4ac3d19ffc778 NeedsCompilation: no Title: Graph Theoretic Association Tests Description: Functions and data used in Balasubramanian, et al. (2004) biocViews: Network, GraphAndNetwork Author: R. Balasubramanian, T. LaFramboise, D. Scholtens Maintainer: Thomas LaFramboise git_url: https://git.bioconductor.org/packages/GraphAT git_branch: RELEASE_3_12 git_last_commit: 9c67583 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GraphAT_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GraphAT_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GraphAT_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 23 Package: graphite Version: 1.36.0 Depends: R (>= 2.10), methods Imports: AnnotationDbi, checkmate, graph (>= 1.67.1), httr, rappdirs, stats, utils Suggests: a4Preproc, ALL, BiocStyle, clipper, codetools, hgu133plus2.db, hgu95av2.db, impute, knitr, org.Hs.eg.db, parallel, R.rsp, RCy3, rmarkdown, SPIA (>= 2.2), testthat, topologyGSA (>= 1.4.0) License: AGPL-3 MD5sum: 3cb65e6dae93b72547fe770f4cbf96d4 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from Biocarta, HumanCyc, KEGG, NCI, Panther, PathBank, PharmGKB, Reactome and SMPDB databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, BioCarta, Metabolomics Author: Gabriele Sales , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales VignetteBuilder: R.rsp git_url: https://git.bioconductor.org/packages/graphite git_branch: RELEASE_3_12 git_last_commit: 9aa4edd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/graphite_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/graphite_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/graphite_1.36.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf, vignettes/graphite/inst/doc/metabolites.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment, metabolites.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: PoTRA, ToPASeq importsMe: EnrichmentBrowser, mogsa, multiGSEA, ReactomePA, StarBioTrek, ICDS suggestsMe: clipper, metaboliteIDmapping, NFP, SourceSet dependencyCount: 39 Package: GraphPAC Version: 1.32.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 0314d922d1ace88be3de2776031d0342 NeedsCompilation: no Title: Identification of Mutational Clusters in Proteins via a Graph Theoretical Approach. Description: Identifies mutational clusters of amino acids in a protein while utilizing the proteins tertiary structure via a graph theoretical model. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/GraphPAC git_branch: RELEASE_3_12 git_last_commit: 8f7bfe2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GraphPAC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GraphPAC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GraphPAC_1.32.0.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GraphPAC/inst/doc/GraphPAC.R dependsOnMe: QuartPAC dependencyCount: 36 Package: GRENITS Version: 1.42.0 Depends: R (>= 2.12.0), Rcpp (>= 0.8.6), RcppArmadillo (>= 0.2.8), ggplot2 (>= 0.9.0) Imports: graphics, grDevices, reshape2, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: network License: GPL (>= 2) Archs: i386, x64 MD5sum: 8257abd52336fad9a9bf8b33228354d9 NeedsCompilation: yes Title: Gene Regulatory Network Inference Using Time Series Description: The package offers four network inference statistical models using Dynamic Bayesian Networks and Gibbs Variable Selection: a linear interaction model, two linear interaction models with added experimental noise (Gaussian and Student distributed) for the case where replicates are available and a non-linear interaction model. biocViews: NetworkInference, GeneRegulation, TimeCourse, GraphAndNetwork, GeneExpression, Network, Bayesian Author: Edward Morrissey Maintainer: Edward Morrissey git_url: https://git.bioconductor.org/packages/GRENITS git_branch: RELEASE_3_12 git_last_commit: c283d0b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GRENITS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GRENITS_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GRENITS_1.42.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRENITS/inst/doc/GRENITS_package.R dependencyCount: 45 Package: GreyListChIP Version: 1.22.0 Depends: R (>= 4.0), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment, stats, utils Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 580066a16f61ed04fd261749e9cc1b6b NeedsCompilation: no Title: Grey Lists -- Mask Artefact Regions Based on ChIP Inputs Description: Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis. biocViews: ChIPSeq, Alignment, Preprocessing, DifferentialPeakCalling, Sequencing, GenomeAnnotation, Coverage Author: Gord Brown Maintainer: Gordon Brown git_url: https://git.bioconductor.org/packages/GreyListChIP git_branch: RELEASE_3_12 git_last_commit: 7026152 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GreyListChIP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GreyListChIP_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GreyListChIP_1.22.0.tgz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GreyListChIP/inst/doc/GreyList-demo.R importsMe: DiffBind dependencyCount: 42 Package: GRmetrics Version: 1.16.0 Depends: R (>= 4.0), SummarizedExperiment Imports: drc, plotly, ggplot2, S4Vectors, stats Suggests: knitr, rmarkdown, BiocStyle, tinytex License: GPL-3 MD5sum: aa22529f474c74e9dde06f4345526e83 NeedsCompilation: no Title: Calculate growth-rate inhibition (GR) metrics Description: Functions for calculating and visualizing growth-rate inhibition (GR) metrics. biocViews: ImmunoOncology, CellBasedAssays, CellBiology, Software, TimeCourse, Visualization Author: Nicholas Clark Maintainer: Nicholas Clark , Mario Medvedovic URL: https://github.com/uc-bd2k/GRmetrics VignetteBuilder: knitr BugReports: https://github.com/uc-bd2k/GRmetrics/issues git_url: https://git.bioconductor.org/packages/GRmetrics git_branch: RELEASE_3_12 git_last_commit: 6545df4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GRmetrics_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GRmetrics_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GRmetrics_1.16.0.tgz vignettes: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.html vignetteTitles: GRmetrics: an R package for calculation and visualization of dose-response metrics based on growth rate inhibition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRmetrics/inst/doc/GRmetrics-vignette.R dependencyCount: 133 Package: groHMM Version: 1.24.0 Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), GenomicAlignments (>= 1.15.6), rtracklayer (>= 1.39.7) Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 0af6164863567bca0b83a8b9fd9a1d42 NeedsCompilation: yes Title: GRO-seq Analysis Pipeline Description: A pipeline for the analysis of GRO-seq data. biocViews: Sequencing, Software Author: Charles G. Danko, Minho Chae, Andre Martins, W. Lee Kraus Maintainer: Anusha Nagari , Tulip Nandu , W. Lee Kraus URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues git_url: https://git.bioconductor.org/packages/groHMM git_branch: RELEASE_3_12 git_last_commit: 40688b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/groHMM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/groHMM_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/groHMM_1.24.0.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/groHMM/inst/doc/groHMM.R dependencyCount: 41 Package: GRridge Version: 1.14.0 Depends: R (>= 3.2), penalized, Iso, survival, methods, graph,stats,glmnet,mvtnorm Suggests: testthat License: GPL-3 MD5sum: c88d47e144186ef0a5501760ebf33837 NeedsCompilation: no Title: Better prediction by use of co-data: Adaptive group-regularized ridge regression Description: This package allows the use of multiple sources of co-data (e.g. external p-values, gene lists, annotation) to improve prediction of binary, continuous and survival response using (logistic, linear or Cox) group-regularized ridge regression. It also facilitates post-hoc variable selection and prediction diagnostics by cross-validation using ROC curves and AUC. biocViews: Classification, Regression, Survival, Bayesian, RNASeq, GenePrediction, GeneExpression, Pathways, GeneSetEnrichment, GO, KEGG, GraphAndNetwork, ImmunoOncology Author: Mark A. van de Wiel , Putri W. Novianti Maintainer: Mark A. van de Wiel git_url: https://git.bioconductor.org/packages/GRridge git_branch: RELEASE_3_12 git_last_commit: 3f189a9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GRridge_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GRridge_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GRridge_1.14.0.tgz vignettes: vignettes/GRridge/inst/doc/GRridge.pdf vignetteTitles: GRridge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GRridge/inst/doc/GRridge.R dependencyCount: 24 Package: GSALightning Version: 1.18.0 Depends: R (>= 3.3.0) Imports: Matrix, data.table, stats Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 8a442e0dba34820fd1e0a18fdf60c2a4 NeedsCompilation: no Title: Fast Permutation-based Gene Set Analysis Description: GSALightning provides a fast implementation of permutation-based gene set analysis for two-sample problem. This package is particularly useful when testing simultaneously a large number of gene sets, or when a large number of permutations is necessary for more accurate p-values estimation. biocViews: Software, BiologicalQuestion, GeneSetEnrichment, DifferentialExpression, GeneExpression, Transcription Author: Billy Heung Wing Chang Maintainer: Billy Heung Wing Chang URL: https://github.com/billyhw/GSALightning VignetteBuilder: knitr BugReports: https://github.com/billyhw/GSALightning/issues git_url: https://git.bioconductor.org/packages/GSALightning git_branch: RELEASE_3_12 git_last_commit: e13ad60 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSALightning_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSALightning_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSALightning_1.18.0.tgz vignettes: vignettes/GSALightning/inst/doc/vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R dependencyCount: 9 Package: GSAR Version: 1.24.0 Depends: R (>= 3.0.1), igraph (>= 0.7.1) Imports: stats, graphics Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: f3d551ace7cbe0e39ab20752e9081ad9 NeedsCompilation: no Title: Gene Set Analysis in R Description: Gene set analysis using specific alternative hypotheses. Tests for differential expression, scale and net correlation structure. biocViews: Software, StatisticalMethod, DifferentialExpression Author: Yasir Rahmatallah , Galina Glazko Maintainer: Yasir Rahmatallah , Galina Glazko git_url: https://git.bioconductor.org/packages/GSAR git_branch: RELEASE_3_12 git_last_commit: b6c9d79 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSAR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSAR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSAR_1.24.0.tgz vignettes: vignettes/GSAR/inst/doc/GSAR.pdf vignetteTitles: Gene Set Analysis in R -- the GSAR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSAR/inst/doc/GSAR.R dependencyCount: 11 Package: GSCA Version: 2.20.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 383d6978752e620580dc6230c614ced0 NeedsCompilation: no Title: GSCA: Gene Set Context Analysis Description: GSCA takes as input several lists of activated and repressed genes. GSCA then searches through a compendium of publicly available gene expression profiles for biological contexts that are enriched with a specified pattern of gene expression. GSCA provides both traditional R functions and interactive, user-friendly user interface. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji git_url: https://git.bioconductor.org/packages/GSCA git_branch: RELEASE_3_12 git_last_commit: 34a9327 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSCA_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSCA_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSCA_2.20.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSCA/inst/doc/GSCA.R dependencyCount: 71 Package: gscreend Version: 1.4.0 Depends: R (>= 3.6) Imports: SummarizedExperiment, nloptr, fGarch, methods, BiocParallel, graphics Suggests: knitr, testthat License: GPL-3 MD5sum: 546eeeb9b4c7abc00451b93329bfb412 NeedsCompilation: no Title: Analysis of pooled genetic screens Description: Package for the analysis of pooled genetic screens (e.g. CRISPR-KO). The analysis of such screens is based on the comparison of gRNA abundances before and after a cell proliferation phase. The gscreend packages takes gRNA counts as input and allows detection of genes whose knockout decreases or increases cell proliferation. biocViews: Software, StatisticalMethod, PooledScreens, CRISPR Author: Katharina Imkeller [cre, aut], Wolfgang Huber [aut] Maintainer: Katharina Imkeller URL: https://github.com/imkeller/gscreend VignetteBuilder: knitr BugReports: https://github.com/imkeller/gscreend/issues git_url: https://git.bioconductor.org/packages/gscreend git_branch: RELEASE_3_12 git_last_commit: bf8ede6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gscreend_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gscreend_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gscreend_1.4.0.tgz vignettes: vignettes/gscreend/inst/doc/gscreend_simulated_data.html vignetteTitles: Example_simulated hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gscreend/inst/doc/gscreend_simulated_data.R dependencyCount: 43 Package: GSEABase Version: 1.52.1 Depends: R (>= 2.6.0), BiocGenerics (>= 0.13.8), Biobase (>= 2.17.8), annotate (>= 1.45.3), methods, graph (>= 1.37.2) Imports: AnnotationDbi, XML Suggests: hgu95av2.db, GO.db, org.Hs.eg.db, Rgraphviz, ReportingTools, testthat, BiocStyle, knitr License: Artistic-2.0 MD5sum: 7a4eff4176b5a004d3e7839b7e48af9d NeedsCompilation: no Title: Gene set enrichment data structures and methods Description: This package provides classes and methods to support Gene Set Enrichment Analysis (GSEA). biocViews: GeneExpression, GeneSetEnrichment, GraphAndNetwork, GO, KEGG Author: Martin Morgan, Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEABase git_branch: RELEASE_3_12 git_last_commit: 257dfcc git_last_commit_date: 2020-12-10 Date/Publication: 2020-12-11 source.ver: src/contrib/GSEABase_1.52.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSEABase_1.52.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GSEABase_1.52.1.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABase/inst/doc/GSEABase.R dependsOnMe: AGDEX, BicARE, CCPROMISE, cpvSNP, npGSEA, PROMISE, splineTimeR, TissueEnrich, GSVAdata importsMe: AUCell, BioCor, canceR, Category, categoryCompare, cellHTS2, EnrichmentBrowser, escape, gep2pep, GISPA, GlobalAncova, GmicR, GSRI, GSVA, MIGSA, miRSM, mogsa, oppar, PCpheno, phenoTest, POST, PROMISE, RcisTarget, ReportingTools, scTGIF, signatureSearch, singleCellTK, singscore, slalom, TFutils, SingscoreAMLMutations, clustermole, immcp, RVA suggestsMe: BiocCaseStudies, BiocSet, gage, globaltest, GOstats, GSAR, MAST, phenoTest, TFEA.ChIP, BaseSet dependencyCount: 39 Package: GSEABenchmarkeR Version: 1.10.1 Depends: Biobase, SummarizedExperiment Imports: AnnotationDbi, AnnotationHub, BiocFileCache, BiocParallel, edgeR, EnrichmentBrowser, ExperimentHub, grDevices, graphics, KEGGandMetacoreDzPathwaysGEO, KEGGdzPathwaysGEO, methods, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: dbb2f8abe63c12de77c41dd3e8044bc5 NeedsCompilation: no Title: Reproducible GSEA Benchmarking Description: The GSEABenchmarkeR package implements an extendable framework for reproducible evaluation of set- and network-based methods for enrichment analysis of gene expression data. This includes support for the efficient execution of these methods on comprehensive real data compendia (microarray and RNA-seq) using parallel computation on standard workstations and institutional computer grids. Methods can then be assessed with respect to runtime, statistical significance, and relevance of the results for the phenotypes investigated. biocViews: ImmunoOncology, Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger [aut, cre], Gergely Csaba [aut], Mara Santarelli [ctb], Lucas Schiffer [ctb], Marcel Ramos [ctb], Ralf Zimmer [aut], Levi Waldron [aut] Maintainer: Ludwig Geistlinger URL: https://github.com/waldronlab/GSEABenchmarkeR VignetteBuilder: knitr BugReports: https://github.com/waldronlab/GSEABenchmarkeR/issues git_url: https://git.bioconductor.org/packages/GSEABenchmarkeR git_branch: RELEASE_3_12 git_last_commit: 4980161 git_last_commit_date: 2020-12-09 Date/Publication: 2020-12-10 source.ver: src/contrib/GSEABenchmarkeR_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSEABenchmarkeR_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GSEABenchmarkeR_1.10.1.tgz vignettes: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.html vignetteTitles: Reproducible GSEA Benchmarking hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEABenchmarkeR/inst/doc/GSEABenchmarkeR.R dependencyCount: 123 Package: GSEAlm Version: 1.50.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 1d2dca984936b25eabd5e4d52d41ad4b NeedsCompilation: no Title: Linear Model Toolset for Gene Set Enrichment Analysis Description: Models and methods for fitting linear models to gene expression data, together with tools for computing and using various regression diagnostics. biocViews: Microarray Author: Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang) Maintainer: Assaf Oron git_url: https://git.bioconductor.org/packages/GSEAlm git_branch: RELEASE_3_12 git_last_commit: 7a53fa2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSEAlm_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSEAlm_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSEAlm_1.50.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSEAlm/inst/doc/GSEAlm.R dependencyCount: 7 Package: GSEAmining Version: 1.0.0 Depends: R (>= 4.0) Imports: dplyr, tidytext, dendextend, tibble, ggplot2, ggwordcloud, stringr, gridExtra, rlang, grDevices, graphics, stats, methods Suggests: knitr, rmarkdown, BiocStyle, clusterProfiler, testthat License: GPL-3 | file LICENSE MD5sum: 9b25f392a9b8831159e1138e531f9622 NeedsCompilation: no Title: Make Biological Sense of Gene Set Enrichment Analysis Outputs Description: Gene Set Enrichment Analysis is a very powerful and interesting computational method that allows an easy correlation between differential expressed genes and biological processes. Unfortunately, although it was designed to help researchers to interpret gene expression data it can generate huge amounts of results whose biological meaning can be difficult to interpret. Many available tools rely on the hierarchically structured Gene Ontology (GO) classification to reduce reundandcy in the results. However, due to the popularity of GSEA many more gene set collections, such as those in the Molecular Signatures Database are emerging. Since these collections are not organized as those in GO, their usage for GSEA do not always give a straightforward answer or, in other words, getting all the meaninful information can be challenging with the currently available tools. For these reasons, GSEAmining was born to be an easy tool to create reproducible reports to help researchers make biological sense of GSEA outputs. Given the results of GSEA, GSEAmining clusters the different gene sets collections based on the presence of the same genes in the leadind edge (core) subset. Leading edge subsets are those genes that contribute most to the enrichment score of each collection of genes or gene sets. For this reason, gene sets that participate in similar biological processes should share genes in common and in turn cluster together. After that, GSEAmining is able to identify and represent for each cluster: - The most enriched terms in the names of gene sets (as wordclouds) - The most enriched genes in the leading edge subsets (as bar plots). In each case, positive and negative enrichments are shown in different colors so it is easy to distinguish biological processes or genes that may be of interest in that particular study. biocViews: GeneSetEnrichment, Clustering, Visualization Author: Oriol Arqués [aut, cre] Maintainer: Oriol Arqués VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GSEAmining git_branch: RELEASE_3_12 git_last_commit: 098fd7e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSEAmining_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSEAmining_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSEAmining_1.0.0.tgz vignettes: vignettes/GSEAmining/inst/doc/GSEAmining.html vignetteTitles: GSEAmining hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSEAmining/inst/doc/GSEAmining.R dependencyCount: 57 Package: gsean Version: 1.10.1 Depends: R (>= 3.5), fgsea, PPInfer Suggests: SummarizedExperiment, knitr, plotly, RANKS, WGCNA, rmarkdown License: Artistic-2.0 MD5sum: 87db37bfa1ceb26a48f3afe047d12ce0 NeedsCompilation: no Title: Gene Set Enrichment Analysis with Networks Description: Biological molecules in a living organism seldom work individually. They usually interact each other in a cooperative way. Biological process is too complicated to understand without considering such interactions. Thus, network-based procedures can be seen as powerful methods for studying complex process. However, many methods are devised for analyzing individual genes. It is said that techniques based on biological networks such as gene co-expression are more precise ways to represent information than those using lists of genes only. This package is aimed to integrate the gene expression and biological network. A biological network is constructed from gene expression data and it is used for Gene Set Enrichment Analysis. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, GeneExpression, NetworkEnrichment, Pathways, DifferentialExpression Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gsean git_branch: RELEASE_3_12 git_last_commit: 774eab7 git_last_commit_date: 2021-04-20 Date/Publication: 2021-04-20 source.ver: src/contrib/gsean_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/gsean_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/gsean_1.10.1.tgz vignettes: vignettes/gsean/inst/doc/gsean.html vignetteTitles: gsean hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gsean/inst/doc/gsean.R dependencyCount: 110 Package: GSgalgoR Version: 1.0.0 Imports: cluster, doParallel, foreach, matchingR, nsga2R, survival, proxy, stats, methods, Suggests: knitr, rmarkdown, ggplot2, BiocStyle, genefu, survcomp, Biobase, survminer, breastCancerTRANSBIG, breastCancerUPP, iC10TrainingData, pamr, testthat License: MIT + file LICENSE MD5sum: f68cc92e24c76f40e3ac052728dbf4a6 NeedsCompilation: no Title: An Evolutionary Framework for the Identification and Study of Prognostic Gene Expression Signatures in Cancer Description: A multi-objective optimization algorithm for disease sub-type discovery based on a non-dominated sorting genetic algorithm. The 'Galgo' framework combines the advantages of clustering algorithms for grouping heterogeneous 'omics' data and the searching properties of genetic algorithms for feature selection. The algorithm search for the optimal number of clusters determination considering the features that maximize the survival difference between sub-types while keeping cluster consistency high. biocViews: GeneExpression, Transcription, Clustering, Classification, Survival Author: Martin Guerrero [aut], Carlos Catania [cre] Maintainer: Carlos Catania URL: https://github.com/harpomaxx/GSgalgoR VignetteBuilder: knitr BugReports: https://github.com/harpomaxx/GSgalgoR/issues git_url: https://git.bioconductor.org/packages/GSgalgoR git_branch: RELEASE_3_12 git_last_commit: 02c2506 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSgalgoR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSgalgoR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSgalgoR_1.0.0.tgz vignettes: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.html, vignettes/GSgalgoR/inst/doc/GSgalgoR.html vignetteTitles: GSgalgoR_callbacks.html, GSgalgoR.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSgalgoR/inst/doc/GSgalgoR_callbacks.R, vignettes/GSgalgoR/inst/doc/GSgalgoR.R dependencyCount: 22 Package: GSReg Version: 1.24.0 Depends: R (>= 2.13.1), Homo.sapiens, org.Hs.eg.db, GenomicFeatures, AnnotationDbi Suggests: GenomicRanges, GSBenchMark License: GPL-2 Archs: i386, x64 MD5sum: 7701b7f39c794f0f0e38a5fee879fa15 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions as well as a method to detect Alternative Splicing Events . It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. For detecting Differentially Spliced genes, it provides an implementation of the Spliced-EVA (SEVA). biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment, AlternativeSplicing Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari , Elana J. Fertig git_url: https://git.bioconductor.org/packages/GSReg git_branch: RELEASE_3_12 git_last_commit: 7725e9d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSReg_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSReg_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSReg_1.24.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSReg/inst/doc/GSReg.R dependencyCount: 96 Package: GSRI Version: 2.38.0 Depends: R (>= 2.14.2), fdrtool Imports: methods, graphics, stats, utils, genefilter, Biobase, GSEABase, les (>= 1.1.6) Suggests: limma, hgu95av2.db Enhances: parallel License: GPL-3 MD5sum: 5ed89fd020879891955b91a8e267d963 NeedsCompilation: no Title: Gene Set Regulation Index Description: The GSRI package estimates the number of differentially expressed genes in gene sets, utilizing the concept of the Gene Set Regulation Index (GSRI). biocViews: Microarray, Transcription, DifferentialExpression, GeneSetEnrichment, GeneRegulation Author: Julian Gehring, Kilian Bartholome, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/GSRI git_branch: RELEASE_3_12 git_last_commit: e70fa93 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GSRI_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSRI_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GSRI_2.38.0.tgz vignettes: vignettes/GSRI/inst/doc/gsri.pdf vignetteTitles: Introduction to the GSRI package: Estimating Regulatory Effects utilizing the Gene Set Regulation Index hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSRI/inst/doc/gsri.R dependencyCount: 56 Package: GSVA Version: 1.38.2 Depends: R (>= 3.5.0) Imports: methods, stats, utils, graphics, BiocGenerics, S4Vectors, IRanges, Biobase, SummarizedExperiment, GSEABase, parallel, BiocParallel Suggests: RUnit, limma, RColorBrewer, genefilter, edgeR, GSVAdata, shiny, shinythemes, ggplot2, data.table, plotly License: GPL (>= 2) Archs: i386, x64 MD5sum: 65c4f4221f57b636cf5e4e1af9dcd176 NeedsCompilation: yes Title: Gene Set Variation Analysis for microarray and RNA-seq data Description: Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for estimating variation of gene set enrichment through the samples of a expression data set. GSVA performs a change in coordinate systems, transforming the data from a gene by sample matrix to a gene-set by sample matrix, thereby allowing the evaluation of pathway enrichment for each sample. This new matrix of GSVA enrichment scores facilitates applying standard analytical methods like functional enrichment, survival analysis, clustering, CNV-pathway analysis or cross-tissue pathway analysis, in a pathway-centric manner. biocViews: Microarray, Pathways, GeneSetEnrichment Author: Justin Guinney [aut, cre], Robert Castelo [aut], Alexey Sergushichev [ctb], Pablo Sebastian Rodriguez [ctb] Maintainer: Justin Guinney URL: https://github.com/rcastelo/GSVA BugReports: https://github.com/rcastelo/GSVA/issues git_url: https://git.bioconductor.org/packages/GSVA git_branch: RELEASE_3_12 git_last_commit: 3acf0ce git_last_commit_date: 2021-02-09 Date/Publication: 2021-02-09 source.ver: src/contrib/GSVA_1.38.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/GSVA_1.38.2.zip mac.binary.ver: bin/macosx/contrib/4.0/GSVA_1.38.2.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.pdf vignetteTitles: Gene Set Variation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GSVA/inst/doc/GSVA.R dependsOnMe: MM2S importsMe: consensusOV, EGSEA, escape, oppar, singleCellTK, TBSignatureProfiler, TNBC.CMS, clustermole, immcp, psSubpathway, scMappR, SIGN, sigQC, SMDIC suggestsMe: MCbiclust dependencyCount: 62 Package: gtrellis Version: 1.22.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.4.8), GetoptLong, grDevices, utils Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, ComplexHeatmap (>= 1.99.0), Cairo, png, jpeg, tiff License: MIT + file LICENSE MD5sum: 80f43d4832a5e31cadebd0add270b1ce NeedsCompilation: no Title: Genome Level Trellis Layout Description: Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and to add self-defined graphics in the plot. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/gtrellis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gtrellis git_branch: RELEASE_3_12 git_last_commit: c071c56 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gtrellis_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gtrellis_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gtrellis_1.22.0.tgz vignettes: vignettes/gtrellis/inst/doc/gtrellis.html vignetteTitles: Make Genome-level Trellis Graph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R importsMe: YAPSA dependencyCount: 26 Package: GUIDEseq Version: 1.20.0 Depends: R (>= 3.2.0), GenomicRanges, BiocGenerics Imports: BiocParallel, Biostrings, CRISPRseek, ChIPpeakAnno, data.table, matrixStats, BSgenome, parallel, IRanges (>= 2.5.5), S4Vectors (>= 0.9.6), GenomicAlignments (>= 1.7.3), GenomeInfoDb, Rsamtools, hash, limma,dplyr Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 899007f762b2839cc3319ce5addc5250 NeedsCompilation: no Title: GUIDE-seq analysis pipeline Description: The package implements GUIDE-seq analysis workflow including functions for obtaining unique insertion sites (proxy of cleavage sites), estimating the locations of the insertion sites, aka, peaks, merging estimated insertion sites from plus and minus strand, and performing off target search of the extended regions around insertion sites. biocViews: ImmunoOncology, GeneRegulation, Sequencing, WorkflowStep, CRISPR Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Hervé Pagès , Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GUIDEseq git_branch: RELEASE_3_12 git_last_commit: ccacc7a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GUIDEseq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GUIDEseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GUIDEseq_1.20.0.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GUIDEseq/inst/doc/GUIDEseq.R importsMe: crisprseekplus dependencyCount: 130 Package: Guitar Version: 2.6.0 Depends: GenomicFeatures, rtracklayer,AnnotationDbi, GenomicRanges, magrittr, ggplot2, methods, stats,utils ,knitr,dplyr License: GPL-2 MD5sum: 9231a0c9c791010b2dd12b276b360160 NeedsCompilation: no Title: Guitar Description: The package is designed for visualization of RNA-related genomic features with respect to the landmarks of RNA transcripts, i.e., transcription starting site, start codon, stop codon and transcription ending site. biocViews: Sequencing, SplicedAlignment, Alignment, DataImport, RNASeq, MethylSeq, QualityControl, Transcription Author: Xiao Du, Hui Liu, Lin Zhang, Jia Meng Maintainer: Jia Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Guitar git_branch: RELEASE_3_12 git_last_commit: fe492f5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Guitar_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Guitar_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Guitar_2.6.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Guitar hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R dependencyCount: 109 Package: Gviz Version: 1.34.1 Depends: R (>= 4.0), methods, S4Vectors (>= 0.9.25), IRanges (>= 1.99.18), GenomicRanges (>= 1.17.20), grid Imports: XVector (>= 0.5.7), rtracklayer (>= 1.25.13), lattice, RColorBrewer, biomaRt (>= 2.11.0), AnnotationDbi (>= 1.27.5), Biobase (>= 2.15.3), GenomicFeatures (>= 1.17.22), ensembldb (>= 2.11.3), BSgenome (>= 1.33.1), Biostrings (>= 2.33.11), biovizBase (>= 1.13.8), Rsamtools (>= 1.17.28), latticeExtra (>= 0.6-26), matrixStats (>= 0.8.14), GenomicAlignments (>= 1.1.16), GenomeInfoDb (>= 1.1.3), BiocGenerics (>= 0.11.3), digest(>= 0.6.8), graphics, grDevices, stats, utils Suggests: BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 36209134e0388df2911da3f772c433e1 NeedsCompilation: no Title: Plotting data and annotation information along genomic coordinates Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. Gviz uses the biomaRt and the rtracklayer packages to perform live annotation queries to Ensembl and UCSC and translates this to e.g. gene/transcript structures in viewports of the grid graphics package. This results in genomic information plotted together with your data. biocViews: Visualization, Microarray, Sequencing Author: Florian Hahne [aut], Steffen Durinck [aut], Robert Ivanek [aut, cre] (), Arne Mueller [aut], Steve Lianoglou [aut], Ge Tan [aut], Lance Parsons [aut], Shraddha Pai [aut], Thomas McCarthy [ctb], Felix Ernst [ctb] Maintainer: Robert Ivanek URL: https://github.com/ivanek/Gviz VignetteBuilder: knitr BugReports: https://github.com/ivanek/Gviz/issues git_url: https://git.bioconductor.org/packages/Gviz git_branch: RELEASE_3_12 git_last_commit: 5aa425a git_last_commit_date: 2021-03-14 Date/Publication: 2021-03-15 source.ver: src/contrib/Gviz_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/Gviz_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.0/Gviz_1.34.1.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.html vignetteTitles: The Gviz User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, chimeraviz, cicero, coMET, cummeRbund, DMRforPairs, Pviz, methylationArrayAnalysis, rnaseqGene importsMe: AllelicImbalance, ALPS, ASpediaFI, ASpli, CAGEfightR, DMRcate, ELMER, GenomicInteractions, GGtools, InPAS, maser, mCSEA, MEAL, methyAnalysis, methylPipe, motifbreakR, Pi, PING, primirTSS, proActiv, regutools, RNAmodR, RNAmodR.AlkAnilineSeq, RNAmodR.RiboMethSeq, SPLINTER, srnadiff, STAN, trackViewer, TVTB, uncoverappLib, VariantFiltering, DMRcatedata suggestsMe: annmap, cellbaseR, CNEr, CNVRanger, DeepBlueR, ensembldb, GenomicRanges, gwascat, interactiveDisplay, InterMineR, pqsfinder, QuasR, RnBeads, SplicingGraphs, TFutils, TxRegInfra, Single.mTEC.Transcriptomes, CAGEWorkflow, chipseqDB, csawUsersGuide, chicane, RTIGER dependencyCount: 137 Package: GWAS.BAYES Version: 1.0.0 Depends: R (>= 4.0), Rcpp (>= 1.0.3), RcppEigen (>= 0.3.3.7.0), GA (>= 3.2), caret (>= 6.0-86), ggplot2 (>= 3.3.0), doParallel (>= 1.0.15), memoise (>= 1.1.0), reshape2 (>= 1.4.4), Matrix (>= 1.2-18) LinkingTo: RcppEigen (>= 0.3.3.7.0),Rcpp (>= 1.0.3) Suggests: BiocStyle, knitr, rmarkdown, formatR, rrBLUP, qqman License: GPL-2 | GPL-3 Archs: i386, x64 MD5sum: 12dd7fb66145baa9212378f531825458 NeedsCompilation: yes Title: GWAS for Selfing Species Description: This package is built to perform GWAS analysis for selfing species. The research related to this package was supported in part by National Science Foundation Award 1853549. biocViews: AssayDomain, SNP Author: Jake Williams [aut, cre], Marco Ferreira [aut], Tieming Ji [aut] Maintainer: Jake Williams VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/GWAS.BAYES git_branch: RELEASE_3_12 git_last_commit: 919eb22 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GWAS.BAYES_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GWAS.BAYES_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GWAS.BAYES_1.0.0.tgz vignettes: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.html vignetteTitles: GWAS.BAYES hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GWAS.BAYES/inst/doc/VignetteGWASBAYES.R dependencyCount: 80 Package: gwascat Version: 2.22.0 Depends: R (>= 3.5.0), methods Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.29.6), GenomicFeatures, readr, Biostrings, AnnotationDbi, BiocFileCache, snpStats, VariantAnnotation Suggests: DO.db, DT, knitr, RBGL, testthat, Gviz, AnnotationHub, Rsamtools, IRanges, graph, ggbio, DelayedArray, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, BiocStyle Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 MD5sum: 59d153e7cf92fff3a30786d6430b953b NeedsCompilation: no Title: representing and modeling data in the EMBL-EBI GWAS catalog Description: Represent and model data in the EMBL-EBI GWAS catalog. biocViews: Genetics Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwascat git_branch: RELEASE_3_12 git_last_commit: 2340948 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gwascat_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gwascat_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gwascat_2.22.0.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.html, vignettes/gwascat/inst/doc/gwascatOnt.html vignetteTitles: gwascat: structuring and querying the NHGRI GWAS catalog, gwascat -- GRanges for GWAS hits in EBI catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R dependsOnMe: vtpnet, liftOver importsMe: circRNAprofiler suggestsMe: GenomicScores, gQTLBase, gQTLstats, hmdbQuery, ldblock, parglms, TFutils, grasp2db dependencyCount: 96 Package: GWASTools Version: 1.36.0 Depends: Biobase Imports: graphics, stats, utils, methods, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth, data.table Suggests: ncdf4, GWASdata, BiocGenerics, RUnit, Biostrings, GenomicRanges, IRanges, SNPRelate, snpStats, S4Vectors, VariantAnnotation, parallel License: Artistic-2.0 MD5sum: 0ff0c2ad4ba3af9a7434f5271815ba64 NeedsCompilation: no Title: Tools for Genome Wide Association Studies Description: Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis. biocViews: SNP, GeneticVariability, QualityControl, Microarray Author: Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Michael Lawrence, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson, David Levine Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/GWASTools git_url: https://git.bioconductor.org/packages/GWASTools git_branch: RELEASE_3_12 git_last_commit: ea79df8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/GWASTools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/GWASTools_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/GWASTools_1.36.0.tgz vignettes: vignettes/GWASTools/inst/doc/Affymetrix.pdf, vignettes/GWASTools/inst/doc/DataCleaning.pdf, vignettes/GWASTools/inst/doc/Formats.pdf vignetteTitles: Preparing Affymetrix Data, GWAS Data Cleaning, Data formats in GWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWASTools/inst/doc/Affymetrix.R, vignettes/GWASTools/inst/doc/DataCleaning.R, vignettes/GWASTools/inst/doc/Formats.R dependsOnMe: mBPCR, GWASdata importsMe: GENESIS, gwasurvivr suggestsMe: podkat dependencyCount: 68 Package: gwasurvivr Version: 1.8.0 Depends: R (>= 3.4.0) Imports: GWASTools, survival, VariantAnnotation, parallel, matrixStats, SummarizedExperiment, stats, utils, SNPRelate Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: a255c21902da8882421c5349fc929abd NeedsCompilation: no Title: gwasurvivr: an R package for genome wide survival analysis Description: gwasurvivr is a package to perform survival analysis using Cox proportional hazard models on imputed genetic data. biocViews: GenomeWideAssociation, Survival, Regression, Genetics, SNP, GeneticVariability, Pharmacogenomics, BiomedicalInformatics Author: Abbas Rizvi, Ezgi Karaesmen, Martin Morgan, Lara Sucheston-Campbell Maintainer: Abbas Rizvi URL: https://github.com/suchestoncampbelllab/gwasurvivr VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/gwasurvivr git_branch: RELEASE_3_12 git_last_commit: 265ed72 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/gwasurvivr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/gwasurvivr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/gwasurvivr_1.8.0.tgz vignettes: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.html vignetteTitles: gwasurvivr Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwasurvivr/inst/doc/gwasurvivr_Introduction.R dependencyCount: 117 Package: GWENA Version: 1.0.1 Depends: R (>= 4.0.0) Imports: WGCNA (>= 1.67), dplyr (>= 0.8.3), dynamicTreeCut (>= 1.63-1), ggplot2 (>= 3.1.1), gprofiler2 (>= 0.1.6), magrittr (>= 1.5), tibble (>= 2.1.1), tidyr (>= 1.0.0), NetRep (>= 1.2.1), igraph (>= 1.2.4.1), RColorBrewer (>= 1.1-2), purrr (>= 0.3.3), rlist (>= 0.4.6.1), matrixStats (>= 0.55.0), SummarizedExperiment (>= 1.14.1), stringr (>= 1.4.0), methods, graphics, stats, utils Suggests: testthat (>= 2.1.0), knitr (>= 1.25), rmarkdown (>= 1.16), prettydoc (>= 0.3.0), httr (>= 1.4.1), S4Vectors (>= 0.22.1), BiocStyle (>= 2.15.8) License: GPL-3 MD5sum: 18800e1a268b72786a2f4cddb001fea3 NeedsCompilation: no Title: Pipeline for augmented co-expression analysis Description: The development of high-throughput sequencing led to increased use of co-expression analysis to go beyong single feature (i.e. gene) focus. We propose GWENA (Gene Whole co-Expression Network Analysis) , a tool designed to perform gene co-expression network analysis and explore the results in a single pipeline. It includes functional enrichment of modules of co-expressed genes, phenotypcal association, topological analysis and comparison of networks configuration between conditions. biocViews: Software, GeneExpression, Network, Clustering, GraphAndNetwork, GeneSetEnrichment, Pathways, Visualization, RNASeq, Transcriptomics, mRNAMicroarray, Microarray, NetworkEnrichment, Sequencing, GO Author: Gwenaëlle Lemoine [aut, cre] () Maintainer: Gwenaëlle Lemoine VignetteBuilder: knitr BugReports: https://github.com/Kumquatum/GWENA/issues git_url: https://git.bioconductor.org/packages/GWENA git_branch: RELEASE_3_12 git_last_commit: 59c2afd git_last_commit_date: 2021-02-15 Date/Publication: 2021-02-15 source.ver: src/contrib/GWENA_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/GWENA_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/GWENA_1.0.1.tgz vignettes: vignettes/GWENA/inst/doc/GWENA_guide.html vignetteTitles: GWENA-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GWENA/inst/doc/GWENA_guide.R dependencyCount: 133 Package: h5vc Version: 2.24.0 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 1.99.1), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rhtslib (>= 1.15.3) Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics License: GPL (>= 3) Archs: i386, x64 MD5sum: 97e27f50d1dfb368d4cb072f541bedcf NeedsCompilation: yes Title: Managing alignment tallies using a hdf5 backend Description: This package contains functions to interact with tally data from NGS experiments that is stored in HDF5 files. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/h5vc git_branch: RELEASE_3_12 git_last_commit: 325e19e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/h5vc_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/h5vc_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/h5vc_2.24.0.tgz vignettes: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html vignetteTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.R, vignettes/h5vc/inst/doc/h5vc.tour.R suggestsMe: h5vcData dependencyCount: 88 Package: hapFabia Version: 1.32.0 Depends: R (>= 3.6.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 169aa7096f762202e7a310c611e5bba7 NeedsCompilation: yes Title: hapFabia: Identification of very short segments of identity by descent (IBD) characterized by rare variants in large sequencing data Description: A package to identify very short IBD segments in large sequencing data by FABIA biclustering. Two haplotypes are identical by descent (IBD) if they share a segment that both inherited from a common ancestor. Current IBD methods reliably detect long IBD segments because many minor alleles in the segment are concordant between the two haplotypes. However, many cohort studies contain unrelated individuals which share only short IBD segments. This package provides software to identify short IBD segments in sequencing data. Knowledge of short IBD segments are relevant for phasing of genotyping data, association studies, and for population genetics, where they shed light on the evolutionary history of humans. The package supports VCF formats, is based on sparse matrix operations, and provides visualization of haplotype clusters in different formats. biocViews: Genetics, GeneticVariability, SNP, Sequencing, Sequencing, Visualization, Clustering, SequenceMatching, Software Author: Sepp Hochreiter Maintainer: Andreas Mitterecker URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html git_url: https://git.bioconductor.org/packages/hapFabia git_branch: RELEASE_3_12 git_last_commit: 83cb41e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hapFabia_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hapFabia_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hapFabia_1.32.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hapFabia/inst/doc/hapfabia.R dependencyCount: 9 Package: Harman Version: 1.18.0 Depends: R (>= 3.6) Imports: Rcpp (>= 0.11.2), graphics, stats, methods LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData, sva License: GPL-3 + file LICENCE Archs: i386, x64 MD5sum: 9f6aa0b753d91e7232352980b05fba87 NeedsCompilation: yes Title: The removal of batch effects from datasets using a PCA and constrained optimisation based technique Description: Harman is a PCA and constrained optimisation based technique that maximises the removal of batch effects from datasets, with the constraint that the probability of overcorrection (i.e. removing genuine biological signal along with batch noise) is kept to a fraction which is set by the end-user. biocViews: BatchEffect, Microarray, MultipleComparison, PrincipalComponent, Normalization, Preprocessing, DNAMethylation, Transcription, Software, StatisticalMethod Author: Josh Bowden [aut], Jason Ross [aut, cre], Yalchin Oytam [aut] Maintainer: Jason Ross URL: http://www.bioinformatics.csiro.au/harman/ VignetteBuilder: knitr BugReports: https://github.com/JasonR055/Harman/issues git_url: https://git.bioconductor.org/packages/Harman git_branch: RELEASE_3_12 git_last_commit: 0bd8e79 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Harman_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Harman_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Harman_1.18.0.tgz vignettes: vignettes/Harman/inst/doc/IntroductionToHarman.html vignetteTitles: IntroductionToHarman hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R importsMe: debrowser dependencyCount: 5 Package: Harshlight Version: 1.62.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 0f1eb183fbf8dd0f78ab23101b16feb5 NeedsCompilation: yes Title: A "corrective make-up" program for microarray chips Description: The package is used to detect extended, diffuse and compact blemishes on microarray chips. Harshlight automatically marks the areas in a collection of chips (affybatch objects) and a corrected AffyBatch object is returned, in which the defected areas are substituted with NAs or the median of the values of the same probe in the other chips in the collection. The new version handle the substitute value as whole matrix to solve the memory problem. biocViews: Microarray, QualityControl, Preprocessing, OneChannel, ReportWriting Author: Mayte Suarez-Farinas, Maurizio Pellegrino, Knut M. Wittkowski, Marcelo O. Magnasco Maintainer: Maurizio Pellegrino URL: http://asterion.rockefeller.edu/Harshlight/ git_url: https://git.bioconductor.org/packages/Harshlight git_branch: RELEASE_3_12 git_last_commit: bb58d1c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Harshlight_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Harshlight_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Harshlight_1.62.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harshlight/inst/doc/Harshlight.R dependencyCount: 24 Package: HCABrowser Version: 1.6.0 Depends: R (>= 3.6.0), dplyr, AnVIL Imports: BiocFileCache, googleAuthR, httr, methods, readr, rlang, utils Suggests: BiocStyle, knitr, rmarkdown, stringr, testthat License: Artistic-2.0 MD5sum: 28dddb43cfa267051e0e249af4cab6fc NeedsCompilation: no Title: Browse the Human Cell Atlas data portal Description: Search, browse, reference, and download resources from the Human Cell Atlas data portal. Development of this package is supported through funds from the Chan / Zuckerberg initiative. biocViews: DataImport, Sequencing, SingleCell Author: Daniel Van Twisk [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/HCABrowser VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCABrowser/issues git_url: https://git.bioconductor.org/packages/HCABrowser git_branch: RELEASE_3_12 git_last_commit: 534ebfd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HCABrowser_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HCABrowser_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HCABrowser_1.6.0.tgz vignettes: vignettes/HCABrowser/inst/doc/HCABrowser.html vignetteTitles: HCABrowser hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCABrowser/inst/doc/HCABrowser.R suggestsMe: HCAMatrixBrowser dependencyCount: 64 Package: HCAExplorer Version: 1.4.0 Depends: R (>= 3.6.0), dplyr Imports: BiocFileCache, HCAMatrixBrowser, S4Vectors, LoomExperiment, vctrs, curl, httr, jsonlite, methods, plyr, readr, rlang, tibble, tidygraph, utils, xml2 Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 13a0b46eafbf7c92c89402eda5722eaf NeedsCompilation: no Title: Browse the Human Cell Atlas data portal Description: Search, browse, reference, and download resources from the Human Cell Atlas data portal. Development of this package is supported through funds from the Chan / Zuckerberg initiative. biocViews: DataImport, Sequencing Author: Daniel Van Twisk [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/HCABrowser VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCABrowser/issues git_url: https://git.bioconductor.org/packages/HCAExplorer git_branch: RELEASE_3_12 git_last_commit: 7ea8f44 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HCAExplorer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HCAExplorer_1.3.1.zip mac.binary.ver: bin/macosx/contrib/4.0/HCAExplorer_1.4.0.tgz vignettes: vignettes/HCAExplorer/inst/doc/HCAExplorer.html vignetteTitles: HCAExplorer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCAExplorer/inst/doc/HCAExplorer.R dependencyCount: 104 Package: HCAMatrixBrowser Version: 1.0.1 Depends: R (>= 4.0.0), AnVIL Imports: BiocFileCache, digest, dplyr, httr, jsonlite, Matrix, methods, progress, rlang, SingleCellExperiment, stats, utils Suggests: BiocStyle, knitr, HCABrowser, LoomExperiment (>= 1.5.3), readr License: Artistic-2.0 MD5sum: 16dbe1f8527dec51c598e4ce51bcfd25 NeedsCompilation: no Title: Extract and manage matrix data from the Human Cell Atlas project Description: The HCAMatrixBrowser queries the HCA matrix endpoint to download expression data and returns a standard Bioconductor object. It uses the LoomExperiment package to serve matrix data that is downloaded as HDF5 loom format. biocViews: Infrastructure, DataRepresentation, Software Author: Marcel Ramos [aut, cre] (), Martin Morgan [aut] (0000-002-5874-8148) Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/HCAMatrixBrowser git_url: https://git.bioconductor.org/packages/HCAMatrixBrowser git_branch: RELEASE_3_12 git_last_commit: f9c47ee git_last_commit_date: 2020-10-28 Date/Publication: 2020-10-28 source.ver: src/contrib/HCAMatrixBrowser_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/HCAMatrixBrowser_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/HCAMatrixBrowser_1.0.1.tgz vignettes: vignettes/HCAMatrixBrowser/inst/doc/HCAMatrix.html, vignettes/HCAMatrixBrowser/inst/doc/HCAMatrixBrowser.html vignetteTitles: HCAMatrix API Queries, HCAMatrixBrowser Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCAMatrixBrowser/inst/doc/HCAMatrix.R, vignettes/HCAMatrixBrowser/inst/doc/HCAMatrixBrowser.R importsMe: HCAExplorer, SingleCellMultiModal dependencyCount: 79 Package: HDF5Array Version: 1.18.1 Depends: R (>= 3.4), methods, DelayedArray (>= 0.15.16), rhdf5 (>= 2.31.6) Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.31.5), S4Vectors, IRanges LinkingTo: S4Vectors (>= 0.27.13), Rhdf5lib Suggests: BiocParallel, GenomicRanges, SummarizedExperiment (>= 1.15.1), h5vcData, ExperimentHub, TENxBrainData, GenomicFeatures, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 1435820d6e182c45139ae142b782677f NeedsCompilation: yes Title: HDF5 backend for DelayedArray objects Description: Implements the HDF5Array and TENxMatrix classes, 2 convenient and memory-efficient array-like containers for on-disk representation of HDF5 datasets. HDF5Array is for datasets that use the conventional (i.e. dense) HDF5 representation. TENxMatrix is for datasets that use the HDF5-based sparse matrix representation from 10x Genomics (e.g. the 1.3 Million Brain Cell Dataset). Both containers being DelayedArray extensions, they support all operations supported by DelayedArray objects. These operations can be either delayed or block-processed. biocViews: Infrastructure, DataRepresentation, DataImport, Sequencing, RNASeq, Coverage, Annotation, GenomeAnnotation, SingleCell, ImmunoOncology Author: Hervé Pagès Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/HDF5Array SystemRequirements: GNU make BugReports: https://github.com/Bioconductor/HDF5Array/issues git_url: https://git.bioconductor.org/packages/HDF5Array git_branch: RELEASE_3_12 git_last_commit: 5bc12e4 git_last_commit_date: 2021-02-04 Date/Publication: 2021-02-04 source.ver: src/contrib/HDF5Array_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/HDF5Array_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/HDF5Array_1.18.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: GenoGAM, TENxBrainData, TENxPBMCData importsMe: biscuiteer, bsseq, clusterExperiment, DelayedMatrixStats, DropletUtils, FRASER, GenomicScores, glmGamPoi, LoomExperiment, methrix, minfi, MOFA2, netSmooth, recountmethylation, scmeth, scry, signatureSearch, MafH5.gnomAD.r3.0.GRCh38, curatedTCGAData, HCAData, MethylSeqData, SingleCellMultiModal suggestsMe: BiocSklearn, DelayedArray, iSEE, MAST, mbkmeans, MultiAssayExperiment, scMerge, scran, sesame, SummarizedExperiment, zellkonverter dependencyCount: 20 Package: HDTD Version: 1.24.0 Depends: R (>= 3.6) Imports: stats, Rcpp (>= 1.0.1) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, markdown License: GPL-3 Archs: i386, x64 MD5sum: 4efe750803ca111e4c29639b94ceb43d NeedsCompilation: yes Title: Statistical Inference about the Mean Matrix and the Covariance Matrices in High-Dimensional Transposable Data (HDTD) Description: Characterization of intra-individual variability using physiologically relevant measurements provides important insights into fundamental biological questions ranging from cell type identity to tumor development. For each individual, the data measurements can be written as a matrix with the different subsamples of the individual recorded in the columns and the different phenotypic units recorded in the rows. Datasets of this type are called high-dimensional transposable data. The HDTD package provides functions for conducting statistical inference for the mean relationship between the row and column variables and for the covariance structure within and between the row and column variables. biocViews: DifferentialExpression, Genetics, GeneExpression, Microarray, Sequencing, StatisticalMethod, Software Author: Anestis Touloumis [cre, aut] (), John C. Marioni [aut] (), Simon Tavar\'{e} [aut] () Maintainer: Anestis Touloumis URL: http://github.com/AnestisTouloumis/HDTD VignetteBuilder: knitr BugReports: http://github.com/AnestisTouloumis/HDTD/issues git_url: https://git.bioconductor.org/packages/HDTD git_branch: RELEASE_3_12 git_last_commit: 0cb481b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HDTD_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HDTD_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HDTD_1.24.0.tgz vignettes: vignettes/HDTD/inst/doc/HDTD.html vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/HDTD.R dependencyCount: 5 Package: heatmaps Version: 1.14.0 Depends: R (>= 3.4) Imports: methods, grDevices, graphics, stats, Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix, Matrix, EBImage, RColorBrewer, BiocGenerics, GenomeInfoDb Suggests: BSgenome.Drerio.UCSC.danRer7, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: b0295d9995560fc8d00c4e77897e8d7f NeedsCompilation: no Title: Flexible Heatmaps for Functional Genomics and Sequence Features Description: This package provides functions for plotting heatmaps of genome-wide data across genomic intervals, such as ChIP-seq signals at peaks or across promoters. Many functions are also provided for investigating sequence features. biocViews: Visualization, SequenceMatching, FunctionalGenomics Author: Malcolm Perry Maintainer: Malcolm Perry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/heatmaps git_branch: RELEASE_3_12 git_last_commit: 6e5cabd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/heatmaps_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/heatmaps_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/heatmaps_1.14.0.tgz vignettes: vignettes/heatmaps/inst/doc/heatmaps.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/heatmaps/inst/doc/heatmaps.R dependencyCount: 40 Package: Heatplus Version: 2.36.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 2032035aedd0a98bd8c7e3076639dab4 NeedsCompilation: no Title: Heatmaps with row and/or column covariates and colored clusters Description: Display a rectangular heatmap (intensity plot) of a data matrix. By default, both samples (columns) and features (row) of the matrix are sorted according to a hierarchical clustering, and the corresponding dendrogram is plotted. Optionally, panels with additional information about samples and features can be added to the plot. biocViews: Microarray, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: https://github.com/alexploner/Heatplus BugReports: https://github.com/alexploner/Heatplus/issues git_url: https://git.bioconductor.org/packages/Heatplus git_branch: RELEASE_3_12 git_last_commit: ccec539 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Heatplus_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Heatplus_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Heatplus_2.36.0.tgz vignettes: vignettes/Heatplus/inst/doc/annHeatmap.pdf, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.pdf, vignettes/Heatplus/inst/doc/oldHeatplus.pdf vignetteTitles: Annotated and regular heatmaps, Commented package source, Old functions (deprecated) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Heatplus/inst/doc/annHeatmap.R, vignettes/Heatplus/inst/doc/annHeatmapCommentedSource.R, vignettes/Heatplus/inst/doc/oldHeatplus.R dependsOnMe: GeneAnswers, phenoTest, tRanslatome suggestsMe: mtbls2, RforProteomics, RAM dependencyCount: 4 Package: HelloRanges Version: 1.16.0 Depends: methods, BiocGenerics, S4Vectors (>= 0.17.39), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.10), Biostrings (>= 2.41.3), BSgenome, GenomicFeatures (>= 1.31.5), VariantAnnotation (>= 1.19.3), Rsamtools, GenomicAlignments (>= 1.15.7), rtracklayer (>= 1.33.8), GenomeInfoDb, SummarizedExperiment Imports: docopt, stats, tools, utils Suggests: HelloRangesData, BiocStyle License: GPL (>= 2) MD5sum: 48bc704d9d45789886f48fed3d97f9e9 NeedsCompilation: no Title: Introduce *Ranges to bedtools users Description: Translates bedtools command-line invocations to R code calling functions from the Bioconductor *Ranges infrastructure. This is intended to educate novice Bioconductor users and to compare the syntax and semantics of the two frameworks. biocViews: Sequencing, Annotation, Coverage, GenomeAnnotation, DataImport, SequenceMatching, VariantAnnotation Author: Michael Lawrence Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/HelloRanges git_branch: RELEASE_3_12 git_last_commit: 49e5245 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HelloRanges_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HelloRanges_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HelloRanges_1.16.0.tgz vignettes: vignettes/HelloRanges/inst/doc/tutorial.pdf vignetteTitles: HelloRanges Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HelloRanges/inst/doc/tutorial.R importsMe: OMICsPCA suggestsMe: plyranges dependencyCount: 91 Package: HELP Version: 1.48.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: 3edcf8e1c5da8cccb0715bc3cbd11082 NeedsCompilation: no Title: Tools for HELP data analysis Description: The package contains a modular pipeline for analysis of HELP microarray data, and includes graphical and mathematical tools with more general applications. biocViews: CpGIsland, DNAMethylation, Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, Visualization Author: Reid F. Thompson , John M. Greally , with contributions from Mark Reimers Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/HELP git_branch: RELEASE_3_12 git_last_commit: d0badf8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HELP_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HELP_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HELP_1.48.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HELP/inst/doc/HELP.R dependencyCount: 8 Package: HEM Version: 1.62.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: e401122668f7017aa186bd1796d1e0b8 NeedsCompilation: yes Title: Heterogeneous error model for identification of differentially expressed genes under multiple conditions Description: This package fits heterogeneous error models for analysis of microarray data biocViews: Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: HyungJun Cho URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/HEM git_branch: RELEASE_3_12 git_last_commit: 9f7f3a0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HEM_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HEM_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HEM_1.62.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: Herper Version: 1.0.2 Depends: R (>= 4.0), reticulate Imports: utils, rjson, withr, stats Suggests: BiocStyle, testthat, knitr, rmarkdown, seqCNA License: GPL-3 MD5sum: c51e80550f966a7afa08ebdb79fb53a2 NeedsCompilation: no Title: The Herper package is a simple toolset to install and manage conda packages and environments from R Description: Many tools for data analysis are not available in R, but are present in public repositories like conda. The Herper package provides a comprehensive set of functions to interact with the conda package managament system. With Herper users can install, manage and run conda packages from the comfort of their R session. Herper also provides an ad-hoc approach to handling external system requirements for R packages. For people developing packages with python conda dependencies we recommend using basilisk (https://bioconductor.org/packages/release/bioc/html/basilisk.html) to internally support these system requirments pre-hoc. biocViews: Infrastructure, Software Author: Matt Paul [aut] (), Thomas Carroll [aut, cre] (), Doug Barrows [aut], Kathryn Rozen-Gagnon [ctb] Maintainer: Thomas Carroll URL: https://github.com/RockefellerUniversity/Herper VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Herper git_branch: RELEASE_3_12 git_last_commit: 3ab5705 git_last_commit_date: 2021-02-26 Date/Publication: 2021-02-26 source.ver: src/contrib/Herper_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/Herper_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.0/Herper_1.0.2.tgz vignettes: vignettes/Herper/inst/doc/Herper.html, vignettes/Herper/inst/doc/QuickStart.html vignetteTitles: Herper, Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Herper/inst/doc/Herper.R, vignettes/Herper/inst/doc/QuickStart.R dependencyCount: 15 Package: hiAnnotator Version: 1.24.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales, methods Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: de0fcd928b9d9084aef1d15871190e74 NeedsCompilation: no Title: Functions for annotating GRanges objects Description: hiAnnotator contains set of functions which allow users to annotate a GRanges object with custom set of annotations. The basic philosophy of this package is to take two GRanges objects (query & subject) with common set of seqnames (i.e. chromosomes) and return associated annotation per seqnames and rows from the query matching seqnames and rows from the subject (i.e. genes or cpg islands). The package comes with three types of annotation functions which calculates if a position from query is: within a feature, near a feature, or count features in defined window sizes. Moreover, each function is equipped with parallel backend to utilize the foreach package. In addition, the package is equipped with wrapper functions, which finds appropriate columns needed to make a GRanges object from a common data frame. biocViews: Software, Annotation Author: Nirav V Malani Maintainer: Nirav V Malani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiAnnotator git_branch: RELEASE_3_12 git_last_commit: ab43fdd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hiAnnotator_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hiAnnotator_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hiAnnotator_1.24.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/Intro.html vignetteTitles: Using hiAnnotator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R dependsOnMe: hiReadsProcessor dependencyCount: 77 Package: HIBAG Version: 1.26.1 Depends: R (>= 3.2.0) Imports: methods, RcppParallel LinkingTo: RcppParallel Suggests: parallel, knitr, gdsfmt, SNPRelate, ggplot2, reshape2 License: GPL-3 Archs: i386, x64 MD5sum: 77569fcceb299cc34a73cdcbe2920f9c NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: Imputes HLA classical alleles using GWAS SNP data, and it relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection. biocViews: Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre, cph] (), Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/HIBAG, https://hibag.s3.amazonaws.com/index.html, http://www.biostat.washington.edu/~bsweir/HIBAG SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIBAG git_branch: RELEASE_3_12 git_last_commit: 6b35b21 git_last_commit_date: 2021-03-21 Date/Publication: 2021-03-23 source.ver: src/contrib/HIBAG_1.26.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/HIBAG_1.26.1.zip mac.binary.ver: bin/macosx/contrib/4.0/HIBAG_1.26.1.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG.html, vignettes/HIBAG/inst/doc/HLA_Association.html, vignettes/HIBAG/inst/doc/Implementation.html vignetteTitles: HIBAG vignette html, HLA association vignette html, HIBAG algorithm implementation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG.R, vignettes/HIBAG/inst/doc/HLA_Association.R, vignettes/HIBAG/inst/doc/Implementation.R dependencyCount: 2 Package: HiCBricks Version: 1.8.0 Depends: R (>= 3.6), utils, curl, rhdf5, R6, grid Imports: ggplot2, viridis, RColorBrewer, scales, reshape2, stringr, data.table, GenomeInfoDb, GenomicRanges, stats, IRanges, grDevices, S4Vectors, digest, tibble, jsonlite, BiocParallel, R.utils, readr, methods Suggests: BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 7481d65d690be04275269b58c705c0b7 NeedsCompilation: no Title: Framework for Storing and Accessing Hi-C Data Through HDF Files Description: HiCBricks is a library designed for handling large high-resolution Hi-C datasets. Over the years, the Hi-C field has experienced a rapid increase in the size and complexity of datasets. HiCBricks is meant to overcome the challenges related to the analysis of such large datasets within the R environment. HiCBricks offers user-friendly and efficient solutions for handling large high-resolution Hi-C datasets. The package provides an R/Bioconductor framework with the bricks to build more complex data analysis pipelines and algorithms. HiCBricks already incorporates example algorithms for calling domain boundaries and functions for high quality data visualization. biocViews: DataImport, Infrastructure, Software, Technology, Sequencing, HiC Author: Koustav Pal [aut, cre], Carmen Livi [ctb], Ilario Tagliaferri [ctb] Maintainer: Koustav Pal VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HiCBricks git_branch: RELEASE_3_12 git_last_commit: 12ba995 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HiCBricks_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HiCBricks_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HiCBricks_1.8.0.tgz vignettes: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.html vignetteTitles: IntroductionToHiCBricks.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCBricks/inst/doc/IntroductionToHiCBricks.R dependencyCount: 78 Package: HiCcompare Version: 1.12.0 Depends: R (>= 3.4.0), dplyr Imports: data.table, ggplot2, gridExtra, mgcv, stats, InteractionSet, GenomicRanges, IRanges, S4Vectors, BiocParallel, QDNAseq, KernSmooth, methods, utils, graphics, pheatmap, gtools, rhdf5 Suggests: knitr, rmarkdown, testthat, multiHiCcompare License: MIT + file LICENSE MD5sum: 8329962d6d012424758ab7157e2986fd NeedsCompilation: no Title: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets Description: HiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. HiCcompare operates on processed Hi-C data in the form of chromosome-specific chromatin interaction matrices. It accepts three-column tab-separated text files storing chromatin interaction matrices in a sparse matrix format which are available from several sources. HiCcompare is designed to give the user the ability to perform a comparative analysis on the 3-Dimensional structure of the genomes of cells in different biological states.`HiCcompare` differs from other packages that attempt to compare Hi-C data in that it works on processed data in chromatin interaction matrix format instead of pre-processed sequencing data. In addition, `HiCcompare` provides a non-parametric method for the joint normalization and removal of biases between two Hi-C datasets for the purpose of comparative analysis. `HiCcompare` also provides a simple yet robust method for detecting differences between Hi-C datasets. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield , Kellen Cresswell , Mikhail Dozmorov Maintainer: John Stansfield , Mikhail Dozmorov URL: https://github.com/dozmorovlab/HiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/HiCcompare/issues git_url: https://git.bioconductor.org/packages/HiCcompare git_branch: RELEASE_3_12 git_last_commit: f28a3b7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HiCcompare_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HiCcompare_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HiCcompare_1.12.0.tgz vignettes: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.html vignetteTitles: HiCcompare Usage Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HiCcompare/inst/doc/HiCcompare-vignette.R importsMe: multiHiCcompare, SpectralTAD, TADCompare dependencyCount: 97 Package: hierGWAS Version: 1.20.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: dfeb486cd7eb6872f98716b4c8d4ad26 NeedsCompilation: no Title: Asessing statistical significance in predictive GWA studies Description: Testing individual SNPs, as well as arbitrarily large groups of SNPs in GWA studies, using a joint model of all SNPs. The method controls the FWER, and provides an automatic, data-driven refinement of the SNP clusters to smaller groups or single markers. biocViews: SNP, LinkageDisequilibrium, Clustering Author: Laura Buzdugan Maintainer: Laura Buzdugan git_url: https://git.bioconductor.org/packages/hierGWAS git_branch: RELEASE_3_12 git_last_commit: 1559099 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hierGWAS_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hierGWAS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hierGWAS_1.20.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hierGWAS/inst/doc/hierGWAS.R dependencyCount: 17 Package: hierinf Version: 1.8.0 Depends: R (>= 3.6.0) Imports: fmsb, glmnet, methods, parallel, stats Suggests: knitr, MASS, testthat License: GPL-3 | file LICENSE MD5sum: 4770d165f275573ca074cc7ae73cdad0 NeedsCompilation: no Title: Hierarchical Inference Description: Tools to perform hierarchical inference for one or multiple studies / data sets based on high-dimensional multivariate (generalised) linear models. A possible application is to perform hierarchical inference for GWA studies to find significant groups or single SNPs (if the signal is strong) in a data-driven and automated procedure. The method is based on an efficient hierarchical multiple testing correction and controls the FWER. The functions can easily be run in parallel. biocViews: Clustering, GenomeWideAssociation, LinkageDisequilibrium, Regression, SNP Author: Claude Renaux [aut, cre], Laura Buzdugan [aut], Markus Kalisch [aut], Peter Bühlmann [aut] Maintainer: Claude Renaux VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hierinf git_branch: RELEASE_3_12 git_last_commit: 6e31840 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hierinf_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hierinf_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hierinf_1.8.0.tgz vignettes: vignettes/hierinf/inst/doc/vignette-hierinf.pdf vignetteTitles: vignette-hierinf.Rnw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hierinf/inst/doc/vignette-hierinf.R dependencyCount: 17 Package: HilbertCurve Version: 1.20.0 Depends: R (>= 3.1.2), grid Imports: methods, utils, HilbertVis, png, grDevices, circlize (>= 0.3.3), IRanges, GenomicRanges, polylabelr Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.99.0), markdown, RColorBrewer, RCurl, GetoptLong License: MIT + file LICENSE MD5sum: be7f1ed6442d17bf9330378da18f6dbb NeedsCompilation: no Title: Making 2D Hilbert Curve Description: Hilbert curve is a type of space-filling curves that fold one dimensional axis into a two dimensional space, but with still preserves the locality. This package aims to provide an easy and flexible way to visualize data through Hilbert curve. biocViews: Software, Visualization, Sequencing, Coverage, GenomeAnnotation Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/HilbertCurve VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HilbertCurve git_branch: RELEASE_3_12 git_last_commit: b100b32 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HilbertCurve_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HilbertCurve_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HilbertCurve_1.20.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/HilbertCurve.html vignetteTitles: Making 2D Hilbert Curve hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R dependencyCount: 28 Package: HilbertVis Version: 1.48.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: i386, x64 MD5sum: f29898aef902618d76e59497e4efe053 NeedsCompilation: yes Title: Hilbert curve visualization Description: Functions to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert git_url: https://git.bioconductor.org/packages/HilbertVis git_branch: RELEASE_3_12 git_last_commit: 09ecd73 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HilbertVis_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HilbertVis_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HilbertVis_1.48.0.tgz vignettes: vignettes/HilbertVis/inst/doc/HilbertVis.pdf vignetteTitles: Visualising very long data vectors with the Hilbert curve hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertVis/inst/doc/HilbertVis.R dependsOnMe: HilbertVisGUI importsMe: ChIPseqR, HilbertCurve dependencyCount: 6 Package: HilbertVisGUI Version: 1.48.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: 09ee1fb95face21606691b92b6aac9b4 NeedsCompilation: yes Title: HilbertVisGUI Description: An interactive tool to visualize long vectors of integer data by means of Hilbert curves biocViews: Visualization Author: Simon Anders Maintainer: Simon Anders URL: http://www.ebi.ac.uk/~anders/hilbert SystemRequirements: gtkmm-2.4, GNU make git_url: https://git.bioconductor.org/packages/HilbertVisGUI git_branch: RELEASE_3_12 git_last_commit: d371910 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HilbertVisGUI_1.48.0.tar.gz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE dependencyCount: 7 Package: HiLDA Version: 1.4.0 Depends: R(>= 3.6), ggplot2 Imports: R2jags, abind, cowplot, grid, forcats, stringr, GenomicRanges, S4Vectors, XVector, Biostrings, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, BiocGenerics, tidyr, grDevices, stats, TxDb.Hsapiens.UCSC.hg19.knownGene, utils, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 16debe0ab9dfde98ed0ba2222dd683de NeedsCompilation: yes Title: Conducting statistical inference on comparing the mutational exposures of mutational signatures by using hierarchical latent Dirichlet allocation Description: A package built under the Bayesian framework of applying hierarchical latent Dirichlet allocation to statistically test whether the mutational exposures of mutational signatures (Shiraishi-model signatures) are different between two groups. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Bayesian Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/HiLDA, https://doi.org/10.1101/577452 SystemRequirements: JAGS 4.2.0 VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/issues git_url: https://git.bioconductor.org/packages/HiLDA git_branch: RELEASE_3_12 git_last_commit: f8470d0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HiLDA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HiLDA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HiLDA_1.4.0.tgz vignettes: vignettes/HiLDA/inst/doc/HiLDA.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/HiLDA/inst/doc/HiLDA.R importsMe: selectKSigs dependencyCount: 116 Package: hipathia Version: 2.6.0 Depends: R (>= 3.6), igraph (>= 1.0.1), AnnotationHub(>= 2.6.5), MultiAssayExperiment(>= 1.4.9), SummarizedExperiment(>= 1.8.1) Imports: coin, stats, limma, grDevices, utils, graphics, preprocessCore, servr, DelayedArray, matrixStats, methods, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: d1272035f3d66dd0e52b984046c98f7c NeedsCompilation: no Title: HiPathia: High-throughput Pathway Analysis Description: Hipathia is a method for the computation of signal transduction along signaling pathways from transcriptomic data. The method is based on an iterative algorithm which is able to compute the signal intensity passing through the nodes of a network by taking into account the level of expression of each gene and the intensity of the signal arriving to it. It also provides a new approach to functional analysis allowing to compute the signal arriving to the functions annotated to each pathway. biocViews: Pathways, GraphAndNetwork, GeneExpression, GeneSignaling, GO Author: Marta R. Hidalgo [aut, cre], José Carbonell-Caballero [ctb], Francisco Salavert [ctb], Alicia Amadoz [ctb], Çankut Cubuk [ctb], Joaquin Dopazo [ctb] Maintainer: Marta R. Hidalgo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hipathia git_branch: RELEASE_3_12 git_last_commit: e436824 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hipathia_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hipathia_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hipathia_2.6.0.tgz vignettes: vignettes/hipathia/inst/doc/hipathia-vignette.pdf vignetteTitles: Hipathia Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hipathia/inst/doc/hipathia-vignette.R dependencyCount: 110 Package: HIPPO Version: 1.2.0 Depends: R (>= 3.6.0) Imports: ggplot2, graphics, stats, reshape2, gridExtra, Rtsne, umap, dplyr, rlang, magrittr, irlba, Matrix, SingleCellExperiment, ggrepel Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: 4b9a1ed76c5695123a213fc25405813a NeedsCompilation: no Title: Heterogeneity-Induced Pre-Processing tOol Description: For scRNA-seq data, it selects features and clusters the cells simultaneously for single-cell UMI data. It has a novel feature selection method using the zero inflation instead of gene variance, and computationally faster than other existing methods since it only relies on PCA+Kmeans rather than graph-clustering or consensus clustering. biocViews: Sequencing, SingleCell, GeneExpression, DifferentialExpression, Clustering Author: Tae Kim [aut, cre], Mengjie Chen [aut] Maintainer: Tae Kim URL: https://github.com/tk382/HIPPO VignetteBuilder: knitr BugReports: https://github.com/tk382/HIPPO/issues git_url: https://git.bioconductor.org/packages/HIPPO git_branch: RELEASE_3_12 git_last_commit: 90c3d90 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/HIPPO_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HIPPO_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HIPPO_1.2.0.tgz vignettes: vignettes/HIPPO/inst/doc/example.html vignetteTitles: Example analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIPPO/inst/doc/example.R dependencyCount: 80 Package: hiReadsProcessor Version: 1.26.0 Depends: Biostrings, GenomicAlignments, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges, readxl, methods Suggests: knitr, testthat License: GPL-3 MD5sum: 8f32c3eb48874f544f3761f40647aa2e NeedsCompilation: no Title: Functions to process LM-PCR reads from 454/Illumina data Description: hiReadsProcessor contains set of functions which allow users to process LM-PCR products sequenced using any platform. Given an excel/txt file containing parameters for demultiplexing and sample metadata, the functions automate trimming of adaptors and identification of the genomic product. Genomic products are further processed for QC and abundance quantification. biocViews: Sequencing, Preprocessing Author: Nirav V Malani Maintainer: Nirav V Malani SystemRequirements: BLAT, UCSC hg18 in 2bit format for BLAT VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hiReadsProcessor git_branch: RELEASE_3_12 git_last_commit: 41c0142 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hiReadsProcessor_1.26.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/hiReadsProcessor_1.26.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/Tutorial.html vignetteTitles: Using hiReadsProcessor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R dependencyCount: 86 Package: HIREewas Version: 1.8.0 Depends: R (>= 3.5.0) Imports: quadprog, gplots, grDevices, stats Suggests: BiocStyle, knitr, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 77a30768fbea18487e069e253f71fabd NeedsCompilation: yes Title: Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies Description: In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. We propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types. The "HIREewas" R package is to implement HIRE model in R. biocViews: DNAMethylation, DifferentialMethylation, FeatureExtraction Author: Xiangyu Luo , Can Yang , Yingying Wei Maintainer: Xiangyu Luo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HIREewas git_branch: RELEASE_3_12 git_last_commit: c2c1623 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HIREewas_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HIREewas_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HIREewas_1.8.0.tgz vignettes: vignettes/HIREewas/inst/doc/HIREewas.pdf vignetteTitles: HIREewas hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIREewas/inst/doc/HIREewas.R dependencyCount: 10 Package: HiTC Version: 1.34.0 Depends: R (>= 2.15.0), methods, IRanges, GenomicRanges Imports: Biostrings, graphics, grDevices, rtracklayer, RColorBrewer, Matrix, parallel, GenomeInfoDb Suggests: BiocStyle, HiCDataHumanIMR90 License: Artistic-2.0 MD5sum: 31fc51938f036f8b450e6b418cf39d09 NeedsCompilation: no Title: High Throughput Chromosome Conformation Capture analysis Description: The HiTC package was developed to explore high-throughput 'C' data such as 5C or Hi-C. Dedicated R classes as well as standard methods for quality controls, normalization, visualization, and further analysis are also provided. biocViews: Sequencing, HighThroughputSequencing, HiC Author: Nicolas Servant Maintainer: Nicolas Servant git_url: https://git.bioconductor.org/packages/HiTC git_branch: RELEASE_3_12 git_last_commit: b68a85e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HiTC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HiTC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HiTC_1.34.0.tgz vignettes: vignettes/HiTC/inst/doc/HiC_analysis.pdf, vignettes/HiTC/inst/doc/HiTC.pdf vignetteTitles: Hi-C data analysis using HiTC, Introduction to HiTC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HiTC/inst/doc/HiC_analysis.R, vignettes/HiTC/inst/doc/HiTC.R suggestsMe: HiCDataHumanIMR90, adjclust dependencyCount: 41 Package: hmdbQuery Version: 1.10.1 Depends: R (>= 3.5), XML Imports: S4Vectors, methods, utils Suggests: knitr, annotate, gwascat, testthat License: Artistic-2.0 MD5sum: 690d74a9798830777e6dd8b6709d9abf NeedsCompilation: no Title: utilities for exploration of human metabolome database Description: Define utilities for exploration of human metabolome database, including functions to retrieve specific metabolite entries and data snapshots with pairwise associations (metabolite-gene,-protein,-disease). biocViews: Metabolomics, Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hmdbQuery git_branch: RELEASE_3_12 git_last_commit: 30801af git_last_commit_date: 2021-02-14 Date/Publication: 2021-02-15 source.ver: src/contrib/hmdbQuery_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/hmdbQuery_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/hmdbQuery_1.10.1.tgz vignettes: vignettes/hmdbQuery/inst/doc/hmdbQuery.html vignetteTitles: hmdbQuery: working with Human Metabolome Database (hmdb.ca) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hmdbQuery/inst/doc/hmdbQuery.R dependencyCount: 9 Package: HMMcopy Version: 1.32.0 Depends: R (>= 2.10.0), data.table (>= 1.11.8) License: GPL-3 Archs: i386, x64 MD5sum: 25f8622f279bf4abd8c4ce56387475bf NeedsCompilation: yes Title: Copy number prediction with correction for GC and mappability bias for HTS data Description: Corrects GC and mappability biases for readcounts (i.e. coverage) in non-overlapping windows of fixed length for single whole genome samples, yielding a rough estimate of copy number for furthur analysis. Designed for rapid correction of high coverage whole genome tumour and normal samples. biocViews: Sequencing, Preprocessing, Visualization, CopyNumberVariation, Microarray Author: Daniel Lai, Gavin Ha, Sohrab Shah Maintainer: Daniel Lai , Sohrab Shah git_url: https://git.bioconductor.org/packages/HMMcopy git_branch: RELEASE_3_12 git_last_commit: ba30e1f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HMMcopy_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HMMcopy_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HMMcopy_1.32.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HMMcopy/inst/doc/HMMcopy.R importsMe: qsea dependencyCount: 2 Package: hopach Version: 2.50.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 0f7a57341b4d6188ad96b11a95972a82 NeedsCompilation: yes Title: Hierarchical Ordered Partitioning and Collapsing Hybrid (HOPACH) Description: The HOPACH clustering algorithm builds a hierarchical tree of clusters by recursively partitioning a data set, while ordering and possibly collapsing clusters at each level. The algorithm uses the Mean/Median Split Silhouette (MSS) criteria to identify the level of the tree with maximally homogeneous clusters. It also runs the tree down to produce a final ordered list of the elements. The non-parametric bootstrap allows one to estimate the probability that each element belongs to each cluster (fuzzy clustering). biocViews: Clustering Author: Katherine S. Pollard, with Mark J. van der Laan and Greg Wall Maintainer: Katherine S. Pollard URL: http://www.stat.berkeley.edu/~laan/, http://docpollard.org/ git_url: https://git.bioconductor.org/packages/hopach git_branch: RELEASE_3_12 git_last_commit: 22812f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hopach_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hopach_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hopach_2.50.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hopach/inst/doc/hopach.R importsMe: phenoTest, scClassify suggestsMe: BiocCaseStudies dependencyCount: 9 Package: HPAanalyze Version: 1.8.1 Depends: R (>= 3.5.0) Imports: dplyr, openxlsx, ggplot2, tibble, xml2, stats, utils, gridExtra Suggests: knitr, rmarkdown, devtools, BiocStyle License: GPL-3 + file LICENSE MD5sum: 4e842192841ef13d2f27b5ba3cc09d76 NeedsCompilation: no Title: Retrieve and analyze data from the Human Protein Atlas Description: Provide functions for retrieving, exploratory analyzing and visualizing the Human Protein Atlas data. biocViews: Proteomics, CellBiology, Visualization, Software Author: Anh Nhat Tran [aut, cre] Maintainer: Anh Nhat Tran VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HPAanalyze git_branch: RELEASE_3_12 git_last_commit: ae410ad git_last_commit_date: 2020-11-24 Date/Publication: 2020-11-24 source.ver: src/contrib/HPAanalyze_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/HPAanalyze_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.0/HPAanalyze_1.8.1.tgz vignettes: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.html, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.html, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.html, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.html, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.html, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.html, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.html vignetteTitles: "1. HPAanalyze quick start guide: Acquire and visualize the Human Protein Atlas (HPA) data in one function", "2. HPAanalyze in-depth: Working with Human Protein Atlas (HPA) data in R", "3. HPAanalyze use case: Combine with your Human Protein Atlas (HPA) queries", "4. HPAanalyze use case: Working with Human Protein Atlas (HPA) xml files offline", "5. HPAanalyze use case: Export Human Protein Atlas (HPA) data as JSON", "6. HPAanalyze use case: Download histology images from the Human Protein Atlas", "99. Code for figures from HPAanalyze paper" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/HPAanalyze/inst/doc/a_HPAanalyze_quick_start.R, vignettes/HPAanalyze/inst/doc/b_HPAanalyze_indepth.R, vignettes/HPAanalyze/inst/doc/c_HPAanalyze_case_query.R, vignettes/HPAanalyze/inst/doc/d_HPAanalyze_case_offline_xml.R, vignettes/HPAanalyze/inst/doc/e_HPAanalyze_case_json.R, vignettes/HPAanalyze/inst/doc/f_HPAanalyze_case_images.R, vignettes/HPAanalyze/inst/doc/z_HPAanalyze_paper_figures.R dependencyCount: 49 Package: hpar Version: 1.32.1 Depends: R (>= 3.5.0) Imports: utils Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 269a025ba8660f2f7f924f1b5d44c55d NeedsCompilation: no Title: Human Protein Atlas in R Description: The hpar package provides a simple R interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto [cre, aut] (), Manon Martin [aut] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hpar git_branch: RELEASE_3_12 git_last_commit: 68bed75 git_last_commit_date: 2020-11-01 Date/Publication: 2020-11-01 source.ver: src/contrib/hpar_1.32.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/hpar_1.32.1.zip mac.binary.ver: bin/macosx/contrib/4.0/hpar_1.32.1.tgz vignettes: vignettes/hpar/inst/doc/hpar.html vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R dependsOnMe: proteomics importsMe: MetaboSignal suggestsMe: pRoloc, RforProteomics dependencyCount: 1 Package: HPAStainR Version: 1.0.3 Depends: R (>= 4.0.0), dplyr, tidyr Imports: utils, stats, scales, stringr, tibble, shiny, data.table Suggests: knitr, qpdf, testthat License: Artistic-2.0 MD5sum: 944114dadd5b0580ff079167f8a5816f NeedsCompilation: no Title: Queries the Human Protein Atlas Staining Data for Multiple Proteins and Genes Description: This package is built around the HPAStainR function. The purpose of the HPAStainR function is to query the visual staining data in the Human Protein Atlas to return a table of staining ranked cell types. The function also has multiple arguements to personalize to output as well to include cancer data, csv readable names, modify the confidence levels of the results and more. The other functions exist exlcusively to easily acquire the data required to run HPAStainR. biocViews: GeneExpression, GeneSetEnrichment Author: Tim O. Nieuwenhuis [aut, cre] () Maintainer: Tim O. Nieuwenhuis SystemRequirements: 4GB of RAM VignetteBuilder: knitr BugReports: https://github.com/tnieuwe/HPAstainR git_url: https://git.bioconductor.org/packages/HPAStainR git_branch: RELEASE_3_12 git_last_commit: ead97ed git_last_commit_date: 2021-02-03 Date/Publication: 2021-02-09 source.ver: src/contrib/HPAStainR_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/HPAStainR_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.0/HPAStainR_1.0.3.tgz vignettes: vignettes/HPAStainR/inst/doc/HPAStainR.html vignetteTitles: HPAStainR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HPAStainR/inst/doc/HPAStainR.R dependencyCount: 57 Package: HTqPCR Version: 1.44.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 8d484a7dde777144e116a346d544a278 NeedsCompilation: no Title: Automated analysis of high-throughput qPCR data Description: Analysis of Ct values from high throughput quantitative real-time PCR (qPCR) assays across multiple conditions or replicates. The input data can be from spatially-defined formats such ABI TaqMan Low Density Arrays or OpenArray; LightCycler from Roche Applied Science; the CFX plates from Bio-Rad Laboratories; conventional 96- or 384-well plates; or microfluidic devices such as the Dynamic Arrays from Fluidigm Corporation. HTqPCR handles data loading, quality assessment, normalization, visualization and parametric or non-parametric testing for statistical significance in Ct values between features (e.g. genes, microRNAs). biocViews: MicrotitrePlateAssay, DifferentialExpression, GeneExpression, DataImport, QualityControl, Preprocessing, Visualization, MultipleComparison, qPCR Author: Heidi Dvinge, Paul Bertone Maintainer: Heidi Dvinge URL: http://www.ebi.ac.uk/bertone/software git_url: https://git.bioconductor.org/packages/HTqPCR git_branch: RELEASE_3_12 git_last_commit: 66a431f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HTqPCR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HTqPCR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HTqPCR_1.44.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTqPCR/inst/doc/HTqPCR.R importsMe: nondetects, unifiedWMWqPCR dependencyCount: 21 Package: HTSeqGenie Version: 4.20.0 Depends: R (>= 3.0.0), gmapR (>= 1.8.0), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.9.25), IRanges (>= 1.21.39), GenomicRanges (>= 1.23.21), Rsamtools (>= 1.8.5), Biostrings (>= 2.24.1), chipseq (>= 1.6.1), hwriter (>= 1.3.0), Cairo (>= 1.5.5), GenomicFeatures (>= 1.9.31), BiocParallel, parallel, tools, rtracklayer (>= 1.17.19), GenomicAlignments, VariantTools (>= 1.7.7), GenomeInfoDb, SummarizedExperiment, methods Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, LungCancerLines, org.Hs.eg.db License: Artistic-2.0 MD5sum: 63d5f7dbe66d40c3b5d8c316c2b5f2b7 NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder git_url: https://git.bioconductor.org/packages/HTSeqGenie git_branch: RELEASE_3_12 git_last_commit: 1967349 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HTSeqGenie_4.20.0.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.R dependencyCount: 100 Package: HTSFilter Version: 1.30.1 Depends: R (>= 4.0) Imports: edgeR, DESeq2, BiocParallel, Biobase, utils, stats, grDevices, graphics, methods Suggests: EDASeq, testthat, knitr, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 1b21f08c274cfe771110bb5cb647bb86 NeedsCompilation: no Title: Filter replicated high-throughput transcriptome sequencing data Description: This package implements a filtering procedure for replicated transcriptome sequencing data based on a global Jaccard similarity index in order to identify genes with low, constant levels of expression across one or more experimental conditions. biocViews: Sequencing, RNASeq, Preprocessing, DifferentialExpression, GeneExpression, Normalization, ImmunoOncology Author: Andrea Rau [cre, aut] (), Melina Gallopin [ctb], Gilles Celeux [ctb], Florence Jaffrézic [ctb] Maintainer: Andrea Rau VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HTSFilter git_branch: RELEASE_3_12 git_last_commit: e4979de git_last_commit_date: 2020-12-16 Date/Publication: 2020-12-16 source.ver: src/contrib/HTSFilter_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/HTSFilter_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.0/HTSFilter_1.30.1.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.html vignetteTitles: HTSFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R importsMe: coseq suggestsMe: HTSCluster dependencyCount: 92 Package: HumanTranscriptomeCompendium Version: 1.6.0 Depends: R (>= 3.6) Imports: shiny, ssrch, S4Vectors, SummarizedExperiment, utils Suggests: knitr, BiocStyle, beeswarm, tximportData, DT, tximport, dplyr, magrittr, BiocFileCache, testthat License: Artistic-2.0 MD5sum: b21314e839618abc0c98e839cf013fde NeedsCompilation: no Title: Tools to work with a Compendium of 181000 human transcriptome sequencing studies Description: Provide tools for working with a compendium of human transcriptome sequences (originally htxcomp). biocViews: Transcriptomics, Infrastructure Author: Sean Davis, Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/HumanTranscriptomeCompendium git_branch: RELEASE_3_12 git_last_commit: 36303f1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HumanTranscriptomeCompendium_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HumanTranscriptomeCompendium_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HumanTranscriptomeCompendium_1.6.0.tgz vignettes: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.html vignetteTitles: HumanTranscriptomeCompendium -- a cloud-resident collection of sequenced human transcriptomes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HumanTranscriptomeCompendium/inst/doc/htxcomp.R dependencyCount: 60 Package: hummingbird Version: 1.0.3 Depends: R (>= 4.0) Imports: Rcpp, graphics, GenomicRanges, SummarizedExperiment, IRanges LinkingTo: Rcpp Suggests: knitr, rmarkdown License: GPL (>=2) Archs: i386, x64 MD5sum: b2524046aa0aebf83ba6ae7a030cefc5 NeedsCompilation: yes Title: Bayesian Hidden Markov Model for the detection of differentially methylated regions Description: A package for detecting differential methylation. It exploits a Bayesian hidden Markov model that incorporates location dependence among genomic loci, unlike most existing methods that assume independence among observations. Bayesian priors are applied to permit information sharing across an entire chromosome for improved power of detection. The direct output of our software package is the best sequence of methylation states, eliminating the use of a subjective, and most of the time an arbitrary, threshold of p-value for determining significance. At last, our methodology does not require replication in either or both of the two comparison groups. biocViews: HiddenMarkovModel, Bayesian, DNAMethylation, BiomedicalInformatics, Sequencing, GeneExpression, DifferentialExpression, DifferentialMethylation Author: Eleni Adam [aut, cre], Tieming Ji [aut], Desh Ranjan [aut] Maintainer: Eleni Adam VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/hummingbird git_branch: RELEASE_3_12 git_last_commit: 457ac10 git_last_commit_date: 2021-04-17 Date/Publication: 2021-04-17 source.ver: src/contrib/hummingbird_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/hummingbird_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.0/hummingbird_1.0.3.tgz vignettes: vignettes/hummingbird/inst/doc/hummingbird.html vignetteTitles: hummingbird hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hummingbird/inst/doc/hummingbird.R dependencyCount: 27 Package: HybridMTest Version: 1.34.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: f4a846981f99612b4b56eaaf0fcd0d09 NeedsCompilation: no Title: Hybrid Multiple Testing Description: Performs hybrid multiple testing that incorporates method selection and assumption evaluations into the analysis using empirical Bayes probability (EBP) estimates obtained by Grenander density estimation. For instance, for 3-group comparison analysis, Hybrid Multiple testing considers EBPs as weighted EBPs between F-test and H-test with EBPs from Shapiro Wilk test of normality as weigth. Instead of just using EBPs from F-test only or using H-test only, this methodology combines both types of EBPs through EBPs from Shapiro Wilk test of normality. This methodology uses then the law of total EBPs. biocViews: GeneExpression, Genetics, Microarray Author: Stan Pounds , Demba Fofana Maintainer: Demba Fofana git_url: https://git.bioconductor.org/packages/HybridMTest git_branch: RELEASE_3_12 git_last_commit: e2f3ab3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/HybridMTest_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/HybridMTest_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/HybridMTest_1.34.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HybridMTest/inst/doc/HybridMTest.R dependencyCount: 15 Package: hypeR Version: 1.6.0 Depends: R (>= 3.6.0) Imports: ggplot2, ggforce, R6, magrittr, dplyr, purrr, stats, stringr, scales, rlang, httr, openxlsx, htmltools, reshape2, reactable, msigdbr, kableExtra, rmarkdown, igraph, visNetwork, shiny Suggests: tidyverse, devtools, testthat, knitr License: GPL-3 + file LICENSE MD5sum: d0366aceda3fe8bfa2ea66bce1428bef NeedsCompilation: no Title: An R Package For Geneset Enrichment Workflows Description: An R Package for Geneset Enrichment Workflows. biocViews: GeneSetEnrichment, Annotation, Pathways Author: Anthony Federico [aut, cre], Stefano Monti [aut] Maintainer: Anthony Federico URL: https://github.com/montilab/hypeR VignetteBuilder: knitr BugReports: https://github.com/montilab/hypeR/issues git_url: https://git.bioconductor.org/packages/hypeR git_branch: RELEASE_3_12 git_last_commit: a4fdcb4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hypeR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hypeR_1.5.1.zip mac.binary.ver: bin/macosx/contrib/4.0/hypeR_1.6.0.tgz vignettes: vignettes/hypeR/inst/doc/hypeR.html vignetteTitles: hypeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/hypeR/inst/doc/hypeR.R dependencyCount: 104 Package: hyperdraw Version: 1.42.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: a5357b57439f678f7cebec0ec945a0f2 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz git_url: https://git.bioconductor.org/packages/hyperdraw git_branch: RELEASE_3_12 git_last_commit: aee0fdc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hyperdraw_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hyperdraw_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hyperdraw_1.42.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hyperdraw/inst/doc/hyperdraw.R dependsOnMe: BiGGR dependencyCount: 12 Package: hypergraph Version: 1.62.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 9d3230d237f9dbc59e15b6f60e767f4c NeedsCompilation: no Title: A package providing hypergraph data structures Description: A package that implements some simple capabilities for representing and manipulating hypergraphs. biocViews: GraphAndNetwork Author: Seth Falcon, Robert Gentleman Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/hypergraph git_branch: RELEASE_3_12 git_last_commit: a286bbb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/hypergraph_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/hypergraph_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/hypergraph_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw dependencyCount: 8 Package: iASeq Version: 1.34.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 000aa80790ce4c08102b46ffbbac609f NeedsCompilation: no Title: iASeq: integrating multiple sequencing datasets for detecting allele-specific events Description: It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies. biocViews: ImmunoOncology, SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei git_url: https://git.bioconductor.org/packages/iASeq git_branch: RELEASE_3_12 git_last_commit: 04b64de git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iASeq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iASeq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iASeq_1.34.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iASeq/inst/doc/iASeqVignette.R dependencyCount: 2 Package: iasva Version: 1.8.0 Depends: R (>= 3.5), Imports: irlba, stats, cluster, graphics, SummarizedExperiment, BiocParallel Suggests: knitr, testthat, rmarkdown, sva, Rtsne, pheatmap, corrplot, DescTools, RColorBrewer License: GPL-2 MD5sum: 32769ce1e40c040a72ff1ff027fa5524 NeedsCompilation: no Title: Iteratively Adjusted Surrogate Variable Analysis Description: Iteratively Adjusted Surrogate Variable Analysis (IA-SVA) is a statistical framework to uncover hidden sources of variation even when these sources are correlated. IA-SVA provides a flexible methodology to i) identify a hidden factor for unwanted heterogeneity while adjusting for all known factors; ii) test the significance of the putative hidden factor for explaining the unmodeled variation in the data; and iii), if significant, use the estimated factor as an additional known factor in the next iteration to uncover further hidden factors. biocViews: Preprocessing, QualityControl, BatchEffect, RNASeq, Software, StatisticalMethod, FeatureExtraction, ImmunoOncology Author: Donghyung Lee [aut, cre], Anthony Cheng [aut], Nathan Lawlor [aut], Duygu Ucar [aut] Maintainer: Donghyung Lee , Anthony Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iasva git_branch: RELEASE_3_12 git_last_commit: df78e19 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iasva_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iasva_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iasva_1.8.0.tgz vignettes: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.html vignetteTitles: "Detecting hidden heterogeneity in single cell RNA-Seq data" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iasva/inst/doc/detecting_hidden_heterogeneity_iasvaV0.95.R dependencyCount: 35 Package: iBBiG Version: 1.34.1 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: i386, x64 MD5sum: dd7b6f8256cc313c9981379cfffc3e26 NeedsCompilation: yes Title: Iterative Binary Biclustering of Genesets Description: iBBiG is a bi-clustering algorithm which is optimizes for binary data analysis. We apply it to meta-gene set analysis of large numbers of gene expression datasets. The iterative algorithm extracts groups of phenotypes from multiple studies that are associated with similar gene sets. iBBiG does not require prior knowledge of the number or scale of clusters and allows discovery of clusters with diverse sizes biocViews: Clustering, Annotation, GeneSetEnrichment Author: Daniel Gusenleitner, Aedin Culhane Maintainer: Aedin Culhane URL: http://bcb.dfci.harvard.edu/~aedin/publications/ git_url: https://git.bioconductor.org/packages/iBBiG git_branch: RELEASE_3_12 git_last_commit: f172e49 git_last_commit_date: 2021-02-27 Date/Publication: 2021-02-28 source.ver: src/contrib/iBBiG_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/iBBiG_1.34.1.zip mac.binary.ver: bin/macosx/contrib/4.0/iBBiG_1.34.1.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBBiG/inst/doc/tutorial.R importsMe: miRSM dependencyCount: 58 Package: ibh Version: 1.38.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: 311accf7388b4cfcc771d28638ba0cb3 NeedsCompilation: no Title: Interaction Based Homogeneity for Evaluating Gene Lists Description: This package contains methods for calculating Interaction Based Homogeneity to evaluate fitness of gene lists to an interaction network which is useful for evaluation of clustering results and gene list analysis. BioGRID interactions are used in the calculation. The user can also provide their own interactions. biocViews: QualityControl, DataImport, GraphAndNetwork, NetworkEnrichment Author: Kircicegi Korkmaz, Volkan Atalay, Rengul Cetin Atalay. Maintainer: Kircicegi Korkmaz git_url: https://git.bioconductor.org/packages/ibh git_branch: RELEASE_3_12 git_last_commit: ad47e92 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ibh_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ibh_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ibh_1.38.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ibh/inst/doc/ibh.R dependencyCount: 1 Package: iBMQ Version: 1.30.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: c74d42d12ff58992266bc4165532cc32 NeedsCompilation: yes Title: integrated Bayesian Modeling of eQTL data Description: integrated Bayesian Modeling of eQTL data biocViews: Microarray, Preprocessing, GeneExpression, SNP Author: Marie-Pier Scott-Boyer and Greg Imholte Maintainer: Greg Imholte URL: http://www.rglab.org SystemRequirements: GSL and OpenMP git_url: https://git.bioconductor.org/packages/iBMQ git_branch: RELEASE_3_12 git_last_commit: 7965381 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iBMQ_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iBMQ_1.30.0.zip vignettes: vignettes/iBMQ/inst/doc/iBMQ.pdf vignetteTitles: iBMQ: An Integrated Hierarchical Bayesian Model for Multivariate eQTL Mapping hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iBMQ/inst/doc/iBMQ.R dependencyCount: 41 Package: iCARE Version: 1.18.0 Depends: R (>= 3.3.0), plotrix, gtools, Hmisc Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 56c364da6220a59fdbc36486d80023f9 NeedsCompilation: yes Title: A Tool for Individualized Coherent Absolute Risk Estimation (iCARE) Description: An R package to compute Individualized Coherent Absolute Risk Estimators. biocViews: Software, StatisticalMethod, GenomeWideAssociation Author: Paige Maas, Parichoy Pal Choudhury, Nilanjan Chatterjee and William Wheeler Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/iCARE git_branch: RELEASE_3_12 git_last_commit: a362686 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iCARE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iCARE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iCARE_1.18.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette_model_validation.pdf, vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette Model Validation, iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette_model_validation.R, vignettes/iCARE/inst/doc/vignette.R dependencyCount: 71 Package: Icens Version: 1.62.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: 406f545b6bb4aacb1e060e552958d7e6 NeedsCompilation: no Title: NPMLE for Censored and Truncated Data Description: Many functions for computing the NPMLE for censored and truncated data. biocViews: Infrastructure Author: R. Gentleman and Alain Vandal Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/Icens git_branch: RELEASE_3_12 git_last_commit: 134c530 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Icens_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Icens_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Icens_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess, icensBKL, interval importsMe: PROcess, icrf, LTRCtrees suggestsMe: ReIns dependencyCount: 10 Package: icetea Version: 1.8.0 Depends: R (>= 4.0) Imports: stats, utils, methods, graphics, grDevices, ggplot2, GenomicFeatures, ShortRead, BiocParallel, Biostrings, S4Vectors, Rsamtools, BiocGenerics, IRanges, GenomicAlignments, GenomicRanges, rtracklayer, SummarizedExperiment, VariantAnnotation, limma, edgeR, csaw, DESeq2, TxDb.Dmelanogaster.UCSC.dm6.ensGene Suggests: knitr, rmarkdown, Rsubread (>= 1.29.0), testthat License: GPL-3 + file LICENSE MD5sum: 1b797c66daaf02b91b2cd1fbe5923f68 NeedsCompilation: no Title: Integrating Cap Enrichment with Transcript Expression Analysis Description: icetea (Integrating Cap Enrichment with Transcript Expression Analysis) provides functions for end-to-end analysis of multiple 5'-profiling methods such as CAGE, RAMPAGE and MAPCap, beginning from raw reads to detection of transcription start sites using replicates. It also allows performing differential TSS detection between group of samples, therefore, integrating the mRNA cap enrichment information with transcript expression analysis. biocViews: ImmunoOncology, Transcription, GeneExpression, Sequencing, RNASeq, Transcriptomics, DifferentialExpression Author: Vivek Bhardwaj [aut, cre] Maintainer: Vivek Bhardwaj URL: https://github.com/vivekbhr/icetea VignetteBuilder: knitr BugReports: https://github.com/vivekbhr/icetea/issues git_url: https://git.bioconductor.org/packages/icetea git_branch: RELEASE_3_12 git_last_commit: 72b27ba git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/icetea_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/icetea_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/icetea_1.8.0.tgz vignettes: vignettes/icetea/inst/doc/mapcap_analysis.html vignetteTitles: Analysing transcript 5'-profiling data using icetea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/icetea/inst/doc/mapcap_analysis.R dependencyCount: 122 Package: iCheck Version: 1.20.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d, utils License: GPL (>= 2) MD5sum: cdf00868c1ad8072f92ecf96d7ad7fe7 NeedsCompilation: no Title: QC Pipeline and Data Analysis Tools for High-Dimensional Illumina mRNA Expression Data Description: QC pipeline and data analysis tools for high-dimensional Illumina mRNA expression data. biocViews: GeneExpression, DifferentialExpression, Microarray, Preprocessing, DNAMethylation, OneChannel, TwoChannel, QualityControl Author: Weiliang Qiu [aut, cre], Brandon Guo [aut, ctb], Christopher Anderson [aut, ctb], Barbara Klanderman [aut, ctb], Vincent Carey [aut, ctb], Benjamin Raby [aut, ctb] Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/iCheck git_branch: RELEASE_3_12 git_last_commit: 1221cbb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iCheck_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iCheck_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iCheck_1.20.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCheck/inst/doc/iCheck.R dependencyCount: 187 Package: iChip Version: 1.44.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: 8f3fc73bca4871990febaf55affbb350 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: Hidden Ising models are implemented to identify enriched genomic regions in ChIP-chip data. They can be used to analyze the data from multiple platforms (e.g., Affymetrix, Agilent, and NimbleGen), and the data with single to multiple replicates. biocViews: ChIPchip, OneChannel, AgilentChip, Microarray Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iChip git_branch: RELEASE_3_12 git_last_commit: cee2370 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iChip_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iChip_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iChip_1.44.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iChip/inst/doc/iChip.R dependencyCount: 6 Package: iClusterPlus Version: 1.26.0 Depends: R (>= 3.3.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 9c5a7d869592fb6f89abc987be217f79 NeedsCompilation: yes Title: Integrative clustering of multi-type genomic data Description: Integrative clustering of multiple genomic data using a joint latent variable model. biocViews: Microarray, Clustering Author: Qianxing Mo, Ronglai Shen Maintainer: Qianxing Mo , Ronglai Shen git_url: https://git.bioconductor.org/packages/iClusterPlus git_branch: RELEASE_3_12 git_last_commit: 4e2e2a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iClusterPlus_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iClusterPlus_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iClusterPlus_1.26.0.tgz vignettes: vignettes/iClusterPlus/inst/doc/iClusterPlus.pdf, vignettes/iClusterPlus/inst/doc/iManual.pdf vignetteTitles: iClusterPlus, iManual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MultiDataSet dependencyCount: 1 Package: iCNV Version: 1.10.0 Depends: R (>= 3.3.1), CODEX Imports: fields, ggplot2, truncnorm, tidyr, data.table, dplyr, grDevices, graphics, stats, utils, rlang Suggests: knitr, rmarkdown, WES.1KG.WUGSC License: GPL-2 MD5sum: 351b5a613d56222d512393fbacaf886f NeedsCompilation: no Title: Integrated Copy Number Variation detection Description: Integrative copy number variation (CNV) detection from multiple platform and experimental design. biocViews: ImmunoOncology, ExomeSeq, WholeGenome, SNP, CopyNumberVariation, HiddenMarkovModel Author: Zilu Zhou, Nancy Zhang Maintainer: Zilu Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/iCNV git_branch: RELEASE_3_12 git_last_commit: ce1ecaf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iCNV_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iCNV_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iCNV_1.10.0.tgz vignettes: vignettes/iCNV/inst/doc/iCNV-vignette.html vignetteTitles: iCNV Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCNV/inst/doc/iCNV-vignette.R dependencyCount: 84 Package: iCOBRA Version: 1.18.1 Depends: R (>= 3.4) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2 (>= 2.0.0), scales, ROCR, dplyr, DT, limma, methods, UpSetR Suggests: knitr, testthat, rmarkdown License: GPL (>=2) MD5sum: cab5e1588e3c2b4d69420f496481f7a1 NeedsCompilation: no Title: Comparison and Visualization of Ranking and Assignment Methods Description: This package provides functions for calculation and visualization of performance metrics for evaluation of ranking and binary classification (assignment) methods. It also contains a shiny application for interactive exploration of results. biocViews: Classification Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/markrobinsonuzh/iCOBRA VignetteBuilder: knitr BugReports: https://github.com/markrobinsonuzh/iCOBRA/issues git_url: https://git.bioconductor.org/packages/iCOBRA git_branch: RELEASE_3_12 git_last_commit: d347e5a git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/iCOBRA_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/iCOBRA_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/iCOBRA_1.18.1.tgz vignettes: vignettes/iCOBRA/inst/doc/iCOBRA.html vignetteTitles: iCOBRA User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R suggestsMe: muscat, SummarizedBenchmark dependencyCount: 82 Package: ideal Version: 1.14.0 Depends: topGO Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, ggplot2 (>= 2.0.0), heatmaply, plotly, pheatmap, pcaExplorer, IHW, gplots, UpSetR, goseq, stringr, dplyr, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, DT, rentrez, rintrojs, ggrepel, knitr, rmarkdown, shinyAce, BiocParallel, grDevices, base64enc, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, DEFormats, edgeR License: MIT + file LICENSE MD5sum: 9751df7c0a6efa47913b04ef43e2dc66 NeedsCompilation: no Title: Interactive Differential Expression AnaLysis Description: This package provides functions for an Interactive Differential Expression AnaLysis of RNA-sequencing datasets, to extract quickly and effectively information downstream the step of differential expression. A Shiny application encapsulates the whole package. biocViews: ImmunoOncology, GeneExpression, DifferentialExpression, RNASeq, Sequencing, Visualization, QualityControl, GUI, GeneSetEnrichment, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/ideal, https://federicomarini.github.io/ideal/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/ideal/issues git_url: https://git.bioconductor.org/packages/ideal git_branch: RELEASE_3_12 git_last_commit: 984d306 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ideal_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ideal_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ideal_1.14.0.tgz vignettes: vignettes/ideal/inst/doc/ideal-usersguide.html vignetteTitles: ideal User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ideal/inst/doc/ideal-usersguide.R dependencyCount: 199 Package: IdeoViz Version: 1.26.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 6e98b6151d499145886c9804fb0fb0fa NeedsCompilation: no Title: Plots data (continuous/discrete) along chromosomal ideogram Description: Plots data associated with arbitrary genomic intervals along chromosomal ideogram. biocViews: Visualization,Microarray Author: Shraddha Pai , Jingliang Ren Maintainer: Shraddha Pai git_url: https://git.bioconductor.org/packages/IdeoViz git_branch: RELEASE_3_12 git_last_commit: 72f01cf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IdeoViz_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IdeoViz_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IdeoViz_1.26.0.tgz vignettes: vignettes/IdeoViz/inst/doc/Vignette.pdf vignetteTitles: IdeoViz: a package for plotting simple data along ideograms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R dependencyCount: 41 Package: idiogram Version: 1.66.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 57091edbdc9bf8317e3a0fcd16463113 NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema git_url: https://git.bioconductor.org/packages/idiogram git_branch: RELEASE_3_12 git_last_commit: 3a2a6a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/idiogram_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/idiogram_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/idiogram_1.66.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idiogram/inst/doc/idiogram.R dependencyCount: 40 Package: idpr Version: 1.0.007 Depends: R (>= 4.0.0) Imports: ggplot2 (>= 3.3.0), magrittr (>= 1.5), dplyr (>= 0.8.5), plyr (>= 1.8.6), jsonlite (>= 1.6.1), rlang (>= 0.4.6), Biostrings (>= 2.56.0), methods (>= 4.0.0) Suggests: knitr, rmarkdown, msa, ape, testthat, seqinr License: LGPL-3 MD5sum: 7f222bd3276e5fc1bc2faeb76d2a6cda NeedsCompilation: no Title: Profiling and Analyzing Intrinsically Disordered Proteins in R Description: ‘idpr’ aims to integrate tools for the computational analysis of intrinsically disordered proteins (IDPs) within R. This package is used to identify known characteristics of IDPs for a sequence of interest with easily reported and dynamic results. Additionally, this package includes tools for IDP-based sequence analysis to be used in conjunction with other R packages. biocViews: StructuralPrediction, Proteomics, CellBiology Author: William M. McFadden [cre, aut], Judith L. Yanowitz [aut, fnd], Michael Buszczak [ctb, fnd] Maintainer: William M. McFadden VignetteBuilder: knitr BugReports: https://github.com/wmm27/idpr/issues git_url: https://git.bioconductor.org/packages/idpr git_branch: RELEASE_3_12 git_last_commit: ebc9324 git_last_commit_date: 2020-12-23 Date/Publication: 2020-12-23 source.ver: src/contrib/idpr_1.0.007.tar.gz win.binary.ver: bin/windows/contrib/4.0/idpr_1.0.007.zip mac.binary.ver: bin/macosx/contrib/4.0/idpr_1.0.007.tgz vignettes: vignettes/idpr/inst/doc/chargeHydropathy-vignette.html, vignettes/idpr/inst/doc/disorderedMatrices-vignette.html, vignettes/idpr/inst/doc/idpr-vignette.html, vignettes/idpr/inst/doc/iupred-vignette.html, vignettes/idpr/inst/doc/sequenceMAP-vignette.html, vignettes/idpr/inst/doc/structuralTendency-vignette.html vignetteTitles: Charge and Hydropathy Vignette, Disordered Matrices Vignette, idpr Package Overview Vignette, IUPred Vignette, Sequence Map Vignette, Structural Tendency Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/idpr/inst/doc/chargeHydropathy-vignette.R, vignettes/idpr/inst/doc/disorderedMatrices-vignette.R, vignettes/idpr/inst/doc/idpr-vignette.R, vignettes/idpr/inst/doc/iupred-vignette.R, vignettes/idpr/inst/doc/sequenceMAP-vignette.R, vignettes/idpr/inst/doc/structuralTendency-vignette.R dependencyCount: 54 Package: idr2d Version: 1.4.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.6), futile.logger (>= 1.4.3), GenomeInfoDb (>= 1.14.0), GenomicRanges (>= 1.30), ggplot2 (>= 3.1.1), grDevices, idr (>= 1.2), IRanges (>= 2.18.0), magrittr (>= 1.5), methods, reticulate (>= 1.13), scales (>= 1.0.0), stats, stringr (>= 1.3.1), utils Suggests: DT (>= 0.4), htmltools (>= 0.3.6), knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: bee14cfa084c5f4c8b22dc29d6eefd18 NeedsCompilation: no Title: Irreproducible Discovery Rate for Genomic Interactions Data Description: A tool to measure reproducibility between genomic experiments that produce two-dimensional peaks (interactions between peaks), such as ChIA-PET, HiChIP, and HiC. idr2d is an extension of the original idr package, which is intended for (one-dimensional) ChIP-seq peaks. biocViews: DNA3DStructure, GeneRegulation, PeakDetection, Epigenetics, FunctionalGenomics, Classification, HiC Author: Konstantin Krismer [aut, cre, cph] (), David Gifford [ths, cph] () Maintainer: Konstantin Krismer URL: https://idr2d.mit.edu SystemRequirements: Python (>= 3.5.0), hic-straw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/idr2d git_branch: RELEASE_3_12 git_last_commit: b78392e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/idr2d_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/idr2d_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/idr2d_1.4.0.tgz vignettes: vignettes/idr2d/inst/doc/idr1d.html, vignettes/idr2d/inst/doc/idr2d.html vignetteTitles: Identify reproducible genomic peaks from replicate ChIP-seq experiments, Identify reproducible genomic interactions from replicate ChIA-PET experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/idr2d/inst/doc/idr1d.R, vignettes/idr2d/inst/doc/idr2d.R dependencyCount: 67 Package: iGC Version: 1.20.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 5d155c35be621488ad7e09ca7a494d77 NeedsCompilation: no Title: An integrated analysis package of Gene expression and Copy number alteration Description: This package is intended to identify differentially expressed genes driven by Copy Number Alterations from samples with both gene expression and CNA data. biocViews: Software, Biological Question, DifferentialExpression, GenomicVariation, AssayDomain, CopyNumberVariation, GeneExpression, ResearchField, Genetics, Technology, Microarray, Sequencing, WorkflowStep, MultipleComparison Author: Yi-Pin Lai [aut], Liang-Bo Wang [aut, cre], Tzu-Pin Lu [aut], Eric Y. Chuang [aut] Maintainer: Liang-Bo Wang URL: http://github.com/ccwang002/iGC VignetteBuilder: knitr BugReports: http://github.com/ccwang002/iGC/issues git_url: https://git.bioconductor.org/packages/iGC git_branch: RELEASE_3_12 git_last_commit: df9181b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iGC_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iGC_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iGC_1.20.0.tgz vignettes: vignettes/iGC/inst/doc/Introduction.html vignetteTitles: Introduction to iGC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R dependencyCount: 5 Package: IgGeneUsage Version: 1.4.0 Depends: methods, R (>= 3.6.0), Rcpp (>= 0.12.0), SummarizedExperiment, StanHeaders (> 2.18.1) Imports: rstan (>= 2.19.2), reshape2 (>= 1.4.3) Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0), ggplot2, ggforce, gridExtra, ggrepel License: file LICENSE MD5sum: 3a813de2a786865faa47afdaee67f276 NeedsCompilation: no Title: Differential gene usage in immune repertoires Description: Decoding the properties of immune repertoires is key in understanding the response of adaptive immunity to challenges such as viral infection. One important task in immune repertoire profiling is the detection of biases in Ig gene usage between biological conditions. IgGeneUsage is a computational tool for the analysis of differential gene usage in immune repertoires. It employs Bayesian hierarchical models to fit complex gene usage data from immune repertoire sequencing experiments and quantifies Ig gene usage biases as probabilities. biocViews: DifferentialExpression, Regression, Genetics, Bayesian Author: Simo Kitanovski [aut, cre] Maintainer: Simo Kitanovski VignetteBuilder: knitr BugReports: https://github.com/snaketron/IgGeneUsage/issues git_url: https://git.bioconductor.org/packages/IgGeneUsage git_branch: RELEASE_3_12 git_last_commit: d9056e6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IgGeneUsage_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IgGeneUsage_1.3.1.zip mac.binary.ver: bin/macosx/contrib/4.0/IgGeneUsage_1.4.1.tgz vignettes: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.html vignetteTitles: User Manual: IgGeneUsage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IgGeneUsage/inst/doc/IgUsageCaseStudies.R dependencyCount: 81 Package: igvR Version: 1.10.0 Depends: R (>= 3.5.0), GenomicRanges, GenomicAlignments, BrowserViz (>= 2.9.1) Imports: methods, BiocGenerics, httpuv, utils, MotifDb, seqLogo, rtracklayer, VariantAnnotation, RColorBrewer Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: eac58c7e258fe4ef7058ca8b53132ec3 NeedsCompilation: no Title: igvR: integrative genomics viewer Description: Access to igv.js, the Integrative Genomics Viewer running in a web browser. biocViews: Visualization, ThirdPartyClient, GenomeBrowsers Author: Paul Shannon Maintainer: Paul Shannon URL: https://paul-shannon.github.io/igvR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/igvR git_branch: RELEASE_3_12 git_last_commit: d74e551 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/igvR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/igvR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/igvR_1.10.0.tgz vignettes: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.html, vignettes/igvR/inst/doc/basicIntro.html, vignettes/igvR/inst/doc/ctcfChipSeq.html vignetteTitles: "Explore VCF variants,, GWAS snps,, promoters and histone marks around the MEF2C gene in Alzheimers Disease", "Introduction: a simple demo", "Explore ChIP-seq alignments from a bam file,, MACS2 narrowPeaks,, conservation,, H3K4me3 methylation and motif matching" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/igvR/inst/doc/alzheimersVariantsNearMEF2C.R, vignettes/igvR/inst/doc/basicIntro.R, vignettes/igvR/inst/doc/ctcfChipSeq.R dependencyCount: 99 Package: IHW Version: 1.18.0 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, dplyr, gridExtra, scales, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: 17f249a751fda79a7bd2e17c5f350522 NeedsCompilation: no Title: Independent Hypothesis Weighting Description: Independent hypothesis weighting (IHW) is a multiple testing procedure that increases power compared to the method of Benjamini and Hochberg by assigning data-driven weights to each hypothesis. The input to IHW is a two-column table of p-values and covariates. The covariate can be any continuous-valued or categorical variable that is thought to be informative on the statistical properties of each hypothesis test, while it is independent of the p-value under the null hypothesis. biocViews: ImmunoOncology, MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IHW git_branch: RELEASE_3_12 git_last_commit: a59642f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IHW_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IHW_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IHW_1.18.0.tgz vignettes: vignettes/IHW/inst/doc/introduction_to_ihw.html vignetteTitles: "Introduction to IHW" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R dependsOnMe: IHWpaper importsMe: ideal, DGEobj.utils suggestsMe: DEWSeq, metagenomeSeq, SummarizedBenchmark, BloodCancerMultiOmics2017, BisRNA dependencyCount: 10 Package: illuminaio Version: 0.32.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: c05675197148dcbc63991c855659296c NeedsCompilation: yes Title: Parsing Illumina Microarray Output Files Description: Tools for parsing Illumina's microarray output files, including IDAT. biocViews: Infrastructure, DataImport, Microarray, ProprietaryPlatforms Author: Keith Baggerly [aut], Henrik Bengtsson [aut], Kasper Daniel Hansen [aut, cre], Matt Ritchie [aut], Mike L. Smith [aut], Tim Triche Jr. [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/HenrikBengtsson/illuminaio BugReports: https://github.com/HenrikBengtsson/illuminaio/issues git_url: https://git.bioconductor.org/packages/illuminaio git_branch: RELEASE_3_12 git_last_commit: e1322c7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/illuminaio_0.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/illuminaio_0.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/illuminaio_0.32.0.tgz vignettes: vignettes/illuminaio/inst/doc/EncryptedFormat.pdf, vignettes/illuminaio/inst/doc/illuminaio.pdf vignetteTitles: Description of Encrypted IDAT Format, Introduction to illuminaio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/illuminaio/inst/doc/illuminaio.R dependsOnMe: normalize450K, RnBeads, wateRmelon, EGSEA123 importsMe: beadarray, crlmm, methylumi, minfi, sesame suggestsMe: limma dependencyCount: 4 Package: ILoReg Version: 1.0.0 Depends: R (>= 4.0.0) Imports: Matrix, parallel, foreach, aricode, LiblineaR, SparseM, ggplot2, cowplot, RSpectra, umap, Rtsne, fastcluster, parallelDist, cluster, dendextend, DescTools, plyr, scales, pheatmap, reshape2, dplyr, doRNG, SingleCellExperiment, SummarizedExperiment, S4Vectors, methods, stats, doSNOW, utils Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f5b976e7d38d18e6133f4482998fd840 NeedsCompilation: no Title: ILoReg: a tool for high-resolution cell population identification from scRNA-Seq data Description: ILoReg is a tool for identification of cell populations from scRNA-seq data. In particular, ILoReg is useful for finding cell populations with subtle transcriptomic differences. The method utilizes a self-supervised learning method, called Iteratitive Clustering Projection (ICP), to find cluster probabilities, which are used in noise reduction prior to PCA and the subsequent hierarchical clustering and t-SNE steps. Additionally, functions for differential expression analysis to find gene markers for the populations and gene expression visualization are provided. biocViews: SingleCell, Software, Clustering, DimensionReduction, RNASeq, Visualization, Transcriptomics, DataRepresentation, DifferentialExpression, Transcription, GeneExpression Author: Johannes Smolander [cre, aut], Sini Junttila [aut], Mikko S Venäläinen [aut], Laura L Elo [aut] Maintainer: Johannes Smolander URL: https://github.com/elolab/ILoReg VignetteBuilder: knitr BugReports: https://github.com/elolab/ILoReg/issues git_url: https://git.bioconductor.org/packages/ILoReg git_branch: RELEASE_3_12 git_last_commit: 9774a06 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ILoReg_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ILoReg_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ILoReg_1.0.0.tgz vignettes: vignettes/ILoReg/inst/doc/ILoReg.html vignetteTitles: ILoReg package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ILoReg/inst/doc/ILoReg.R dependencyCount: 113 Package: imageHTS Version: 1.40.0 Depends: R (>= 2.9.0), EBImage (>= 4.3.12), cellHTS2 (>= 2.10.0) Imports: tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 Suggests: BiocStyle, MASS License: LGPL-2.1 MD5sum: ba2a998b5ae134239ecf976bbe965d57 NeedsCompilation: no Title: Analysis of high-throughput microscopy-based screens Description: imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets. biocViews: ImmunoOncology, Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry git_url: https://git.bioconductor.org/packages/imageHTS git_branch: RELEASE_3_12 git_last_commit: 6be79b0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/imageHTS_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/imageHTS_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/imageHTS_1.40.0.tgz vignettes: vignettes/imageHTS/inst/doc/imageHTS-introduction.pdf vignetteTitles: Analysis of high-throughput microscopy-based screens with imageHTS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/imageHTS/inst/doc/imageHTS-introduction.R dependencyCount: 100 Package: IMAS Version: 1.14.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, IVAS Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, stats, ggfortify, grDevices, methods, Matrix, utils, graphics, gridExtra, grid, lattice, Rsamtools, survival, BiocParallel, GenomicAlignments, parallel Suggests: BiocStyle, RUnit License: GPL-2 MD5sum: 926db15c62e8d1dcc233361d31b037d0 NeedsCompilation: no Title: Integrative analysis of Multi-omics data for Alternative Splicing Description: Integrative analysis of Multi-omics data for Alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Younghee Lee Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IMAS git_branch: RELEASE_3_12 git_last_commit: 541abe0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IMAS_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IMAS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IMAS_1.14.0.tgz vignettes: vignettes/IMAS/inst/doc/IMAS.pdf vignetteTitles: IMAS : Integrative analysis of Multi-omics data for Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMAS/inst/doc/IMAS.R dependencyCount: 119 Package: Imetagene Version: 1.19.0 Depends: R (>= 3.2.0), metagene, shiny Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2 Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 233f5e74a0e46a6ccf66e9c0bd390c2f NeedsCompilation: no Title: A graphical interface for the metagene package Description: This package provide a graphical user interface to the metagene package. This will allow people with minimal R experience to easily complete metagene analysis. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Audrey Lemacon , Charles Joly Beauparlant , Arnaud Droit Maintainer: Audrey Lemacon VignetteBuilder: knitr BugReports: https://github.com/andronekomimi/Imetagene/issues git_url: https://git.bioconductor.org/packages/Imetagene git_branch: master git_last_commit: dc20248 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/Imetagene_1.19.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Imetagene_1.19.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Imetagene_1.19.0.tgz vignettes: vignettes/Imetagene/inst/doc/imetagene.html vignetteTitles: Presentation of Imetagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Imetagene/inst/doc/imetagene.R dependencyCount: 137 Package: IMMAN Version: 1.10.0 Imports: STRINGdb, Biostrings, igraph, graphics, utils, seqinr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 29aa52aedf96dee134a2e50b5ac90db8 NeedsCompilation: no Title: Interlog protein network reconstruction by Mapping and Mining ANalysis Description: Reconstructing Interlog Protein Network (IPN) integrated from several Protein protein Interaction Networks (PPINs). Using this package, overlaying different PPINs to mine conserved common networks between diverse species will be applicable. biocViews: SequenceMatching, Alignment, SystemsBiology, GraphAndNetwork, Network, Proteomics Author: Minoo Ashtiani, Payman Nickchi, Abdollah Safari, Mehdi Mirzaie, Mohieddin Jafari Maintainer: Minoo Ashtiani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IMMAN git_branch: RELEASE_3_12 git_last_commit: b4cfedb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IMMAN_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IMMAN_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IMMAN_1.10.0.tgz vignettes: vignettes/IMMAN/inst/doc/IMMAN.html vignetteTitles: IMMAN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IMMAN/inst/doc/IMMAN.R dependencyCount: 62 Package: ImmuneSpaceR Version: 1.18.1 Depends: R (>= 3.5.0) Imports: utils, R6, data.table, curl, httr, Rlabkey (>= 2.3.1), Biobase, pheatmap, ggplot2 (>= 3.2.0), scales, stats, gplots, plotly, heatmaply (>= 0.7.0), jsonlite, rmarkdown, preprocessCore, flowCore, flowWorkspace, digest Suggests: knitr, testthat License: GPL-2 MD5sum: 6bcd5583d08c65ea539cd8d9d72f1125 NeedsCompilation: no Title: A Thin Wrapper around the ImmuneSpace Database Description: Provides a convenient API for accessing data sets within ImmuneSpace (www.immunespace.org), the data repository and analysis platform of the Human Immunology Project Consortium (HIPC). biocViews: DataImport, DataRepresentation, ThirdPartyClient Author: Greg Finak [aut], Renan Sauteraud [aut], Mike Jiang [aut], Gil Guday [aut], Leo Dashevskiy [aut], Evan Henrich [aut], Ju Yeong Kim [aut], Lauren Wolfe [aut], Helen Miller [aut], Raphael Gottardo [aut], ImmuneSpace Package Maintainer [cre, cph] Maintainer: ImmuneSpace Package Maintainer URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues git_url: https://git.bioconductor.org/packages/ImmuneSpaceR git_branch: RELEASE_3_12 git_last_commit: e8ef8fc git_last_commit_date: 2020-12-18 Date/Publication: 2020-12-19 source.ver: src/contrib/ImmuneSpaceR_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ImmuneSpaceR_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ImmuneSpaceR_1.18.1.tgz vignettes: vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.html, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.html, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html vignetteTitles: Downloading Datasets with getDataset, Handling Expression Matrices with ImmuneSpaceR, interactive_netrc() Function Walkthrough, An Introduction to the ImmuneSpaceR Package, SDY144: Correlation of HAI/Virus Neutralizition Titer and Cell Counts, SDY180: Abundance of Plasmablasts Measured by Multiparameter Flow Cytometry, SDY269: Correlating HAI with Flow Cytometry and ELISPOT Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/getGEMatrix.R, vignettes/ImmuneSpaceR/inst/doc/interactiveNetrc.R, vignettes/ImmuneSpaceR/inst/doc/Intro_to_ImmuneSpaceR.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R dependencyCount: 128 Package: immunoClust Version: 1.22.0 Depends: R(>= 3.6), flowCore Imports: methods, stats, graphics, grid, lattice, grDevices Suggests: BiocStyle, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: 6e84e0b42638123e20c964a536780ee6 NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: immunoClust is a model based clustering approach for Flow Cytometry samples. The cell-events of single Flow Cytometry samples are modelled by a mixture of multinominal normal- or t-distributions. The cell-event clusters of several samples are modelled by a mixture of multinominal normal-distributions aiming stable co-clusters across these samples. biocViews: Clustering, FlowCytometry, SingleCell, CellBasedAssays, ImmunoOncology Author: Till Soerensen Maintainer: Till Soerensen git_url: https://git.bioconductor.org/packages/immunoClust git_branch: RELEASE_3_12 git_last_commit: f078aa9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/immunoClust_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/immunoClust_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/immunoClust_1.22.0.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/immunoClust/inst/doc/immunoClust.R dependencyCount: 21 Package: IMPCdata Version: 1.26.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: a9ef5eca02db43c84af818c039b12f62 NeedsCompilation: no Title: Retrieves data from IMPC database Description: Package contains methods for data retrieval from IMPC Database. biocViews: ExperimentData Author: Natalja Kurbatova, Jeremy Mason Maintainer: Jeremy Mason git_url: https://git.bioconductor.org/packages/IMPCdata git_branch: RELEASE_3_12 git_last_commit: 070c677 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IMPCdata_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IMPCdata_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IMPCdata_1.26.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IMPCdata/inst/doc/IMPCdata.R dependencyCount: 1 Package: impute Version: 1.64.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 659f0e29d6f9d8325bd89918a06a2698 NeedsCompilation: yes Title: impute: Imputation for microarray data Description: Imputation for microarray data (currently KNN only) biocViews: Microarray Author: Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu Maintainer: Balasubramanian Narasimhan git_url: https://git.bioconductor.org/packages/impute git_branch: RELEASE_3_12 git_last_commit: 31a5636 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/impute_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/impute_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/impute_1.64.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AMARETTO, CGHcall, TIN, curatedBreastData, MetaGxOvarian, FAMT, iC10, imputeLCMD, moduleColor, snpReady, swamp importsMe: biscuiteer, CancerSubtypes, cola, doppelgangR, EGAD, fastLiquidAssociation, genefu, genomation, MEAT, MethylMix, miRLAB, MSnbase, netboost, Pigengene, pmp, POMA, REMP, RNAAgeCalc, Rnits, MetaGxBreast, MetaGxPancreas, armada, DIscBIO, lilikoi, maGUI, Rnmr1D, samr, speaq, specmine, WGCNA suggestsMe: BioNet, graphite, MethPed, MsCoreUtils, QFeatures, RnBeads, scp, TCGAutils, DDPNA, DGCA, GSA dependencyCount: 0 Package: INDEED Version: 2.4.0 Depends: glasso (>= 1.8), R (>= 3.5) Imports: devtools (>= 1.13.0), graphics (>= 3.3.1), stats (>= 3.3.1), utils (>= 3.3.1), igraph (>= 1.2.4), visNetwork(>= 2.0.6) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8), testthat (>= 2.0.0) License: Artistic-2.0 MD5sum: 0c12a16a28e1907b9a431ea2b948f37d NeedsCompilation: no Title: Interactive Visualization of Integrated Differential Expression and Differential Network Analysis for Biomarker Candidate Selection Package Description: An R package for integrated differential expression and differential network analysis based on omic data for cancer biomarker discovery. Both correlation and partial correlation can be used to generate differential network to aid the traditional differential expression analysis to identify changes between biomolecules on both their expression and pairwise association levels. A detailed description of the methodology has been published in Methods journal (PMID: 27592383). An interactive visualization feature allows for the exploration and selection of candidate biomarkers. biocViews: ImmunoOncology, Software, ResearchField, BiologicalQuestion, StatisticalMethod, DifferentialExpression, MassSpectrometry, Metabolomics Author: Yiming Zuo , Kian Ghaffari , Zhenzhi Li Maintainer: Ressom group , Yiming Zuo URL: http://github.com/ressomlab/INDEED VignetteBuilder: knitr BugReports: http://github.com/ressomlab/INDEED/issues git_url: https://git.bioconductor.org/packages/INDEED git_branch: RELEASE_3_12 git_last_commit: 467a185 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/INDEED_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/INDEED_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/INDEED_2.4.0.tgz vignettes: vignettes/INDEED/inst/doc/Introduction_to_INDEED.pdf vignetteTitles: INDEED R package for cancer biomarker discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INDEED/inst/doc/Introduction_to_INDEED.R dependencyCount: 87 Package: infercnv Version: 1.6.0 Depends: R(>= 4.0) Imports: graphics, grDevices, RColorBrewer, gplots, futile.logger, stats, utils, methods, ape, Matrix, fastcluster, dplyr, HiddenMarkov, ggplot2, edgeR, coin, caTools, digest, reshape, rjags, fitdistrplus, future, foreach, doParallel, BiocGenerics, SummarizedExperiment, SingleCellExperiment, tidyr, parallel, coda, gridExtra, argparse Suggests: BiocStyle, knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 17efb11a85c4f7dd7d4cacf51ecd2c7e NeedsCompilation: no Title: Infer Copy Number Variation from Single-Cell RNA-Seq Data Description: Using single-cell RNA-Seq expression to visualize CNV in cells. biocViews: Software, CopyNumberVariation, VariantDetection, StructuralVariation, GenomicVariation, Genetics, Transcriptomics, StatisticalMethod, Bayesian, HiddenMarkovModel, SingleCell Author: Timothy Tickle [aut], Itay Tirosh [aut], Christophe Georgescu [aut, cre], Maxwell Brown [aut], Brian Haas [aut] Maintainer: Christophe Georgescu URL: https://github.com/broadinstitute/inferCNV/wiki SystemRequirements: JAGS 4.x.y VignetteBuilder: knitr BugReports: https://github.com/broadinstitute/inferCNV/issues git_url: https://git.bioconductor.org/packages/infercnv git_branch: RELEASE_3_12 git_last_commit: eb60cdc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/infercnv_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/infercnv_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/infercnv_1.6.0.tgz vignettes: vignettes/infercnv/inst/doc/inferCNV.html vignetteTitles: Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/infercnv/inst/doc/inferCNV.R dependencyCount: 104 Package: infinityFlow Version: 1.0.0 Depends: R (>= 4.0.0), flowCore Imports: stats, grDevices, utils, graphics, pbapply, matlab, png, raster, grid, uwot, gtools, Biobase, generics, parallel, methods, xgboost Suggests: knitr, rmarkdown, keras, tensorflow, glmnetUtils, e1071 License: GPL-3 MD5sum: 51bf7c2564d015dcec626d04b9120972 NeedsCompilation: no Title: Augmenting Massively Parallel Cytometry Experiments Using Multivariate Non-Linear Regressions Description: Pipeline to analyze and merge data files produced by BioLegend's LEGENDScreen or BD Human Cell Surface Marker Screening Panel (BD Lyoplates). biocViews: Software, FlowCytometry, CellBasedAssays, SingleCell, Proteomics Author: Etienne Becht [cre, aut] Maintainer: Etienne Becht VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/infinityFlow git_branch: RELEASE_3_12 git_last_commit: 8b856b0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/infinityFlow_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/infinityFlow_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/infinityFlow_1.0.0.tgz vignettes: vignettes/infinityFlow/inst/doc/basic_usage.html, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.html vignetteTitles: Basic usage of the infinityFlow package, Training non default regression models hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/infinityFlow/inst/doc/basic_usage.R, vignettes/infinityFlow/inst/doc/training_non_default_regression_models.R dependencyCount: 42 Package: Informeasure Version: 1.0.0 Depends: R (>= 4.0) Imports: entropy, matrixStats Suggests: knitr, rmarkdown, testthat, SummarizedExperiment License: GPL-3 MD5sum: 80e15daf2a0d9dc0b69eb53c13ca3935 NeedsCompilation: no Title: R implementation of Information measures Description: This package compiles most information measures currently available: mutual information, conditional mutual information, interaction information, partial information decomposition and part mutual information. Using gene expression profile data, all these estimators can be employed to quantify nonlinear dependence between variables in biological regulatory network inference. The first estimator is used to infer bivariate network while the last four estimators are dedicated to analyze trivariate networks. biocViews: GeneExpression, NetworkInference, Network Author: Chu Pan [aut, cre] Maintainer: Chu Pan URL: https://github.com/chupan1218/Informeasure VignetteBuilder: knitr BugReports: https://github.com/chupan1218/Informeasure/issues git_url: https://git.bioconductor.org/packages/Informeasure git_branch: RELEASE_3_12 git_last_commit: 04ee364 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Informeasure_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Informeasure_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Informeasure_1.0.0.tgz vignettes: vignettes/Informeasure/inst/doc/Informeasure.html vignetteTitles: Informeasure: a tool to quantify non-linear dependence between variables in biological regulatory networks from an information theory perspective hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Informeasure/inst/doc/Informeasure.R dependencyCount: 2 Package: InPAS Version: 1.22.0 Depends: R (>= 3.1), methods, Biobase, GenomicRanges, GenomicFeatures, S4Vectors Imports: AnnotationDbi, BSgenome, cleanUpdTSeq, Gviz, seqinr, preprocessCore, IRanges, GenomeInfoDb, depmixS4, limma, BiocParallel Suggests: RUnit, BiocGenerics, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, org.Hs.eg.db, org.Mm.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, rtracklayer, knitr License: GPL (>= 2) MD5sum: 6f355b7e87e8e8ab24a97e0f19694fed NeedsCompilation: no Title: InPAS: a bioconductor package for the identification of novel alternative PolyAdenylation Sites (PAS) using RNA-seq data Description: Alternative polyadenylation (APA) is one of the important post-transcriptional regulation mechanisms which occurs in most human genes. InPAS facilitates the discovery of novel APA sites and the differential usage of APA sites from RNA-Seq data. It leverages cleanUpdTSeq to fine tune identified APA sites by removing false sites due to internal-priming. biocViews: RNASeq, Sequencing, AlternativeSplicing, Coverage, DifferentialSplicing, GeneRegulation, Transcription, ImmunoOncology Author: Jianhong Ou, Sungmi M. Park, Michael R. Green and Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InPAS git_branch: RELEASE_3_12 git_last_commit: f692ba0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/InPAS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/InPAS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/InPAS_1.22.0.tgz vignettes: vignettes/InPAS/inst/doc/InPAS.html vignetteTitles: InPAS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R dependencyCount: 153 Package: INPower Version: 1.26.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: d2b62e3886529d73bc56f482db2937e8 NeedsCompilation: no Title: An R package for computing the number of susceptibility SNPs Description: An R package for computing the number of susceptibility SNPs and power of future studies biocViews: SNP Author: Ju-Hyun Park Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/INPower git_branch: RELEASE_3_12 git_last_commit: 2cca7d9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/INPower_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/INPower_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/INPower_1.26.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/INPower/inst/doc/vignette.R dependencyCount: 3 Package: INSPEcT Version: 1.20.0 Depends: R (>= 3.6), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, KernSmooth, gdata, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors, GenomeInfoDb, DESeq2, plgem, rtracklayer, SummarizedExperiment, TxDb.Mmusculus.UCSC.mm9.knownGene, shiny Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: b1a67ef651a4727ae064f6c1179b012a NeedsCompilation: no Title: Modeling RNA synthesis, processing and degradation with RNA-seq data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates from Transcriptomic data) RNA-seq data in time-course experiments or steady-state conditions, with or without the support of nascent RNA data. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis , Mattia Furlan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/INSPEcT git_branch: RELEASE_3_12 git_last_commit: d952c98 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/INSPEcT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/INSPEcT_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/INSPEcT_1.20.0.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.html, vignettes/INSPEcT/inst/doc/INSPEcT.html vignetteTitles: INSPEcT_GUI.html, INSPEcT - INference of Synthesis,, Processing and dEgradation rates from Transcriptomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT_GUI.R, vignettes/INSPEcT/inst/doc/INSPEcT.R dependencyCount: 132 Package: InTAD Version: 1.10.0 Depends: R (>= 3.5), methods, S4Vectors, IRanges, GenomicRanges, MultiAssayExperiment, SummarizedExperiment,stats Imports: BiocGenerics,Biobase,rtracklayer,parallel,graphics,mclust,qvalue, ggplot2,utils,ggpubr Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: ef7a7a9b674b6f377cda435ff9e927ff NeedsCompilation: no Title: Search for correlation between epigenetic signals and gene expression in TADs Description: The package is focused on the detection of correlation between expressed genes and selected epigenomic signals (i.e. enhancers obtained from ChIP-seq data) either within topologically associated domains (TADs) or between chromatin contact loop anchors. Various parameters can be controlled to investigate the influence of external factors and visualization plots are available for each analysis step. biocViews: Epigenetics, Sequencing, ChIPSeq, RNASeq, HiC, GeneExpression,ImmunoOncology Author: Konstantin Okonechnikov, Serap Erkek, Lukas Chavez Maintainer: Konstantin Okonechnikov VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InTAD git_branch: RELEASE_3_12 git_last_commit: e127d1a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/InTAD_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/InTAD_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/InTAD_1.10.0.tgz vignettes: vignettes/InTAD/inst/doc/InTAD.html vignetteTitles: Correlation of epigenetic signals and genes in TADs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InTAD/inst/doc/InTAD.R dependencyCount: 136 Package: intansv Version: 1.30.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: MIT + file LICENSE MD5sum: 3351a12352caed01bf5ef67efe32b123 NeedsCompilation: no Title: Integrative analysis of structural variations Description: This package provides efficient tools to read and integrate structural variations predicted by popular softwares. Annotation and visulation of structural variations are also implemented in the package. biocViews: Genetics, Annotation, Sequencing, Software Author: Wen Yao Maintainer: Wen Yao git_url: https://git.bioconductor.org/packages/intansv git_branch: RELEASE_3_12 git_last_commit: 2bbd8ca git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/intansv_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/intansv_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/intansv_1.30.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R dependencyCount: 149 Package: InteractionSet Version: 1.18.1 Depends: GenomicRanges, SummarizedExperiment Imports: methods, Matrix, Rcpp, BiocGenerics, S4Vectors (>= 0.27.12), IRanges, GenomeInfoDb LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: b89ea751ceccfed53738b956b8188123 NeedsCompilation: yes Title: Base Classes for Storing Genomic Interaction Data Description: Provides the GInteractions, InteractionSet and ContactMatrix objects and associated methods for storing and manipulating genomic interaction data from Hi-C and ChIA-PET experiments. biocViews: Infrastructure, DataRepresentation, Software, HiC Author: Aaron Lun [aut, cre], Malcolm Perry [aut], Elizabeth Ing-Simmons [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/InteractionSet git_branch: RELEASE_3_12 git_last_commit: f39d50d git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/InteractionSet_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/InteractionSet_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/InteractionSet_1.18.1.tgz vignettes: vignettes/InteractionSet/inst/doc/interactions.html vignetteTitles: Genomic interaction classes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R dependsOnMe: diffHic, GenomicInteractions, MACPET, sevenC importsMe: CAGEfightR, HiCcompare, trackViewer suggestsMe: CAGEWorkflow dependencyCount: 27 Package: interactiveDisplay Version: 1.28.0 Depends: R (>= 2.10), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: b355bf2c95a9d845d52065f68c78742a NeedsCompilation: no Title: Package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplay package contains the methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplay git_branch: RELEASE_3_12 git_last_commit: 55ae0c4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/interactiveDisplay_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/interactiveDisplay_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/interactiveDisplay_1.28.0.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.R suggestsMe: metagenomeSeq dependencyCount: 97 Package: interactiveDisplayBase Version: 1.28.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny, DT Suggests: knitr Enhances: rstudioapi License: Artistic-2.0 MD5sum: 2614ca1597a495273ebed4a6b9529110 NeedsCompilation: no Title: Base package for enabling powerful shiny web displays of Bioconductor objects Description: The interactiveDisplayBase package contains the the basic methods needed to generate interactive Shiny based display methods for Bioconductor objects. biocViews: GO, GeneExpression, Microarray, Sequencing, Classification, Network, QualityControl, Visualization, Visualization, Genetics, DataRepresentation, GUI, AnnotationData Author: Shawn Balcome [aut, cre], Marc Carlson [ctb], Marcel Ramos [ctb] Maintainer: Shawn Balcome VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/interactiveDisplayBase git_branch: RELEASE_3_12 git_last_commit: a74c02c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/interactiveDisplayBase_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/interactiveDisplayBase_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/interactiveDisplayBase_1.28.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html vignetteTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R importsMe: AnnotationHub, interactiveDisplay suggestsMe: recount3 dependencyCount: 41 Package: IntEREst Version: 1.14.0 Depends: R (>= 3.4), GenomicRanges, Rsamtools, SummarizedExperiment, edgeR, S4Vectors Imports: seqLogo, Biostrings, GenomicFeatures (>= 1.39.4), IRanges, seqinr, graphics, grDevices, stats, utils, grid, methods, DBI, RMySQL, GenomicAlignments, BiocParallel, BiocGenerics, DEXSeq, DESeq2 Suggests: clinfun, knitr, BSgenome.Hsapiens.UCSC.hg19 License: GPL-2 MD5sum: 1a4c5fd03ea01a9ada6d33ab78451eaa NeedsCompilation: no Title: Intron-Exon Retention Estimator Description: This package performs Intron-Exon Retention analysis on RNA-seq data (.bam files). biocViews: Software, AlternativeSplicing, Coverage, DifferentialSplicing, Sequencing, RNASeq, Alignment, Normalization, DifferentialExpression, ImmunoOncology Author: Ali Oghabian , Dario Greco , Mikko Frilander Maintainer: Ali Oghabian , Mikko Frilander VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IntEREst git_branch: RELEASE_3_12 git_last_commit: 514e896 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IntEREst_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IntEREst_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IntEREst_1.14.0.tgz vignettes: vignettes/IntEREst/inst/doc/IntEREst.html vignetteTitles: IntEREst,, Intron Exon Retention Estimator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntEREst/inst/doc/IntEREst.R dependencyCount: 123 Package: InterMineR Version: 1.12.3 Depends: R (>= 3.4.1) Imports: Biostrings, RCurl, XML, xml2, RJSONIO, sqldf, igraph, httr, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, methods Suggests: BiocStyle, Gviz, knitr, rmarkdown, GeneAnswers, GO.db, org.Hs.eg.db License: LGPL MD5sum: 350123a8844ca055485e1fe0859108de NeedsCompilation: no Title: R Interface with InterMine-Powered Databases Description: Databases based on the InterMine platform such as FlyMine, modMine (modENCODE), RatMine, YeastMine, HumanMine and TargetMine are integrated databases of genomic, expression and protein data for various organisms. Integrating data makes it possible to run sophisticated data mining queries that span domains of biological knowledge. This R package provides interfaces with these databases through webservices. It makes most from the correspondence of the data frame object in R and the table object in databases, while hiding the details of data exchange through XML or JSON. biocViews: GeneExpression, SNP, GeneSetEnrichment, DifferentialExpression, GeneRegulation, GenomeAnnotation, GenomeWideAssociation, FunctionalPrediction, AlternativeSplicing, ComparativeGenomics, FunctionalGenomics, Proteomics, SystemsBiology, Microarray, MultipleComparison, Pathways, GO, KEGG, Reactome, Visualization Author: Bing Wang, Julie Sullivan, Rachel Lyne, Konstantinos Kyritsis, Celia Sanchez Maintainer: InterMine Team VignetteBuilder: knitr BugReports: https://github.com/intermine/intermineR/issues git_url: https://git.bioconductor.org/packages/InterMineR git_branch: RELEASE_3_12 git_last_commit: ecd9a83 git_last_commit_date: 2021-04-28 Date/Publication: 2021-04-28 source.ver: src/contrib/InterMineR_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/InterMineR_1.12.3.zip mac.binary.ver: bin/macosx/contrib/4.0/InterMineR_1.12.3.tgz vignettes: vignettes/InterMineR/inst/doc/InterMineR.html vignetteTitles: InterMineR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/InterMineR/inst/doc/InterMineR.R dependencyCount: 60 Package: IntramiRExploreR Version: 1.12.0 Depends: R (>= 3.4) Imports: igraph (>= 1.0.1), FGNet (>= 3.0.7), knitr (>= 1.12.3), stats, utils, grDevices, graphics Suggests: RDAVIDWebService, gProfileR, topGO, KEGGprofile, org.Dm.eg.db, rmarkdown, testthat License: GPL-2 MD5sum: a93cf41fbbb403a7e5957e6860aee7cb NeedsCompilation: no Title: Predicting Targets for Drosophila Intragenic miRNAs Description: Intra-miR-ExploreR, an integrative miRNA target prediction bioinformatics tool, identifies targets combining expression and biophysical interactions of a given microRNA (miR). Using the tool, we have identified targets for 92 intragenic miRs in D. melanogaster, using available microarray expression data, from Affymetrix 1 and Affymetrix2 microarray array platforms, providing a global perspective of intragenic miR targets in Drosophila. Predicted targets are grouped according to biological functions using the DAVID Gene Ontology tool and are ranked based on a biologically relevant scoring system, enabling the user to identify functionally relevant targets for a given miR. biocViews: Software, Microarray, GeneTarget, StatisticalMethod, GeneExpression, GenePrediction Author: Surajit Bhattacharya and Daniel Cox Maintainer: Surajit Bhattacharya URL: https://github.com/sbhattacharya3/IntramiRExploreR/ VignetteBuilder: knitr BugReports: https://github.com/sbhattacharya3/IntramiRExploreR/issues git_url: https://git.bioconductor.org/packages/IntramiRExploreR git_branch: RELEASE_3_12 git_last_commit: f5bc850 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IntramiRExploreR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IntramiRExploreR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IntramiRExploreR_1.12.0.tgz vignettes: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.html vignetteTitles: IntramiRExploreR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IntramiRExploreR/inst/doc/IntramiRExploreR_vignettes.R dependencyCount: 34 Package: inveRsion Version: 1.38.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 79ca2ed6b2987c4bbed56ae3bff9f307 NeedsCompilation: yes Title: Inversions in genotype data Description: Package to find genetic inversions in genotype (SNP array) data. biocViews: Microarray, SNP Author: Alejandro Caceres Maintainer: Alejandro Caceres git_url: https://git.bioconductor.org/packages/inveRsion git_branch: RELEASE_3_12 git_last_commit: 06c5951 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/inveRsion_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/inveRsion_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/inveRsion_1.38.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inveRsion/inst/doc/inveRsion.R dependencyCount: 86 Package: IONiseR Version: 2.14.0 Depends: R (>= 3.4) Imports: rhdf5, dplyr, magrittr, tidyr, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector, tibble, stats, BiocParallel, bit64, stringr, utils Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: 7cd4ddb9f37f9563edbeddecb9fe65c0 NeedsCompilation: no Title: Quality Assessment Tools for Oxford Nanopore MinION data Description: IONiseR provides tools for the quality assessment of Oxford Nanopore MinION data. It extracts summary statistics from a set of fast5 files and can be used either before or after base calling. In addition to standard summaries of the read-types produced, it provides a number of plots for visualising metrics relative to experiment run time or spatially over the surface of a flowcell. biocViews: QualityControl, DataImport, Sequencing Author: Mike Smith [aut, cre] Maintainer: Mike Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IONiseR git_branch: RELEASE_3_12 git_last_commit: ccf91a8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IONiseR_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IONiseR_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IONiseR_2.14.0.tgz vignettes: vignettes/IONiseR/inst/doc/IONiseR.html vignetteTitles: Quality assessment tools for nanopore data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R dependencyCount: 85 Package: iPAC Version: 1.34.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 322241da6c560ca54d9463e4b80f69e4 NeedsCompilation: no Title: Identification of Protein Amino acid Clustering Description: iPAC is a novel tool to identify somatic amino acid mutation clustering within proteins while taking into account protein structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/iPAC git_branch: RELEASE_3_12 git_last_commit: 1cfe274 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iPAC_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iPAC_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iPAC_1.34.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iPAC/inst/doc/iPAC.R dependsOnMe: QuartPAC dependencyCount: 26 Package: ipdDb Version: 1.8.0 Depends: R (>= 3.5.0), methods, AnnotationDbi (>= 1.43.1), AnnotationHub Imports: Biostrings, GenomicRanges, RSQLite, DBI, IRanges, stats, assertthat Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: c5d8ebd2b3b9333424ebd51df3714fd1 NeedsCompilation: no Title: IPD IMGT/HLA and IPD KIR database for Homo sapiens Description: All alleles from the IPD IMGT/HLA and IPD KIR database for Homo sapiens. Reference: Robinson J, Maccari G, Marsh SGE, Walter L, Blokhuis J, Bimber B, Parham P, De Groot NG, Bontrop RE, Guethlein LA, and Hammond JA KIR Nomenclature in non-human species Immunogenetics (2018), in preparation. biocViews: GenomicVariation, SequenceMatching, VariantAnnotation, DataRepresentation Author: Steffen Klasberg Maintainer: Steffen Klasberg URL: https://github.com/DKMS-LSL/ipdDb organism: Homo sapiens VignetteBuilder: knitr BugReports: https://github.com/DKMS-LSL/ipdDb/issues/new git_url: https://git.bioconductor.org/packages/ipdDb git_branch: RELEASE_3_12 git_last_commit: 797685e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ipdDb_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ipdDb_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ipdDb_1.8.0.tgz vignettes: vignettes/ipdDb/inst/doc/Readme.html vignetteTitles: ipdDb hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ipdDb/inst/doc/Readme.R dependencyCount: 84 Package: IPO Version: 1.16.0 Depends: xcms (>= 1.50.0), rsm, CAMERA, grDevices, graphics, stats, utils Imports: BiocParallel Suggests: RUnit, BiocGenerics, msdata, mtbls2, faahKO, knitr Enhances: parallel License: GPL (>= 2) + file LICENSE MD5sum: 8a51b2a815583f5a8eedebb1df690025 NeedsCompilation: no Title: Automated Optimization of XCMS Data Processing parameters Description: The outcome of XCMS data processing strongly depends on the parameter settings. IPO (`Isotopologue Parameter Optimization`) is a parameter optimization tool that is applicable for different kinds of samples and liquid chromatography coupled to high resolution mass spectrometry devices, fast and free of labeling steps. IPO uses natural, stable 13C isotopes to calculate a peak picking score. Retention time correction is optimized by minimizing the relative retention time differences within features and grouping parameters are optimized by maximizing the number of features showing exactly one peak from each injection of a pooled sample. The different parameter settings are achieved by design of experiment. The resulting scores are evaluated using response surface models. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Gunnar Libiseller , Christoph Magnes , Thomas Riebenbauer Maintainer: Thomas Riebenbauer URL: https://github.com/rietho/IPO VignetteBuilder: knitr BugReports: https://github.com/rietho/IPO/issues/new git_url: https://git.bioconductor.org/packages/IPO git_branch: RELEASE_3_12 git_last_commit: af1e447 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IPO_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IPO_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IPO_1.16.0.tgz vignettes: vignettes/IPO/inst/doc/IPO.html vignetteTitles: XCMS Parameter Optimization with IPO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IPO/inst/doc/IPO.R dependencyCount: 128 Package: IRanges Version: 2.24.1 Depends: R (>= 4.0.0), methods, utils, stats, BiocGenerics (>= 0.36.0), S4Vectors (>= 0.27.12) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, Rsamtools, GenomicAlignments, GenomicFeatures, BSgenome.Celegans.UCSC.ce2, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: d80e152955f2984fea28f4c7eb704508 NeedsCompilation: yes Title: Foundation of integer range manipulation in Bioconductor Description: Provides efficient low-level and highly reusable S4 classes for storing, manipulating and aggregating over annotated ranges of integers. Implements an algebra of range operations, including efficient algorithms for finding overlaps and nearest neighbors. Defines efficient list-like classes for storing, transforming and aggregating large grouped data, i.e., collections of atomic vectors and DataFrames. biocViews: Infrastructure, DataRepresentation Author: H. Pagès, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/IRanges BugReports: https://github.com/Bioconductor/IRanges/issues git_url: https://git.bioconductor.org/packages/IRanges git_branch: RELEASE_3_12 git_last_commit: 6c61fdd git_last_commit_date: 2020-12-11 Date/Publication: 2020-12-12 source.ver: src/contrib/IRanges_2.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/IRanges_2.24.1.zip mac.binary.ver: bin/macosx/contrib/4.0/IRanges_2.24.1.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Overview of the IRanges package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, BaalChIP, bambu, biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter, CAFE, casper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, CODEX, consensusSeekeR, CSAR, CSSQ, customProDB, deepSNV, DelayedArray, DESeq2, DEXSeq, 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qpgraph, qPLEXanalyzer, qsea, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RaggedExperiment, RareVariantVis, Rariant, Rcade, RCAS, recount, REDseq, regioneR, regutools, REMP, Repitools, ReportingTools, rfaRm, RiboProfiling, riboSeqR, ribosomeProfilingQC, RIPAT, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAprobR, rnaSeqMap, RnBeads, roar, Rqc, Rsamtools, RSVSim, RTN, rtracklayer, sarks, SCAN.UPC, scHOT, segmentSeq, SeqArray, seqCAT, seqPattern, seqplots, seqsetvis, SeqSQC, SeqVarTools, sesame, sevenC, ShortRead, signeR, SimFFPE, SMITE, snapcount, SNPhood, soGGi, SomaticSignatures, SparseSignatures, Spectra, spicyR, SplicingGraphs, SPLINTER, srnadiff, STAN, strandCheckR, SummarizedExperiment, SynExtend, systemPipeR, TAPseq, target, TarSeqQC, TCGAbiolinks, TCGAutils, TCseq, TFBSTools, TFEA.ChIP, TFHAZ, TitanCNA, TnT, tracktables, trackViewer, transcriptR, TransView, tRNA, tRNAdbImport, tRNAscanImport, tscR, TSRchitect, TVTB, tximeta, TxRegInfra, UMI4Cats, Uniquorn, universalmotif, VanillaICE, VariantAnnotation, VariantExperiment, VariantFiltering, vasp, VaSP, wavClusteR, wiggleplotr, XCIR, xcms, XVector, yamss, fitCons.UCSC.hg19, GenomicState, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.charm.hg18.example, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.mirna.3.1, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP142.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, cgdv17, chipenrich.data, leeBamViews, MethylSeqData, pd.atdschip.tiling, SomaticCancerAlterations, spatialLIBD, ActiveDriverWGS, alakazam, BinQuasi, crispRdesignR, ExomeDepth, geno2proteo, HiCfeat, hoardeR, ICAMS, LoopRig, MAFDash, noisyr, PACVr, RapidoPGS, RTIGER, Signac, simMP, STRMPS, tidygenomics, VALERIE suggestsMe: annotate, AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, epivizrChart, Glimma, gQTLBase, gwascat, GWASTools, HilbertVis, HilbertVisGUI, martini, MiRaGE, multicrispr, regionReport, RTCGA, S4Vectors, SigsPack, splatter, StructuralVariantAnnotation, TFutils, yeastRNASeq, cancerTiming, fuzzyjoin, gkmSVM, LDheatmap, pagoo, polyRAD, rliger, seqmagick, Seurat, sigminer, valr linksToMe: Biostrings, CNEr, DECIPHER, GenomicAlignments, GenomicRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 8 Package: ISAnalytics Version: 1.0.11 Depends: R (>= 4.0), magrittr Imports: utils, reactable, htmltools, dplyr, readr, tidyr, purrr, rlang, forcats, tibble, BiocParallel, stringr, fs, zip, lubridate, lifecycle, ggplot2, ggrepel, stats, upsetjs, psych, grDevices, data.table, readxl, tools Suggests: testthat, covr, knitr, BiocStyle, knitcitations, sessioninfo, rmarkdown, roxygen2, vegan, withr License: CC BY 4.0 MD5sum: b0a1ec2bcb8ea78c6f6f66b7be279dbe NeedsCompilation: no Title: Analyze gene therapy vector insertion sites data identified from genomics next generation sequencing reads for clonal tracking studies Description: In gene therapy, stem cells are modified using viral vectors to deliver the therapeutic transgene and replace functional properties since the genetic modification is stable and inherited in all cell progeny. The retrieval and mapping of the sequences flanking the virus-host DNA junctions allows the identification of insertion sites (IS), essential for monitoring the evolution of genetically modified cells in vivo. A comprehensive toolkit for the analysis of IS is required to foster clonal trackign studies and supporting the assessment of safety and long term efficacy in vivo. This package is aimed at (1) supporting automation of IS workflow, (2) performing base and advance analysis for IS tracking (clonal abundance, clonal expansions and statistics for insertional mutagenesis, etc.), (3) providing basic biology insights of transduced stem cells in vivo. biocViews: BiomedicalInformatics, Sequencing, SingleCell Author: Andrea Calabria [aut, cre], Giulio Spinozzi [aut], Giulia Pais [aut] Maintainer: Andrea Calabria URL: https://calabrialab.github.io/ISAnalytics, https://github.com//calabrialab/isanalytics VignetteBuilder: knitr BugReports: https://github.com/calabrialab/ISAnalytics/issues git_url: https://git.bioconductor.org/packages/ISAnalytics git_branch: RELEASE_3_12 git_last_commit: a1014e7 git_last_commit_date: 2021-04-08 Date/Publication: 2021-04-08 source.ver: src/contrib/ISAnalytics_1.0.11.tar.gz win.binary.ver: bin/windows/contrib/4.0/ISAnalytics_1.0.11.zip mac.binary.ver: bin/macosx/contrib/4.0/ISAnalytics_1.0.11.tgz vignettes: vignettes/ISAnalytics/inst/doc/aggregate_function_usage.html, vignettes/ISAnalytics/inst/doc/collision_removal.html, vignettes/ISAnalytics/inst/doc/how_to_import_functions.html, vignettes/ISAnalytics/inst/doc/no_rstudio_usage.html vignetteTitles: Working with aggregate functions, Collision removal functionality, How to use import functions, Using ISAnalytics without RStudio support hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ISAnalytics/inst/doc/aggregate_function_usage.R, vignettes/ISAnalytics/inst/doc/collision_removal.R, vignettes/ISAnalytics/inst/doc/how_to_import_functions.R, vignettes/ISAnalytics/inst/doc/no_rstudio_usage.R dependencyCount: 81 Package: iSEE Version: 2.2.4 Depends: SummarizedExperiment, SingleCellExperiment Imports: methods, BiocGenerics, S4Vectors, utils, stats, shiny, shinydashboard, shinyAce, shinyjs, DT, rintrojs, ggplot2, ggrepel, colourpicker, igraph, vipor, mgcv, graphics, grDevices, viridisLite, shinyWidgets, ComplexHeatmap, circlize, grid Suggests: testthat, BiocStyle, knitr, rmarkdown, scRNAseq, TENxPBMCData, scater, DelayedArray, HDF5Array, RColorBrewer, viridis, htmltools License: MIT + file LICENSE MD5sum: e190494e252390f07fdf140087d5c864 NeedsCompilation: no Title: Interactive SummarizedExperiment Explorer Description: Create an interactive Shiny-based graphical user interface for exploring data stored in SummarizedExperiment objects, including row- and column-level metadata. The interface supports transmission of selections between plots and tables, code tracking, interactive tours, interactive or programmatic initialization, preservation of app state, and extensibility to new panel types via S4 classes. Special attention is given to single-cell data in a SingleCellExperiment object with visualization of dimensionality reduction results. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (), Federico Marini [aut] (), Charlotte Soneson [aut] (), Aaron Lun [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEE VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEE/issues git_url: https://git.bioconductor.org/packages/iSEE git_branch: RELEASE_3_12 git_last_commit: b5d95ef git_last_commit_date: 2021-02-01 Date/Publication: 2021-02-01 source.ver: src/contrib/iSEE_2.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/iSEE_2.2.4.zip mac.binary.ver: bin/macosx/contrib/4.0/iSEE_2.2.4.tgz vignettes: vignettes/iSEE/inst/doc/basic.html, vignettes/iSEE/inst/doc/bigdata.html, vignettes/iSEE/inst/doc/configure.html, vignettes/iSEE/inst/doc/custom.html, vignettes/iSEE/inst/doc/ecm.html, vignettes/iSEE/inst/doc/links.html, vignettes/iSEE/inst/doc/voice.html vignetteTitles: 1. The iSEE User's Guide, 6. Using iSEE with big data, 3. Configuring iSEE apps, 5. Deploying custom panels, 4. The ExperimentColorMap Class, 2. Sharing information across panels, 7. Speech recognition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEE/inst/doc/basic.R, vignettes/iSEE/inst/doc/bigdata.R, vignettes/iSEE/inst/doc/configure.R, vignettes/iSEE/inst/doc/custom.R, vignettes/iSEE/inst/doc/ecm.R, vignettes/iSEE/inst/doc/links.R, vignettes/iSEE/inst/doc/voice.R dependsOnMe: iSEEu suggestsMe: schex, DuoClustering2018, HCAData, TabulaMurisData dependencyCount: 101 Package: iSEEu Version: 1.2.0 Depends: iSEE Imports: methods, S4Vectors, shiny, SummarizedExperiment, SingleCellExperiment, ggplot2, DT, stats, colourpicker Suggests: scRNAseq, scater, scran, airway, edgeR, AnnotationDbi, org.Hs.eg.db, GO.db, KEGGREST, knitr, igraph, rmarkdown, BiocStyle, htmltools, Rtsne, uwot, testthat (>= 2.1.0), covr License: MIT + file LICENSE MD5sum: 80e4cd06090689bb9288f95dabc4bfb8 NeedsCompilation: no Title: iSEE Universe Description: iSEEu (the iSEE universe) contains diverse functionality to extend the usage of the iSEE package, including additional classes for the panels, or modes allowing easy configuration of iSEE applications. biocViews: ImmunoOncology, Visualization, GUI, DimensionReduction, FeatureExtraction, Clustering, Transcription, GeneExpression, Transcriptomics, SingleCell, CellBasedAssays Author: Kevin Rue-Albrecht [aut, cre] (), Charlotte Soneson [aut] (), Federico Marini [aut] (), Aaron Lun [aut] (), Michael Stadler [ctb] Maintainer: Kevin Rue-Albrecht URL: https://github.com/iSEE/iSEEu VignetteBuilder: knitr BugReports: https://github.com/iSEE/iSEEu/issues git_url: https://git.bioconductor.org/packages/iSEEu git_branch: RELEASE_3_12 git_last_commit: c573172 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iSEEu_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iSEEu_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iSEEu_1.2.0.tgz vignettes: vignettes/iSEEu/inst/doc/universe.html vignetteTitles: Panel universe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iSEEu/inst/doc/universe.R dependencyCount: 102 Package: iSeq Version: 1.42.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: d5a061d5cc3168e8100dba03a2d1c8f7 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: Bayesian hidden Ising models are implemented to identify IP-enriched genomic regions from ChIP-seq data. They can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo git_url: https://git.bioconductor.org/packages/iSeq git_branch: RELEASE_3_12 git_last_commit: b08ccd7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iSeq_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iSeq_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iSeq_1.42.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iSeq/inst/doc/iSeq.R dependencyCount: 0 Package: isobar Version: 1.36.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr, biomaRt, ggplot2 Suggests: MSnbase, OrgMassSpecR, XML, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, DBI, MASS License: LGPL-2 MD5sum: e3249c71e17c4caf2e123f8b8ebcf588 NeedsCompilation: no Title: Analysis and quantitation of isobarically tagged MSMS proteomics data Description: isobar provides methods for preprocessing, normalization, and report generation for the analysis of quantitative mass spectrometry proteomics data labeled with isobaric tags, such as iTRAQ and TMT. Features modules for integrating and validating PTM-centric datasets (isobar-PTM). More information on http://www.ms-isobar.org. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Bioinformatics, MultipleComparisons, QualityControl Author: Florian P Breitwieser and Jacques Colinge , with contributions from Alexey Stukalov , Xavier Robin and Florent Gluck Maintainer: Florian P Breitwieser URL: https://github.com/fbreitwieser/isobar BugReports: https://github.com/fbreitwieser/isobar/issues git_url: https://git.bioconductor.org/packages/isobar git_branch: RELEASE_3_12 git_last_commit: 37cd3b5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/isobar_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/isobar_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/isobar_1.36.0.tgz vignettes: vignettes/isobar/inst/doc/isobar-devel.pdf, vignettes/isobar/inst/doc/isobar-ptm.pdf, vignettes/isobar/inst/doc/isobar-usecases.pdf, vignettes/isobar/inst/doc/isobar.pdf vignetteTitles: isobar for developers, isobar for quantification of PTM datasets, Usecases for isobar package, isobar package for iTRAQ and TMT protein quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/isobar/inst/doc/isobar-devel.R, vignettes/isobar/inst/doc/isobar-ptm.R, vignettes/isobar/inst/doc/isobar-usecases.R, vignettes/isobar/inst/doc/isobar.R suggestsMe: RforProteomics dependencyCount: 83 Package: IsoCorrectoR Version: 1.8.0 Depends: R (>= 3.5) Imports: dplyr, magrittr, methods, quadprog, readr, readxl, stringr, tibble, tools, utils, pracma, WriteXLS Suggests: IsoCorrectoRGUI, knitr, rmarkdown, testthat License: GPL-3 MD5sum: f8fd26c1990a32e9834274e40a76058c NeedsCompilation: no Title: Correction for natural isotope abundance and tracer purity in MS and MS/MS data from stable isotope labeling experiments Description: IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as ultra-high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). See the Bioconductor package IsoCorrectoRGUI for a graphical user interface to IsoCorrectoR. NOTE: With R version 4.0.0, writing correction results to Excel files may currently not work on Windows. However, writing results to csv works as before. biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoR/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoR git_branch: RELEASE_3_12 git_last_commit: db97f98 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IsoCorrectoR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IsoCorrectoR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IsoCorrectoR_1.8.0.tgz vignettes: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoR/inst/doc/IsoCorrectoR.R importsMe: IsoCorrectoRGUI dependencyCount: 40 Package: IsoCorrectoRGUI Version: 1.6.0 Depends: R (>= 3.6) Imports: IsoCorrectoR, readxl, tcltk2, tcltk, utils Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: c61fece449f046b2d8703beba9786dde NeedsCompilation: no Title: Graphical User Interface for IsoCorrectoR Description: IsoCorrectoRGUI is a Graphical User Interface for the IsoCorrectoR package. IsoCorrectoR performs the correction of mass spectrometry data from stable isotope labeling/tracing metabolomics experiments with regard to natural isotope abundance and tracer impurity. Data from both MS and MS/MS measurements can be corrected (with any tracer isotope: 13C, 15N, 18O...), as well as high resolution MS data from multiple-tracer experiments (e.g. 13C and 15N used simultaneously). biocViews: Software, Metabolomics, MassSpectrometry, Preprocessing, GUI, ImmunoOncology Author: Christian Kohler [cre, aut], Paul Kuerner [aut], Paul Heinrich [aut] Maintainer: Christian Kohler URL: https://genomics.ur.de/files/IsoCorrectoRGUI VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IsoCorrectoRGUI git_branch: RELEASE_3_12 git_last_commit: 1cb35ea git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IsoCorrectoRGUI_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IsoCorrectoRGUI_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IsoCorrectoRGUI_1.6.0.tgz vignettes: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.html vignetteTitles: IsoCorrectoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoCorrectoRGUI/inst/doc/IsoCorrectoRGUI.R suggestsMe: IsoCorrectoR dependencyCount: 43 Package: IsoformSwitchAnalyzeR Version: 1.12.0 Depends: R (>= 3.5), limma, DEXSeq, ggplot2 Imports: methods, BSgenome, plyr, reshape2, gridExtra, Biostrings (>= 2.50.0), IRanges, GenomicRanges, DRIMSeq, RColorBrewer, rtracklayer, VennDiagram, DBI, grDevices, graphics, stats, utils, GenomeInfoDb, grid, tximport (>= 1.7.1), tximeta (>= 1.7.12), edgeR, futile.logger, stringr, dplyr, magrittr, readr, tibble, XVector, BiocGenerics, RCurl Suggests: knitr, BSgenome.Hsapiens.UCSC.hg19, cummeRbund License: GPL (>= 2) Archs: i386, x64 MD5sum: 02993bb8a0286b4ec33643fffd170635 NeedsCompilation: yes Title: Identify, Annotate and Visualize Alternative Splicing and Isoform Switches with Functional Consequences from both short- and long-read RNA-seq data. Description: IsoformSwitchAnalyzeR enables identification and analysis of alternative splicing and isoform switches with predicted functional consequences (e.g. gain/loss of protein domains etc.) from quantification of all types of RNASeq by tools such as Kallisto, Salmon, Cufflinks/Cuffdiff, RSEM etc. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Visualization, StatisticalMethod, TranscriptomeVariant, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, Transcriptomics, RNASeq, Annotation, FunctionalPrediction, GenePrediction, DataImport, MultipleComparison, BatchEffect, ImmunoOncology Author: Kristoffer Vitting-Seerup Maintainer: Kristoffer Vitting-Seerup URL: http://bioconductor.org/packages/IsoformSwitchAnalyzeR/ VignetteBuilder: knitr BugReports: https://github.com/kvittingseerup/IsoformSwitchAnalyzeR/issues git_url: https://git.bioconductor.org/packages/IsoformSwitchAnalyzeR git_branch: RELEASE_3_12 git_last_commit: c799e51 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IsoformSwitchAnalyzeR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IsoformSwitchAnalyzeR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IsoformSwitchAnalyzeR_1.12.0.tgz vignettes: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.html vignetteTitles: IsoformSwitchAnalyzeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoformSwitchAnalyzeR/inst/doc/IsoformSwitchAnalyzeR.R dependencyCount: 151 Package: IsoGeneGUI Version: 2.26.0 Depends: tcltk, xlsx Imports: Rcpp, tkrplot, multtest, relimp, geneplotter, RColorBrewer, Iso, IsoGene, ORCME, ORIClust, orQA, goric, ff, Biobase, jpeg Suggests: RUnit License: GPL-2 MD5sum: 97c4fc18b58e7dd8cedcf3de1d493691 NeedsCompilation: no Title: A graphical user interface to conduct a dose-response analysis of microarray data Description: The IsoGene Graphical User Interface (IsoGene-GUI) is a user friendly interface of the IsoGene package which is aimed to identify for genes with a monotonic trend in the expression levels with respect to the increasing doses. Additionally, GUI extension of original package contains various tools to perform clustering of dose-response profiles. Testing is addressed through several test statistics: global likelihood ratio test (E2), Bartholomew 1961, Barlow et al. 1972 and Robertson et al. 1988), Williams (1971, 1972), Marcus (1976), the M (Hu et al. 2005) and the modified M (Lin et al. 2007). The p-values of the global likelihood ratio test (E2) are obtained using the exact distribution and permutations. The other four test statistics are obtained using permutations. Several p-values adjustment are provided: Bonferroni, Holm (1979), Hochberg (1988), and Sidak procedures for controlling the family-wise Type I error rate (FWER), and BH (Benjamini and Hochberg 1995) and BY (Benjamini and Yekutieli 2001) procedures are used for controlling the FDR. The inference is based on resampling methods, which control the False Discovery Rate (FDR), for both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM, Tusher et al., 2001). Clustering methods are outsourced from CRAN packages ORCME, ORIClust. The package ORCME is based on delta-clustering method (Cheng and Church, 2000) and ORIClust on Order Restricted Information Criterion (Liu et al., 2009), both perform same task but from different perspective and their outputs are clusters of genes. Additionally, profile selection for given gene based on Generalized ORIC (Kuiper et al., 2014) from package goric and permutation test for E2 based on package orQA are included in IsoGene-GUI. None of these four packages has GUI. biocViews: Microarray, DifferentialExpression, GUI Author: Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke, Martin Otava Maintainer: Setia Pramana URL: http://ibiostat.be/online-resources/online-resources/isogenegui/isogenegui-package git_url: https://git.bioconductor.org/packages/IsoGeneGUI git_branch: RELEASE_3_12 git_last_commit: 5abb439 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IsoGeneGUI_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IsoGeneGUI_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IsoGeneGUI_2.26.0.tgz vignettes: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.pdf vignetteTitles: IsoGeneGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IsoGeneGUI/inst/doc/IsoGeneGUI.R dependencyCount: 72 Package: ISoLDE Version: 1.18.1 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: i386, x64 MD5sum: ad4a39c011f70c97233f0af93db18c4c NeedsCompilation: yes Title: Integrative Statistics of alleLe Dependent Expression Description: This package provides ISoLDE a new method for identifying imprinted genes. This method is dedicated to data arising from RNA sequencing technologies. The ISoLDE package implements original statistical methodology described in the publication below. biocViews: ImmunoOncology, GeneExpression, Transcription, GeneSetEnrichment, Genetics, Sequencing, RNASeq, MultipleComparison, SNP, GeneticVariability, Epigenetics, MathematicalBiology, GeneRegulation Author: Christelle Reynès [aut, cre], Marine Rohmer [aut], Guilhem Kister [aut] Maintainer: Christelle Reynès URL: www.r-project.org git_url: https://git.bioconductor.org/packages/ISoLDE git_branch: RELEASE_3_12 git_last_commit: 258a2b0 git_last_commit_date: 2021-01-08 Date/Publication: 2021-01-08 source.ver: src/contrib/ISoLDE_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ISoLDE_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ISoLDE_1.18.1.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: isomiRs Version: 1.18.1 Depends: R (>= 3.5), DiscriMiner, SummarizedExperiment Imports: AnnotationDbi, assertive.sets, BiocGenerics, Biobase, broom, cluster, cowplot, DEGreport, DESeq2, IRanges, dplyr, GenomicRanges, gplots, ggplot2, gtools, gridExtra, grid, grDevices, graphics, GGally, limma, methods, RColorBrewer, readr, reshape, rlang, stats, stringr, S4Vectors, tidyr, tibble Suggests: knitr, org.Mm.eg.db, targetscan.Hs.eg.db, pheatmap, BiocStyle, testthat License: MIT + file LICENSE MD5sum: a4607daba55561baead7eccd5c1f9860 NeedsCompilation: no Title: Analyze isomiRs and miRNAs from small RNA-seq Description: Characterization of miRNAs and isomiRs, clustering and differential expression. biocViews: miRNA, RNASeq, DifferentialExpression, Clustering, ImmunoOncology Author: Lorena Pantano [aut, cre], Georgia Escaramis [aut] Maintainer: Lorena Pantano VignetteBuilder: knitr BugReports: https://github.com/lpantano/isomiRs/issues git_url: https://git.bioconductor.org/packages/isomiRs git_branch: RELEASE_3_12 git_last_commit: 3f07703 git_last_commit_date: 2021-01-28 Date/Publication: 2021-01-29 source.ver: src/contrib/isomiRs_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/isomiRs_1.18.1.zip mac.binary.ver: bin/macosx/contrib/4.0/isomiRs_1.18.1.tgz vignettes: vignettes/isomiRs/inst/doc/isomiRs.html vignetteTitles: miRNA and isomiR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs.R dependencyCount: 147 Package: ITALICS Version: 2.50.0 Depends: R (>= 2.0.0), GLAD, ITALICSData, oligo, affxparser, pd.mapping50k.xba240 Imports: affxparser, DBI, GLAD, oligo, oligoClasses, stats Suggests: pd.mapping50k.hind240, pd.mapping250k.sty, pd.mapping250k.nsp License: GPL-2 MD5sum: 791641158dc407f4368dd3c506a7e372 NeedsCompilation: no Title: ITALICS Description: A Method to normalize of Affymetrix GeneChip Human Mapping 100K and 500K set biocViews: Microarray, CopyNumberVariation Author: Guillem Rigaill, Philippe Hupe Maintainer: Guillem Rigaill URL: http://bioinfo.curie.fr git_url: https://git.bioconductor.org/packages/ITALICS git_branch: RELEASE_3_12 git_last_commit: d9e57ac git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ITALICS_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ITALICS_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ITALICS_2.50.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ITALICS/inst/doc/ITALICS.R dependencyCount: 60 Package: iterativeBMA Version: 1.48.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: c16e334203775b26b0e815c073b5660d NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) algorithm Description: The iterative Bayesian Model Averaging (BMA) algorithm is a variable selection and classification algorithm with an application of classifying 2-class microarray samples, as described in Yeung, Bumgarner and Raftery (Bioinformatics 2005, 21: 2394-2402). biocViews: Microarray, Classification Author: Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter Maintainer: Ka Yee Yeung URL: http://faculty.washington.edu/kayee/research.html git_url: https://git.bioconductor.org/packages/iterativeBMA git_branch: RELEASE_3_12 git_last_commit: 23ef7f4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iterativeBMA_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iterativeBMA_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iterativeBMA_1.48.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMA/inst/doc/iterativeBMA.R dependencyCount: 22 Package: iterativeBMAsurv Version: 1.48.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: a9a9563c4493c10005ec30852e9a2553 NeedsCompilation: no Title: The Iterative Bayesian Model Averaging (BMA) Algorithm For Survival Analysis Description: The iterative Bayesian Model Averaging (BMA) algorithm for survival analysis is a variable selection method for applying survival analysis to microarray data. biocViews: Microarray Author: Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA Maintainer: Ka Yee Yeung URL: http://expression.washington.edu/ibmasurv/protected git_url: https://git.bioconductor.org/packages/iterativeBMAsurv git_branch: RELEASE_3_12 git_last_commit: 6a0b2b1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iterativeBMAsurv_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iterativeBMAsurv_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iterativeBMAsurv_1.48.0.tgz vignettes: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm For Survival Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iterativeBMAsurv/inst/doc/iterativeBMAsurv.R dependencyCount: 19 Package: iterClust Version: 1.12.0 Depends: R (>= 3.4.1) Imports: Biobase, cluster, stats, methods Suggests: tsne, bcellViper License: file LICENSE MD5sum: 5cc83491faaf51399d0c9e104724d329 NeedsCompilation: no Title: Iterative Clustering Description: A framework for performing clustering analysis iteratively. biocViews: StatisticalMethod, Clustering Author: Hongxu Ding and Andrea Califano Maintainer: Hongxu Ding URL: https://github.com/hd2326/iterClust BugReports: https://github.com/hd2326/iterClust/issues git_url: https://git.bioconductor.org/packages/iterClust git_branch: RELEASE_3_12 git_last_commit: b687925 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iterClust_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iterClust_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iterClust_1.12.0.tgz vignettes: vignettes/iterClust/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iterClust/inst/doc/introduction.R dependencyCount: 9 Package: iteremoval Version: 1.10.0 Depends: R (>= 3.5.0), ggplot2 (>= 2.2.1) Imports: magrittr, graphics, utils, GenomicRanges, SummarizedExperiment Suggests: testthat, knitr License: GPL-2 MD5sum: 5c4870facedfee7564b5caf720635e93 NeedsCompilation: no Title: Iteration removal method for feature selection Description: The package provides a flexible algorithm to screen features of two distinct groups in consideration of overfitting and overall performance. It was originally tailored for methylation locus screening of NGS data, and it can also be used as a generic method for feature selection. Each step of the algorithm provides a default method for simple implemention, and the method can be replaced by a user defined function. biocViews: StatisticalMethod Author: Jiacheng Chuan [aut, cre] Maintainer: Jiacheng Chuan URL: https://github.com/cihga39871/iteremoval VignetteBuilder: knitr BugReports: https://github.com/cihga39871/iteremoval/issues git_url: https://git.bioconductor.org/packages/iteremoval git_branch: RELEASE_3_12 git_last_commit: 9e0618c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/iteremoval_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/iteremoval_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/iteremoval_1.10.0.tgz vignettes: vignettes/iteremoval/inst/doc/iteremoval.html vignetteTitles: An introduction to iteremoval hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iteremoval/inst/doc/iteremoval.R dependencyCount: 56 Package: IVAS Version: 2.10.0 Depends: R (> 3.0.0),GenomicFeatures, ggplot2, Biobase Imports: doParallel, lme4, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb, ggfortify, grDevices, methods, Matrix, BiocParallel,utils, stats Suggests: BiocStyle License: GPL-2 MD5sum: 481ecc97552cdd82ba12072fb98d91e8 NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han git_url: https://git.bioconductor.org/packages/IVAS git_branch: RELEASE_3_12 git_last_commit: 5ca83c4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IVAS_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IVAS_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IVAS_2.10.0.tgz vignettes: vignettes/IVAS/inst/doc/IVAS.pdf vignetteTitles: IVAS : Identification of genetic Variants affecting Alternative Splicing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IVAS/inst/doc/IVAS.R dependsOnMe: IMAS importsMe: ASpediaFI dependencyCount: 117 Package: ivygapSE Version: 1.12.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: shiny, survival, survminer, hwriter, plotly, ggplot2, S4Vectors, graphics, stats, utils, UpSetR Suggests: knitr, png, limma, grid, DT, randomForest, digest, testthat License: Artistic-2.0 MD5sum: f5e55d94b315d2c409e72d345b0b5028 NeedsCompilation: no Title: A SummarizedExperiment for Ivy-GAP data Description: Define a SummarizedExperiment and exploratory app for Ivy-GAP glioblastoma image, expression, and clinical data. biocViews: Transcription, Software, Visualization, Survival, GeneExpression, Sequencing Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ivygapSE git_branch: RELEASE_3_12 git_last_commit: 077e1ec git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ivygapSE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ivygapSE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ivygapSE_1.12.0.tgz vignettes: vignettes/ivygapSE/inst/doc/ivygapSE.html vignetteTitles: ivygapSE -- SummarizedExperiment for Ivy-GAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ivygapSE/inst/doc/ivygapSE.R dependencyCount: 161 Package: IWTomics Version: 1.14.0 Depends: GenomicRanges Imports: parallel,gtable,grid,graphics,methods,IRanges,KernSmooth,fda,S4Vectors,grDevices,stats,utils,tools Suggests: knitr License: GPL (>=2) MD5sum: 64d448a3476393bde5f6b2bc244085ca NeedsCompilation: no Title: Interval-Wise Testing for Omics Data Description: Implementation of the Interval-Wise Testing (IWT) for omics data. This inferential procedure tests for differences in "Omics" data between two groups of genomic regions (or between a group of genomic regions and a reference center of symmetry), and does not require fixing location and scale at the outset. biocViews: StatisticalMethod, MultipleComparison, DifferentialExpression, DifferentialMethylation, DifferentialPeakCalling, GenomeAnnotation, DataImport Author: Marzia A Cremona, Alessia Pini, Francesca Chiaromonte, Simone Vantini Maintainer: Marzia A Cremona VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/IWTomics git_branch: RELEASE_3_12 git_last_commit: f304876 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/IWTomics_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/IWTomics_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/IWTomics_1.14.0.tgz vignettes: vignettes/IWTomics/inst/doc/IWTomics.pdf vignetteTitles: Introduction to IWTomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IWTomics/inst/doc/IWTomics.R dependencyCount: 70 Package: karyoploteR Version: 1.16.0 Depends: R (>= 3.4), regioneR, GenomicRanges, methods Imports: regioneR, GenomicRanges, IRanges, Rsamtools, stats, graphics, memoise, rtracklayer, GenomeInfoDb, S4Vectors, biovizBase, digest, bezier, GenomicFeatures, bamsignals, AnnotationDbi, grDevices, VariantAnnotation Suggests: BiocStyle, knitr, testthat, magrittr, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, org.Hs.eg.db, org.Mm.eg.db, pasillaBamSubset License: Artistic-2.0 MD5sum: 9b18eb40b53b8871bd8fb1a1ef5d2ba3 NeedsCompilation: no Title: Plot customizable linear genomes displaying arbitrary data Description: karyoploteR creates karyotype plots of arbitrary genomes and offers a complete set of functions to plot arbitrary data on them. It mimicks many R base graphics functions coupling them with a coordinate change function automatically mapping the chromosome and data coordinates into the plot coordinates. In addition to the provided data plotting functions, it is easy to add new ones. biocViews: Visualization, CopyNumberVariation, Sequencing, Coverage, DNASeq, ChIPSeq, MethylSeq, DataImport, OneChannel Author: Bernat Gel Maintainer: Bernat Gel URL: https://github.com/bernatgel/karyoploteR VignetteBuilder: knitr BugReports: https://github.com/bernatgel/karyoploteR/issues git_url: https://git.bioconductor.org/packages/karyoploteR git_branch: RELEASE_3_12 git_last_commit: 7562c22 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/karyoploteR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/karyoploteR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/karyoploteR_1.16.0.tgz vignettes: vignettes/karyoploteR/inst/doc/karyoploteR.html vignetteTitles: karyoploteR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/karyoploteR/inst/doc/karyoploteR.R dependsOnMe: CopyNumberPlots importsMe: CNVfilteR, multicrispr, RIPAT suggestsMe: Category dependencyCount: 140 Package: KCsmart Version: 2.48.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 0332192f7b1ba155efa00ee5e360c9c2 NeedsCompilation: no Title: Multi sample aCGH analysis package using kernel convolution Description: Multi sample aCGH analysis package using kernel convolution biocViews: CopyNumberVariation, Visualization, aCGH, Microarray Author: Jorma de Ronde, Christiaan Klijn, Arno Velds Maintainer: Jorma de Ronde git_url: https://git.bioconductor.org/packages/KCsmart git_branch: RELEASE_3_12 git_last_commit: 69342a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/KCsmart_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/KCsmart_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/KCsmart_2.48.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KCsmart/inst/doc/KCS.R dependencyCount: 19 Package: kebabs Version: 1.24.0 Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.27.3), e1071, LiblineaR, graphics, grDevices, utils, apcluster LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, Biobase, BiocGenerics, knitr License: GPL (>= 2.1) Archs: i386, x64 MD5sum: d5dc11c3df015c4083ebd720404e98ac NeedsCompilation: yes Title: Kernel-Based Analysis Of Biological Sequences Description: The package provides functionality for kernel-based analysis of DNA, RNA, and amino acid sequences via SVM-based methods. As core functionality, kebabs implements following sequence kernels: spectrum kernel, mismatch kernel, gappy pair kernel, and motif kernel. Apart from an efficient implementation of standard position-independent functionality, the kernels are extended in a novel way to take the position of patterns into account for the similarity measure. Because of the flexibility of the kernel formulation, other kernels like the weighted degree kernel or the shifted weighted degree kernel with constant weighting of positions are included as special cases. An annotation-specific variant of the kernels uses annotation information placed along the sequence together with the patterns in the sequence. The package allows for the generation of a kernel matrix or an explicit feature representation in dense or sparse format for all available kernels which can be used with methods implemented in other R packages. With focus on SVM-based methods, kebabs provides a framework which simplifies the usage of existing SVM implementations in kernlab, e1071, and LiblineaR. Binary and multi-class classification as well as regression tasks can be used in a unified way without having to deal with the different functions, parameters, and formats of the selected SVM. As support for choosing hyperparameters, the package provides cross validation - including grouped cross validation, grid search and model selection functions. For easier biological interpretation of the results, the package computes feature weights for all SVMs and prediction profiles which show the contribution of individual sequence positions to the prediction result and indicate the relevance of sequence sections for the learning result and the underlying biological functions. biocViews: SupportVectorMachine, Classification, Clustering, Regression Author: Johannes Palme Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/kebabs/ https://github.com/UBod/kebabs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/kebabs git_branch: RELEASE_3_12 git_last_commit: e4fb714 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/kebabs_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/kebabs_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/kebabs_1.24.0.tgz vignettes: vignettes/kebabs/inst/doc/kebabs.pdf vignetteTitles: KeBABS - An R Package for Kernel Based Analysis of Biological Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kebabs/inst/doc/kebabs.R dependsOnMe: procoil importsMe: FindMyFriends, odseq suggestsMe: apcluster dependencyCount: 26 Package: KEGGgraph Version: 1.50.0 Depends: R (>= 2.10.0) Imports: methods, XML (>= 2.3-0), graph, utils, RCurl Suggests: Rgraphviz, RBGL, testthat, RColorBrewer, KEGG.db, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: d891d5e728179f324ab2e3992db40eaf NeedsCompilation: no Title: KEGGgraph: A graph approach to KEGG PATHWAY in R and Bioconductor Description: KEGGGraph is an interface between KEGG pathway and graph object as well as a collection of tools to analyze, dissect and visualize these graphs. It parses the regularly updated KGML (KEGG XML) files into graph models maintaining all essential pathway attributes. The package offers functionalities including parsing, graph operation, visualization and etc. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: Jitao David Zhang, with inputs from Paul Shannon Maintainer: Jitao David Zhang URL: http://www.nextbiomotif.com git_url: https://git.bioconductor.org/packages/KEGGgraph git_branch: RELEASE_3_12 git_last_commit: 3335e85 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/KEGGgraph_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/KEGGgraph_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/KEGGgraph_1.50.0.tgz vignettes: vignettes/KEGGgraph/inst/doc/KEGGgraph.pdf, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.pdf vignetteTitles: KEGGgraph: graph approach to KEGG PATHWAY, KEGGgraph: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGgraph/inst/doc/KEGGgraph.R, vignettes/KEGGgraph/inst/doc/KEGGgraphApp.R dependsOnMe: ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, MetaboSignal, MWASTools, NCIgraph, pathview, iCARH, kangar00, NFP, pathfindR suggestsMe: DEGraph, GenomicRanges, maGUI, rags2ridges, specmine dependencyCount: 11 Package: KEGGlincs Version: 1.16.0 Depends: R (>= 3.3), KOdata, hgu133a.db, org.Hs.eg.db (>= 3.3.0) Imports: AnnotationDbi,KEGGgraph,igraph,plyr,gtools,httr,RJSONIO,KEGGREST, methods,graphics,stats,utils, XML, grDevices Suggests: BiocManager (>= 1.20.3), knitr, graph License: GPL-3 MD5sum: 9291db1d724965e4c19ac34d8e269781 NeedsCompilation: no Title: Visualize all edges within a KEGG pathway and overlay LINCS data Description: See what is going on 'under the hood' of KEGG pathways by explicitly re-creating the pathway maps from information obtained from KGML files. biocViews: NetworkInference, GeneExpression, DataRepresentation, ThirdPartyClient,CellBiology,GraphAndNetwork,Pathways,KEGG,Network Author: Shana White Maintainer: Shana White , Mario Medvedovic SystemRequirements: Cytoscape (>= 3.3.0), Java (>= 8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGlincs git_branch: RELEASE_3_12 git_last_commit: cc5f8bd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/KEGGlincs_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/KEGGlincs_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/KEGGlincs_1.16.0.tgz vignettes: vignettes/KEGGlincs/inst/doc/Example-workflow.html vignetteTitles: KEGGlincs Workflows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGlincs/inst/doc/Example-workflow.R dependencyCount: 58 Package: keggorthology Version: 2.42.0 Depends: R (>= 2.5.0),stats,graph,hgu95av2.db Imports: AnnotationDbi,graph,DBI, graph, grDevices, methods, stats, tools, utils Suggests: RBGL,ALL License: Artistic-2.0 MD5sum: da9081f0201b31e18192c31137e091e4 NeedsCompilation: no Title: graph support for KO, KEGG Orthology Description: graphical representation of the Feb 2010 KEGG Orthology. The KEGG orthology is a set of pathway IDs that are not to be confused with the KEGG ortholog IDs. biocViews: Pathways, GraphAndNetwork, Visualization, KEGG Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/keggorthology git_branch: RELEASE_3_12 git_last_commit: 9ccff01 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/keggorthology_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/keggorthology_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/keggorthology_2.42.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/keggorthology/inst/doc/keggorth.R suggestsMe: MLInterfaces dependencyCount: 31 Package: KEGGprofile Version: 1.32.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt,RCurl,ggplot2,reshape2 License: GPL (>= 2) MD5sum: ed02f7b7432b0f25734deb5087e9cb6a NeedsCompilation: no Title: An annotation and visualization package for multi-types and multi-groups expression data in KEGG pathway Description: KEGGprofile is an annotation and visualization tool which integrated the expression profiles and the function annotation in KEGG pathway maps. The multi-types and multi-groups expression data can be visualized in one pathway map. KEGGprofile facilitated more detailed analysis about the specific function changes inner pathway or temporal correlations in different genes and samples. biocViews: Pathways, KEGG Author: Shilin Zhao, Yan Guo, Yu Shyr Maintainer: Shilin Zhao git_url: https://git.bioconductor.org/packages/KEGGprofile git_branch: RELEASE_3_12 git_last_commit: 1f969f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/KEGGprofile_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/KEGGprofile_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/KEGGprofile_1.32.0.tgz vignettes: vignettes/KEGGprofile/inst/doc/KEGGprofile.pdf vignetteTitles: KEGGprofile: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGprofile/inst/doc/KEGGprofile.R suggestsMe: IntramiRExploreR dependencyCount: 90 Package: KEGGREST Version: 1.30.1 Depends: R (>= 3.5.0) Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: ab6124a830a7d326ecfca529eb969156 NeedsCompilation: no Title: Client-side REST access to the Kyoto Encyclopedia of Genes and Genomes (KEGG) Description: A package that provides a client interface to the Kyoto Encyclopedia of Genes and Genomes (KEGG) REST server. Based on KEGGSOAP by J. Zhang, R. Gentleman, and Marc Carlson, and KEGG (python package) by Aurelien Mazurie. biocViews: Annotation, Pathways, ThirdPartyClient, KEGG Author: Dan Tenenbaum [aut], Jeremy Volkening [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KEGGREST git_branch: RELEASE_3_12 git_last_commit: fd9970e git_last_commit_date: 2020-11-23 Date/Publication: 2020-11-23 source.ver: src/contrib/KEGGREST_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/KEGGREST_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.0/KEGGREST_1.30.1.tgz vignettes: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html vignetteTitles: Accessing the KEGG REST API hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R dependsOnMe: ROntoTools, Hiiragi2013 importsMe: ADAM, attract, BiocSet, ChIPpeakAnno, CNEr, EnrichmentBrowser, famat, FELLA, gage, MetaboSignal, MWASTools, PADOG, pathview, SBGNview, SMITE, transomics2cytoscape, YAPSA, g2f, MetaDBparse, omu, pathfindR suggestsMe: globaltest, iSEEu, padma, maGUI, ptm, specmine dependencyCount: 24 Package: KinSwingR Version: 1.8.0 Depends: R (>= 3.5) Imports: data.table, BiocParallel, sqldf, stats, grid, grDevices Suggests: knitr, rmarkdown License: GPL-3 MD5sum: bfb2bcceb998df55f5bd91fa1a2f0e9b NeedsCompilation: no Title: KinSwingR: network-based kinase activity prediction Description: KinSwingR integrates phosphosite data derived from mass-spectrometry data and kinase-substrate predictions to predict kinase activity. Several functions allow the user to build PWM models of kinase-subtrates, statistically infer PWM:substrate matches, and integrate these data to infer kinase activity. biocViews: Proteomics, SequenceMatching, Network Author: Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KinSwingR git_branch: RELEASE_3_12 git_last_commit: 0ec3ba6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/KinSwingR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/KinSwingR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/KinSwingR_1.8.0.tgz vignettes: vignettes/KinSwingR/inst/doc/KinSwingR.html vignetteTitles: KinSwingR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KinSwingR/inst/doc/KinSwingR.R dependencyCount: 34 Package: kissDE Version: 1.10.0 Imports: aod, Biobase, DESeq2, DSS, ggplot2, gplots, graphics, grDevices, matrixStats, stats, utils, foreach, doParallel, parallel Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: bffd155a5feaf8ce9299ec82bc1544a3 NeedsCompilation: no Title: Retrieves Condition-Specific Variants in RNA-Seq Data Description: Retrieves condition-specific variants in RNA-seq data (SNVs, alternative-splicings, indels). It has been developed as a post-treatment of 'KisSplice' but can also be used with user's own data. biocViews: AlternativeSplicing, DifferentialSplicing, ExperimentalDesign, GenomicVariation, RNASeq, Transcriptomics Author: Clara Benoit-Pilven [aut], Camille Marchet [aut], Janice Kielbassa [aut], Lilia Brinza [aut], Audric Cologne [aut], Aurélie Siberchicot [aut, cre], Vincent Lacroix [aut], Frank Picard [ctb], Laurent Jacob [ctb], Vincent Miele [ctb] Maintainer: Aurélie Siberchicot git_url: https://git.bioconductor.org/packages/kissDE git_branch: RELEASE_3_12 git_last_commit: 44e2b8d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/kissDE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/kissDE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/kissDE_1.10.0.tgz vignettes: vignettes/kissDE/inst/doc/kissDE.pdf vignetteTitles: kissDE.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kissDE/inst/doc/kissDE.R dependencyCount: 120 Package: KnowSeq Version: 1.4.5 Depends: R (>= 4.0), cqn (>= 1.28.1) Imports: stringr, methods, ggplot2 (>= 3.3.0), jsonlite, kernlab, rlist, rmarkdown, reshape2, e1071, randomForest, caret, XML, praznik, R.utils, httr, sva (>= 3.30.1), edgeR (>= 3.24.3), limma (>= 3.38.3), grDevices, graphics, stats, utils, Hmisc (>= 4.4.0), gridExtra Suggests: knitr License: GPL (>=2) MD5sum: 749d096b8d3053bd13c59fb15ff06fb5 NeedsCompilation: no Title: KnowSeq R/Bioc package: The Smart Transcriptomic Pipeline Description: KnowSeq proposes a novel methodology that comprises the most relevant steps in the Transcriptomic gene expression analysis. KnowSeq expects to serve as an integrative tool that allows to process and extract relevant biomarkers, as well as to assess them through a Machine Learning approaches. Finally, the last objective of KnowSeq is the biological knowledge extraction from the biomarkers (Gene Ontology enrichment, Pathway listing and Visualization and Evidences related to the addressed disease). Although the package allows analyzing all the data manually, the main strenght of KnowSeq is the possibilty of carrying out an automatic and intelligent HTML report that collect all the involved steps in one document. It is important to highligh that the pipeline is totally modular and flexible, hence it can be started from whichever of the different steps. KnowSeq expects to serve as a novel tool to help to the experts in the field to acquire robust knowledge and conclusions for the data and diseases to study. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataImport, Classification, FeatureExtraction, Sequencing, RNASeq, BatchEffect, Normalization, Preprocessing, QualityControl, Genetics, Transcriptomics, Microarray, Alignment, Pathways, SystemsBiology, GO, ImmunoOncology Author: Daniel Castillo-Secilla [aut, cre], Juan Manuel Galvez [ctb], Francisco Carrillo-Perez [ctb], Marta Verona-Almeida [ctb], Daniel Redondo-Sanchez [ctb], Francisco Manuel Ortuno [ctb], Luis Javier Herrera [ctb], Ignacio Rojas [ctb] Maintainer: Daniel Castillo-Secilla VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/KnowSeq git_branch: RELEASE_3_12 git_last_commit: d577d76 git_last_commit_date: 2021-04-15 Date/Publication: 2021-04-15 source.ver: src/contrib/KnowSeq_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/KnowSeq_1.4.5.zip mac.binary.ver: bin/macosx/contrib/4.0/KnowSeq_1.4.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 152 Package: LACE Version: 1.2.1 Depends: R (>= 4.0.0) Imports: graphics, grDevices, igraph, parallel, RColorBrewer, Rfast, stats, SummarizedExperiment, utils Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: df6348b1dd8a1c78f4cb0847bf9f7b91 NeedsCompilation: no Title: Longitudinal Analysis of Cancer Evolution (LACE) Description: LACE is an algorithmic framework that processes single-cell somatic mutation profiles from cancer samples collected at different time points and in distinct experimental settings, to produce longitudinal models of cancer evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a weighed likelihood function computed on multiple time points. biocViews: BiomedicalInformatics, SingleCell, SomaticMutation Author: Daniele Ramazzotti [aut] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [ctb] Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/LACE VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/LACE git_url: https://git.bioconductor.org/packages/LACE git_branch: RELEASE_3_12 git_last_commit: a4abd41 git_last_commit_date: 2020-11-27 Date/Publication: 2020-11-27 source.ver: src/contrib/LACE_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/LACE_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/LACE_1.2.1.tgz vignettes: vignettes/LACE/inst/doc/vignette.pdf vignetteTitles: LACE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LACE/inst/doc/vignette.R dependencyCount: 35 Package: lapmix Version: 1.56.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: dc48653cbe5786c8cc4061f201dd907e NeedsCompilation: no Title: Laplace Mixture Model in Microarray Experiments Description: Laplace mixture modelling of microarray experiments. A hierarchical Bayesian approach is used, and the hyperparameters are estimated using empirical Bayes. The main purpose is to identify differentially expressed genes. biocViews: Microarray, OneChannel, DifferentialExpression Author: Yann Ruffieux, contributions from Debjani Bhowmick, Anthony C. Davison, and Darlene R. Goldstein Maintainer: Yann Ruffieux URL: http://www.r-project.org, http://www.bioconductor.org, http://stat.epfl.ch git_url: https://git.bioconductor.org/packages/lapmix git_branch: RELEASE_3_12 git_last_commit: ea5f402 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lapmix_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lapmix_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lapmix_1.56.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lapmix/inst/doc/lapmix-example.R dependencyCount: 9 Package: LBE Version: 1.58.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 7730f74cf519dc998ab7c696a2f85d2c NeedsCompilation: no Title: Estimation of the false discovery rate. Description: LBE is an efficient procedure for estimating the proportion of true null hypotheses, the false discovery rate (and so the q-values) in the framework of estimating procedures based on the marginal distribution of the p-values without assumption for the alternative hypothesis. biocViews: MultipleComparison Author: Cyril Dalmasso Maintainer: Cyril Dalmasso git_url: https://git.bioconductor.org/packages/LBE git_branch: RELEASE_3_12 git_last_commit: 85ef77c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LBE_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LBE_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LBE_1.58.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LBE/inst/doc/LBE.R dependsOnMe: PhViD dependencyCount: 5 Package: ldblock Version: 1.20.0 Depends: R (>= 3.5), methods Imports: Matrix, snpStats, VariantAnnotation, GenomeInfoDb, httr, ensembldb, EnsDb.Hsapiens.v75, Rsamtools, GenomicFiles (>= 1.13.6), BiocGenerics (>= 0.25.1) Suggests: RUnit, knitr, BiocStyle, gwascat License: Artistic-2.0 MD5sum: a689de8a7c7b6e83f1e226a301ef9000 NeedsCompilation: no Title: data structures for linkage disequilibrium measures in populations Description: Define data structures for linkage disequilibrium measures in populations. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ldblock git_branch: RELEASE_3_12 git_last_commit: bf913a7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ldblock_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ldblock_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ldblock_1.20.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.html vignetteTitles: ldblock package: linkage disequilibrium data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R suggestsMe: gQTLstats dependencyCount: 99 Package: LEA Version: 3.2.0 Depends: R (>= 3.3.0), methods, stats, utils, graphics Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: f9b69bbc4be8bc25e318b2840bc61837 NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to population genomics, landscape genomics and ecological association tests. LEA can run analyses of population structure and genomewide tests for local adaptation. The package includes statistical methods for estimating ancestry coefficients from large genotypic matrices and for evaluating the number of ancestral populations (snmf, pca). It performs statistical tests using latent factor mixed models for identifying genetic polymorphisms that exhibit association with environmental gradients or phenotypic traits (lfmm and lfmm2). LEA is mainly based on optimized programs that can scale with the dimension of large data sets. biocViews: Software, Statistical Method, Clustering, Regression Author: Eric Frichot , Olivier Francois , Clement Gain Maintainer: Olivier Francois , Eric Frichot URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LEA git_branch: RELEASE_3_12 git_last_commit: eb50e8f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LEA_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LEA_3.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LEA_3.2.0.tgz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LEA/inst/doc/LEA.R dependencyCount: 4 Package: LedPred Version: 1.24.0 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: c37048697ae7d050229fdc71bebceb58 NeedsCompilation: no Title: Learning from DNA to Predict Enhancers Description: This package aims at creating a predictive model of regulatory sequences used to score unknown sequences based on the content of DNA motifs, next-generation sequencing (NGS) peaks and signals and other numerical scores of the sequences using supervised classification. The package contains a workflow based on the support vector machine (SVM) algorithm that maps features to sequences, optimize SVM parameters and feature number and creates a model that can be stored and used to score the regulatory potential of unknown sequences. biocViews: SupportVectorMachine, Software, MotifAnnotation, ChIPSeq, Sequencing, Classification Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues git_url: https://git.bioconductor.org/packages/LedPred git_branch: RELEASE_3_12 git_last_commit: 88baa51 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LedPred_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LedPred_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LedPred_1.24.0.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LedPred/inst/doc/LedPred.R dependencyCount: 74 Package: lefser Version: 1.0.0 Depends: SummarizedExperiment, R (>= 4.0.0) Imports: coin, MASS, ggplot2, stats, methods Suggests: knitr, rmarkdown, curatedMetagenomicData, BiocStyle, testthat, pkgdown, covr, withr License: Artistic-2.0 MD5sum: 41f8283a1eb0e969c3633ca807f65b0c NeedsCompilation: no Title: R implementation of the LEfSE method for microbiome biomarker discovery Description: lefser is an implementation in R of the popular "LDA Effect Size (LEfSe)" method for microbiome biomarker discovery. It uses the Kruskal-Wallis test, Wilcoxon-Rank Sum test, and Linear Discriminant Analysis to find biomarkers of groups and sub-groups. biocViews: Software, Sequencing, DifferentialExpression, Microbiome, StatisticalMethod, Classification Author: Asya Khleborodova [cre, aut], Ludwig Geistlinger [ctb], Marcel Ramos [ctb] (), Levi Waldron [ctb] Maintainer: Asya Khleborodova URL: https://github.com/waldronlab/lefser VignetteBuilder: knitr BugReports: https://github.com/waldronlab/lefser/issues git_url: https://git.bioconductor.org/packages/lefser git_branch: RELEASE_3_12 git_last_commit: 1d9f7b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lefser_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lefser_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lefser_1.0.0.tgz vignettes: vignettes/lefser/inst/doc/lefser.html vignetteTitles: Introduction to the lefser R implementation of the popular LEfSE software for biomarker discovery in microbiome analysis. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lefser/inst/doc/lefser.R dependencyCount: 66 Package: les Version: 1.40.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: a7489e0d50aceb0ff121e2c4fc02e923 NeedsCompilation: no Title: Identifying Differential Effects in Tiling Microarray Data Description: The 'les' package estimates Loci of Enhanced Significance (LES) in tiling microarray data. These are regions of regulation such as found in differential transcription, CHiP-chip, or DNA modification analysis. The package provides a universal framework suitable for identifying differential effects in tiling microarray data sets, and is independent of the underlying statistics at the level of single probes. biocViews: Microarray, DifferentialExpression, ChIPchip, DNAMethylation, Transcription Author: Julian Gehring, Clemens Kreutz, Jens Timmer Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/les git_branch: RELEASE_3_12 git_last_commit: 69fa046 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/les_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/les_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/les_1.40.0.tgz vignettes: vignettes/les/inst/doc/les.pdf vignetteTitles: Introduction to the les package: Identifying Differential Effects in Tiling Microarray Data with the Loci of Enhanced Significance Framework hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/les/inst/doc/les.R importsMe: GSRI dependencyCount: 13 Package: levi Version: 1.8.0 Imports: DT(>= 0.4), RColorBrewer(>= 1.1-2), colorspace(>= 1.3-2), dplyr(>= 0.7.4), ggplot2(>= 2.2.1), httr(>= 1.3.1), igraph(>= 1.2.1), reshape2(>= 1.4.3), shiny(>= 1.0.5), shinydashboard(>= 0.7.0), shinyjs(>= 1.0), xml2(>= 1.2.0), knitr, Rcpp (>= 0.12.18), grid, grDevices, stats, utils, testthat, methods LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: 398a1aa579176905285b4f78348ebd0e NeedsCompilation: yes Title: Landscape Expression Visualization Interface Description: The tool integrates data from biological networks with transcriptomes, displaying a heatmap with surface curves to evidence the altered regions. biocViews: GeneExpression, Sequencing, Network, Software Author: Jose Rafael Pilan , Isabelle Mira da Silva , Agnes Alessandra Sekijima Takeda , Jose Luiz Rybarczyk Filho Maintainer: Jose Luiz Rybarczyk Filho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/levi git_branch: RELEASE_3_12 git_last_commit: 5d3e354 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/levi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/levi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/levi_1.8.0.tgz vignettes: vignettes/levi/inst/doc/levi.html vignetteTitles: "Using levi" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/levi/inst/doc/levi.R dependencyCount: 98 Package: lfa Version: 1.20.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: c9e6dbf20bc5b6ddd5be19a0c8f77bc7 NeedsCompilation: yes Title: Logistic Factor Analysis for Categorical Data Description: LFA is a method for a PCA analogue on Binomial data via estimation of latent structure in the natural parameter. biocViews: SNP, DimensionReduction, PrincipalComponent Author: Wei Hao, Minsun Song, John D. Storey Maintainer: Wei Hao , John D. Storey URL: https://github.com/StoreyLab/lfa VignetteBuilder: knitr BugReports: https://github.com/StoreyLab/lfa/issues git_url: https://git.bioconductor.org/packages/lfa git_branch: RELEASE_3_12 git_last_commit: cea356f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lfa_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lfa_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lfa_1.20.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lfa/inst/doc/lfa.R importsMe: gcatest, jackstraw suggestsMe: genio, popkin dependencyCount: 2 Package: limma Version: 3.46.0 Depends: R (>= 3.6.0) Imports: grDevices, graphics, stats, utils, methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, gplots, illuminaio, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) Archs: i386, x64 MD5sum: 9cea3df170cedf139a4e158f9f7cd8e5 NeedsCompilation: yes Title: Linear Models for Microarray Data Description: Data analysis, linear models and differential expression for microarray data. biocViews: ExonArray, GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, DataImport, Bayesian, Clustering, Regression, TimeCourse, Microarray, MicroRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, Sequencing, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl, BiomedicalInformatics, CellBiology, Cheminformatics, Epigenetics, FunctionalGenomics, Genetics, ImmunoOncology, Metabolomics, Proteomics, SystemsBiology, Transcriptomics Author: Gordon Smyth [cre,aut], Yifang Hu [ctb], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Davis McCarthy [ctb], Di Wu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Aaron Lun [ctb], Natalie Thorne [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Yunshun Chen [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma git_url: https://git.bioconductor.org/packages/limma git_branch: RELEASE_3_12 git_last_commit: ff03542 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/limma_3.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/limma_3.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/limma_3.46.0.tgz vignettes: vignettes/limma/inst/doc/intro.pdf, vignettes/limma/inst/doc/usersguide.pdf vignetteTitles: Limma One Page Introduction, usersguide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AffyExpress, ASpli, BLMA, cghMCR, codelink, convert, Cormotif, deco, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, genefu, HTqPCR, IsoformSwitchAnalyzeR, maigesPack, marray, metagenomeSeq, metaseqR, metaseqR2, mpra, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, splineTimeR, SRGnet, TOAST, tRanslatome, TurboNorm, variancePartition, wateRmelon, CCl4, Fletcher2013a, HD2013SGI, ReactomeGSA.data, EGSEA123, maEndToEnd, methylationArrayAnalysis, RNAseq123, BALLI, BioInsight, CORM, countTransformers, cp4p, DAAGbio, DRomics, PerfMeas importsMe: a4Base, ABSSeq, affycoretools, affylmGUI, AMARETTO, animalcules, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, artMS, ASpediaFI, ATACseqQC, attract, AWFisher, ballgown, BatchQC, beadarray, biotmle, bsseq, BubbleTree, bumphunter, CancerMutationAnalysis, CancerSubtypes, casper, ChAMP, clusterExperiment, CNVRanger, coexnet, combi, compcodeR, consensusDE, consensusOV, CountClust, crlmm, crossmeta, csaw, cTRAP, ctsGE, CytoTree, DAMEfinder, DaMiRseq, debrowser, DEP, derfinderPlot, DEsubs, DiffBind, diffcyt, diffHic, diffloop, distinct, DMRcate, Doscheda, DRIMSeq, eegc, EGAD, EGSEA, eisaR, EnrichmentBrowser, erccdashboard, escape, EventPointer, explorase, ExploreModelMatrix, flowBin, flowSpy, gCrisprTools, GDCRNATools, GeneSelectMMD, GEOquery, GGBase, Glimma, GOsummaries, gQTLstats, hipathia, HTqPCR, icetea, iCheck, iChip, iCOBRA, ideal, InPAS, isomiRs, KnowSeq, limmaGUI, Linnorm, lipidr, lmdme, mAPKL, MBQN, mCSEA, MEAL, methylKit, MethylMix, methyvim, microbiomeExplorer, MIGSA, minfi, miRLAB, missMethyl, MLSeq, monocle, MoonlightR, msImpute, MSstats, MSstatsTMT, MultiDataSet, muscat, NADfinder, nethet, nondetects, NormalyzerDE, OLIN, omicRexposome, oppti, OVESEG, PAA, PADOG, PathoStat, pcaExplorer, PECA, pepStat, phantasus, phenoTest, PhosR, polyester, POMA, projectR, psichomics, pwrEWAS, qPLEXanalyzer, qsea, RegEnrich, regsplice, Ringo, RNAinteract, RNAither, ROSeq, RTCGAToolbox, RTN, RTopper, scClassify, scone, scran, SEPIRA, seqsetvis, SimBindProfiles, SingleCellSignalR, singleCellTK, snapCGH, SPsimSeq, SSPA, STATegRa, sva, systemPipeR, timecourse, TimeSeriesExperiment, ToPASeq, ToxicoGx, TPP, TPP2D, transcriptogramer, TVTB, tweeDEseq, vsn, weitrix, Wrench, yamss, yarn, BeadArrayUseCases, DmelSGI, signatureSearchData, ExpressionNormalizationWorkflow, recountWorkflow, aliases2entrez, BPM, Cascade, cinaR, DCGL, DGEobj.utils, DiPALM, dsb, GWASbyCluster, immcp, INCATome, lilikoi, lipidomeR, maGUI, metaMA, MiDA, miRtest, MKmisc, MKomics, nlcv, Patterns, plfMA, RANKS, rmRNAseq, robustSingleCell, RPPanalyzer, scBio, scRNAtools, SQDA, ssizeRNA, statVisual, wrProteo suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocCaseStudies, BiocSet, BioNet, Category, categoryCompare, celaref, CellBench, CellMixS, ChIPpeakAnno, ClassifyR, CMA, coGPS, cydar, dearseq, DEGreport, derfinder, DEScan2, dyebias, easyreporting, ELBOW, fgsea, gage, glmGamPoi, GSRI, GSVA, Harman, Heatplus, isobar, ivygapSE, les, lumi, MAST, mdgsa, methylumi, MLP, npGSEA, oligo, oppar, piano, PREDA, proDA, puma, QFeatures, randRotation, Rcade, recountmethylation, ribosomeProfilingQC, RTopper, rtracklayer, stageR, subSeq, SummarizedBenchmark, TCGAbiolinks, tidybulk, topconfects, tximeta, tximport, ViSEAGO, zFPKM, BloodCancerMultiOmics2017, GeuvadisTranscriptExpr, mammaPrintData, seventyGeneData, arrays, CAGEWorkflow, fluentGenomics, simpleSingleCell, aroma.affymetrix, canvasXpress, COCONUT, corncob, dnet, hexbin, LPS, NACHO, propr, protti, seqgendiff, Seurat, st, tcgsaseq, wrGraph, wrMisc, wrTopDownFrag dependencyCount: 5 Package: limmaGUI Version: 1.66.0 Imports: methods, grDevices, graphics, limma, R2HTML, tcltk, tkrplot, xtable, utils License: GPL (>=2) MD5sum: c421b835b4d2fa677f0bb0a706739210 NeedsCompilation: no Title: GUI for limma Package With Two Color Microarrays Description: A Graphical User Interface for differential expression analysis of two-color microarray data using the limma package. biocViews: GUI, GeneExpression, DifferentialExpression, DataImport, Bayesian, Regression, TimeCourse, Microarray, mRNAMicroarray, TwoChannel, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl Author: James Wettenhall [aut], Gordon Smyth [aut], Keith Satterley [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limmaGUI/ git_url: https://git.bioconductor.org/packages/limmaGUI git_branch: RELEASE_3_12 git_last_commit: b5b2b41 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/limmaGUI_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/limmaGUI_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/limmaGUI_1.66.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf, vignettes/limmaGUI/inst/doc/about.html, vignettes/limmaGUI/inst/doc/CustMenu.html, vignettes/limmaGUI/inst/doc/import.html, vignettes/limmaGUI/inst/doc/index.html, vignettes/limmaGUI/inst/doc/InputFiles.html, vignettes/limmaGUI/inst/doc/lgDevel.html, vignettes/limmaGUI/inst/doc/windowsFocus.html vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf, about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R dependencyCount: 10 Package: LineagePulse Version: 1.10.0 Imports: BiocParallel, circlize, compiler, ComplexHeatmap, ggplot2, gplots, grDevices, grid, knitr, Matrix, methods, RColorBrewer, SingleCellExperiment, splines, stats, SummarizedExperiment, utils License: Artistic-2.0 MD5sum: c25ae9ae6b62a2065b0f5e31752f7697 NeedsCompilation: no Title: Differential expression analysis and model fitting for single-cell RNA-seq data Description: LineagePulse is a differential expression and expression model fitting package tailored to single-cell RNA-seq data (scRNA-seq). LineagePulse accounts for batch effects, drop-out and variable sequencing depth. One can use LineagePulse to perform longitudinal differential expression analysis across pseudotime as a continuous coordinate or between discrete groups of cells (e.g. pre-defined clusters or experimental conditions). Expression model fits can be directly extracted from LineagePulse. biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays, SingleCell Author: David S Fischer [aut, cre], Fabian Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/LineagePulse/issues git_url: https://git.bioconductor.org/packages/LineagePulse git_branch: RELEASE_3_12 git_last_commit: 9ccb5a6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LineagePulse_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LineagePulse_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LineagePulse_1.10.0.tgz vignettes: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.html vignetteTitles: LineagePulse hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LineagePulse/inst/doc/LineagePulse_Tutorial.R dependencyCount: 88 Package: LinkHD Version: 1.4.0 Depends: R(>= 3.6.0), methods, ggplot2, stats Imports: scales, cluster, graphics, ggpubr, gridExtra, vegan, rio, MultiAssayExperiment, emmeans, reshape2, data.table Suggests: MASS (>= 7.3.0), knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 57371842aacf925e374b1d24d5b3a2a6 NeedsCompilation: no Title: LinkHD: a versatile framework to explore and integrate heterogeneous data Description: Here we present Link-HD, an approach to integrate heterogeneous datasets, as a generalization of STATIS-ACT (“Structuration des Tableaux A Trois Indices de la Statistique–Analyse Conjointe de Tableaux”), a family of methods to join and compare information from multiple subspaces. However, STATIS-ACT has some drawbacks since it only allows continuous data and it is unable to establish relationships between samples and features. In order to tackle these constraints, we incorporate multiple distance options and a linear regression based Biplot model in order to stablish relationships between observations and variable and perform variable selection. biocViews: Classification,MultipleComparison,Regression,Software Author: Laura M. Zingaretti [aut, cre] Maintainer: "Laura M Zingaretti" VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LinkHD git_branch: RELEASE_3_12 git_last_commit: 5ac4205 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LinkHD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LinkHD_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LinkHD_1.4.0.tgz vignettes: vignettes/LinkHD/inst/doc/LinkHD.html vignetteTitles: Annotating Genomic Variants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LinkHD/inst/doc/LinkHD.R dependencyCount: 129 Package: Linnorm Version: 2.14.0 Depends: R(>= 3.4) Imports: Rcpp (>= 0.12.2), RcppArmadillo (>= 0.8.100.1.0), fpc, vegan, mclust, apcluster, ggplot2, ellipse, limma, utils, statmod, MASS, igraph, grDevices, graphics, fastcluster, ggdendro, zoo, stats, amap, Rtsne, gmodels LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, gplots, RColorBrewer, moments, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: ff192695ea885e61b0c4e20a1cd2b458 NeedsCompilation: yes Title: Linear model and normality based normalization and transformation method (Linnorm) Description: Linnorm is an algorithm for normalizing and transforming RNA-seq, single cell RNA-seq, ChIP-seq count data or any large scale count data. It has been independently reviewed by Tian et al. on Nature Methods (https://doi.org/10.1038/s41592-019-0425-8). Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect, PeakDetection, Clustering, Network, SingleCell Author: Shun Hang Yip , Panwen Wang , Jean-Pierre Kocher , Pak Chung Sham , Junwen Wang Maintainer: Ken Shun Hang Yip URL: https://doi.org/10.1093/nar/gkx828 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Linnorm git_branch: RELEASE_3_12 git_last_commit: 5004ac6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Linnorm_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Linnorm_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Linnorm_2.14.0.tgz vignettes: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.pdf vignetteTitles: Linnorm User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Linnorm/inst/doc/Linnorm_User_Manual.R importsMe: mnem dependencyCount: 69 Package: lionessR Version: 1.4.0 Depends: R (>= 3.6.0) Imports: stats, SummarizedExperiment, S4Vectors Suggests: knitr, rmarkdown, igraph, reshape2, limma, License: MIT + file LICENSE MD5sum: 4fc1c2e420acbbc3286a566cd361834a NeedsCompilation: no Title: Modeling networks for individual samples using LIONESS Description: LIONESS, or Linear Interpolation to Obtain Network Estimates for Single Samples, can be used to reconstruct single-sample networks (https://arxiv.org/abs/1505.06440). This code implements the LIONESS equation in the lioness function in R to reconstruct single-sample networks. The default network reconstruction method we use is based on Pearson correlation. However, lionessR can run on any network reconstruction algorithms that returns a complete, weighted adjacency matrix. lionessR works for both unipartite and bipartite networks. biocViews: Network, NetworkInference, GeneExpression Author: Marieke Lydia Kuijjer [aut] (), Ping-Han Hsieh [cre] () Maintainer: Ping-Han Hsieh URL: https://github.com/mararie/lionessR VignetteBuilder: knitr BugReports: https://github.com/mararie/lionessR/issues git_url: https://git.bioconductor.org/packages/lionessR git_branch: RELEASE_3_12 git_last_commit: af0927f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lionessR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lionessR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lionessR_1.4.0.tgz vignettes: vignettes/lionessR/inst/doc/lionessR.html vignetteTitles: lionessR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lionessR/inst/doc/lionessR.R dependencyCount: 26 Package: lipidr Version: 2.4.0 Depends: R (>= 3.6.0), SummarizedExperiment Imports: methods, stats, utils, data.table, S4Vectors, rlang, dplyr, tidyr, forcats, ggplot2, limma, fgsea, ropls, imputeLCMD, magrittr Suggests: knitr, rmarkdown, BiocStyle, ggrepel, plotly, iheatmapr, spelling, testthat License: MIT + file LICENSE MD5sum: 92c416c4f0bb65d60404257545c46a7c NeedsCompilation: no Title: Data Mining and Analysis of Lipidomics Datasets Description: lipidr an easy-to-use R package implementing a complete workflow for downstream analysis of targeted and untargeted lipidomics data. lipidomics results can be imported into lipidr as a numerical matrix or a Skyline export, allowing integration into current analysis frameworks. Data mining of lipidomics datasets is enabled through integration with Metabolomics Workbench API. lipidr allows data inspection, normalization, univariate and multivariate analysis, displaying informative visualizations. lipidr also implements a novel Lipid Set Enrichment Analysis (LSEA), harnessing molecular information such as lipid class, total chain length and unsaturation. biocViews: Lipidomics, MassSpectrometry, Normalization, QualityControl, Visualization Author: Ahmed Mohamed [cre] (), Ahmed Mohamed [aut], Jeffrey Molendijk [aut] Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/lipidr VignetteBuilder: knitr BugReports: https://github.com/ahmohamed/lipidr/issues/ git_url: https://git.bioconductor.org/packages/lipidr git_branch: RELEASE_3_12 git_last_commit: fcfbe01 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lipidr_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lipidr_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lipidr_2.4.0.tgz vignettes: vignettes/lipidr/inst/doc/workflow.html vignetteTitles: lipidr_workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/lipidr/inst/doc/workflow.R dependencyCount: 90 Package: LiquidAssociation Version: 1.44.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 9867495104ea785e8a81639f44beba6a NeedsCompilation: no Title: LiquidAssociation Description: The package contains functions for calculate direct and model-based estimators for liquid association. It also provides functions for testing the existence of liquid association given a gene triplet data. biocViews: Pathways, GeneExpression, CellBiology, Genetics, Network, TimeCourse Author: Yen-Yi Ho Maintainer: Yen-Yi Ho git_url: https://git.bioconductor.org/packages/LiquidAssociation git_branch: RELEASE_3_12 git_last_commit: 18390c9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LiquidAssociation_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LiquidAssociation_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LiquidAssociation_1.44.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.R dependsOnMe: fastLiquidAssociation dependencyCount: 51 Package: lmdme Version: 1.32.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 5ba4ccaecc5b1a54b168d247528ebc68 NeedsCompilation: no Title: Linear Model decomposition for Designed Multivariate Experiments Description: linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS. biocViews: Microarray, OneChannel, TwoChannel, Visualization, DifferentialExpression, ExperimentData, Cancer Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/?page_id=38 git_url: https://git.bioconductor.org/packages/lmdme git_branch: RELEASE_3_12 git_last_commit: 7ea7ff0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lmdme_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lmdme_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lmdme_1.32.0.tgz vignettes: vignettes/lmdme/inst/doc/lmdme-vignette.pdf vignetteTitles: lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lmdme/inst/doc/lmdme-vignette.R dependencyCount: 8 Package: LOBSTAHS Version: 1.16.0 Depends: R (>= 3.4), xcms, CAMERA, methods Imports: utils Suggests: PtH2O2lipids, knitr, rmarkdown License: GPL (>= 3) + file LICENSE MD5sum: be2e9be2d198851c4b95de62681e8cd7 NeedsCompilation: no Title: Lipid and Oxylipin Biomarker Screening through Adduct Hierarchy Sequences Description: LOBSTAHS is a multifunction package for screening, annotation, and putative identification of mass spectral features in large, HPLC-MS lipid datasets. In silico data for a wide range of lipids, oxidized lipids, and oxylipins can be generated from user-supplied structural criteria with a database generation function. LOBSTAHS then applies these databases to assign putative compound identities to features in any high-mass accuracy dataset that has been processed using xcms and CAMERA. Users can then apply a series of orthogonal screening criteria based on adduct ion formation patterns, chromatographic retention time, and other properties, to evaluate and assign confidence scores to this list of preliminary assignments. During the screening routine, LOBSTAHS rejects assignments that do not meet the specified criteria, identifies potential isomers and isobars, and assigns a variety of annotation codes to assist the user in evaluating the accuracy of each assignment. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Lipidomics, DataImport Author: James Collins [aut, cre], Helen Fredricks [aut], Bethanie Edwards [aut], Benjamin Van Mooy [aut] Maintainer: James Collins URL: http://bioconductor.org/packages/LOBSTAHS VignetteBuilder: knitr BugReports: https://github.com/vanmooylipidomics/LOBSTAHS/issues/new git_url: https://git.bioconductor.org/packages/LOBSTAHS git_branch: RELEASE_3_12 git_last_commit: 5117564 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LOBSTAHS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LOBSTAHS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LOBSTAHS_1.16.0.tgz vignettes: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.html vignetteTitles: Discovery,, Identification,, and Screening of Lipids and Oxylipins in HPLC-MS Datasets Using LOBSTAHS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/LOBSTAHS/inst/doc/LOBSTAHS.R dependsOnMe: PtH2O2lipids dependencyCount: 126 Package: loci2path Version: 1.10.0 Depends: R (>= 3.4) Imports: pheatmap, wordcloud, RColorBrewer, data.table, methods, grDevices, stats, graphics, GenomicRanges, BiocParallel, S4Vectors Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 30f0bb5565e036284f97d6d9e8ba3326 NeedsCompilation: no Title: Loci2path: regulatory annotation of genomic intervals based on tissue-specific expression QTLs Description: loci2path performs statistics-rigorous enrichment analysis of eQTLs in genomic regions of interest. Using eQTL collections provided by the Genotype-Tissue Expression (GTEx) project and pathway collections from MSigDB. biocViews: FunctionalGenomics, Genetics, GeneSetEnrichment, Software, GeneExpression, Sequencing, Coverage, BioCarta Author: Tianlei Xu Maintainer: Tianlei Xu URL: https://github.com/StanleyXu/loci2path VignetteBuilder: knitr BugReports: https://github.com/StanleyXu/loci2path/issues git_url: https://git.bioconductor.org/packages/loci2path git_branch: RELEASE_3_12 git_last_commit: b21f7ef git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/loci2path_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/loci2path_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/loci2path_1.10.0.tgz vignettes: vignettes/loci2path/inst/doc/loci2path-vignette.html vignetteTitles: loci2path hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/loci2path/inst/doc/loci2path-vignette.R dependencyCount: 42 Package: logicFS Version: 2.10.0 Depends: LogicReg, mcbiopi, survival Imports: graphics, methods, stats Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 561ebc992e2dbfe96f672416697dfc01 NeedsCompilation: no Title: Identification of SNP Interactions Description: Identification of interactions between binary variables using Logic Regression. Can, e.g., be used to find interesting SNP interactions. Contains also a bagging version of logic regression for classification. biocViews: SNP, Classification, Genetics Author: Holger Schwender, Tobias Tietz Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/logicFS git_branch: RELEASE_3_12 git_last_commit: 39c9de4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/logicFS_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/logicFS_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/logicFS_2.10.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logicFS/inst/doc/logicFS.R suggestsMe: trio dependencyCount: 12 Package: logitT Version: 1.48.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) Archs: i386, x64 MD5sum: 9d0b8d78fb55602b23af9278990186b8 NeedsCompilation: yes Title: logit-t Package Description: The logitT library implements the Logit-t algorithm introduced in --A high performance test of differential gene expression for oligonucleotide arrays-- by William J Lemon, Sandya Liyanarachchi and Ming You for use with Affymetrix data stored in an AffyBatch object in R. biocViews: Microarray, DifferentialExpression Author: Tobias Guennel Maintainer: Tobias Guennel URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/logitT git_branch: RELEASE_3_12 git_last_commit: 2781658 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/logitT_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/logitT_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/logitT_1.48.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/logitT/inst/doc/logitT.R dependencyCount: 13 Package: LOLA Version: 1.20.0 Depends: R (>= 2.10) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, reshape2, utils, stats, methods Suggests: parallel, testthat, knitr, BiocStyle, rmarkdown Enhances: simpleCache, qvalue, ggplot2 License: GPL-3 MD5sum: a26e21ee56583787082013f558d4a37f NeedsCompilation: no Title: Locus overlap analysis for enrichment of genomic ranges Description: Provides functions for testing overlap of sets of genomic regions with public and custom region set (genomic ranges) databases. This makes it possible to do automated enrichment analysis for genomic region sets, thus facilitating interpretation of functional genomics and epigenomics data. biocViews: GeneSetEnrichment, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing Author: Nathan Sheffield [aut, cre], Christoph Bock [ctb] Maintainer: Nathan Sheffield URL: http://code.databio.org/LOLA VignetteBuilder: knitr BugReports: http://github.com/nsheff/LOLA git_url: https://git.bioconductor.org/packages/LOLA git_branch: RELEASE_3_12 git_last_commit: 640802d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LOLA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LOLA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LOLA_1.20.0.tgz vignettes: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html vignetteTitles: 3. Choosing a Universe, 1. Getting Started with LOLA, 2. Using LOLA Core hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LOLA/inst/doc/choosingUniverse.R, vignettes/LOLA/inst/doc/gettingStarted.R, vignettes/LOLA/inst/doc/usingLOLACore.R suggestsMe: COCOA, DeepBlueR, MIRA dependencyCount: 25 Package: LoomExperiment Version: 1.8.0 Depends: R (>= 3.5.0), S4Vectors, SingleCellExperiment, SummarizedExperiment, methods, rhdf5, rtracklayer Imports: DelayedArray, GenomicRanges, HDF5Array, Matrix, stats, stringr, utils Suggests: testthat, BiocStyle, knitr, reticulate License: Artistic-2.0 MD5sum: 657fa3f09d9585eabc2688b3ffd2a835 NeedsCompilation: no Title: LoomExperiment container Description: The LoomExperiment class provide a means to easily convert Bioconductor's "Experiment" classes to loom files and vice versa. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Martin Morgan, Daniel Van Twisk Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LoomExperiment git_branch: RELEASE_3_12 git_last_commit: affa25d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LoomExperiment_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LoomExperiment_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LoomExperiment_1.8.0.tgz vignettes: vignettes/LoomExperiment/inst/doc/LoomExperiment.html vignetteTitles: An introduction to the LoomExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LoomExperiment/inst/doc/LoomExperiment.R importsMe: HCAExplorer suggestsMe: HCAMatrixBrowser dependencyCount: 49 Package: LowMACA Version: 1.20.0 Depends: R (>= 2.10) Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings, httr, grid, gridBase Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 0b4c773ef02baa744a6d139332daa9a4 NeedsCompilation: no Title: LowMACA - Low frequency Mutation Analysis via Consensus Alignment Description: The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest. biocViews: SomaticMutation, SequenceMatching, WholeGenome, Sequencing, Alignment, DataImport, MultipleSequenceAlignment Author: Stefano de Pretis , Giorgio Melloni Maintainer: Stefano de Pretis , Giorgio Melloni SystemRequirements: clustalo, gs, perl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LowMACA git_branch: RELEASE_3_12 git_last_commit: 37c0c2e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-28 source.ver: src/contrib/LowMACA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LowMACA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LowMACA_1.20.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.html vignetteTitles: Bioconductor style for HTML documents hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R dependencyCount: 84 Package: LPE Version: 1.64.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: cfa7974f939d692207d5d9df68d3ca72 NeedsCompilation: no Title: Methods for analyzing microarray data using Local Pooled Error (LPE) method Description: This LPE library is used to do significance analysis of microarray data with small number of replicates. It uses resampling based FDR adjustment, and gives less conservative results than traditional 'BH' or 'BY' procedures. Data accepted is raw data in txt format from MAS4, MAS5 or dChip. Data can also be supplied after normalization. LPE library is primarily used for analyzing data between two conditions. To use it for paired data, see LPEP library. For using LPE in multiple conditions, use HEM library. biocViews: Microarray, DifferentialExpression Author: Nitin Jain , Michael O'Connell , Jae K. Lee . Includes R source code contributed by HyungJun Cho Maintainer: Nitin Jain URL: http://www.r-project.org, http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/, http://sourceforge.net/projects/r-lpe/ git_url: https://git.bioconductor.org/packages/LPE git_branch: RELEASE_3_12 git_last_commit: 7e12bf8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LPE_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LPE_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LPE_1.64.0.tgz vignettes: vignettes/LPE/inst/doc/LPE.pdf vignetteTitles: LPE test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPE/inst/doc/LPE.R dependsOnMe: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray dependencyCount: 1 Package: LPEadj Version: 1.50.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 5691e2df2ed7f79f0b54b4060aafaaa1 NeedsCompilation: no Title: A correction of the local pooled error (LPE) method to replace the asymptotic variance adjustment with an unbiased adjustment based on sample size. Description: Two options are added to the LPE algorithm. The original LPE method sets all variances below the max variance in the ordered distribution of variances to the maximum variance. in LPEadj this option is turned off by default. The second option is to use a variance adjustment based on sample size rather than pi/2. By default the LPEadj uses the sample size based variance adjustment. biocViews: Microarray, Proteomics Author: Carl Murie , Robert Nadon Maintainer: Carl Murie git_url: https://git.bioconductor.org/packages/LPEadj git_branch: RELEASE_3_12 git_last_commit: d2bd8b5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LPEadj_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LPEadj_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LPEadj_1.50.0.tgz vignettes: vignettes/LPEadj/inst/doc/LPEadj.pdf vignetteTitles: LPEadj test for microarray data with small number of replicates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LPEadj/inst/doc/LPEadj.R dependencyCount: 2 Package: lpNet Version: 2.22.0 Depends: lpSolve License: Artistic License 2.0 MD5sum: d8054f079dbce8a009269bf148ff9c5f NeedsCompilation: no Title: Linear Programming Model for Network Inference Description: lpNet aims at infering biological networks, in particular signaling and gene networks. For that it takes perturbation data, either steady-state or time-series, as input and generates an LP model which allows the inference of signaling networks. For parameter identification either leave-one-out cross-validation or stratified n-fold cross-validation can be used. biocViews: NetworkInference Author: Bettina Knapp, Marta R. A. Matos, Johanna Mazur, Lars Kaderali Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/lpNet git_branch: RELEASE_3_12 git_last_commit: da8eaca git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lpNet_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lpNet_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lpNet_2.22.0.tgz vignettes: vignettes/lpNet/inst/doc/vignette_lpNet.pdf vignetteTitles: lpNet,, network inference with a linear optimization program. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpNet/inst/doc/vignette_lpNet.R dependencyCount: 1 Package: lpsymphony Version: 1.18.0 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr, testthat Enhances: slam License: EPL Archs: i386, x64 MD5sum: e200b92797ceaa0426e21df20dbcf397 NeedsCompilation: yes Title: Symphony integer linear programming solver in R Description: This package was derived from Rsymphony_0.1-17 from CRAN. These packages provide an R interface to SYMPHONY, an open-source linear programming solver written in C++. The main difference between this package and Rsymphony is that it includes the solver source code (SYMPHONY version 5.6), while Rsymphony expects to find header and library files on the users' system. Thus the intention of lpsymphony is to provide an easy to install interface to SYMPHONY. For Windows, precompiled DLLs are included in this package. biocViews: Infrastructure, ThirdPartyClient Author: Vladislav Kim [aut, cre], Ted Ralphs [ctb], Menal Guzelsoy [ctb], Ashutosh Mahajan [ctb], Reinhard Harter [ctb], Kurt Hornik [ctb], Cyrille Szymanski [ctb], Stefan Theussl [ctb] Maintainer: Vladislav Kim URL: http://R-Forge.R-project.org/projects/rsymphony, https://projects.coin-or.org/SYMPHONY, http://www.coin-or.org/download/source/SYMPHONY/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/lpsymphony git_branch: RELEASE_3_12 git_last_commit: e36fafe git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lpsymphony_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lpsymphony_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lpsymphony_1.18.0.tgz vignettes: vignettes/lpsymphony/inst/doc/lpsymphony.pdf vignetteTitles: Introduction to lpsymphony hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lpsymphony/inst/doc/lpsymphony.R importsMe: IHW, Maaslin2 suggestsMe: oppr, prioritizr, TestDesign dependencyCount: 0 Package: LRBaseDbi Version: 2.0.0 Depends: R (>= 3.5.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 44464713db3eb2e65188a07c8b6594da NeedsCompilation: no Title: DBI to construct LRBase-related package Description: Interface to construct LRBase package (LRBase.XXX.eg.db). biocViews: Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki VignetteBuilder: utils git_url: https://git.bioconductor.org/packages/LRBaseDbi git_branch: RELEASE_3_12 git_last_commit: e14b784 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LRBaseDbi_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LRBaseDbi_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LRBaseDbi_2.0.0.tgz vignettes: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.pdf vignetteTitles: LRBaseDbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LRBaseDbi/inst/doc/LRBaseDbi.R dependsOnMe: LRBase.Ath.eg.db, LRBase.Bta.eg.db, LRBase.Cel.eg.db, LRBase.Dme.eg.db, LRBase.Dre.eg.db, LRBase.Gga.eg.db, LRBase.Hsa.eg.db, LRBase.Mmu.eg.db, LRBase.Pab.eg.db, LRBase.Rno.eg.db, LRBase.Ssc.eg.db, LRBase.Xtr.eg.db suggestsMe: scTensor dependencyCount: 26 Package: lumi Version: 2.42.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, lattice, mgcv (>= 1.4-0), nleqslv, KernSmooth, preprocessCore, RSQLite, DBI, AnnotationDbi, MASS, graphics, stats, stats4, methods Suggests: beadarray, limma, vsn, lumiBarnes, lumiHumanAll.db, lumiHumanIDMapping, genefilter, RColorBrewer License: LGPL (>= 2) MD5sum: 3506254b8013f74eba2fb79d9ed95bcc NeedsCompilation: no Title: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays Description: The lumi package provides an integrated solution for the Illumina microarray data analysis. It includes functions of Illumina BeadStudio (GenomeStudio) data input, quality control, BeadArray-specific variance stabilization, normalization and gene annotation at the probe level. It also includes the functions of processing Illumina methylation microarrays, especially Illumina Infinium methylation microarrays. biocViews: Microarray, OneChannel, Preprocessing, DNAMethylation, QualityControl, TwoChannel Author: Pan Du, Richard Bourgon, Gang Feng, Simon Lin Maintainer: Lei Huang git_url: https://git.bioconductor.org/packages/lumi git_branch: RELEASE_3_12 git_last_commit: a643b3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/lumi_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/lumi_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/lumi_2.42.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: arrayMvout, iCheck, wateRmelon, lumiHumanIDMapping, lumiMouseIDMapping, lumiRatIDMapping, ffpeExampleData, lumiBarnes, MAQCsubset, MAQCsubsetILM, mvoutData, eQTL importsMe: ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre, beadarrayFilter, maGUI dependencyCount: 150 Package: LymphoSeq Version: 1.18.0 Depends: R (>= 3.3), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats, ggtree, msa, Biostrings, phangorn, stringdist, UpSetR Suggests: knitr, pheatmap, wordcloud, rmarkdown License: Artistic-2.0 MD5sum: 5ffabb92a3b2b23d43c4cb297e194a78 NeedsCompilation: no Title: Analyze high-throughput sequencing of T and B cell receptors Description: This R package analyzes high-throughput sequencing of T and B cell receptor complementarity determining region 3 (CDR3) sequences generated by Adaptive Biotechnologies' ImmunoSEQ assay. Its input comes from tab-separated value (.tsv) files exported from the ImmunoSEQ analyzer. biocViews: Software, Technology, Sequencing, TargetedResequencing, Alignment, MultipleSequenceAlignment Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/LymphoSeq git_branch: RELEASE_3_12 git_last_commit: 294dfbb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/LymphoSeq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/LymphoSeq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/LymphoSeq_1.18.0.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.html vignetteTitles: Analysis of high-throughput sequencing of T and B cell receptors with LymphoSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LymphoSeq/inst/doc/LymphoSeq.R dependencyCount: 85 Package: M3C Version: 1.12.0 Depends: R (>= 3.5.0) Imports: ggplot2, Matrix, doSNOW, cluster, parallel, foreach, doParallel, matrixcalc, Rtsne, corpcor, umap Suggests: knitr, rmarkdown License: AGPL-3 MD5sum: 3570780fb5e36cba2d42ce1e7fa1429a NeedsCompilation: no Title: Monte Carlo Reference-based Consensus Clustering Description: M3C is a consensus clustering algorithm that uses a Monte Carlo simulation to eliminate overestimation of K and can reject the null hypothesis K=1. biocViews: Clustering, GeneExpression, Transcription, RNASeq, Sequencing, ImmunoOncology Author: Christopher John, David Watson Maintainer: Christopher John VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/M3C git_branch: RELEASE_3_12 git_last_commit: 3d1bcd0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/M3C_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/M3C_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/M3C_1.12.0.tgz vignettes: vignettes/M3C/inst/doc/M3Cvignette.pdf vignetteTitles: M3C hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3C/inst/doc/M3Cvignette.R importsMe: lilikoi suggestsMe: parameters dependencyCount: 60 Package: M3Drop Version: 1.16.0 Depends: R (>= 3.4), numDeriv Imports: RColorBrewer, gplots, bbmle, statmod, grDevices, graphics, stats, matrixStats, Matrix, irlba, reldist, Hmisc, methods Suggests: ROCR, knitr, M3DExampleData, scater, SingleCellExperiment, monocle, Seurat, Biobase License: GPL (>=2) MD5sum: 10cea4d66acd57bb749d4056de66cdb1 NeedsCompilation: no Title: Michaelis-Menten Modelling of Dropouts in single-cell RNASeq Description: This package fits a Michaelis-Menten model to the pattern of dropouts in single-cell RNASeq data. This model is used as a null to identify significantly variable (i.e. differentially expressed) genes for use in downstream analysis, such as clustering cells. biocViews: RNASeq, Sequencing, Transcriptomics, GeneExpression, Software, DifferentialExpression, DimensionReduction, FeatureExtraction Author: Tallulah Andrews Maintainer: Tallulah Andrews URL: https://github.com/tallulandrews/M3Drop VignetteBuilder: knitr BugReports: https://github.com/tallulandrews/M3Drop/issues git_url: https://git.bioconductor.org/packages/M3Drop git_branch: RELEASE_3_12 git_last_commit: 17f0450 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/M3Drop_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/M3Drop_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/M3Drop_1.16.0.tgz vignettes: vignettes/M3Drop/inst/doc/M3Drop_Vignette.pdf vignetteTitles: Introduction to M3Drop hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3Drop/inst/doc/M3Drop_Vignette.R importsMe: scMerge dependencyCount: 83 Package: maanova Version: 1.60.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) Archs: i386, x64 MD5sum: db446c08a77e46e3f5872aaef66bc8d7 NeedsCompilation: yes Title: Tools for analyzing Micro Array experiments Description: Analysis of N-dye Micro Array experiment using mixed model effect. Containing analysis of variance, permutation and bootstrap, cluster and consensus tree. biocViews: Microarray, DifferentialExpression, Clustering Author: Hao Wu, modified by Hyuna Yang and Keith Sheppard with ideas from Gary Churchill, Katie Kerr and Xiangqin Cui. Maintainer: Keith Sheppard URL: http://research.jax.org/faculty/churchill git_url: https://git.bioconductor.org/packages/maanova git_branch: RELEASE_3_12 git_last_commit: 7eedb69 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/maanova_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/maanova_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/maanova_1.60.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: Maaslin2 Version: 1.4.0 Depends: R (>= 3.6) Imports: robustbase, biglm, pcaPP, edgeR, metagenomeSeq, lpsymphony, pbapply, car, dplyr, vegan, chemometrics, ggplot2, pheatmap, logging, data.table, lmerTest, hash, optparse, MuMIn, grDevices, stats, utils, glmmTMB, MASS, cplm, pscl Suggests: knitr, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 575a122a4da4f0b2be6cd917d5ef7a70 NeedsCompilation: no Title: Maaslin2 Description: MaAsLin2 is comprehensive R package for efficiently determining multivariable association between clinical metadata and microbial meta'omic features. MaAsLin2 relies on general linear models to accommodate most modern epidemiological study designs, including cross-sectional and longitudinal, and offers a variety of data exploration, normalization, and transformation methods. MaAsLin2 is the next generation of MaAsLin. biocViews: Metagenomics, Software, Microbiome, Normalization Author: Himel Mallick [aut], Ali Rahnavard [aut], Lauren McIver [aut, cre] Maintainer: Lauren McIver URL: http://huttenhower.sph.harvard.edu/maaslin2 VignetteBuilder: knitr BugReports: https://github.com/biobakery/maaslin2/issues git_url: https://git.bioconductor.org/packages/Maaslin2 git_branch: RELEASE_3_12 git_last_commit: a352d2d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Maaslin2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Maaslin2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Maaslin2_1.4.0.tgz vignettes: vignettes/Maaslin2/inst/doc/maaslin2.html vignetteTitles: MaAsLin2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Maaslin2/inst/doc/maaslin2.R importsMe: MMUPHin dependencyCount: 150 Package: macat Version: 1.64.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: bf72653f9e08d84f101f02a09f3620b6 NeedsCompilation: no Title: MicroArray Chromosome Analysis Tool Description: This library contains functions to investigate links between differential gene expression and the chromosomal localization of the genes. MACAT is motivated by the common observation of phenomena involving large chromosomal regions in tumor cells. MACAT is the implementation of a statistical approach for identifying significantly differentially expressed chromosome regions. The functions have been tested on a publicly available data set about acute lymphoblastic leukemia (Yeoh et al.Cancer Cell 2002), which is provided in the library 'stjudem'. biocViews: Microarray, DifferentialExpression, Visualization Author: Benjamin Georgi, Matthias Heinig, Stefan Roepcke, Sebastian Schmeier, Joern Toedling Maintainer: Joern Toedling git_url: https://git.bioconductor.org/packages/macat git_branch: RELEASE_3_12 git_last_commit: 0612249 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/macat_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/macat_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/macat_1.64.0.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/macat/inst/doc/macat.R dependencyCount: 38 Package: maCorrPlot Version: 1.60.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 6feb41847ac19ff71c6d8b039e46f44c NeedsCompilation: no Title: Visualize artificial correlation in microarray data Description: Graphically displays correlation in microarray data that is due to insufficient normalization biocViews: Microarray, Preprocessing, Visualization Author: Alexander Ploner Maintainer: Alexander Ploner URL: http://www.pubmedcentral.gov/articlerender.fcgi?tool=pubmed&pubmedid=15799785 git_url: https://git.bioconductor.org/packages/maCorrPlot git_branch: RELEASE_3_12 git_last_commit: 95a3cd1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/maCorrPlot_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/maCorrPlot_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/maCorrPlot_1.60.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maCorrPlot/inst/doc/maCorrPlot.R dependencyCount: 6 Package: MACPET Version: 1.10.0 Depends: R (>= 3.6.1), InteractionSet (>= 1.13.0), bigmemory (>= 4.5.33), BH (>= 1.66.0.1), Rcpp (>= 1.0.1) Imports: intervals (>= 0.15.1), plyr (>= 1.8.4), Rsamtools (>= 2.1.3), stats (>= 3.6.1), utils (>= 3.6.1), methods (>= 3.6.1), GenomicRanges (>= 1.37.14), S4Vectors (>= 0.23.17), IRanges (>= 2.19.10), GenomeInfoDb (>= 1.21.1), gtools (>= 3.8.1), GenomicAlignments (>= 1.21.4), knitr (>= 1.23), rtracklayer (>= 1.45.1), BiocParallel (>= 1.19.0), Rbowtie (>= 1.25.0), GEOquery (>= 2.53.0), Biostrings (>= 2.53.2), ShortRead (>= 1.43.0), futile.logger (>= 1.4.3) LinkingTo: Rcpp, bigmemory, BH Suggests: ggplot2 (>= 3.2.0), igraph (>= 1.2.4.1), rmarkdown (>= 1.14), reshape2 (>= 1.4.3), BiocStyle (>= 2.13.2) License: GPL-3 Archs: i386, x64 MD5sum: 1bc7c8b71eaf1281f43d28693820402c NeedsCompilation: yes Title: Model based analysis for paired-end data Description: The MACPET package can be used for complete interaction analysis for ChIA-PET data. MACPET reads ChIA-PET data in BAM or SAM format and separates the data into Self-ligated, Intra- and Inter-chromosomal PETs. Furthermore, MACPET breaks the genome into regions and applies 2D mixture models for identifying candidate peaks/binding sites using skewed generalized students-t distributions (SGT). It then uses a local poisson model for finding significant binding sites. Finally it runs an additive interaction-analysis model for calling for significant interactions between those peaks. MACPET is mainly written in C++, and it also supports the BiocParallel package. biocViews: Software, DNA3DStructure, PeakDetection, StatisticalMethod, Clustering, Classification, HiC Author: Ioannis Vardaxis Maintainer: Ioannis Vardaxis SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACPET git_branch: RELEASE_3_12 git_last_commit: bf2919e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MACPET_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MACPET_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MACPET_1.10.0.tgz vignettes: vignettes/MACPET/inst/doc/MACPET.pdf vignetteTitles: MACPET hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACPET/inst/doc/MACPET.R dependencyCount: 94 Package: MACSQuantifyR Version: 1.4.0 Imports: readxl, graphics, tools, utils, grDevices, ggplot2, ggrepel, methods, stats, latticeExtra, lattice, rmarkdown, png, grid, gridExtra, prettydoc, rvest, xml2 Suggests: knitr, testthat, R.utils, spelling License: Artistic-2.0 MD5sum: 7ed6c76af7519028238968057c10d549 NeedsCompilation: no Title: Fast treatment of MACSQuantify FACS data Description: Automatically process the metadata of MACSQuantify FACS sorter. It runs multiple modules: i) imports of raw file and graphical selection of duplicates in well plate, ii) computes statistics on data and iii) can compute combination index. biocViews: DataImport, Preprocessing, Normalization, FlowCytometry, DataRepresentation, GUI Author: Raphaël Bonnet [aut, cre], Marielle Nebout [dtc],Giulia Biondani [dtc], Jean-François Peyron[aut,ths], Inserm [fnd] Maintainer: Raphaël Bonnet VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MACSQuantifyR git_branch: RELEASE_3_12 git_last_commit: 79a3ba1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MACSQuantifyR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MACSQuantifyR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MACSQuantifyR_1.4.0.tgz vignettes: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.html, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.html vignetteTitles: MACSQuantifyR_step_by_step_analysis, MACSQuantifyR_simple_pipeline, MACSQuantifyR_quick_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_combo.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR_pipeline.R, vignettes/MACSQuantifyR/inst/doc/MACSQuantifyR.R dependencyCount: 74 Package: made4 Version: 1.64.0 Depends: RColorBrewer,gplots,scatterplot3d, Biobase, SummarizedExperiment Imports: ade4 Suggests: affy, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: f5ef0b7acc977d043c2e32531e56c587 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include for supervised dimension reduction (between group analysis) and joint dimension reduction of 2 datasets (coinertia analysis). It contains functions that require R package ade4. biocViews: Clustering, Classification, DimensionReduction, PrincipalComponent,Transcriptomics, MultipleComparison, GeneExpression, Sequencing, Microarray Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/made4 git_branch: RELEASE_3_12 git_last_commit: 89d0a81 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/made4_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/made4_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/made4_1.64.0.tgz vignettes: vignettes/made4/inst/doc/introduction.html vignetteTitles: Authoring R Markdown vignettes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R importsMe: deco, omicade4 dependencyCount: 47 Package: MADSEQ Version: 1.16.0 Depends: R(>= 3.4), rjags(>= 4-6), Imports: VGAM, coda, BSgenome, BSgenome.Hsapiens.UCSC.hg19, S4Vectors, methods, preprocessCore, GenomicAlignments, Rsamtools, Biostrings, GenomicRanges, IRanges, VariantAnnotation, SummarizedExperiment, GenomeInfoDb, rtracklayer, graphics, stats, grDevices, utils, zlibbioc, vcfR Suggests: knitr License: GPL(>=2) MD5sum: a475eecdb0ba39ebd41f284f4b82a79e NeedsCompilation: no Title: Mosaic Aneuploidy Detection and Quantification using Massive Parallel Sequencing Data Description: The MADSEQ package provides a group of hierarchical Bayeisan models for the detection of mosaic aneuploidy, the inference of the type of aneuploidy and also for the quantification of the fraction of aneuploid cells in the sample. biocViews: GenomicVariation, SomaticMutation, VariantDetection, Bayesian, CopyNumberVariation, Sequencing, Coverage Author: Yu Kong, Adam Auton, John Murray Greally Maintainer: Yu Kong URL: https://github.com/ykong2/MADSEQ VignetteBuilder: knitr BugReports: https://github.com/ykong2/MADSEQ/issues git_url: https://git.bioconductor.org/packages/MADSEQ git_branch: RELEASE_3_12 git_last_commit: 970e1fe git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MADSEQ_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MADSEQ_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MADSEQ_1.16.0.tgz vignettes: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.html vignetteTitles: R Package MADSEQ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MADSEQ/inst/doc/MADSEQ-vignette.R dependencyCount: 107 Package: maftools Version: 2.6.05 Depends: R (>= 3.3) Imports: data.table, RColorBrewer, methods, grDevices, survival Suggests: knitr, rmarkdown, BSgenome, Biostrings, NMF, mclust, berryFunctions License: MIT + file LICENSE MD5sum: 048df541fa263ff0ec32c6dfcedc4b87 NeedsCompilation: no Title: Summarize, Analyze and Visualize MAF Files Description: Analyze and visualize Mutation Annotation Format (MAF) files from large scale sequencing studies. This package provides various functions to perform most commonly used analyses in cancer genomics and to create feature rich customizable visualzations with minimal effort. biocViews: DataRepresentation, DNASeq, Visualization, DriverMutation, VariantAnnotation, FeatureExtraction, Classification, SomaticMutation, Sequencing, FunctionalGenomics, Survival Author: Anand Mayakonda [aut, cre] () Maintainer: Anand Mayakonda URL: https://github.com/PoisonAlien/maftools VignetteBuilder: knitr BugReports: https://github.com/PoisonAlien/maftools/issues git_url: https://git.bioconductor.org/packages/maftools git_branch: RELEASE_3_12 git_last_commit: 9263447 git_last_commit_date: 2021-02-04 Date/Publication: 2021-02-04 source.ver: src/contrib/maftools_2.6.05.tar.gz win.binary.ver: bin/windows/contrib/4.0/maftools_2.6.05.zip mac.binary.ver: bin/macosx/contrib/4.0/maftools_2.6.05.tgz vignettes: vignettes/maftools/inst/doc/maftools.html, vignettes/maftools/inst/doc/oncoplots.html vignetteTitles: 01: Summarize,, Analyze,, and Visualize MAF Files, 02: Customizing oncoplots hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maftools/inst/doc/maftools.R, vignettes/maftools/inst/doc/oncoplots.R importsMe: musicatk, TCGAbiolinksGUI, TCGAWorkflow, MAFDash, Rediscover, sigminer, SMDIC suggestsMe: survtype, TCGAbiolinks dependencyCount: 12 Package: MAGeCKFlute Version: 1.10.0 Depends: R (>= 3.5) Imports: Biobase, clusterProfiler (>= 3.16.1), enrichplot, gridExtra, ggplot2, ggrepel, grDevices, grid, reshape2, stats, utils Suggests: biomaRt, BiocStyle, DOSE, dendextend, graphics, knitr, msigdbr, pheatmap, png, pathview, scales, sva, testthat, License: GPL (>=3) MD5sum: 55534c138a0bc921fac332eb314cc19d NeedsCompilation: no Title: Integrative Analysis Pipeline for Pooled CRISPR Functional Genetic Screens Description: CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK and MAGeCK-VISPR, to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows. biocViews: FunctionalGenomics, CRISPR, PooledScreens, QualityControl, Normalization, GeneSetEnrichment, Pathways, Visualization, GeneTarget, KEGG Author: Binbin Wang, Wubing Zhang, Feizhen Wu, Wei Li & X. Shirley Liu Maintainer: Wubing Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MAGeCKFlute git_branch: RELEASE_3_12 git_last_commit: 52ae9ae git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MAGeCKFlute_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MAGeCKFlute_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MAGeCKFlute_1.10.0.tgz vignettes: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.html, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.html vignetteTitles: MAGeCKFlute_enrichment.Rmd, MAGeCKFlute.Rmd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute_enrichment.R, vignettes/MAGeCKFlute/inst/doc/MAGeCKFlute.R dependencyCount: 101 Package: maigesPack Version: 1.54.0 Depends: R (>= 2.10), convert, graph, limma, marray, methods Suggests: amap, annotate, class, e1071, MASS, multtest, OLIN, R2HTML, rgl, som License: GPL (>= 2) Archs: i386, x64 MD5sum: 427328f0a6ed2d2f4574340362679e25 NeedsCompilation: yes Title: Functions to handle cDNA microarray data, including several methods of data analysis Description: This package uses functions of various other packages together with other functions in a coordinated way to handle and analyse cDNA microarray data biocViews: Microarray, TwoChannel, Preprocessing, ThirdPartyClient, DifferentialExpression, Clustering, Classification, GraphAndNetwork Author: Gustavo H. Esteves , with contributions from Roberto Hirata Jr , E. Jordao Neves , Elier B. Cristo , Ana C. Simoes and Lucas Fahham Maintainer: Gustavo H. Esteves URL: http://www.maiges.org/en/software/ git_url: https://git.bioconductor.org/packages/maigesPack git_branch: RELEASE_3_12 git_last_commit: 5910e59 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/maigesPack_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/maigesPack_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/maigesPack_1.54.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maigesPack/inst/doc/maigesPack_tutorial.R dependencyCount: 13 Package: MAIT Version: 1.24.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Suggests: faahKO Enhances: rgl License: GPL-2 MD5sum: 5385b047aba499328533d2757af98fde NeedsCompilation: no Title: Statistical Analysis of Metabolomic Data Description: The MAIT package contains functions to perform end-to-end statistical analysis of LC/MS Metabolomic Data. Special emphasis is put on peak annotation and in modular function design of the functions. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Pol Sola-Santos git_url: https://git.bioconductor.org/packages/MAIT git_branch: RELEASE_3_12 git_last_commit: 6469a14 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MAIT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MAIT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MAIT_1.24.0.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: \maketitleMAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAIT/inst/doc/MAIT_Vignette.R suggestsMe: specmine dependencyCount: 194 Package: makecdfenv Version: 1.66.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: 94892bf6eb0bcf26feb772193f2ba3a5 NeedsCompilation: yes Title: CDF Environment Maker Description: This package has two functions. One reads a Affymetrix chip description file (CDF) and creates a hash table environment containing the location/probe set membership mapping. The other creates a package that automatically loads that environment. biocViews: OneChannel, DataImport, Preprocessing Author: Rafael A. Irizarry , Laurent Gautier , Wolfgang Huber , Ben Bolstad Maintainer: James W. MacDonald git_url: https://git.bioconductor.org/packages/makecdfenv git_branch: RELEASE_3_12 git_last_commit: 02aa975 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/makecdfenv_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/makecdfenv_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/makecdfenv_1.66.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/makecdfenv/inst/doc/makecdfenv.R dependsOnMe: altcdfenvs dependencyCount: 13 Package: MANOR Version: 1.62.0 Depends: R (>= 2.10) Imports: GLAD, graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, bookdown License: GPL-2 Archs: i386, x64 MD5sum: 02d4980f5ffe18a8cb30054b3064ddca NeedsCompilation: yes Title: CGH Micro-Array NORmalization Description: Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments. biocViews: Microarray, TwoChannel, DataImport, QualityControl, Preprocessing, CopyNumberVariation, Normalization Author: Pierre Neuvial , Philippe Hupé Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html VignetteBuilder: knitr BugReports: https://github.com/pneuvial/MANOR/issues git_url: https://git.bioconductor.org/packages/MANOR git_branch: RELEASE_3_12 git_last_commit: 5c15ed5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MANOR_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MANOR_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MANOR_1.62.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.html vignetteTitles: Overview of the MANOR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R dependencyCount: 9 Package: MantelCorr Version: 1.60.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: a2e8545052f1cc91c747dba39491ba4c NeedsCompilation: no Title: Compute Mantel Cluster Correlations Description: Computes Mantel cluster correlations from a (p x n) numeric data matrix (e.g. microarray gene-expression data). biocViews: Clustering Author: Brian Steinmeyer and William Shannon Maintainer: Brian Steinmeyer git_url: https://git.bioconductor.org/packages/MantelCorr git_branch: RELEASE_3_12 git_last_commit: 2e3818b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MantelCorr_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MantelCorr_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MantelCorr_1.60.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MantelCorr/inst/doc/MantelCorrVignette.R dependencyCount: 1 Package: mAPKL Version: 1.20.0 Depends: R (>= 3.6.0), Biobase Imports: multtest, clusterSim, apcluster, limma, e1071, AnnotationDbi, methods, parmigene,igraph,reactome.db Suggests: BiocStyle, knitr, mAPKLData, hgu133plus2.db, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 5da738f868220c873657e1e55edd8b8e NeedsCompilation: no Title: A Hybrid Feature Selection method for gene expression data Description: We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes. biocViews: FeatureExtraction, DifferentialExpression, Microarray, GeneExpression Author: Argiris Sakellariou Maintainer: Argiris Sakellariou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mAPKL git_branch: RELEASE_3_12 git_last_commit: 68b77e0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mAPKL_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mAPKL_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mAPKL_1.20.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mAPKL/inst/doc/mAPKL.R dependencyCount: 93 Package: maPredictDSC Version: 1.28.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: b4eb3092419e1b61c89b19dbe35fb1f1 NeedsCompilation: no Title: Phenotype prediction using microarray data: approach of the best overall team in the IMPROVER Diagnostic Signature Challenge Description: This package implements the classification pipeline of the best overall team (Team221) in the IMPROVER Diagnostic Signature Challenge. Additional functionality is added to compare 27 combinations of data preprocessing, feature selection and classifier types. biocViews: Microarray, Classification Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/maPredictDSC git_url: https://git.bioconductor.org/packages/maPredictDSC git_branch: RELEASE_3_12 git_last_commit: 10d1ff1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/maPredictDSC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/maPredictDSC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/maPredictDSC_1.28.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maPredictDSC/inst/doc/maPredictDSC.R dependencyCount: 114 Package: mapscape Version: 1.14.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), base64enc (>= 0.1-3), stringr (>= 1.0.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: 525937d64657c9fddc9e380577e5f710 NeedsCompilation: no Title: mapscape Description: MapScape integrates clonal prevalence, clonal hierarchy, anatomic and mutational information to provide interactive visualization of spatial clonal evolution. There are four inputs to MapScape: (i) the clonal phylogeny, (ii) clonal prevalences, (iii) an image reference, which may be a medical image or drawing and (iv) pixel locations for each sample on the referenced image. Optionally, MapScape can accept a data table of mutations for each clone and their variant allele frequencies in each sample. The output of MapScape consists of a cropped anatomical image surrounded by two representations of each tumour sample. The first, a cellular aggregate, visually displays the prevalence of each clone. The second shows a skeleton of the clonal phylogeny while highlighting only those clones present in the sample. Together, these representations enable the analyst to visualize the distribution of clones throughout anatomic space. biocViews: Visualization Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mapscape git_branch: RELEASE_3_12 git_last_commit: 62d05bb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mapscape_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mapscape_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mapscape_1.14.0.tgz vignettes: vignettes/mapscape/inst/doc/mapscape_vignette.html vignetteTitles: MapScape vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mapscape/inst/doc/mapscape_vignette.R dependencyCount: 16 Package: marr Version: 1.00.03 Depends: R (>= 4.0) Imports: Rcpp, SummarizedExperiment, utils, methods, ggplot2, dplyr, magrittr, rlang, S4Vectors LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, covr License: GPL (>= 3) Archs: i386, x64 MD5sum: 8df537cd1114b176ca713a8780c27d41 NeedsCompilation: yes Title: Maximum rank reproducibility Description: marr (Maximum Rank Reproducibility) is a nonparametric approach that detects reproducible signals using a maximal rank statistic for high-dimensional biological data. In this R package, we implement functions that measures the reproducibility of features per sample pair and sample pairs per feature in high-dimensional biological replicate experiments. The user-friendly plot functions in this package also plot histograms of the reproducibility of features per sample pair and sample pairs per feature. Furthermore, our approach also allows the users to select optimal filtering threshold values for the identification of reproducible features and sample pairs based on output visualization checks (histograms). This package also provides the subset of data filtered by reproducible features and/or sample pairs. biocViews: QualityControl, Metabolomics, MassSpectrometry, RNASeq, ChIPSeq Author: Tusharkanti Ghosh [aut, cre], Max McGrath [aut], Daisy Philtron [aut], Katerina Kechris [aut], Debashis Ghosh [aut, cph] Maintainer: Tusharkanti Ghosh VignetteBuilder: knitr BugReports: https://github.com/Ghoshlab/marr/issues git_url: https://git.bioconductor.org/packages/marr git_branch: RELEASE_3_12 git_last_commit: a59d771 git_last_commit_date: 2021-04-27 Date/Publication: 2021-04-28 source.ver: src/contrib/marr_1.00.03.tar.gz win.binary.ver: bin/windows/contrib/4.0/marr_1.00.03.zip mac.binary.ver: bin/macosx/contrib/4.0/marr_1.00.03.tgz vignettes: vignettes/marr/inst/doc/MarrVignette.html vignetteTitles: The marr user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/marr/inst/doc/MarrVignette.R dependencyCount: 61 Package: marray Version: 1.68.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: c094d8d6d3818bd5bd3e5f9a3c10ad64 NeedsCompilation: no Title: Exploratory analysis for two-color spotted microarray data Description: Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking. biocViews: Microarray, TwoChannel, Preprocessing Author: Yee Hwa (Jean) Yang with contributions from Agnes Paquet and Sandrine Dudoit. Maintainer: Yee Hwa (Jean) Yang URL: http://www.maths.usyd.edu.au/u/jeany/ git_url: https://git.bioconductor.org/packages/marray git_branch: RELEASE_3_12 git_last_commit: 67b3080 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/marray_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/marray_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/marray_1.68.0.tgz vignettes: vignettes/marray/inst/doc/marray.pdf, vignettes/marray/inst/doc/marrayClasses.pdf, vignettes/marray/inst/doc/marrayClassesShort.pdf, vignettes/marray/inst/doc/marrayInput.pdf, vignettes/marray/inst/doc/marrayNorm.pdf, vignettes/marray/inst/doc/marrayPlots.pdf vignetteTitles: marray Overview, marrayClasses Overview, marrayClasses Tutorial (short), marrayInput Introduction, marray Normalization, marrayPlots Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/marray/inst/doc/marray.R, vignettes/marray/inst/doc/marrayClasses.R, vignettes/marray/inst/doc/marrayClassesShort.R, vignettes/marray/inst/doc/marrayInput.R, vignettes/marray/inst/doc/marrayNorm.R, vignettes/marray/inst/doc/marrayPlots.R dependsOnMe: CGHbase, convert, dyebias, maigesPack, MineICA, nnNorm, OLIN, RBM, stepNorm, TurboNorm, beta7, dyebiasexamples importsMe: arrayQuality, ChAMP, methylPipe, MSstats, nnNorm, OLIN, OLINgui, piano, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz, hexbin, maGUI dependencyCount: 6 Package: martini Version: 1.10.0 Depends: R (>= 3.6) Imports: igraph (>= 1.0.1), Matrix, methods (>= 3.3.2), Rcpp (>= 0.12.8), snpStats (>= 1.20.0), S4Vectors (>= 0.12.2), stats, utils LinkingTo: Rgin, Rcpp, RcppEigen (>= 0.3.3.5.0) Suggests: biomaRt (>= 2.34.1), httr (>= 1.2.1), IRanges (>= 2.8.2), knitr, testthat, readr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: aa4d21f8cb656b468f3bdf49b2d80250 NeedsCompilation: yes Title: GWAS Incorporating Networks Description: martini deals with the low power inherent to GWAS studies by using prior knowledge represented as a network. SNPs are the vertices of the network, and the edges represent biological relationships between them (genomic adjacency, belonging to the same gene, physical interaction between protein products). The network is scanned using SConES, which looks for groups of SNPs maximally associated with the phenotype, that form a close subnetwork. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre] (), Chloe-Agathe Azencott [aut] () Maintainer: Hector Climente-Gonzalez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/martini git_branch: RELEASE_3_12 git_last_commit: 7bf768f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/martini_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/martini_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/martini_1.10.0.tgz vignettes: vignettes/martini/inst/doc/scones_usage.html, vignettes/martini/inst/doc/simulate_phenotype.html vignetteTitles: Running SConES, Simulating SConES-based phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/martini/inst/doc/scones_usage.R, vignettes/martini/inst/doc/simulate_phenotype.R dependencyCount: 22 Package: maser Version: 1.8.0 Depends: R (>= 3.5.0), ggplot2, GenomicRanges Imports: dplyr, rtracklayer, reshape2, Gviz, DT, GenomeInfoDb, stats, utils, IRanges, methods, BiocGenerics, parallel, data.table Suggests: testthat, knitr, rmarkdown, BiocStyle, AnnotationHub License: MIT + file LICENSE MD5sum: 45fa586363933c9cb6d9b4fb6831d742 NeedsCompilation: no Title: Mapping Alternative Splicing Events to pRoteins Description: This package provides functionalities for downstream analysis, annotation and visualizaton of alternative splicing events generated by rMATS. biocViews: AlternativeSplicing, Transcriptomics, Visualization Author: Diogo F.T. Veiga [aut, cre] Maintainer: Diogo F.T. Veiga URL: https://github.com/DiogoVeiga/maser VignetteBuilder: knitr BugReports: https://github.com/DiogoVeiga/maser/issues git_url: https://git.bioconductor.org/packages/maser git_branch: RELEASE_3_12 git_last_commit: e86964d git_last_commit_date: 2020-10-27 Date/Publication: 2021-03-15 source.ver: src/contrib/maser_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/maser_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/maser_1.8.0.tgz vignettes: vignettes/maser/inst/doc/Introduction.html, vignettes/maser/inst/doc/Protein_mapping.html vignetteTitles: Introduction, Mapping protein features to splicing events hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/maser/inst/doc/Introduction.R, vignettes/maser/inst/doc/Protein_mapping.R dependencyCount: 144 Package: maSigPro Version: 1.62.0 Depends: R (>= 2.3.1) Imports: Biobase, graphics, grDevices, venn, mclust, stats, MASS License: GPL (>= 2) MD5sum: 19aa4c67c0d8d0182a9bdb658c769b11 NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Gene Expression Data Description: maSigPro is a regression based approach to find genes for which there are significant gene expression profile differences between experimental groups in time course microarray and RNA-Seq experiments. biocViews: Microarray, RNA-Seq, Differential Expression, TimeCourse Author: Ana Conesa , Maria Jose Nueda Maintainer: Maria Jose Nueda URL: http://bioinfo.cipf.es/ git_url: https://git.bioconductor.org/packages/maSigPro git_branch: RELEASE_3_12 git_last_commit: 3b9da93 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/maSigPro_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/maSigPro_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/maSigPro_1.62.0.tgz vignettes: vignettes/maSigPro/inst/doc/maSigPro.pdf, vignettes/maSigPro/inst/doc/maSigProUsersGuide.pdf vignetteTitles: maSigPro Vignette, maSigProUsersGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: maskBAD Version: 1.34.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe, hgu95av2cdf License: GPL (>= 2) MD5sum: 1c7fb69f5967ba2e1ed0b1715bd1825f NeedsCompilation: no Title: Masking probes with binding affinity differences Description: Package includes functions to analyze and mask microarray expression data. biocViews: Microarray Author: Michael Dannemann Maintainer: Michael Dannemann git_url: https://git.bioconductor.org/packages/maskBAD git_branch: RELEASE_3_12 git_last_commit: 31f2548 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/maskBAD_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/maskBAD_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/maskBAD_1.34.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/maskBAD/inst/doc/maskBAD.R dependencyCount: 22 Package: MassArray Version: 1.42.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, stats, utils License: GPL (>=2) MD5sum: 2d07fdfca17ed7889473396b9320c5f2 NeedsCompilation: no Title: Analytical Tools for MassArray Data Description: This package is designed for the import, quality control, analysis, and visualization of methylation data generated using Sequenom's MassArray platform. The tools herein contain a highly detailed amplicon prediction for optimal assay design. Also included are quality control measures of data, such as primer dimer and bisulfite conversion efficiency estimation. Methylation data are calculated using the same algorithms contained in the EpiTyper software package. Additionally, automatic SNP-detection can be used to flag potentially confounded data from specific CG sites. Visualization includes barplots of methylation data as well as UCSC Genome Browser-compatible BED tracks. Multiple assays can be positionally combined for integrated analysis. biocViews: ImmunoOncology, DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson git_url: https://git.bioconductor.org/packages/MassArray git_branch: RELEASE_3_12 git_last_commit: b6db508 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MassArray_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MassArray_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MassArray_1.42.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassArray/inst/doc/MassArray.R dependencyCount: 5 Package: massiR Version: 1.26.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: 5c1638ddc70fac17c6a57d7f7913b212 NeedsCompilation: no Title: massiR: MicroArray Sample Sex Identifier Description: Predicts the sex of samples in gene expression microarray datasets biocViews: Software, Microarray, GeneExpression, Clustering, Classification, QualityControl Author: Sam Buckberry Maintainer: Sam Buckberry git_url: https://git.bioconductor.org/packages/massiR git_branch: RELEASE_3_12 git_last_commit: 9aa04f2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/massiR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/massiR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/massiR_1.26.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/massiR/inst/doc/massiR_Vignette.R dependencyCount: 15 Package: MassSpecWavelet Version: 1.56.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) Archs: i386, x64 MD5sum: eb74f0197875143823ca5c61861bafe4 NeedsCompilation: yes Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Pan Du, Warren Kibbe, Simon Lin Maintainer: Pan Du git_url: https://git.bioconductor.org/packages/MassSpecWavelet git_branch: RELEASE_3_12 git_last_commit: 0e274f0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MassSpecWavelet_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MassSpecWavelet_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MassSpecWavelet_1.56.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.R importsMe: cosmiq, xcms, Rnmr1D, speaq dependencyCount: 5 Package: MAST Version: 1.16.0 Depends: SingleCellExperiment (>= 1.2.0), R(>= 3.5) Imports: Biobase, BiocGenerics, S4Vectors, data.table, ggplot2, plyr, stringr, abind, methods, parallel, reshape2, stats, stats4, graphics, utils, SummarizedExperiment(>= 1.5.3), progress Suggests: knitr, rmarkdown, testthat, lme4(>= 1.0), blme, roxygen2(> 6.0.0), numDeriv, car, gdata, lattice, GGally, GSEABase, NMF, TxDb.Hsapiens.UCSC.hg19.knownGene, rsvd, limma, RColorBrewer, BiocStyle, scater, DelayedArray, Matrix, HDF5Array, zinbwave, dplyr License: GPL(>= 2) MD5sum: e6389f9dfb33e0f87578340d2c371cf7 NeedsCompilation: no Title: Model-based Analysis of Single Cell Transcriptomics Description: Methods and models for handling zero-inflated single cell assay data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, RNASeq, Transcriptomics, SingleCell Author: Andrew McDavid [aut, cre], Greg Finak [aut], Masanao Yajima [aut] Maintainer: Andrew McDavid URL: https://github.com/RGLab/MAST/ VignetteBuilder: knitr BugReports: https://github.com/RGLab/MAST/issues git_url: https://git.bioconductor.org/packages/MAST git_branch: RELEASE_3_12 git_last_commit: 7c88cab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MAST_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MAST_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MAST_1.16.0.tgz vignettes: vignettes/MAST/inst/doc/MAITAnalysis.html, vignettes/MAST/inst/doc/MAST-interoperability.html, vignettes/MAST/inst/doc/MAST-Intro.html vignetteTitles: Using MAST for filtering,, differential expression and gene set enrichment in MAIT cells, Interoptability between MAST and SingleCellExperiment-derived packages, An Introduction to MAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MAST/inst/doc/MAITAnalysis.R, vignettes/MAST/inst/doc/MAST-interoperability.R, vignettes/MAST/inst/doc/MAST-Intro.R importsMe: celaref, celda, singleCellTK suggestsMe: clusterExperiment, Seurat dependencyCount: 67 Package: matchBox Version: 1.32.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 16fe6045e7a0800b3f006ba2225d47d2 NeedsCompilation: no Title: Utilities to compute, compare, and plot the agreement between ordered vectors of features (ie. distinct genomic experiments). The package includes Correspondence-At-the-TOP (CAT) analysis. Description: The matchBox package enables comparing ranked vectors of features, merging multiple datasets, removing redundant features, using CAT-plots and Venn diagrams, and computing statistical significance. biocViews: Software, Annotation, Microarray, MultipleComparison, Visualization Author: Luigi Marchionni , Anuj Gupta Maintainer: Luigi Marchionni , Anuj Gupta git_url: https://git.bioconductor.org/packages/matchBox git_branch: RELEASE_3_12 git_last_commit: 1bef80c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/matchBox_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/matchBox_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/matchBox_1.32.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matchBox/inst/doc/matchBox.R dependencyCount: 0 Package: MatrixGenerics Version: 1.2.1 Depends: matrixStats (> 0.57.0) Imports: methods Suggests: sparseMatrixStats, DelayedMatrixStats, SummarizedExperiment, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 3e246622875066b62f93dc21a71baefa NeedsCompilation: no Title: S4 Generic Summary Statistic Functions that Operate on Matrix-Like Objects Description: S4 generic functions modeled after the 'matrixStats' API for alternative matrix implementations. Packages with alternative matrix implementation can depend on this package and implement the generic functions that are defined here for a useful set of row and column summary statistics. Other package developers can import this package and handle a different matrix implementations without worrying about incompatibilities. biocViews: Infrastructure, Software Author: Constantin Ahlmann-Eltze [aut] (), Peter Hickey [aut, cre] (), Hervé Pagès [aut] Maintainer: Peter Hickey URL: https://bioconductor.org/packages/MatrixGenerics BugReports: https://github.com/Bioconductor/MatrixGenerics/issues git_url: https://git.bioconductor.org/packages/MatrixGenerics git_branch: RELEASE_3_12 git_last_commit: abcc9ca git_last_commit_date: 2021-01-29 Date/Publication: 2021-01-30 source.ver: src/contrib/MatrixGenerics_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MatrixGenerics_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MatrixGenerics_1.2.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DelayedArray, DelayedMatrixStats, GenomicFiles, sparseMatrixStats, SummarizedExperiment, VariantAnnotation importsMe: MinimumDistance, RaggedExperiment, VanillaICE dependencyCount: 2 Package: MatrixRider Version: 1.22.0 Depends: R (>= 3.1.2) Imports: methods, TFBSTools, IRanges, XVector, Biostrings LinkingTo: IRanges, XVector, Biostrings, S4Vectors Suggests: RUnit, BiocGenerics, BiocStyle, JASPAR2014 License: GPL-3 Archs: i386, x64 MD5sum: 86e1f64d695b46805c0e1c61f5899610 NeedsCompilation: yes Title: Obtain total affinity and occupancies for binding site matrices on a given sequence Description: Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar. biocViews: GeneRegulation, Genetics, MotifAnnotation Author: Elena Grassi Maintainer: Elena Grassi git_url: https://git.bioconductor.org/packages/MatrixRider git_branch: RELEASE_3_12 git_last_commit: d5858bb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MatrixRider_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MatrixRider_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MatrixRider_1.22.0.tgz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MatrixRider/inst/doc/MatrixRider.R dependencyCount: 113 Package: matter Version: 1.16.0 Depends: R (>= 3.5), BiocParallel, Matrix, methods, stats, biglm Imports: BiocGenerics, ProtGenerics, digest, irlba, utils Suggests: BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: a53171cead59bf4501f1e801f9a5d0e6 NeedsCompilation: yes Title: A framework for rapid prototyping with file-based data structures Description: Memory-efficient reading, writing, and manipulation of structured binary data as file-based vectors, matrices, arrays, lists, and data frames. biocViews: Infrastructure, DataRepresentation Author: Kylie A. Bemis Maintainer: Kylie A. Bemis URL: https://github.com/kuwisdelu/matter git_url: https://git.bioconductor.org/packages/matter git_branch: RELEASE_3_12 git_last_commit: 764f943 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/matter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/matter_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/matter_1.16.0.tgz vignettes: vignettes/matter/inst/doc/matter-supp1.pdf, vignettes/matter/inst/doc/matter-supp2.pdf, vignettes/matter/inst/doc/matter.pdf vignetteTitles: matter: Supplementary 1 - Simulations and comparative benchmarks, matter: Supplementary 2 - 3D mass spectrometry imaging case study, matter: Rapid prototyping with data on disk hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/matter/inst/doc/matter-supp1.R, vignettes/matter/inst/doc/matter-supp2.R, vignettes/matter/inst/doc/matter.R importsMe: Cardinal dependencyCount: 22 Package: MBAmethyl Version: 1.24.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 6bc2d79722fd36f207c7770000d1a9cd NeedsCompilation: no Title: Model-based analysis of DNA methylation data Description: This package provides a function for reconstructing DNA methylation values from raw measurements. It iteratively implements the group fused lars to smooth related-by-location methylation values and the constrained least squares to remove probe affinity effect across multiple sequences. biocViews: DNAMethylation, MethylationArray Author: Tao Wang, Mengjie Chen Maintainer: Tao Wang git_url: https://git.bioconductor.org/packages/MBAmethyl git_branch: RELEASE_3_12 git_last_commit: 91d8a99 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MBAmethyl_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MBAmethyl_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MBAmethyl_1.24.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBAmethyl/inst/doc/MBAmethyl.R dependencyCount: 0 Package: MBASED Version: 1.24.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: ed2515257bcd0579b82f070eda827f61 NeedsCompilation: no Title: Package containing functions for ASE analysis using Meta-analysis Based Allele-Specific Expression Detection Description: The package implements MBASED algorithm for detecting allele-specific gene expression from RNA count data, where allele counts at individual loci (SNVs) are integrated into a gene-specific measure of ASE, and utilizes simulations to appropriately assess the statistical significance of observed ASE. biocViews: Sequencing, GeneExpression, Transcription Author: Oleg Mayba, Houston Gilbert Maintainer: Oleg Mayba git_url: https://git.bioconductor.org/packages/MBASED git_branch: RELEASE_3_12 git_last_commit: 3d1c13e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MBASED_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MBASED_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MBASED_1.24.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBASED/inst/doc/MBASED.R dependencyCount: 34 Package: MBCB Version: 1.44.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 9f92f467979da0c7ddbec18054c43bea NeedsCompilation: no Title: MBCB (Model-based Background Correction for Beadarray) Description: This package provides a model-based background correction method, which incorporates the negative control beads to pre-process Illumina BeadArray data. biocViews: Microarray, Preprocessing Author: Yang Xie Maintainer: Jeff Allen URL: http://www.utsouthwestern.edu git_url: https://git.bioconductor.org/packages/MBCB git_branch: RELEASE_3_12 git_last_commit: f60d92d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MBCB_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MBCB_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MBCB_1.44.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBCB/inst/doc/MBCB.R dependencyCount: 5 Package: mbkmeans Version: 1.6.1 Depends: R (>= 3.6) Imports: methods, DelayedArray, Rcpp, S4Vectors, SingleCellExperiment, SummarizedExperiment, bluster, ClusterR, benchmarkme, Matrix, BiocParallel LinkingTo: Rcpp, RcppArmadillo (>= 0.7.2), Rhdf5lib, beachmat, ClusterR Suggests: beachmat, HDF5Array, Rhdf5lib, BiocStyle, TENxPBMCData, scater, DelayedMatrixStats, knitr, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 0a30eb3a31bdde4a7fd67e13e6bde8ae NeedsCompilation: yes Title: Mini-batch K-means Clustering for Single-Cell RNA-seq Description: Implements the mini-batch k-means algorithm for large datasets, including support for on-disk data representation. biocViews: Clustering, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Yuwei Ni [aut, cph], Davide Risso [aut, cre, cph], Stephanie Hicks [aut, cph], Elizabeth Purdom [aut, cph] Maintainer: Davide Risso SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/drisso/mbkmeans/issues git_url: https://git.bioconductor.org/packages/mbkmeans git_branch: RELEASE_3_12 git_last_commit: ee9603a git_last_commit_date: 2020-11-13 Date/Publication: 2020-11-13 source.ver: src/contrib/mbkmeans_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/mbkmeans_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/mbkmeans_1.6.1.tgz vignettes: vignettes/mbkmeans/inst/doc/Vignette.html vignetteTitles: mbkmeans vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mbkmeans/inst/doc/Vignette.R importsMe: clusterExperiment dependencyCount: 93 Package: mBPCR Version: 1.44.0 Depends: oligoClasses, GWASTools Imports: Biobase, graphics, methods, utils, grDevices Suggests: xtable License: GPL (>= 2) MD5sum: 7b2348db3f34b0af40132750e62ea79e NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: It contains functions for estimating the DNA copy number profile using mBPCR with the aim of detecting regions with copy number changes. biocViews: aCGH, SNP, Microarray, CopyNumberVariation Author: P.M.V. Rancoita , with contributions from M. Hutter Maintainer: P.M.V. Rancoita URL: http://www.idsia.ch/~paola/mBPCR git_url: https://git.bioconductor.org/packages/mBPCR git_branch: RELEASE_3_12 git_last_commit: d64dedf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mBPCR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mBPCR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mBPCR_1.44.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mBPCR/inst/doc/mBPCR.R dependencyCount: 90 Package: MBQN Version: 2.2.0 Depends: R (>= 4.0) Imports: stats, graphics, utils, limma (>= 3.30.13), SummarizedExperiment (>= 1.10.0), preprocessCore (>= 1.36.0), BiocFileCache, rappdirs, rpx, xml2, RCurl, ggplot2, PairedData Suggests: knitr License: GPL-3 + file LICENSE MD5sum: 3664b8681beda79e0a6381745b8c8820 NeedsCompilation: no Title: Mean/Median-balanced quantile normalization Description: Modified quantile normalization for omics or other matrix-like data distorted in location and scale. biocViews: Normalization, Preprocessing, Proteomics, Software Author: Ariane Schad [aut, cre] (), Clemens Kreutz [aut, ctb] (), Eva Brombacher [aut, ctb] () Maintainer: Ariane Schad URL: https://github.com/arianeschad/mbqn VignetteBuilder: knitr BugReports: https://github.com/arianeschad/MBQN/issues git_url: https://git.bioconductor.org/packages/MBQN git_branch: RELEASE_3_12 git_last_commit: f2f16aa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MBQN_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MBQN_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MBQN_2.2.0.tgz vignettes: vignettes/MBQN/inst/doc/MBQNpackage.html vignetteTitles: MBQN Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MBQN/inst/doc/MBQNpackage.R dependencyCount: 92 Package: MBttest Version: 1.18.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: f68da82bb89f4f564adf7a8b35ff545b NeedsCompilation: no Title: Multiple Beta t-Tests Description: MBttest method was developed from beta t-test method of Baggerly et al(2003). Compared to baySeq (Hard castle and Kelly 2010), DESeq (Anders and Huber 2010) and exact test (Robinson and Smyth 2007, 2008) and the GLM of McCarthy et al(2012), MBttest is of high work efficiency,that is, it has high power, high conservativeness of FDR estimation and high stability. MBttest is suit- able to transcriptomic data, tag data, SAGE data (count data) from small samples or a few replicate libraries. It can be used to identify genes, mRNA isoforms or tags differentially expressed between two conditions. biocViews: Sequencing, DifferentialExpression, MultipleComparison, SAGE, GeneExpression, Transcription, AlternativeSplicing,Coverage, DifferentialSplicing Author: Yuan-De Tan Maintainer: Yuan-De Tan git_url: https://git.bioconductor.org/packages/MBttest git_branch: RELEASE_3_12 git_last_commit: d9901a4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MBttest_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MBttest_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MBttest_1.18.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest-manual.pdf, vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: MBttest-manual.pdf, Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R dependencyCount: 11 Package: mcaGUI Version: 1.37.0 Depends: lattice, MASS, proto, foreign, gWidgets(>= 0.0-36), gWidgetsRGtk2(>= 0.0-53), OTUbase, vegan, bpca Enhances: iplots, reshape, ggplot2, cairoDevice, OTUbase License: GPL (>= 2) MD5sum: 895ddc33b6387389990ad088ab40d54d NeedsCompilation: no Title: Microbial Community Analysis GUI Description: Microbial community analysis GUI for R using gWidgets. biocViews: GUI, Visualization, Clustering, Sequencing Author: Wade K. Copeland, Vandhana Krishnan, Daniel Beck, Matt Settles, James Foster, Kyu-Chul Cho, Mitch Day, Roxana Hickey, Ursel M.E. Schutte, Xia Zhou, Chris Williams, Larry J. Forney, Zaid Abdo, Poor Man's GUI (PMG) base code by John Verzani with contributions by Yvonnick Noel Maintainer: Wade K. Copeland URL: http://www.ibest.uidaho.edu/ibest/index.php git_url: https://git.bioconductor.org/packages/mcaGUI git_branch: master git_last_commit: 09aaaff git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/mcaGUI_1.37.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mcaGUI_1.37.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mcaGUI_1.37.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 104 Package: MCbiclust Version: 1.14.0 Depends: R (>= 3.4) Imports: BiocParallel, graphics, utils, stats, AnnotationDbi, GO.db, org.Hs.eg.db, GGally, ggplot2, scales, cluster, WGCNA Suggests: gplots, knitr, rmarkdown, BiocStyle, gProfileR, MASS, dplyr, pander, devtools, testthat, GSVA License: GPL-2 MD5sum: 51c59beae66b639181fc13885e194560 NeedsCompilation: no Title: Massive correlating biclusters for gene expression data and associated methods Description: Custom made algorithm and associated methods for finding, visualising and analysing biclusters in large gene expression data sets. Algorithm is based on with a supplied gene set of size n, finding the maximum strength correlation matrix containing m samples from the data set. biocViews: ImmunoOncology, Clustering, Microarray, StatisticalMethod, Software, RNASeq, GeneExpression Author: Robert Bentham Maintainer: Robert Bentham VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MCbiclust git_branch: RELEASE_3_12 git_last_commit: 6b1a37f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MCbiclust_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MCbiclust_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MCbiclust_1.14.0.tgz vignettes: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.html vignetteTitles: Introduction to MCbiclust hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCbiclust/inst/doc/MCbiclust_vignette.R dependencyCount: 118 Package: mCSEA Version: 1.10.0 Depends: R (>= 3.5), mCSEAdata, Homo.sapiens Imports: biomaRt, fgsea, GenomicFeatures, GenomicRanges, ggplot2, graphics, grDevices, Gviz, IRanges, limma, methods, parallel, S4Vectors, stats, SummarizedExperiment, utils Suggests: Biobase, BiocGenerics, BiocStyle, FlowSorted.Blood.450k, knitr, leukemiasEset, minfi, minfiData, rmarkdown, RUnit License: GPL-2 MD5sum: 7b66f71c81b3f567d7ee0ec2a9f92f4a NeedsCompilation: no Title: Methylated CpGs Set Enrichment Analysis Description: Identification of diferentially methylated regions (DMRs) in predefined regions (promoters, CpG islands...) from the human genome using Illumina's 450K or EPIC microarray data. Provides methods to rank CpG probes based on linear models and includes plotting functions. biocViews: ImmunoOncology, DifferentialMethylation, DNAMethylation, Epigenetics, Genetics, GenomeAnnotation, MethylationArray, Microarray, MultipleComparison, TwoChannel Author: Jordi Martorell-Marugán and Pedro Carmona-Sáez Maintainer: Jordi Martorell-Marugán VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mCSEA git_branch: RELEASE_3_12 git_last_commit: a480d3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mCSEA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mCSEA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mCSEA_1.10.0.tgz vignettes: vignettes/mCSEA/inst/doc/mCSEA.pdf vignetteTitles: Predefined DMRs identification with mCSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mCSEA/inst/doc/mCSEA.R dependencyCount: 150 Package: mdgsa Version: 1.22.0 Depends: R (>= 2.14) Imports: AnnotationDbi, DBI, GO.db, KEGG.db, cluster, Matrix Suggests: BiocStyle, knitr, rmarkdown, limma, ALL, hgu95av2.db, RUnit, BiocGenerics License: GPL MD5sum: e4b7a6d180bb1d34db922788daeb24a0 NeedsCompilation: no Title: Multi Dimensional Gene Set Analysis. Description: Functions to preform a Gene Set Analysis in several genomic dimensions. Including methods for miRNAs. biocViews: GeneSetEnrichment, Annotation, Pathways, GO Author: David Montaner Maintainer: David Montaner URL: https://github.com/dmontaner/mdgsa, http://www.dmontaner.com VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdgsa git_branch: RELEASE_3_12 git_last_commit: fcb676e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mdgsa_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mdgsa_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mdgsa_1.22.0.tgz vignettes: vignettes/mdgsa/inst/doc/mdgsa_vignette.pdf vignetteTitles: mdgsa_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdgsa/inst/doc/mdgsa_vignette.R dependencyCount: 33 Package: mdp Version: 1.10.0 Depends: R (>= 4.0) Imports: ggplot2, gridExtra, grid, stats, utils Suggests: testthat, knitr, rmarkdown, fgsea, BiocManager License: GPL-3 MD5sum: 671e7880728ab631f0740fe843050f7d NeedsCompilation: no Title: Molecular Degree of Perturbation calculates scores for transcriptome data samples based on their perturbation from controls Description: The Molecular Degree of Perturbation webtool quantifies the heterogeneity of samples. It takes a data.frame of omic data that contains at least two classes (control and test) and assigns a score to all samples based on how perturbed they are compared to the controls. It is based on the Molecular Distance to Health (Pankla et al. 2009), and expands on this algorithm by adding the options to calculate the z-score using the modified z-score (using median absolute deviation), change the z-score zeroing threshold, and look at genes that are most perturbed in the test versus control classes. biocViews: BiomedicalInformatics, QualityControl, Transcriptomics, SystemsBiology, Microarray, QualityControl Author: Melissa Lever [aut], Pedro Russo [aut], Helder Nakaya [aut, cre] Maintainer: Helder Nakaya URL: https://mdp.sysbio.tools/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mdp git_branch: RELEASE_3_12 git_last_commit: 048cca6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mdp_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mdp_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mdp_1.10.0.tgz vignettes: vignettes/mdp/inst/doc/my-vignette.html vignetteTitles: Running the mdp package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdp/inst/doc/my-vignette.R dependencyCount: 39 Package: mdqc Version: 1.52.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 972b6d8dbbffd205ecfd1533abfe6fba NeedsCompilation: no Title: Mahalanobis Distance Quality Control for microarrays Description: MDQC is a multivariate quality assessment method for microarrays based on quality control (QC) reports. The Mahalanobis distance of an array's quality attributes is used to measure the similarity of the quality of that array against the quality of the other arrays. Then, arrays with unusually high distances can be flagged as potentially low-quality. biocViews: Microarray, QualityControl Author: Justin Harrington Maintainer: Gabriela Cohen-Freue git_url: https://git.bioconductor.org/packages/mdqc git_branch: RELEASE_3_12 git_last_commit: e34bc43 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mdqc_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mdqc_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mdqc_1.52.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdqc/inst/doc/mdqcvignette.R importsMe: arrayMvout dependencyCount: 7 Package: MDTS Version: 1.10.0 Depends: R (>= 3.5.0) Imports: GenomicAlignments, GenomicRanges, IRanges, Biostrings, DNAcopy, Rsamtools, parallel, stringr Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 81742327e87c31984d500faa3db288be NeedsCompilation: no Title: Detection of de novo deletion in targeted sequencing trios Description: A package for the detection of de novo copy number deletions in targeted sequencing of trios with high sensitivity and positive predictive value. biocViews: StatisticalMethod, Technology, Sequencing, TargetedResequencing, Coverage, DataImport Author: Jack M.. Fu [aut, cre] Maintainer: Jack M.. Fu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MDTS git_branch: RELEASE_3_12 git_last_commit: f64bdf7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MDTS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MDTS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MDTS_1.10.0.tgz vignettes: vignettes/MDTS/inst/doc/mdts.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MDTS/inst/doc/mdts.R dependencyCount: 43 Package: MEAL Version: 1.20.4 Depends: R (>= 3.6.0), Biobase, MultiDataSet Imports: GenomicRanges, limma, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, parallel, ggplot2 (>= 2.0.0), permute, Gviz, missMethyl, isva, SummarizedExperiment, SmartSVA, graphics, stats, utils, matrixStats Suggests: testthat, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, BiocStyle, rmarkdown, brgedata License: Artistic-2.0 MD5sum: d380fbfbf2e85798ff0f8662a5564334 NeedsCompilation: no Title: Perform methylation analysis Description: Package to integrate methylation and expression data. It can also perform methylation or expression analysis alone. Several plotting functionalities are included as well as a new region analysis based on redundancy analysis. Effect of SNPs on a region can also be estimated. biocViews: DNAMethylation, Microarray, Software, WholeGenome Author: Carlos Ruiz-Arenas [aut, cre], Juan R. Gonzalez [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEAL git_branch: RELEASE_3_12 git_last_commit: f173c88 git_last_commit_date: 2021-05-03 Date/Publication: 2021-05-03 source.ver: src/contrib/MEAL_1.20.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/MEAL_1.20.4.zip mac.binary.ver: bin/macosx/contrib/4.0/MEAL_1.20.4.tgz vignettes: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html vignetteTitles: Expression and Methylation Analysis with MEAL, Methylation Analysis with MEAL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R dependencyCount: 203 Package: MeasurementError.cor Version: 1.62.0 License: LGPL MD5sum: b10e421284612340a4a018b42abd503b NeedsCompilation: no Title: Measurement Error model estimate for correlation coefficient Description: Two-stage measurement error model for correlation estimation with smaller bias than the usual sample correlation biocViews: StatisticalMethod Author: Beiying Ding Maintainer: Beiying Ding git_url: https://git.bioconductor.org/packages/MeasurementError.cor git_branch: RELEASE_3_12 git_last_commit: 05d46b1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MeasurementError.cor_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MeasurementError.cor_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MeasurementError.cor_1.62.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.R dependencyCount: 0 Package: MEAT Version: 1.2.2 Depends: R (>= 4.0) Imports: impute (>= 1.58), dynamicTreeCut (>= 1.63), glmnet (>= 2.0), grDevices, graphics, stats, utils, stringr, tibble, RPMM (>= 1.25), minfi (>= 1.30), dplyr, SummarizedExperiment, wateRmelon Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: d509a298226b79761bb66a01258a70ff NeedsCompilation: no Title: Muscle Epigenetic Age Test Description: This package estimates epigenetic age in skeletal muscle, using DNA methylation data generated with the Illumina Infinium technology (HM27, HM450 and HMEPIC). biocViews: Epigenetics, DNAMethylation, Microarray, Normalization, BiomedicalInformatics, MethylationArray, Preprocessing Author: Sarah Voisin [aut, cre] (), Steve Horvath [ctb] () Maintainer: Sarah Voisin URL: https://github.com/sarah-voisin/MEAT VignetteBuilder: knitr BugReports: https://github.com/sarah-voisin/MEAT/issues git_url: https://git.bioconductor.org/packages/MEAT git_branch: RELEASE_3_12 git_last_commit: 23febaf git_last_commit_date: 2021-03-28 Date/Publication: 2021-03-31 source.ver: src/contrib/MEAT_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/MEAT_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/MEAT_1.2.2.tgz vignettes: vignettes/MEAT/inst/doc/MEAT.html vignetteTitles: MEAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEAT/inst/doc/MEAT.R dependencyCount: 166 Package: MEB Version: 1.4.0 Depends: R (>= 3.6.0) Imports: e1071, SummarizedExperiment Suggests: knitr,rmarkdown,BiocStyle License: GPL-2 MD5sum: 970f7376030475891ba2b3769dce8023 NeedsCompilation: no Title: A normalization-invariant minimum enclosing ball method to detect differentially expressed genes for RNA-seq data Description: Identifying differentially expressed genes between the same or different species is an urgent demand for biological and medical research. For RNA-seq data, systematic technical effects and different sequencing depths are usually encountered when conducting experiments. Normalization is regarded as an essential step in the discovery of biologically important changes in expression. The present methods usually involve normalization of the data with a scaling factor, followed by detection of significant genes. However, more than one scaling factor may exist because of the complexity of real data. Consequently, methods that normalize data by a single scaling factor may deliver suboptimal performance or may not even work. The development of modern machine learning techniques has provided a new perspective regarding discrimination between differentially expressed (DE) and non-DE genes. However, in reality, the non-DE genes comprise only a small set and may contain housekeeping genes (in same species) or conserved orthologous genes (in different species). Therefore, the process of detecting DE genes can be formulated as a one-class classification problem, where only non-DE genes are observed, while DE genes are completely absent from the training data. We transform the problem to an outlier detection problem by treating DE genes as outliers, and we propose a normalization-invariant minimum enclosing ball (NIMEB) method to construct a smallest possible ball to contain the known non-DE genes in a feature space. The genes outside the minimum enclosing ball can then be naturally considered to be DE genes. Compared with the existing methods, the proposed NIMEB method does not require data normalization, which is particularly attractive when the RNA-seq data include more than one scaling factor. Furthermore, the NIMEB method could be easily extended to different species without normalization. biocViews: DifferentialExpression, GeneExpression, Normalization, Classification, Sequencing Author: Yan Zhou, Jiadi Zhu Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn>, Yan Zhou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEB git_branch: RELEASE_3_12 git_last_commit: 59e4dd5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MEB_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MEB_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MEB_1.4.0.tgz vignettes: vignettes/MEB/inst/doc/NIMEB.html vignetteTitles: MEB Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEB/inst/doc/NIMEB.R dependencyCount: 30 Package: MEDIPS Version: 1.42.0 Depends: R (>= 3.0), BSgenome, Rsamtools Imports: GenomicRanges, Biostrings, graphics, gtools, IRanges, methods, stats, utils, edgeR, DNAcopy, biomaRt, rtracklayer, preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, BiocStyle License: GPL (>=2) MD5sum: 61cb23ab6c229ed030c0774147e6c3e8 NeedsCompilation: no Title: DNA IP-seq data analysis Description: MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, MEDIPS provides functionalities for the analysis of any kind of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential coverage between groups of samples and saturation and correlation analysis. biocViews: DNAMethylation, CpGIsland, DifferentialExpression, Sequencing, ChIPSeq, Preprocessing, QualityControl, Visualization, Microarray, Genetics, Coverage, GenomeAnnotation, CopyNumberVariation, SequenceMatching Author: Lukas Chavez, Matthias Lienhard, Joern Dietrich, Isaac Lopez Moyado Maintainer: Lukas Chavez git_url: https://git.bioconductor.org/packages/MEDIPS git_branch: RELEASE_3_12 git_last_commit: 661e51c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MEDIPS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MEDIPS_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MEDIPS_1.42.0.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDIPS/inst/doc/MEDIPS.R dependencyCount: 94 Package: MEDME Version: 1.50.0 Depends: R (>= 2.15), grDevices, graphics, methods, stats, utils Imports: Biostrings, MASS, drc Suggests: BSgenome.Hsapiens.UCSC.hg18, BSgenome.Mmusculus.UCSC.mm9 License: GPL (>= 2) Archs: i386, x64 MD5sum: 43bbb60258bc0c86db4b51a4b05c0e88 NeedsCompilation: yes Title: Modelling Experimental Data from MeDIP Enrichment Description: Description: MEDME allows the prediction of absolute and relative methylation levels based on measures obtained by MeDIP-microarray experiments biocViews: Microarray, CpGIsland, DNAMethylation Author: Mattia Pelizzola and Annette Molinaro Maintainer: Mattia Pelizzola git_url: https://git.bioconductor.org/packages/MEDME git_branch: RELEASE_3_12 git_last_commit: 94e4b45 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MEDME_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MEDME_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MEDME_1.50.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEDME/inst/doc/MEDME.R dependencyCount: 107 Package: megadepth Version: 1.0.3 Imports: xfun, utils, fs, GenomicRanges, readr, cmdfun Suggests: covr, knitr, BiocStyle, sessioninfo, rmarkdown, rtracklayer, derfinder, GenomeInfoDb, tools, RefManageR, testthat License: Artistic-2.0 MD5sum: 2f9c945becdefcfb5e6f5e8c73701d67 NeedsCompilation: no Title: megadepth: BigWig and BAM related utilities Description: This package provides an R interface to Megadepth by Christopher Wilks available at https://github.com/ChristopherWilks/megadepth. It is particularly useful for computing the coverage of a set of genomic regions across bigWig or BAM files. With this package, you can build base-pair coverage matrices for regions or annotations of your choice from BigWig files. Megadepth was used to create the raw files provided by https://bioconductor.org/packages/recount3. biocViews: Software, Coverage, DataImport, Transcriptomics, RNASeq, Preprocessing Author: Leonardo Collado-Torres [aut] (), David Zhang [aut, cre] () Maintainer: David Zhang URL: https://github.com/LieberInstitute/megadepth SystemRequirements: megadepth () VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/megadepth git_url: https://git.bioconductor.org/packages/megadepth git_branch: RELEASE_3_12 git_last_commit: 4cef55c git_last_commit_date: 2021-02-02 Date/Publication: 2021-02-03 source.ver: src/contrib/megadepth_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/megadepth_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.0/megadepth_1.0.3.tgz vignettes: vignettes/megadepth/inst/doc/megadepth.html vignetteTitles: megadepth quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/megadepth/inst/doc/megadepth.R importsMe: dasper dependencyCount: 77 Package: MEIGOR Version: 1.24.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR, knitr License: GPL-3 MD5sum: 5bc24fe41bfd4e55c62f465f4de1ba8f NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose A. Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose A. Egea VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MEIGOR git_branch: RELEASE_3_12 git_last_commit: a8a0bea git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MEIGOR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MEIGOR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MEIGOR_1.24.0.tgz vignettes: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.pdf vignetteTitles: Main vignette:Global Optimization for Bioinformatics and Systems Biology hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R importsMe: bioOED dependencyCount: 61 Package: Melissa Version: 1.6.0 Depends: R (>= 3.5.0), BPRMeth, GenomicRanges Imports: data.table, parallel, ROCR, matrixcalc, mclust, ggplot2, doParallel, foreach, MCMCpack, cowplot, magrittr, mvtnorm, truncnorm, assertthat, BiocStyle, stats, utils Suggests: testthat, knitr, rmarkdown License: GPL-3 | file LICENSE MD5sum: 6ac7d346d4cbf6acd2a5db80de936304 NeedsCompilation: no Title: Bayesian clustering and imputationa of single cell methylomes Description: Melissa is a Baysian probabilistic model for jointly clustering and imputing single cell methylomes. This is done by taking into account local correlations via a Generalised Linear Model approach and global similarities using a mixture modelling approach. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, GeneRegulation, Epigenetics, Genetics, Clustering, FeatureExtraction, Regression, RNASeq, Bayesian, KEGG, Sequencing, Coverage, SingleCell Author: C. A. Kapourani [aut, cre] Maintainer: C. A. Kapourani VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Melissa git_branch: RELEASE_3_12 git_last_commit: d9eb3dc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Melissa_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Melissa_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Melissa_1.6.0.tgz vignettes: vignettes/Melissa/inst/doc/process_files.html, vignettes/Melissa/inst/doc/run_melissa.html vignetteTitles: 1: Process and filter scBS-seq data, 2: Cluster and impute scBS-seq data using Melissa hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Melissa/inst/doc/process_files.R, vignettes/Melissa/inst/doc/run_melissa.R dependencyCount: 105 Package: Mergeomics Version: 1.18.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 159979d2af14b0f2d66905ef423e649e NeedsCompilation: no Title: Integrative network analysis of omics data Description: The Mergeomics pipeline serves as a flexible framework for integrating multidimensional omics-disease associations, functional genomics, canonical pathways and gene-gene interaction networks to generate mechanistic hypotheses. It includes two main parts, 1) Marker set enrichment analysis (MSEA); 2) Weighted Key Driver Analysis (wKDA). biocViews: Software Author: Ville-Petteri Makinen, Le Shu, Yuqi Zhao, Zeyneb Kurt, Bin Zhang, Xia Yang Maintainer: Zeyneb Kurt git_url: https://git.bioconductor.org/packages/Mergeomics git_branch: RELEASE_3_12 git_last_commit: 94997e4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Mergeomics_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Mergeomics_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Mergeomics_1.18.0.tgz vignettes: vignettes/Mergeomics/inst/doc/Mergeomics.pdf vignetteTitles: Mergeomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mergeomics/inst/doc/Mergeomics.R dependencyCount: 0 Package: MeSHDbi Version: 1.26.0 Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: RUnit License: Artistic-2.0 MD5sum: 9ae89879e3915048630343b65e726518 NeedsCompilation: no Title: DBI to construct MeSH-related package from sqlite file Description: The package is unified implementation of MeSH.db, MeSH.AOR.db, and MeSH.PCR.db and also is interface to construct Gene-MeSH package (MeSH.XXX.eg.db). loadMeSHDbiPkg import sqlite file and generate MeSH.XXX.eg.db. biocViews: Annotation, AnnotationData, Infrastructure Author: Koki Tsuyuzaki Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/MeSHDbi git_branch: RELEASE_3_12 git_last_commit: 5e8565a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MeSHDbi_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MeSHDbi_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MeSHDbi_1.26.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: MeSH.Aca.eg.db, MeSH.Aga.PEST.eg.db, MeSH.Ame.eg.db, MeSH.Aml.eg.db, MeSH.Ana.eg.db, MeSH.Ani.FGSC.eg.db, MeSH.AOR.db, MeSH.Ath.eg.db, MeSH.Bfl.eg.db, MeSH.Bsu.168.eg.db, MeSH.Bta.eg.db, MeSH.Cal.SC5314.eg.db, MeSH.Cbr.eg.db, MeSH.Cel.eg.db, MeSH.Cfa.eg.db, MeSH.Cin.eg.db, MeSH.Cja.eg.db, MeSH.Cpo.eg.db, MeSH.Cre.eg.db, MeSH.Dan.eg.db, MeSH.db, MeSH.Dda.3937.eg.db, MeSH.Ddi.AX4.eg.db, MeSH.Der.eg.db, MeSH.Dgr.eg.db, MeSH.Dme.eg.db, MeSH.Dmo.eg.db, MeSH.Dpe.eg.db, MeSH.Dre.eg.db, MeSH.Dse.eg.db, MeSH.Dsi.eg.db, MeSH.Dvi.eg.db, MeSH.Dya.eg.db, MeSH.Eca.eg.db, MeSH.Eco.55989.eg.db, MeSH.Eco.ED1a.eg.db, MeSH.Eco.IAI39.eg.db, MeSH.Eco.K12.MG1655.eg.db, MeSH.Eco.O157.H7.Sakai.eg.db, MeSH.Eco.UMN026.eg.db, MeSH.Eqc.eg.db, MeSH.Gga.eg.db, MeSH.Gma.eg.db, MeSH.Hsa.eg.db, MeSH.Laf.eg.db, MeSH.Lma.eg.db, MeSH.Mdo.eg.db, MeSH.Mes.eg.db, MeSH.Mga.eg.db, MeSH.Miy.eg.db, MeSH.Mml.eg.db, MeSH.Mmu.eg.db, MeSH.Mtr.eg.db, MeSH.Nle.eg.db, MeSH.Oan.eg.db, MeSH.Ocu.eg.db, MeSH.Oni.eg.db, MeSH.Osa.eg.db, MeSH.Pab.eg.db, MeSH.Pae.PAO1.eg.db, MeSH.PCR.db, MeSH.Pfa.3D7.eg.db, MeSH.Pto.eg.db, MeSH.Ptr.eg.db, MeSH.Rno.eg.db, MeSH.Sce.S288c.eg.db, MeSH.Sco.A32.eg.db, MeSH.Sil.eg.db, MeSH.Spu.eg.db, MeSH.Ssc.eg.db, MeSH.Syn.eg.db, MeSH.Tbr.9274.eg.db, MeSH.Tgo.ME49.eg.db, MeSH.Tgu.eg.db, MeSH.Vvi.eg.db, MeSH.Xla.eg.db, MeSH.Xtr.eg.db, MeSH.Zma.eg.db importsMe: meshr, scTensor dependencyCount: 26 Package: meshes Version: 1.16.0 Depends: R (>= 3.6.0) Imports: AnnotationDbi, DOSE, enrichplot, GOSemSim, MeSH.db, methods, rvcheck, utils Suggests: knitr, MeSH.Cel.eg.db, MeSH.Hsa.eg.db, prettydoc License: Artistic-2.0 MD5sum: 6629bc0ef570268e41df3d355eeded59 NeedsCompilation: no Title: MeSH Enrichment and Semantic analyses Description: MeSH (Medical Subject Headings) is the NLM controlled vocabulary used to manually index articles for MEDLINE/PubMed. MeSH terms were associated by Entrez Gene ID by three methods, gendoo, gene2pubmed and RBBH. This association is fundamental for enrichment and semantic analyses. meshes supports enrichment analysis (over-representation and gene set enrichment analysis) of gene list or whole expression profile. The semantic comparisons of MeSH terms provide quantitative ways to compute similarities between genes and gene groups. meshes implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively and supports more than 70 species. biocViews: Annotation, Clustering, MultipleComparison, Software Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/meshes/issues git_url: https://git.bioconductor.org/packages/meshes git_branch: RELEASE_3_12 git_last_commit: 9bdbdc5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/meshes_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/meshes_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/meshes_1.16.0.tgz vignettes: vignettes/meshes/inst/doc/meshes.html vignetteTitles: meshes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshes/inst/doc/meshes.R dependencyCount: 101 Package: meshr Version: 1.26.0 Depends: R (>= 3.0.1) Imports: methods, stats, utils, fdrtool, MeSH.db, MeSH.AOR.db, MeSH.PCR.db, MeSHDbi, MeSH.Hsa.eg.db, MeSH.Aca.eg.db, MeSH.Bsu.168.eg.db, MeSH.Syn.eg.db, cummeRbund, org.Hs.eg.db, Category, S4Vectors, BiocGenerics, RSQLite License: Artistic-2.0 MD5sum: 362c6b1faa262b3e2511332e0f641f3a NeedsCompilation: no Title: Tools for conducting enrichment analysis of MeSH Description: A set of annotation maps describing the entire MeSH assembled using data from MeSH. biocViews: AnnotationData, FunctionalAnnotation, Bioinformatics, Statistics, Annotation, MultipleComparisons, MeSHDb Author: Koki Tsuyuzaki, Itoshi Nikaido, Gota Morota Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/meshr git_branch: RELEASE_3_12 git_last_commit: 9e8ccbf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/meshr_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/meshr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/meshr_1.26.0.tgz vignettes: vignettes/meshr/inst/doc/MeSH.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/meshr/inst/doc/MeSH.R importsMe: scTensor dependencyCount: 159 Package: MesKit Version: 1.0.1 Depends: R (>= 4.0.0) Imports: methods, data.table, Biostrings, dplyr, tidyr (>= 1.0.0), ape (>= 5.4.1), ggrepel, pracma, ggridges, AnnotationDbi, IRanges, circlize, cowplot, mclust, phangorn, ComplexHeatmap (>= 1.9.3), ggplot2, RColorBrewer, grDevices, stats, utils, S4Vectors Suggests: shiny, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), org.Hs.eg.db, clusterProfiler, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 MD5sum: 1058743a3bbf53ce36cda242efee260c NeedsCompilation: no Title: A tool kit for dissecting cancer evolution from multi-region derived tumor biopsies via somatic alterations Description: MesKit provides commonly used analysis and visualization modules based on mutational data generated by multi-region sequencing (MRS). This package allows to depict mutational profiles, measure heterogeneity within or between tumors from the same patient, track evolutionary dynamics, as well as characterize mutational patterns on different levels. Shiny application was also developed for a need of GUI-based analysis. As a handy tool, MesKit can facilitate the interpretation of tumor heterogeneity and the understanding of evolutionary relationship between regions in MRS study. Author: Mengni Liu [aut, cre] (), Jianyu Chen [aut, ctb] (), Xin Wang [aut, ctb] () Maintainer: Mengni Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MesKit git_branch: RELEASE_3_12 git_last_commit: 5896d35 git_last_commit_date: 2021-03-26 Date/Publication: 2021-03-27 source.ver: src/contrib/MesKit_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MesKit_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MesKit_1.0.1.tgz vignettes: vignettes/MesKit/inst/doc/MesKit.html vignetteTitles: Analyze and Visualize Multi-region Whole-exome Sequencing Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MesKit/inst/doc/MesKit.R dependencyCount: 88 Package: messina Version: 1.26.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) Archs: i386, x64 MD5sum: a178e97d945df2151a8f8291eef92cf1 NeedsCompilation: yes Title: Single-gene classifiers and outlier-resistant detection of differential expression for two-group and survival problems Description: Messina is a collection of algorithms for constructing optimally robust single-gene classifiers, and for identifying differential expression in the presence of outliers or unknown sample subgroups. The methods have application in identifying lead features to develop into clinical tests (both diagnostic and prognostic), and in identifying differential expression when a fraction of samples show unusual patterns of expression. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Survival Author: Mark Pinese [aut], Mark Pinese [cre], Mark Pinese [cph] Maintainer: Mark Pinese VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/messina git_branch: RELEASE_3_12 git_last_commit: a628730 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/messina_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/messina_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/messina_1.26.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/messina/inst/doc/messina.R dependencyCount: 44 Package: Metab Version: 1.24.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: e6e9e413b84b20e638497eb857f834d7 NeedsCompilation: no Title: Metab: An R Package for a High-Throughput Analysis of Metabolomics Data Generated by GC-MS. Description: Metab is an R package for high-throughput processing of metabolomics data analysed by the Automated Mass Spectral Deconvolution and Identification System (AMDIS) (http://chemdata.nist.gov/mass-spc/amdis/downloads/). In addition, it performs statistical hypothesis test (t-test) and analysis of variance (ANOVA). Doing so, Metab considerably speed up the data mining process in metabolomics and produces better quality results. Metab was developed using interactive features, allowing users with lack of R knowledge to appreciate its functionalities. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio git_url: https://git.bioconductor.org/packages/Metab git_branch: RELEASE_3_12 git_last_commit: 6aacd1a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Metab_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Metab_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Metab_1.24.0.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Metab/inst/doc/MetabPackage.R dependencyCount: 96 Package: metabCombiner Version: 1.0.1 Depends: R (>= 4.0), dplyr (>= 1.0) Imports: methods, mgcv, caret, S4Vectors, stats, utils, rlang, graphics, matrixStats Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: f3bfffe45bcca4a82d25d2a2ded55a03 NeedsCompilation: yes Title: Method for Combining LC-MS Metabolomics Feature Measurements Description: This package aligns LC-HRMS metabolomics datasets acquired from biologically similar specimens analyzed under similar, but not necessarily identical, conditions. Two peak-picked and aligned metabolomics feature tables (consisting of m/z, rt, and per-sample abundance measurements, plus optional identifiers & adduct annotations) are accepted as input. The package outputs a combined table of feature pair alignments, organized into groups of similar m/z, and ranked by a similarity score. Input tables are assumed to be acquired using similar (but not necessarily identical) analytical methods. biocViews: Software, MassSpectrometry, Metabolomics Author: Hani Habra [aut, cre], Alla Karnovsky [ths] Maintainer: Hani Habra VignetteBuilder: knitr BugReports: https://www.github.com/hhabra/metabCombiner/issues git_url: https://git.bioconductor.org/packages/metabCombiner git_branch: RELEASE_3_12 git_last_commit: 6fde963 git_last_commit_date: 2020-12-07 Date/Publication: 2020-12-08 source.ver: src/contrib/metabCombiner_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/metabCombiner_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/metabCombiner_1.0.1.tgz vignettes: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.html vignetteTitles: Combine LC-MS Metabolomics Datasets with metabCombiner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabCombiner/inst/doc/metabCombiner_vignette.R dependencyCount: 76 Package: metabolomicsWorkbenchR Version: 1.0.0 Depends: R (>= 4.0) Imports: data.table, httr, jsonlite, methods, MultiAssayExperiment, struct, SummarizedExperiment, utils Suggests: BiocStyle, covr, knitr, rmarkdown, testthat License: GPL-3 MD5sum: 11e99c8c02f8fd4437f95f1b1b1bfeba NeedsCompilation: no Title: Metabolomics Workbench in R Description: This package provides functions for interfacing with the Metabolomics Workbench RESTful API. Study, compound, protein and gene information can be searched for using the API. Methods to obtain study data in common Bioconductor formats such as SummarizedExperiment and MultiAssayExperiment are also included. biocViews: Software, Metabolomics Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metabolomicsWorkbenchR git_branch: RELEASE_3_12 git_last_commit: 7c8fe4c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metabolomicsWorkbenchR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metabolomicsWorkbenchR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metabolomicsWorkbenchR_1.0.0.tgz vignettes: vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.html vignetteTitles: Introduction_to_metabolomicsWorkbenchR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabolomicsWorkbenchR/inst/doc/Introduction_to_metabolomicsWorkbenchR.R dependencyCount: 66 Package: metabomxtr Version: 1.24.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest, BiocParallel, ggplot2 Suggests: xtable, reshape2 License: GPL-2 MD5sum: 639c4705a3e5f42c79d693a262e90df5 NeedsCompilation: no Title: A package to run mixture models for truncated metabolomics data with normal or lognormal distributions Description: The functions in this package return optimized parameter estimates and log likelihoods for mixture models of truncated data with normal or lognormal distributions. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski git_url: https://git.bioconductor.org/packages/metabomxtr git_branch: RELEASE_3_12 git_last_commit: fe3d767 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metabomxtr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metabomxtr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metabomxtr_1.24.0.tgz vignettes: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.pdf, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.pdf vignetteTitles: metabomxtr, mixnorm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metabomxtr/inst/doc/Metabomxtr_Vignette.R, vignettes/metabomxtr/inst/doc/mixnorm_Vignette.R dependencyCount: 56 Package: MetaboSignal Version: 1.20.0 Depends: R(>= 3.3) Imports: KEGGgraph, hpar, igraph, RCurl, KEGGREST, EnsDb.Hsapiens.v75, stats, graphics, utils, org.Hs.eg.db, biomaRt, AnnotationDbi, MWASTools, mygene Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: 134d6084f0dd4e7167c63cbafa39faf9 NeedsCompilation: no Title: MetaboSignal: a network-based approach to overlay and explore metabolic and signaling KEGG pathways Description: MetaboSignal is an R package that allows merging, analyzing and customizing metabolic and signaling KEGG pathways. It is a network-based approach designed to explore the topological relationship between genes (signaling- or enzymatic-genes) and metabolites, representing a powerful tool to investigate the genetic landscape and regulatory networks of metabolic phenotypes. biocViews: GraphAndNetwork, GeneSignaling, GeneTarget, Network, Pathways, KEGG, Reactome, Software Author: Andrea Rodriguez-Martinez, Rafael Ayala, Joram M. Posma, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaboSignal git_branch: RELEASE_3_12 git_last_commit: 5554397 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MetaboSignal_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MetaboSignal_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MetaboSignal_1.20.0.tgz vignettes: vignettes/MetaboSignal/inst/doc/MetaboSignal.html, vignettes/MetaboSignal/inst/doc/MetaboSignal2.html vignetteTitles: MetaboSignal, MetaboSignal 2: merging KEGG with additional interaction resources hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaboSignal/inst/doc/MetaboSignal.R, vignettes/MetaboSignal/inst/doc/MetaboSignal2.R dependencyCount: 190 Package: metaCCA Version: 1.18.0 Suggests: knitr License: MIT + file LICENSE MD5sum: 7a0ed4340e0a930e55a0a29bf2d2ab62 NeedsCompilation: no Title: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis Description: metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness. biocViews: GenomeWideAssociation, SNP, Genetics, Regression, StatisticalMethod, Software Author: Anna Cichonska Maintainer: Anna Cichonska URL: https://doi.org/10.1093/bioinformatics/btw052 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metaCCA git_branch: RELEASE_3_12 git_last_commit: 49dd918 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metaCCA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metaCCA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metaCCA_1.18.0.tgz vignettes: vignettes/metaCCA/inst/doc/metaCCA.pdf vignetteTitles: metaCCA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metaCCA/inst/doc/metaCCA.R dependencyCount: 0 Package: MetaCyto Version: 1.12.0 Depends: R (>= 3.4) Imports: flowCore (>= 1.4),tidyr (>= 0.7),fastcluster,ggplot2,metafor,cluster,FlowSOM, grDevices, graphics, stats, utils Suggests: knitr, dplyr License: GPL (>= 2) MD5sum: c6fee822aedd6d10627dcbf5e2ba57c6 NeedsCompilation: no Title: MetaCyto: A package for meta-analysis of cytometry data Description: This package provides functions for preprocessing, automated gating and meta-analysis of cytometry data. It also provides functions that facilitate the collection of cytometry data from the ImmPort database. biocViews: ImmunoOncology, CellBiology, FlowCytometry, Clustering, StatisticalMethod, Software, CellBasedAssays, Preprocessing Author: Zicheng Hu, Chethan Jujjavarapu, Sanchita Bhattacharya, Atul J. Butte Maintainer: Zicheng Hu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaCyto git_branch: RELEASE_3_12 git_last_commit: 66b8205 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MetaCyto_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MetaCyto_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MetaCyto_1.12.0.tgz vignettes: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaCyto/inst/doc/MetaCyto_Vignette.R dependencyCount: 132 Package: metagene Version: 2.22.0 Depends: R (>= 3.5.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, DBChIP, matrixStats, purrr, data.table, magrittr, methods, utils, ensembldb, EnsDb.Hsapiens.v86, stringr Suggests: BiocGenerics, similaRpeak, RUnit, knitr, BiocStyle, rmarkdown, similaRpeak License: Artistic-2.0 | file LICENSE MD5sum: a6e7a6a07e2b1d539a2c6efaa81a8d32 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare the behavior of DNA-interacting proteins at selected groups of genes/features. Bam files are used to increase the resolution. Multiple combination of group of bam files and/or group of genomic regions can be compared in a single analysis. Bootstraping analysis is used to compare the groups and locate regions with statistically different enrichment profiles. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Charles Joly Beauparlant , Fabien Claude Lamaze , Rawane Samb , Cedric Lippens , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues git_url: https://git.bioconductor.org/packages/metagene git_branch: RELEASE_3_12 git_last_commit: cdf1dbb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metagene_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metagene_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metagene_2.22.0.tgz vignettes: vignettes/metagene/inst/doc/metagene_rnaseq.html, vignettes/metagene/inst/doc/metagene.html vignetteTitles: RNA-seq exp ext, Introduction to metagene hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene_rnaseq.R, vignettes/metagene/inst/doc/metagene.R dependsOnMe: Imetagene dependencyCount: 119 Package: metagene2 Version: 1.6.1 Depends: R (>= 4.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, tools, GenomicAlignments, GenomeInfoDb, IRanges, ggplot2, Rsamtools, purrr, data.table, methods, dplyr, magrittr, reshape2 Suggests: BiocGenerics, RUnit, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: c43c3636a925f109c1dc3b65cdd78271 NeedsCompilation: no Title: A package to produce metagene plots Description: This package produces metagene plots to compare coverages of sequencing experiments at selected groups of genomic regions. It can be used for such analyses as assessing the binding of DNA-interacting proteins at promoter regions or surveying antisense transcription over the length of a gene. The metagene2 package can manage all aspects of the analysis, from normalization of coverages to plot facetting according to experimental metadata. Bootstraping analysis is used to provide confidence intervals of per-sample mean coverages. biocViews: ChIPSeq, Genetics, MultipleComparison, Coverage, Alignment, Sequencing Author: Eric Fournier [cre, aut], Charles Joly Beauparlant [aut], Cedric Lippens [aut], Arnaud Droit [aut] Maintainer: Eric Fournier URL: https://github.com/ArnaudDroitLab/metagene2 VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/metagene2/issues git_url: https://git.bioconductor.org/packages/metagene2 git_branch: RELEASE_3_12 git_last_commit: 2610629 git_last_commit_date: 2021-03-17 Date/Publication: 2021-03-18 source.ver: src/contrib/metagene2_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/metagene2_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/metagene2_1.6.1.tgz vignettes: vignettes/metagene2/inst/doc/metagene2.html vignetteTitles: Introduction to metagene2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagene2/inst/doc/metagene2.R dependencyCount: 79 Package: metagenomeFeatures Version: 2.10.0 Depends: R (>= 3.5), Biobase (>= 2.17.8) Imports: Biostrings (>= 2.36.4), S4Vectors (>= 0.23.18), dplyr (>= 0.7.0), dbplyr(>= 1.0.0), stringr (>= 1.0.0), lazyeval (>= 0.1.10), RSQLite (>= 1.0.0), magrittr (>= 1.5), methods (>= 3.3.1), lattice (>= 0.20.33), ape (>= 3.5), DECIPHER (>= 2.4.0) Suggests: knitr (>= 1.11), testthat (>= 0.10.0), rmarkdown (>= 1.3), devtools (>= 1.13.5), ggtree(>= 1.8.2), BiocStyle (>= 2.8.2), phyloseq (>= 1.24.2), forcats (>= 0.3.0), ggplot2 (>= 3.0.0) License: Artistic-2.0 MD5sum: a007bdf66408c5eef42a7a2c30bce892 NeedsCompilation: no Title: Exploration of marker-gene sequence taxonomic annotations Description: metagenomeFeatures was developed for use in exploring the taxonomic annotations for a marker-gene metagenomic sequence dataset. The package can be used to explore the taxonomic composition of a marker-gene database or annotated sequences from a marker-gene metagenome experiment. biocViews: ImmunoOncology, Microbiome, Metagenomics, Annotation, Infrastructure, Sequencing, Software Author: Nathan D. Olson, Joseph Nathaniel Paulson, Hector Corrada Bravo Maintainer: Nathan D. Olson URL: https://github.com/HCBravoLab/metagenomeFeatures VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/metagenomeFeatures/issues git_url: https://git.bioconductor.org/packages/metagenomeFeatures git_branch: RELEASE_3_12 git_last_commit: 880eaad git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metagenomeFeatures_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metagenomeFeatures_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metagenomeFeatures_2.10.0.tgz vignettes: vignettes/metagenomeFeatures/inst/doc/database-explore.html, vignettes/metagenomeFeatures/inst/doc/MgDb_and_mgFeatures_classes.html, vignettes/metagenomeFeatures/inst/doc/retrieve-feature-data.html vignetteTitles: Exploring sequence and phylogenetic diversity for a taxonomic group of interest., `metagenomeFeatures` classes and methods., Using metagenomeFeatures to Retrieve Feature Data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeFeatures/inst/doc/database-explore.R, vignettes/metagenomeFeatures/inst/doc/MgDb_and_mgFeatures_classes.R, vignettes/metagenomeFeatures/inst/doc/retrieve-feature-data.R importsMe: greengenes13.5MgDb, ribosomaldatabaseproject11.5MgDb, silva128.1MgDb dependencyCount: 54 Package: metagenomeSeq Version: 1.32.0 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots, graphics, grDevices, stats, utils, Wrench Suggests: annotate, BiocGenerics, biomformat, knitr, gss, testthat (>= 0.8), vegan, interactiveDisplay, IHW License: Artistic-2.0 MD5sum: b0a3fe1e3e42522b89dfab33de717045 NeedsCompilation: no Title: Statistical analysis for sparse high-throughput sequencing Description: metagenomeSeq is designed to determine features (be it Operational Taxanomic Unit (OTU), species, etc.) that are differentially abundant between two or more groups of multiple samples. metagenomeSeq is designed to address the effects of both normalization and under-sampling of microbial communities on disease association detection and the testing of feature correlations. biocViews: ImmunoOncology, Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, Nathan D. Olson, Domenick J. Braccia, Justin Wagner, Hisham Talukder, Mihai Pop, Hector Corrada Bravo Maintainer: Joseph N. Paulson URL: https://github.com/nosson/metagenomeSeq/ VignetteBuilder: knitr BugReports: https://github.com/nosson/metagenomeSeq/issues git_url: https://git.bioconductor.org/packages/metagenomeSeq git_branch: RELEASE_3_12 git_last_commit: 22ffea0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metagenomeSeq_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metagenomeSeq_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metagenomeSeq_1.32.0.tgz vignettes: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.pdf, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.pdf vignetteTitles: fitTimeSeries: differential abundance analysis through time or location, metagenomeSeq: statistical analysis for sparse high-throughput sequencing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metagenomeSeq/inst/doc/fitTimeSeries.R, vignettes/metagenomeSeq/inst/doc/metagenomeSeq.R dependsOnMe: metavizr, microbiomeExplorer, etec16s importsMe: Maaslin2, microbiomeDASim, MetaLonDA suggestsMe: interactiveDisplay, phyloseq, Wrench, curatedMetagenomicData dependencyCount: 28 Package: metahdep Version: 1.48.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: i386, x64 MD5sum: f14d7f00d154ccdfc5a6d830dd101445 NeedsCompilation: yes Title: Hierarchical Dependence in Meta-Analysis Description: Tools for meta-analysis in the presence of hierarchical (and/or sampling) dependence, including with gene expression studies biocViews: Microarray, DifferentialExpression Author: John R. Stevens, Gabriel Nicholas Maintainer: John R. Stevens git_url: https://git.bioconductor.org/packages/metahdep git_branch: RELEASE_3_12 git_last_commit: d556de9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metahdep_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metahdep_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metahdep_1.48.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metahdep/inst/doc/metahdep.R dependencyCount: 1 Package: metaMS Version: 1.26.0 Depends: R (>= 4.0), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics, graphics, stats, utils Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 8384efd1bc0d3ed2ec534e4002f67f23 NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Ron Wehrens [aut] (author of GC-MS part, Initial Maintainer), Pietro Franceschi [aut] (author of LC-MS part), Nir Shahaf [ctb], Matthias Scholz [ctb], Georg Weingart [ctb] (development of GC-MS approach), Elisabete Carvalho [ctb] (testing and feedback of GC-MS pipeline), Yann Guitton [ctb, cre] (), Julien Saint-Vanne [ctb] Maintainer: Yann Guitton URL: https://github.com/yguitton/metaMS git_url: https://git.bioconductor.org/packages/metaMS git_branch: RELEASE_3_12 git_last_commit: c3d5531 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metaMS_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metaMS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metaMS_1.26.0.tgz vignettes: vignettes/metaMS/inst/doc/runGC.pdf, vignettes/metaMS/inst/doc/runLC.pdf vignetteTitles: runGC, runLC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaMS/inst/doc/runGC.R, vignettes/metaMS/inst/doc/runLC.R suggestsMe: CluMSID dependencyCount: 126 Package: MetaNeighbor Version: 1.10.0 Depends: R(>= 3.5) Imports: grDevices, graphics, methods, stats (>= 3.4), utils (>= 3.4), Matrix (>= 1.2), matrixStats (>= 0.54), beanplot (>= 1.2), gplots (>= 3.0.1), RColorBrewer (>= 1.1.2), SummarizedExperiment (>= 1.12), SingleCellExperiment, igraph, dplyr, tidyr, tibble, ggplot2 Suggests: knitr (>= 1.17), rmarkdown (>= 1.6), testthat (>= 1.0.2), UpSetR License: MIT + file LICENSE MD5sum: 0bfcc0e719f411e88beca4275912dbb1 NeedsCompilation: no Title: Single cell replicability analysis Description: MetaNeighbor allows users to quantify cell type replicability across datasets using neighbor voting. biocViews: ImmunoOncology, GeneExpression, GO, MultipleComparison, SingleCell, Transcriptomics Author: Megan Crow [aut, cre], Sara Ballouz [ctb], Manthan Shah [ctb], Stephan Fischer [ctb], Jesse Gillis [aut] Maintainer: Stephan Fischer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaNeighbor git_branch: RELEASE_3_12 git_last_commit: c4000c0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MetaNeighbor_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MetaNeighbor_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MetaNeighbor_1.10.0.tgz vignettes: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.pdf vignetteTitles: MetaNeighbor user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MetaNeighbor/inst/doc/MetaNeighbor.R dependencyCount: 69 Package: metaSeq Version: 1.30.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 8bd8fea15d8dff9cea6eadd1dfa62adb NeedsCompilation: no Title: Meta-analysis of RNA-Seq count data in multiple studies Description: The probabilities by one-sided NOISeq are combined by Fisher's method or Stouffer's method biocViews: RNASeq, DifferentialExpression, Sequencing, ImmunoOncology Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki git_url: https://git.bioconductor.org/packages/metaSeq git_branch: RELEASE_3_12 git_last_commit: 4b676e6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metaSeq_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metaSeq_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metaSeq_1.30.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaSeq/inst/doc/metaSeq.R dependencyCount: 15 Package: metaseqR Version: 1.30.0 Depends: R (>= 3.4.0), EDASeq, DESeq, limma, qvalue Imports: edgeR, NOISeq, baySeq, NBPSeq, biomaRt, utils, gplots, corrplot, vsn, brew, rjson, log4r Suggests: BiocGenerics, GenomicRanges, rtracklayer, Rsamtools, survcomp, VennDiagram, knitr, zoo, RUnit, BiocManager, BSgenome, RSQLite Enhances: parallel, TCC, RMySQL License: GPL (>= 3) MD5sum: 98302550242e550aae10ee42d48bc5b5 NeedsCompilation: no Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms. Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: ImmunoOncology, Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/metaseqR git_branch: RELEASE_3_12 git_last_commit: 42215cd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metaseqR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metaseqR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metaseqR_1.30.0.tgz vignettes: vignettes/metaseqR/inst/doc/metaseqr-pdf.pdf vignetteTitles: RNA-Seq data analysis using mulitple statistical algorithms with metaseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR/inst/doc/metaseqr-pdf.R dependencyCount: 137 Package: metaseqR2 Version: 1.2.0 Depends: R (>= 4.0.0), DESeq2, limma, locfit, splines Imports: ABSSeq, baySeq, Biobase, BiocGenerics, BiocParallel, biomaRt, Biostrings, corrplot, DSS, DT, EDASeq, edgeR, genefilter, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, gplots, graphics, grDevices, harmonicmeanp, heatmaply, htmltools, httr, IRanges, jsonlite, lattice, log4r, magrittr, MASS, Matrix, methods, NBPSeq, pander, parallel, qvalue, rmarkdown, rmdformats, Rsamtools, RSQLite, rtracklayer, S4Vectors, stats, stringr, SummarizedExperiment, survcomp, utils, VennDiagram, vsn, yaml, zoo Suggests: BiocManager, BSgenome, knitr, RMySQL, RUnit Enhances: TCC License: GPL (>= 3) Archs: i386, x64 MD5sum: 4d8cbde15c56963cf669abf3266033b9 NeedsCompilation: yes Title: An R package for the analysis and result reporting of RNA-Seq data by combining multiple statistical algorithms Description: Provides an interface to several normalization and statistical testing packages for RNA-Seq gene expression data. Additionally, it creates several diagnostic plots, performs meta-analysis by combinining the results of several statistical tests and reports the results in an interactive way. biocViews: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq, Transcription, Sequencing, Transcriptomics, Bayesian, Clustering, CellBiology, BiomedicalInformatics, FunctionalGenomics, SystemsBiology, ImmunoOncology, AlternativeSplicing, DifferentialSplicing, MultipleComparison, TimeCourse, DataImport, ATACSeq, Epigenetics, Regression, ProprietaryPlatforms, GeneSetEnrichment, BatchEffect, ChIPSeq Author: Panagiotis Moulos [aut, cre] Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr BugReports: https://github.com/pmoulos/metaseqR2/issues git_url: https://git.bioconductor.org/packages/metaseqR2 git_branch: RELEASE_3_12 git_last_commit: 2bcc067 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metaseqR2_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metaseqR2_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metaseqR2_1.2.0.tgz vignettes: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.html, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.html vignetteTitles: Building an annotation database for metaseqR2, RNA-Seq data analysis with metaseqR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaseqR2/inst/doc/metaseqr2-annotation.R, vignettes/metaseqR2/inst/doc/metaseqr2-statistics.R dependencyCount: 211 Package: metavizr Version: 1.14.0 Depends: R (>= 3.4), metagenomeSeq (>= 1.17.1), methods, data.table, Biobase, digest Imports: epivizr, epivizrData, epivizrServer, epivizrStandalone, vegan, GenomeInfoDb, phyloseq, httr Suggests: knitr, BiocStyle, matrixStats, msd16s (>= 0.109.1), etec16s, testthat, gss, curatedMetagenomicData License: MIT + file LICENSE MD5sum: 613366b7901e6461f2aa14ebacdf452e NeedsCompilation: no Title: R Interface to the metaviz web app for interactive metagenomics data analysis and visualization Description: This package provides Websocket communication to the metaviz web app (http://metaviz.cbcb.umd.edu) for interactive visualization of metagenomics data. Objects in R/bioc interactive sessions can be displayed in plots and data can be explored using a facetzoom visualization. Fundamental Bioconductor data structures are supported (e.g., MRexperiment objects), while providing an easy mechanism to support other data structures. Visualizations (using d3.js) can be easily added to the web app as well. biocViews: Visualization, Infrastructure, GUI, Metagenomics, ImmunoOncology Author: Hector Corrada Bravo [cre, aut], Florin Chelaru [aut], Justin Wagner [aut], Jayaram Kancherla [aut], Joseph Paulson [aut] Maintainer: Hector Corrada Bravo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/metavizr git_branch: RELEASE_3_12 git_last_commit: 36ae820 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/metavizr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/metavizr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/metavizr_1.14.0.tgz vignettes: vignettes/metavizr/inst/doc/IntroToMetavizr.html vignetteTitles: Introduction to metavizr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metavizr/inst/doc/IntroToMetavizr.R dependencyCount: 154 Package: MetaVolcanoR Version: 1.4.0 Depends: R (>= 3.6.0) Imports: methods, data.table, dplyr, tidyr, plotly, ggplot2, cowplot, parallel, metafor, metap, rlang, topconfects, grDevices, graphics, stats, htmlwidgets Suggests: knitr, testthat License: GPL-3 MD5sum: 04b2d1efab6d90b2b90e52f2bef6e288 NeedsCompilation: no Title: Gene Expression Meta-analysis Visualization Tool Description: MetaVolcanoR combines differential gene expression results. It implements three strategies to summarize differential gene expression from different studies. i) Random Effects Model (REM) approach, ii) a p-value combining-approach, and iii) a vote-counting approach. In all cases, MetaVolcano exploits the Volcano plot reasoning to visualize the gene expression meta-analysis results. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, mRNAMicroarray, RNASeq Author: Cesar Prada [aut, cre], Diogenes Lima [aut], Helder Nakaya [aut, ths] Maintainer: Cesar Prada VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetaVolcanoR git_branch: RELEASE_3_12 git_last_commit: 5f1bf82 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MetaVolcanoR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MetaVolcanoR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MetaVolcanoR_1.4.0.tgz vignettes: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.html, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.html vignetteTitles: MetaVolcanoR: Differential expression meta-analysis tool, MetaVolcanoR inputs: differential expression examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetaVolcanoR/inst/doc/MetaVolcano.R, vignettes/MetaVolcanoR/inst/doc/PrepareDatasets.R dependencyCount: 96 Package: MetCirc Version: 1.20.0 Depends: R (>= 3.5), amap (>= 0.8), circlize (>= 0.3.9), scales (>= 0.3.0), shiny (>= 1.0.0), MSnbase (>= 2.15.3), Imports: ggplot2 (>= 3.2.1), S4Vectors (>= 0.22.0) Suggests: BiocGenerics, graphics (>= 3.5), grDevices (>= 3.5), knitr (>= 1.11), methods (>= 3.5), stats (>= 3.5), testthat (>= 2.2.1) License: GPL (>= 3) MD5sum: 299436809b4c0474c1eaf7064cd9285b NeedsCompilation: no Title: Navigating mass spectral similarity in high-resolution MS/MS metabolomics data Description: MetCirc comprises a workflow to interactively explore high-resolution MS/MS metabolomics data. MetCirc uses the Spectrum2 and MSpectra infrastructure defined in the package MSnbase that stores MS/MS spectra. MetCirc offers functionality to calculate similarity between precursors based on the normalised dot product, neutral losses or user-defined functions and visualise similarities in a circular layout. Within the interactive framework the user can annotate MS/MS features based on their similarity to (known) related MS/MS features. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Visualization Author: Thomas Naake , Johannes Rainer and Emmanuel Gaquerel Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetCirc git_branch: RELEASE_3_12 git_last_commit: 4d686d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MetCirc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MetCirc_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MetCirc_1.20.0.tgz vignettes: vignettes/MetCirc/inst/doc/MetCirc.pdf vignetteTitles: Workflow for Metabolomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetCirc/inst/doc/MetCirc.R dependencyCount: 97 Package: MethCP Version: 1.4.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, S4Vectors, bsseq, DSS, methylKit, DNAcopy, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel Suggests: testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 84bb50535f91db8f7acbabcf0ee5ebb7 NeedsCompilation: no Title: Differential methylation anlsysis for bisulfite sequencing data Description: MethCP is a differentially methylated region (DMR) detecting method for whole-genome bisulfite sequencing (WGBS) data, which is applicable for a wide range of experimental designs beyond the two-group comparisons, such as time-course data. MethCP identifies DMRs based on change point detection, which naturally segments the genome and provides region-level differential analysis. biocViews: DifferentialMethylation, Sequencing, WholeGenome, TimeCourse Author: Boying Gong [aut, cre] Maintainer: Boying Gong VignetteBuilder: knitr BugReports: https://github.com/boyinggong/methcp/issues git_url: https://git.bioconductor.org/packages/MethCP git_branch: RELEASE_3_12 git_last_commit: 06c4008 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MethCP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MethCP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MethCP_1.4.0.tgz vignettes: vignettes/MethCP/inst/doc/methcp.html vignetteTitles: methcp: User’s Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethCP/inst/doc/methcp.R dependencyCount: 104 Package: methimpute Version: 1.12.0 Depends: R (>= 3.4.0), GenomicRanges, ggplot2 Imports: Rcpp (>= 0.12.4.5), methods, utils, grDevices, stats, GenomeInfoDb, IRanges, Biostrings, reshape2, minpack.lm, data.table LinkingTo: Rcpp Suggests: knitr, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 17f8d2626db0d775bdef67a1d3afb68c NeedsCompilation: yes Title: Imputation-guided re-construction of complete methylomes from WGBS data Description: This package implements functions for calling methylation for all cytosines in the genome. biocViews: ImmunoOncology, Software, DNAMethylation, Epigenetics, HiddenMarkovModel, Sequencing, Coverage Author: Aaron Taudt Maintainer: Aaron Taudt VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methimpute git_branch: RELEASE_3_12 git_last_commit: a13a2af git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methimpute_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methimpute_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methimpute_1.12.0.tgz vignettes: vignettes/methimpute/inst/doc/methimpute.pdf vignetteTitles: Methylation status calling with METHimpute hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methimpute/inst/doc/methimpute.R dependencyCount: 59 Package: methInheritSim Version: 1.12.0 Depends: R (>= 3.4) Imports: methylKit, GenomicRanges, GenomeInfoDb, parallel, BiocGenerics, S4Vectors, methods, stats, IRanges, msm Suggests: BiocStyle, knitr, rmarkdown, RUnit, methylInheritance License: Artistic-2.0 MD5sum: 158dd7ee7fdfaac74d07930ecfdfc674 NeedsCompilation: no Title: Simulating Whole-Genome Inherited Bisulphite Sequencing Data Description: Simulate a multigeneration methylation case versus control experiment with inheritance relation using a real control dataset. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Pascal Belleau, Astrid Deschênes and Arnaud Droit Maintainer: Pascal Belleau URL: https://github.com/belleau/methInheritSim VignetteBuilder: knitr BugReports: https://github.com/belleau/methInheritSim/issues git_url: https://git.bioconductor.org/packages/methInheritSim git_branch: RELEASE_3_12 git_last_commit: 36e0739 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methInheritSim_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methInheritSim_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methInheritSim_1.12.0.tgz vignettes: vignettes/methInheritSim/inst/doc/methInheritSim.html vignetteTitles: Simulating Whole-Genome Inherited Bisulphite Sequencing Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methInheritSim/inst/doc/methInheritSim.R suggestsMe: methylInheritance dependencyCount: 94 Package: MethPed Version: 1.18.0 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: 8cc8d020865af609ce56e307e323b392 NeedsCompilation: no Title: A DNA methylation classifier tool for the identification of pediatric brain tumor subtypes Description: Classification of pediatric tumors into biologically defined subtypes is challenging and multifaceted approaches are needed. For this aim, we developed a diagnostic classifier based on DNA methylation profiles. We offer MethPed as an easy-to-use toolbox that allows researchers and clinical diagnosticians to test single samples as well as large cohorts for subclass prediction of pediatric brain tumors. The current version of MethPed can classify the following tumor diagnoses/subgroups: Diffuse Intrinsic Pontine Glioma (DIPG), Ependymoma, Embryonal tumors with multilayered rosettes (ETMR), Glioblastoma (GBM), Medulloblastoma (MB) - Group 3 (MB_Gr3), Group 4 (MB_Gr3), Group WNT (MB_WNT), Group SHH (MB_SHH) and Pilocytic Astrocytoma (PiloAstro). biocViews: ImmunoOncology, DNAMethylation, Classification, Epigenetics Author: Mohammad Tanvir Ahamed [aut, trl], Anna Danielsson [aut], Szilárd Nemes [aut, trl], Helena Carén [aut, cre, cph] Maintainer: Helena Carén VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethPed git_branch: RELEASE_3_12 git_last_commit: 311faed git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MethPed_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MethPed_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MethPed_1.18.0.tgz vignettes: vignettes/MethPed/inst/doc/MethPed-vignette.html vignetteTitles: MethPed User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R dependencyCount: 9 Package: MethReg Version: 1.0.0 Depends: R (>= 4.0) Imports: dplyr, plyr, GenomicRanges, SummarizedExperiment, DelayedArray, ggplot2, ggpubr, tibble, tidyr, S4Vectors, sesameData, stringr, readr, methods, stats, Matrix, MASS, rlang, pscl, IRanges, sfsmisc, progress Suggests: rmarkdown, BiocStyle, testthat (>= 2.1.0), parallel, downloader, R.utils, doParallel, reshape2, JASPAR2020, TFBSTools, motifmatchr, matrixStats, biomaRt, dorothea, viper, stageR, BiocFileCache, png, htmltools, knitr, jpeg License: GPL-3 MD5sum: c6b3bc378c1790c9e5d2281b960dc11f NeedsCompilation: no Title: Assessing the regulatory potential of DNA methylation regions or sites on gene transcription Description: Epigenome-wide association studies (EWAS) detects a large number of DNA methylation differences, often hundreds of differentially methylated regions and thousands of CpGs, that are significantly associated with a disease, many are located in non-coding regions. Therefore, there is a critical need to better understand the functional impact of these CpG methylations and to further prioritize the significant changes. MethReg is an R package for integrative modeling of DNA methylation, target gene expression and transcription factor binding sites data, to systematically identify and rank functional CpG methylations. MethReg evaluates, prioritizes and annotates CpG sites with high regulatory potential using matched methylation and gene expression data, along with external TF-target interaction databases based on manually curation, ChIP-seq experiments or gene regulatory network analysis. biocViews: MethylationArray, Regression, GeneExpression, Epigenetics, GeneTarget, Transcription Author: Tiago Silva [aut, cre] (), Lily Wang [aut] Maintainer: Tiago Silva VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/MethReg/issues/ git_url: https://git.bioconductor.org/packages/MethReg git_branch: RELEASE_3_12 git_last_commit: 2932aa8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MethReg_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MethReg_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MethReg_1.0.0.tgz vignettes: vignettes/MethReg/inst/doc/MethReg.html vignetteTitles: MethReg: estimating regulatory potential of DNA methylation in gene transcription hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethReg/inst/doc/MethReg.R dependencyCount: 167 Package: methrix Version: 1.4.07 Depends: R (>= 3.6), data.table (>= 1.12.4), SummarizedExperiment Imports: rtracklayer, DelayedArray, HDF5Array, BSgenome, DelayedMatrixStats, parallel, methods, ggplot2, matrixStats, graphics, stats, utils, GenomicRanges, IRanges Suggests: knitr, rmarkdown, DSS, bsseq, plotly, BSgenome.Mmusculus.UCSC.mm9, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, GenomicScores, Biostrings, RColorBrewer, GenomeInfoDb, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: a296592e123053f98bd40bf515b4a3ea NeedsCompilation: no Title: Fast and efficient summarization of generic bedGraph files from Bisufite sequencing Description: Bedgraph files generated by Bisulfite pipelines often come in various flavors. Critical downstream step requires summarization of these files into methylation/coverage matrices. This step of data aggregation is done by Methrix, including many other useful downstream functions. biocViews: DNAMethylation, Sequencing, Coverage Author: Anand Mayakonda [aut, cre] (), Reka Toth [aut] (), Rajbir Batra [ctb], Clarissa Feuerstein-Akgöz [ctb], Joschka Hey [ctb], Maximilian Schönung [ctb], Pavlo Lutsik [ctb] Maintainer: Anand Mayakonda URL: https://github.com/CompEpigen/methrix VignetteBuilder: knitr BugReports: https://github.com/CompEpigen/methrix/issues git_url: https://git.bioconductor.org/packages/methrix git_branch: RELEASE_3_12 git_last_commit: 012a4cc git_last_commit_date: 2021-02-11 Date/Publication: 2021-02-11 source.ver: src/contrib/methrix_1.4.07.tar.gz win.binary.ver: bin/windows/contrib/4.0/methrix_1.4.07.zip mac.binary.ver: bin/macosx/contrib/4.0/methrix_1.4.07.tgz vignettes: vignettes/methrix/inst/doc/methrix.html vignetteTitles: Methrix tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methrix/inst/doc/methrix.R dependencyCount: 78 Package: MethTargetedNGS Version: 1.22.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: 7e626eace2f2306bd1882ab55dde758b NeedsCompilation: no Title: Perform Methylation Analysis on Next Generation Sequencing Data Description: Perform step by step methylation analysis of Next Generation Sequencing data. biocViews: ResearchField, Genetics, Sequencing, Alignment, SequenceMatching, DataImport Author: Muhammad Ahmer Jamil with Contribution of Prof. Holger Frohlich and Priv.-Doz. Dr. Osman El-Maarri Maintainer: Muhammad Ahmer Jamil SystemRequirements: HMMER3 git_url: https://git.bioconductor.org/packages/MethTargetedNGS git_branch: RELEASE_3_12 git_last_commit: a88d091 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MethTargetedNGS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MethTargetedNGS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MethTargetedNGS_1.22.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.R dependencyCount: 41 Package: methyAnalysis Version: 1.32.0 Depends: R (>= 2.10), grid, BiocGenerics, IRanges, GenomeInfoDb (>= 1.22.0), GenomicRanges, Biobase (>= 2.34.0), org.Hs.eg.db Imports: grDevices, stats, utils, lumi, methylumi, Gviz, genoset, SummarizedExperiment, IRanges, GenomicRanges, VariantAnnotation, rtracklayer, bigmemoryExtras,GenomicFeatures, annotate, Biobase (>= 2.5.5), AnnotationDbi, genefilter, biomaRt, methods, parallel Suggests: FDb.InfiniumMethylation.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 3f3171a32bcc499823cbdf52d675aa90 NeedsCompilation: no Title: DNA methylation data analysis and visualization Description: The methyAnalysis package aims for the DNA methylation data analysis and visualization. A MethyGenoSet class is defined to keep the chromosome location information together with the data. The package also includes functions of estimating the methylation levels from Methy-Seq data. biocViews: Microarray, DNAMethylation, Visualization Author: Pan Du, Richard Bourgon Maintainer: Lei Huang git_url: https://git.bioconductor.org/packages/methyAnalysis git_branch: RELEASE_3_12 git_last_commit: 722c722 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methyAnalysis_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methyAnalysis_1.31.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methyAnalysis_1.32.0.tgz vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf vignetteTitles: An Introduction to the methyAnalysis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methyAnalysis/inst/doc/methyAnalysis.R suggestsMe: methylumi dependencyCount: 188 Package: MethylAid Version: 1.24.0 Depends: R (>= 3.4) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.22.0), methods, RColorBrewer, shiny, stats, SummarizedExperiment, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, minfiDataEPIC, RUnit License: GPL (>= 2) MD5sum: f00efbf705869cace991575c74f5114b NeedsCompilation: no Title: Visual and interactive quality control of large Illumina DNA Methylation array data sets Description: A visual and interactive web application using RStudio's shiny package. Bad quality samples are detected using sample-dependent and sample-independent controls present on the array and user adjustable thresholds. In depth exploration of bad quality samples can be performed using several interactive diagnostic plots of the quality control probes present on the array. Furthermore, the impact of any batch effect provided by the user can be explored. biocViews: DNAMethylation, MethylationArray, Microarray, TwoChannel, QualityControl, BatchEffect, Visualization, GUI Author: Maarten van Iterson [aut, cre], Elmar Tobi[ctb], Roderick Slieker[ctb], Wouter den Hollander[ctb], Rene Luijk[ctb] and Bas Heijmans[ctb] Maintainer: L.J.Sinke VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylAid git_branch: RELEASE_3_12 git_last_commit: ff8facb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MethylAid_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MethylAid_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MethylAid_1.24.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and Interactive quality control of Illumina Human DNA Methylation array data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylAid/inst/doc/MethylAid.R dependsOnMe: MethylAidData dependencyCount: 154 Package: methylCC Version: 1.4.0 Depends: R (>= 3.6), FlowSorted.Blood.450k Imports: Biobase, GenomicRanges, IRanges, S4Vectors, dplyr, magrittr, minfi, bsseq, quadprog, plyranges, stats, utils, bumphunter, genefilter, methods, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: knitr, testthat (>= 2.1.0), BiocGenerics, BiocStyle, tidyr, ggplot2 License: CC BY 4.0 MD5sum: 1d9d48426be88ef5908029395f4ad022 NeedsCompilation: no Title: Estimate the cell composition of whole blood in DNA methylation samples Description: A tool to estimate the cell composition of DNA methylation whole blood sample measured on any platform technology (microarray and sequencing). biocViews: Microarray, Sequencing, DNAMethylation, MethylationArray, MethylSeq, WholeGenome Author: Stephanie C. Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks URL: https://github.com/stephaniehicks/methylCC/ VignetteBuilder: knitr BugReports: https://github.com/stephaniehicks/methylCC/ git_url: https://git.bioconductor.org/packages/methylCC git_branch: RELEASE_3_12 git_last_commit: f078962 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methylCC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylCC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methylCC_1.4.0.tgz vignettes: vignettes/methylCC/inst/doc/methylCC.html vignetteTitles: The methylCC user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylCC/inst/doc/methylCC.R dependencyCount: 148 Package: methylGSA Version: 1.8.0 Depends: R (>= 3.5) Imports: RobustRankAggreg, ggplot2, stringr, stats, clusterProfiler, missMethyl, org.Hs.eg.db, reactome.db, BiocParallel, GO.db, AnnotationDbi, shiny, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Suggests: knitr, rmarkdown, testthat, enrichplot License: GPL-2 MD5sum: 60c3d0cb9ea866c8019177949857ce74 NeedsCompilation: no Title: Gene Set Analysis Using the Outcome of Differential Methylation Description: The main functions for methylGSA are methylglm and methylRRA. methylGSA implements logistic regression adjusting number of probes as a covariate. methylRRA adjusts multiple p-values of each gene by Robust Rank Aggregation. For more detailed help information, please see the vignette. biocViews: DNAMethylation,DifferentialMethylation,GeneSetEnrichment,Regression, GeneRegulation,Pathways Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/methylGSA VignetteBuilder: knitr BugReports: https://github.com/reese3928/methylGSA/issues git_url: https://git.bioconductor.org/packages/methylGSA git_branch: RELEASE_3_12 git_last_commit: 1c2e892 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methylGSA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylGSA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methylGSA_1.8.0.tgz vignettes: vignettes/methylGSA/inst/doc/methylGSA-vignette.html vignetteTitles: methylGSA: Gene Set Analysis for DNA Methylation Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylGSA/inst/doc/methylGSA-vignette.R dependencyCount: 194 Package: methylInheritance Version: 1.14.0 Depends: R (>= 3.5) Imports: methylKit, BiocParallel, GenomicRanges, IRanges, S4Vectors, methods, parallel, ggplot2, gridExtra, rebus Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit, methInheritSim License: Artistic-2.0 MD5sum: bd56a3e2eab518e9f469c0cf6fd3ec99 NeedsCompilation: no Title: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect Description: Permutation analysis, based on Monte Carlo sampling, for testing the hypothesis that the number of conserved differentially methylated elements, between several generations, is associated to an effect inherited from a treatment and that stochastic effect can be dismissed. biocViews: BiologicalQuestion, Epigenetics, DNAMethylation, DifferentialMethylation, MethylSeq, Software, ImmunoOncology, StatisticalMethod, WholeGenome, Sequencing Author: Astrid Deschênes, Pascal Belleau and Arnaud Droit Maintainer: Astrid Deschenes URL: https://github.com/adeschen/methylInheritance VignetteBuilder: knitr BugReports: https://github.com/adeschen/methylInheritance/issues git_url: https://git.bioconductor.org/packages/methylInheritance git_branch: RELEASE_3_12 git_last_commit: ef7dbdb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methylInheritance_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylInheritance_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methylInheritance_1.14.0.tgz vignettes: vignettes/methylInheritance/inst/doc/methylInheritance.html vignetteTitles: Permutation-Based Analysis associating Conserved Differentially Methylated Elements Across Multiple Generations to a Treatment Effect hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylInheritance/inst/doc/methylInheritance.R suggestsMe: methInheritSim dependencyCount: 97 Package: methylKit Version: 1.16.1 Depends: R (>= 3.5.0), GenomicRanges (>= 1.18.1), methods Imports: IRanges, data.table (>= 1.9.6), parallel, S4Vectors (>= 0.13.13), GenomeInfoDb, KernSmooth, qvalue, emdbook, Rsamtools, gtools, fastseg, rtracklayer, mclust, mgcv, Rcpp, R.utils, limma, grDevices, graphics, stats, utils LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc Suggests: testthat (>= 2.1.0), knitr, rmarkdown, genomation, BiocManager License: Artistic-2.0 Archs: i386, x64 MD5sum: bb87f51ebc30d1bdbfaa4d1a0d3ca899 NeedsCompilation: yes Title: DNA methylation analysis from high-throughput bisulfite sequencing results Description: methylKit is an R package for DNA methylation analysis and annotation from high-throughput bisulfite sequencing. The package is designed to deal with sequencing data from RRBS and its variants, but also target-capture methods and whole genome bisulfite sequencing. It also has functions to analyze base-pair resolution 5hmC data from experimental protocols such as oxBS-Seq and TAB-Seq. Methylation calling can be performed directly from Bismark aligned BAM files. biocViews: DNAMethylation, Sequencing, MethylSeq Author: Altuna Akalin [aut, cre], Matthias Kormaksson [aut], Sheng Li [aut], Arsene Wabo [ctb], Adrian Bierling [aut], Alexander Gosdschan [aut] Maintainer: Altuna Akalin , Alexander Gosdschan URL: http://code.google.com/p/methylkit/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylKit git_branch: RELEASE_3_12 git_last_commit: a48c559 git_last_commit_date: 2021-01-28 Date/Publication: 2021-01-28 source.ver: src/contrib/methylKit_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylKit_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.0/methylKit_1.16.1.tgz vignettes: vignettes/methylKit/inst/doc/methylKit.html vignetteTitles: methylKit: User Guide v`r packageVersion('methylKit')` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylKit/inst/doc/methylKit.R importsMe: MethCP, methInheritSim, methylInheritance dependencyCount: 90 Package: MethylMix Version: 2.20.0 Depends: R (>= 3.2.0) Imports: foreach, RPMM, RColorBrewer, ggplot2, RCurl, impute, data.table, limma, R.matlab, digest Suggests: BiocStyle, doParallel, testthat, knitr, rmarkdown License: GPL-2 MD5sum: d94872d61bb43e66e5d7dbe47d54ac34 NeedsCompilation: no Title: MethylMix: Identifying methylation driven cancer genes Description: MethylMix is an algorithm implemented to identify hyper and hypomethylated genes for a disease. MethylMix is based on a beta mixture model to identify methylation states and compares them with the normal DNA methylation state. MethylMix uses a novel statistic, the Differential Methylation value or DM-value defined as the difference of a methylation state with the normal methylation state. Finally, matched gene expression data is used to identify, besides differential, functional methylation states by focusing on methylation changes that effect gene expression. References: Gevaert 0. MethylMix: an R package for identifying DNA methylation-driven genes. Bioinformatics (Oxford, England). 2015;31(11):1839-41. doi:10.1093/bioinformatics/btv020. Gevaert O, Tibshirani R, Plevritis SK. Pancancer analysis of DNA methylation-driven genes using MethylMix. Genome Biology. 2015;16(1):17. doi:10.1186/s13059-014-0579-8. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray,DifferentialExpression,Pathways,Network Author: Olivier Gevaert Maintainer: Olivier Gevaert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MethylMix git_branch: RELEASE_3_12 git_last_commit: 79c129c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MethylMix_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MethylMix_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MethylMix_2.20.0.tgz vignettes: vignettes/MethylMix/inst/doc/vignettes.html vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/vignettes.R dependencyCount: 53 Package: methylMnM Version: 1.28.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: i386, x64 MD5sum: bc66eaa63c8540cf2e387dfb1f823177 NeedsCompilation: yes Title: detect different methylation level (DMR) Description: To give the exactly p-value and q-value of MeDIP-seq and MRE-seq data for different samples comparation. biocViews: Software, DNAMethylation, Sequencing Author: Yan Zhou, Bo Zhang, Nan Lin, BaoXue Zhang and Ting Wang Maintainer: Yan Zhou git_url: https://git.bioconductor.org/packages/methylMnM git_branch: RELEASE_3_12 git_last_commit: 64fb516 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methylMnM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylMnM_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methylMnM_1.28.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylMnM/inst/doc/methylMnM.R importsMe: SIMD dependencyCount: 12 Package: methylPipe Version: 1.24.0 Depends: R (>= 3.2.0), methods, grDevices, graphics, stats, utils, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rsamtools Imports: marray, gplots, IRanges, BiocGenerics, Gviz, GenomicAlignments, Biostrings, parallel, data.table, GenomeInfoDb, S4Vectors Suggests: BSgenome.Hsapiens.UCSC.hg18, TxDb.Hsapiens.UCSC.hg18.knownGene, knitr, MethylSeekR License: GPL(>=2) Archs: i386, x64 MD5sum: 0a0a5d4749494616b382d50ab0e740da NeedsCompilation: yes Title: Base resolution DNA methylation data analysis Description: Memory efficient analysis of base resolution DNA methylation data in both the CpG and non-CpG sequence context. Integration of DNA methylation data derived from any methodology providing base- or low-resolution data. biocViews: MethylSeq, DNAMethylation, Coverage, Sequencing Author: Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/methylPipe git_branch: RELEASE_3_12 git_last_commit: 5afa573 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methylPipe_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylPipe_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methylPipe_1.24.0.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylPipe/inst/doc/methylPipe.R dependsOnMe: ListerEtAlBSseq importsMe: compEpiTools dependencyCount: 144 Package: MethylSeekR Version: 1.30.0 Depends: rtracklayer (>= 1.16.3), parallel (>= 2.15.1), mhsmm (>= 0.4.4) Imports: IRanges (>= 1.16.3), BSgenome (>= 1.26.1), GenomicRanges (>= 1.10.5), geneplotter (>= 1.34.0), graphics (>= 2.15.2), grDevices (>= 2.15.2), parallel (>= 2.15.2), stats (>= 2.15.2), utils (>= 2.15.2) Suggests: BSgenome.Hsapiens.UCSC.hg18 License: GPL (>=2) MD5sum: 1036b75fec464eb2e9ec4bcd6ac7e4bf NeedsCompilation: no Title: Segmentation of Bis-seq data Description: This is a package for the discovery of regulatory regions from Bis-seq data biocViews: Sequencing, MethylSeq, DNAMethylation Author: Lukas Burger, Dimos Gaidatzis, Dirk Schubeler and Michael Stadler Maintainer: Lukas Burger git_url: https://git.bioconductor.org/packages/MethylSeekR git_branch: RELEASE_3_12 git_last_commit: f3dee06 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MethylSeekR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MethylSeekR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MethylSeekR_1.30.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylSeekR/inst/doc/MethylSeekR.R suggestsMe: methylPipe, RnBeads dependencyCount: 71 Package: methylSig Version: 1.2.0 Depends: R (>= 3.6) Imports: bsseq, DelayedArray, DelayedMatrixStats, DSS, IRanges, GenomeInfoDb, GenomicRanges, methods, parallel, stats, S4Vectors Suggests: BiocStyle, bsseqData, knitr, rmarkdown, testthat (>= 2.1.0), covr License: GPL-3 MD5sum: 128a3a288242be31b83cfc88c07a4b44 NeedsCompilation: no Title: MethylSig: Differential Methylation Testing for WGBS and RRBS Data Description: MethylSig is a package for testing for differentially methylated cytosines (DMCs) or regions (DMRs) in whole-genome bisulfite sequencing (WGBS) or reduced representation bisulfite sequencing (RRBS) experiments. MethylSig uses a beta binomial model to test for significant differences between groups of samples. Several options exist for either site-specific or sliding window tests, and variance estimation. biocViews: DNAMethylation, DifferentialMethylation, Epigenetics, Regression, MethylSeq Author: Yongseok Park [aut], Raymond G. Cavalcante [aut, cre] Maintainer: Raymond G. Cavalcante VignetteBuilder: knitr BugReports: https://github.com/sartorlab/methylSig/issues git_url: https://git.bioconductor.org/packages/methylSig git_branch: RELEASE_3_12 git_last_commit: 649ed96 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methylSig_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylSig_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methylSig_1.2.0.tgz vignettes: vignettes/methylSig/inst/doc/updating-methylSig-code.html, vignettes/methylSig/inst/doc/using-methylSig.html vignetteTitles: Updating methylSig code, Using methylSig hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylSig/inst/doc/updating-methylSig-code.R, vignettes/methylSig/inst/doc/using-methylSig.R dependencyCount: 71 Package: methylumi Version: 2.36.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, methyAnalysis, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: e5c5cf78b7f6a4611540a297281e7708 NeedsCompilation: no Title: Handle Illumina methylation data Description: This package provides classes for holding and manipulating Illumina methylation data. Based on eSet, it can contain MIAME information, sample information, feature information, and multiple matrices of data. An "intelligent" import function, methylumiR can read the Illumina text files and create a MethyLumiSet. methylumIDAT can directly read raw IDAT files from HumanMethylation27 and HumanMethylation450 microarrays. Normalization, background correction, and quality control features for GoldenGate, Infinium, and Infinium HD arrays are also included. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl, CpGIsland Author: Sean Davis, Pan Du, Sven Bilke, Tim Triche, Jr., Moiz Bootwalla Maintainer: Sean Davis VignetteBuilder: knitr BugReports: https://github.com/seandavi/methylumi/issues/new git_url: https://git.bioconductor.org/packages/methylumi git_branch: RELEASE_3_12 git_last_commit: 5fb0b60 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/methylumi_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methylumi_2.35.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methylumi_2.36.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methylumi/inst/doc/methylumi.R, vignettes/methylumi/inst/doc/methylumi450k.R dependsOnMe: bigmelon, RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, methyAnalysis, missMethyl dependencyCount: 144 Package: methyvim Version: 1.11.0 Depends: R (>= 3.4.0) Imports: stats, cluster, methods, ggplot2, ggsci, gridExtra, superheat, dplyr, gtools, tmle (>= 1.4.0.1), future, doFuture, S4Vectors, BiocGenerics, BiocParallel, SummarizedExperiment, GenomeInfoDb, bumphunter, IRanges, limma, minfi Suggests: testthat, knitr, rmarkdown, BiocStyle, SuperLearner, earth, nnet, gam, arm, snow, parallel, BatchJobs, minfiData, methyvimData License: file LICENSE MD5sum: b6b4a5071fcd9defc71b131768425055 NeedsCompilation: no Title: Targeted, Robust, and Model-free Differential Methylation Analysis Description: This package provides facilities for differential methylation analysis based on variable importance measures (VIMs), a class of statistical target parameters that arise in causal inference. The estimation and inference procedures provided are nonparametric, relying on ensemble machine learning to flexibly assess functional relationships among covariates and the outcome of interest. These tools can be applied to differential methylation at the level of CpG sites, to obtain valid statistical inference even after corrections for multiple hypothesis testing. biocViews: Clustering, DNAMethylation, DifferentialMethylation, MethylationArray, MethylSeq Author: Nima Hejazi [aut, cre, cph] (), Rachael Phillips [ctb] (), Mark van der Laan [aut, ths] (), Alan Hubbard [ctb, ths] () Maintainer: Nima Hejazi URL: https://github.com/nhejazi/methyvim VignetteBuilder: knitr BugReports: https://github.com/nhejazi/methyvim/issues git_url: https://git.bioconductor.org/packages/methyvim git_branch: master git_last_commit: 7b4ee9f git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/methyvim_1.11.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/methyvim_1.11.0.zip mac.binary.ver: bin/macosx/contrib/4.0/methyvim_1.11.0.tgz vignettes: vignettes/methyvim/inst/doc/using_methyvim.html vignetteTitles: Targeted Data-Adaptive Estimation and Inference for Differential Methylation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/methyvim/inst/doc/using_methyvim.R dependencyCount: 162 Package: MetID Version: 1.8.0 Depends: R (>= 3.5) Imports: utils (>= 3.3.1), stats (>= 3.4.2), devtools (>= 1.13.0), stringr (>= 1.3.0), Matrix (>= 1.2-12), igraph (>= 1.2.1), ChemmineR (>= 2.30.2) Suggests: knitr (>= 1.19), rmarkdown (>= 1.8) License: Artistic-2.0 MD5sum: 57112e47542a2c7a915529e64a66e590 NeedsCompilation: no Title: Network-based prioritization of putative metabolite IDs Description: This package uses an innovative network-based approach that will enhance our ability to determine the identities of significant ions detected by LC-MS. biocViews: AssayDomain, BiologicalQuestion, Infrastructure, ResearchField, StatisticalMethod, Technology, WorkflowStep, Network, KEGG Author: Zhenzhi Li Maintainer: Zhenzhi Li URL: https://github.com/ressomlab/MetID VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetID git_branch: RELEASE_3_12 git_last_commit: c5f3d4c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MetID_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MetID_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MetID_1.8.0.tgz vignettes: vignettes/MetID/inst/doc/Introduction_to_MetID.html vignetteTitles: Introduction to MetID hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetID/inst/doc/Introduction_to_MetID.R dependencyCount: 115 Package: MetNet Version: 1.8.0 Depends: R (>= 3.6) Imports: bnlearn (>= 4.3), BiocParallel (>= 1.12.0), GENIE3 (>= 1.7.0), methods (>= 3.5), mpmi (>= 0.42), parmigene (>= 1.0.2), ppcor (>= 1.1), sna (>= 2.4), stabs (>= 0.6), stats (>= 3.6) Suggests: BiocGenerics (>= 0.24.0), BiocStyle (>= 2.6.1), glmnet (>= 2.0-18), igraph (>= 1.1.2), knitr (>= 1.11), rmarkdown (>= 1.15), testthat (>= 2.2.1) License: GPL (>= 3) MD5sum: 41f31e1ed88f5a2c71ad4c60a832ce5d NeedsCompilation: no Title: Inferring metabolic networks from untargeted high-resolution mass spectrometry data Description: MetNet contains functionality to infer metabolic network topologies from quantitative data and high-resolution mass/charge information. Using statistical models (including correlation, mutual information, regression and Bayes statistics) and quantitative data (intensity values of features) adjacency matrices are inferred that can be combined to a consensus matrix. Mass differences calculated between mass/charge values of features will be matched against a data frame of supplied mass/charge differences referring to transformations of enzymatic activities. In a third step, the two matrices are combined to form a adjacency matrix inferred from both quantitative and structure information. biocViews: ImmunoOncology, Metabolomics, MassSpectrometry, Network, Regression Author: Thomas Naake [aut, cre] Maintainer: Thomas Naake VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MetNet git_branch: RELEASE_3_12 git_last_commit: d0b799b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MetNet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MetNet_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MetNet_1.8.0.tgz vignettes: vignettes/MetNet/inst/doc/MetNet.html vignetteTitles: Workflow for high-resolution metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MetNet/inst/doc/MetNet.R dependencyCount: 47 Package: mfa Version: 1.12.0 Depends: R (>= 3.4.0) Imports: methods, stats, ggplot2, Rcpp, dplyr, ggmcmc, MCMCpack, MCMCglmm, coda, magrittr, tibble, Biobase LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: abe9721dcca13b4b2804820b91b96bbd NeedsCompilation: yes Title: Bayesian hierarchical mixture of factor analyzers for modelling genomic bifurcations Description: MFA models genomic bifurcations using a Bayesian hierarchical mixture of factor analysers. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mfa git_branch: RELEASE_3_12 git_last_commit: ed3c0b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mfa_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mfa_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mfa_1.12.0.tgz vignettes: vignettes/mfa/inst/doc/introduction_to_mfa.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mfa/inst/doc/introduction_to_mfa.R suggestsMe: splatter dependencyCount: 71 Package: Mfuzz Version: 2.50.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 85457b8078daad46522f4f6e3d9bd5f3 NeedsCompilation: no Title: Soft clustering of time series gene expression data Description: Package for noise-robust soft clustering of gene expression time-series data (including a graphical user interface) biocViews: Microarray, Clustering, TimeCourse, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://mfuzz.sysbiolab.eu/ git_url: https://git.bioconductor.org/packages/Mfuzz git_branch: RELEASE_3_12 git_last_commit: 5943b62 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Mfuzz_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Mfuzz_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Mfuzz_2.50.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mfuzz/inst/doc/Mfuzz.R dependsOnMe: cycle, TimiRGeN importsMe: Patterns suggestsMe: pwOmics dependencyCount: 17 Package: MGFM Version: 1.24.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 22fe706275d750f4ba89f439b7b36568 NeedsCompilation: no Title: Marker Gene Finder in Microarray gene expression data Description: The package is designed to detect marker genes from Microarray gene expression data sets biocViews: Genetics, GeneExpression, Microarray Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFM git_branch: RELEASE_3_12 git_last_commit: 3fcefea git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MGFM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MGFM_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MGFM_1.24.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFM/inst/doc/MGFM.R dependsOnMe: sampleClassifier dependencyCount: 38 Package: MGFR Version: 1.16.0 Depends: R (>= 3.5) Imports: biomaRt, annotate License: GPL-3 MD5sum: b3f4407db4d1e90cac2e55e46d202cce NeedsCompilation: no Title: Marker Gene Finder in RNA-seq data Description: The package is designed to detect marker genes from RNA-seq data. biocViews: ImmunoOncology, Genetics, GeneExpression, RNASeq Author: Khadija El Amrani Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/MGFR git_branch: RELEASE_3_12 git_last_commit: 93b83a8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MGFR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MGFR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MGFR_1.16.0.tgz vignettes: vignettes/MGFR/inst/doc/MGFR.pdf vignetteTitles: Using MGFR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MGFR/inst/doc/MGFR.R dependsOnMe: sampleClassifier dependencyCount: 63 Package: mgsa Version: 1.38.0 Depends: R (>= 2.14.0), methods, gplots Imports: graphics, stats, utils Suggests: DBI, RSQLite, GO.db, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 2ce5f5ea1166d4431d7fc1075cfbf340 NeedsCompilation: yes Title: Model-based gene set analysis Description: Model-based Gene Set Analysis (MGSA) is a Bayesian modeling approach for gene set enrichment. The package mgsa implements MGSA and tools to use MGSA together with the Gene Ontology. biocViews: Pathways, GO, GeneSetEnrichment Author: Sebastian Bauer , Julien Gagneur Maintainer: Sebastian Bauer URL: https://github.com/sba1/mgsa-bioc git_url: https://git.bioconductor.org/packages/mgsa git_branch: RELEASE_3_12 git_last_commit: 82398b1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mgsa_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mgsa_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mgsa_1.38.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mgsa/inst/doc/mgsa.R dependencyCount: 9 Package: MiChip Version: 1.44.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 2bef98a5980811d9d7951acdf8f33b50 NeedsCompilation: no Title: MiChip Parsing and Summarizing Functions Description: This package takes the MiChip miRNA microarray .grp scanner output files and parses these out, providing summary and plotting functions to analyse MiChip hybridizations. A set of hybridizations is packaged into an ExpressionSet allowing it to be used by other BioConductor packages. biocViews: Microarray, Preprocessing Author: Jonathon Blake Maintainer: Jonathon Blake git_url: https://git.bioconductor.org/packages/MiChip git_branch: RELEASE_3_12 git_last_commit: ddea57d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MiChip_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MiChip_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MiChip_1.44.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiChip/inst/doc/MiChip.R dependencyCount: 7 Package: microbiome Version: 1.12.0 Depends: R (>= 3.6.0), phyloseq, ggplot2 Imports: dplyr, reshape2, Rtsne, scales, stats, tibble, tidyr, utils, vegan Suggests: BiocGenerics, BiocStyle, Cairo, knitcitations, knitr, rmarkdown, testthat License: BSD_2_clause + file LICENSE MD5sum: cb9f05ff8b0ab803d8924a57bf713ef7 NeedsCompilation: no Title: Microbiome Analytics Description: Utilities for microbiome analysis. biocViews: Metagenomics,Microbiome,Sequencing,SystemsBiology Author: Leo Lahti [aut, cre], Sudarshan Shetty [aut] Maintainer: Leo Lahti URL: http://microbiome.github.io/microbiome VignetteBuilder: knitr BugReports: https://github.com/microbiome/microbiome/issues git_url: https://git.bioconductor.org/packages/microbiome git_branch: RELEASE_3_12 git_last_commit: 44e86d2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/microbiome_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/microbiome_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/microbiome_1.12.0.tgz vignettes: vignettes/microbiome/inst/doc/vignette.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiome/inst/doc/vignette.R importsMe: ANCOMBC suggestsMe: ANCOMBC dependencyCount: 83 Package: microbiomeDASim Version: 1.4.0 Depends: R (>= 3.6.0) Imports: graphics, ggplot2, MASS, tmvtnorm, Matrix, mvtnorm, pbapply, stats, phyloseq, metagenomeSeq, Biobase Suggests: testthat (>= 2.1.0), knitr, devtools License: MIT + file LICENSE MD5sum: c2089278dd7a8ea0cf64fd2e91b4e01b NeedsCompilation: no Title: Microbiome Differential Abundance Simulation Description: A toolkit for simulating differential microbiome data designed for longitudinal analyses. Several functional forms may be specified for the mean trend. Observations are drawn from a multivariate normal model. The objective of this package is to be able to simulate data in order to accurately compare different longitudinal methods for differential abundance. biocViews: Microbiome, Visualization, Software Author: Justin Williams, Hector Corrada Bravo, Jennifer Tom, Joseph Nathaniel Paulson Maintainer: Justin Williams URL: https://github.com/williazo/microbiomeDASim VignetteBuilder: knitr BugReports: https://github.com/williazo/microbiomeDASim/issues git_url: https://git.bioconductor.org/packages/microbiomeDASim git_branch: RELEASE_3_12 git_last_commit: 55fb35a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/microbiomeDASim_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/microbiomeDASim_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/microbiomeDASim_1.4.0.tgz vignettes: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.pdf vignetteTitles: microbiomeDASim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeDASim/inst/doc/microbiomeDASim.R dependencyCount: 94 Package: microbiomeExplorer Version: 1.0.1 Depends: shiny, magrittr, metagenomeSeq, Biobase Imports: shinyjs, shinydashboard, shinycssloaders, shinyWidgets, rmarkdown (>= 1.9.0), DESeq2, RColorBrewer, dplyr, tidyr, rlang, knitr, readr, DT (>= 0.12.0), biomformat, tools, stringr, vegan, matrixStats, heatmaply, car, broom, limma, reshape2, tibble, forcats, lubridate, methods, plotly (>= 4.9.1) Suggests: V8, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: ededaa0576ddaf9c75dbc072931b41cc NeedsCompilation: no Title: Microbiome Exploration App Description: The MicrobiomeExplorer R package is designed to facilitate the analysis and visualization of marker-gene survey feature data. It allows a user to perform and visualize typical microbiome analytical workflows either through the command line or an interactive Shiny application included with the package. In addition to applying common analytical workflows the application enables automated analysis report generation. biocViews: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software, ImmunoOncology Author: Joseph Paulson [aut], Janina Reeder [aut, cre], Mo Huang [aut], Genentech [cph, fnd] Maintainer: Janina Reeder VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/microbiomeExplorer git_branch: RELEASE_3_12 git_last_commit: 17f035a git_last_commit_date: 2020-10-28 Date/Publication: 2020-10-29 source.ver: src/contrib/microbiomeExplorer_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/microbiomeExplorer_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/microbiomeExplorer_1.0.1.tgz vignettes: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.html vignetteTitles: microbiomeExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/microbiomeExplorer/inst/doc/exploreMouseData.R dependencyCount: 201 Package: MicrobiotaProcess Version: 1.2.2 Depends: R (>= 4.0.0) Imports: ape, plyr, tidyr, ggplot2, phyloseq, magrittr, dplyr, Biostrings, ggrepel, vegan, reshape, zoo, ggtree, tidytree, gtools, MASS, methods, rlang, tibble, grDevices, stats, utils, coin, ggsignif, Rmisc, patchwork, ggstar Suggests: DT, rmarkdown, prettydoc, treeio, tidyverse, testthat, knitr, nlme, phangorn, DECIPHER, randomForest, biomformat, scales, yaml License: GPL (>= 3.0) MD5sum: afd069d9c0a1cbf956dddb61cd19d12e NeedsCompilation: no Title: an R package for analysis, visualization and biomarker discovery of microbiome Description: MicrobiotaProcess is an R package for analysis, visualization and biomarker discovery of microbial datasets. It supports calculating alpha index and provides functions to visualize rarefaction curves. Moreover, it also supports visualizing the abundance of taxonomy of samples. And It also provides functions to perform the PCA, PCoA and hierarchical cluster analysis. In addition, MicrobiotaProcess also provides a method for the biomarker discovery of metagenome or other datasets. biocViews: Visualization, Microbiome, Software, MultipleComparison, FeatureExtraction Author: Shuangbin Xu [aut, cre] (), Guangchuang Yu [aut, ctb] () Maintainer: Shuangbin Xu URL: https://github.com/YuLab-SMU/MicrobiotaProcess/ VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/MicrobiotaProcess/issues git_url: https://git.bioconductor.org/packages/MicrobiotaProcess git_branch: RELEASE_3_12 git_last_commit: 3645ef5 git_last_commit_date: 2021-04-17 Date/Publication: 2021-04-17 source.ver: src/contrib/MicrobiotaProcess_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/MicrobiotaProcess_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/MicrobiotaProcess_1.2.2.tgz vignettes: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess-basics.html, vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess-biomaker-discovery.html vignetteTitles: MicrobiotaProcess: basics analysis using MicrobiotaProcess., MicrobiotaProcess: biomarker discovery using MicrobiotaProcess. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess-basics.R, vignettes/MicrobiotaProcess/inst/doc/MicrobiotaProcess-biomaker-discovery.R dependencyCount: 106 Package: microRNA Version: 1.48.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) License: Artistic-2.0 Archs: i386, x64 MD5sum: 5b492de8979ab1b3496369b48ccf5211 NeedsCompilation: yes Title: Data and functions for dealing with microRNAs Description: Different data resources for microRNAs and some functions for manipulating them. biocViews: Infrastructure, GenomeAnnotation, SequenceMatching Author: R. Gentleman, S. Falcon Maintainer: "James F. Reid" git_url: https://git.bioconductor.org/packages/microRNA git_branch: RELEASE_3_12 git_last_commit: 6afc34c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/microRNA_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/microRNA_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/microRNA_1.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rtracklayer dependencyCount: 15 Package: MIGSA Version: 1.14.1 Depends: R (>= 3.4), methods, BiocGenerics Imports: AnnotationDbi, Biobase, BiocParallel, compiler, data.table, edgeR, futile.logger, ggdendro, ggplot2, GO.db, GOstats, graph, graphics, grDevices, grid, GSEABase, ismev, jsonlite, limma, matrixStats, org.Hs.eg.db, RBGL, reshape2, Rgraphviz, stats, utils, vegan Suggests: BiocStyle, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX, knitr, mGSZ, MIGSAdata, RUnit License: GPL (>= 2) MD5sum: 50e1286b04099e0d2c79d91f1d3ad8cd NeedsCompilation: no Title: Massive and Integrative Gene Set Analysis Description: Massive and Integrative Gene Set Analysis. The MIGSA package allows to perform a massive and integrative gene set analysis over several expression and gene sets simultaneously. It provides a common gene expression analytic framework that grants a comprehensive and coherent analysis. Only a minimal user parameter setting is required to perform both singular and gene set enrichment analyses in an integrative manner by means of the best available methods, i.e. dEnricher and mGSZ respectively. The greatest strengths of this big omics data tool are the availability of several functions to explore, analyze and visualize its results in order to facilitate the data mining task over huge information sources. MIGSA package also provides several functions that allow to easily load the most updated gene sets from several repositories. biocViews: Software, GeneSetEnrichment, Visualization, GeneExpression, Microarray, RNASeq, KEGG Author: Juan C. Rodriguez, Cristobal Fresno, Andrea S. Llera and Elmer A. Fernandez Maintainer: Juan C. Rodriguez URL: https://github.com/jcrodriguez1989/MIGSA/ BugReports: https://github.com/jcrodriguez1989/MIGSA/issues git_url: https://git.bioconductor.org/packages/MIGSA git_branch: RELEASE_3_12 git_last_commit: 270bd75 git_last_commit_date: 2020-10-28 Date/Publication: 2020-10-29 source.ver: src/contrib/MIGSA_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MIGSA_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MIGSA_1.14.1.tgz vignettes: vignettes/MIGSA/inst/doc/gettingPbcmcData.pdf, vignettes/MIGSA/inst/doc/gettingTcgaData.pdf, vignettes/MIGSA/inst/doc/MIGSA.pdf vignetteTitles: Getting pbcmc datasets, Getting TCGA datasets, Massive and Integrative Gene Set Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIGSA/inst/doc/gettingPbcmcData.R, vignettes/MIGSA/inst/doc/gettingTcgaData.R, vignettes/MIGSA/inst/doc/MIGSA.R dependencyCount: 101 Package: mimager Version: 1.14.0 Depends: Biobase Imports: BiocGenerics, S4Vectors, preprocessCore, grDevices, methods, grid, gtable, scales, DBI, affy, affyPLM, oligo, oligoClasses Suggests: knitr, rmarkdown, BiocStyle, testthat, lintr, Matrix, abind, affydata, hgu95av2cdf, oligoData, pd.hugene.1.0.st.v1 License: MIT + file LICENSE MD5sum: 5376014d2d487c0377a1e994bfeab839 NeedsCompilation: no Title: mimager: The Microarray Imager Description: Easily visualize and inspect microarrays for spatial artifacts. biocViews: Infrastructure, Visualization, Microarray Author: Aaron Wolen [aut, cre, cph] Maintainer: Aaron Wolen URL: https://github.com/aaronwolen/mimager VignetteBuilder: knitr BugReports: https://github.com/aaronwolen/mimager/issues git_url: https://git.bioconductor.org/packages/mimager git_branch: RELEASE_3_12 git_last_commit: 79b8f00 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mimager_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mimager_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mimager_1.14.0.tgz vignettes: vignettes/mimager/inst/doc/introduction.html vignetteTitles: mimager overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mimager/inst/doc/introduction.R dependencyCount: 67 Package: MIMOSA Version: 1.28.1 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, dplyr, tidyr, rlang LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: MIT + file LICENSE Archs: i386, x64 MD5sum: f0f8ccd4d4d5f7590d8471d480ea3ad0 NeedsCompilation: yes Title: Mixture Models for Single-Cell Assays Description: Modeling count data using Dirichlet-multinomial and beta-binomial mixtures with applications to single-cell assays. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MIMOSA git_branch: RELEASE_3_12 git_last_commit: 79e49a2 git_last_commit_date: 2020-11-09 Date/Publication: 2020-11-10 source.ver: src/contrib/MIMOSA_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MIMOSA_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MIMOSA_1.28.1.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R dependencyCount: 91 Package: MineICA Version: 1.30.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.13.8), Biobase, plyr, ggplot2, scales, foreach, xtable, biomaRt, gtools, GOstats, cluster, marray, mclust, RColorBrewer, colorspace, igraph, Rgraphviz, graph, annotate, Hmisc, fastICA, JADE Imports: AnnotationDbi, lumi, fpc, lumiHumanAll.db Suggests: biomaRt, GOstats, cluster, hgu133a.db, mclust, igraph, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerVDX, future, future.apply Enhances: doMC License: GPL-2 MD5sum: f2ab701e9cdf2a3e269d13e1d725b0de NeedsCompilation: no Title: Analysis of an ICA decomposition obtained on genomics data Description: The goal of MineICA is to perform Independent Component Analysis (ICA) on multiple transcriptome datasets, integrating additional data (e.g molecular, clinical and pathological). This Integrative ICA helps the biological interpretation of the components by studying their association with variables (e.g sample annotations) and gene sets, and enables the comparison of components from different datasets using correlation-based graph. biocViews: Visualization, MultipleComparison Author: Anne Biton Maintainer: Anne Biton git_url: https://git.bioconductor.org/packages/MineICA git_branch: RELEASE_3_12 git_last_commit: 136dd69 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MineICA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MineICA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MineICA_1.30.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MineICA/inst/doc/MineICA.R dependencyCount: 199 Package: minet Version: 3.48.0 Imports: infotheo License: Artistic-2.0 Archs: i386, x64 MD5sum: 4c6cb2b6be2ffec6c7a39a6b5774101d NeedsCompilation: yes Title: Mutual Information NETworks Description: This package implements various algorithms for inferring mutual information networks from data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference Author: Patrick E. Meyer, Frederic Lafitte, Gianluca Bontempi Maintainer: Patrick E. Meyer URL: http://minet.meyerp.com git_url: https://git.bioconductor.org/packages/minet git_branch: RELEASE_3_12 git_last_commit: f4d0391 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/minet_3.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/minet_3.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/minet_3.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: coexnet, epiNEM, RTN, TCGAWorkflow, TGS suggestsMe: CNORfeeder, predictionet, TCGAbiolinks, WGCNA dependencyCount: 1 Package: minfi Version: 1.36.0 Depends: methods, BiocGenerics (>= 0.15.3), GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, Biobase (>= 2.33.2), IRanges, beanplot, RColorBrewer, lattice, nor1mix, siggenes, limma, preprocessCore, illuminaio (>= 0.23.2), DelayedMatrixStats (>= 1.3.4), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils, DelayedArray (>= 0.15.16), HDF5Array, BiocParallel Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.18.0), minfiDataEPIC, FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown, tools License: Artistic-2.0 MD5sum: 7058aab43dbb644a75ad7c5e8489d20d NeedsCompilation: no Title: Analyze Illumina Infinium DNA methylation arrays Description: Tools to analyze & visualize Illumina Infinium methylation arrays. biocViews: ImmunoOncology, DNAMethylation, DifferentialMethylation, Epigenetics, Microarray, MethylationArray, MultiChannel, TwoChannel, DataImport, Normalization, Preprocessing, QualityControl Author: Kasper Daniel Hansen [cre, aut], Martin Aryee [aut], Rafael A. Irizarry [aut], Andrew E. Jaffe [ctb], Jovana Maksimovic [ctb], E. Andres Houseman [ctb], Jean-Philippe Fortin [ctb], Tim Triche [ctb], Shan V. Andrews [ctb], Peter F. Hickey [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/hansenlab/minfi VignetteBuilder: knitr BugReports: https://github.com/hansenlab/minfi/issues git_url: https://git.bioconductor.org/packages/minfi git_branch: RELEASE_3_12 git_last_commit: 94301da git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/minfi_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/minfi_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/minfi_1.36.0.tgz vignettes: vignettes/minfi/inst/doc/minfi.html vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: bigmelon, ChAMP, compartmap, conumee, DMRcate, methylumi, REMP, shinyMethyl, IlluminaHumanMethylation27kanno.ilmn12.hg19, IlluminaHumanMethylation27kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICanno.ilm10b3.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19, IlluminaHumanMethylationEPICmanifest, FlowSorted.Blood.450k, FlowSorted.Blood.EPIC, FlowSorted.CordBlood.450k, FlowSorted.CordBloodCombined.450k, FlowSorted.CordBloodNorway.450k, FlowSorted.DLPFC.450k, methyvimData, minfiData, minfiDataEPIC, methylationArrayAnalysis importsMe: EnMCB, funtooNorm, MEAL, MEAT, MethylAid, methylCC, methylumi, methyvim, missMethyl, quantro, recountmethylation, skewr suggestsMe: epivizr, epivizrChart, Harman, mCSEA, MultiDataSet, RnBeads, sesame, brgedata, MLML2R dependencyCount: 129 Package: MinimumDistance Version: 1.34.0 Depends: R (>= 3.5.0), VanillaICE (>= 1.47.1) Imports: methods, BiocGenerics, MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 1.15.4), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: 29f7f8d8366c780b855ad72472f07199 NeedsCompilation: no Title: A Package for De Novo CNV Detection in Case-Parent Trios Description: Analysis of de novo copy number variants in trios from high-dimensional genotyping platforms. biocViews: Microarray, SNP, CopyNumberVariation Author: Robert B Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/MinimumDistance git_branch: RELEASE_3_12 git_last_commit: 50ebfdd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MinimumDistance_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MinimumDistance_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MinimumDistance_1.34.0.tgz vignettes: vignettes/MinimumDistance/inst/doc/MinimumDistance.pdf vignetteTitles: Detection of de novo copy number alterations in case-parent trios hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MinimumDistance/inst/doc/MinimumDistance.R dependencyCount: 81 Package: MiPP Version: 1.62.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: 200eec9d3b771676a87f9426ad1362e1 NeedsCompilation: no Title: Misclassification Penalized Posterior Classification Description: This package finds optimal sets of genes that seperate samples into two or more classes. biocViews: Microarray, Classification Author: HyungJun Cho , Sukwoo Kim , Mat Soukup , and Jae K. Lee Maintainer: Sukwoo Kim URL: http://www.healthsystem.virginia.edu/internet/hes/biostat/bioinformatics/ git_url: https://git.bioconductor.org/packages/MiPP git_branch: RELEASE_3_12 git_last_commit: 08355af git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MiPP_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MiPP_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MiPP_1.62.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 12 Package: MIRA Version: 1.12.0 Depends: R (>= 3.5) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table, ggplot2, Biobase, stats, bsseq, methods Suggests: knitr, parallel, testthat, BiocStyle, rmarkdown, AnnotationHub, LOLA License: GPL-3 MD5sum: 783f43260beeb34056a59296b9a7fcd3 NeedsCompilation: no Title: Methylation-Based Inference of Regulatory Activity Description: DNA methylation contains information about the regulatory state of the cell. MIRA aggregates genome-scale DNA methylation data into a DNA methylation profile for a given region set with shared biological annotation. Using this profile, MIRA infers and scores the collective regulatory activity for the region set. MIRA facilitates regulatory analysis in situations where classical regulatory assays would be difficult and allows public sources of region sets to be leveraged for novel insight into the regulatory state of DNA methylation datasets. biocViews: ImmunoOncology, DNAMethylation, GeneRegulation, GenomeAnnotation, SystemsBiology, FunctionalGenomics, ChIPSeq, MethylSeq, Sequencing, Epigenetics, Coverage Author: Nathan Sheffield [aut], Christoph Bock [ctb], John Lawson [aut, cre] Maintainer: John Lawson URL: http://databio.org/mira VignetteBuilder: knitr BugReports: https://github.com/databio/MIRA git_url: https://git.bioconductor.org/packages/MIRA git_branch: RELEASE_3_12 git_last_commit: c1a0840 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MIRA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MIRA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MIRA_1.12.0.tgz vignettes: vignettes/MIRA/inst/doc/BiologicalApplication.html, vignettes/MIRA/inst/doc/GettingStarted.html vignetteTitles: Applying MIRA to a Biological Question, Getting Started with Methylation-based Inference of Regulatory Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIRA/inst/doc/BiologicalApplication.R, vignettes/MIRA/inst/doc/GettingStarted.R importsMe: COCOA dependencyCount: 87 Package: MiRaGE Version: 1.32.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi, BiocManager Suggests: seqinr (>= 3.0.7), biomaRt (>= 2.19.1), GenomicFeatures (>= 1.15.4), Biostrings (>= 2.31.3), BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, miRNATarget, humanStemCell, IRanges, GenomicRanges (>= 1.8.3), BSgenome, beadarrayExampleData License: GPL MD5sum: 9aeb415c6e876f13e3b6b166bea9de93 NeedsCompilation: no Title: MiRNA Ranking by Gene Expression Description: The package contains functions for inferece of target gene regulation by miRNA, based on only target gene expression profile. biocViews: ImmunoOncology, Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi git_url: https://git.bioconductor.org/packages/MiRaGE git_branch: RELEASE_3_12 git_last_commit: 6e2e75a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MiRaGE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MiRaGE_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MiRaGE_1.32.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MiRaGE/inst/doc/MiRaGE.R dependencyCount: 27 Package: miRBaseConverter Version: 1.14.0 Depends: R (>= 3.4) Imports: stats Suggests: BiocGenerics, RUnit, knitr, rtracklayer, utils License: GPL (>= 2) MD5sum: ee864f94ab1cdb12764d529ef93a3bbd NeedsCompilation: no Title: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions Description: A comprehensive tool for converting and retrieving the miRNA Name, Accession, Sequence, Version, History and Family information in different miRBase versions. It can process a huge number of miRNAs in a short time without other depends. biocViews: Software, miRNA Author: Taosheng Xu, Thuc Le Maintainer: Taosheng Xu URL: https://github.com/taoshengxu/miRBaseConverter VignetteBuilder: knitr BugReports: https://github.com/taoshengxu/miRBaseConverter/issues git_url: https://git.bioconductor.org/packages/miRBaseConverter git_branch: RELEASE_3_12 git_last_commit: 6e8f8d4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/miRBaseConverter_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRBaseConverter_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/miRBaseConverter_1.14.0.tgz vignettes: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.html vignetteTitles: "miRBaseConverter: A comprehensive and high-efficiency tool for converting and retrieving the information of miRNAs in different miRBase versions" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRBaseConverter/inst/doc/miRBaseConverter-vignette.R dependencyCount: 1 Package: miRcomp Version: 1.20.0 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics, shiny License: GPL-3 | file LICENSE MD5sum: 63ee249d3ff19dd03b1c27a174dc98dd NeedsCompilation: no Title: Tools to assess and compare miRNA expression estimatation methods Description: Based on a large miRNA dilution study, this package provides tools to read in the raw amplification data and use these data to assess the performance of methods that estimate expression from the amplification curves. biocViews: Software, qPCR, Preprocessing, QualityControl Author: Matthew N. McCall , Lauren Kemperman Maintainer: Matthew N. McCall VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRcomp git_branch: RELEASE_3_12 git_last_commit: 659117c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/miRcomp_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRcomp_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/miRcomp_1.20.0.tgz vignettes: vignettes/miRcomp/inst/doc/miRcomp.html vignetteTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R dependencyCount: 9 Package: mirIntegrator Version: 1.20.0 Depends: R (>= 3.3) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: fdaa9d155700dc9da2a14410fb584638 NeedsCompilation: no Title: Integrating microRNA expression into signaling pathways for pathway analysis Description: Tools for augmenting signaling pathways to perform pathway analysis of microRNA and mRNA expression levels. biocViews: Network, Microarray, GraphAndNetwork, Pathways, KEGG Author: Diana Diaz Maintainer: Diana Diaz URL: http://datad.github.io/mirIntegrator/ git_url: https://git.bioconductor.org/packages/mirIntegrator git_branch: RELEASE_3_12 git_last_commit: ff6ceea git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mirIntegrator_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mirIntegrator_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mirIntegrator_1.20.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mirIntegrator/inst/doc/mirIntegrator.R dependencyCount: 77 Package: miRLAB Version: 1.20.0 Imports: methods, stats, utils, RCurl, httr, stringr, Hmisc, energy, entropy, Roleswitch, gplots, glmnet, impute, limma, pcalg,TCGAbiolinks,dplyr,SummarizedExperiment, ctc, heatmap.plus, InvariantCausalPrediction, Category, GOstats, org.Hs.eg.db Suggests: knitr,BiocGenerics, AnnotationDbi,RUnit License: GPL (>=2) MD5sum: dbaf406b697b38a6336d45580399bcd3 NeedsCompilation: no Title: Dry lab for exploring miRNA-mRNA relationships Description: Provide tools exploring miRNA-mRNA relationships, including popular miRNA target prediction methods, ensemble methods that integrate individual methods, functions to get data from online resources, functions to validate the results, and functions to conduct enrichment analyses. biocViews: miRNA, GeneExpression, NetworkInference, Network Author: Thuc Duy Le, Junpeng Zhang, Mo Chen, Vu Viet Hoang Pham Maintainer: Thuc Duy Le URL: https://github.com/pvvhoang/miRLAB VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRLAB git_branch: RELEASE_3_12 git_last_commit: 07d532a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/miRLAB_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRLAB_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/miRLAB_1.20.0.tgz vignettes: vignettes/miRLAB/inst/doc/miRLAB-vignette.html vignetteTitles: miRLAB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R dependencyCount: 185 Package: miRmine Version: 1.12.0 Depends: R (>= 3.4), SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, DESeq2 License: GPL (>= 3) MD5sum: b7b852bc522f02f1002e797dc00e7a89 NeedsCompilation: no Title: Data package with miRNA-seq datasets from miRmine database as RangedSummarizedExperiment Description: miRmine database is a collection of expression profiles from different publicly available miRNA-seq datasets, Panwar et al (2017) miRmine: A Database of Human miRNA Expression, prepared with this data package as RangedSummarizedExperiment. biocViews: Homo_sapiens_Data, RNASeqData, SequencingData, ExpressionData Author: Dusan Randjelovic [aut, cre] Maintainer: Dusan Randjelovic VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRmine git_branch: RELEASE_3_12 git_last_commit: 9e3497d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/miRmine_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRmine_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/miRmine_1.12.0.tgz vignettes: vignettes/miRmine/inst/doc/miRmine.html vignetteTitles: miRmine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRmine/inst/doc/miRmine.R dependencyCount: 26 Package: miRNAmeConverter Version: 1.18.0 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi, reshape2 Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: a941296fe52771eef5b8d27f1b906b97 NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Translating mature miRNA names to different miRBase versions, sequence retrieval, checking names for validity and detecting miRBase version of a given set of names (data from http://www.mirbase.org/). biocViews: Preprocessing, miRNA Author: Stefan Haunsberger [aut, cre] Maintainer: Stefan J. Haunsberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/miRNAmeConverter git_branch: RELEASE_3_12 git_last_commit: ffa8bdb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/miRNAmeConverter_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRNAmeConverter_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/miRNAmeConverter_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 34 Package: miRNApath Version: 1.50.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: 41f2664642344c157d3131939f8a9885 NeedsCompilation: no Title: miRNApath: Pathway Enrichment for miRNA Expression Data Description: This package provides pathway enrichment techniques for miRNA expression data. Specifically, the set of methods handles the many-to-many relationship between miRNAs and the multiple genes they are predicted to target (and thus affect.) It also handles the gene-to-pathway relationships separately. Both steps are designed to preserve the additive effects of miRNAs on genes, many miRNAs affecting one gene, one miRNA affecting multiple genes, or many miRNAs affecting many genes. biocViews: Annotation, Pathways, DifferentialExpression, NetworkEnrichment, miRNA Author: James M. Ward with contributions from Yunling Shi, Cindy Richards, John P. Cogswell Maintainer: James M. Ward git_url: https://git.bioconductor.org/packages/miRNApath git_branch: RELEASE_3_12 git_last_commit: 1e8d60f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/miRNApath_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRNApath_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/miRNApath_1.50.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNApath/inst/doc/miRNApath.R dependencyCount: 1 Package: miRNAtap Version: 1.24.0 Depends: R (>= 3.3.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: acd86da2827c970e6f76ac60fe662031 NeedsCompilation: no Title: miRNAtap: microRNA Targets - Aggregated Predictions Description: The package facilitates implementation of workflows requiring miRNA predictions, it allows to integrate ranked miRNA target predictions from multiple sources available online and aggregate them with various methods which improves quality of predictions above any of the single sources. Currently predictions are available for Homo sapiens, Mus musculus and Rattus norvegicus (the last one through homology translation). biocViews: Software, Classification, Microarray, Sequencing, miRNA Author: Maciej Pajak, T. Ian Simpson Maintainer: T. Ian Simpson git_url: https://git.bioconductor.org/packages/miRNAtap git_branch: RELEASE_3_12 git_last_commit: 653c3f1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/miRNAtap_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRNAtap_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/miRNAtap_1.24.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R dependsOnMe: miRNAtap.db importsMe: SpidermiR, miRNAtap.db dependencyCount: 35 Package: miRSM Version: 1.8.4 Depends: R (>= 3.5.0) Imports: WGCNA, flashClust, dynamicTreeCut, GFA, igraph, linkcomm, MCL, NMF, biclust, iBBiG, fabia, BicARE, isa2, s4vd, BiBitR, rqubic, Biobase, PMA, stats, dbscan, subspace, mclust, SOMbrero, ppclust, miRspongeR, Rcpp, utils, SummarizedExperiment, GSEABase, org.Hs.eg.db, MatrixCorrelation, energy Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: x64 MD5sum: 0e64d07fc0bc5b81cf7c090b55034759 NeedsCompilation: yes Title: Inferring miRNA sponge modules in heterogeneous data Description: The package aims to identify miRNA sponge modules in heterogeneous data. It provides several functions to study miRNA sponge modules, including popular methods for inferring gene modules (candidate miRNA sponge modules), and a function to identify miRNA sponge modules, as well as several functions to conduct modular analysis of miRNA sponge modules. biocViews: GeneExpression, BiomedicalInformatics, Clustering, GeneSetEnrichment, Microarray, Software, GeneRegulation, GeneTarget Author: Junpeng Zhang [aut, cre] Maintainer: Junpeng Zhang URL: https://github.com/zhangjunpeng411/miRSM VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRSM/issues git_url: https://git.bioconductor.org/packages/miRSM git_branch: RELEASE_3_12 git_last_commit: 6477b9b git_last_commit_date: 2021-04-15 Date/Publication: 2021-04-15 source.ver: src/contrib/miRSM_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRSM_1.8.4.zip mac.binary.ver: bin/macosx/contrib/4.0/miRSM_1.8.4.tgz vignettes: vignettes/miRSM/inst/doc/miRSM.html vignetteTitles: miRSM: inferring miRNA sponge modules in heterogeneous data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRSM/inst/doc/miRSM.R dependencyCount: 240 Package: miRspongeR Version: 1.16.2 Depends: R (>= 3.5.0) Imports: corpcor, parallel, igraph, MCL, clusterProfiler, ReactomePA, DOSE, survival, grDevices, graphics, stats, varhandle, linkcomm, utils, Rcpp, org.Hs.eg.db Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: f6a3f16fb111c362e9055fb658e10181 NeedsCompilation: yes Title: Identification and analysis of miRNA sponge interaction networks and modules Description: This package provides several functions to study miRNA sponge (also called ceRNA or miRNA decoy), including popular methods for identifying miRNA sponge interactions, and the integrative method to integrate miRNA sponge interactions from different methods, as well as the functions to validate miRNA sponge interactions, and infer miRNA sponge modules, conduct enrichment analysis of modules, and conduct survival analysis of modules. biocViews: GeneExpression, BiomedicalInformatics, NetworkEnrichment, Survival, Microarray, Software Author: Junpeng Zhang Maintainer: Junpeng Zhang URL: VignetteBuilder: knitr BugReports: https://github.com/zhangjunpeng411/miRspongeR/issues git_url: https://git.bioconductor.org/packages/miRspongeR git_branch: RELEASE_3_12 git_last_commit: ca92261 git_last_commit_date: 2020-11-17 Date/Publication: 2020-11-17 source.ver: src/contrib/miRspongeR_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/miRspongeR_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.0/miRspongeR_1.16.2.tgz vignettes: vignettes/miRspongeR/inst/doc/miRspongeR.html vignetteTitles: miRspongeR: identification and analysis of miRNA sponge interaction networks and modules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRspongeR/inst/doc/miRspongeR.R importsMe: miRSM dependencyCount: 123 Package: missMethyl Version: 1.24.0 Depends: R (>= 3.6.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b4.hg19 Imports: AnnotationDbi, BiasedUrn, Biobase, BiocGenerics, GenomicRanges, GO.db, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylationEPICmanifest, IRanges, limma, methods, methylumi, minfi, org.Hs.eg.db, ruv, S4Vectors, statmod, stringr, SummarizedExperiment Suggests: BiocStyle, edgeR, knitr, minfiData, rmarkdown, tweeDEseqCountData, DMRcate, ExperimentHub License: GPL-2 MD5sum: 4ee1a591215a732d4e2ed30dc0cb4efa NeedsCompilation: no Title: Analysing Illumina HumanMethylation BeadChip Data Description: Normalisation, testing for differential variability and differential methylation and gene set testing for data from Illumina's Infinium HumanMethylation arrays. The normalisation procedure is subset-quantile within-array normalisation (SWAN), which allows Infinium I and II type probes on a single array to be normalised together. The test for differential variability is based on an empirical Bayes version of Levene's test. Differential methylation testing is performed using RUV, which can adjust for systematic errors of unknown origin in high-dimensional data by using negative control probes. Gene ontology analysis is performed by taking into account the number of probes per gene on the array, as well as taking into account multi-gene associated probes. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic , Andrew Lonsdale VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missMethyl git_branch: RELEASE_3_12 git_last_commit: f6c8604 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/missMethyl_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/missMethyl_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/missMethyl_1.24.0.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.html vignetteTitles: missMethyl: Analysing Illumina HumanMethylation BeadChip Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R dependsOnMe: methylationArrayAnalysis importsMe: DMRcate, MEAL, methylGSA suggestsMe: RnBeads dependencyCount: 154 Package: missRows Version: 1.10.0 Depends: R (>= 3.5), methods, ggplot2, grDevices, MultiAssayExperiment Imports: plyr, stats, gtools, S4Vectors Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: 05f24c1eeedf8f4f0e880f99ddf82535 NeedsCompilation: no Title: Handling Missing Individuals in Multi-Omics Data Integration Description: The missRows package implements the MI-MFA method to deal with missing individuals ('biological units') in multi-omics data integration. The MI-MFA method generates multiple imputed datasets from a Multiple Factor Analysis model, then the yield results are combined in a single consensus solution. The package provides functions for estimating coordinates of individuals and variables, imputing missing individuals, and various diagnostic plots to inspect the pattern of missingness and visualize the uncertainty due to missing values. biocViews: Software, StatisticalMethod, DimensionReduction, PrincipalComponent, MathematicalBiology, Visualization Author: Ignacio Gonzalez and Valentin Voillet Maintainer: Gonzalez Ignacio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/missRows git_branch: RELEASE_3_12 git_last_commit: c0d0d8a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/missRows_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/missRows_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/missRows_1.10.0.tgz vignettes: vignettes/missRows/inst/doc/missRows.pdf vignetteTitles: missRows hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missRows/inst/doc/missRows.R dependencyCount: 66 Package: mitch Version: 1.2.2 Depends: R (>= 4.0) Imports: stats, grDevices, graphics, utils, MASS, plyr, reshape2, parallel, GGally, grid, gridExtra, knitr, rmarkdown, ggplot2, gplots, beeswarm, echarts4r Suggests: stringi, testthat (>= 2.1.0) License: CC BY-SA 4.0 + file LICENSE MD5sum: ef7319698705282794483cd04227b249 NeedsCompilation: no Title: Multi-Contrast Gene Set Enrichment Analysis Description: mitch is an R package for multi-contrast enrichment analysis. At it’s heart, it uses a rank-MANOVA based statistical approach to detect sets of genes that exhibit enrichment in the multidimensional space as compared to the background. The rank-MANOVA concept dates to work by Cox and Mann (https://doi.org/10.1186/1471-2105-13-S16-S12). mitch is useful for pathway analysis of profiling studies with one, two or more contrasts, or in studies with multiple omics profiling, for example proteomic, transcriptomic, epigenomic analysis of the same samples. mitch is perfectly suited for pathway level differential analysis of scRNA-seq data. The main strengths of mitch are that it can import datasets easily from many upstream tools and has advanced plotting features to visualise these enrichments. biocViews: GeneExpression, GeneSetEnrichment, SingleCell, Transcriptomics, Epigenetics, Proteomics, DifferentialExpression, Reactome Author: Mark Ziemann [aut, cre, cph], Antony Kaspi [aut, cph] Maintainer: Mark Ziemann URL: https://github.com/markziemann/mitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mitch git_branch: RELEASE_3_12 git_last_commit: 7f9d0ec git_last_commit_date: 2020-11-05 Date/Publication: 2020-11-05 source.ver: src/contrib/mitch_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/mitch_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/mitch_1.2.2.tgz vignettes: vignettes/mitch/inst/doc/mitchWorkflow.html vignetteTitles: mitch Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/mitch/inst/doc/mitchWorkflow.R dependencyCount: 97 Package: mixOmics Version: 6.14.1 Depends: R (>= 3.5.0), MASS, lattice, ggplot2 Imports: igraph, ellipse, corpcor, RColorBrewer, parallel, dplyr, tidyr, reshape2, methods, matrixStats, rARPACK, gridExtra, grDevices, graphics, stats, ggrepel, BiocParallel, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, rgl License: GPL (>= 2) MD5sum: 1558f10fe83332bd26655dd916a18382 NeedsCompilation: no Title: Omics Data Integration Project Description: Multivariate methods are well suited to large omics data sets where the number of variables (e.g. genes, proteins, metabolites) is much larger than the number of samples (patients, cells, mice). They have the appealing properties of reducing the dimension of the data by using instrumental variables (components), which are defined as combinations of all variables. Those components are then used to produce useful graphical outputs that enable better understanding of the relationships and correlation structures between the different data sets that are integrated. mixOmics offers a wide range of multivariate methods for the exploration and integration of biological datasets with a particular focus on variable selection. The package proposes several sparse multivariate models we have developed to identify the key variables that are highly correlated, and/or explain the biological outcome of interest. The data that can be analysed with mixOmics may come from high throughput sequencing technologies, such as omics data (transcriptomics, metabolomics, proteomics, metagenomics etc) but also beyond the realm of omics (e.g. spectral imaging). The methods implemented in mixOmics can also handle missing values without having to delete entire rows with missing data. A non exhaustive list of methods include variants of generalised Canonical Correlation Analysis, sparse Partial Least Squares and sparse Discriminant Analysis. Recently we implemented integrative methods to combine multiple data sets: N-integration with variants of Generalised Canonical Correlation Analysis and P-integration with variants of multi-group Partial Least Squares. biocViews: ImmunoOncology, Microarray, Sequencing, Metabolomics, Metagenomics, Proteomics, GenePrediction, MultipleComparison, Classification, Regression Author: Kim-Anh Le Cao [aut, cre], Florian Rohart [aut], Ignacio Gonzalez [aut], Sebastien Dejean [aut], Al Abadi [ctb], Benoit Gautier [ctb], Francois Bartolo [ctb], Pierre Monget [ctb], Jeff Coquery [ctb], FangZou Yao [ctb], Benoit Liquet [ctb] Maintainer: Kim-Anh Le Cao URL: http://www.mixOmics.org VignetteBuilder: knitr BugReports: https://github.com/mixOmicsTeam/mixOmics/issues/ git_url: https://git.bioconductor.org/packages/mixOmics git_branch: RELEASE_3_12 git_last_commit: 05bff25 git_last_commit_date: 2021-04-13 Date/Publication: 2021-04-14 source.ver: src/contrib/mixOmics_6.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/mixOmics_6.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/mixOmics_6.14.1.tgz vignettes: vignettes/mixOmics/inst/doc/vignette.html vignetteTitles: mixOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mixOmics/inst/doc/vignette.R dependsOnMe: compartmap, timeOmics, bootsPLS, mixKernel, RGCxGC, sgPLS importsMe: AlpsNMR, DepecheR, POMA, MetabolomicsBasics, plsmod, plsRcox, RVAideMemoire suggestsMe: PLSbiplot1, SelectBoost dependencyCount: 67 Package: MLInterfaces Version: 1.70.0 Depends: R (>= 3.5), Rcpp, methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, genefilter, fpc, ggvis, shiny, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4, tools, grDevices, graphics, stats Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party, klaR, testthat Enhances: parallel, rda License: LGPL MD5sum: 3f35a3c6f426b4b7c0b51ba66930ac43 NeedsCompilation: no Title: Uniform interfaces to R machine learning procedures for data in Bioconductor containers Description: This package provides uniform interfaces to machine learning code for data in R and Bioconductor containers. biocViews: Classification, Clustering Author: Vince Carey , Robert Gentleman, Jess Mar, and contributions from Jason Vertrees and Laurent Gatto Maintainer: V. Carey git_url: https://git.bioconductor.org/packages/MLInterfaces git_branch: RELEASE_3_12 git_last_commit: 7b076c3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MLInterfaces_1.70.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MLInterfaces_1.70.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MLInterfaces_1.70.0.tgz vignettes: vignettes/MLInterfaces/inst/doc/MLint_devel.pdf, vignettes/MLInterfaces/inst/doc/MLInterfaces.pdf, vignettes/MLInterfaces/inst/doc/MLprac2_2.pdf, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.pdf vignetteTitles: MLInterfaces devel for schema-based MLearn, MLInterfaces Primer, A machine learning tutorial: applications of the Bioconductor MLInterfaces package to expression and ChIP-Seq data, MLInterfaces Computer Cluster hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLInterfaces/inst/doc/MLint_devel.R, vignettes/MLInterfaces/inst/doc/MLInterfaces.R, vignettes/MLInterfaces/inst/doc/MLprac2_2.R, vignettes/MLInterfaces/inst/doc/xvalComputerClusters.R dependsOnMe: pRoloc, SigCheck, proteomics, dGAselID, nlcv suggestsMe: BiocCaseStudies dependencyCount: 102 Package: MLP Version: 1.38.0 Depends: AnnotationDbi, affy, plotrix, gplots, gmodels, gdata, gtools Suggests: GO.db, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Cf.eg.db, KEGG.db, annotate, Rgraphviz, GOstats, limma, mouse4302.db, reactome.db License: GPL-3 MD5sum: 1ba06947db5d2d9a73c35836a8c29e0d NeedsCompilation: no Title: MLP Description: Mean Log P Analysis biocViews: Genetics, Reactome, KEGG Author: Nandini Raghavan, Tobias Verbeke, An De Bondt with contributions by Javier Cabrera, Dhammika Amaratunga, Tine Casneuf and Willem Ligtenberg Maintainer: Tobias Verbeke git_url: https://git.bioconductor.org/packages/MLP git_branch: RELEASE_3_12 git_last_commit: ced1e9d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/MLP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MLP_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MLP_1.38.0.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLP/inst/doc/UsingMLP.R importsMe: esetVis suggestsMe: a4 dependencyCount: 41 Package: MLSeq Version: 2.8.0 Depends: caret, ggplot2 Imports: methods, DESeq2, edgeR, limma, Biobase, SummarizedExperiment, plyr, foreach, utils, sSeq, xtable Suggests: knitr, testthat, BiocStyle, VennDiagram, pamr License: GPL(>=2) MD5sum: 6dd09cdd31e68e7d8cc82ebbcbb9c8c3 NeedsCompilation: no Title: Machine Learning Interface for RNA-Seq Data Description: This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART to RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz [aut, cre], Dincer Goksuluk [aut], Selcuk Korkmaz [aut], Vahap Eldem [aut], Izzet Parug Duru [ctb], Ahmet Ozturk [aut], Ahmet Ergun Karaagaoglu [aut, ths] Maintainer: Gokmen Zararsiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MLSeq git_branch: RELEASE_3_12 git_last_commit: 1b39bbe git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MLSeq_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MLSeq_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MLSeq_2.8.0.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: Beginner's guide to the "MLSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R importsMe: GARS dependencyCount: 124 Package: MMAPPR2 Version: 1.4.0 Depends: R (>= 3.6.0) Imports: ensemblVEP (>= 1.20.0), gmapR, Rsamtools, VariantAnnotation, BiocParallel, Biobase, BiocGenerics, dplyr, GenomeInfoDb, GenomicRanges, IRanges, S4Vectors, tidyr, VariantTools, magrittr, methods, grDevices, graphics, stats, utils, stringr, data.table Suggests: testthat, mockery, roxygen2, knitr, rmarkdown, BiocStyle, MMAPPR2data License: GPL-3 OS_type: unix MD5sum: f834605687a8b50a35df6a0409eac7d2 NeedsCompilation: no Title: Mutation Mapping Analysis Pipeline for Pooled RNA-Seq Description: MMAPPR2 maps mutations resulting from pooled RNA-seq data from the F2 cross of forward genetic screens. Its predecessor is described in a paper published in Genome Research (Hill et al. 2013). MMAPPR2 accepts aligned BAM files as well as a reference genome as input, identifies loci of high sequence disparity between the control and mutant RNA sequences, predicts variant effects using Ensembl's Variant Effect Predictor, and outputs a ranked list of candidate mutations. biocViews: RNASeq, PooledScreens, DNASeq, VariantDetection Author: Kyle Johnsen [aut], Nathaniel Jenkins [aut], Jonathon Hill [cre] Maintainer: Jonathon Hill URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3613585/, https://github.com/kjohnsen/MMAPPR2 SystemRequirements: Ensembl VEP, Samtools VignetteBuilder: knitr BugReports: https://github.com/kjohnsen/MMAPPR2/issues git_url: https://git.bioconductor.org/packages/MMAPPR2 git_branch: RELEASE_3_12 git_last_commit: 2727adf git_last_commit_date: 2020-10-27 Date/Publication: 2020-11-04 source.ver: src/contrib/MMAPPR2_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/MMAPPR2_1.4.0.tgz vignettes: vignettes/MMAPPR2/inst/doc/MMAPPR2.html vignetteTitles: An Introduction to MMAPPR2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMAPPR2/inst/doc/MMAPPR2.R dependencyCount: 96 Package: MMDiff2 Version: 1.18.0 Depends: R (>= 3.3), Rsamtools, Biobase, Imports: GenomicRanges, locfit, BSgenome, Biostrings, shiny, ggplot2, RColorBrewer, graphics, grDevices, parallel, S4Vectors, methods Suggests: MMDiffBamSubset, MotifDb, knitr, BiocStyle, BSgenome.Mmusculus.UCSC.mm9 License: Artistic-2.0 MD5sum: 1d0a6eec20256e14d2ebf672fa1e1bd5 NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant differences between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). biocViews: ChIPSeq, DifferentialPeakCalling, Sequencing, Software Author: Gabriele Schweikert [cre, aut], David Kuo [aut] Maintainer: Gabriele Schweikert VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMDiff2 git_branch: RELEASE_3_12 git_last_commit: f7a6d8f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MMDiff2_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MMDiff2_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MMDiff2_1.18.0.tgz vignettes: vignettes/MMDiff2/inst/doc/MMDiff2.pdf vignetteTitles: An Introduction to the MMDiff2 method hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff2/inst/doc/MMDiff2.R suggestsMe: MMDiffBamSubset dependencyCount: 90 Package: MMUPHin Version: 1.4.2 Depends: R (>= 3.6) Imports: Maaslin2, metafor, fpc, igraph, ggplot2, dplyr, tidyr, cowplot, utils, stats, grDevices Suggests: testthat, BiocStyle, knitr, rmarkdown, magrittr, vegan, phyloseq, curatedMetagenomicData, genefilter License: MIT + file LICENSE MD5sum: b061192f2cfabf8e1afd8144d6300e4c NeedsCompilation: no Title: Meta-analysis Methods with Uniform Pipeline for Heterogeneity in Microbiome Studies Description: MMUPHin is an R package for meta-analysis tasks of microbiome cohorts. It has function interfaces for: a) covariate-controlled batch- and cohort effect adjustment, b) meta-analysis differential abundance testing, c) meta-analysis unsupervised discrete structure (clustering) discovery, and d) meta-analysis unsupervised continuous structure discovery. biocViews: Metagenomics, Microbiome, BatchEffect Author: Siyuan Ma Maintainer: Siyuan MA VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MMUPHin git_branch: RELEASE_3_12 git_last_commit: 7338d99 git_last_commit_date: 2021-04-07 Date/Publication: 2021-04-08 source.ver: src/contrib/MMUPHin_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/MMUPHin_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/MMUPHin_1.3.0.tgz vignettes: vignettes/MMUPHin/inst/doc/MMUPHin.html vignetteTitles: MMUPHin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MMUPHin/inst/doc/MMUPHin.R dependencyCount: 160 Package: mnem Version: 1.6.5 Depends: R (>= 3.6) Imports: cluster, graph, Rgraphviz, flexclust, lattice, naturalsort, snowfall, stats4, tsne, methods, graphics, stats, utils, Linnorm, data.table, Rcpp, RcppEigen, matrixStats, grDevices, e1071 LinkingTo: Rcpp, RcppEigen Suggests: knitr, devtools, rmarkdown, BiocGenerics, RUnit, epiNEM License: GPL-3 Archs: i386, x64 MD5sum: a6597ddb07766228f7a018c06f380638 NeedsCompilation: yes Title: Mixture Nested Effects Models Description: Mixture Nested Effects Models (mnem) is an extension of Nested Effects Models and allows for the analysis of single cell perturbation data provided by methods like Perturb-Seq (Dixit et al., 2016) or Crop-Seq (Datlinger et al., 2017). In those experiments each of many cells is perturbed by a knock-down of a specific gene, i.e. several cells are perturbed by a knock-down of gene A, several by a knock-down of gene B, ... and so forth. The observed read-out has to be multi-trait and in the case of the Perturb-/Crop-Seq gene are expression profiles for each cell. mnem uses a mixture model to simultaneously cluster the cell population into k clusters and and infer k networks causally linking the perturbed genes for each cluster. The mixture components are inferred via an expectation maximization algorithm. biocViews: Pathways, SystemsBiology, NetworkInference, Network, RNASeq, PooledScreens, SingleCell, CRISPR, ATACSeq, DNASeq, GeneExpression Author: Martin Pirkl Maintainer: Martin Pirkl VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mnem git_branch: RELEASE_3_12 git_last_commit: 7431a37 git_last_commit_date: 2020-11-16 Date/Publication: 2020-11-16 source.ver: src/contrib/mnem_1.6.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/mnem_1.6.5.zip mac.binary.ver: bin/macosx/contrib/4.0/mnem_1.6.5.tgz vignettes: vignettes/mnem/inst/doc/mnem.html vignetteTitles: mnem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mnem/inst/doc/mnem.R importsMe: epiNEM dependencyCount: 83 Package: MODA Version: 1.16.0 Depends: R (>= 3.3) Imports: grDevices, graphics, stats, utils, WGCNA, dynamicTreeCut, igraph, cluster, AMOUNTAIN, RColorBrewer Suggests: BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: 4dc3f5c212b51b710ea8714ebd95faf0 NeedsCompilation: no Title: MODA: MOdule Differential Analysis for weighted gene co-expression network Description: MODA can be used to estimate and construct condition-specific gene co-expression networks, and identify differentially expressed subnetworks as conserved or condition specific modules which are potentially associated with relevant biological processes. biocViews: GeneExpression, Microarray, DifferentialExpression, Network Author: Dong Li, James B. Brown, Luisa Orsini, Zhisong Pan, Guyu Hu and Shan He Maintainer: Dong Li git_url: https://git.bioconductor.org/packages/MODA git_branch: RELEASE_3_12 git_last_commit: ce64c31 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MODA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MODA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MODA_1.16.0.tgz vignettes: vignettes/MODA/inst/doc/MODA.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 99 Package: Modstrings Version: 1.6.0 Depends: R (>= 3.6), Biostrings (>= 2.51.5) Imports: methods, BiocGenerics, GenomicRanges, S4Vectors, IRanges, XVector, stringi, stringr, crayon, grDevices Suggests: BiocStyle, knitr, rmarkdown, testthat, usethis License: Artistic-2.0 MD5sum: f2fbfed5b004e3c88ed336a845a070e8 NeedsCompilation: no Title: Working with modified nucleotide sequences Description: Representing nucleotide modifications in a nucleotide sequence is usually done via special characters from a number of sources. This represents a challenge to work with in R and the Biostrings package. The Modstrings package implements this functionallity for RNA and DNA sequences containing modified nucleotides by translating the character internally in order to work with the infrastructure of the Biostrings package. For this the ModRNAString and ModDNAString classes and derivates and functions to construct and modify these objects despite the encoding issues are implemenented. In addition the conversion from sequences to list like location information (and the reverse operation) is implemented as well. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Modstrings/issues git_url: https://git.bioconductor.org/packages/Modstrings git_branch: RELEASE_3_12 git_last_commit: 7ee54eb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Modstrings_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Modstrings_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Modstrings_1.6.0.tgz vignettes: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.html, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.html, vignettes/Modstrings/inst/doc/Modstrings.html vignetteTitles: Modstrings-DNA-alphabet, Modstrings-RNA-alphabet, Modstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Modstrings/inst/doc/ModDNAString-alphabet.R, vignettes/Modstrings/inst/doc/ModRNAString-alphabet.R, vignettes/Modstrings/inst/doc/Modstrings.R dependsOnMe: EpiTxDb, RNAmodR, tRNAdbImport importsMe: tRNA suggestsMe: EpiTxDb.Hs.hg38 dependencyCount: 24 Package: MOFA Version: 1.6.2 Depends: R (>= 3.5) Imports: rhdf5, dplyr, reshape2, pheatmap, corrplot, ggplot2, ggbeeswarm, methods, scales, GGally, RColorBrewer, cowplot, ggrepel, MultiAssayExperiment, Biobase, doParallel, foreach, reticulate, grDevices, stats, utils Suggests: knitr, MOFAdata License: LGPL-3 | file LICENSE MD5sum: 25fc4151a836a1c55774c54657bce6ff NeedsCompilation: yes Title: Multi-Omics Factor Analysis (MOFA) Description: Multi-Omics Factor Analysis: an unsupervised framework for the integration of multi-omics data sets. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet, Britta Velten, Damien Arnol, Florian Buettner, Wolfgang Huber, Oliver Stegle Maintainer: Britta Velten SystemRequirements: Python (>=2.7.0), numpy, pandas, h5py, scipy, sklearn, mofapy VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/MOFA git_branch: RELEASE_3_12 git_last_commit: 0ae14cf git_last_commit_date: 2021-02-08 Date/Publication: 2021-02-09 source.ver: src/contrib/MOFA_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/MOFA_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.0/MOFA_1.6.2.tgz vignettes: vignettes/MOFA/inst/doc/MOFA_example_CLL.html, vignettes/MOFA/inst/doc/MOFA_example_scMT.html, vignettes/MOFA/inst/doc/MOFA_example_simulated.html, vignettes/MOFA/inst/doc/MOFA.html vignetteTitles: MOFA: applications to a multi-omics data set of CLL patients, MOFA: Application to a single-cell multi-omics data set, MOFA: How to assess model robustness and do model selection, Introduction to MOFA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOFA/inst/doc/MOFA_example_CLL.R, vignettes/MOFA/inst/doc/MOFA_example_scMT.R, vignettes/MOFA/inst/doc/MOFA_example_simulated.R, vignettes/MOFA/inst/doc/MOFA.R dependencyCount: 92 Package: MOFA2 Version: 1.0.1 Depends: R (>= 4.0) Imports: rhdf5, dplyr, tidyr, reshape2, pheatmap, ggplot2, methods, GGally, RColorBrewer, cowplot, ggrepel, reticulate, HDF5Array, grDevices, stats, magrittr, forcats, utils, corrplot, DelayedArray, Rtsne, uwot, basilisk Suggests: knitr, testthat, Seurat, ggpubr, foreach, psych, MultiAssayExperiment, SummarizedExperiment, SingleCellExperiment, ggrastr License: GPL (>= 2) MD5sum: daa3835bc9a5b04cdc644feda8a6f730 NeedsCompilation: yes Title: Multi-Omics Factor Analysis v2 Description: The MOFA2 package contains a collection of tools for running and analysing MOFA models. biocViews: DimensionReduction, Bayesian, Visualization Author: Ricard Argelaguet [aut] (), Damien Arnol [aut] (), Danila Bredikhin [aut] (), Britta Velten [aut, cre] () Maintainer: Britta Velten URL: https://biofam.github.io/MOFA2/index.html SystemRequirements: Python (>=3), numpy, pandas, h5py, scipy, argparse, sklearn, mofapy2 VignetteBuilder: knitr BugReports: https://github.com/bioFAM/MOFA2 git_url: https://git.bioconductor.org/packages/MOFA2 git_branch: RELEASE_3_12 git_last_commit: 1b28109 git_last_commit_date: 2020-11-03 Date/Publication: 2020-11-03 source.ver: src/contrib/MOFA2_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MOFA2_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MOFA2_1.0.1.tgz vignettes: vignettes/MOFA2/inst/doc/downstream_analysis.html, vignettes/MOFA2/inst/doc/getting_started_R.html vignetteTitles: Downstream analysis: Overview, MOFA2: How to train a model in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOFA2/inst/doc/downstream_analysis.R, vignettes/MOFA2/inst/doc/getting_started_R.R dependencyCount: 90 Package: MOGAMUN Version: 1.0.1 Imports: stats, utils, RCy3, stringr, graphics, grDevices, RUnit, BiocParallel, igraph Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 79ddf382ada073a59e5dca9b7b787795 NeedsCompilation: no Title: MOGAMUN: A Multi-Objective Genetic Algorithm to Find Active Modules in Multiplex Biological Networks Description: MOGAMUN is a multi-objective genetic algorithm that identifies active modules in a multiplex biological network. This allows analyzing different biological networks at the same time. MOGAMUN is based on NSGA-II (Non-Dominated Sorting Genetic Algorithm, version II), which we adapted to work on networks. biocViews: SystemsBiology, GraphAndNetwork, DifferentialExpression, BiomedicalInformatics, Transcriptomics, Clustering, Network Author: Elva-María Novoa-del-Toro [aut, cre] () Maintainer: Elva-María Novoa-del-Toro URL: https://github.com/elvanov/MOGAMUN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MOGAMUN git_branch: RELEASE_3_12 git_last_commit: a65e3fb git_last_commit_date: 2021-02-12 Date/Publication: 2021-02-12 source.ver: src/contrib/MOGAMUN_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MOGAMUN_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MOGAMUN_1.0.1.tgz vignettes: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.html vignetteTitles: Finding active modules with MOGAMUN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MOGAMUN/inst/doc/MOGAMUN_Vignette.R dependencyCount: 41 Package: mogsa Version: 1.24.0 Depends: R (>= 3.4.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, org.Hs.eg.db License: GPL-2 MD5sum: 3ce8033234c97a0ed6ad85e05ba1f4cf NeedsCompilation: no Title: Multiple omics data integrative clustering and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Clustering, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mogsa git_branch: RELEASE_3_12 git_last_commit: 82a0fec git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mogsa_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mogsa_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mogsa_1.24.0.tgz vignettes: vignettes/mogsa/inst/doc/moCluster-knitr.pdf, vignettes/mogsa/inst/doc/mogsa-knitr.pdf vignetteTitles: moCluster: Integrative clustering using multiple omics data, mogsa: gene set analysis on multiple omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mogsa/inst/doc/moCluster-knitr.R, vignettes/mogsa/inst/doc/mogsa-knitr.R dependencyCount: 59 Package: MOMA Version: 1.2.0 Depends: R (>= 4.0) Imports: circlize, cluster, ComplexHeatmap, dplyr, ggplot2, graphics, grid, grDevices, magrittr, methods, MKmisc, MultiAssayExperiment, parallel, qvalue, RColorBrewer, readr, reshape2, rlang, stats, stringr, tibble, tidyr, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, viper License: GPL-3 MD5sum: 215530f9b6cb4a959844b86f0ba49791 NeedsCompilation: no Title: Multi Omic Master Regulator Analysis Description: This package implements the inference of candidate master regulator proteins from multi-omics' data (MOMA) algorithm, as well as ancillary analysis and visualization functions. biocViews: Software, NetworkEnrichment, NetworkInference, Network, FeatureExtraction, Clustering, FunctionalGenomics, Transcriptomics, SystemsBiology Author: Evan Paull [aut], Sunny Jones [aut, cre], Mariano Alvarez [aut] Maintainer: Sunny Jones VignetteBuilder: knitr BugReports: https://github.com/califano-lab/MOMA/issues git_url: https://git.bioconductor.org/packages/MOMA git_branch: RELEASE_3_12 git_last_commit: 4a9d5ef git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MOMA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MOMA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MOMA_1.2.0.tgz vignettes: vignettes/MOMA/inst/doc/moma.html vignetteTitles: MOMA - Multi Omic Master Regulator Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOMA/inst/doc/moma.R dependencyCount: 87 Package: monocle Version: 2.18.0 Depends: R (>= 2.10.0), methods, Matrix (>= 1.2-6), Biobase, ggplot2 (>= 1.0.0), VGAM (>= 1.0-6), DDRTree (>= 0.1.4), Imports: parallel, igraph (>= 1.0.1), BiocGenerics, HSMMSingleCell (>= 0.101.5), plyr, cluster, combinat, fastICA, grid, irlba (>= 2.0.0), matrixStats, densityClust (>= 0.3), Rtsne, MASS, reshape2, limma, tibble, dplyr, qlcMatrix, pheatmap, stringr, proxy, slam, viridis, stats, biocViews, RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: destiny, Hmisc, knitr, Seurat, scater, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 9f899263f2582c9818fd148b39fd5c75 NeedsCompilation: yes Title: Clustering, differential expression, and trajectory analysis for single- cell RNA-Seq Description: Monocle performs differential expression and time-series analysis for single-cell expression experiments. It orders individual cells according to progress through a biological process, without knowing ahead of time which genes define progress through that process. Monocle also performs differential expression analysis, clustering, visualization, and other useful tasks on single cell expression data. It is designed to work with RNA-Seq and qPCR data, but could be used with other types as well. biocViews: ImmunoOncology, Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/monocle git_branch: RELEASE_3_12 git_last_commit: 14ac1ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/monocle_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/monocle_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/monocle_2.18.0.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Cell counting,, differential expression,, and trajectory analysis for single-cell RNA-Seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R dependsOnMe: cicero, ctgGEM, phemd importsMe: tradeSeq, uSORT suggestsMe: M3Drop, scran, sincell, Seurat dependencyCount: 85 Package: MoonlightR Version: 1.16.0 Depends: R (>= 3.5), doParallel, foreach Imports: parmigene, randomForest, SummarizedExperiment, gplots, circlize, RColorBrewer, HiveR, clusterProfiler, DOSE, Biobase, limma, grDevices, graphics, TCGAbiolinks, GEOquery, stats, RISmed, grid, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, png License: GPL (>= 3) MD5sum: 62c9d17b32e9e73fe288650323b51a41 NeedsCompilation: no Title: Identify oncogenes and tumor suppressor genes from omics data Description: Motivation: The understanding of cancer mechanism requires the identification of genes playing a role in the development of the pathology and the characterization of their role (notably oncogenes and tumor suppressors). Results: We present an R/bioconductor package called MoonlightR which returns a list of candidate driver genes for specific cancer types on the basis of TCGA expression data. The method first infers gene regulatory networks and then carries out a functional enrichment analysis (FEA) (implementing an upstream regulator analysis, URA) to score the importance of well-known biological processes with respect to the studied cancer type. Eventually, by means of random forests, MoonlightR predicts two specific roles for the candidate driver genes: i) tumor suppressor genes (TSGs) and ii) oncogenes (OCGs). As a consequence, this methodology does not only identify genes playing a dual role (e.g. TSG in one cancer type and OCG in another) but also helps in elucidating the biological processes underlying their specific roles. In particular, MoonlightR can be used to discover OCGs and TSGs in the same cancer type. This may help in answering the question whether some genes change role between early stages (I, II) and late stages (III, IV) in breast cancer. In the future, this analysis could be useful to determine the causes of different resistances to chemotherapeutic treatments. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Survival, GeneSetEnrichment, NetworkEnrichment Author: Antonio Colaprico*, Catharina Olsen*, Claudia Cava, Thilde Terkelsen, Laura Cantini, Andre Olsen, Gloria Bertoli, Andrei Zinovyev, Emmanuel Barillot, Isabella Castiglioni, Elena Papaleo, Gianluca Bontempi Maintainer: Antonio Colaprico , Catharina Olsen URL: https://github.com/ibsquare/MoonlightR VignetteBuilder: knitr BugReports: https://github.com/ibsquare/MoonlightR/issues git_url: https://git.bioconductor.org/packages/MoonlightR git_branch: RELEASE_3_12 git_last_commit: 69c53b7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MoonlightR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MoonlightR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MoonlightR_1.16.0.tgz vignettes: vignettes/MoonlightR/inst/doc/Moonlight.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoonlightR/inst/doc/Moonlight.R dependencyCount: 187 Package: mosaics Version: 2.28.0 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges, GenomicRanges, GenomicAlignments, Rsamtools, GenomeInfoDb, S4Vectors LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: e314a1a16d519030c0ade8ce774ed3d8 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS and MOSAiCS-HMM, a statistical framework to analyze one-sample or two-sample ChIP-seq data of transcription factor binding and histone modification. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl git_url: https://git.bioconductor.org/packages/mosaics git_branch: RELEASE_3_12 git_last_commit: 4f6e125 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mosaics_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mosaics_2.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mosaics_2.28.0.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mosaics/inst/doc/mosaics-example.R dependencyCount: 41 Package: MOSim Version: 1.4.0 Depends: R (>= 3.6) Imports: HiddenMarkov, zoo, methods, matrixStats, dplyr, stringi, lazyeval, rlang, stats, utils, purrr, scales, stringr, tibble, tidyr, ggplot2, Biobase, IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 3abb6342edd444021935dd93eba62421 NeedsCompilation: no Title: Multi-Omics Simulation (MOSim) Description: MOSim package simulates multi-omic experiments that mimic regulatory mechanisms within the cell, allowing flexible experimental design including time course and multiple groups. biocViews: Software, TimeCourse, ExperimentalDesign, RNASeq Author: Carlos Martínez [cre, aut], Sonia Tarazona [aut] Maintainer: Carlos Martínez URL: https://github.com/Neurergus/MOSim VignetteBuilder: knitr BugReports: https://github.com/Neurergus/MOSim/issues git_url: https://git.bioconductor.org/packages/MOSim git_branch: RELEASE_3_12 git_last_commit: abf3b1d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MOSim_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MOSim_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MOSim_1.4.0.tgz vignettes: vignettes/MOSim/inst/doc/MOSim.pdf vignetteTitles: MOSim hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MOSim/inst/doc/MOSim.R dependencyCount: 57 Package: motifbreakR Version: 2.4.0 Depends: R (>= 3.5.0), grid, MotifDb Imports: methods, compiler, grDevices, grImport, stringr, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Biostrings, BSgenome, rtracklayer, VariantAnnotation, BiocParallel, motifStack, Gviz, matrixStats, TFMPvalue, SummarizedExperiment Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP142.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 MD5sum: 0e8a5a1f240f0e6168c29e30e23d6054 NeedsCompilation: no Title: A Package For Predicting The Disruptiveness Of Single Nucleotide Polymorphisms On Transcription Factor Binding Sites Description: We introduce motifbreakR, which allows the biologist to judge in the first place whether the sequence surrounding the polymorphism is a good match, and in the second place how much information is gained or lost in one allele of the polymorphism relative to another. MotifbreakR is both flexible and extensible over previous offerings; giving a choice of algorithms for interrogation of genomes with motifs from public sources that users can choose from; these are 1) a weighted-sum probability matrix, 2) log-probabilities, and 3) weighted by relative entropy. MotifbreakR can predict effects for novel or previously described variants in public databases, making it suitable for tasks beyond the scope of its original design. Lastly, it can be used to interrogate any genome curated within Bioconductor (currently there are 22). biocViews: ChIPSeq, Visualization, MotifAnnotation Author: Simon Gert Coetzee [aut, cre], Dennis J. Hazelett [aut] Maintainer: Simon Gert Coetzee VignetteBuilder: knitr BugReports: https://github.com/Simon-Coetzee/motifbreakR/issues git_url: https://git.bioconductor.org/packages/motifbreakR git_branch: RELEASE_3_12 git_last_commit: b7ea7f4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/motifbreakR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/motifbreakR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/motifbreakR_2.4.0.tgz vignettes: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html vignetteTitles: motifbreakR: an Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R dependencyCount: 147 Package: motifcounter Version: 1.14.0 Depends: R(>= 3.0) Imports: Biostrings, methods Suggests: knitr, rmarkdown, testthat, MotifDb, seqLogo, prettydoc License: GPL-2 Archs: i386, x64 MD5sum: 2c4a9ce02dfd10bd99e0fb617c8b414b NeedsCompilation: yes Title: R package for analysing TFBSs in DNA sequences Description: 'motifcounter' provides motif matching, motif counting and motif enrichment functionality based on position frequency matrices. The main features of the packages include the utilization of higher-order background models and accounting for self-overlapping motif matches when determining motif enrichment. The background model allows to capture dinucleotide (or higher-order nucleotide) composition adequately which may reduced model biases and misleading results compared to using simple GC background models. When conducting a motif enrichment analysis based on the motif match count, the package relies on a compound Poisson distribution or alternatively a combinatorial model. These distribution account for self-overlapping motif structures as exemplified by repeat-like or palindromic motifs, and allow to determine the p-value and fold-enrichment for a set of observed motif matches. biocViews: Transcription,MotifAnnotation,SequenceMatching,Software Author: Wolfgang Kopp [aut, cre] Maintainer: Wolfgang Kopp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifcounter git_branch: RELEASE_3_12 git_last_commit: 021b279 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/motifcounter_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/motifcounter_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/motifcounter_1.14.0.tgz vignettes: vignettes/motifcounter/inst/doc/motifcounter.html vignetteTitles: Introduction to the `motifcounter` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifcounter/inst/doc/motifcounter.R dependencyCount: 15 Package: MotifDb Version: 1.32.0 Depends: R (>= 3.5.0), methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, Biostrings Imports: rtracklayer, splitstackshape Suggests: RUnit, seqLogo, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: feb87a49ba4e1339ff8f7322f72a47ed NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 9900 annotated position frequency matrices from 14 public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon, Matt Richards Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MotifDb git_branch: RELEASE_3_12 git_last_commit: 99f1d5a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MotifDb_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MotifDb_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MotifDb_1.32.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.html vignetteTitles: "A collection of PWMs" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR, trena, generegulation importsMe: igvR, rTRMui suggestsMe: ATACseqQC, DiffLogo, MMDiff2, motifcounter, motifStack, profileScoreDist, PWMEnrich, rTRM, TFutils, universalmotif, vtpnet dependencyCount: 42 Package: motifmatchr Version: 1.12.0 Depends: R (>= 3.3) Imports: Matrix, Rcpp, methods, TFBSTools, Biostrings, BSgenome, S4Vectors, SummarizedExperiment, GenomicRanges, IRanges, Rsamtools, GenomeInfoDb LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: a4dc16ebf2d1b0e5ca264b5cd4ff40ad NeedsCompilation: yes Title: Fast Motif Matching in R Description: Quickly find motif matches for many motifs and many sequences. Wraps C++ code from the MOODS motif calling library, which was developed by Pasi Rastas, Janne Korhonen, and Petri Martinmäki. biocViews: MotifAnnotation Author: Alicia Schep [aut, cre], Stanford University [cph] Maintainer: Alicia Schep SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifmatchr git_branch: RELEASE_3_12 git_last_commit: 015a8b1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/motifmatchr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/motifmatchr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/motifmatchr_1.12.0.tgz vignettes: vignettes/motifmatchr/inst/doc/motifmatchr.html vignetteTitles: motifmatchr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/motifmatchr/inst/doc/motifmatchr.R importsMe: enrichTF, esATAC, pageRank suggestsMe: chromVAR, MethReg, CAGEWorkflow, Signac dependencyCount: 114 Package: motifStack Version: 1.34.0 Depends: R (>= 2.15.1), methods, grid Imports: ade4, Biostrings, ggplot2, grDevices, graphics, htmlwidgets, stats, stats4, utils, XML Suggests: grImport, grImport2, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr License: GPL (>= 2) MD5sum: 9f2a5294f6277c5e51d1a963c71833c4 NeedsCompilation: no Title: Plot stacked logos for single or multiple DNA, RNA and amino acid sequence Description: The motifStack package is designed for graphic representation of multiple motifs with different similarity scores. It works with both DNA/RNA sequence motif and amino acid sequence motif. In addition, it provides the flexibility for users to customize the graphic parameters such as the font type and symbol colors. biocViews: SequenceMatching, Visualization, Sequencing, Microarray, Alignment, ChIPchip, ChIPSeq, MotifAnnotation, DataImport Author: Jianhong Ou, Michael Brodsky, Scot Wolfe and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/motifStack git_branch: RELEASE_3_12 git_last_commit: 600df2d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/motifStack_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/motifStack_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/motifStack_1.34.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack_HTML.html vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R dependsOnMe: generegulation importsMe: ATACseqQC, atSNP, dagLogo, LowMACA, motifbreakR, ribosomeProfilingQC, TCGAWorkflow suggestsMe: ChIPpeakAnno, TFutils, universalmotif dependencyCount: 59 Package: MouseFM Version: 1.0.0 Depends: R (>= 4.0.0) Imports: httr, curl, GenomicRanges, dplyr, ggplot2, reshape2, scales, gtools, tidyr, data.table, jsonlite, rlist, GenomeInfoDb, methods, biomaRt, stats, IRanges Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: ba7d910206c0bf588bf1b8b47218c168 NeedsCompilation: no Title: In-silico methods for genetic finemapping in inbred mice Description: This package provides methods for genetic finemapping in inbred mice by taking advantage of their very high homozygosity rate (>95%). biocViews: Genetics, SNP, GeneTarget, VariantAnnotation, GenomicVariation, MultipleComparison, SystemsBiology, MathematicalBiology, PatternLogic, GenePrediction, BiomedicalInformatics, FunctionalGenomics Author: Matthias Munz [aut, cre] (), Inken Wohlers [aut] (), Hauke Busch [aut] () Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/MouseFM/issues git_url: https://git.bioconductor.org/packages/MouseFM git_branch: RELEASE_3_12 git_last_commit: b63d244 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MouseFM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MouseFM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MouseFM_1.0.0.tgz vignettes: vignettes/MouseFM/inst/doc/fetch.html, vignettes/MouseFM/inst/doc/finemap.html, vignettes/MouseFM/inst/doc/prio.html vignetteTitles: Fetch, Finemapping, Prioritization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MouseFM/inst/doc/fetch.R, vignettes/MouseFM/inst/doc/finemap.R, vignettes/MouseFM/inst/doc/prio.R dependencyCount: 94 Package: MPFE Version: 1.26.0 License: GPL (>= 3) MD5sum: 7860fa51cced17bab40f82bf07a017a4 NeedsCompilation: no Title: Estimation of the amplicon methylation pattern distribution from bisulphite sequencing data Description: Estimate distribution of methylation patterns from a table of counts from a bisulphite sequencing experiment given a non-conversion rate and read error rate. biocViews: HighThroughputSequencingData, DNAMethylation, MethylSeq Author: Peijie Lin, Sylvain Foret, Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/MPFE git_branch: RELEASE_3_12 git_last_commit: 69355d5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MPFE_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MPFE_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MPFE_1.26.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPFE/inst/doc/MPFE.R dependencyCount: 0 Package: mpra Version: 1.12.1 Depends: R (>= 3.4.0), methods, BiocGenerics, SummarizedExperiment, limma Imports: S4Vectors, scales, stats, graphics, statmod Suggests: BiocStyle, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 89aa1fdbbff1d30db806f43db49d5bc8 NeedsCompilation: no Title: Analyze massively parallel reporter assays Description: Tools for data management, count preprocessing, and differential analysis in massively parallel report assays (MPRA). biocViews: Software, GeneRegulation, Sequencing, FunctionalGenomics Author: Leslie Myint [cre, aut], Kasper D. Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/mpra VignetteBuilder: knitr BugReports: https://github.com/hansenlab/mpra/issues git_url: https://git.bioconductor.org/packages/mpra git_branch: RELEASE_3_12 git_last_commit: fbb0870 git_last_commit_date: 2021-02-26 Date/Publication: 2021-02-26 source.ver: src/contrib/mpra_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/mpra_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/mpra_1.12.1.tgz vignettes: vignettes/mpra/inst/doc/mpra.html vignetteTitles: mpra User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mpra/inst/doc/mpra.R dependencyCount: 39 Package: MPRAnalyze Version: 1.8.0 Imports: BiocParallel, methods, progress, stats, SummarizedExperiment Suggests: knitr License: GPL-3 MD5sum: 473fd4304c52e8d567c19b522134bca7 NeedsCompilation: no Title: Statistical Analysis of MPRA data Description: MPRAnalyze provides statistical framework for the analysis of data generated by Massively Parallel Reporter Assays (MPRAs), used to directly measure enhancer activity. MPRAnalyze can be used for quantification of enhancer activity, classification of active enhancers and comparative analyses of enhancer activity between conditions. MPRAnalyze construct a nested pair of generalized linear models (GLMs) to relate the DNA and RNA observations, easily adjustable to various experimental designs and conditions, and provides a set of rigorous statistical testig schemes. biocViews: ImmunoOncology, Software, StatisticalMethod, Sequencing, GeneExpression, CellBiology, CellBasedAssays, DifferentialExpression Author: Tal Ashuach [aut, cre], David S Fischer [aut], Nir Yosef [ctb], Fabian J Theis [ctb], Maintainer: Tal Ashuach URL: https://github.com/YosefLab/MPRAnalyze VignetteBuilder: knitr BugReports: https://github.com/YosefLab/MPRAnalyze git_url: https://git.bioconductor.org/packages/MPRAnalyze git_branch: RELEASE_3_12 git_last_commit: 1836ce5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MPRAnalyze_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MPRAnalyze_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MPRAnalyze_1.8.0.tgz vignettes: vignettes/MPRAnalyze/inst/doc/vignette.html vignetteTitles: Analyzing MPRA data with MPRAnalyze hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MPRAnalyze/inst/doc/vignette.R dependencyCount: 44 Package: msa Version: 1.22.0 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.40.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr, ape, phangorn License: GPL (>= 2) Archs: i386, x64 MD5sum: 0548f245627b38c12d945ce8fcdfb460 NeedsCompilation: yes Title: Multiple Sequence Alignment Description: The 'msa' package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade. biocViews: MultipleSequenceAlignment, Alignment, MultipleComparison, Sequencing Author: Enrico Bonatesta, Christoph Horejs-Kainrath, Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/msa/ SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/msa git_branch: RELEASE_3_12 git_last_commit: 78bb238 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/msa_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/msa_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/msa_1.22.0.tgz vignettes: vignettes/msa/inst/doc/msa.pdf vignetteTitles: msa - An R Package for Multiple Sequence Alignment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msa/inst/doc/msa.R importsMe: LymphoSeq, odseq suggestsMe: idpr, bio3d dependencyCount: 16 Package: MsCoreUtils Version: 1.2.0 Depends: R (>= 3.6.0) Imports: methods, S4Vectors, MASS, stats LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown, roxygen2, imputeLCMD, impute, norm, pcaMethods, vsn, preprocessCore License: Artistic-2.0 Archs: i386, x64 MD5sum: c89aff62508d009d0eb36a40a2f00fb8 NeedsCompilation: yes Title: Core Utils for Mass Spectrometry Data Description: MsCoreUtils defines low-level functions for mass spectrometry data and is independent of any high-level data structures. These functions include mass spectra processing functions (noise estimation, smoothing, binning), quantitative aggregation functions (median polish, robust summarisation, ...), missing data imputation, data normalisation (quantiles, vsn, ...) as well as misc helper functions, that are used across high-level data structure within the R for Mass Spectrometry packages. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] (), Adriaan Sticker [ctb], Sigurdur Smarason [ctb], Thomas Naake [ctb] Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/MsCoreUtils VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/MsCoreUtils/issues git_url: https://git.bioconductor.org/packages/MsCoreUtils git_branch: RELEASE_3_12 git_last_commit: 963c37c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MsCoreUtils_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MsCoreUtils_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MsCoreUtils_1.2.0.tgz vignettes: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.html vignetteTitles: Core Utils for Mass Spectrometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MsCoreUtils/inst/doc/MsCoreUtils.R importsMe: QFeatures, Spectra, xcms dependencyCount: 11 Package: MSEADbi Version: 1.0.0 Depends: R (>= 4.0) Imports: methods, stats, utils, AnnotationDbi, RSQLite, DBI, Biobase Suggests: RUnit, BiocGenerics, BiocStyle, knitr, testthat (>= 2.1.0) License: Artistic-2.0 MD5sum: 72a5d3c0a4b06e68e8880722ab56c82b NeedsCompilation: no Title: DBI to construct MSEA-related package Description: Interface to construct annotation package for MSEA (MSEA.XXX.pb.db). The program design is same as Bioconductor LRBaseDbi or MeSHDbi pacakge, and the usage is also the same as these packages. biocViews: Infrastructure Author: Kozo Nishida [aut, cre] (), Koki Tsuyuzaki [aut] (), Atsushi Fukushima [aut] () Maintainer: Kozo Nishida VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSEADbi git_branch: RELEASE_3_12 git_last_commit: 9f800ce git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSEADbi_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSEADbi_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSEADbi_1.0.0.tgz vignettes: vignettes/MSEADbi/inst/doc/MSEADbi.html vignetteTitles: MSEADbi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSEADbi/inst/doc/MSEADbi.R dependencyCount: 26 Package: msgbsR Version: 1.14.0 Depends: R (>= 3.4), GenomicRanges, methods Imports: BSgenome, easyRNASeq, edgeR, GenomicAlignments, GenomicFeatures, GenomeInfoDb, ggbio, ggplot2, IRanges, parallel, plyr, Rsamtools, R.utils, stats, SummarizedExperiment, S4Vectors, utils Suggests: roxygen2, BSgenome.Rnorvegicus.UCSC.rn6 License: GPL-2 MD5sum: b1d28095cf464b352ad5e47f5b506d1b NeedsCompilation: no Title: msgbsR: methylation sensitive genotyping by sequencing (MS-GBS) R functions Description: Pipeline for the anaysis of a MS-GBS experiment. biocViews: ImmunoOncology, DifferentialMethylation, DataImport, Epigenetics, MethylSeq Author: Benjamin Mayne Maintainer: Benjamin Mayne git_url: https://git.bioconductor.org/packages/msgbsR git_branch: RELEASE_3_12 git_last_commit: 9b64d08 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/msgbsR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/msgbsR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/msgbsR_1.14.0.tgz vignettes: vignettes/msgbsR/inst/doc/msgbsR_Vignette.pdf vignetteTitles: msgbsR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msgbsR/inst/doc/msgbsR_Vignette.R dependencyCount: 156 Package: MSGFgui Version: 1.24.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 3506c931e7e5a4256e55f63851a4c359 NeedsCompilation: no Title: A shiny GUI for MSGFplus Description: This package makes it possible to perform analyses using the MSGFplus package in a GUI environment. Furthermore it enables the user to investigate the results using interactive plots, summary statistics and filtering. Lastly it exposes the current results to another R session so the user can seamlessly integrate the gui into other workflows. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSGFgui git_branch: RELEASE_3_12 git_last_commit: 8f8155d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSGFgui_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSGFgui_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSGFgui_1.24.0.tgz vignettes: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R dependencyCount: 61 Package: MSGFplus Version: 1.24.0 Depends: methods Imports: mzID, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: c3d57e5483ee31db2008fd7c08f32833 NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using the MS-GF+ algorithm. The package contains functionality for building up a parameter set both in code and through a simple GUI, as well as running the algorithm in batches, potentially asynchronously. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSGFplus git_branch: RELEASE_3_12 git_last_commit: 1f43e06 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSGFplus_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSGFplus_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSGFplus_1.24.0.tgz vignettes: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html vignetteTitles: Using MSGFgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R dependsOnMe: proteomics importsMe: MSGFgui dependencyCount: 12 Package: msImpute Version: 1.0.0 Depends: R (>= 4.0) Imports: softImpute, methods, stats, graphics, pdist, reticulate, scran, data.table, FNN, matrixStats, rdetools, limma, mvtnorm Suggests: BiocStyle, knitr, rmarkdown, ComplexHeatmap, imputeLCMD License: GPL (>=2) MD5sum: d641986e6f1e1c31978626ccc4b4e515 NeedsCompilation: no Title: Imputation of label-free mass spectrometry peptides Description: MsImpute is a package for imputation of peptide intensity in proteomics experiments. It additionally contains tools for MAR/MNAR diagnosis and assessment of distortions to the probability distribution of the data post imputation. Currently, msImpute completes missing values by low-rank approximation of the underlying data matrix. biocViews: MassSpectrometry, Proteomics, Software Author: Soroor Hediyeh-zadeh [aut, cre] () Maintainer: Soroor Hediyeh-zadeh SystemRequirements: python VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/msImpute/issues git_url: https://git.bioconductor.org/packages/msImpute git_branch: RELEASE_3_12 git_last_commit: cf79c8f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/msImpute_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/msImpute_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/msImpute_1.0.0.tgz vignettes: vignettes/msImpute/inst/doc/msImpute-vignette.html vignetteTitles: msImpute: proteomics missing values imputation and diagnosis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msImpute/inst/doc/msImpute-vignette.R dependencyCount: 70 Package: msmsEDA Version: 1.28.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: da31304153bdc38a35abec6e42796a8c NeedsCompilation: no Title: Exploratory Data Analysis of LC-MS/MS data by spectral counts Description: Exploratory data analysis to assess the quality of a set of LC-MS/MS experiments, and visualize de influence of the involved factors. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori git_url: https://git.bioconductor.org/packages/msmsEDA git_branch: RELEASE_3_12 git_last_commit: a55b390 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/msmsEDA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/msmsEDA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/msmsEDA_1.28.0.tgz vignettes: vignettes/msmsEDA/inst/doc/msmsData-Vignette.pdf vignetteTitles: msmsEDA: Batch effects detection in LC-MSMS experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsEDA/inst/doc/msmsData-Vignette.R dependsOnMe: msmsTests suggestsMe: Harman, RforProteomics dependencyCount: 79 Package: msmsTests Version: 1.28.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: a19346f682982035df4e3f256eb6d3c6 NeedsCompilation: no Title: LC-MS/MS Differential Expression Tests Description: Statistical tests for label-free LC-MS/MS data by spectral counts, to discover differentially expressed proteins between two biological conditions. Three tests are available: Poisson GLM regression, quasi-likelihood GLM regression, and the negative binomial of the edgeR package.The three models admit blocking factors to control for nuissance variables.To assure a good level of reproducibility a post-test filter is available, where we may set the minimum effect size considered biologicaly relevant, and the minimum expression of the most abundant condition. biocViews: ImmunoOncology, Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font git_url: https://git.bioconductor.org/packages/msmsTests git_branch: RELEASE_3_12 git_last_commit: d21b2c9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/msmsTests_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/msmsTests_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/msmsTests_1.28.0.tgz vignettes: vignettes/msmsTests/inst/doc/msmsTests-Vignette.pdf, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.pdf vignetteTitles: msmsTests: post test filters to improve reproducibility, msmsTests: controlling batch effects by blocking hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/msmsTests/inst/doc/msmsTests-Vignette.R, vignettes/msmsTests/inst/doc/msmsTests-Vignette2.R importsMe: MSnID suggestsMe: RforProteomics dependencyCount: 87 Package: MSnbase Version: 2.16.1 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR (>= 2.19.6), S4Vectors, ProtGenerics (>= 1.19.3) Imports: BiocParallel, IRanges (>= 2.13.28), plyr, preprocessCore, vsn, grid, stats4, affy, impute, pcaMethods, MALDIquant (>= 1.16), mzID (>= 1.5.2), digest, lattice, ggplot2, XML, scales, MASS, Rcpp LinkingTo: Rcpp Suggests: testthat, pryr, gridExtra, microbenchmark, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata (>= 0.19.3), roxygen2, rgl, rpx, AnnotationHub, BiocStyle (>= 2.5.19), rmarkdown, imputeLCMD, norm, gplots, shiny, magrittr, SummarizedExperiment License: Artistic-2.0 Archs: i386, x64 MD5sum: 945b7ca886039835658e465af91107ac NeedsCompilation: yes Title: Base Functions and Classes for Mass Spectrometry and Proteomics Description: MSnbase provides infrastructure for manipulation, processing and visualisation of mass spectrometry and proteomics data, ranging from raw to quantitative and annotated data. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto, Johannes Rainer and Sebastian Gibb with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Arne Smits, Martina Fisher, Ludger Goeminne, Adriaan Sticker and Lieven Clement. Maintainer: Laurent Gatto URL: https://lgatto.github.io/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues git_url: https://git.bioconductor.org/packages/MSnbase git_branch: RELEASE_3_12 git_last_commit: 4d88b4e git_last_commit_date: 2021-01-21 Date/Publication: 2021-01-21 source.ver: src/contrib/MSnbase_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSnbase_2.15.7.zip mac.binary.ver: bin/macosx/contrib/4.0/MSnbase_2.16.1.tgz vignettes: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.html, vignettes/MSnbase/inst/doc/v02-MSnbase-io.html, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.html, vignettes/MSnbase/inst/doc/v04-benchmarking.html, vignettes/MSnbase/inst/doc/v05-MSnbase-development.html vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase IO capabilities, MSnbase: centroiding of profile-mode MS data, MSnbase benchmarking, A short introduction to `MSnbase` development hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/v01-MSnbase-demo.R, vignettes/MSnbase/inst/doc/v02-MSnbase-io.R, vignettes/MSnbase/inst/doc/v03-MSnbase-centroiding.R, vignettes/MSnbase/inst/doc/v04-benchmarking.R, vignettes/MSnbase/inst/doc/v05-MSnbase-development.R dependsOnMe: Autotuner, MetCirc, msmsEDA, msmsTests, pRoloc, pRolocGUI, qPLEXanalyzer, synapter, xcms, pRolocdata, RforProteomics, proteomics importsMe: cliqueMS, CluMSID, DEP, MSnID, MSstatsQC, peakPantheR, POMA, PrInCE, ProteomicsAnnotationHubData, topdownr, qPLEXdata, RAMClustR suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, proDA, qcmetrics, wpm, msdata, enviGCMS, pmd dependencyCount: 73 Package: MSnID Version: 1.24.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, methods, iterators, data.table, Biobase, ProtGenerics, reshape2, dplyr, mzR, BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics, Biostrings, purrr, rlang, stringr, tibble, AnnotationHub, AnnotationDbi, xtable License: Artistic-2.0 MD5sum: 2ccf3b6175766623a0d8be301555eace NeedsCompilation: no Title: Utilities for Exploration and Assessment of Confidence of LC-MSn Proteomics Identifications Description: Extracts MS/MS ID data from mzIdentML (leveraging mzID package) or text files. After collating the search results from multiple datasets it assesses their identification quality and optimize filtering criteria to achieve the maximum number of identifications while not exceeding a specified false discovery rate. Also contains a number of utilities to explore the MS/MS results and assess missed and irregular enzymatic cleavages, mass measurement accuracy, etc. biocViews: Proteomics, MassSpectrometry, ImmunoOncology Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk git_url: https://git.bioconductor.org/packages/MSnID git_branch: RELEASE_3_12 git_last_commit: f0cef77 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSnID_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSnID_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSnID_1.24.0.tgz vignettes: vignettes/MSnID/inst/doc/handling_mods.pdf, vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: Handling Modifications with MSnID, MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/handling_mods.R, vignettes/MSnID/inst/doc/msnid_vignette.R dependsOnMe: proteomics suggestsMe: RforProteomics dependencyCount: 151 Package: MSPrep Version: 1.0.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, S4Vectors, pcaMethods (>= 1.24.0), VIM, crmn, preprocessCore, sva, dplyr (>= 0.7), tidyr, tibble (>= 1.2), magrittr, rlang, stats, stringr, methods, ddpcr Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 1.0.2) License: GPL-3 MD5sum: d5effc947c46dfc5dd65ff3897f95277 NeedsCompilation: no Title: Package for Summarizing, Filtering, Imputing, and Normalizing Metabolomics Data Description: Package performs summarization of replicates, filtering by frequency, several different options for imputing missing data, and a variety of options for transforming, batch correcting, and normalizing data. biocViews: Metabolomics, MassSpectrometry, Preprocessing Author: Max McGrath [aut, cre], Matt Mulvahill [aut], Grant Hughes [aut], Sean Jacobson [aut], Harrison Pielke-lombardo [aut], Katerina Kechris [aut, cph, ths] Maintainer: Max McGrath URL: https://github.com/KechrisLab/MSPrep VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/MSPrep/issues git_url: https://git.bioconductor.org/packages/MSPrep git_branch: RELEASE_3_12 git_last_commit: c320918 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSPrep_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSPrep_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSPrep_1.0.0.tgz vignettes: vignettes/MSPrep/inst/doc/using_MSPrep.html vignetteTitles: Using MSPrep hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSPrep/inst/doc/using_MSPrep.R dependencyCount: 177 Package: msPurity Version: 1.16.2 Depends: Rcpp Imports: plyr, dplyr, dbplyr, magrittr, foreach, parallel, doSNOW, stringr, mzR, reshape2, fastcluster, ggplot2, DBI, RSQLite, uuid, jsonlite Suggests: testthat, xcms, BiocStyle, knitr, rmarkdown, msPurityData, CAMERA, RPostgres, RMySQL License: GPL-3 + file LICENSE MD5sum: 4dd49fb525b132e8f8d185a8f582afbf NeedsCompilation: no Title: Automated Evaluation of Precursor Ion Purity for Mass Spectrometry Based Fragmentation in Metabolomics Description: msPurity R package was developed to: 1) Assess the spectral quality of fragmentation spectra by evaluating the "precursor ion purity". 2) Process fragmentation spectra. 3) Perform spectral matching. What is precursor ion purity? -What we call "Precursor ion purity" is a measure of the contribution of a selected precursor peak in an isolation window used for fragmentation. The simple calculation involves dividing the intensity of the selected precursor peak by the total intensity of the isolation window. When assessing MS/MS spectra this calculation is done before and after the MS/MS scan of interest and the purity is interpolated at the recorded time of the MS/MS acquisition. Additionally, isotopic peaks can be removed, low abundance peaks are removed that are thought to have limited contribution to the resulting MS/MS spectra and the isolation efficiency of the mass spectrometer can be used to normalise the intensities used for the calculation. biocViews: MassSpectrometry, Metabolomics, Software Author: Thomas N. Lawson [aut, cre] (), Ralf Weber [ctb], Martin Jones [ctb], Julien Saint-Vanne [ctb], Andris Jankevics [ctb], Mark Viant [ths], Warwick Dunn [ths] Maintainer: Thomas N. Lawson URL: https://github.com/computational-metabolomics/msPurity/ VignetteBuilder: knitr BugReports: https://github.com/computational-metabolomics/msPurity/issues/new git_url: https://git.bioconductor.org/packages/msPurity git_branch: RELEASE_3_12 git_last_commit: 452a749 git_last_commit_date: 2021-01-12 Date/Publication: 2021-01-12 source.ver: src/contrib/msPurity_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/msPurity_1.16.2.zip mac.binary.ver: bin/macosx/contrib/4.0/msPurity_1.16.2.tgz vignettes: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.html, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.html, vignettes/msPurity/inst/doc/msPurity-vignette.html vignetteTitles: msPurity spectral matching, msPurity spectral database schema, msPurity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/msPurity/inst/doc/msPurity-lcmsms-data-processing-and-spectral-matching-vignette.R, vignettes/msPurity/inst/doc/msPurity-spectral-database-vignette.R, vignettes/msPurity/inst/doc/msPurity-vignette.R dependencyCount: 75 Package: MSstats Version: 3.22.1 Depends: R (>= 3.6) Imports: lme4, marray, limma, gplots, ggplot2, methods, grid, ggrepel, preprocessCore, reshape2, survival, statmod, minpack.lm, utils, grDevices, graphics, stats, doSNOW, snow, foreach, data.table, MASS, dplyr, tidyr, broom, purrr, stringr, Suggests: BiocStyle, knitr, rmarkdown, MSstatsBioData License: Artistic-2.0 MD5sum: 116aa1cef57e5bef988fd562f7b63670 NeedsCompilation: no Title: Protein Significance Analysis in DDA, SRM and DIA for Label-free or Label-based Proteomics Experiments Description: A set of tools for statistical relative protein significance analysis in DDA, SRM and DIA experiments. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, Normalization, QualityControl, TimeCourse Author: Meena Choi [aut, cre], Tsung-Heng Tsai [aut], Cyril Galitzine [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Meena Choi URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstats git_branch: RELEASE_3_12 git_last_commit: 8f68dc8 git_last_commit_date: 2021-02-25 Date/Publication: 2021-02-26 source.ver: src/contrib/MSstats_3.22.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstats_3.22.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstats_3.22.1.tgz vignettes: vignettes/MSstats/inst/doc/MSstats.html vignetteTitles: MSstats: Protein/Peptide significance analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstats/inst/doc/MSstats.R importsMe: artMS, MSstatsSampleSize, MSstatsTMT suggestsMe: MSstatsTMTPTM, MSstatsBioData dependencyCount: 76 Package: MSstatsConvert Version: 1.0.0 Depends: R (>= 4.0) Imports: data.table, log4r, methods, checkmate, utils Suggests: tinytest, covr, knitr, rmarkdown License: Artistic-2.0 MD5sum: 9fa5067172944093e405f6d3f3ab6e14 NeedsCompilation: no Title: Import Data from Various Mass Spectrometry Signal Processing Tools to MSstats Format Description: MSstatsConvert provides tools for importing reports of Mass Spectrometry data processing tools into R format suitable for statistical analysis using the MSstats and MSstatsTMT packages. biocViews: MassSpectrometry, Proteomics, Software, DataImport, QualityControl Author: Mateusz Staniak [aut, cre], Meena Choi [aut], Ting Huang [aut], Olga Vitek [aut] Maintainer: Mateusz Staniak VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MSstatsConvert git_branch: RELEASE_3_12 git_last_commit: 90d207a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSstatsConvert_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstatsConvert_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstatsConvert_1.0.0.tgz vignettes: vignettes/MSstatsConvert/inst/doc/msstats_data_format.html vignetteTitles: Working with MSstatsConvert hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsConvert/inst/doc/msstats_data_format.R dependencyCount: 6 Package: MSstatsPTM Version: 1.0.0 Depends: R (>= 4.0) Imports: broom, dplyr, rlang, stats, tibble, tidyr, tidyselect, Biostrings Suggests: knitr, rmarkdown, testthat (>= 2.1.0), BiocStyle License: Artistic-2.0 MD5sum: 16778f88203c406dbd70331cf405a56a NeedsCompilation: no Title: Statistical Characterization of Post-translational Modifications Description: MSstatsPTM provides general statistical methods for quantitative characterization of post-translational modifications (PTMs). Typically, the analysis involves the quantification of PTM sites (i.e., modified residues) and their corresponding proteins, as well as the integration of the quantification results. MSstatsPTM provides functions for summarization, estimation of PTM site abundance, and detection of changes in PTMs across experimental conditions. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression Author: Tsung-Heng Tsai [aut, cre], Olga Vitek [aut] Maintainer: Tsung-Heng Tsai VignetteBuilder: knitr BugReports: https://github.com/tsunghengtsai/MSstatsPTM git_url: https://git.bioconductor.org/packages/MSstatsPTM git_branch: RELEASE_3_12 git_last_commit: d8c642c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSstatsPTM_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstatsPTM_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstatsPTM_1.0.0.tgz vignettes: vignettes/MSstatsPTM/inst/doc/MSstatsPTM.html vignetteTitles: Introduction to MSstatsPTM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsPTM/inst/doc/MSstatsPTM.R dependencyCount: 38 Package: MSstatsQC Version: 2.8.0 Depends: R (>= 3.5.0) Imports: dplyr,plotly,ggplot2,ggExtra, stats,grid, MSnbase, qcmetrics Suggests: knitr,rmarkdown, testthat, RforProteomics License: Artistic License 2.0 MD5sum: 6909f4b899cfc7494561ea9ed63ceb57 NeedsCompilation: no Title: Longitudinal system suitability monitoring and quality control for proteomic experiments Description: MSstatsQC is an R package which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQC git_branch: RELEASE_3_12 git_last_commit: 3a5ce77 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSstatsQC_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstatsQC_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstatsQC_2.8.0.tgz vignettes: vignettes/MSstatsQC/inst/doc/MSstatsQC.html vignetteTitles: MSstatsQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQC/inst/doc/MSstatsQC.R importsMe: MSstatsQCgui dependencyCount: 124 Package: MSstatsQCgui Version: 1.10.0 Imports: shiny, MSstatsQC, ggExtra, gridExtra, plotly, dplyr, grid Suggests: knitr License: Artistic License 2.0 MD5sum: 9ba13be10dee4e9de726257b93349627 NeedsCompilation: no Title: A graphical user interface for MSstatsQC package Description: MSstatsQCgui is a Shiny app which provides longitudinal system suitability monitoring and quality control tools for proteomic experiments. biocViews: Software, QualityControl, Proteomics, MassSpectrometry, GUI Author: Eralp Dogu [aut, cre], Sara Taheri [aut], Olga Vitek [aut] Maintainer: Eralp Dogu URL: http://msstats.org/msstatsqc VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstatsqc git_url: https://git.bioconductor.org/packages/MSstatsQCgui git_branch: RELEASE_3_12 git_last_commit: fbef053 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSstatsQCgui_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstatsQCgui_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstatsQCgui_1.10.0.tgz vignettes: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.html vignetteTitles: MSstatsQCgui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsQCgui/inst/doc/MSstatsQCgui.R dependencyCount: 126 Package: MSstatsSampleSize Version: 1.4.0 Depends: R (>= 3.6) Imports: ggplot2, BiocParallel, caret, gridExtra, reshape2, stats, utils, grDevices, graphics, MSstats Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: ba30e8a91e634998148466b1aa5ba5d3 NeedsCompilation: no Title: Simulation tool for optimal design of high-dimensional MS-based proteomics experiment Description: The packages estimates the variance in the input protein abundance data and simulates data with predefined number of biological replicates based on the variance estimation. It reports the mean predictive accuracy of the classifier and mean protein importance over multiple iterations of the simulation. biocViews: MassSpectrometry, Proteomics, Software, DifferentialExpression, Classification, PrincipalComponent, ExperimentalDesign, Visualization Author: Ting Huang [aut, cre], Meena Choi [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsSampleSize git_branch: RELEASE_3_12 git_last_commit: d48be27 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MSstatsSampleSize_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstatsSampleSize_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstatsSampleSize_1.4.0.tgz vignettes: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.html vignetteTitles: MSstatsSampleSize User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsSampleSize/inst/doc/MSstatsSampleSize.R dependencyCount: 100 Package: MSstatsTMT Version: 1.8.2 Depends: R (>= 4.0) Imports: limma, lme4, lmerTest, dplyr, tidyr, statmod, methods, reshape2, data.table, matrixStats, stats, utils, ggplot2, grDevices, graphics, MSstats Suggests: BiocStyle, knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: d7c7389433a76cce47b7b41e18256975 NeedsCompilation: no Title: Protein Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: The package provides statistical tools for detecting differentially abundant proteins in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software Author: Ting Huang [aut, cre], Meena Choi [aut], Mateusz Staniak [aut], Sicheng Hao [aut], Olga Vitek [aut] Maintainer: Ting Huang URL: http://msstats.org/msstatstmt/ VignetteBuilder: knitr BugReports: https://groups.google.com/forum/#!forum/msstats git_url: https://git.bioconductor.org/packages/MSstatsTMT git_branch: RELEASE_3_12 git_last_commit: d8b9d49 git_last_commit_date: 2020-12-10 Date/Publication: 2020-12-18 source.ver: src/contrib/MSstatsTMT_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstatsTMT_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstatsTMT_1.8.2.tgz vignettes: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.html vignetteTitles: MSstatsTMT User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMT/inst/doc/MSstatsTMT.R importsMe: MSstatsTMTPTM dependencyCount: 80 Package: MSstatsTMTPTM Version: 1.0.2 Depends: R (>= 4.0) Imports: dplyr, gridExtra, stringr, reshape2, stats, utils, ggplot2, grDevices, graphics, MSstatsTMT, Rcpp LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat, MSstats, covr License: Artistic-2.0 Archs: i386, x64 MD5sum: 35268ed9410bae542aa94339a427c102 NeedsCompilation: yes Title: Post Translational Modification (PTM) Significance Analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling Description: Tools for Post Translational Modification (PTM) and protein significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling. The functions in this package should be used after PTM/protein summarization. They can be used to both plot the summarized results and model the summarized datasets. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Software, DifferentialExpression, OneChannel, TwoChannel, Normalization, QualityControl Author: Devon Kohler [aut, cre], Ting Huang [aut], Mateusz Staniak [aut], Meena Choi [aut], Tsung-Heng Tsai [aut], Olga Vitek [aut] Maintainer: Devon Kohler VignetteBuilder: knitr BugReports: https://github.com/Vitek-Lab/MSstatsTMTPTM/issues git_url: https://git.bioconductor.org/packages/MSstatsTMTPTM git_branch: RELEASE_3_12 git_last_commit: eb86988 git_last_commit_date: 2021-02-15 Date/Publication: 2021-02-16 source.ver: src/contrib/MSstatsTMTPTM_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/MSstatsTMTPTM_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.0/MSstatsTMTPTM_1.0.2.tgz vignettes: vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.html, vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.Workflow.html vignetteTitles: MSstatsTMTPTM : A package for post translational modification (PTM) significance analysis in shotgun mass spectrometry-based proteomic experiments with tandem mass tag (TMT) labeling", MSstatsTMTPTM.Workflow.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.R, vignettes/MSstatsTMTPTM/inst/doc/MSstatsTMTPTM.Workflow.R dependencyCount: 82 Package: Mulcom Version: 1.40.0 Depends: R (>= 2.10), Biobase Imports: graphics, grDevices, stats, methods, fields License: GPL-2 Archs: i386, x64 MD5sum: 035f6183e637b93641b3c3fb8bf00589 NeedsCompilation: yes Title: Calculates Mulcom test Description: Identification of differentially expressed genes and false discovery rate (FDR) calculation by Multiple Comparison test. biocViews: StatisticalMethod, MultipleComparison, Microarray, DifferentialExpression, GeneExpression Author: Claudio Isella Maintainer: Claudio Isella git_url: https://git.bioconductor.org/packages/Mulcom git_branch: RELEASE_3_12 git_last_commit: 660bb26 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Mulcom_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Mulcom_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Mulcom_1.40.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 13 Package: MultiAssayExperiment Version: 1.16.0 Depends: R (>= 4.0.0), SummarizedExperiment (>= 1.3.81) Imports: methods, GenomicRanges (>= 1.25.93), BiocGenerics, S4Vectors (>= 0.23.19), IRanges, Biobase, stats, tidyr, utils Suggests: BiocStyle, testthat, knitr, rmarkdown, R.rsp, HDF5Array, RaggedExperiment, UpSetR, survival, survminer License: Artistic-2.0 MD5sum: 6888cd383b88539214eec8761b82b0fe NeedsCompilation: no Title: Software for the integration of multi-omics experiments in Bioconductor Description: MultiAssayExperiment harmonizes data management of multiple experimental assays performed on an overlapping set of specimens. It provides a familiar Bioconductor user experience by extending concepts from SummarizedExperiment, supporting an open-ended mix of standard data classes for individual assays, and allowing subsetting by genomic ranges or rownames. Facilities are provided for reshaping data into wide and long formats for adaptability to graphing and downstream analysis. biocViews: Infrastructure, DataRepresentation Author: Marcel Ramos [aut, cre], Levi Waldron [aut], MultiAssay SIG [ctb] Maintainer: Marcel Ramos URL: http://waldronlab.io/MultiAssayExperiment/ VignetteBuilder: knitr, R.rsp Video: https://youtu.be/w6HWAHaDpyk, https://youtu.be/Vh0hVVUKKFM BugReports: https://github.com/waldronlab/MultiAssayExperiment/issues git_url: https://git.bioconductor.org/packages/MultiAssayExperiment git_branch: RELEASE_3_12 git_last_commit: edebfc9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MultiAssayExperiment_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MultiAssayExperiment_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MultiAssayExperiment_1.16.0.tgz vignettes: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment_cheatsheet.pdf, vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.html, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.html, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.html vignetteTitles: MultiAssayExperiment_cheatsheet.pdf, Coordinating Analysis of Multi-Assay Experiments, Quick-start Guide, HDF5Array and MultiAssayExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiAssayExperiment/inst/doc/MultiAssayExperiment.R, vignettes/MultiAssayExperiment/inst/doc/QuickStartMultiAssay.R, vignettes/MultiAssayExperiment/inst/doc/UsingHDF5Array.R dependsOnMe: CAGEr, cBioPortalData, ClassifyR, evaluomeR, glmSparseNet, hipathia, InTAD, missRows, QFeatures, TimiRGeN, curatedTCGAData, OMICsPCAdata, SingleCellMultiModal, yriMulti importsMe: AffiXcan, AMARETTO, animalcules, corral, ELMER, GOpro, LinkHD, metabolomicsWorkbenchR, MOFA, MOMA, MultiBaC, OMICsPCA, omicsPrint, padma, scp, TCGAutils, HMP2Data suggestsMe: BiocOncoTK, CNVRanger, deco, MOFA2, MultiDataSet, RaggedExperiment, brgedata, MOFAdata dependencyCount: 46 Package: MultiBaC Version: 1.0.0 Imports: Matrix, ggplot2, MultiAssayExperiment, ropls, graphics, methods Suggests: knitr, rmarkdown, BiocStyle, devtools License: GPL-3 MD5sum: b43445ba421996d210681736069527da NeedsCompilation: no Title: Multiomic Batch effect Correction Description: MultiBaC is a strategy to correct batch effects from multiomic datasets distributed across different labs or data acquisition events. MultiBaC is the first Batch effect correction algorithm that dealing with batch effect correction in multiomics datasets. MultiBaC is able to remove batch effects across different omics generated within separate batches provided that at least one common omic data type is included in all the batches considered. biocViews: Software, StatisticalMethod, PrincipalComponent, DataRepresentation, GeneExpression, Transcription, BatchEffect Author: person("Manuel", "Ugidos", email = "manuelugidos@gmail.com"), person("Sonia", "Tarazona", email = "sotacam@gmail.com"), person("María José", "Nueda", email = "mjnueda@ua.es") Maintainer: The package maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiBaC git_branch: RELEASE_3_12 git_last_commit: f79df0f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MultiBaC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MultiBaC_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MultiBaC_1.0.0.tgz vignettes: vignettes/MultiBaC/inst/doc/MultiBaC.html vignetteTitles: MultiBaC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MultiBaC/inst/doc/MultiBaC.R dependencyCount: 70 Package: multiClust Version: 1.20.0 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 92420216df0e165659412bfe4b78385c NeedsCompilation: no Title: multiClust: An R-package for Identifying Biologically Relevant Clusters in Cancer Transcriptome Profiles Description: Clustering is carried out to identify patterns in transcriptomics profiles to determine clinically relevant subgroups of patients. Feature (gene) selection is a critical and an integral part of the process. Currently, there are many feature selection and clustering methods to identify the relevant genes and perform clustering of samples. However, choosing an appropriate methodology is difficult. In addition, extensive feature selection methods have not been supported by the available packages. Hence, we developed an integrative R-package called multiClust that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. Using multiClust, we identified the best performing clustering methodology in the context of clinical outcome. Our observations demonstrate that simple methods such as variance-based ranking perform well on the majority of data sets, provided that the appropriate number of genes is selected. However, different gene ranking and selection methods remain relevant as no methodology works for all studies. biocViews: FeatureExtraction, Clustering, GeneExpression, Survival Author: Nathan Lawlor [aut, cre], Peiyong Guan [aut], Alec Fabbri [aut], Krish Karuturi [aut], Joshy George [aut] Maintainer: Nathan Lawlor VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/multiClust git_branch: RELEASE_3_12 git_last_commit: 4aec467 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/multiClust_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/multiClust_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/multiClust_1.20.0.tgz vignettes: vignettes/multiClust/inst/doc/multiClust.html vignetteTitles: "A Guide to multiClust" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R dependencyCount: 47 Package: multicrispr Version: 1.0.0 Depends: R (>= 4.0) Imports: assertive, BiocGenerics, Biostrings, BSgenome, CRISPRseek, data.table, GenomeInfoDb, GenomicFeatures, GenomicRanges, ggplot2, grid, karyoploteR, magrittr, methods, parallel, plyranges, Rbowtie, reticulate, rtracklayer, stats, stringi, tidyr, tidyselect, utils Suggests: AnnotationHub, BiocStyle, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Scerevisiae.UCSC.sacCer1, ensembldb, IRanges, knitr, magick, rmarkdown, testthat, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-2 MD5sum: 24ce54d21114bec0ff0fb29560cc2667 NeedsCompilation: no Title: Multi-locus multi-purpose Crispr/Cas design Description: This package is for designing Crispr/Cas9 and Prime Editing experiments. It contains functions to (1) define and transform genomic targets, (2) find spacers (4) count offtarget (mis)matches, and (5) compute Doench2016/2014 targeting efficiency. Care has been taken for multicrispr to scale well towards large target sets, enabling the design of large Crispr/Cas9 libraries. biocViews: CRISPR, Software Author: Aditya Bhagwat [aut, cre], Johannes Graumann [sad, ctb], Mette Bentsen [ctb], Jens Preussner [ctb], Michael Lawrence [ctb], Hervé Pagès [ctb], Mario Looso [sad, rth] Maintainer: Aditya Bhagwat URL: https://loosolab.pages.gwdg.de/software/multicrispr/ VignetteBuilder: knitr BugReports: https://gitlab.gwdg.de/loosolab/software/multicrispr/-/issues git_url: https://git.bioconductor.org/packages/multicrispr git_branch: RELEASE_3_12 git_last_commit: c2d9878 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/multicrispr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/multicrispr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/multicrispr_1.0.0.tgz vignettes: vignettes/multicrispr/inst/doc/crispr_grna_design.html, vignettes/multicrispr/inst/doc/genome_arithmetics.html, vignettes/multicrispr/inst/doc/prime_editing.html vignetteTitles: grna_design, genome_arithmetics, prime_editing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multicrispr/inst/doc/crispr_grna_design.R, vignettes/multicrispr/inst/doc/genome_arithmetics.R, vignettes/multicrispr/inst/doc/prime_editing.R dependencyCount: 173 Package: MultiDataSet Version: 1.18.2 Depends: R (>= 3.3), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, methods, utils, ggplot2, ggrepel, qqman, limma Suggests: brgedata, minfi, minfiData, knitr, rmarkdown, testthat, omicade4, iClusterPlus, GEOquery, MultiAssayExperiment, BiocStyle, RaggedExperiment License: file LICENSE MD5sum: eb112bbb3c021de2d306c93e057d6138 NeedsCompilation: no Title: Implementation of MultiDataSet and ResultSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and ResultSet. MultiDataSet is designed for integrating multi omics data sets and ResultSet is a container for omics results. This package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzalez [aut] Maintainer: Xavier Escrib Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MultiDataSet git_branch: RELEASE_3_12 git_last_commit: 94a0f5a git_last_commit_date: 2021-05-03 Date/Publication: 2021-05-03 source.ver: src/contrib/MultiDataSet_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/MultiDataSet_1.18.2.zip mac.binary.ver: bin/macosx/contrib/4.0/MultiDataSet_1.18.2.tgz vignettes: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.html, vignettes/MultiDataSet/inst/doc/MultiDataSet.html vignetteTitles: Adding a new type of data to MultiDataSet objects, Introduction to MultiDataSet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet_Extending_Proteome.R, vignettes/MultiDataSet/inst/doc/MultiDataSet.R dependsOnMe: MEAL importsMe: biosigner, omicRexposome, ropls dependencyCount: 61 Package: multiGSEA Version: 1.0.2 Depends: R (>= 4.0.0) Imports: magrittr, graphite, AnnotationDbi, dplyr, fgsea, metap, rappdirs, rlang, methods Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Ss.eg.db, org.Bt.eg.db, org.Ce.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.Gg.eg.db, org.Xl.eg.db, org.Cf.eg.db, metaboliteIDmapping, knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0) License: GPL-3 MD5sum: 1ed89dbf468da750fcd76a6cf96a8c9b NeedsCompilation: no Title: Combining GSEA-based pathway enrichment with multi omics data integration Description: Extracted features from pathways derived from 8 different databases (KEGG, Reactome, Biocarta, etc.) can be used on transcriptomic, proteomic, and/or metabolomic level to calculate a combined GSEA-based enrichment score. biocViews: GeneSetEnrichment, Pathways, Reactome, BioCarta Author: Sebastian Canzler [aut, cre] (), Jörg Hackermüller [aut] () Maintainer: Sebastian Canzler URL: https://github.com/yigbt/multiGSEA VignetteBuilder: knitr BugReports: https://github.com/yigbt/multiGSEA/issues git_url: https://git.bioconductor.org/packages/multiGSEA git_branch: RELEASE_3_12 git_last_commit: 2dc0f3c git_last_commit_date: 2021-04-21 Date/Publication: 2021-04-22 source.ver: src/contrib/multiGSEA_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/multiGSEA_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.0/multiGSEA_1.0.2.tgz vignettes: vignettes/multiGSEA/inst/doc/multiGSEA.html vignetteTitles: multiGSEA.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiGSEA/inst/doc/multiGSEA.R dependencyCount: 108 Package: multiHiCcompare Version: 1.8.0 Depends: R (>= 4.0.0) Imports: data.table, dplyr, HiCcompare, edgeR, BiocParallel, qqman, pheatmap, methods, metap, GenomicRanges, graphics, stats, utils, pbapply, GenomeInfoDbData, BLMA, GenomeInfoDb Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE MD5sum: 8692d56d407c98271d0e6aa356461a8e NeedsCompilation: no Title: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available Description: multiHiCcompare provides functions for joint normalization and difference detection in multiple Hi-C datasets. This extension of the original HiCcompare package now allows for Hi-C experiments with more than 2 groups and multiple samples per group. multiHiCcompare operates on processed Hi-C data in the form of sparse upper triangular matrices. It accepts four column (chromosome, region1, region2, IF) tab-separated text files storing chromatin interaction matrices. multiHiCcompare provides cyclic loess and fast loess (fastlo) methods adapted to jointly normalizing Hi-C data. Additionally, it provides a general linear model (GLM) framework adapting the edgeR package to detect differences in Hi-C data in a distance dependent manner. biocViews: Software, HiC, Sequencing, Normalization Author: John Stansfield , Mikhail Dozmorov Maintainer: John Stansfield , Mikhail Dozmorov URL: https://github.com/dozmorovlab/multiHiCcompare VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/multiHiCcompare/issues git_url: https://git.bioconductor.org/packages/multiHiCcompare git_branch: RELEASE_3_12 git_last_commit: d2e0a2e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/multiHiCcompare_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/multiHiCcompare_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/multiHiCcompare_1.8.0.tgz vignettes: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.html, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.html vignetteTitles: Visualizing results in Juicebox, multiHiCcompare Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiHiCcompare/inst/doc/juiceboxVisualization.R, vignettes/multiHiCcompare/inst/doc/multiHiCcompare.R suggestsMe: HiCcompare dependencyCount: 163 Package: MultiMed Version: 2.12.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: f5ba937dcf14ba3c1d7604bd44431411 NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements methods for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Ruth Heller, Joshua N. Sampson Maintainer: Simina M. Boca git_url: https://git.bioconductor.org/packages/MultiMed git_branch: RELEASE_3_12 git_last_commit: d0bc4ea git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MultiMed_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MultiMed_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MultiMed_2.12.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiMed/inst/doc/MultiMed.R dependencyCount: 0 Package: multiMiR Version: 1.12.0 Depends: R (>= 3.4) Imports: stats, XML, RCurl, purrr (>= 0.2.2), tibble (>= 1.2), methods, BiocGenerics, AnnotationDbi, dplyr, Suggests: BiocStyle, edgeR, knitr, rmarkdown, testthat (>= 1.0.2) License: MIT + file LICENSE MD5sum: 6d6f63e919705e80fb48c88befb6e305 NeedsCompilation: no Title: Integration of multiple microRNA-target databases with their disease and drug associations Description: A collection of microRNAs/targets from external resources, including validated microRNA-target databases (miRecords, miRTarBase and TarBase), predicted microRNA-target databases (DIANA-microT, ElMMo, MicroCosm, miRanda, miRDB, PicTar, PITA and TargetScan) and microRNA-disease/drug databases (miR2Disease, Pharmaco-miR VerSe and PhenomiR). biocViews: miRNAData, Homo_sapiens_Data, Mus_musculus_Data, Rattus_norvegicus_Data, OrganismData Author: Yuanbin Ru [aut], Matt Mulvahill [cre, aut], Spencer Mahaffey [aut], Katerina Kechris [aut, cph, ths] Maintainer: Matt Mulvahill URL: https://github.com/KechrisLab/multiMiR VignetteBuilder: knitr BugReports: https://github.com/KechrisLab/multiMiR/issues git_url: https://git.bioconductor.org/packages/multiMiR git_branch: RELEASE_3_12 git_last_commit: 21b8901 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/multiMiR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/multiMiR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/multiMiR_1.12.0.tgz vignettes: vignettes/multiMiR/inst/doc/multiMiR.html vignetteTitles: The multiMiR user's guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/multiMiR/inst/doc/multiMiR.R dependencyCount: 43 Package: multiOmicsViz Version: 1.14.0 Depends: R (>= 3.3.2) Imports: methods, parallel, doParallel, foreach, grDevices, graphics, utils, SummarizedExperiment, stats Suggests: BiocGenerics License: LGPL MD5sum: 68c2e9c3f7618a5b6557ae4b2a019301 NeedsCompilation: no Title: Plot the effect of one omics data on other omics data along the chromosome Description: Calculate the spearman correlation between the source omics data and other target omics data, identify the significant correlations and plot the significant correlations on the heat map in which the x-axis and y-axis are ordered by the chromosomal location. biocViews: Software, Visualization, SystemsBiology Author: Jing Wang Maintainer: Jing Wang git_url: https://git.bioconductor.org/packages/multiOmicsViz git_branch: RELEASE_3_12 git_last_commit: c861d88 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/multiOmicsViz_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/multiOmicsViz_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/multiOmicsViz_1.14.0.tgz vignettes: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.pdf vignetteTitles: multiOmicsViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiOmicsViz/inst/doc/multiOmicsViz.R dependencyCount: 30 Package: multiscan Version: 1.50.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 86dcf23c37e27268b82d800867411f53 NeedsCompilation: yes Title: R package for combining multiple scans Description: Estimates gene expressions from several laser scans of the same microarray biocViews: Microarray, Preprocessing Author: Mizanur Khondoker , Chris Glasbey, Bruce Worton. Maintainer: Mizanur Khondoker git_url: https://git.bioconductor.org/packages/multiscan git_branch: RELEASE_3_12 git_last_commit: 242d5ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/multiscan_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/multiscan_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/multiscan_1.50.0.tgz vignettes: vignettes/multiscan/inst/doc/multiscan.pdf vignetteTitles: An R Package for Estimating Gene Expressions using Multiple Scans hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiscan/inst/doc/multiscan.R dependencyCount: 7 Package: multtest Version: 2.46.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: i386, x64 MD5sum: df4a53716450db2c7eb67da8bb616dc7 NeedsCompilation: yes Title: Resampling-based multiple hypothesis testing Description: Non-parametric bootstrap and permutation resampling-based multiple testing procedures (including empirical Bayes methods) for controlling the family-wise error rate (FWER), generalized family-wise error rate (gFWER), tail probability of the proportion of false positives (TPPFP), and false discovery rate (FDR). Several choices of bootstrap-based null distribution are implemented (centered, centered and scaled, quantile-transformed). Single-step and step-wise methods are available. Tests based on a variety of t- and F-statistics (including t-statistics based on regression parameters from linear and survival models as well as those based on correlation parameters) are included. When probing hypotheses with t-statistics, users may also select a potentially faster null distribution which is multivariate normal with mean zero and variance covariance matrix derived from the vector influence function. Results are reported in terms of adjusted p-values, confidence regions and test statistic cutoffs. The procedures are directly applicable to identifying differentially expressed genes in DNA microarray experiments. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Katherine S. Pollard, Houston N. Gilbert, Yongchao Ge, Sandra Taylor, Sandrine Dudoit Maintainer: Katherine S. Pollard git_url: https://git.bioconductor.org/packages/multtest git_branch: RELEASE_3_12 git_last_commit: c4dd27b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/multtest_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/multtest_2.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/multtest_2.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: aCGH, BicARE, iPAC, KCsmart, PREDA, rain, REDseq, SAGx, siggenes, webbioc, cp4p, DiffCorr, GExMap, PCS importsMe: a4Base, ABarray, adSplit, ALDEx2, anota, ChIPpeakAnno, IsoGeneGUI, mAPKL, metabomxtr, nethet, OCplus, phyloseq, RTopper, SingleCellSignalR, singleCellTK, synapter, webbioc, hddplot, INCATome, MetaIntegrator, mutoss, nlcv, pRF, TcGSA suggestsMe: annaffy, BiocCaseStudies, ecolitk, factDesign, GGtools, GOstats, gQTLstats, GSEAlm, maigesPack, pcot2, ropls, topGO, xcms, cherry, metagam, POSTm dependencyCount: 15 Package: muscat Version: 1.4.0 Depends: R (>= 4.0) Imports: BiocParallel, blme, ComplexHeatmap, data.table, DESeq2, dplyr, edgeR, ggplot2, glmmTMB, grDevices, grid, limma, lmerTest, lme4, Matrix, matrixStats, methods, progress, purrr, S4Vectors, scales, scater, sctransform, stats, SingleCellExperiment, SummarizedExperiment, variancePartition, viridis Suggests: BiocStyle, countsimQC, cowplot, ExperimentHub, iCOBRA, knitr, phylogram, RColorBrewer, reshape2, rmarkdown, testthat, UpSetR License: GPL (>= 2) MD5sum: 35613af5ef0c1b7fb43bc21815e6352f NeedsCompilation: no Title: Multi-sample multi-group scRNA-seq data analysis tools Description: `muscat` provides various methods and visualization tools for DS analysis in multi-sample, multi-group, multi-(cell-)subpopulation scRNA-seq data, including cell-level mixed models and methods based on aggregated “pseudobulk” data, as well as a flexible simulation platform that mimics both single and multi-sample scRNA-seq data. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, SingleCell, Software, StatisticalMethod, Visualization Author: Helena L. Crowell [aut, cre], Pierre-Luc Germain [aut], Charlotte Soneson [aut], Anthony Sonrel [aut], Mark D. Robinson [aut, fnd] Maintainer: Helena L. Crowell URL: https://github.com/HelenaLC/muscat VignetteBuilder: knitr BugReports: https://github.com/HelenaLC/muscat/issues git_url: https://git.bioconductor.org/packages/muscat git_branch: RELEASE_3_12 git_last_commit: f9eef39 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/muscat_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/muscat_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/muscat_1.4.0.tgz vignettes: vignettes/muscat/inst/doc/analysis.html, vignettes/muscat/inst/doc/simulation.html vignetteTitles: "1. DS analysis", "2. Data simulation" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscat/inst/doc/analysis.R, vignettes/muscat/inst/doc/simulation.R suggestsMe: muscData dependencyCount: 172 Package: muscle Version: 3.32.0 Depends: Biostrings License: Unlimited Archs: i386, x64 MD5sum: d1a4f150e357e5997179d9a6a330d583 NeedsCompilation: yes Title: Multiple Sequence Alignment with MUSCLE Description: MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences. biocViews: MultipleSequenceAlignment, Alignment, Sequencing, Genetics, SequenceMatching, DataImport Author: Algorithm by Robert C. Edgar. R port by Alex T. Kalinka. Maintainer: Alex T. Kalinka URL: http://www.drive5.com/muscle/ git_url: https://git.bioconductor.org/packages/muscle git_branch: RELEASE_3_12 git_last_commit: 6c17633 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/muscle_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/muscle_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/muscle_3.32.0.tgz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/muscle/inst/doc/muscle-vignette.R importsMe: ptm suggestsMe: seqmagick dependencyCount: 15 Package: musicatk Version: 1.0.0 Depends: R (>= 4.0.0), NMF Imports: SummarizedExperiment, VariantAnnotation, cowplot, Biostrings, base, methods, magrittr, tibble, tidyr, gtools, gridExtra, maftools, MCMCprecision, data.table, dplyr, rlang, BSgenome, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, S4Vectors, uwot, ggplot2, stringr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Mmusculus.UCSC.mm10, deconstructSigs, decompTumor2Sig, topicmodels, ggrepel, withr, plotly, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, survival, XVector, qpdf, covr License: LGPL-3 MD5sum: fee44567069c89c2f133e6e860bfe97c NeedsCompilation: no Title: Mutational Signature Comprehensive Analysis Toolkit Description: Mutational signatures are carcinogenic exposures or aberrant cellular processes that can cause alterations to the genome. We created musicatk (MUtational SIgnature Comprehensive Analysis ToolKit) to address shortcomings in versatility and ease of use in other pre-existing computational tools. Although many different types of mutational data have been generated, current software packages do not have a flexible framework to allow users to mix and match different types of mutations in the mutational signature inference process. Musicatk enables users to count and combine multiple mutation types, including SBS, DBS, and indels. Musicatk calculates replication strand, transcription strand and combinations of these features along with discovery from unique and proprietary genomic feature associated with any mutation type. Musicatk also implements several methods for discovery of new signatures as well as methods to infer exposure given an existing set of signatures. Musicatk provides functions for visualization and downstream exploratory analysis including the ability to compare signatures between cohorts and find matching signatures in COSMIC V2 or COSMIC V3. biocViews: Software, BiologicalQuestion, SomaticMutation, VariantAnnotation Author: Aaron Chevalier [cre] (0000-0002-3968-9250), Joshua D. Campbell [aut] () Maintainer: Aaron Chevalier VignetteBuilder: knitr BugReports: https://github.com/campbio/musicatk/issues git_url: https://git.bioconductor.org/packages/musicatk git_branch: RELEASE_3_12 git_last_commit: 22a0020 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/musicatk_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/musicatk_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/musicatk_1.0.0.tgz vignettes: vignettes/musicatk/inst/doc/musicatk.html vignetteTitles: Mutational Signature Comprehensive Analysis Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/musicatk/inst/doc/musicatk.R dependencyCount: 165 Package: MutationalPatterns Version: 3.0.1 Depends: R (>= 4.0.0), GenomicRanges (>= 1.24.0), NMF (>= 0.20.6) Imports: stats, S4Vectors, BiocGenerics (>= 0.18.0), BSgenome (>= 1.40.0), VariantAnnotation (>= 1.18.1), dplyr (>= 0.8.3), tibble(>= 2.1.3), purrr (>= 0.3.2), tidyr (>= 1.0.0), stringr (>= 1.4.0), magrittr (>= 1.5), ggplot2 (>= 2.1.0), pracma (>= 1.8.8), IRanges (>= 2.6.0), GenomeInfoDb (>= 1.12.0), Biostrings (>= 2.40.0), ggdendro (>= 0.1-20), cowplot (>= 0.9.2), ggalluvial (>= 0.12.2) Suggests: BSgenome.Hsapiens.UCSC.hg19 (>= 1.4.0), BiocStyle (>= 2.0.3), TxDb.Hsapiens.UCSC.hg19.knownGene (>= 3.2.2), biomaRt (>= 2.28.0), gridExtra (>= 2.2.1), rtracklayer (>= 1.32.2), ccfindR (>= 1.6.0), GenomicFeatures, AnnotationDbi, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 027f34ce1fe55fd6b84dc1c301cb8b1d NeedsCompilation: no Title: Comprehensive genome-wide analysis of mutational processes Description: Mutational processes leave characteristic footprints in genomic DNA. This package provides a comprehensive set of flexible functions that allows researchers to easily evaluate and visualize a multitude of mutational patterns in base substitution catalogues of e.g. healthy samples, tumour samples, or DNA-repair deficient cells. The package covers a wide range of patterns including: mutational signatures, transcriptional and replicative strand bias, lesion segregation, genomic distribution and association with genomic features, which are collectively meaningful for studying the activity of mutational processes. The package works with single nucleotide variants (SNVs), insertions and deletions (Indels), double base substitutions (DBSs) and larger multi base substitutions (MBSs). The package provides functionalities for both extracting mutational signatures de novo and determining the contribution of previously identified mutational signatures on a single sample level. MutationalPatterns integrates with common R genomic analysis workflows and allows easy association with (publicly available) annotation data. biocViews: Genetics, SomaticMutation Author: Freek Manders [aut] (), Francis Blokzijl [aut] (), Roel Janssen [aut] (), Jurrian de Kanter [ctb] (), Rurika Oka [cre] (), Ruben van Boxtel [aut, cph] (), Edwin Cuppen [aut] () Maintainer: Rurika Oka URL: https://doi.org/10.1186/s13073-018-0539-0 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MutationalPatterns git_branch: RELEASE_3_12 git_last_commit: 72983a7 git_last_commit_date: 2020-11-12 Date/Publication: 2020-11-12 source.ver: src/contrib/MutationalPatterns_3.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/MutationalPatterns_3.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/MutationalPatterns_3.0.1.tgz vignettes: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.html vignetteTitles: Introduction to MutationalPatterns hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MutationalPatterns/inst/doc/Introduction_to_MutationalPatterns.R dependencyCount: 126 Package: MVCClass Version: 1.64.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 3ff02ba6bcbc8110e6e6175d61ebe334 NeedsCompilation: no Title: Model-View-Controller (MVC) Classes Description: Creates classes used in model-view-controller (MVC) design biocViews: Visualization, Infrastructure, GraphAndNetwork Author: Elizabeth Whalen Maintainer: Elizabeth Whalen git_url: https://git.bioconductor.org/packages/MVCClass git_branch: RELEASE_3_12 git_last_commit: a573d43 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MVCClass_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MVCClass_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MVCClass_1.64.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass dependencyCount: 1 Package: MWASTools Version: 1.14.0 Depends: R(>= 3.4) Imports: glm2, ppcor, qvalue, car, boot, grid, ggplot2, gridExtra, igraph, SummarizedExperiment, KEGGgraph, RCurl, KEGGREST, ComplexHeatmap, stats, utils Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: CC BY-NC-ND 4.0 MD5sum: 788f54f43625e397b02df8569860465d NeedsCompilation: no Title: MWASTools: an integrated pipeline to perform metabolome-wide association studies Description: MWASTools provides a complete pipeline to perform metabolome-wide association studies. Key functionalities of the package include: quality control analysis of metabonomic data; MWAS using different association models (partial correlations; generalized linear models); model validation using non-parametric bootstrapping; visualization of MWAS results; NMR metabolite identification using STOCSY; and biological interpretation of MWAS results. biocViews: Metabolomics, Lipidomics, Cheminformatics, SystemsBiology, QualityControl Author: Andrea Rodriguez-Martinez, Joram M. Posma, Rafael Ayala, Ana L. Neves, Maryam Anwar, Jeremy K. Nicholson, Marc-Emmanuel Dumas Maintainer: Andrea Rodriguez-Martinez , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/MWASTools git_branch: RELEASE_3_12 git_last_commit: 6fba3b0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/MWASTools_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/MWASTools_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/MWASTools_1.14.0.tgz vignettes: vignettes/MWASTools/inst/doc/MWASTools.html vignetteTitles: MWASTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MWASTools/inst/doc/MWASTools.R importsMe: MetaboSignal dependencyCount: 137 Package: mygene Version: 1.26.0 Depends: R (>= 3.2.1), GenomicFeatures, Imports: httr (>= 0.3), jsonlite (>= 0.9.7), S4Vectors, Hmisc, sqldf, plyr Suggests: BiocStyle License: Artistic-2.0 MD5sum: 2993fa896f7169766bfbb4df409d12c1 NeedsCompilation: no Title: Access MyGene.Info_ services Description: MyGene.Info_ provides simple-to-use REST web services to query/retrieve gene annotation data. It's designed with simplicity and performance emphasized. *mygene*, is an easy-to-use R wrapper to access MyGene.Info_ services. biocViews: Annotation Author: Adam Mark, Ryan Thompson, Cyrus Afrasiabi, Chunlei Wu Maintainer: Adam Mark, Cyrus Afrasiabi, Chunlei Wu git_url: https://git.bioconductor.org/packages/mygene git_branch: RELEASE_3_12 git_last_commit: f612b92 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mygene_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mygene_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mygene_1.26.0.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mygene/inst/doc/mygene.R importsMe: MetaboSignal dependencyCount: 134 Package: myvariant Version: 1.20.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 7614afa2301eaae2e0013939b256a6b0 NeedsCompilation: no Title: Accesses MyVariant.info variant query and annotation services Description: MyVariant.info is a comprehensive aggregation of variant annotation resources. myvariant is a wrapper for querying MyVariant.info services biocViews: VariantAnnotation, Annotation, GenomicVariation Author: Adam Mark Maintainer: Adam Mark, Chunlei Wu git_url: https://git.bioconductor.org/packages/myvariant git_branch: RELEASE_3_12 git_last_commit: 719e034 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/myvariant_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/myvariant_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/myvariant_1.20.0.tgz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/myvariant/inst/doc/myvariant.R dependencyCount: 132 Package: mzID Version: 1.28.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 2d52a55a972baaa6f23ab16c50df4fd9 NeedsCompilation: no Title: An mzIdentML parser for R Description: A parser for mzIdentML files implemented using the XML package. The parser tries to be general and able to handle all types of mzIdentML files with the drawback of having less 'pretty' output than a vendor specific parser. Please contact the maintainer with any problems and supply an mzIdentML file so the problems can be fixed quickly. biocViews: ImmunoOncology, DataImport, MassSpectrometry, Proteomics Author: Laurent Gatto [ctb, cre] (), Thomas Pedersen [aut] (), Vladislav Petyuk [ctb] Maintainer: Laurent Gatto VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/mzID git_branch: RELEASE_3_12 git_last_commit: cd00663 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/mzID_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/mzID_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/mzID_1.28.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzID/inst/doc/HOWTO_mzID.R dependsOnMe: proteomics importsMe: MSGFgui, MSGFplus, MSnbase, MSnID suggestsMe: mzR, RforProteomics dependencyCount: 11 Package: mzR Version: 2.24.1 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics (>= 1.17.3), ncdf4 LinkingTo: Rcpp, zlibbioc, Rhdf5lib (>= 1.1.4) Suggests: msdata (>= 0.15.1), RUnit, mzID, BiocStyle (>= 2.5.19), knitr, XML License: Artistic-2.0 Archs: i386, x64 MD5sum: 7681fb96bb7e8a43cdf7f049b2b70ac3 NeedsCompilation: yes Title: parser for netCDF, mzXML, mzData and mzML and mzIdentML files (mass spectrometry data) Description: mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a wrapper for the ISB random access parser for mass spectrometry mzXML, mzData and mzML files. The package contains the original code written by the ISB, and a subset of the proteowizard library for mzML and mzIdentML. The netCDF reading code has previously been used in XCMS. biocViews: ImmunoOncology, Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou, Johannes Rainer Maintainer: Steffen Neumann , Laurent Gatto , Qiang Kou URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ git_url: https://git.bioconductor.org/packages/mzR git_branch: RELEASE_3_12 git_last_commit: e1d4de8 git_last_commit_date: 2020-11-18 Date/Publication: 2020-11-18 source.ver: src/contrib/mzR_2.24.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/mzR_2.24.1.zip mac.binary.ver: bin/macosx/contrib/4.0/mzR_2.24.1.tgz vignettes: vignettes/mzR/inst/doc/mzR.html vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mzR/inst/doc/mzR.R dependsOnMe: MSGFgui, MSnbase, proteomics importsMe: adductomicsR, Autotuner, CluMSID, DIAlignR, MSnID, msPurity, peakPantheR, ProteomicsAnnotationHubData, SIMAT, topdownr, xcms, yamss suggestsMe: AnnotationHub, qcmetrics, Spectra, msdata, RforProteomics, baitmet, erah dependencyCount: 12 Package: NADfinder Version: 1.14.0 Depends: R (>= 3.4), BiocGenerics, IRanges, GenomicRanges, S4Vectors, SummarizedExperiment Imports: graphics, methods, baseline, signal, GenomicAlignments, GenomeInfoDb, rtracklayer, limma, trackViewer, stats, utils, Rsamtools, metap, EmpiricalBrownsMethod,ATACseqQC, corrplot, csaw Suggests: RUnit, BiocStyle, knitr, BSgenome.Mmusculus.UCSC.mm10, testthat, BiocManager License: GPL (>= 2) MD5sum: a47c08dc30cb622302ef5b47d425f3cd NeedsCompilation: no Title: Call wide peaks for sequencing data Description: Nucleolus is an important structure inside the nucleus in eukaryotic cells. It is the site for transcribing rDNA into rRNA and for assembling ribosomes, aka ribosome biogenesis. In addition, nucleoli are dynamic hubs through which numerous proteins shuttle and contact specific non-rDNA genomic loci. Deep sequencing analyses of DNA associated with isolated nucleoli (NAD- seq) have shown that specific loci, termed nucleolus- associated domains (NADs) form frequent three- dimensional associations with nucleoli. NAD-seq has been used to study the biological functions of NAD and the dynamics of NAD distribution during embryonic stem cell (ESC) differentiation. Here, we developed a Bioconductor package NADfinder for bioinformatic analysis of the NAD-seq data, including baseline correction, smoothing, normalization, peak calling, and annotation. biocViews: Sequencing, DNASeq, GeneRegulation, PeakDetection Author: Jianhong Ou, Haibo Liu, Jun Yu, Hervé Pagès, Paul Kaufman, Lihua Julie Zhu Maintainer: Jianhong Ou , Lihua Julie Zhu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NADfinder git_branch: RELEASE_3_12 git_last_commit: e5dde58 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NADfinder_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NADfinder_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NADfinder_1.14.0.tgz vignettes: vignettes/NADfinder/inst/doc/NADfinder.html hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NADfinder/inst/doc/NADfinder.R dependencyCount: 213 Package: NanoMethViz Version: 1.0.0 Depends: R (>= 4.0.0), methods, ggplot2 Imports: S4Vectors, SummarizedExperiment, bsseq, forcats, assertthat, AnnotationDbi, Rcpp, dplyr, data.table, e1071, fs, GenomicRanges, ggthemes, glue, patchwork, purrr, readr, rlang, RSQLite, Rsamtools, scales, stats, stringr, tibble, tidyr, utils, zlibbioc LinkingTo: Rcpp Suggests: DSS, Mus.musculus, Homo.sapiens, knitr, rmarkdown License: Apache License (>= 2.0) Archs: i386, x64 MD5sum: e46cc017b0b64ab7acc3f3da8f13c425 NeedsCompilation: yes Title: Visualise methlation data from Oxford Nanopore sequencing Description: NanoMethViz is a toolkit for visualising methylation data from Oxford Nanopore sequencing. It can be used to explore methylation patterns from reads derived from Oxford Nanopore direct DNA sequencing with methylation called by callers including nanopolish, f5c and megalodon. The plots in this package allow the visualisation of methylation profiles aggregated over experimental groups and across classes of genomic features. biocViews: Software, Visualization, DifferentialMethylation Author: Shian Su [cre, aut] Maintainer: Shian Su URL: https://github.com/shians/NanoMethViz SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Shians/NanoMethViz/issues git_url: https://git.bioconductor.org/packages/NanoMethViz git_branch: RELEASE_3_12 git_last_commit: 60b2e12 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NanoMethViz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NanoMethViz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NanoMethViz_1.0.0.tgz vignettes: vignettes/NanoMethViz/inst/doc/ImportingData.html, vignettes/NanoMethViz/inst/doc/Introduction.html vignetteTitles: Importing Data, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoMethViz/inst/doc/ImportingData.R, vignettes/NanoMethViz/inst/doc/Introduction.R dependencyCount: 116 Package: NanoStringDiff Version: 1.20.0 Depends: Biobase Imports: matrixStats, methods, Rcpp LinkingTo: Rcpp Suggests: testthat, BiocStyle License: GPL Archs: i386, x64 MD5sum: bf91dbd7089b4cfc4c4696f0e70b7d59 NeedsCompilation: yes Title: Differential Expression Analysis of NanoString nCounter Data Description: This Package utilizes a generalized linear model(GLM) of the negative binomial family to characterize count data and allows for multi-factor design. NanoStrongDiff incorporate size factors, calculated from positive controls and housekeeping controls, and background level, obtained from negative controls, in the model framework so that all the normalization information provided by NanoString nCounter Analyzer is fully utilized. biocViews: DifferentialExpression, Normalization Author: hong wang , tingting zhai , chi wang Maintainer: tingting zhai ,hong wang git_url: https://git.bioconductor.org/packages/NanoStringDiff git_branch: RELEASE_3_12 git_last_commit: a3c1534 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NanoStringDiff_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NanoStringDiff_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NanoStringDiff_1.20.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.R dependencyCount: 9 Package: NanoStringQCPro Version: 1.22.0 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.12), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: c97ba9f5f8fcdc827d9a94ec702e89a1 NeedsCompilation: no Title: Quality metrics and data processing methods for NanoString mRNA gene expression data Description: NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data -- i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output. biocViews: Microarray, mRNAMicroarray, Preprocessing, Normalization, QualityControl, ReportWriting Author: Dorothee Nickles , Thomas Sandmann , Robert Ziman , Richard Bourgon Maintainer: Robert Ziman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NanoStringQCPro git_branch: RELEASE_3_12 git_last_commit: 11526ca git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NanoStringQCPro_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NanoStringQCPro_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NanoStringQCPro_1.22.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: vignetteNanoStringQCPro.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 82 Package: nanotatoR Version: 1.6.0 Depends: R (>= 3.6) Imports: hash(>= 2.2.6), openxlsx(>= 4.0.17), rentrez(>= 1.1.0), stats, grDevices, graphics, stringr, knitr, testthat, utils, AnnotationDbi, httr, org.Hs.eg.db, rtracklayer Suggests: rmarkdown, yaml License: file LICENSE MD5sum: d60c6f34c6309d360c3a4dea73543c2c NeedsCompilation: no Title: nanotatoR: next generation structural variant annotation and classification Description: Whole genome sequencing (WGS) has successfully been used to identify single-nucleotide variants (SNV), small insertions and deletions and, more recently, small copy number variants. However, due to utilization of short reads, it is not well suited for identification of structural variants (SV) and the majority of SV calling tools having high false positive and negative rates.Optical next-generation mapping (NGM) utilizes long fluorescently labeled native-state DNA molecules for de novo genome assembly to overcome the limitations of WGS. NGM allows for a significant increase in SV detection capability. However, accuracy of SV annotation is highly important for variant classification and filtration to determine pathogenicity.Here we create a new tool in R, for SV annotation, including population frequency and gene function and expression, using publicly available datasets. We use DGV (Database of Genomic Variants), to calculate the population frequency of the SVs identified by the Bionano SVCaller in the NGM dataset of a cohort of 50 undiagnosed patients with a variety of phenotypes. The new annotation tool, nanotatoR, also calculates the internal frequency of SVs, which could be beneficial in identification of potential false positive or common calls. The software creates a primary gene list (PG) from NCBI databases based on patient phenotype and compares the list to the set of genes overlapping the patient’s SVs generated by SVCaller, providing analysts with an easy way to identify variants affecting genes in the PG. The output is given in an Excel file format, which is subdivided into multiple sheets based on SV type. Users then have a choice to filter SVs using the provided annotation for identification of inherited, de novo or rare variants. nanotatoR provides integrated annotation and the expression patterns to enable users to identify potential pathogenic SVs with greater precision and faster turnaround times. biocViews: Software, WorkflowStep, GenomeAssembly, VariantAnnotation Author: Surajit Bhattacharya,Hayk Barsheghyan, Emmanuele C Delot and Eric Vilain Maintainer: Surajit Bhattacharya URL: https://github.com/VilainLab/Nanotator VignetteBuilder: knitr BugReports: https://github.com/VilainLab/Nanotator/issues git_url: https://git.bioconductor.org/packages/nanotatoR git_branch: RELEASE_3_12 git_last_commit: ac03239 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-28 source.ver: src/contrib/nanotatoR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nanotatoR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nanotatoR_1.6.0.tgz vignettes: vignettes/nanotatoR/inst/doc/nanotatoR.html vignetteTitles: nanotatoR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nanotatoR/inst/doc/nanotatoR.R dependencyCount: 99 Package: NBAMSeq Version: 1.6.1 Depends: R (>= 3.6), SummarizedExperiment, S4Vectors Imports: DESeq2, mgcv(>= 1.8-24), BiocParallel, genefilter, methods, stats, Suggests: knitr, rmarkdown, testthat, ggplot2 License: GPL-2 MD5sum: 8adef5d6629f24fbf74fdcb77e528e2f NeedsCompilation: no Title: Negative Binomial Additive Model for RNA-Seq Data Description: High-throughput sequencing experiments followed by differential expression analysis is a widely used approach to detect genomic biomarkers. A fundamental step in differential expression analysis is to model the association between gene counts and covariates of interest. NBAMSeq a flexible statistical model based on the generalized additive model and allows for information sharing across genes in variance estimation. biocViews: RNASeq, DifferentialExpression, GeneExpression, Sequencing, Coverage Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/NBAMSeq VignetteBuilder: knitr BugReports: https://github.com/reese3928/NBAMSeq/issues git_url: https://git.bioconductor.org/packages/NBAMSeq git_branch: RELEASE_3_12 git_last_commit: 854d381 git_last_commit_date: 2020-10-28 Date/Publication: 2020-10-29 source.ver: src/contrib/NBAMSeq_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/NBAMSeq_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/NBAMSeq_1.6.1.tgz vignettes: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.html vignetteTitles: Negative Binomial Additive Model for RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBAMSeq/inst/doc/NBAMSeq-vignette.R dependencyCount: 90 Package: NBSplice Version: 1.8.0 Depends: R (>= 3.5), methods Imports: edgeR, stats, MASS, car, mppa, BiocParallel, ggplot2, reshape2 Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=2) MD5sum: 704abc0dc68095298645e80464d41701 NeedsCompilation: no Title: Negative Binomial Models to detect Differential Splicing Description: The package proposes a differential splicing evaluation method based on isoform quantification. It applies generalized linear models with negative binomial distribution to infer changes in isoform relative expression. biocViews: Software, StatisticalMethod, AlternativeSplicing, Regression, DifferentialExpression, DifferentialSplicing, RNASeq, ImmunoOncology Author: Gabriela A. Merino and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NBSplice git_branch: RELEASE_3_12 git_last_commit: 870d8c4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NBSplice_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NBSplice_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NBSplice_1.8.0.tgz vignettes: vignettes/NBSplice/inst/doc/NBSplice-vignette.html vignetteTitles: NBSplice-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NBSplice/inst/doc/NBSplice-vignette.R dependencyCount: 107 Package: ncdfFlow Version: 2.36.0 Depends: R (>= 2.14.0), flowCore(>= 1.51.7), RcppArmadillo, methods, BH Imports: Biobase,BiocGenerics,flowCore,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH, Rhdf5lib Suggests: testthat,parallel,flowStats,knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: c60d11ee9f5c4a260096b128cf85644e NeedsCompilation: yes Title: ncdfFlow: A package that provides HDF5 based storage for flow cytometry data. Description: Provides HDF5 storage based methods and functions for manipulation of flow cytometry data. biocViews: ImmunoOncology, FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang , Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ncdfFlow git_branch: RELEASE_3_12 git_last_commit: 786f1c0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ncdfFlow_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ncdfFlow_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ncdfFlow_2.36.0.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncdfFlow/inst/doc/ncdfFlow.R dependsOnMe: ggcyto importsMe: flowStats, flowWorkspace suggestsMe: COMPASS, cydar dependencyCount: 20 Package: ncGTW Version: 1.4.0 Depends: methods, BiocParallel, xcms Imports: Rcpp, grDevices, graphics, stats LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: fa0caaa03426e7220bdb76a4b3f3fba1 NeedsCompilation: yes Title: Alignment of LC-MS Profiles by Neighbor-wise Compound-specific Graphical Time Warping with Misalignment Detection Description: The purpose of ncGTW is to help XCMS for LC-MS data alignment. Currently, ncGTW can detect the misaligned feature groups by XCMS, and the user can choose to realign these feature groups by ncGTW or not. biocViews: Software, MassSpectrometry, Metabolomics, Alignment Author: Chiung-Ting Wu Maintainer: Chiung-Ting Wu VignetteBuilder: knitr BugReports: https://github.com/ChiungTingWu/ncGTW/issues git_url: https://git.bioconductor.org/packages/ncGTW git_branch: RELEASE_3_12 git_last_commit: 5e5f086 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ncGTW_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ncGTW_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ncGTW_1.4.0.tgz vignettes: vignettes/ncGTW/inst/doc/ncGTW.html vignetteTitles: ncGTW User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncGTW/inst/doc/ncGTW.R dependencyCount: 92 Package: NCIgraph Version: 1.38.0 Depends: R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCy3, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 2bce91c1f1770903f6e3f30d03823565 NeedsCompilation: no Title: Pathways from the NCI Pathways Database Description: Provides various methods to load the pathways from the NCI Pathways Database in R graph objects and to re-format them. biocViews: Pathways, GraphAndNetwork Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/NCIgraph git_branch: RELEASE_3_12 git_last_commit: 1f618f0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NCIgraph_1.38.0.tar.gz vignettes: vignettes/NCIgraph/inst/doc/NCIgraph.pdf vignetteTitles: NCIgraph: networks from the NCI pathway integrated database as graphNEL objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NCIgraph/inst/doc/NCIgraph.R importsMe: DEGraph suggestsMe: DEGraph dependencyCount: 35 Package: ncRNAtools Version: 1.0.0 Imports: httr, xml2, utils, methods, grDevices, ggplot2, IRanges, GenomicRanges, S4Vectors Suggests: knitr, BiocStyle, rmarkdown, RUnit, BiocGenerics License: GPL-3 MD5sum: 03fbd0b630f8214f6954dc3abe46c278 NeedsCompilation: no Title: An R toolkit for non-coding RNA Description: ncRNAtools provides a set of basic tools for handling and analyzing non-coding RNAs. These include tools to access the RNAcentral database and to predict and visualize the secondary structure of non-coding RNAs. The package also provides tools to read, write and interconvert the file formats most commonly used for representing such secondary structures. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, StructuralPrediction Author: Lara Selles Vidal [cre, aut] (), Rafael Ayala [aut] (), Guy-Bart Stan [aut] (), Rodrigo Ledesma-Amaro [aut] () Maintainer: Lara Selles Vidal VignetteBuilder: knitr BugReports: https://github.com/LaraSellesVidal/ncRNAtools/issues git_url: https://git.bioconductor.org/packages/ncRNAtools git_branch: RELEASE_3_12 git_last_commit: d20e688 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ncRNAtools_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ncRNAtools_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ncRNAtools_1.0.0.tgz vignettes: vignettes/ncRNAtools/inst/doc/ncRNAtools.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ncRNAtools/inst/doc/ncRNAtools.R dependencyCount: 59 Package: ndexr Version: 1.12.1 Depends: igraph Imports: httr, jsonlite, plyr, tidyr Suggests: BiocStyle, testthat, knitr, rmarkdown License: BSD MD5sum: d6e6add322a0c4cee9a822e278a72f87 NeedsCompilation: no Title: NDEx R client library Description: This package offers an interface to NDEx servers, e.g. the public server at http://ndexbio.org/. It can retrieve and save networks via the API. Networks are offered as RCX object and as igraph representation. biocViews: Pathways, DataImport, Network Author: Florian Auer , Frank Kramer , Alex Ishkin , Dexter Pratt Maintainer: Florian Auer URL: https://github.com/frankkramer-lab/ndexr VignetteBuilder: knitr BugReports: https://github.com/frankkramer-lab/ndexr/issues git_url: https://git.bioconductor.org/packages/ndexr git_branch: RELEASE_3_12 git_last_commit: f470b1d git_last_commit_date: 2021-03-10 Date/Publication: 2021-03-11 source.ver: src/contrib/ndexr_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ndexr_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ndexr_1.12.1.tgz vignettes: vignettes/ndexr/inst/doc/ndexr-vignette.html vignetteTitles: NDExR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ndexr/inst/doc/ndexr-vignette.R dependencyCount: 39 Package: nearBynding Version: 1.0.0 Depends: R (>= 4.0) Imports: R.utils, matrixStats, plyranges, transport, Rsamtools, S4Vectors, grDevices, graphics, rtracklayer, dplyr, GenomeInfoDb, methods, GenomicRanges, utils, stats, magrittr, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, ggplot2, gplots, BiocGenerics, rlang Suggests: knitr License: Artistic-2.0 MD5sum: 7646ebaa672aae56b2e1af8e0d0619dc NeedsCompilation: no Title: Discern RNA structure proximal to protein binding Description: Provides a pipeline to discern RNA structure at and proximal to the site of protein binding within regions of the transcriptome defined by the user. CLIP protein-binding data can be input as either aligned BAM or peak-called bedGraph files. RNA structure can either be predicted internally from sequence or users have the option to input their own RNA structure data. RNA structure binding profiles can be visually and quantitatively compared across multiple formats. biocViews: Visualization, MotifDiscovery, DataRepresentation, StructuralPrediction, Clustering, MultipleComparison Author: Veronica Busa [cre] Maintainer: Veronica Busa SystemRequirements: bedtools (>= 2.28.0), Stereogene (>= v2.20), CapR (>= 1.1.1) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nearBynding git_branch: RELEASE_3_12 git_last_commit: ccf44ab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/nearBynding_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nearBynding_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nearBynding_1.0.0.tgz vignettes: vignettes/nearBynding/inst/doc/nearBynding.pdf vignetteTitles: nearBynding Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nearBynding/inst/doc/nearBynding.R dependencyCount: 116 Package: Nebulosa Version: 1.0.2 Depends: R (>= 4.0), ggplot2, patchwork Imports: Seurat, SingleCellExperiment, SummarizedExperiment, SeuratObject, ks, Matrix, stats, methods Suggests: testthat, BiocStyle, knitr, rmarkdown, covr, scater, scran, DropletUtils, igraph, BiocFileCache License: GPL-3 MD5sum: 50db3a6760a0bc1e51030909caadefc5 NeedsCompilation: no Title: Single-Cell Data Visualisation Using Kernel Gene-Weighted Density Estimation Description: This package provides a enhanced visualization of single-cell data based on gene-weighted density estimation. Nebulosa recovers the signal from dropped-out features and allows the inspection of the joint expression from multiple features (e.g. genes). Seurat and SingleCellExperiment objects can be used within Nebulosa. biocViews: Software, GeneExpression, SingleCell, Visualization, DimensionReduction Author: Jose Alquicira-Hernandez [aut, cre] () Maintainer: Jose Alquicira-Hernandez URL: https://github.com/powellgenomicslab/Nebulosa VignetteBuilder: knitr BugReports: https://github.com/powellgenomicslab/Nebulosa/issues git_url: https://git.bioconductor.org/packages/Nebulosa git_branch: RELEASE_3_12 git_last_commit: 0cdc5b6 git_last_commit_date: 2021-03-23 Date/Publication: 2021-03-23 source.ver: src/contrib/Nebulosa_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/Nebulosa_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.0/Nebulosa_1.0.2.tgz vignettes: vignettes/Nebulosa/inst/doc/introduction.html, vignettes/Nebulosa/inst/doc/nebulosa_seurat.html vignetteTitles: Visualization of gene expression with Nebulosa, Visualization of gene expression with Nebulosa (in Seurat) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Nebulosa/inst/doc/introduction.R, vignettes/Nebulosa/inst/doc/nebulosa_seurat.R dependencyCount: 161 Package: NeighborNet Version: 1.8.0 Depends: methods Imports: graph, stats License: CC BY-NC-ND 4.0 MD5sum: 684b5efccc507d07155e5ff000b05223 NeedsCompilation: no Title: Neighbor_net analysis Description: Identify the putative mechanism explaining the active interactions between genes in the investigated phenotype. biocViews: Software, GeneExpression, StatisticalMethod, GraphAndNetwork Author: Sahar Ansari and Sorin Draghici Maintainer: Sahar Ansari git_url: https://git.bioconductor.org/packages/NeighborNet git_branch: RELEASE_3_12 git_last_commit: 17e335b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NeighborNet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NeighborNet_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NeighborNet_1.8.0.tgz vignettes: vignettes/NeighborNet/inst/doc/neighborNet.pdf vignetteTitles: NeighborNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NeighborNet/inst/doc/neighborNet.R dependencyCount: 8 Package: netbiov Version: 1.24.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 37dd1021357e59da3cd3971376127832 NeedsCompilation: no Title: A package for visualizing complex biological network Description: A package that provides an effective visualization of large biological networks biocViews: GraphAndNetwork, Network, Software, Visualization Author: Shailesh tripathi and Frank Emmert-Streib Maintainer: Shailesh tripathi URL: http://www.bio-complexity.com git_url: https://git.bioconductor.org/packages/netbiov git_branch: RELEASE_3_12 git_last_commit: cda7778 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/netbiov_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/netbiov_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/netbiov_1.24.0.tgz vignettes: vignettes/netbiov/inst/doc/netbiov-intro.pdf vignetteTitles: netbiov: An R package for visualizing biological networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbiov/inst/doc/netbiov-intro.R dependencyCount: 11 Package: netboost Version: 1.6.0 Depends: R (>= 3.6.0) Imports: Rcpp, RcppParallel, parallel, grDevices, graphics, stats, utils, dynamicTreeCut, WGCNA, impute, colorspace, methods, R.utils LinkingTo: Rcpp, RcppParallel Suggests: knitr License: GPL-3 OS_type: unix MD5sum: ee2b80c3c009f4d691b777caa07450fd NeedsCompilation: yes Title: Network Analysis Supported by Boosting Description: Boosting supported network analysis for high-dimensional omics applications. This package comes bundled with the MC-UPGMA clustering package by Yaniv Loewenstein. biocViews: Software, StatisticalMethod, GraphAndNetwork, Network, Clustering, DimensionReduction, BiomedicalInformatics, Epigenetics, Metabolomics, Transcriptomics Author: Pascal Schlosser [aut, cre], Jochen Knaus [aut, ctb], Yaniv Loewenstein [aut] Maintainer: Pascal Schlosser URL: https://bioconductor.org/packages/release/bioc/html/netboost.html SystemRequirements: GNU make, Bash, Perl, Gzip VignetteBuilder: knitr BugReports: https://github.com/PascalSchlosser/netboost/issues git_url: https://git.bioconductor.org/packages/netboost git_branch: RELEASE_3_12 git_last_commit: 95408d2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/netboost_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/netboost_1.6.0.tgz vignettes: vignettes/netboost/inst/doc/netboost.html vignetteTitles: The Netboost users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netboost/inst/doc/netboost.R dependencyCount: 101 Package: netboxr Version: 1.2.0 Depends: R (>= 4.0.0), igraph (>= 1.2.4.1), parallel Imports: RColorBrewer, DT, stats, clusterProfiler, data.table, gplots, jsonlite, plyr Suggests: paxtoolsr, BiocStyle, org.Hs.eg.db, knitr, rmarkdown, testthat, cgdsr License: LGPL-3 + file LICENSE MD5sum: b20d18374937b8cdd5c5268b3a33bb2e NeedsCompilation: no Title: netboxr Description: NetBox is a network-based approach that combines prior knowledge with a network clustering algorithm. The algorithm allows for the identification of functional modules and allows for combining multiple data types, such as mutations and copy number alterations. NetBox performs network analysis on human interaction networks, and comes pre-loaded with a Human Interaction Network (HIN) derived from four literature curated data sources, including the Human Protein Reference Database (HPRD), Reactome, NCI-Nature Pathway Interaction (PID) Database, and the MSKCC Cancer Cell Map. biocViews: Software,Network,Pathways,GraphAndNetwork,Reactome, SystemsBiology, GeneSetEnrichment, NetworkEnrichment, KEGG Author: Eric Minwei Liu [aut,cre], Augustin Luna [aut], Ethan Cerami [aut] Maintainer: Eirc Minwei Liu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netboxr git_branch: RELEASE_3_12 git_last_commit: 52f9bc4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/netboxr_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/netboxr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/netboxr_1.2.0.tgz vignettes: vignettes/netboxr/inst/doc/netboxrTutorial.html vignetteTitles: NetBoxR Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netboxr/inst/doc/netboxrTutorial.R dependencyCount: 116 Package: netDx Version: 1.2.2 Depends: R (>= 3.6) Imports: ROCR,pracma,ggplot2,RCy3,glmnet,igraph,reshape2,parallel,stats,utils,MultiAssayExperiment,graphics,grDevices,methods,BiocFileCache,GenomicRanges,bigmemory,doParallel,foreach,combinat,rappdirs,GenomeInfoDb,S4Vectors,IRanges,RColorBrewer, scater, netSmooth, clusterExperiment,Rtsne,httr Suggests: curatedTCGAData, TCGAutils, rmarkdown, testthat, knitr License: MIT + file LICENSE MD5sum: d5b437190914e09145e31b433ab14d09 NeedsCompilation: no Title: Network-based patient classifier Description: netDx is a general-purpose algorithm to build a patient classifier from heterogenous patient data. The method converts data into patient similarity networks at the level of features. Feature selection identifies features of predictive value to each class. Methods are provided for versatile predictor design and performance evaluation using standard measures. netDx natively groups molecular data into pathway-level features and connects with Cytoscape for network visualization of pathway themes. For method details see: Pai et al. (2019). netDx: interpretable patient classification using integrated patient similarity networks. Molecular Systems Biology. 15, e8497 biocViews: Classification, BiomedicalInformatics, Network, SystemsBiology Author: Shraddha Pai [aut, cre] (), Philipp Weber [aut], Ahmad Shah [aut], Luca Giudice [aut], Shirley Hui [aut], Ruth Isserlin [aut], Hussam Kaka [aut], Gary Bader [aut] Maintainer: Shraddha Pai URL: http://netdx.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netDx git_branch: RELEASE_3_12 git_last_commit: 60ed124 git_last_commit_date: 2020-12-08 Date/Publication: 2020-12-09 source.ver: src/contrib/netDx_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/netDx_1.1.4.zip mac.binary.ver: bin/macosx/contrib/4.0/netDx_1.2.2.tgz vignettes: vignettes/netDx/inst/doc/BuildPredictor.html, vignettes/netDx/inst/doc/Predict_CaseControl_from_CNV.html, vignettes/netDx/inst/doc/ThreeWayClassifier.html vignetteTitles: 01. Build binary predictor and view performance,, top features and integrated Patient Similarity Network, 03. Build classifier from sparse genetic data, 02. Build three-way classifier (N-way; N>2) from multi-omic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/netDx/inst/doc/BuildPredictor.R, vignettes/netDx/inst/doc/Predict_CaseControl_from_CNV.R, vignettes/netDx/inst/doc/ThreeWayClassifier.R dependencyCount: 186 Package: nethet Version: 1.22.0 Imports: glasso, mvtnorm, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2, grDevices, graphics, stats, utils Suggests: knitr, xtable, BiocStyle, testthat License: GPL-2 Archs: i386, x64 MD5sum: 06740fb93d3601c971c324ef4bcbd287 NeedsCompilation: yes Title: A bioconductor package for high-dimensional exploration of biological network heterogeneity Description: Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013). biocViews: Clustering, GraphAndNetwork Author: Nicolas Staedler, Frank Dondelinger Maintainer: Nicolas Staedler , Frank Dondelinger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nethet git_branch: RELEASE_3_12 git_last_commit: 09eb075 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/nethet_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nethet_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nethet_1.22.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nethet/inst/doc/nethet.R dependencyCount: 73 Package: NetPathMiner Version: 1.26.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, graph, knitr, rmarkdown, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: be269554466a8f61ec43a6e16620e568 NeedsCompilation: yes Title: NetPathMiner for Biological Network Construction, Path Mining and Visualization Description: NetPathMiner is a general framework for network path mining using genome-scale networks. It constructs networks from KGML, SBML and BioPAX files, providing three network representations, metabolic, reaction and gene representations. NetPathMiner finds active paths and applies machine learning methods to summarize found paths for easy interpretation. It also provides static and interactive visualizations of networks and paths to aid manual investigation. biocViews: GraphAndNetwork, Pathways, Network, Clustering, Classification Author: Ahmed Mohamed , Tim Hancock , Ichigaku Takigawa , Nicolas Wicker Maintainer: Ahmed Mohamed URL: https://github.com/ahmohamed/NetPathMiner SystemRequirements: libxml2, libSBML (>= 5.5) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NetPathMiner git_branch: RELEASE_3_12 git_last_commit: 57dbe8c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NetPathMiner_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NetPathMiner_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NetPathMiner_1.26.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.html vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R dependencyCount: 11 Package: netprioR Version: 1.16.0 Depends: methods, graphics, R(>= 3.3) Imports: stats, Matrix, dplyr, doParallel, foreach, parallel, sparseMVN, ggplot2, gridExtra, pROC Suggests: knitr, BiocStyle, pander License: GPL-3 MD5sum: f38562b2282990b24721102d48b92332 NeedsCompilation: no Title: A model for network-based prioritisation of genes Description: A model for semi-supervised prioritisation of genes integrating network data, phenotypes and additional prior knowledge about TP and TN gene labels from the literature or experts. biocViews: ImmunoOncology, CellBasedAssays, Preprocessing, Network Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://bioconductor.org/packages/netprioR VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/netprioR git_branch: RELEASE_3_12 git_last_commit: 7b3be4e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/netprioR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/netprioR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/netprioR_1.16.0.tgz vignettes: vignettes/netprioR/inst/doc/netprioR.html vignetteTitles: netprioR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netprioR/inst/doc/netprioR.R dependencyCount: 52 Package: netReg Version: 1.14.0 Depends: R (>= 3.4), tensorflow (>= 1.14.0), tfprobability (>= 0.7.0) Imports: Rcpp, stats, reticulate, nloptr, methods LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, testthat, knitr, rmarkdown, lintr, styler, LaplacesDemon, grplasso License: GPL-3 MD5sum: 08a1f3c5b13b8455523cd5d4c164ffdb NeedsCompilation: yes Title: Network-Regularized Regression Models Description: netReg fits linear regression models using network-penalization. Graph prior knowledge, in the form of biological networks, is being incorporated into the loss function of the linear model. The networks describe biological relationships such as co-regulation or dependency of the same transcription factors/metabolites/etc. yielding a part sparse and part smooth solution for coefficient profiles. biocViews: Software, StatisticalMethod, Regression, FeatureExtraction, Network, GraphAndNetwork Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/dirmeier/netReg SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/dirmeier/netReg/issues PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/netReg git_branch: RELEASE_3_12 git_last_commit: 3863166 git_last_commit_date: 2020-10-27 Date/Publication: 2021-04-21 source.ver: src/contrib/netReg_1.14.0.tar.gz vignettes: vignettes/netReg/inst/doc/netReg.html vignetteTitles: netReg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netReg/inst/doc/netReg.R dependencyCount: 37 Package: netresponse Version: 1.50.0 Depends: R (>= 2.15.1), Rgraphviz, methods, minet, mclust, reshape2 Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer Suggests: knitr License: GPL (>=2) Archs: i386, x64 MD5sum: 6fc0f208cfad146dbec94f76f58b0a79 NeedsCompilation: yes Title: Functional Network Analysis Description: Algorithms for functional network analysis. Includes an implementation of a variational Dirichlet process Gaussian mixture model for nonparametric mixture modeling. biocViews: CellBiology, Clustering, GeneExpression, Genetics, Network, GraphAndNetwork, DifferentialExpression, Microarray, NetworkInference, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse VignetteBuilder: knitr BugReports: https://github.com/antagomir/netresponse/issues git_url: https://git.bioconductor.org/packages/netresponse git_branch: RELEASE_3_12 git_last_commit: 3309644 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/netresponse_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/netresponse_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/netresponse_1.50.0.tgz vignettes: vignettes/netresponse/inst/doc/NetResponse.html vignetteTitles: microbiome R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netresponse/inst/doc/NetResponse.R dependencyCount: 56 Package: NetSAM Version: 1.30.0 Depends: R (>= 2.15.1), methods, igraph (>= 0.6-1), seriation (>= 1.0-6), graph (>= 1.34.0) Imports: methods Suggests: RUnit, BiocGenerics License: LGPL MD5sum: 943c607518a00278a3293bc9ecbda365 NeedsCompilation: no Title: Network Seriation And Modularization Description: The NetSAM (Network Seriation and Modularization) package takes an edge-list representation of a network as an input, performs network seriation and modularization analysis, and generates as files that can be used as an input for the one-dimensional network visualization tool NetGestalt (http://www.netgestalt.org) or other network analysis. biocViews: Visualization, Network Author: Jing Wang Maintainer: Bing Zhang git_url: https://git.bioconductor.org/packages/NetSAM git_branch: RELEASE_3_12 git_last_commit: d866104 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NetSAM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NetSAM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NetSAM_1.30.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetSAM/inst/doc/NetSAM.R dependencyCount: 61 Package: netSmooth Version: 1.10.0 Depends: R (>= 3.5), scater (>= 1.15.11), clusterExperiment (>= 2.1.6) Imports: entropy, SummarizedExperiment, SingleCellExperiment, Matrix, cluster, data.table, stats, methods, DelayedArray, HDF5Array (>= 1.15.13) Suggests: knitr, testthat, Rtsne, biomaRt, igraph, STRINGdb, NMI, pheatmap, ggplot2, BiocStyle, rmarkdown, BiocParallel, uwot License: GPL-3 MD5sum: df87282a913d18a2895310b7375a89b8 NeedsCompilation: no Title: Network smoothing for scRNAseq Description: netSmooth is an R package for network smoothing of single cell RNA sequencing data. Using bio networks such as protein-protein interactions as priors for gene co-expression, netsmooth improves cell type identification from noisy, sparse scRNAseq data. biocViews: Network, GraphAndNetwork, SingleCell, RNASeq, GeneExpression, Sequencing, Transcriptomics, Normalization, Preprocessing, Clustering, DimensionReduction Author: Jonathan Ronen [aut, cre], Altuna Akalin [aut] Maintainer: Jonathan Ronen URL: https://github.com/BIMSBbioinfo/netSmooth VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/netSmooth/issues git_url: https://git.bioconductor.org/packages/netSmooth git_branch: RELEASE_3_12 git_last_commit: 3e13f53 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/netSmooth_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/netSmooth_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/netSmooth_1.10.0.tgz vignettes: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.html, vignettes/netSmooth/inst/doc/netSmoothIntro.html vignetteTitles: Generation of PPI graph, netSmooth example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netSmooth/inst/doc/buildingPPIsFromStringDB.R, vignettes/netSmooth/inst/doc/netSmoothIntro.R importsMe: netDx dependencyCount: 165 Package: networkBMA Version: 2.30.1 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1), leaps LinkingTo: Rcpp, RcppArmadillo, RcppEigen, BH License: GPL (>= 2) Archs: i386, x64 MD5sum: 1dcf8c66ab8e7297c9443adb409012c5 NeedsCompilation: yes Title: Regression-based network inference using Bayesian Model Averaging Description: An extension of Bayesian Model Averaging (BMA) for network construction using time series gene expression data. Includes assessment functions and sample test data. biocViews: GraphsAndNetwork, NetworkInference, GeneExpression, GeneTarget, Network, Bayesian Author: Chris Fraley, Wm. Chad Young, Ling-Hong Hung, Kaiyuan Shi, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung SystemRequirements: liblapack-dev git_url: https://git.bioconductor.org/packages/networkBMA git_branch: RELEASE_3_12 git_last_commit: 35c18a2 git_last_commit_date: 2021-01-25 Date/Publication: 2021-01-26 source.ver: src/contrib/networkBMA_2.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/networkBMA_2.30.1.zip mac.binary.ver: bin/macosx/contrib/4.0/networkBMA_2.30.1.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/networkBMA/inst/doc/networkBMA.R suggestsMe: DREAM4 dependencyCount: 23 Package: NewWave Version: 1.0.2 Depends: R (>= 4.0), SummarizedExperiment, SharedObject(>= 1.3.15) Imports: methods, SingleCellExperiment, parallel, irlba, Matrix, DelayedArray, BiocSingular, stats Suggests: testthat, rmarkdown, splatter, mclust, Rtsne, ggplot2, Rcpp, BiocStyle, knitr License: GPL-3 MD5sum: ff27e598b2579454e9c101c31e9ffa14 NeedsCompilation: no Title: Negative binomial model for scRNA-seq Description: A model designed for dimensionality reduction and batch effect removal for scRNA-seq data. It is designed to be massively parallelizable using shared objects that prevent memory duplication, and it can be used with different mini-batch approaches in order to reduce time consumption. It assumes a negative binomial distribution for the data with a dispersion parameter that can be both commonwise across gene both genewise. biocViews: Software, GeneExpression, Transcriptomics, SingleCell, BatchEffect, Sequencing, Coverage, Regression Author: Federico Agostinis [aut, cre], Chiara Romualdi [aut], Gabriele Sales [aut], Davide Risso [aut] Maintainer: Federico Agostinis VignetteBuilder: knitr BugReports: https://github.com/fedeago/NewWave/issues git_url: https://git.bioconductor.org/packages/NewWave git_branch: RELEASE_3_12 git_last_commit: b343548 git_last_commit_date: 2020-12-21 Date/Publication: 2020-12-22 source.ver: src/contrib/NewWave_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/NewWave_1.0.2.zip mac.binary.ver: bin/macosx/contrib/4.0/NewWave_1.0.2.tgz vignettes: vignettes/NewWave/inst/doc/vignette.html vignetteTitles: vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NewWave/inst/doc/vignette.R dependencyCount: 40 Package: ngsReports Version: 1.6.1 Depends: R (>= 4.0.0), BiocGenerics, ggplot2, tibble (>= 1.3.1) Imports: Biostrings, checkmate, dplyr (>= 1.0.0), DT, FactoMineR, forcats, ggdendro, grDevices, grid, lifecycle, lubridate, methods, pander, parallel, plotly, readr, reshape2, rmarkdown, Rsamtools, scales, ShortRead, stats, stringr, tidyr, tidyselect (>= 0.2.3), utils, viridisLite, zoo Suggests: BiocStyle, Cairo, knitr, testthat, truncnorm License: file LICENSE MD5sum: b830ed2f26c5d78f650db136b44a3179 NeedsCompilation: no Title: Load FastqQC reports and other NGS related files Description: This package provides methods and object classes for parsing FastQC reports and output summaries from other NGS tools into R, as well as visualising the data loaded from these files. biocViews: QualityControl, ReportWriting Author: Steve Pederson [aut, cre], Christopher Ward [aut], Thu-Hien To [aut] Maintainer: Steve Pederson URL: https://github.com/steveped/ngsReports VignetteBuilder: knitr BugReports: https://github.com/steveped/ngsReports/issues git_url: https://git.bioconductor.org/packages/ngsReports git_branch: RELEASE_3_12 git_last_commit: 5f7c1da git_last_commit_date: 2020-11-21 Date/Publication: 2020-11-21 source.ver: src/contrib/ngsReports_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ngsReports_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ngsReports_1.6.1.tgz vignettes: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.html vignetteTitles: An Introduction To ngsReports hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ngsReports/inst/doc/ngsReportsIntroduction.R dependencyCount: 155 Package: nnNorm Version: 2.54.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: ea84c0e2af67605e48de3e63ddf1f4d7 NeedsCompilation: no Title: Spatial and intensity based normalization of cDNA microarray data based on robust neural nets Description: This package allows to detect and correct for spatial and intensity biases with two-channel microarray data. The normalization method implemented in this package is based on robust neural networks fitting. biocViews: Microarray, TwoChannel, Preprocessing Author: Adi Laurentiu Tarca Maintainer: Adi Laurentiu Tarca URL: http://bioinformaticsprb.med.wayne.edu/tarca/ git_url: https://git.bioconductor.org/packages/nnNorm git_branch: RELEASE_3_12 git_last_commit: afcbca7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/nnNorm_2.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nnNorm_2.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nnNorm_2.54.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nnNorm/inst/doc/nnNorm.R dependencyCount: 8 Package: NOISeq Version: 2.34.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 8cfebb7c3f60dbe0cc29c91ff40f6e67 NeedsCompilation: no Title: Exploratory analysis and differential expression for RNA-seq data Description: Analysis of RNA-seq expression data or other similar kind of data. Exploratory plots to evualuate saturation, count distribution, expression per chromosome, type of detected features, features length, etc. Differential expression between two experimental conditions with no parametric assumptions. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona git_url: https://git.bioconductor.org/packages/NOISeq git_branch: RELEASE_3_12 git_last_commit: 21f5d1f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NOISeq_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NOISeq_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NOISeq_2.34.0.tgz vignettes: vignettes/NOISeq/inst/doc/NOISeq.pdf, vignettes/NOISeq/inst/doc/QCreport.pdf vignetteTitles: NOISeq User's Guide, QCreport.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NOISeq/inst/doc/NOISeq.R dependsOnMe: metaSeq importsMe: CNVPanelizer, metaseqR suggestsMe: compcodeR dependencyCount: 12 Package: nondetects Version: 2.20.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, arm, HTqPCR (>= 1.16.0) Suggests: knitr, rmarkdown, BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: a1fb2efdcdd7a1c866ba4cf0fbe73c18 NeedsCompilation: no Title: Non-detects in qPCR data Description: Methods to model and impute non-detects in the results of qPCR experiments. biocViews: Software, AssayDomain, GeneExpression, Technology, qPCR, WorkflowStep, Preprocessing Author: Matthew N. McCall , Valeriia Sherina Maintainer: Valeriia Sherina VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nondetects git_branch: RELEASE_3_12 git_last_commit: 0447257 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/nondetects_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nondetects_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nondetects_2.20.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.html vignetteTitles: Title of your vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nondetects/inst/doc/nondetects.R dependencyCount: 96 Package: NoRCE Version: 1.2.0 Depends: R (>= 4.0) Imports: KEGGREST,png,dplyr,graphics,RSQLite,DBI,tidyr,grDevices, S4Vectors,SummarizedExperiment,reactome.db,rWikiPathways,RCurl, dbplyr,utils,ggplot2,igraph,stats,reshape2,readr, GO.db,zlibbioc, biomaRt,rtracklayer,IRanges,GenomicRanges,GenomicFeatures,AnnotationDbi Suggests: knitr, TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Drerio.UCSC.danRer10.refGene, TxDb.Mmusculus.UCSC.mm10.knownGene,TxDb.Dmelanogaster.UCSC.dm6.ensGene, testthat,TxDb.Celegans.UCSC.ce11.refGene,rmarkdown, TxDb.Rnorvegicus.UCSC.rn6.refGene,TxDb.Hsapiens.UCSC.hg19.knownGene, org.Mm.eg.db, org.Rn.eg.db,org.Hs.eg.db,org.Dr.eg.db,BiocGenerics, org.Sc.sgd.db, org.Ce.eg.db,org.Dm.eg.db, methods, License: MIT + file LICENSE MD5sum: ca45f80d8681fa8e6d7adf322eaae56f NeedsCompilation: no Title: NoRCE: Noncoding RNA Sets Cis Annotation and Enrichment Description: While some non-coding RNAs (ncRNAs) have been found to play critical regulatory roles in biological processes, most remain functionally uncharacterized. This presents a challenge whenever an interesting set of ncRNAs set needs to be analyzed in a functional context. Transcripts located close-by on the genome are often regulated together, and this spatial proximity hints at a functional association. Based on this idea, we present an R package, NoRCE, that performs cis enrichment analysis for a given set of ncRNAs. Enrichment is carried out by using the functional annotations of the coding genes located proximally to the input ncRNAs. NoRCE allows incorporating other biological information such as the topologically associating domain (TAD) regions, co-expression patterns, and miRNA target information. NoRCE repository includes several data files, such as cell line specific TAD regions, functional gene sets, and cancer expression data. Additionally, users can input custom data files. Results can be retrieved in a tabular format or viewed as graphs. NoRCE is currently available for the following species: human, mouse, rat, zebrafish, fruit fly, worm and yeast. biocViews: BiologicalQuestion, DifferentialExpression, GenomeAnnotation, GeneSetEnrichment, GeneTarget, GenomeAssembly, GO Author: Gulden Olgun [aut, cre] Maintainer: Gulden Olgun VignetteBuilder: knitr BugReports: https://github.com/guldenolgun/NoRCE/issues git_url: https://git.bioconductor.org/packages/NoRCE git_branch: RELEASE_3_12 git_last_commit: 0ff701b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NoRCE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NoRCE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NoRCE_1.2.0.tgz vignettes: vignettes/NoRCE/inst/doc/NoRCE.html vignetteTitles: Noncoding RNA Set Cis Annotation and Enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/NoRCE/inst/doc/NoRCE.R dependencyCount: 117 Package: normalize450K Version: 1.18.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 4f3171e982c938f39fc0dff6b32f7746 NeedsCompilation: no Title: Preprocessing of Illumina Infinium 450K data Description: Precise measurements are important for epigenome-wide studies investigating DNA methylation in whole blood samples, where effect sizes are expected to be small in magnitude. The 450K platform is often affected by batch effects and proper preprocessing is recommended. This package provides functions to read and normalize 450K '.idat' files. The normalization corrects for dye bias and biases related to signal intensity and methylation of probes using local regression. No adjustment for probe type bias is performed to avoid the trade-off of precision for accuracy of beta-values. biocViews: Normalization, DNAMethylation, Microarray, TwoChannel, Preprocessing, MethylationArray Author: Jonathan Alexander Heiss Maintainer: Jonathan Alexander Heiss git_url: https://git.bioconductor.org/packages/normalize450K git_branch: RELEASE_3_12 git_last_commit: 8010988 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/normalize450K_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/normalize450K_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/normalize450K_1.18.0.tgz vignettes: vignettes/normalize450K/inst/doc/read_and_normalize450K.pdf vignetteTitles: Normalization of 450K data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/normalize450K/inst/doc/read_and_normalize450K.R dependencyCount: 13 Package: NormalyzerDE Version: 1.8.0 Depends: R (>= 3.6) Imports: vsn, preprocessCore, limma, MASS, ape, car, ggplot2, methods, Biobase, RcmdrMisc, raster, utils, stats, SummarizedExperiment, matrixStats, ggforce Suggests: knitr, testthat, rmarkdown, roxygen2, hexbin, BiocStyle License: Artistic-2.0 MD5sum: 796c42a226a41b654335f59056949aea NeedsCompilation: no Title: Evaluation of normalization methods and calculation of differential expression analysis statistics Description: NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis. biocViews: Normalization, MultipleComparison, Visualization, Bayesian, Proteomics, Metabolomics, DifferentialExpression Author: Jakob Willforss Maintainer: Jakob Willforss URL: https://github.com/ComputationalProteomics/NormalyzerDE VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NormalyzerDE git_branch: RELEASE_3_12 git_last_commit: 59c5a24 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NormalyzerDE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NormalyzerDE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NormalyzerDE_1.8.0.tgz vignettes: vignettes/NormalyzerDE/inst/doc/vignette.pdf vignetteTitles: Differential expression and countering technical biases using NormalyzerDE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormalyzerDE/inst/doc/vignette.R dependencyCount: 146 Package: NormqPCR Version: 1.36.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: b1a3c275883506b3565496b2af5f56ef NeedsCompilation: no Title: Functions for normalisation of RT-qPCR data Description: Functions for the selection of optimal reference genes and the normalisation of real-time quantitative PCR data. biocViews: MicrotitrePlateAssay, GeneExpression, qPCR Author: Matthias Kohl, James Perkins, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: www.bioconductor.org/packages/release/bioc/html/NormqPCR.html git_url: https://git.bioconductor.org/packages/NormqPCR git_branch: RELEASE_3_12 git_last_commit: 3d0cc46 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NormqPCR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NormqPCR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NormqPCR_1.36.0.tgz vignettes: vignettes/NormqPCR/inst/doc/NormqPCR.pdf vignetteTitles: NormqPCR: Functions for normalisation of RT-qPCR data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NormqPCR/inst/doc/NormqPCR.R dependencyCount: 66 Package: normr Version: 1.16.0 Depends: R (>= 3.3.0) Imports: methods, stats, utils, grDevices, parallel, GenomeInfoDb, GenomicRanges, IRanges, Rcpp (>= 0.11), qvalue (>= 2.2), bamsignals (>= 1.4), rtracklayer (>= 1.32) LinkingTo: Rcpp Suggests: BiocStyle, testthat (>= 1.0), knitr, rmarkdown Enhances: BiocParallel License: GPL-2 Archs: i386, x64 MD5sum: 4b5a1d31af77652114e9fd702e60f003 NeedsCompilation: yes Title: Normalization and difference calling in ChIP-seq data Description: Robust normalization and difference calling procedures for ChIP-seq and alike data. Read counts are modeled jointly as a binomial mixture model with a user-specified number of components. A fitted background estimate accounts for the effect of enrichment in certain regions and, therefore, represents an appropriate null hypothesis. This robust background is used to identify significantly enriched or depleted regions. biocViews: Bayesian, DifferentialPeakCalling, Classification, DataImport, ChIPSeq, RIPSeq, FunctionalGenomics, Genetics, MultipleComparison, Normalization, PeakDetection, Preprocessing, Alignment Author: Johannes Helmuth [aut, cre], Ho-Ryun Chung [aut] Maintainer: Johannes Helmuth URL: https://github.com/your-highness/normR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/your-highness/normR/issues git_url: https://git.bioconductor.org/packages/normr git_branch: RELEASE_3_12 git_last_commit: d829918 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/normr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/normr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/normr_1.16.0.tgz vignettes: vignettes/normr/inst/doc/normr.html vignetteTitles: Introduction to the normR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/normr/inst/doc/normr.R dependencyCount: 76 Package: NPARC Version: 1.2.0 Depends: R (>= 4.0.0) Imports: dplyr, tidyr, BiocParallel, broom, MASS, rlang, magrittr, stats, methods Suggests: testthat, devtools, knitr, rprojroot, rmarkdown, ggplot2, BiocStyle License: GPL-3 MD5sum: dd07dc4522d4668d7edbd9abf59b4792 NeedsCompilation: no Title: Non-parametric analysis of response curves for thermal proteome profiling experiments Description: Perform non-parametric analysis of response curves as described by Childs, Bach, Franken et al. (2019): Non-parametric analysis of thermal proteome profiles reveals novel drug-binding proteins. biocViews: Software, Proteomics Author: Dorothee Childs, Nils Kurzawa Maintainer: Nils Kurzawa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/NPARC git_branch: RELEASE_3_12 git_last_commit: 6cb84fd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NPARC_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NPARC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NPARC_1.2.0.tgz vignettes: vignettes/NPARC/inst/doc/NPARC.html vignetteTitles: Analysing thermal proteome profiling data with the NPARC package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NPARC/inst/doc/NPARC.R dependencyCount: 39 Package: npGSEA Version: 1.26.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 1d301ebeb9a6455a21e37ffe00716dfa NeedsCompilation: no Title: Permutation approximation methods for gene set enrichment analysis (non-permutation GSEA) Description: Current gene set enrichment methods rely upon permutations for inference. These approaches are computationally expensive and have minimum achievable p-values based on the number of permutations, not on the actual observed statistics. We have derived three parametric approximations to the permutation distributions of two gene set enrichment test statistics. We are able to reduce the computational burden and granularity issues of permutation testing with our method, which is implemented in this package. npGSEA calculates gene set enrichment statistics and p-values without the computational cost of permutations. It is applicable in settings where one or many gene sets are of interest. There are also built-in plotting functions to help users visualize results. biocViews: GeneSetEnrichment, Microarray, StatisticalMethod, Pathways Author: Jessica Larson and Art Owen Maintainer: Jessica Larson git_url: https://git.bioconductor.org/packages/npGSEA git_branch: RELEASE_3_12 git_last_commit: 0231dd4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/npGSEA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/npGSEA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/npGSEA_1.26.0.tgz vignettes: vignettes/npGSEA/inst/doc/npGSEA.pdf vignetteTitles: Running gene set enrichment analysis with the "npGSEA" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/npGSEA/inst/doc/npGSEA.R dependencyCount: 40 Package: NTW Version: 1.40.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: b3de30ce606bac1cbd046c84696ed8d6 NeedsCompilation: no Title: Predict gene network using an Ordinary Differential Equation (ODE) based method Description: This package predicts the gene-gene interaction network and identifies the direct transcriptional targets of the perturbation using an ODE (Ordinary Differential Equation) based method. biocViews: Preprocessing Author: Wei Xiao, Yin Jin, Darong Lai, Xinyi Yang, Yuanhua Liu, Christine Nardini Maintainer: Yuanhua Liu git_url: https://git.bioconductor.org/packages/NTW git_branch: RELEASE_3_12 git_last_commit: 82f22e3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NTW_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NTW_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NTW_1.40.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NTW/inst/doc/NTW.R dependencyCount: 4 Package: nucleoSim Version: 1.18.0 Imports: stats, IRanges, S4Vectors, graphics, methods Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: fa44f3e1a63b431ee994578a12745d32 NeedsCompilation: no Title: Generate synthetic nucleosome maps Description: This package can generate a synthetic map with reads covering the nucleosome regions as well as a synthetic map with forward and reverse reads emulating next-generation sequencing. The user has choice between three different distributions for the read positioning: Normal, Student and Uniform. biocViews: Genetics, Sequencing, Software, StatisticalMethod, Alignment Author: Rawane Samb [aut], Astrid Deschênes [cre, aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/arnauddroitlab/nucleoSim VignetteBuilder: knitr BugReports: https://github.com/arnauddroitlab/nucleoSim/issues git_url: https://git.bioconductor.org/packages/nucleoSim git_branch: RELEASE_3_12 git_last_commit: 3f9ae33 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/nucleoSim_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nucleoSim_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nucleoSim_1.18.0.tgz vignettes: vignettes/nucleoSim/inst/doc/nucleoSim.html vignetteTitles: Generate synthetic nucleosome maps hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R suggestsMe: RJMCMCNucleosomes dependencyCount: 9 Package: nucleR Version: 2.22.0 Depends: methods Imports: Biobase, BiocGenerics, Biostrings, GenomeInfoDb, GenomicRanges, IRanges, Rsamtools, S4Vectors, ShortRead, dplyr, ggplot2, magrittr, parallel, stats, utils, grDevices Suggests: Starr, BiocStyle, knitr, rmarkdown, testthat License: LGPL (>= 3) MD5sum: c780f966dd1750522172477f602a5dbb NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: NucleosomePositioning, Coverage, ChIPSeq, Microarray, Sequencing, Genetics, QualityControl, DataImport Author: Oscar Flores, Ricard Illa Maintainer: Diego Gallego VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/nucleR git_branch: RELEASE_3_12 git_last_commit: 1620ac7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/nucleR_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nucleR_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nucleR_2.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 76 Package: nuCpos Version: 1.8.0 Depends: R (>= 3.6) Imports: graphics, methods Suggests: NuPoP, Biostrings, testthat License: file LICENSE Archs: i386, x64 MD5sum: db2dc5c535856fa892cee42eb2bbd3a3 NeedsCompilation: yes Title: An R package for prediction of nucleosome positions Description: nuCpos, a derivative of NuPoP, is an R package for prediction of nucleosome positions. In nuCpos, a duration hidden Markov model is trained with a chemical map of nucleosomes either from budding yeast, fission yeast, or mouse embryonic stem cells. nuCpos outputs the Viterbi (most probable) path of nucleosome-linker states, predicted nucleosome occupancy scores and histone binding affinity (HBA) scores as NuPoP does. nuCpos can also calculate local and whole nucleosomal HBA scores for a given 147-bp sequence. Furthermore, effect of genetic alterations on nucleosome occupancy can be predicted with this package. The parental package NuPoP, which is based on an MNase-seq-based map of budding yeast nucleosomes, was developed by Ji-Ping Wang and Liqun Xi, licensed under GPL-2. biocViews: Genetics, Epigenetics, NucleosomePositioning, HiddenMarkovModel, ImmunoOncology Author: Hiroaki Kato, Takeshi Urano Maintainer: Hiroaki Kato git_url: https://git.bioconductor.org/packages/nuCpos git_branch: RELEASE_3_12 git_last_commit: b77ccdf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/nuCpos_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/nuCpos_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/nuCpos_1.8.0.tgz vignettes: vignettes/nuCpos/inst/doc/nuCpos-intro.pdf vignetteTitles: An R package for prediction of nucleosome positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/nuCpos/inst/doc/nuCpos-intro.R dependencyCount: 2 Package: NuPoP Version: 1.40.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 0f9a3e927c3f1d8b5b43f7152c011bce NeedsCompilation: yes Title: An R package for nucleosome positioning prediction Description: NuPoP is an R package for Nucleosome Positioning Prediction.This package is built upon a duration hidden Markov model proposed in Xi et al, 2010; Wang et al, 2008. The core of the package was written in Fotran. In addition to the R package, a stand-alone Fortran software tool is also available at http://nucleosome.stats.northwestern.edu. biocViews: Genetics,Visualization,Classification Author: Ji-Ping Wang ; Liqun Xi Maintainer: Ji-Ping Wang git_url: https://git.bioconductor.org/packages/NuPoP git_branch: RELEASE_3_12 git_last_commit: b53c2c5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/NuPoP_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/NuPoP_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/NuPoP_1.40.0.tgz vignettes: vignettes/NuPoP/inst/doc/NuPoP-intro.pdf vignetteTitles: An R package for Nucleosome positioning prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NuPoP/inst/doc/NuPoP-intro.R suggestsMe: nuCpos dependencyCount: 0 Package: occugene Version: 1.50.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: ee1b21cfec81f330e3b926841500a050 NeedsCompilation: no Title: Functions for Multinomial Occupancy Distribution Description: Statistical tools for building random mutagenesis libraries for prokaryotes. The package has functions for handling the occupancy distribution for a multinomial and for estimating the number of essential genes in random transposon mutagenesis libraries. biocViews: Annotation, Pathways Author: Oliver Will Maintainer: Oliver Will git_url: https://git.bioconductor.org/packages/occugene git_branch: RELEASE_3_12 git_last_commit: 2166673 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/occugene_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/occugene_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/occugene_1.50.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/occugene/inst/doc/occugene.R dependencyCount: 0 Package: OCplus Version: 1.64.0 Depends: R (>= 2.1.0) Imports: multtest (>= 1.7.3), graphics, grDevices, stats, akima License: LGPL MD5sum: 218582fb08d45f93ceda60b11efa0e24 NeedsCompilation: no Title: Operating characteristics plus sample size and local fdr for microarray experiments Description: This package allows to characterize the operating characteristics of a microarray experiment, i.e. the trade-off between false discovery rate and the power to detect truly regulated genes. The package includes tools both for planned experiments (for sample size assessment) and for already collected data (identification of differentially expressed genes). biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Yudi Pawitan and Alexander Ploner Maintainer: Alexander Ploner git_url: https://git.bioconductor.org/packages/OCplus git_branch: RELEASE_3_12 git_last_commit: 41fcc52 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OCplus_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OCplus_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OCplus_1.64.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OCplus/inst/doc/OCplus.R dependencyCount: 18 Package: odseq Version: 1.18.0 Depends: R (>= 3.2.3) Imports: msa (>= 1.2.1), kebabs (>= 1.4.1), mclust (>= 5.1) Suggests: knitr(>= 1.11) License: MIT + file LICENSE MD5sum: 0eeff9f48a09c8647fa55d256857042c NeedsCompilation: no Title: Outlier detection in multiple sequence alignments Description: Performs outlier detection of sequences in a multiple sequence alignment using bootstrap of predefined distance metrics. Outlier sequences can make downstream analyses unreliable or make the alignments less accurate while they are being constructed. This package implements the OD-seq algorithm proposed by Jehl et al (doi 10.1186/s12859-015-0702-1) for aligned sequences and a variant using string kernels for unaligned sequences. biocViews: Alignment, MultipleSequenceAlignment Author: José Jiménez Maintainer: José Jiménez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/odseq git_branch: RELEASE_3_12 git_last_commit: 7922c00 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/odseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/odseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/odseq_1.18.0.tgz vignettes: vignettes/odseq/inst/doc/vignette.pdf vignetteTitles: A quick tutorial to outlier detection in MSAs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/odseq/inst/doc/vignette.R dependencyCount: 29 Package: oligo Version: 1.54.1 Depends: R (>= 3.2.0), BiocGenerics (>= 0.13.11), oligoClasses (>= 1.29.6), Biobase (>= 2.27.3), Biostrings (>= 2.35.12) Imports: affyio (>= 1.35.0), affxparser (>= 1.39.4), DBI (>= 0.3.1), ff, graphics, methods, preprocessCore (>= 1.29.0), RSQLite (>= 1.0.0), splines, stats, stats4, utils, zlibbioc LinkingTo: preprocessCore Suggests: BSgenome.Hsapiens.UCSC.hg18, hapmap100kxba, pd.hg.u95av2, pd.mapping50k.xba240, pd.huex.1.0.st.v2, pd.hg18.60mer.expr, pd.hugene.1.0.st.v1, maqcExpression4plex, genefilter, limma, RColorBrewer, oligoData, BiocStyle, knitr, RUnit, biomaRt, AnnotationDbi, ACME, RCurl Enhances: doMC, doMPI License: LGPL (>= 2) Archs: i386, x64 MD5sum: 49e01715da8ced8d22fbcc59e6b47b6e NeedsCompilation: yes Title: Preprocessing tools for oligonucleotide arrays Description: A package to analyze oligonucleotide arrays (expression/SNP/tiling/exon) at probe-level. It currently supports Affymetrix (CEL files) and NimbleGen arrays (XYS files). biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, SNP, DifferentialExpression, ExonArray, GeneExpression, DataImport Author: Benilton Carvalho and Rafael Irizarry Maintainer: Benilton Carvalho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oligo git_branch: RELEASE_3_12 git_last_commit: 4988b63 git_last_commit_date: 2020-11-03 Date/Publication: 2020-11-04 source.ver: src/contrib/oligo_1.54.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/oligo_1.54.1.zip mac.binary.ver: bin/macosx/contrib/4.0/oligo_1.54.1.tgz vignettes: vignettes/oligo/inst/doc/oug.pdf vignetteTitles: oligo User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, puma, SCAN.UPC, oligoData, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, pumadata, maEndToEnd importsMe: ArrayExpress, cn.farms, crossmeta, frma, ITALICS, mimager suggestsMe: fastseg, frmaTools, hapmap100khind, hapmap100kxba, hapmap500knsp, hapmap500ksty, hapmapsnp5, hapmapsnp6, maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 53 Package: oligoClasses Version: 1.52.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.27.1), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), foreach, BiocManager, utils, S4Vectors (>= 0.9.25), RSQLite, DBI, ff Suggests: hapmapsnp5, hapmapsnp6, pd.genomewidesnp.6, pd.genomewidesnp.5, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.mapping250k.sty, pd.mapping250k.nsp, genomewidesnp6Crlmm (>= 1.0.7), genomewidesnp5Crlmm (>= 1.0.6), RUnit, human370v1cCrlmm, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 8cf6636b898393a44761a3284e4f4322 NeedsCompilation: no Title: Classes for high-throughput arrays supported by oligo and crlmm Description: This package contains class definitions, validity checks, and initialization methods for classes used by the oligo and crlmm packages. biocViews: Infrastructure Author: Benilton Carvalho and Robert Scharpf Maintainer: Benilton Carvalho and Robert Scharpf git_url: https://git.bioconductor.org/packages/oligoClasses git_branch: RELEASE_3_12 git_last_commit: 7995efb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/oligoClasses_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/oligoClasses_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/oligoClasses_1.52.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, pd.081229.hg18.promoter.medip.hx1, pd.2006.07.18.hg18.refseq.promoter, pd.2006.07.18.mm8.refseq.promoter, pd.2006.10.31.rn34.refseq.promoter, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, pd.bovine, pd.bsubtilis, pd.cangene.1.0.st, pd.cangene.1.1.st, pd.canine, pd.canine.2, pd.celegans, pd.charm.hg18.example, pd.chicken, pd.chigene.1.0.st, pd.chigene.1.1.st, pd.chogene.2.0.st, pd.chogene.2.1.st, pd.citrus, pd.clariom.d.human, pd.clariom.s.human, pd.clariom.s.human.ht, pd.clariom.s.mouse, pd.clariom.s.mouse.ht, pd.clariom.s.rat, pd.clariom.s.rat.ht, pd.cotton, pd.cyngene.1.0.st, pd.cyngene.1.1.st, pd.cyrgene.1.0.st, pd.cyrgene.1.1.st, pd.cytogenetics.array, pd.drogene.1.0.st, pd.drogene.1.1.st, pd.drosgenome1, pd.drosophila.2, pd.e.coli.2, pd.ecoli, pd.ecoli.asv2, pd.elegene.1.0.st, pd.elegene.1.1.st, pd.equgene.1.0.st, pd.equgene.1.1.st, pd.feinberg.hg18.me.hx1, pd.feinberg.mm8.me.hx1, pd.felgene.1.0.st, pd.felgene.1.1.st, pd.fingene.1.0.st, pd.fingene.1.1.st, pd.genomewidesnp.5, pd.genomewidesnp.6, pd.guigene.1.0.st, pd.guigene.1.1.st, pd.hc.g110, pd.hg.focus, pd.hg.u133.plus.2, pd.hg.u133a, pd.hg.u133a.2, pd.hg.u133a.tag, pd.hg.u133b, pd.hg.u219, pd.hg.u95a, pd.hg.u95av2, pd.hg.u95b, pd.hg.u95c, pd.hg.u95d, pd.hg.u95e, pd.hg18.60mer.expr, pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.3.1, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, pd.atdschip.tiling, maEndToEnd importsMe: affycoretools, frma, ITALICS, mimager, MinimumDistance, pdInfoBuilder, puma, VanillaICE suggestsMe: hapmapsnp6, aroma.affymetrix, scrime dependencyCount: 49 Package: OLIN Version: 1.68.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 37da7af43f40148f802881946d0aa88d NeedsCompilation: no Title: Optimized local intensity-dependent normalisation of two-color microarrays Description: Functions for normalisation of two-color microarrays by optimised local regression and for detection of artefacts in microarray data biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLIN git_branch: RELEASE_3_12 git_last_commit: d7a45b4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OLIN_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OLIN_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OLIN_1.68.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLIN/inst/doc/OLIN.R dependsOnMe: OLINgui importsMe: OLINgui suggestsMe: maigesPack dependencyCount: 10 Package: OLINgui Version: 1.64.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: d4bdaa33786327de8eeb59e868f4c02a NeedsCompilation: no Title: Graphical user interface for OLIN Description: Graphical user interface for the OLIN package biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, Visualization Author: Matthias Futschik Maintainer: Matthias Futschik URL: http://olin.sysbiolab.eu git_url: https://git.bioconductor.org/packages/OLINgui git_branch: RELEASE_3_12 git_last_commit: 28f6a55 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OLINgui_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OLINgui_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OLINgui_1.64.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OLINgui/inst/doc/OLINgui.R dependencyCount: 16 Package: OmaDB Version: 2.6.1 Depends: R (>= 3.5), httr (>= 1.2.1), plyr(>= 1.8.4) Imports: utils, ape, Biostrings, GenomicRanges, IRanges, methods, topGO, jsonlite Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 7bcad3e1be86251fa8fa87052bc8ac45 NeedsCompilation: no Title: R wrapper for the OMA REST API Description: A package for the orthology prediction data download from OMA database. biocViews: Software, ComparativeGenomics, FunctionalGenomics, Genetics, Annotation, GO, FunctionalPrediction Author: Klara Kaleb Maintainer: Klara Kaleb , Adrian Altenhoff URL: https://github.com/DessimozLab/OmaDB VignetteBuilder: knitr BugReports: https://github.com/DessimozLab/OmaDB/issues git_url: https://git.bioconductor.org/packages/OmaDB git_branch: RELEASE_3_12 git_last_commit: f70bed3 git_last_commit_date: 2020-11-10 Date/Publication: 2020-11-11 source.ver: src/contrib/OmaDB_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/OmaDB_2.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/OmaDB_2.6.1.tgz vignettes: vignettes/OmaDB/inst/doc/exploring_hogs.html, vignettes/OmaDB/inst/doc/OmaDB.html, vignettes/OmaDB/inst/doc/sequence_mapping.html, vignettes/OmaDB/inst/doc/tree_visualisation.html vignetteTitles: Exploring Hierarchical orthologous groups with OmaDB, Get started with OmaDB, Sequence Mapping with OmaDB, Exploring Taxonomic trees with OmaDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmaDB/inst/doc/exploring_hogs.R, vignettes/OmaDB/inst/doc/OmaDB.R, vignettes/OmaDB/inst/doc/sequence_mapping.R, vignettes/OmaDB/inst/doc/tree_visualisation.R importsMe: PhyloProfile dependencyCount: 55 Package: omicade4 Version: 1.30.0 Depends: R (>= 3.0.0), ade4 Imports: made4, Biobase Suggests: BiocStyle License: GPL-2 MD5sum: c934622287cf20aaec0d1b94bf070a7b NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: This package performes multiple co-inertia analysis of omics datasets. biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng git_url: https://git.bioconductor.org/packages/omicade4 git_branch: RELEASE_3_12 git_last_commit: 009afa9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/omicade4_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/omicade4_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/omicade4_1.30.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicade4/inst/doc/omicade4.R importsMe: omicRexposome suggestsMe: biosigner, MultiDataSet, ropls dependencyCount: 48 Package: OmicCircos Version: 1.28.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 4341aee1eed26a7dc0e660b7d5d7d359 NeedsCompilation: no Title: High-quality circular visualization of omics data Description: OmicCircos is an R application and package for generating high-quality circular plots for omics data. biocViews: Visualization,Statistics,Annotation Author: Ying Hu Chunhua Yan Maintainer: Ying Hu git_url: https://git.bioconductor.org/packages/OmicCircos git_branch: RELEASE_3_12 git_last_commit: 29c6a41 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OmicCircos_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OmicCircos_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OmicCircos_1.28.0.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.R dependencyCount: 17 Package: omicplotR Version: 1.10.0 Depends: R (>= 3.6), ALDEx2 (>= 1.18.0) Imports: compositions, DT, grDevices, knitr, jsonlite, matrixStats, rmarkdown, shiny, stats, vegan, zCompositions License: MIT + file LICENSE MD5sum: 8e212901fbac03a151cce4baed148473 NeedsCompilation: no Title: Visual Exploration of Omic Datasets Using a Shiny App Description: A Shiny app for visual exploration of omic datasets as compositions, and differential abundance analysis using ALDEx2. Useful for exploring RNA-seq, meta-RNA-seq, 16s rRNA gene sequencing with visualizations such as principal component analysis biplots (coloured using metadata for visualizing each variable), dendrograms and stacked bar plots, and effect plots (ALDEx2). Input is a table of counts and metadata file (if metadata exists), with options to filter data by count or by metadata to remove low counts, or to visualize select samples according to selected metadata. biocViews: Software, DifferentialExpression, GeneExpression, GUI, RNASeq, DNASeq, Metagenomics, Transcriptomics, Bayesian, Microbiome, Visualization, Sequencing, ImmunoOncology Author: Daniel Giguere [aut, cre], Jean Macklaim [aut], Greg Gloor [aut] Maintainer: Daniel Giguere VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicplotR git_branch: RELEASE_3_12 git_last_commit: 5696727 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/omicplotR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/omicplotR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/omicplotR_1.10.0.tgz vignettes: vignettes/omicplotR/inst/doc/omicplotR.html vignetteTitles: omicplotR: A tool for visualization of omic datasets as compositions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicplotR/inst/doc/omicplotR.R dependencyCount: 94 Package: omicRexposome Version: 1.12.1 Depends: R (>= 3.4), Biobase Imports: stats, utils, grDevices, graphics, methods, rexposome, limma, sva, ggplot2, ggrepel, PMA, omicade4, gridExtra, MultiDataSet, SmartSVA, isva, parallel, SummarizedExperiment, stringr Suggests: BiocStyle, knitr, rmarkdown, snpStats, brgedata License: MIT + file LICENSE MD5sum: 3c6447fff8122a60c5282bd43e57f556 NeedsCompilation: no Title: Exposome and omic data associatin and integration analysis Description: omicRexposome systematizes the association evaluation between exposures and omic data, taking advantage of MultiDataSet for coordinated data management, rexposome for exposome data definition and limma for association testing. Also to perform data integration mixing exposome and omic data using multi co-inherent analysis (omicade4) and multi-canonical correlation analysis (PMA). biocViews: ImmunoOncology, WorkflowStep, MultipleComparison, Visualization, GeneExpression, DifferentialExpression, DifferentialMethylation, GeneRegulation, Epigenetics, Proteomics, Transcriptomics, StatisticalMethod, Regression Author: Carles Hernandez-Ferrer [aut, cre], Juan R. González [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicRexposome git_branch: RELEASE_3_12 git_last_commit: 8fef5d9 git_last_commit_date: 2021-01-22 Date/Publication: 2021-01-22 source.ver: src/contrib/omicRexposome_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/omicRexposome_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/omicRexposome_1.12.1.tgz vignettes: vignettes/omicRexposome/inst/doc/exposome_omic_integration.html vignetteTitles: Exposome Data Integration with Omic Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/omicRexposome/inst/doc/exposome_omic_integration.R dependencyCount: 208 Package: OmicsLonDA Version: 1.6.0 Depends: R(>= 3.6) Imports: SummarizedExperiment, gss, plyr, zoo, pracma, ggplot2, BiocParallel, parallel, grDevices, graphics, stats, utils, methods, BiocGenerics Suggests: knitr, rmarkdown, testthat, devtools, BiocManager License: MIT + file LICENSE MD5sum: ee7db3ce41eb9cfc7782eb18544bf25f NeedsCompilation: no Title: Omics Longitudinal Differential Analysis Description: Statistical method that provides robust identification of time intervals where omics features (such as proteomics, lipidomics, metabolomics, transcriptomics, microbiome, as well as physiological parameters captured by wearable sensors such as heart rhythm, body temperature, and activity level) are significantly different between groups. biocViews: TimeCourse, Survival, Microbiome, Metabolomics, Proteomics, Lipidomics, Transcriptomics, Regression Author: Ahmed A. Metwally, Tom Zhang, Michael Snyder Maintainer: Ahmed A. Metwally URL: https://github.com/aametwally/OmicsLonDA VignetteBuilder: knitr BugReports: https://github.com/aametwally/OmicsLonDA/issues git_url: https://git.bioconductor.org/packages/OmicsLonDA git_branch: RELEASE_3_12 git_last_commit: b81adbb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OmicsLonDA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OmicsLonDA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OmicsLonDA_1.6.0.tgz vignettes: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.html vignetteTitles: OmicsLonDA Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmicsLonDA/inst/doc/OmicsLonDA.R dependencyCount: 68 Package: OMICsPCA Version: 1.8.0 Depends: R (>= 3.5.0), OMICsPCAdata Imports: HelloRanges, fpc, stats, MultiAssayExperiment, pdftools, methods, grDevices, utils,clValid, NbClust, cowplot, rmarkdown, kableExtra, rtracklayer, IRanges, GenomeInfoDb, reshape2, ggplot2, factoextra, rgl, corrplot, MASS, graphics, FactoMineR, PerformanceAnalytics, tidyr, data.table, cluster, magick Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 2c788bd92e548f01ffa4f6f9c9c70a96 NeedsCompilation: no Title: An R package for quantitative integration and analysis of multiple omics assays from heterogeneous samples Description: OMICsPCA is an analysis pipeline designed to integrate multi OMICs experiments done on various subjects (e.g. Cell lines, individuals), treatments (e.g. disease/control) or time points and to analyse such integrated data from various various angles and perspectives. In it's core OMICsPCA uses Principal Component Analysis (PCA) to integrate multiomics experiments from various sources and thus has ability to over data insufficiency issues by using the ingegrated data as representatives. OMICsPCA can be used in various application including analysis of overall distribution of OMICs assays across various samples /individuals /time points; grouping assays by user-defined conditions; identification of source of variation, similarity/dissimilarity between assays, variables or individuals. biocViews: ImmunoOncology, MultipleComparison, PrincipalComponent, DataRepresentation, Workflow, Visualization, DimensionReduction, Clustering, BiologicalQuestion, EpigeneticsWorkflow, Transcription, GeneticVariability, GUI, BiomedicalInformatics, Epigenetics, FunctionalGenomics, SingleCell Author: Subhadeep Das [aut, cre], Dr. Sucheta Tripathy [ctb] Maintainer: Subhadeep Das VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OMICsPCA git_branch: RELEASE_3_12 git_last_commit: 156d64b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OMICsPCA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OMICsPCA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OMICsPCA_1.8.0.tgz vignettes: vignettes/OMICsPCA/inst/doc/vignettes.html vignetteTitles: OMICsPCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OMICsPCA/inst/doc/vignettes.R dependencyCount: 223 Package: omicsPrint Version: 1.10.0 Depends: R (>= 3.5), MASS Imports: methods, matrixStats, graphics, stats, SummarizedExperiment, MultiAssayExperiment, RaggedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, GEOquery, VariantAnnotation, Rsamtools, BiocParallel, GenomicRanges, FDb.InfiniumMethylation.hg19, snpStats License: GPL (>= 2) MD5sum: e93e014bcb4d24d0110b58638df6075a NeedsCompilation: no Title: Cross omic genetic fingerprinting Description: omicsPrint provides functionality for cross omic genetic fingerprinting, for example, to verify sample relationships between multiple omics data types, i.e. genomic, transcriptomic and epigenetic (DNA methylation). biocViews: QualityControl, Genetics, Epigenetics, Transcriptomics, DNAMethylation, Transcription, GeneticVariability, ImmunoOncology Author: Maarten van Iterson [aut], Davy Cats [cre] Maintainer: Davy Cats VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/omicsPrint git_branch: RELEASE_3_12 git_last_commit: de4e896 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/omicsPrint_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/omicsPrint_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/omicsPrint_1.10.0.tgz vignettes: vignettes/omicsPrint/inst/doc/omicsPrint.html vignetteTitles: omicsPrint: detection of data linkage errors in multiple omics studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/omicsPrint/inst/doc/omicsPrint.R dependencyCount: 49 Package: Omixer Version: 1.0.4 Depends: R (>= 3.0.0) Imports: dplyr, ggplot2, forcats, tibble, gridExtra, magrittr, readr, tidyselect, grid, stats, stringr Suggests: knitr, rmarkdown, BiocStyle, magick, testthat License: MIT + file LICENSE MD5sum: 008fd7cdf24b4b57266448aba8e7becc NeedsCompilation: no Title: Randomize Samples for -omics Profiling Description: Omixer - an R package for multivariate and reproducible randomization with lab-friendly sample layouts. Omixer ensures optimal sample distribution across batches with well-documented methods, and can output intuitive sample sheets for the wet lab if needed. biocViews: DataRepresentation, ExperimentalDesign, QualityControl, Software, Visualization Author: Lucy Sinke [cre, aut] Maintainer: Lucy Sinke VignetteBuilder: knitr BugReports: l.j.sinke@lumc.nl git_url: https://git.bioconductor.org/packages/Omixer git_branch: RELEASE_3_12 git_last_commit: 9d8528f git_last_commit_date: 2021-02-02 Date/Publication: 2021-02-02 source.ver: src/contrib/Omixer_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/Omixer_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.0/Omixer_1.0.4.tgz vignettes: vignettes/Omixer/inst/doc/omixer-vignette.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Omixer/inst/doc/omixer-vignette.R dependencyCount: 52 Package: OmnipathR Version: 2.0.0 Depends: R(>= 4.0), igraph, graphics, methods, utils, jsonlite Imports: dplyr, stats, rlang, tidyr Suggests: dnet, gprofiler2, BiocStyle, testthat, knitr, rmarkdown, ggplot2, ggraph License: MIT + file LICENSE MD5sum: bd7324b88a510cee63058798fc0884db NeedsCompilation: no Title: OmniPath web service client Description: A client for the OmniPath web service (https://www.omnipathdb.org). It also includes functions to transform and pretty print some of the downloaded data. biocViews: GraphAndNetwork, Network, Pathways, Software, ThirdPartyClient, DataImport, DataRepresentation, GeneSignaling, GeneRegulation, SystemsBiology Author: Alberto Valdeolivas [aut] (), Denes Turei [cre, aut] (), Attila Gabor [aut] Maintainer: Denes Turei URL: https://saezlab.github.io/OmnipathR/ VignetteBuilder: knitr BugReports: https://github.com/saezlab/OmnipathR/issues git_url: https://git.bioconductor.org/packages/OmnipathR git_branch: RELEASE_3_12 git_last_commit: 2c98139 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OmnipathR_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OmnipathR_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OmnipathR_2.0.0.tgz vignettes: vignettes/OmnipathR/inst/doc/drug_targets.html, vignettes/OmnipathR/inst/doc/omnipath_intro.html vignetteTitles: Building protein networks around drug-targets using OmnipathR, OmnipathR: utility functions to work with OmniPath in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OmnipathR/inst/doc/drug_targets.R, vignettes/OmnipathR/inst/doc/omnipath_intro.R dependencyCount: 30 Package: Onassis Version: 1.12.0 Depends: R (>= 4.0), rJava, OnassisJavaLibs Imports: GEOmetadb, RSQLite, data.table, methods, tools, utils, AnnotationDbi, RCurl, stats, DT, data.table, knitr, Rtsne, dendextend, clusteval, ggplot2, ggfortify Suggests: BiocStyle, rmarkdown, htmltools, org.Hs.eg.db, gplots, GenomicRanges, kableExtra License: GPL-2 MD5sum: de3fac525a73b557bd02c2a7c130b83f NeedsCompilation: no Title: OnASSIs Ontology Annotation and Semantic SImilarity software Description: A package that allows the annotation of text with ontology terms (mainly from OBO ontologies) and the computation of semantic similarity measures based on the structure of the ontology between different annotated samples. biocViews: Annotation, DataImport, Clustering, Network, Software, GeneTarget Author: Eugenia Galeota Maintainer: Eugenia Galeota SystemRequirements: Java (>= 1.8) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Onassis git_branch: RELEASE_3_12 git_last_commit: f8644e7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Onassis_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Onassis_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Onassis_1.12.0.tgz vignettes: vignettes/Onassis/inst/doc/Onassis.html vignetteTitles: Onassis: Ontology Annotation and Semantic Similarity software hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Onassis/inst/doc/Onassis.R dependencyCount: 106 Package: oncomix Version: 1.12.0 Depends: R (>= 3.4.0) Imports: ggplot2, ggrepel, RColorBrewer, mclust, stats, SummarizedExperiment Suggests: knitr, rmarkdown, testthat, RMySQL License: GPL-3 MD5sum: 19d05c9c8f50202d024629383d54518d NeedsCompilation: no Title: Identifying Genes Overexpressed in Subsets of Tumors from Tumor-Normal mRNA Expression Data Description: This package helps identify mRNAs that are overexpressed in subsets of tumors relative to normal tissue. Ideal inputs would be paired tumor-normal data from the same tissue from many patients (>15 pairs). This unsupervised approach relies on the observation that oncogenes are characteristically overexpressed in only a subset of tumors in the population, and may help identify oncogene candidates purely based on differences in mRNA expression between previously unknown subtypes. biocViews: GeneExpression, Sequencing Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oncomix git_branch: RELEASE_3_12 git_last_commit: fb053e2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/oncomix_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/oncomix_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/oncomix_1.12.0.tgz vignettes: vignettes/oncomix/inst/doc/oncomix.html vignetteTitles: OncoMix Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oncomix/inst/doc/oncomix.R dependencyCount: 59 Package: OncoScore Version: 1.18.0 Depends: R (>= 4.0.0), Imports: biomaRt, grDevices, graphics, utils, methods, Suggests: BiocGenerics, BiocStyle, knitr, testthat, License: file LICENSE MD5sum: a33848eb8100f4bf218b93f8c503694e NeedsCompilation: no Title: A tool to identify potentially oncogenic genes Description: OncoScore is a tool to measure the association of genes to cancer based on citation frequencies in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Luca De Sano [aut] (), Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb], Daniele Ramazzotti [aut, cre], Roberta Spinelli [ctb] Maintainer: Luca De Sano URL: https://github.com/danro9685/OncoScore VignetteBuilder: knitr BugReports: https://github.com/danro9685/OncoScore git_url: https://git.bioconductor.org/packages/OncoScore git_branch: RELEASE_3_12 git_last_commit: 73d475a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OncoScore_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OncoScore_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OncoScore_1.18.0.tgz vignettes: vignettes/OncoScore/inst/doc/vignette.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OncoScore/inst/doc/vignette.R dependencyCount: 61 Package: OncoSimulR Version: 2.20.0 Depends: R (>= 3.3.0) Imports: Rcpp (>= 0.12.4), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices, car, dplyr, smatr, ggplot2, ggrepel LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat (>= 1.0.0), rmarkdown, bookdown, pander License: GPL (>= 3) Archs: i386, x64 MD5sum: 2790589220d2d8fac081c713f386675f NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progression with Epistasis Description: Functions for forward population genetic simulation in asexual populations, with special focus on cancer progression. Fitness can be an arbitrary function of genetic interactions between multiple genes or modules of genes, including epistasis, order restrictions in mutation accumulation, and order effects. Mutation rates can differ between genes, and we can include mutator/antimutator genes (to model mutator phenotypes). Simulations use continuous-time models and can include driver and passenger genes and modules. Also included are functions for: simulating random DAGs of the type found in Oncogenetic Trees, Conjunctive Bayesian Networks, and other cancer progression models; plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling; plotting the parent-child relationships of the clones; generating random fitness landscapes (from Rough Mount Fuji, House of Cards, additive, NK, Ising, and Eggbox models) and plotting them. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Sergio Sanchez-Carrillo [aut], Juan Antonio Miguel Gonzalez [aut], Mark Taylor [ctb], Arash Partow [ctb], Sophie Brouillet [ctb], Sebastian Matuszewski [ctb], Harry Annoni [ctb], Luca Ferretti [ctb], Guillaume Achaz [ctb], Guillermo Gorines Cordero [ctb], Ivan Lorca Alonso [ctb], Francisco Mu\~noz Lopez [ctb], David Roncero Moro\~no [ctb], Alvaro Quevedo [ctb], Pablo Perez [ctb], Cristina Devesa [ctb], Alejandro Herrador [ctb], Holger Froehlich [ctb], Florian Markowetz [ctb], Achim Tresch [ctb], Theresa Niederberger [ctb], Christian Bender [ctb], Matthias Maneck [ctb], Claudio Lottaz [ctb], Tim Beissbarth [ctb], Sara Dorado Alfaro [ctb], Miguel Hernandez del Valle [ctb], Alvaro Huertas Garcia [ctb], Diego Ma\~nanes Cayero [ctb], Alejandro Martin Mu\~noz [ctb], Marta Couce Iglesias [ctb], Silvia Garcia Cobos [ctb], Carlos Madariaga Aramendi [ctb], Ana Rodriguez Ronchel [ctb], Lucia Sanchez Garcia [ctb], Yolanda Benitez Quesada [ctb], Asier Fernandez Pato [ctb], Esperanza Lopez Lopez [ctb], Alberto Manuel Parra Perez [ctb], Jorge Garcia Calleja [ctb], Ana del Ramo Galian [ctb], Alejandro de los Reyes Benitez [ctb], Guillermo Garcia Hoyos [ctb], Rosalia Palomino Cabrera [ctb], Rafael Barrero Rodriguez [ctb], Silvia Talavera Marcos [ctb], Niklas Endres [ctb] Maintainer: Ramon Diaz-Uriarte URL: https://github.com/rdiaz02/OncoSimul, https://popmodels.cancercontrol.cancer.gov/gsr/packages/oncosimulr/ VignetteBuilder: knitr BugReports: https://github.com/rdiaz02/OncoSimul/issues git_url: https://git.bioconductor.org/packages/OncoSimulR git_branch: RELEASE_3_12 git_last_commit: 1990185 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OncoSimulR_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OncoSimulR_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OncoSimulR_2.20.0.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.html vignetteTitles: OncoSimulR: forward genetic simulation in asexual populations with arbitrary epistatic interactions and a focus on modeling tumor progression. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R dependencyCount: 103 Package: oneSENSE Version: 1.12.0 Depends: R (>= 3.4), webshot, shiny, shinyFiles, scatterplot3d Imports: Rtsne, plotly, gplots, grDevices, graphics, stats, utils, methods, flowCore Suggests: knitr, rmarkdown License: GPL (>=3) MD5sum: f4d90d29d23a94680ca338fb1709458f NeedsCompilation: no Title: One-Dimensional Soli-Expression by Nonlinear Stochastic Embedding (OneSENSE) Description: A graphical user interface that facilitates the dimensional reduction method based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm, for categorical analysis of mass cytometry data. With One-SENSE, measured parameters are grouped into predefined categories, and cells are projected onto a space composed of one dimension for each category. Each dimension is informative and can be annotated through the use of heatplots aligned in parallel to each axis, allowing for simultaneous visualization of two catergories across a two-dimensional plot. The cellular occupancy of the resulting plots alllows for direct assessment of the relationships between the categories. biocViews: ImmunoOncology, Software, FlowCytometry, GUI, DimensionReduction Author: Cheng Yang, Evan Newell, Yong Kee Tan Maintainer: Yong Kee Tan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oneSENSE git_branch: RELEASE_3_12 git_last_commit: 3004881 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/oneSENSE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/oneSENSE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/oneSENSE_1.12.0.tgz vignettes: vignettes/oneSENSE/inst/doc/quickstart.html vignetteTitles: Introduction to oneSENSE GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/oneSENSE/inst/doc/quickstart.R dependencyCount: 100 Package: onlineFDR Version: 1.8.0 Imports: stats Suggests: knitr, rmarkdown, testthat, covr License: GPL-3 MD5sum: 2a536f0c90efaa32d749675703827cc8 NeedsCompilation: no Title: Online error control Description: This package allows users to control the false discovery rate (FDR) or familywise error rate (FWER) for online hypothesis testing, where hypotheses arrive sequentially in a stream. In this framework, a null hypothesis is rejected based only on the previous decisions, as the future p-values and the number of hypotheses to be tested are unknown. biocViews: MultipleComparison, Software, StatisticalMethod Author: David S. Robertson [aut, cre], Lathan Liou [aut], Aaditya Ramdas [aut], Adel Javanmard [aut], Andrea Montanari [aut], Jinjin Tian [aut], Tijana Zrnic [aut], Natasha A. Karp [aut] Maintainer: David S. Robertson URL: https://dsrobertson.github.io/onlineFDR/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/onlineFDR git_branch: RELEASE_3_12 git_last_commit: fbadef5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/onlineFDR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/onlineFDR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/onlineFDR_1.8.0.tgz vignettes: vignettes/onlineFDR/inst/doc/onlineFDR-vignette.html vignetteTitles: Using the onlineFDR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/onlineFDR/inst/doc/onlineFDR-vignette.R dependencyCount: 1 Package: ontoProc Version: 1.12.0 Depends: R (>= 3.5), ontologyIndex Imports: Biobase, S4Vectors, methods, AnnotationDbi, stats, utils, BiocFileCache, shiny, graph, Rgraphviz, ontologyPlot, dplyr, magrittr, DT, igraph Suggests: knitr, org.Hs.eg.db, org.Mm.eg.db, testthat, BiocStyle, SingleCellExperiment, celldex License: Artistic-2.0 MD5sum: 5954d402b55c16041c70952bac6a651f NeedsCompilation: no Title: processing of ontologies of anatomy, cell lines, and so on Description: Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies. biocViews: Infrastructure, GO Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ontoProc git_branch: RELEASE_3_12 git_last_commit: fc0996a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ontoProc_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ontoProc_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ontoProc_1.12.0.tgz vignettes: vignettes/ontoProc/inst/doc/ontoProc.html vignetteTitles: ontoProc: some ontology-oriented utilites with single-cell focus for Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ontoProc/inst/doc/ontoProc.R importsMe: pogos, tenXplore suggestsMe: BiocOncoTK, TxRegInfra dependencyCount: 81 Package: openCyto Version: 2.2.0 Depends: R (>= 3.5.0) Imports: methods,Biobase,BiocGenerics,gtools,flowCore(>= 1.99.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace(>= 3.99.1),flowStats(>= 3.99.1),flowClust(>= 3.11.4),MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, testthat, utils, tools, parallel, ggcyto, CytoML License: Artistic-2.0 Archs: i386, x64 MD5sum: ea991d8f53811caae5a761cfd911a306 NeedsCompilation: yes Title: Hierarchical Gating Pipeline for flow cytometry data Description: This package is designed to facilitate the automated gating methods in sequential way to mimic the manual gating strategy. biocViews: ImmunoOncology, FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang ,Jake Wagner VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openCyto git_branch: RELEASE_3_12 git_last_commit: 039c1d7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/openCyto_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/openCyto_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/openCyto_2.2.0.tgz vignettes: vignettes/openCyto/inst/doc/HowToAutoGating.html, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html vignetteTitles: How to use different auto gating functions, How to write a csv gating template, An Introduction to the openCyto package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openCyto/inst/doc/HowToAutoGating.R, vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R importsMe: CytoML suggestsMe: CATALYST, flowClust, flowCore, flowStats, flowTime, flowWorkspace, ggcyto dependencyCount: 117 Package: openPrimeR Version: 1.12.1 Depends: R (>= 4.0.0) Imports: Biostrings (>= 2.38.4), XML (>= 3.98-1.4), scales (>= 0.4.0), reshape2 (>= 1.4.1), seqinr (>= 3.3-3), IRanges (>= 2.4.8), GenomicRanges (>= 1.22.4), ggplot2 (>= 2.1.0), plyr (>= 1.8.4), dplyr (>= 0.5.0), stringdist (>= 0.9.4.1), stringr (>= 1.0.0), RColorBrewer (>= 1.1-2), DECIPHER (>= 1.16.1), lpSolveAPI (>= 5.5.2.0-17), digest (>= 0.6.9), Hmisc (>= 3.17-4), ape (>= 3.5), BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.11), foreach (>= 1.4.3), magrittr (>= 1.5), distr (>= 2.6), distrEx (>= 2.6), fitdistrplus (>= 1.0-7), uniqtag (>= 1.0), openxlsx (>= 4.0.17), grid (>= 3.1.0), grDevices (>= 3.1.0), stats (>= 3.1.0), utils (>= 3.1.0), methods (>= 3.1.0) Suggests: testthat (>= 1.0.2), knitr (>= 1.13), rmarkdown (>= 1.0), devtools (>= 1.12.0), doParallel (>= 1.0.10), pander (>= 0.6.0), learnr (>= 0.9) License: GPL-2 MD5sum: 732253b93190f343b113811e44937b9e NeedsCompilation: no Title: Multiplex PCR Primer Design and Analysis Description: An implementation of methods for designing, evaluating, and comparing primer sets for multiplex PCR. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. A Shiny app providing a user interface for the functionalities of this package is provided by the 'openPrimeRui' package. biocViews: Software, Technology, Coverage, MultipleComparison Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring SystemRequirements: MAFFT (>= 7.305), OligoArrayAux (>= 3.8), ViennaRNA (>= 2.4.1), MELTING (>= 5.1.1), Pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeR git_branch: RELEASE_3_12 git_last_commit: cf7a7af git_last_commit_date: 2020-11-14 Date/Publication: 2020-11-14 source.ver: src/contrib/openPrimeR_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/openPrimeR_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/openPrimeR_1.12.1.tgz vignettes: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.html vignetteTitles: openPrimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeR/inst/doc/openPrimeR_vignette.R dependsOnMe: openPrimeRui dependencyCount: 122 Package: openPrimeRui Version: 1.12.0 Depends: R (>= 4.0.0), openPrimeR (>= 0.99.0) Imports: shiny (>= 1.0.2), shinyjs (>= 0.9), shinyBS (>= 0.61), DT (>= 0.2), rmarkdown (>= 1.0) Suggests: knitr (>= 1.13) License: GPL-2 MD5sum: b5253bd27966c3e80421d404ae2ffec6 NeedsCompilation: no Title: Shiny Application for Multiplex PCR Primer Design and Analysis Description: A Shiny application providing methods for designing, evaluating, and comparing primer sets for multiplex polymerase chain reaction. Primers are designed by solving a set cover problem such that the number of covered template sequences is maximized with the smallest possible set of primers. To guarantee that high-quality primers are generated, only primers fulfilling constraints on their physicochemical properties are selected. biocViews: Software, Technology Author: Matthias Döring [aut, cre], Nico Pfeifer [aut] Maintainer: Matthias Döring VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/openPrimeRui git_branch: RELEASE_3_12 git_last_commit: d296b9d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/openPrimeRui_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/openPrimeRui_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/openPrimeRui_1.12.0.tgz vignettes: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.html vignetteTitles: openPrimeRui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openPrimeRui/inst/doc/openPrimeRui_vignette.R dependencyCount: 142 Package: OpenStats Version: 1.2.0 Depends: nlme Imports: MASS, jsonlite, Hmisc, methods, knitr, AICcmodavg, car, rlist, summarytools, graphics, stats, utils Suggests: rmarkdown License: GPL (>= 2) MD5sum: 70a25d52409c196f83adcb2633a33bbd NeedsCompilation: no Title: A Robust and Scalable Software Package for Reproducible Analysis of High-Throughput genotype-phenotype association Description: Package contains several methods for statistical analysis of genotype to phenotype association in high-throughput screening pipelines. biocViews: StatisticalMethod, BatchEffect, Bayesian Author: Hamed Haseli Mashhadi Maintainer: Hamed Haseli Mashhadi URL: https://git.io/Jv5w0 VignetteBuilder: knitr BugReports: https://git.io/Jv5wg git_url: https://git.bioconductor.org/packages/OpenStats git_branch: RELEASE_3_12 git_last_commit: 284378d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OpenStats_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OpenStats_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OpenStats_1.2.0.tgz vignettes: vignettes/OpenStats/inst/doc/OpenStats.html vignetteTitles: OpenStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OpenStats/inst/doc/OpenStats.R dependencyCount: 131 Package: oposSOM Version: 2.8.0 Depends: R (>= 3.0), igraph (>= 1.0.0) Imports: fastICA, tsne, scatterplot3d, pixmap, fdrtool, ape, biomaRt, Biobase, RcppParallel, Rcpp LinkingTo: RcppParallel, Rcpp License: GPL (>=2) Archs: i386, x64 MD5sum: a9f705219a43724610953ae58cbe4bcd NeedsCompilation: yes Title: Comprehensive analysis of transciptome data Description: This package translates microarray expression data into metadata of reduced dimension. It provides various sample-centered and group-centered visualizations, sample similarity analyses and functional enrichment analyses. The underlying SOM algorithm combines feature clustering, multidimensional scaling and dimension reduction, along with strong visualization capabilities. It enables extraction and description of functional expression modules inherent in the data. biocViews: GeneExpression, DifferentialExpression, GeneSetEnrichment, DataRepresentation, Visualization Author: Henry Loeffler-Wirth , Hoang Thanh Le and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de git_url: https://git.bioconductor.org/packages/oposSOM git_branch: RELEASE_3_12 git_last_commit: a3dbe7e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/oposSOM_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/oposSOM_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/oposSOM_2.8.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oposSOM/inst/doc/Vignette.R dependencyCount: 73 Package: oppar Version: 1.18.0 Depends: R (>= 3.3) Imports: Biobase, methods, GSEABase, GSVA Suggests: knitr, rmarkdown, limma, org.Hs.eg.db, GO.db, snow, parallel License: GPL-2 Archs: i386, x64 MD5sum: 8f367d32009eacc9f1e614cc9180634a NeedsCompilation: yes Title: Outlier profile and pathway analysis in R Description: The R implementation of mCOPA package published by Wang et al. (2012). Oppar provides methods for Cancer Outlier profile Analysis. Although initially developed to detect outlier genes in cancer studies, methods presented in oppar can be used for outlier profile analysis in general. In addition, tools are provided for gene set enrichment and pathway analysis. biocViews: Pathways, GeneSetEnrichment, SystemsBiology, GeneExpression, Software Author: Chenwei Wang [aut], Alperen Taciroglu [aut], Stefan R Maetschke [aut], Colleen C Nelson [aut], Mark Ragan [aut], Melissa Davis [aut], Soroor Hediyeh zadeh [cre], Momeneh Foroutan [ctr] Maintainer: Soroor Hediyeh zadeh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/oppar git_branch: RELEASE_3_12 git_last_commit: 9fcf67c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/oppar_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/oppar_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/oppar_1.18.0.tgz vignettes: vignettes/oppar/inst/doc/oppar.html vignetteTitles: OPPAR: Outlier Profile and Pathway Analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R dependencyCount: 63 Package: oppti Version: 1.4.0 Depends: R (>= 3.6) Imports: limma, stats, reshape, ggplot2, grDevices, RColorBrewer, pheatmap, knitr, methods, devtools License: MIT MD5sum: 9fcf3e755e34a56373f978b0146afe3a NeedsCompilation: no Title: Outlier Protein and Phosphosite Target Identifier Description: The aim of oppti is to analyze protein (and phosphosite) expressions to find outlying markers for each sample in the given cohort(s) for the discovery of personalized actionable targets. biocViews: Proteomics, Regression, DifferentialExpression, BiomedicalInformatics, GeneTarget, GeneExpression, Network Author: Abdulkadir Elmas Maintainer: Abdulkadir Elmas URL: https://github.com/Huang-lab/oppti VignetteBuilder: knitr BugReports: https://github.com/Huang-lab/oppti/issues git_url: https://git.bioconductor.org/packages/oppti git_branch: RELEASE_3_12 git_last_commit: 0014713 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/oppti_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/oppti_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/oppti_1.4.0.tgz vignettes: vignettes/oppti/inst/doc/analysis.html vignetteTitles: Outlier Protein and Phosphosite Target Identifier hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppti/inst/doc/analysis.R dependencyCount: 99 Package: optimalFlow Version: 1.2.0 Depends: dplyr, optimalFlowData, rlang (>= 0.4.0) Imports: transport, parallel, Rfast, robustbase, dbscan, randomForest, foreach, graphics, doParallel, stats, flowMeans, rgl, ellipse Suggests: knitr, BiocStyle, rmarkdown, magick License: Artistic-2.0 MD5sum: 69a705b177ba2992fe50d1a48750ac69 NeedsCompilation: no Title: optimalFlow Description: Optimal-transport techniques applied to supervised flow cytometry gating. biocViews: Software, FlowCytometry, Technology Author: Hristo Inouzhe Maintainer: Hristo Inouzhe VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/optimalFlow git_branch: RELEASE_3_12 git_last_commit: e837f61 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/optimalFlow_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/optimalFlow_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/optimalFlow_1.2.0.tgz vignettes: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.html vignetteTitles: optimalFlow: optimal-transport approach to Flow Cytometry analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/optimalFlow/inst/doc/optimalFlow_vignette.R dependencyCount: 112 Package: OPWeight Version: 1.12.0 Depends: R (>= 3.4.0), Imports: graphics, qvalue, MASS, tibble, stats, Suggests: airway, BiocStyle, cowplot, DESeq2, devtools, ggplot2, gridExtra, knitr, Matrix, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: 7151cdea908161eb698c48a5711f68da NeedsCompilation: no Title: Optimal p-value weighting with independent information Description: This package perform weighted-pvalue based multiple hypothesis test and provides corresponding information such as ranking probability, weight, significant tests, etc . To conduct this testing procedure, the testing method apply a probabilistic relationship between the test rank and the corresponding test effect size. biocViews: ImmunoOncology, BiomedicalInformatics, MultipleComparison, Regression, RNASeq, SNP Author: Mohamad Hasan [aut, cre], Paul Schliekelman [aut] Maintainer: Mohamad Hasan URL: https://github.com/mshasan/OPWeight VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OPWeight git_branch: RELEASE_3_12 git_last_commit: 3043767 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OPWeight_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OPWeight_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OPWeight_1.12.0.tgz vignettes: vignettes/OPWeight/inst/doc/OPWeight.html vignetteTitles: "Introduction to OPWeight" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OPWeight/inst/doc/OPWeight.R dependencyCount: 45 Package: OrderedList Version: 1.62.0 Depends: R (>= 3.6.1), Biobase, twilight Imports: methods License: GPL (>= 2) MD5sum: 94ea44dab63cf6b5d8857312676e0be5 NeedsCompilation: no Title: Similarities of Ordered Gene Lists Description: Detection of similarities between ordered lists of genes. Thereby, either simple lists can be compared or gene expression data can be used to deduce the lists. Significance of similarities is evaluated by shuffling lists or by resampling in microarray data, respectively. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Xinan Yang, Stefanie Scheid, Claudio Lottaz Maintainer: Claudio Lottaz URL: http://compdiag.molgen.mpg.de/software/OrderedList.shtml git_url: https://git.bioconductor.org/packages/OrderedList git_branch: RELEASE_3_12 git_last_commit: 5faa03d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OrderedList_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OrderedList_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OrderedList_1.62.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrderedList/inst/doc/tr_2006_01.R dependencyCount: 10 Package: ORFik Version: 1.10.13 Depends: R (>= 3.6.0), IRanges (>= 2.17.1), GenomicRanges (>= 1.35.1), GenomicAlignments (>= 1.19.0) Imports: S4Vectors (>= 0.21.3), GenomeInfoDb (>= 1.15.5), GenomicFeatures (>= 1.31.10), AnnotationDbi (>= 1.45.0), rtracklayer (>= 1.43.0), Rcpp (>= 1.0.0), data.table (>= 1.11.8), Biostrings (>= 2.51.1), biomartr, BiocGenerics (>= 0.29.1), BiocParallel (>= 1.19.0), BSgenome, DESeq2 (>= 1.24.0), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), cowplot (>= 1.0.0), GGally (>= 1.4.0), methods (>= 3.6.0), R.utils, RCurl, Rsamtools (>= 1.35.0), utils, stats, SummarizedExperiment (>= 1.14.0), fst (>= 0.9.2), xml2 (>= 1.2.0), tools LinkingTo: Rcpp Suggests: testthat, rmarkdown, knitr, BiocStyle, BSgenome.Hsapiens.UCSC.hg19 License: MIT + file LICENSE Archs: i386, x64 MD5sum: 6f382b5a4f52e56e8901043e0237f032 NeedsCompilation: yes Title: Open Reading Frames in Genomics Description: R package for analysis of transcript and translation features through manipulation of sequence data and NGS data like Ribo-Seq, RNA-Seq, TCP-Seq and CAGE. It is generalized in the sense that any transcript region can be analysed, as the name hints to it was made with investigation of ribosomal patterns over Open Reading Frames (ORFs) as it's primary use case. ORFik is extremely fast through use of C++, data.table and GenomicRanges. Package allows to reassign starts of the transcripts with the use of CAGE-Seq data, automatic shifting of RiboSeq reads, finding of Open Reading Frames for whole genomes and much more. biocViews: ImmunoOncology, Software, Sequencing, RiboSeq, RNASeq, FunctionalGenomics, Coverage, Alignment, DataImport Author: Haakon Tjeldnes [aut, cre, dtc], Kornel Labun [aut, cph], Katarzyna Chyzynska [ctb, dtc], Yamila Torres Cleuren [ctb, ths], Evind Valen [ths, fnd] Maintainer: Haakon Tjeldnes URL: https://github.com/Roleren/ORFik VignetteBuilder: knitr BugReports: https://github.com/Roleren/ORFik/issues git_url: https://git.bioconductor.org/packages/ORFik git_branch: RELEASE_3_12 git_last_commit: 3f2976e git_last_commit_date: 2021-03-26 Date/Publication: 2021-03-26 source.ver: src/contrib/ORFik_1.10.13.tar.gz win.binary.ver: bin/windows/contrib/4.0/ORFik_1.10.13.zip mac.binary.ver: bin/macosx/contrib/4.0/ORFik_1.10.13.tgz vignettes: vignettes/ORFik/inst/doc/ORFik_Annotation_Alignment.html, vignettes/ORFik/inst/doc/ORFik_Ribo-seq_pipeline.html, vignettes/ORFik/inst/doc/ORFikExperiment.html, vignettes/ORFik/inst/doc/ORFikOverview.html vignetteTitles: ORFik_Annotation_Alignment.html, ORFik_Ribo-seq_pipeline.html, ORFikExperiment.html, ORFik Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ORFik/inst/doc/ORFik_Annotation_Alignment.R, vignettes/ORFik/inst/doc/ORFik_Ribo-seq_pipeline.R, vignettes/ORFik/inst/doc/ORFikExperiment.R, vignettes/ORFik/inst/doc/ORFikOverview.R dependencyCount: 132 Package: Organism.dplyr Version: 1.18.0 Depends: R (>= 3.4), dplyr (>= 0.7.0), AnnotationFilter (>= 1.1.3) Imports: RSQLite, S4Vectors, GenomeInfoDb, IRanges, GenomicRanges, GenomicFeatures, AnnotationDbi, rlang, methods, tools, utils, BiocFileCache, DBI, dbplyr, tibble Suggests: org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.ensGene, testthat, knitr, rmarkdown, BiocStyle, ggplot2 License: Artistic-2.0 MD5sum: 1c4a55030a2d72a6a0fef124bd6cf43a NeedsCompilation: no Title: dplyr-based Access to Bioconductor Annotation Resources Description: This package provides an alternative interface to Bioconductor 'annotation' resources, in particular the gene identifier mapping functionality of the 'org' packages (e.g., org.Hs.eg.db) and the genome coordinate functionality of the 'TxDb' packages (e.g., TxDb.Hsapiens.UCSC.hg38.knownGene). biocViews: Annotation, Sequencing, GenomeAnnotation Author: Martin Morgan [aut, cre], Daniel van Twisk [ctb], Yubo Cheng [aut] Maintainer: Martin Morgan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Organism.dplyr git_branch: RELEASE_3_12 git_last_commit: f57cfd5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Organism.dplyr_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Organism.dplyr_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Organism.dplyr_1.18.0.tgz vignettes: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.html vignetteTitles: Organism.dplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Organism.dplyr/inst/doc/Organism.dplyr.R dependsOnMe: annotation importsMe: Ularcirc dependencyCount: 90 Package: OrganismDbi Version: 1.32.0 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.39.4) Imports: Biobase, BiocManager, GenomicRanges (>= 1.31.13), graph, IRanges, RBGL, DBI, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, mirbase.db, rtracklayer, biomaRt, RUnit, RMariaDB License: Artistic-2.0 MD5sum: 90d3e741295a39254275e9811378b64a NeedsCompilation: no Title: Software to enable the smooth interfacing of different database packages Description: The package enables a simple unified interface to several annotation packages each of which has its own schema by taking advantage of the fact that each of these packages implements a select methods. biocViews: Annotation, Infrastructure Author: Marc Carlson, Hervé Pagès, Martin Morgan, Valerie Obenchain Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/OrganismDbi git_branch: RELEASE_3_12 git_last_commit: c8100c4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OrganismDbi_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OrganismDbi_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OrganismDbi_1.32.0.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OrganismDbi/inst/doc/OrganismDbi.R dependsOnMe: Homo.sapiens, Mus.musculus, Rattus.norvegicus importsMe: AnnotationHubData, epivizrData, ggbio, gpart, uncoverappLib suggestsMe: ChIPpeakAnno, epivizrStandalone dependencyCount: 91 Package: OSAT Version: 1.38.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: cc5bac97a0993d333e19a3ab85c9c533 NeedsCompilation: no Title: OSAT: Optimal Sample Assignment Tool Description: A sizable genomics study such as microarray often involves the use of multiple batches (groups) of experiment due to practical complication. To minimize batch effects, a careful experiment design should ensure the even distribution of biological groups and confounding factors across batches. OSAT (Optimal Sample Assignment Tool) is developed to facilitate the allocation of collected samples to different batches. With minimum steps, it produces setup that optimizes the even distribution of samples in groups of biological interest into different batches, reducing the confounding or correlation between batches and the biological variables of interest. It can also optimize the even distribution of confounding factors across batches. Our tool can handle challenging instances where incomplete and unbalanced sample collections are involved as well as ideal balanced RCBD. OSAT provides a number of predefined layout for some of the most commonly used genomics platform. Related paper can be find at http://www.biomedcentral.com/1471-2164/13/689 . biocViews: DataRepresentation, Visualization, ExperimentalDesign, QualityControl Author: Li Yan Maintainer: Li Yan URL: http://www.biomedcentral.com/1471-2164/13/689 git_url: https://git.bioconductor.org/packages/OSAT git_branch: RELEASE_3_12 git_last_commit: dead1e7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OSAT_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OSAT_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OSAT_1.38.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OSAT/inst/doc/OSAT.R dependencyCount: 2 Package: Oscope Version: 1.20.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 53d682ed9750efdc3d708c6dae6c5559 NeedsCompilation: no Title: Oscope - A statistical pipeline for identifying oscillatory genes in unsynchronized single cell RNA-seq Description: Oscope is a statistical pipeline developed to identifying and recovering the base cycle profiles of oscillating genes in an unsynchronized single cell RNA-seq experiment. The Oscope pipeline includes three modules: a sine model module to search for candidate oscillator pairs; a K-medoids clustering module to cluster candidate oscillators into groups; and an extended nearest insertion module to recover the base cycle order for each oscillator group. biocViews: ImmunoOncology, StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng git_url: https://git.bioconductor.org/packages/Oscope git_branch: RELEASE_3_12 git_last_commit: f9e6398 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Oscope_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Oscope_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Oscope_1.20.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Oscope/inst/doc/Oscope_vignette.R dependencyCount: 56 Package: OTUbase Version: 1.40.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 27c3ddb0f48dca2764a45a3b7937634e NeedsCompilation: no Title: Provides structure and functions for the analysis of OTU data Description: Provides a platform for Operational Taxonomic Unit based analysis biocViews: Sequencing, DataImport Author: Daniel Beck, Matt Settles, and James A. Foster Maintainer: Daniel Beck git_url: https://git.bioconductor.org/packages/OTUbase git_branch: RELEASE_3_12 git_last_commit: adc701f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OTUbase_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OTUbase_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OTUbase_1.40.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.R dependsOnMe: mcaGUI dependencyCount: 51 Package: OutlierD Version: 1.53.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: 827f1d7594aac3a1d1219913a9440402 NeedsCompilation: no Title: Outlier detection using quantile regression on the M-A scatterplots of high-throughput data Description: This package detects outliers using quantile regression on the M-A scatterplots of high-throughput data. biocViews: Microarray Author: HyungJun Cho Maintainer: Sukwoo Kim URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/OutlierD git_branch: master git_last_commit: 0afc730 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/OutlierD_1.53.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OutlierD_1.53.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OutlierD_1.53.0.tgz vignettes: vignettes/OutlierD/inst/doc/OutlierD.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OutlierD/inst/doc/OutlierD.R dependencyCount: 18 Package: OUTRIDER Version: 1.8.0 Depends: R (>= 3.6), BiocParallel, GenomicFeatures, SummarizedExperiment, data.table, methods Imports: BBmisc, BiocGenerics, DESeq2 (>= 1.16.1), generics, GenomicRanges, ggplot2, grDevices, heatmaply, pheatmap, graphics, IRanges, matrixStats, plotly, plyr, pcaMethods, PRROC, RColorBrewer, Rcpp, reshape2, S4Vectors, scales, splines, stats, utils LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, rmarkdown, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, RMariaDB, AnnotationDbi, beeswarm, covr License: MIT + file LICENSE Archs: i386, x64 MD5sum: 9a498bd5cf7196d0a62ee42bd1cbc2f9 NeedsCompilation: yes Title: OUTRIDER - OUTlier in RNA-Seq fInDER Description: Identification of aberrant gene expression in RNA-seq data. Read count expectations are modeled by an autoencoder to control for confounders in the data. Given these expectations, the RNA-seq read counts are assumed to follow a negative binomial distribution with a gene-specific dispersion. Outliers are then identified as read counts that significantly deviate from this distribution. Furthermore, OUTRIDER provides useful plotting functions to analyze and visualize the results. biocViews: ImmunoOncology, RNASeq, Transcriptomics, Alignment, Sequencing, GeneExpression, Genetics Author: Felix Brechtmann [aut], Christian Mertes [aut, cre], Agne Matuseviciute [aut], Michaela Fee Müller [ctb], Vicente Yepez [aut], Julien Gagneur [aut] Maintainer: Christian Mertes URL: https://github.com/gagneurlab/OUTRIDER VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/OUTRIDER git_branch: RELEASE_3_12 git_last_commit: ab85565 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OUTRIDER_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OUTRIDER_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OUTRIDER_1.8.0.tgz vignettes: vignettes/OUTRIDER/inst/doc/OUTRIDER.pdf vignetteTitles: OUTRIDER: OUTlier in RNA-seq fInDER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/OUTRIDER/inst/doc/OUTRIDER.R importsMe: FRASER dependencyCount: 154 Package: OVESEG Version: 1.6.0 Depends: R (>= 3.6) Imports: stats, utils, methods, BiocParallel, SummarizedExperiment, limma, fdrtool, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, BiocStyle, testthat, ggplot2, gridExtra, grid, reshape2, scales License: GPL-2 Archs: i386, x64 MD5sum: 2c5bfa5fa9e3f878e9d98098a66d23b0 NeedsCompilation: yes Title: OVESEG-test to detect tissue/cell-specific markers Description: An R package for multiple-group comparison to detect tissue/cell-specific marker genes among subtypes. It provides functions to compute OVESEG-test statistics, derive component weights in the mixture null distribution model and estimate p-values from weightedly aggregated permutations. Obtained posterior probabilities of component null hypotheses can also portrait all kinds of upregulation patterns among subtypes. biocViews: Software, MultipleComparison, CellBiology, GeneExpression Author: Lulu Chen Maintainer: Lulu Chen SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/Lululuella/OVESEG git_url: https://git.bioconductor.org/packages/OVESEG git_branch: RELEASE_3_12 git_last_commit: 5809f13 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/OVESEG_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/OVESEG_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/OVESEG_1.6.0.tgz vignettes: vignettes/OVESEG/inst/doc/OVESEG.html vignetteTitles: OVESEG User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OVESEG/inst/doc/OVESEG.R dependencyCount: 36 Package: PAA Version: 1.24.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, gtools, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: c50e990a2d1f7e00ee760d4c7ed0ff6e NeedsCompilation: yes Title: PAA (Protein Array Analyzer) Description: PAA imports single color (protein) microarray data that has been saved in gpr file format - esp. ProtoArray data. After preprocessing (background correction, batch filtering, normalization) univariate feature preselection is performed (e.g., using the "minimum M statistic" approach - hereinafter referred to as "mMs"). Subsequently, a multivariate feature selection is conducted to discover biomarker candidates. Therefore, either a frequency-based backwards elimination aproach or ensemble feature selection can be used. PAA provides a complete toolbox of analysis tools including several different plots for results examination and evaluation. biocViews: Classification, Microarray, OneChannel, Proteomics Author: Michael Turewicz [aut, cre], Martin Eisenacher [ctb, cre] Maintainer: Michael Turewicz , Martin Eisenacher URL: http://www.ruhr-uni-bochum.de/mpc/software/PAA/ SystemRequirements: C++ software package Random Jungle git_url: https://git.bioconductor.org/packages/PAA git_branch: RELEASE_3_12 git_last_commit: 33da63e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PAA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PAA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PAA_1.24.0.tgz vignettes: vignettes/PAA/inst/doc/PAA_1.7.1.pdf, vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA_1.7.1.pdf, PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAA/inst/doc/PAA_vignette.R dependencyCount: 73 Package: packFinder Version: 1.2.0 Depends: R (>= 4.0.0) Imports: Biostrings, GenomicRanges, kmer, ape, methods, IRanges, S4Vectors Suggests: biomartr, knitr, rmarkdown, testthat, dendextend, biocViews, BiocCheck, BiocStyle License: GPL-2 MD5sum: 3b94f0c674f90e3494b9fe7185dae730 NeedsCompilation: no Title: de novo Annotation of Pack-TYPE Transposable Elements Description: Algorithm and tools for in silico pack-TYPE transposon discovery. Filters a given genome for properties unique to DNA transposons and provides tools for the investigation of returned matches. Sequences are input in DNAString format, and ranges are returned as a dataframe (in the format returned by as.dataframe(GRanges)). biocViews: Genetics, SequenceMatching, Annotation Author: Jack Gisby [aut, cre], Marco Catoni [aut] Maintainer: Jack Gisby URL: https://github.com/jackgisby/packFinder VignetteBuilder: knitr BugReports: https://github.com/jackgisby/packFinder/issues git_url: https://git.bioconductor.org/packages/packFinder git_branch: RELEASE_3_12 git_last_commit: 25949e3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/packFinder_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/packFinder_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/packFinder_1.2.0.tgz vignettes: vignettes/packFinder/inst/doc/packFinder.html vignetteTitles: packFinder hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/packFinder/inst/doc/packFinder.R dependencyCount: 30 Package: padma Version: 1.0.0 Depends: R (>= 4.0.0), SummarizedExperiment, S4Vectors Imports: FactoMineR, MultiAssayExperiment, methods, graphics, stats, utils Suggests: testthat, BiocStyle, knitr, rmarkdown, KEGGREST, missMDA, ggplot2, ggrepel, car, cowplot License: GPL (>=3) MD5sum: 28e0de906af885cf4b554da84c4e8739 NeedsCompilation: no Title: Individualized Multi-Omic Pathway Deviation Scores Using Multiple Factor Analysis Description: Use multiple factor analysis to calculate individualized pathway-centric scores of deviation with respect to the sampled population based on multi-omic assays (e.g., RNA-seq, copy number alterations, methylation, etc). Graphical and numerical outputs are provided to identify highly aberrant individuals for a particular pathway of interest, as well as the gene and omics drivers of aberrant multi-omic profiles. biocViews: Software, StatisticalMethod, PrincipalComponent, GeneExpression, Pathways, RNASeq, BioCarta, MethylSeq Author: Andrea Rau [cre, aut] (), Regina Manansala [aut], Florence Jaffrézic [ctb], Denis Laloë [aut], Paul Auer [aut] Maintainer: Andrea Rau URL: https://github.com/andreamrau/padma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/padma git_branch: RELEASE_3_12 git_last_commit: cedf579 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/padma_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/padma_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/padma_1.0.0.tgz vignettes: vignettes/padma/inst/doc/padma.html vignetteTitles: padma package:Quick-start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/padma/inst/doc/padma.R dependencyCount: 127 Package: PADOG Version: 1.32.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGGREST, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: 0ccd224fc1b6e2f496dbebc8be398be5 NeedsCompilation: no Title: Pathway Analysis with Down-weighting of Overlapping Genes (PADOG) Description: This package implements a general purpose gene set analysis method called PADOG that downplays the importance of genes that apear often accross the sets of genes to be analyzed. The package provides also a benchmark for gene set analysis methods in terms of sensitivity and ranking using 24 public datasets from KEGGdzPathwaysGEO package. biocViews: Microarray, OneChannel, TwoChannel Author: Adi Laurentiu Tarca ; Zhonghui Xu Maintainer: Adi Laurentiu Tarca git_url: https://git.bioconductor.org/packages/PADOG git_branch: RELEASE_3_12 git_last_commit: 8cfe38b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PADOG_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PADOG_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PADOG_1.32.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PADOG/inst/doc/PADOG.R dependsOnMe: BLMA importsMe: EGSEA dependencyCount: 57 Package: pageRank Version: 1.0.0 Depends: R (>= 4.0) Imports: GenomicRanges, igraph, motifmatchr, stats, utils, grDevices, graphics Suggests: bcellViper, BSgenome.Hsapiens.UCSC.hg19, JASPAR2018, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db, TFBSTools, GenomicFeatures, annotate License: GPL-2 MD5sum: 8636b6c65519ad53c902300d9fcf24e8 NeedsCompilation: no Title: Temporal and Multiplex PageRank for Gene Regulatory Network Analysis Description: Implemented temporal PageRank analysis as defined by Rozenshtein and Gionis. Implemented multiplex PageRank as defined by Halu et al. Applied temporal and multiplex PageRank in gene regulatory network analysis. biocViews: StatisticalMethod, GeneTarget, Network Author: Hongxu Ding [aut, cre, ctb, cph] Maintainer: Hongxu Ding URL: https://github.com/hd2326/pageRank BugReports: https://github.com/hd2326/pageRank/issues git_url: https://git.bioconductor.org/packages/pageRank git_branch: RELEASE_3_12 git_last_commit: 8a30acb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pageRank_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pageRank_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pageRank_1.0.0.tgz vignettes: vignettes/pageRank/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pageRank/inst/doc/introduction.R dependencyCount: 116 Package: PAIRADISE Version: 1.6.0 Depends: R (>= 3.6), nloptr Imports: SummarizedExperiment, S4Vectors, stats, methods, abind, BiocParallel Suggests: testthat, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: 4e82dfa987f9ca77ffbd08ced11f2c19 NeedsCompilation: no Title: PAIRADISE: Paired analysis of differential isoform expression Description: This package implements the PAIRADISE procedure for detecting differential isoform expression between matched replicates in paired RNA-Seq data. biocViews: RNASeq, DifferentialExpression, AlternativeSplicing, StatisticalMethod, ImmunoOncology Author: Levon Demirdjian, Ying Nian Wu, Yi Xing Maintainer: Qiang Hu , Levon Demirdjian VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PAIRADISE git_branch: RELEASE_3_12 git_last_commit: 130c6dc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PAIRADISE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PAIRADISE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PAIRADISE_1.6.0.tgz vignettes: vignettes/PAIRADISE/inst/doc/pairadise.html vignetteTitles: PAIRADISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAIRADISE/inst/doc/pairadise.R dependencyCount: 35 Package: paircompviz Version: 1.28.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: 4d705a8b5b7d963e53560fe3667d5a3d NeedsCompilation: no Title: Multiple comparison test visualization Description: This package provides visualization of the results from the multiple (i.e. pairwise) comparison tests such as pairwise.t.test, pairwise.prop.test or pairwise.wilcox.test. The groups being compared are visualized as nodes in Hasse diagram. Such approach enables very clear and vivid depiction of which group is significantly greater than which others, especially if comparing a large number of groups. biocViews: GraphAndNetwork Author: Michal Burda Maintainer: Michal Burda git_url: https://git.bioconductor.org/packages/paircompviz git_branch: RELEASE_3_12 git_last_commit: 3f166f1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/paircompviz_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/paircompviz_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/paircompviz_1.28.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paircompviz/inst/doc/vignette.R dependencyCount: 11 Package: pandaR Version: 1.22.0 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: knitr License: GPL-2 MD5sum: 8d846c7af32a6ab5d0dd8502a4677345 NeedsCompilation: no Title: PANDA Algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complementary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: Dan Schlauch, Joseph N. Paulson, Albert Young, John Quackenbush, Kimberly Glass Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pandaR git_branch: RELEASE_3_12 git_last_commit: 144a864 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pandaR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pandaR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pandaR_1.22.0.tgz vignettes: vignettes/pandaR/inst/doc/pandaR.html vignetteTitles: pandaR Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pandaR/inst/doc/pandaR.R dependencyCount: 48 Package: panelcn.mops Version: 1.12.0 Depends: R (>= 3.4), cn.mops, methods, utils, stats, graphics Imports: GenomicRanges, Rsamtools, IRanges, S4Vectors, GenomeInfoDb, grDevices Suggests: knitr, rmarkdown, RUnit, BiocGenerics License: LGPL (>= 2.0) MD5sum: 906665df9a2d633732e267863814a359 NeedsCompilation: no Title: CNV detection tool for targeted NGS panel data Description: CNV detection tool for targeted NGS panel data. Extension of the cn.mops package. biocViews: Sequencing, CopyNumberVariation, CellBiology, GenomicVariation, VariantDetection, Genetics Author: Verena Haunschmid, Gundula Povysil Maintainer: Gundula Povysil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/panelcn.mops git_branch: RELEASE_3_12 git_last_commit: 31ca715 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/panelcn.mops_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/panelcn.mops_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/panelcn.mops_1.12.0.tgz vignettes: vignettes/panelcn.mops/inst/doc/panelcn.mops.pdf vignetteTitles: panelcn.mops: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panelcn.mops/inst/doc/panelcn.mops.R suggestsMe: CopyNumberPlots dependencyCount: 32 Package: panp Version: 1.60.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 356eaa7da39770cedb90c6ce8daa1309 NeedsCompilation: no Title: Presence-Absence Calls from Negative Strand Matching Probesets Description: A function to make gene presence/absence calls based on distance from negative strand matching probesets (NSMP) which are derived from Affymetrix annotation. PANP is applied after gene expression values are created, and therefore can be used after any preprocessing method such as MAS5 or GCRMA, or PM-only methods like RMA. NSMP sets have been established for the HGU133A and HGU133-Plus-2.0 chipsets to date. biocViews: Infrastructure Author: Peter Warren Maintainer: Peter Warren git_url: https://git.bioconductor.org/packages/panp git_branch: RELEASE_3_12 git_last_commit: 660189d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/panp_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/panp_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/panp_1.60.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/panp/inst/doc/panp.R dependencyCount: 13 Package: PANR Version: 1.36.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: a8108810d65f5f4840fabaca45f8ac32 NeedsCompilation: no Title: Posterior association networks and functional modules inferred from rich phenotypes of gene perturbations Description: This package provides S4 classes and methods for inferring functional gene networks with edges encoding posterior beliefs of gene association types and nodes encoding perturbation effects. biocViews: ImmunoOncology, NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang git_url: https://git.bioconductor.org/packages/PANR git_branch: RELEASE_3_12 git_last_commit: 4badc77 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PANR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PANR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PANR_1.36.0.tgz vignettes: vignettes/PANR/inst/doc/PANR-Vignette.pdf vignetteTitles: Main vignette:Posterior association network and enriched functional gene modules inferred from rich phenotypes of gene perturbations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PANR/inst/doc/PANR-Vignette.R dependencyCount: 14 Package: PanVizGenerator Version: 1.18.0 Depends: methods Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, digest License: GPL (>= 2) MD5sum: e2ba5712839e876b8db4a071817d9fb3 NeedsCompilation: no Title: Generate PanViz visualisations from your pangenome Description: PanViz is a JavaScript based visualisation tool for functionaly annotated pangenomes. PanVizGenerator is a companion for PanViz that facilitates the necessary data preprocessing step necessary to create a working PanViz visualization. The output is fully self-contained so the recipient of the visualization does not need R or PanVizGenerator installed. biocViews: ComparativeGenomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen URL: https://github.com/thomasp85/PanVizGenerator VignetteBuilder: knitr BugReports: https://github.com/thomasp85/PanVizGenerator/issues git_url: https://git.bioconductor.org/packages/PanVizGenerator git_branch: RELEASE_3_12 git_last_commit: cc80476 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PanVizGenerator_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PanVizGenerator_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PanVizGenerator_1.18.0.tgz vignettes: vignettes/PanVizGenerator/inst/doc/panviz_howto.html vignetteTitles: Creating PanViz visualizations with PanVizGenerator hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R dependencyCount: 94 Package: parglms Version: 1.22.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS, knitr, GenomeInfoDb, GenomicRanges, gwascat, BiocStyle License: Artistic-2.0 MD5sum: 2d4daa5c3145aca0290784360bbcc0e7 NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: This package provides support for parallelized estimation of GLMs/GEEs, catering for dispersed data. Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parglms git_branch: RELEASE_3_12 git_last_commit: 26c2582 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/parglms_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/parglms_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/parglms_1.22.0.tgz vignettes: vignettes/parglms/inst/doc/parglms.pdf vignetteTitles: parglms: parallelized GLM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parglms/inst/doc/parglms.R dependencyCount: 36 Package: parody Version: 1.48.0 Depends: R (>= 3.5.0), tools, utils Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 482d5825584a5b19ec243fed052ebd3f NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: Provide routines for univariate and multivariate outlier detection with a focus on parametric methods, but support for some methods based on resistant statistics. biocViews: MultipleComparison Author: VJ Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/parody git_branch: RELEASE_3_12 git_last_commit: 6944f02 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/parody_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/parody_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/parody_1.48.0.tgz vignettes: vignettes/parody/inst/doc/parody.html vignetteTitles: parody: parametric and resistant outlier dytection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout dependencyCount: 2 Package: PAST Version: 1.6.0 Depends: R (>= 4.0) Imports: stats, utils, dplyr, rlang, iterators, parallel, foreach, doParallel, qvalue, rtracklayer, ggplot2, GenomicRanges, S4Vectors Suggests: knitr, rmarkdown License: GPL (>=3) + file LICENSE MD5sum: a427240485e31a7cf3b41e40aaa587c0 NeedsCompilation: no Title: Pathway Association Study Tool (PAST) Description: PAST takes GWAS output and assigns SNPs to genes, uses those genes to find pathways associated with the genes, and plots pathways based on significance. Implements methods for reading GWAS input data, finding genes associated with SNPs, calculating enrichment score and significance of pathways, and plotting pathways. biocViews: Pathways, GeneSetEnrichment Author: Thrash Adam [cre, aut], DeOrnellis Mason [aut] Maintainer: Thrash Adam URL: https://github.com/IGBB/past VignetteBuilder: knitr BugReports: https://github.com/IGBB/past/issues git_url: https://git.bioconductor.org/packages/PAST git_branch: RELEASE_3_12 git_last_commit: eefb1b4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PAST_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PAST_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PAST_1.6.0.tgz vignettes: vignettes/PAST/inst/doc/past.html vignetteTitles: PAST hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PAST/inst/doc/past.R dependencyCount: 83 Package: Path2PPI Version: 1.20.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 83e72434a6f6fe2a75a9b464666b6bf7 NeedsCompilation: no Title: Prediction of pathway-related protein-protein interaction networks Description: Package to predict protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI predicts PPI networks based on sets of proteins which can belong to a certain pathway from well-established model organisms. It helps to combine and transfer information of a certain pathway or biological process from several reference organisms to one target organism. Path2PPI only depends on the sequence similarity of the involved proteins. biocViews: NetworkInference, SystemsBiology, Network, Proteomics, Pathways Author: Oliver Philipp [aut, cre], Ina Koch [ctb] Maintainer: Oliver Philipp URL: http://www.bioinformatik.uni-frankfurt.de/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Path2PPI git_branch: RELEASE_3_12 git_last_commit: 57601d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Path2PPI_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Path2PPI_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Path2PPI_1.20.0.tgz vignettes: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html vignetteTitles: Path2PPI - A brief tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R dependencyCount: 11 Package: pathifier Version: 1.28.0 Imports: R.oo, princurve (>= 2.0.4) License: Artistic-1.0 MD5sum: 7ff3d5c43ac4cd9211d643c84e92b4ee NeedsCompilation: no Title: Quantify deregulation of pathways in cancer Description: Pathifier is an algorithm that infers pathway deregulation scores for each tumor sample on the basis of expression data. This score is determined, in a context-specific manner, for every particular dataset and type of cancer that is being investigated. The algorithm transforms gene-level information into pathway-level information, generating a compact and biologically relevant representation of each sample. biocViews: Network Author: Yotam Drier Maintainer: Assif Yitzhaky git_url: https://git.bioconductor.org/packages/pathifier git_branch: RELEASE_3_12 git_last_commit: 1582051 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pathifier_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pathifier_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pathifier_1.28.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathifier/inst/doc/Overview.R importsMe: lilikoi dependencyCount: 9 Package: PathNet Version: 1.30.0 Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: bd08fa9083378610c334d7de92c7617a NeedsCompilation: no Title: An R package for pathway analysis using topological information Description: PathNet uses topological information present in pathways and differential expression levels of genes (obtained from microarray experiment) to identify pathways that are 1) significantly enriched and 2) associated with each other in the context of differential expression. The algorithm is described in: PathNet: A tool for pathway analysis using topological information. Dutta B, Wallqvist A, and Reifman J. Source Code for Biology and Medicine 2012 Sep 24;7(1):10. biocViews: Pathways, DifferentialExpression, MultipleComparison, KEGG, NetworkEnrichment, Network Author: Bhaskar Dutta , Anders Wallqvist , and Jaques Reifman Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/PathNet git_branch: RELEASE_3_12 git_last_commit: 4638552 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PathNet_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PathNet_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PathNet_1.30.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathNet/inst/doc/PathNet.R dependencyCount: 0 Package: PathoStat Version: 1.16.0 Depends: R (>= 3.5) Imports: limma, corpcor,matrixStats, reshape2, scales, ggplot2, rentrez, DT, tidyr, plyr, dplyr, phyloseq, shiny, stats, methods, XML, graphics, utils, BiocStyle, edgeR, DESeq2, ComplexHeatmap, plotly, webshot, vegan, shinyjs, glmnet, gmodels, ROCR, RColorBrewer, knitr, devtools, ape Suggests: rmarkdown, testthat License: GPL (>= 2) MD5sum: 5c7b62d75a4bf88ce4e8cce08d2a362e NeedsCompilation: no Title: PathoStat Statistical Microbiome Analysis Package Description: The purpose of this package is to perform Statistical Microbiome Analysis on metagenomics results from sequencing data samples. In particular, it supports analyses on the PathoScope generated report files. PathoStat provides various functionalities including Relative Abundance charts, Diversity estimates and plots, tests of Differential Abundance, Time Series visualization, and Core OTU analysis. biocViews: Microbiome, Metagenomics, GraphAndNetwork, Microarray, PatternLogic, PrincipalComponent, Sequencing, Software, Visualization, RNASeq, ImmunoOncology Author: Solaiappan Manimaran , Matthew Bendall , Sandro Valenzuela Diaz , Eduardo Castro , Tyler Faits , Yue Zhao , Anthony Nicholas Federico , W. Evan Johnson Maintainer: Solaiappan Manimaran , Yue Zhao URL: https://github.com/mani2012/PathoStat VignetteBuilder: knitr BugReports: https://github.com/mani2012/PathoStat/issues git_url: https://git.bioconductor.org/packages/PathoStat git_branch: RELEASE_3_12 git_last_commit: d3ef0ce git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PathoStat_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PathoStat_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PathoStat_1.16.0.tgz vignettes: vignettes/PathoStat/inst/doc/PathoStat-vignette.html vignetteTitles: PathoStat intro hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PathoStat/inst/doc/PathoStat-vignette.R dependencyCount: 206 Package: pathRender Version: 1.58.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 63b09c6ab2b4df0ec86a5acbf490e555 NeedsCompilation: no Title: Render molecular pathways Description: build graphs from pathway databases, render them by Rgraphviz. biocViews: GraphAndNetwork, Pathways, Visualization Author: Li Long Maintainer: Vince Carey URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/pathRender git_branch: RELEASE_3_12 git_last_commit: 509b2c0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pathRender_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pathRender_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pathRender_1.58.0.tgz vignettes: vignettes/pathRender/inst/doc/pathRender.pdf, vignettes/pathRender/inst/doc/plotExG.pdf vignetteTitles: pathRender overview, pathway graphs colored by expression map hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathRender/inst/doc/pathRender.R, vignettes/pathRender/inst/doc/plotExG.R dependencyCount: 32 Package: pathVar Version: 1.20.0 Depends: R (>= 3.3.0), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table, stats, grDevices, graphics, utils License: LGPL (>= 2.0) MD5sum: 2dc3072ed0fefa8846bebd22a51d7eb2 NeedsCompilation: no Title: Methods to Find Pathways with Significantly Different Variability Description: This package contains the functions to find the pathways that have significantly different variability than a reference gene set. It also finds the categories from this pathway that are significant where each category is a cluster of genes. The genes are separated into clusters by their level of variability. biocViews: GeneticVariability, GeneSetEnrichment, Pathways Author: Laurence de Torrente, Samuel Zimmerman, Jessica Mar Maintainer: Samuel Zimmerman git_url: https://git.bioconductor.org/packages/pathVar git_branch: RELEASE_3_12 git_last_commit: 6ee4b13 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pathVar_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pathVar_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pathVar_1.20.0.tgz vignettes: vignettes/pathVar/inst/doc/pathVar.pdf vignetteTitles: Tutorial on How to Use the Functions in the \texttt{PathVar} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathVar/inst/doc/pathVar.R dependencyCount: 43 Package: pathview Version: 1.30.1 Depends: R (>= 2.10) Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, org.Hs.eg.db, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: d6b355bc95526839e7f748b1a34749af NeedsCompilation: no Title: a tool set for pathway based data integration and visualization Description: Pathview is a tool set for pathway based data integration and visualization. It maps and renders a wide variety of biological data on relevant pathway graphs. All users need is to supply their data and specify the target pathway. Pathview automatically downloads the pathway graph data, parses the data file, maps user data to the pathway, and render pathway graph with the mapped data. In addition, Pathview also seamlessly integrates with pathway and gene set (enrichment) analysis tools for large-scale and fully automated analysis. biocViews: Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing Author: Weijun Luo Maintainer: Weijun Luo URL: https://pathview.uncc.edu/ git_url: https://git.bioconductor.org/packages/pathview git_branch: RELEASE_3_12 git_last_commit: a6a3239 git_last_commit_date: 2020-12-09 Date/Publication: 2020-12-10 source.ver: src/contrib/pathview_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/pathview_1.30.1.zip mac.binary.ver: bin/macosx/contrib/4.0/pathview_1.30.1.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathview/inst/doc/pathview.R dependsOnMe: BioNetStat, EGSEA, RNASeqR, SBGNview importsMe: CompGO, debrowser, EnrichmentBrowser, GDCRNATools, TCGAbiolinksGUI, TCGAWorkflow, lilikoi suggestsMe: gage, MAGeCKFlute, TCGAbiolinks, gageData, CAGEWorkflow dependencyCount: 50 Package: pathwayPCA Version: 1.6.3 Depends: R (>= 3.1) Imports: lars, methods, parallel, stats, survival, utils Suggests: airway, circlize, grDevices, knitr, RCurl, reshape2, rmarkdown, SummarizedExperiment, survminer, testthat, tidyverse License: GPL-3 MD5sum: 4d5db2f740490570c0f98028ef9a99db NeedsCompilation: no Title: Integrative Pathway Analysis with Modern PCA Methodology and Gene Selection Description: pathwayPCA is an integrative analysis tool that implements the principal component analysis (PCA) based pathway analysis approaches described in Chen et al. (2008), Chen et al. (2010), and Chen (2011). pathwayPCA allows users to: (1) Test pathway association with binary, continuous, or survival phenotypes. (2) Extract relevant genes in the pathways using the SuperPCA and AES-PCA approaches. (3) Compute principal components (PCs) based on the selected genes. These estimated latent variables represent pathway activities for individual subjects, which can then be used to perform integrative pathway analysis, such as multi-omics analysis. (4) Extract relevant genes that drive pathway significance as well as data corresponding to these relevant genes for additional in-depth analysis. (5) Perform analyses with enhanced computational efficiency with parallel computing and enhanced data safety with S4-class data objects. (6) Analyze studies with complex experimental designs, with multiple covariates, and with interaction effects, e.g., testing whether pathway association with clinical phenotype is different between male and female subjects. Citations: Chen et al. (2008) ; Chen et al. (2010) ; and Chen (2011) . biocViews: CopyNumberVariation, DNAMethylation, GeneExpression, SNP, Transcription, GenePrediction, GeneSetEnrichment, GeneSignaling, GeneTarget, GenomeWideAssociation, GenomicVariation, CellBiology, Epigenetics, FunctionalGenomics, Genetics, Lipidomics, Metabolomics, Proteomics, SystemsBiology, Transcriptomics, Classification, DimensionReduction, FeatureExtraction, PrincipalComponent, Regression, Survival, MultipleComparison, Pathways Author: Gabriel Odom [aut, cre], James Ban [aut], Lizhong Liu [aut], Lily Wang [aut], Steven Chen [aut] Maintainer: Gabriel Odom URL: VignetteBuilder: knitr BugReports: https://github.com/gabrielodom/pathwayPCA/issues git_url: https://git.bioconductor.org/packages/pathwayPCA git_branch: RELEASE_3_12 git_last_commit: b913a06 git_last_commit_date: 2020-12-14 Date/Publication: 2020-12-14 source.ver: src/contrib/pathwayPCA_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/pathwayPCA_1.6.3.zip mac.binary.ver: bin/macosx/contrib/4.0/pathwayPCA_1.6.3.tgz vignettes: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.html, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.html, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.html, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.html, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.html, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.html vignetteTitles: Integrative Pathway Analysis with pathwayPCA, Suppl. 1. Quickstart Guide, Suppl. 2. Importing Data, Suppl. 3. Create Data Objects, Suppl. 4. Test Pathway Significance, Suppl. 5. Visualizing the Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pathwayPCA/inst/doc/Introduction_to_pathwayPCA.R, vignettes/pathwayPCA/inst/doc/Supplement1-Quickstart_Guide.R, vignettes/pathwayPCA/inst/doc/Supplement2-Importing_Data.R, vignettes/pathwayPCA/inst/doc/Supplement3-Create_Omics_Objects.R, vignettes/pathwayPCA/inst/doc/Supplement4-Methods_Walkthrough.R, vignettes/pathwayPCA/inst/doc/Supplement5-Analyse_Results.R importsMe: fcoex dependencyCount: 12 Package: paxtoolsr Version: 1.24.0 Depends: R (>= 3.2), rJava (>= 0.9-8), methods, XML Imports: utils, httr, igraph, plyr, rjson, R.utils, jsonlite, readr Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, foreach, doSNOW, parallel, org.Hs.eg.db, clusterProfiler License: LGPL-3 MD5sum: 21b7549d5b9f6f774843f3b11a7cd5a9 NeedsCompilation: no Title: PaxtoolsR: Access Pathways from Multiple Databases through BioPAX and Pathway Commons Description: The package provides a set of R functions for interacting with BioPAX OWL files using Paxtools and the querying Pathway Commons (PC) molecular interaction database that are hosted by the Computational Biology Center at Memorial Sloan-Kettering Cancer Center (MSKCC). Pathway Commons databases include: BIND, BioGRID, CORUM, CTD, DIP, DrugBank, HPRD, HumanCyc, IntAct, KEGG, MirTarBase, Panther, PhosphoSitePlus, Reactome, RECON, TRANSFAC. biocViews: GeneSetEnrichment, GraphAndNetwork, Pathways, Software, SystemsBiology, NetworkEnrichment, Network, Reactome, KEGG Author: Augustin Luna Maintainer: Augustin Luna URL: https://github.com/BioPAX/paxtoolsr SystemRequirements: Java (>= 1.6) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/paxtoolsr git_branch: RELEASE_3_12 git_last_commit: 24770ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/paxtoolsr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/paxtoolsr_1.24.0.zip vignettes: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html vignetteTitles: Using PaxtoolsR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R suggestsMe: netboxr dependencyCount: 44 Package: pcaExplorer Version: 2.16.0 Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), heatmaply, plotly, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, shinyAce, threejs, biomaRt, pheatmap, knitr, rmarkdown, base64enc, tidyr, grDevices, methods Suggests: testthat, BiocStyle, airway, org.Hs.eg.db, htmltools License: MIT + file LICENSE MD5sum: 94bbae62ac9a0a6a15796fd427f3fde0 NeedsCompilation: no Title: Interactive Visualization of RNA-seq Data Using a Principal Components Approach Description: This package provides functionality for interactive visualization of RNA-seq datasets based on Principal Components Analysis. The methods provided allow for quick information extraction and effective data exploration. A Shiny application encapsulates the whole analysis. biocViews: ImmunoOncology, Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI, ReportWriting Author: Federico Marini [aut, cre] () Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer, https://federicomarini.github.io/pcaExplorer/ VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues git_url: https://git.bioconductor.org/packages/pcaExplorer git_branch: RELEASE_3_12 git_last_commit: d1867b5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pcaExplorer_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pcaExplorer_2.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pcaExplorer_2.16.0.tgz vignettes: vignettes/pcaExplorer/inst/doc/pcaExplorer.html, vignettes/pcaExplorer/inst/doc/upandrunning.html vignetteTitles: pcaExplorer User Guide, Up and running with pcaExplorer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R, vignettes/pcaExplorer/inst/doc/upandrunning.R importsMe: ideal dependencyCount: 182 Package: pcaMethods Version: 1.82.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) Archs: i386, x64 MD5sum: 4b1f1d41ca1b7a881c2de8d974d0f4e6 NeedsCompilation: yes Title: A collection of PCA methods Description: Provides Bayesian PCA, Probabilistic PCA, Nipals PCA, Inverse Non-Linear PCA and the conventional SVD PCA. A cluster based method for missing value estimation is included for comparison. BPCA, PPCA and NipalsPCA may be used to perform PCA on incomplete data as well as for accurate missing value estimation. A set of methods for printing and plotting the results is also provided. All PCA methods make use of the same data structure (pcaRes) to provide a common interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. biocViews: Bayesian Author: Wolfram Stacklies, Henning Redestig, Kevin Wright Maintainer: Henning Redestig URL: https://github.com/hredestig/pcamethods SystemRequirements: Rcpp BugReports: https://github.com/hredestig/pcamethods/issues git_url: https://git.bioconductor.org/packages/pcaMethods git_branch: RELEASE_3_12 git_last_commit: d500b33 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pcaMethods_1.82.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pcaMethods_1.82.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pcaMethods_1.82.0.tgz vignettes: vignettes/pcaMethods/inst/doc/missingValues.pdf, vignettes/pcaMethods/inst/doc/outliers.pdf, vignettes/pcaMethods/inst/doc/pcaMethods.pdf vignetteTitles: Missing value imputation, Data with outliers, Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaMethods/inst/doc/missingValues.R, vignettes/pcaMethods/inst/doc/outliers.R, vignettes/pcaMethods/inst/doc/pcaMethods.R dependsOnMe: DeconRNASeq, crmn, DiffCorr, imputeLCMD importsMe: CompGO, consensusDE, destiny, FRASER, MSnbase, MSPrep, OUTRIDER, PanVizGenerator, PhosR, pmp, scde, SomaticSignatures, ADAPTS, LOST, MetabolomicsBasics, missCompare, multiDimBio, polyRAD, RAMClustR, santaR, scMappR suggestsMe: MsCoreUtils, QFeatures, mtbls2, pagoda2 dependencyCount: 10 Package: PCAN Version: 1.18.0 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 060838d04dd487ea87e419e8644066b4 NeedsCompilation: no Title: Phenotype Consensus ANalysis (PCAN) Description: Phenotypes comparison based on a pathway consensus approach. Assess the relationship between candidate genes and a set of phenotypes based on additional genes related to the candidate (e.g. Pathways or network neighbors). biocViews: Annotation, Sequencing, Genetics, FunctionalPrediction, VariantAnnotation, Pathways, Network Author: Matthew Page and Patrice Godard Maintainer: Matthew Page and Patrice Godard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAN git_branch: RELEASE_3_12 git_last_commit: 05cdf32 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PCAN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PCAN_1.17.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PCAN_1.18.0.tgz vignettes: vignettes/PCAN/inst/doc/PCAN.html vignetteTitles: Assessing gene relevance for a set of phenotypes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R dependencyCount: 12 Package: PCAtools Version: 2.2.0 Depends: ggplot2, ggrepel Imports: lattice, grDevices, cowplot, methods, reshape2, stats, Matrix, DelayedMatrixStats, DelayedArray, BiocSingular, BiocParallel, Rcpp, dqrng LinkingTo: Rcpp, beachmat, BH, dqrng Suggests: testthat, scran, BiocGenerics, knitr, Biobase, GEOquery, hgu133a.db, ggplotify, beachmat, RMTstat, ggalt License: GPL-3 Archs: i386, x64 MD5sum: 1058dbffff57b393d770633184213320 NeedsCompilation: yes Title: PCAtools: Everything Principal Components Analysis Description: Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data. biocViews: RNASeq, GeneExpression, Transcription, SingleCell, PrincipalComponent Author: Kevin Blighe [aut, cre], Anna-Leigh Brown [ctb], Vincent Carey [ctb], Guido Hooiveld [ctb], Aaron Lun [aut, ctb] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/PCAtools SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PCAtools git_branch: RELEASE_3_12 git_last_commit: 5dfce3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PCAtools_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PCAtools_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PCAtools_2.2.0.tgz vignettes: vignettes/PCAtools/inst/doc/PCAtools.html vignetteTitles: PCAtools: everything Principal Component Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAtools/inst/doc/PCAtools.R suggestsMe: scDataviz dependencyCount: 73 Package: pcot2 Version: 1.58.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) MD5sum: ffaa53c62f2c3440a030cac45c5fecd0 NeedsCompilation: no Title: Principal Coordinates and Hotelling's T-Square method Description: PCOT2 is a permutation-based method for investigating changes in the activity of multi-gene networks. It utilizes inter-gene correlation information to detect significant alterations in gene network activities. Currently it can be applied to two-sample comparisons. biocViews: Microarray, DifferentialExpression, KEGG, GeneExpression, Network Author: Sarah Song, Mik Black Maintainer: Sarah Song git_url: https://git.bioconductor.org/packages/pcot2 git_branch: RELEASE_3_12 git_last_commit: e1f54b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pcot2_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pcot2_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pcot2_1.58.0.tgz vignettes: vignettes/pcot2/inst/doc/pcot2.pdf vignetteTitles: PCOT2 Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcot2/inst/doc/pcot2.R dependencyCount: 9 Package: PCpheno Version: 1.52.0 Depends: R (>= 2.10), Category, ScISI (>= 1.3.0), SLGI, ppiStats, ppiData, annotate (>= 1.17.4) Imports: AnnotationDbi, Biobase, Category, GO.db, graph, graphics, GSEABase, KEGG.db, methods, ScISI, stats, stats4 Suggests: KEGG.db, GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 844d3b5800809f0ed059126a0616da03 NeedsCompilation: no Title: Phenotypes and cellular organizational units Description: Tools to integrate, annotate, and link phenotypes to cellular organizational units such as protein complexes and pathways. biocViews: GraphAndNetwork, Proteomics, Network Author: Nolwenn Le Meur and Robert Gentleman Maintainer: Nolwenn Le Meur git_url: https://git.bioconductor.org/packages/PCpheno git_branch: RELEASE_3_12 git_last_commit: 3715f48 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PCpheno_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PCpheno_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PCpheno_1.52.0.tgz vignettes: vignettes/PCpheno/inst/doc/PCpheno.pdf vignetteTitles: PCpheno Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCpheno/inst/doc/PCpheno.R dependencyCount: 62 Package: pcxn Version: 2.12.0 Depends: R (>= 3.4), pcxnData Imports: methods, grDevices, utils, pheatmap Suggests: igraph, annotate, org.Hs.eg.db License: MIT + file LICENSE MD5sum: 45bc0578e9ae41cd0ab3d3eb51f16696 NeedsCompilation: no Title: Exploring, analyzing and visualizing functions utilizing the pcxnData package Description: Discover the correlated pathways/gene sets of a single pathway/gene set or discover correlation relationships among multiple pathways/gene sets. Draw a heatmap or create a network of your query and extract members of each pathway/gene set found in the available collections (MSigDB H hallmark, MSigDB C2 Canonical pathways, MSigDB C5 GO BP and Pathprint). biocViews: ExperimentData, ExpressionData, MicroarrayData, GEO, Homo_sapiens_Data, OneChannelData, PathwayInteractionDatabase Author: Sokratis Kariotis, Yered Pita-Juarez, Winston Hide, Wenbin Wei Maintainer: Sokratis Kariotis git_url: https://git.bioconductor.org/packages/pcxn git_branch: RELEASE_3_12 git_last_commit: aa92e0c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pcxn_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pcxn_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pcxn_2.12.0.tgz vignettes: vignettes/pcxn/inst/doc/using_pcxn.pdf vignetteTitles: pcxn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcxn/inst/doc/using_pcxn.R suggestsMe: pcxnData dependencyCount: 20 Package: pdInfoBuilder Version: 1.54.0 Depends: R (>= 3.2.0), methods, Biobase (>= 2.27.3), RSQLite (>= 1.0.0), affxparser (>= 1.39.4), oligo (>= 1.31.5) Imports: Biostrings (>= 2.35.12), BiocGenerics (>= 0.13.11), DBI (>= 0.3.1), IRanges (>= 2.1.43), oligoClasses (>= 1.29.6), S4Vectors (>= 0.5.22) License: Artistic-2.0 Archs: i386, x64 MD5sum: b387f9361bde790282eadf8eb0114fd3 NeedsCompilation: yes Title: Platform Design Information Package Builder Description: Builds platform design information packages. These consist of a SQLite database containing feature-level data such as x, y position on chip and featureSet ID. The database also incorporates featureSet-level annotation data. The products of this packages are used by the oligo pkg. biocViews: Annotation, Infrastructure Author: Seth Falcon, Vince Carey, Matt Settles, Kristof de Beuf, Benilton Carvalho Maintainer: Benilton Carvalho git_url: https://git.bioconductor.org/packages/pdInfoBuilder git_branch: RELEASE_3_12 git_last_commit: 7d43340 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pdInfoBuilder_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pdInfoBuilder_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pdInfoBuilder_1.54.0.tgz vignettes: vignettes/pdInfoBuilder/inst/doc/BuildingPDInfoPkgs.pdf, vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.pdf vignetteTitles: Building Annotation Packages with pdInfoBuilder for Use with the oligo Package, PDInfo Package Building Affymetrix Mapping Chips hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdInfoBuilder/inst/doc/howto-AffymetrixMapping.R suggestsMe: maqcExpression4plex, aroma.affymetrix, maGUI dependencyCount: 54 Package: PeacoQC Version: 0.99.25 Depends: R (>= 4.0) Imports: circlize, ComplexHeatmap, flowCore, flowWorkspace, ggplot2, grDevices, grid, gridExtra, methods, plyr, stats, utils Suggests: knitr, rmarkdown, BiocStyle License: GPL (>=3) MD5sum: 1797c2f230f261ddf208c618cc8835e1 NeedsCompilation: no Title: Peak-based selection of high quality cytometry data Description: This is a package that includes pre-processing and quality control functions that can remove margin events, compensate and transform the data and that will use PeacoQCSignalStability for quality control. This last function will first detect peaks in each channel of the flowframe. It will remove anomalies based on the IsolationTree function and the MAD outlier detection method. This package can be used for both flow- and mass cytometry data. biocViews: FlowCytometry, QualityControl, Preprocessing, PeakDetection Author: Annelies Emmaneel [aut, cre] Maintainer: Annelies Emmaneel URL: http://github.com/saeyslab/PeacoQC VignetteBuilder: knitr BugReports: http://github.com/saeyslab/PeacoQC/issues git_url: https://git.bioconductor.org/packages/PeacoQC git_branch: master git_last_commit: 739ae21 git_last_commit_date: 2020-05-28 Date/Publication: 2020-06-02 source.ver: src/contrib/PeacoQC_0.99.25.tar.gz win.binary.ver: bin/windows/contrib/4.0/PeacoQC_0.99.25.zip mac.binary.ver: bin/macosx/contrib/4.0/PeacoQC_0.99.25.tgz vignettes: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.html vignetteTitles: PeacoQC_Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PeacoQC/inst/doc/PeacoQC_Vignette.R dependencyCount: 91 Package: peakPantheR Version: 1.4.0 Depends: R (>= 4.0) Imports: foreach (>= 1.4.4), doParallel (>= 1.0.11), ggplot2 (>= 2.2.1), gridExtra (>= 2.3), MSnbase (>= 2.4.0), mzR (>= 2.12.0), stringr (>= 1.2.0), methods (>= 3.4.0), XML (>= 3.98.1.10), minpack.lm (>= 1.2.1), scales(>= 0.5.0), shiny (>= 1.0.5), shinythemes (>= 1.1.1), shinycssloaders (>= 1.0.0), DT (>= 0.15), utils Suggests: testthat, faahKO, msdata, knitr, rmarkdown, pander, BiocStyle License: GPL-3 MD5sum: 4e1f907b7017559e9c6238c24feeb61f NeedsCompilation: no Title: Peak Picking and Annotation of High Resolution Experiments Description: An automated pipeline for the detection, integration and reporting of predefined features across a large number of mass spectrometry data files. It enables the real time annotation of multiple compounds in a single file, or the parallel annotation of multiple compounds in multiple files. A graphical user interface as well as command line functions will assist in assessing the quality of annotation and update fitting parameters until a satisfactory result is obtained. biocViews: MassSpectrometry, Metabolomics, PeakDetection Author: Arnaud Wolfer [aut, cre] (), Goncalo Correia [aut] (), Jake Pearce [ctb], Caroline Sands [ctb] Maintainer: Arnaud Wolfer URL: https://github.com/phenomecentre/peakPantheR VignetteBuilder: knitr BugReports: https://github.com/phenomecentre/peakPantheR/issues/new git_url: https://git.bioconductor.org/packages/peakPantheR git_branch: RELEASE_3_12 git_last_commit: 1365db6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/peakPantheR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/peakPantheR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/peakPantheR_1.4.0.tgz vignettes: vignettes/peakPantheR/inst/doc/getting-started.html, vignettes/peakPantheR/inst/doc/parallel-annotation.html, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.html, vignettes/peakPantheR/inst/doc/real-time-annotation.html vignetteTitles: Getting Started with the peakPantheR package, Parallel Annotation, peakPantheR Graphical User Interface, Real Time Annotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peakPantheR/inst/doc/getting-started.R, vignettes/peakPantheR/inst/doc/parallel-annotation.R, vignettes/peakPantheR/inst/doc/peakPantheR-GUI.R, vignettes/peakPantheR/inst/doc/real-time-annotation.R dependencyCount: 104 Package: PECA Version: 1.26.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 741a2004cedcadb4ca90e883b1b87230 NeedsCompilation: no Title: Probe-level Expression Change Averaging Description: Calculates Probe-level Expression Change Averages (PECA) to identify differential expression in Affymetrix gene expression microarray studies or in proteomic studies using peptide-level mesurements respectively. biocViews: Software, Proteomics, Microarray, DifferentialExpression, GeneExpression, ExonArray, DifferentialSplicing Author: Tomi Suomi, Jukka Hiissa, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/PECA git_branch: RELEASE_3_12 git_last_commit: a39553d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PECA_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PECA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PECA_1.26.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PECA/inst/doc/PECA.R dependencyCount: 76 Package: peco Version: 1.2.0 Depends: R (>= 2.10) Imports: assertthat, circular, conicfit, doParallel, foreach, genlasso (>= 1.4), graphics, methods, parallel, scater, SingleCellExperiment, SummarizedExperiment, stats, utils Suggests: knitr, rmarkdown License: GPL (>= 3) MD5sum: ef27da7a7845f952455c37e62b39df6f NeedsCompilation: no Title: A Supervised Approach for **P**r**e**dicting **c**ell Cycle Pr**o**gression using scRNA-seq data Description: Our approach provides a way to assign continuous cell cycle phase using scRNA-seq data, and consequently, allows to identify cyclic trend of gene expression levels along the cell cycle. This package provides method and training data, which includes scRNA-seq data collected from 6 individual cell lines of induced pluripotent stem cells (iPSCs), and also continuous cell cycle phase derived from FUCCI fluorescence imaging data. biocViews: Sequencing, RNASeq, GeneExpression, Transcriptomics, SingleCell, Software, StatisticalMethod, Classification, Visualization Author: Chiaowen Joyce Hsiao [aut, cre], Matthew Stephens [aut], John Blischak [ctb], Peter Carbonetto [ctb] Maintainer: Chiaowen Joyce Hsiao URL: https://github.com/jhsiao999/peco VignetteBuilder: knitr BugReports: https://github.com/jhsiao999/peco/issues git_url: https://git.bioconductor.org/packages/peco git_branch: RELEASE_3_12 git_last_commit: c468fc5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/peco_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/peco_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/peco_1.2.0.tgz vignettes: vignettes/peco/inst/doc/vignette.html vignetteTitles: An example of predicting cell cycle phase using peco hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/peco/inst/doc/vignette.R dependencyCount: 97 Package: PepsNMR Version: 1.8.1 Depends: R (>= 3.6) Imports: Matrix, ptw, ggplot2, gridExtra, matrixStats, reshape2, methods, graphics, stats Suggests: knitr, markdown, rmarkdown, BiocStyle, PepsNMRData License: GPL-2 | file LICENSE MD5sum: 974226e295d0ec923b8134ec373f5ccc NeedsCompilation: no Title: Pre-process 1H-NMR FID signals Description: This package provides R functions for common pre-procssing steps that are applied on 1H-NMR data. It also provides a function to read the FID signals directly in the Bruker format. biocViews: Software, Preprocessing, Visualization, Metabolomics, DataImport Author: Manon Martin [aut, cre], Bernadette Govaerts [aut, ths], Benoît Legat [aut], Paul H.C. Eilers [aut], Pascal de Tullio [dtc], Bruno Boulanger [ctb], Julien Vanwinsberghe [ctb] Maintainer: Manon Martin URL: https://github.com/ManonMartin/PepsNMR VignetteBuilder: knitr BugReports: https://github.com/ManonMartin/PepsNMR/issues git_url: https://git.bioconductor.org/packages/PepsNMR git_branch: RELEASE_3_12 git_last_commit: 65af160 git_last_commit_date: 2021-01-14 Date/Publication: 2021-01-14 source.ver: src/contrib/PepsNMR_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/PepsNMR_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.0/PepsNMR_1.8.1.tgz vignettes: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.html vignetteTitles: Application of PepsNMR on the Human Serum dataset hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PepsNMR/inst/doc/PepsNMR_minimal_example.R importsMe: ASICS dependencyCount: 48 Package: pepStat Version: 1.24.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: eb941162e1dfe8e05a47fded3f82c80a NeedsCompilation: no Title: Statistical analysis of peptide microarrays Description: Statistical analysis of peptide microarrays biocViews: Microarray, Preprocessing Author: Raphael Gottardo, Gregory C Imholte, Renan Sauteraud, Mike Jiang Maintainer: Gregory C Imholte URL: https://github.com/RGLab/pepStat VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pepStat git_branch: RELEASE_3_12 git_last_commit: edd0354 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pepStat_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pepStat_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pepStat_1.24.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepStat/inst/doc/pepStat.R dependencyCount: 60 Package: pepXMLTab Version: 1.24.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 7cee487e6dbd890007b9d113ed481fac NeedsCompilation: no Title: Parsing pepXML files and filter based on peptide FDR. Description: Parsing pepXML files based one XML package. The package tries to handle pepXML files generated from different softwares. The output will be a peptide-spectrum-matching tabular file. The package also provide function to filter the PSMs based on FDR. biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/pepXMLTab git_branch: RELEASE_3_12 git_last_commit: 6980d38 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pepXMLTab_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pepXMLTab_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pepXMLTab_1.24.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pepXMLTab/inst/doc/pepXMLTab.R dependencyCount: 3 Package: PERFect Version: 1.4.0 Depends: R (>= 3.6.0), sn (>= 1.5.2) Imports: ggplot2 (>= 3.0.0), phyloseq (>= 1.28.0), zoo (>= 1.8.3), psych (>= 1.8.4), stats (>= 3.6.0), Matrix (>= 1.2.14), fitdistrplus (>= 1.0.12), parallel (>= 3.6.0) Suggests: knitr, rmarkdown, kableExtra, ggpubr License: Artistic-2.0 MD5sum: 38094fa93c2daefb8ba4e721b00286e1 NeedsCompilation: no Title: Permutation filtration for microbiome data Description: PERFect is a novel permutation filtering approach designed to address two unsolved problems in microbiome data processing: (i) define and quantify loss due to filtering by implementing thresholds, and (ii) introduce and evaluate a permutation test for filtering loss to provide a measure of excessive filtering. biocViews: Software, Microbiome, Sequencing, Classification, Metagenomics Author: Ekaterina Smirnova , Quy Cao Maintainer: Quy Cao URL: https://github.com/cxquy91/PERFect VignetteBuilder: knitr BugReports: https://github.com/cxquy91/PERFect/issues git_url: https://git.bioconductor.org/packages/PERFect git_branch: RELEASE_3_12 git_last_commit: 69a0a0a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PERFect_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PERFect_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PERFect_1.4.0.tgz vignettes: vignettes/PERFect/inst/doc/MethodIllustration.html vignetteTitles: Method Illustration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PERFect/inst/doc/MethodIllustration.R dependencyCount: 89 Package: periodicDNA Version: 1.0.0 Depends: R (>= 4.0), Biostrings, GenomicRanges, IRanges, BSgenome, BiocParallel Imports: S4Vectors, rtracklayer, stats, GenomeInfoDb, magrittr, zoo, ggplot2, methods, parallel, cowplot Suggests: BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Celegans.UCSC.ce11, BSgenome.Dmelanogaster.UCSC.dm6, BSgenome.Drerio.UCSC.danRer10, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, reticulate, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 + file LICENSE MD5sum: a6be4f0ff97d6c7ce4089f21f115923e NeedsCompilation: no Title: Set of tools to identify periodic occurrences of k-mers in DNA sequences Description: This R package helps the user identify k-mers (e.g. di- or tri-nucleotides) present periodically in a set of genomic loci (typically regulatory elements). The functions of this package provide a straightforward approach to find periodic occurrences of k-mers in DNA sequences, such as regulatory elements. It is not aimed at identifying motifs separated by a conserved distance; for this type of analysis, please visit MEME website. biocViews: SequenceMatching, MotifDiscovery, MotifAnnotation, Sequencing, Coverage, Alignment, DataImport Author: Jacques Serizay [aut, cre] () Maintainer: Jacques Serizay URL: https://github.com/js2264/periodicDNA VignetteBuilder: knitr BugReports: https://github.com/js2264/periodicDNA/issues git_url: https://git.bioconductor.org/packages/periodicDNA git_branch: RELEASE_3_12 git_last_commit: f4416e8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/periodicDNA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/periodicDNA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/periodicDNA_1.0.0.tgz vignettes: vignettes/periodicDNA/inst/doc/periodicDNA.html vignetteTitles: Introduction to periodicDNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/periodicDNA/inst/doc/periodicDNA.R dependencyCount: 72 Package: perturbatr Version: 1.10.0 Depends: R (>= 3.5), methods, stats Imports: dplyr, ggplot2, tidyr, assertthat, lme4, splines, igraph, foreach, parallel, doParallel, diffusr, lazyeval, tibble, grid, utils, graphics, scales, magrittr, formula.tools, rlang Suggests: testthat, lintr, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 18608b0086e71d143dbcea2927e3f776 NeedsCompilation: no Title: Statistical Analysis of High-Throughput Genetic Perturbation Screens Description: perturbatr does stage-wise analysis of large-scale genetic perturbation screens for integrated data sets consisting of multiple screens. For multiple integrated perturbation screens a hierarchical model that considers the variance between different biological conditions is fitted. The resulting list of gene effects is then further extended using a network propagation algorithm to correct for false negatives. biocViews: ImmunoOncology, Regression, CellBasedAssays, Network Author: Simon Dirmeier [aut, cre] Maintainer: Simon Dirmeier URL: https://github.com/cbg-ethz/perturbatr VignetteBuilder: knitr BugReports: https://github.com/cbg-ethz/perturbatr/issues git_url: https://git.bioconductor.org/packages/perturbatr git_branch: RELEASE_3_12 git_last_commit: 267b2b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/perturbatr_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/perturbatr_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/perturbatr_1.10.0.tgz vignettes: vignettes/perturbatr/inst/doc/perturbatr.html vignetteTitles: perturbatr cookbook hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/perturbatr/inst/doc/perturbatr.R dependencyCount: 63 Package: PGA Version: 1.20.0 Depends: R (>= 4.0.0), IRanges, GenomicRanges, Biostrings (>= 2.26.3), data.table, rTANDEM Imports: S4Vectors (>= 0.9.25), Rsamtools (>= 1.10.2), GenomicFeatures (>= 1.19.8), biomaRt (>= 2.17.1), stringr, RCurl, Nozzle.R1, VariantAnnotation (>= 1.7.28), rtracklayer, RSQLite, ggplot2, AnnotationDbi, customProDB (>= 1.21.5), pheatmap, dplyr, processx, readr, seqinr Suggests: RMariaDB, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocGenerics, BiocStyle, knitr, R.utils License: GPL-2 MD5sum: b382882ecf5eef6e174245f4c78bfcbe NeedsCompilation: no Title: An package for identification of novel peptides by customized database derived from RNA-Seq Description: This package provides functions for construction of customized protein databases based on RNA-Seq data with/without genome guided, database searching, post-processing and report generation. This kind of customized protein database includes both the reference database (such as Refseq or ENSEMBL) and the novel peptide sequences form RNA-Seq data. biocViews: Proteomics, ImmunoOncology, MassSpectrometry, Software, ReportWriting, RNASeq, Sequencing Author: Shaohang Xu, Bo Wen Maintainer: Bo Wen , Shaohang Xu VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/PGA git_branch: RELEASE_3_12 git_last_commit: 4e7418a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PGA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PGA_1.20.0.zip vignettes: vignettes/PGA/inst/doc/PGA.pdf vignetteTitles: PGA tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGA/inst/doc/PGA.R dependencyCount: 122 Package: pgca Version: 1.14.0 Imports: utils, stats Suggests: knitr, testthat License: GPL (>= 2) MD5sum: ab4b998305101347b187322e4d7d80d2 NeedsCompilation: no Title: PGCA: An Algorithm to Link Protein Groups Created from MS/MS Data Description: Protein Group Code Algorithm (PGCA) is a computationally inexpensive algorithm to merge protein summaries from multiple experimental quantitative proteomics data. The algorithm connects two or more groups with overlapping accession numbers. In some cases, pairwise groups are mutually exclusive but they may still be connected by another group (or set of groups) with overlapping accession numbers. Thus, groups created by PGCA from multiple experimental runs (i.e., global groups) are called "connected" groups. These identified global protein groups enable the analysis of quantitative data available for protein groups instead of unique protein identifiers. biocViews: WorkflowStep,AssayDomain,Proteomics,MassSpectrometry,ImmunoOncology Author: Gabriela Cohen-Freue Maintainer: Gabriela Cohen-Freue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pgca git_branch: RELEASE_3_12 git_last_commit: e95d2d4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pgca_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pgca_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pgca_1.14.0.tgz vignettes: vignettes/pgca/inst/doc/intro.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pgca/inst/doc/intro.R dependencyCount: 2 Package: phantasus Version: 1.10.0 Depends: R (>= 3.5) Imports: ggplot2, protolite, Biobase, GEOquery, Rook, htmltools, httpuv, jsonlite, limma, opencpu, assertthat, methods, httr, rhdf5, utils, parallel, stringr, fgsea (>= 1.9.4), svglite, gtable, stats, Matrix, pheatmap, scales, ccaPP, grid, grDevices, AnnotationDbi, DESeq2, curl Suggests: testthat, BiocStyle, knitr, rmarkdown, data.table License: MIT + file LICENSE MD5sum: b268e715c5b8945110589701f45fb77d NeedsCompilation: no Title: Visual and interactive gene expression analysis Description: Phantasus is a web-application for visual and interactive gene expression analysis. Phantasus is based on Morpheus – a web-based software for heatmap visualisation and analysis, which was integrated with an R environment via OpenCPU API. Aside from basic visualization and filtering methods, R-based methods such as k-means clustering, principal component analysis or differential expression analysis with limma package are supported. biocViews: GeneExpression, GUI, Visualization, DataRepresentation, Transcriptomics, RNASeq, Microarray, Normalization, Clustering, DifferentialExpression, PrincipalComponent, ImmunoOncology Author: Daria Zenkova [aut], Vladislav Kamenev [aut], Rita Sablina [ctb], Maxim Kleverov [ctb], Maxim Artyomov [aut], Alexey Sergushichev [aut, cre] Maintainer: Alexey Sergushichev URL: https://genome.ifmo.ru/phantasus, https://artyomovlab.wustl.edu/phantasus VignetteBuilder: knitr BugReports: https://github.com/ctlab/phantasus/issues git_url: https://git.bioconductor.org/packages/phantasus git_branch: RELEASE_3_12 git_last_commit: efcdf05 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/phantasus_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/phantasus_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/phantasus_1.10.0.tgz vignettes: vignettes/phantasus/inst/doc/phantasus-tutorial.html vignetteTitles: Using phantasus application hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/phantasus/inst/doc/phantasus-tutorial.R dependencyCount: 139 Package: PharmacoGx Version: 2.2.4 Depends: R (>= 3.6), CoreGx Imports: Biobase, S4Vectors, SummarizedExperiment, BiocParallel, ggplot2, magicaxis, RColorBrewer, parallel, caTools, methods, downloader, stats, utils, graphics, grDevices, reshape2, jsonlite, data.table Suggests: pander, rmarkdown, knitr, knitcitations, crayon, testthat, BiocGenerics License: Artistic-2.0 MD5sum: d17ee6d5628a4a5ea7a86e0a3776a35e NeedsCompilation: no Title: Analysis of Large-Scale Pharmacogenomic Data Description: Contains a set of functions to perform large-scale analysis of pharmaco-genomic data. These include the PharmacoSet object for storing the results of pharmacogenomic experiments, as well as a number of functions for computing common summaries of drug-dose response and correlating them with the molecular features in a cancer cell-line. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Petr Smirnov [aut], Zhaleh Safikhani [aut], Christopher Eeles [aut], Mark Freeman [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/PharmacoGx/issues git_url: https://git.bioconductor.org/packages/PharmacoGx git_branch: RELEASE_3_12 git_last_commit: 30c7992 git_last_commit_date: 2021-02-26 Date/Publication: 2021-02-26 source.ver: src/contrib/PharmacoGx_2.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/PharmacoGx_2.2.4.zip mac.binary.ver: bin/macosx/contrib/4.0/PharmacoGx_2.2.4.tgz vignettes: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.pdf, vignettes/PharmacoGx/inst/doc/PharmacoGx.pdf vignetteTitles: Creating a PharmacoSet Object, PharmacoGx: An R Package for Analysis of Large Pharmacogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PharmacoGx/inst/doc/CreatingPharmacoSet.R, vignettes/PharmacoGx/inst/doc/PharmacoGx.R importsMe: Xeva suggestsMe: ToxicoGx dependencyCount: 123 Package: phemd Version: 1.6.0 Depends: R (>= 3.5), monocle Imports: SingleCellExperiment, RColorBrewer, igraph, transport, pracma, cluster, Rtsne, destiny, Seurat, RANN, ggplot2, maptree, pheatmap, scatterplot3d, VGAM, methods, grDevices, graphics, stats, utils, cowplot, S4Vectors, BiocGenerics, SummarizedExperiment, Biobase, phateR, reticulate Suggests: knitr License: GPL-2 MD5sum: 7b9aa655ed6145dcabc8cfdae229b369 NeedsCompilation: no Title: Phenotypic EMD for comparison of single-cell samples Description: Package for comparing and generating a low-dimensional embedding of multiple single-cell samples. biocViews: Clustering, ComparativeGenomics, Proteomics, Transcriptomics, Sequencing, DimensionReduction, SingleCell, DataRepresentation, Visualization, MultipleComparison Author: William S Chen Maintainer: William S Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phemd git_branch: RELEASE_3_12 git_last_commit: cc357fc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/phemd_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/phemd_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/phemd_1.6.0.tgz vignettes: vignettes/phemd/inst/doc/phemd.html vignetteTitles: PhEMD vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phemd/inst/doc/phemd.R dependencyCount: 235 Package: phenopath Version: 1.14.0 Imports: Rcpp (>= 0.12.8), SummarizedExperiment, methods, stats, dplyr, tibble, ggplot2, tidyr LinkingTo: Rcpp Suggests: knitr, rmarkdown, forcats, testthat, BiocStyle, SingleCellExperiment License: Apache License (== 2.0) Archs: i386, x64 MD5sum: f28c0b183c7b65952d410b6f39bda0ad NeedsCompilation: yes Title: Genomic trajectories with heterogeneous genetic and environmental backgrounds Description: PhenoPath infers genomic trajectories (pseudotimes) in the presence of heterogeneous genetic and environmental backgrounds and tests for interactions between them. biocViews: ImmunoOncology, RNASeq, GeneExpression, Bayesian, SingleCell, PrincipalComponent Author: Kieran Campbell Maintainer: Kieran Campbell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phenopath git_branch: RELEASE_3_12 git_last_commit: 1a99e25 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/phenopath_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/phenopath_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/phenopath_1.14.0.tgz vignettes: vignettes/phenopath/inst/doc/introduction_to_phenopath.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenopath/inst/doc/introduction_to_phenopath.R suggestsMe: splatter dependencyCount: 63 Package: phenoTest Version: 1.38.0 Depends: R (>= 2.12.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2, Hmisc Imports: survival, limma, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, hgu133a.db, ellipse Suggests: GSEABase, KEGG.db, GO.db Enhances: parallel, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, org.Dm.eg.db License: GPL (>=2) MD5sum: ed1a351558465f190372fb8e000f7cca NeedsCompilation: no Title: Tools to test association between gene expression and phenotype in a way that is efficient, structured, fast and scalable. We also provide tools to do GSEA (Gene set enrichment analysis) and copy number variation. Description: Tools to test correlation between gene expression and phenotype in a way that is efficient, structured, fast and scalable. GSEA is also provided. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Evarist Planet Maintainer: Evarist Planet git_url: https://git.bioconductor.org/packages/phenoTest git_branch: RELEASE_3_12 git_last_commit: 8d87466 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/phenoTest_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/phenoTest_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/phenoTest_1.38.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoTest/inst/doc/phenoTest.R importsMe: canceR dependencyCount: 132 Package: PhenStat Version: 2.26.0 Depends: R (>= 3.5.0) Imports: SmoothWin, methods, car, nlme, nortest, MASS, msgps, logistf, knitr, tools, pingr, ggplot2, reshape, corrplot, graph, lme4, graphics, grDevices, utils, stats Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: dbcdd74ac948507dd599a18785dd3af4 NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. biocViews: StatisticalMethod Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason, Hamed Haselimashhadi Maintainer: Hamed Haselimashhadi git_url: https://git.bioconductor.org/packages/PhenStat git_branch: RELEASE_3_12 git_last_commit: 65ed760 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PhenStat_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PhenStat_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PhenStat_2.26.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf vignetteTitles: PhenStat Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R dependencyCount: 114 Package: philr Version: 1.16.0 Imports: ape, phangorn, tidyr, ggplot2, ggtree Suggests: testthat, knitr, rmarkdown, BiocStyle, phyloseq, glmnet, dplyr License: GPL-3 MD5sum: de943446828a9935f5eeada443c3c3bb NeedsCompilation: no Title: Phylogenetic partitioning based ILR transform for metagenomics data Description: PhILR is short for Phylogenetic Isometric Log-Ratio Transform. This package provides functions for the analysis of compositional data (e.g., data representing proportions of different variables/parts). Specifically this package allows analysis of compositional data where the parts can be related through a phylogenetic tree (as is common in microbiota survey data) and makes available the Isometric Log Ratio transform built from the phylogenetic tree and utilizing a weighted reference measure. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Software Author: Justin Silverman Maintainer: Justin Silverman URL: https://github.com/jsilve24/philr VignetteBuilder: knitr BugReports: https://github.com/jsilve24/philr/issues git_url: https://git.bioconductor.org/packages/philr git_branch: RELEASE_3_12 git_last_commit: 686f4ac git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/philr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/philr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/philr_1.16.0.tgz vignettes: vignettes/philr/inst/doc/philr-intro.html vignetteTitles: Introduction to PhILR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/philr/inst/doc/philr-intro.R suggestsMe: balance dependencyCount: 61 Package: phosphonormalizer Version: 1.14.3 Depends: R (>= 4.0) Imports: plyr, stats, graphics, matrixStats, methods Suggests: knitr, rmarkdown, testthat Enhances: MSnbase License: GPL (>= 2) MD5sum: 40d8d3e2d938b4b16e469976a9f7f53b NeedsCompilation: no Title: Compensates for the bias introduced by median normalization in phosphoproteomics. This is done by taking enriched and non-enriched data and creating a normalization factor. Description: It uses the overlap between enriched and non-enriched datasets to compensate for the bias introduced in global phosphorylation after applying median normalization. biocViews: Software, StatisticalMethod, WorkflowStep, Normalization, Proteomics Author: Sohrab Saraei [aut, cre], Tomi Suomi [ctb], Otto Kauko [ctb], Laura Elo [ths] Maintainer: Sohrab Saraei VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/phosphonormalizer git_branch: RELEASE_3_12 git_last_commit: 4a2562b git_last_commit_date: 2020-12-08 Date/Publication: 2020-12-08 source.ver: src/contrib/phosphonormalizer_1.14.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/phosphonormalizer_1.14.3.zip mac.binary.ver: bin/macosx/contrib/4.0/phosphonormalizer_1.14.3.tgz vignettes: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.pdf, vignettes/phosphonormalizer/inst/doc/vignette.html vignetteTitles: phosphonormalizer: Phosphoproteomics Normalization, Pairwise normalization of phosphoproteomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phosphonormalizer/inst/doc/phosphonormalizer.R, vignettes/phosphonormalizer/inst/doc/vignette.R dependencyCount: 7 Package: PhosR Version: 1.0.0 Depends: R (>= 4.0.0) Imports: ruv, e1071, calibrate, dendextend, limma, pcaMethods, stats, RColorBrewer, circlize, dplyr, igraph, pheatmap, preprocessCore, tidyr, rlang, graphics, grDevices, utils Suggests: testthat, knitr, GGally, network, reshape2, ClueR, directPA, ggplot2, ggpubr License: GPL-3 + file LICENSE MD5sum: a730450acacb48e1e9975bc42f3b15bb NeedsCompilation: no Title: A set of methods and tools for comprehensive analysis of phosphoproteomics data Description: PhosR is a package for the comprenhensive analysis of phosphoproteomic data. There are two major components to PhosR: processing and downstream analysis. PhosR consists of various processing tools for phosphoproteomics data including filtering, imputation, normalisation, and functional analysis for inferring active kinases and signalling pathways. biocViews: Software, ResearchField, SystemsBiology Author: Pengyi Yang [aut], Taiyun Kim [aut, cre], Jieun Hani Kim [aut] Maintainer: Taiyun Kim VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PhosR git_branch: RELEASE_3_12 git_last_commit: 39a78ff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PhosR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PhosR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PhosR_1.0.0.tgz vignettes: vignettes/PhosR/inst/doc/PhosR.pdf vignetteTitles: An introduction to PhosR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhosR/inst/doc/PhosR.R dependencyCount: 65 Package: PhyloProfile Version: 1.4.11 Depends: R (>= 4.0.0) Imports: ape, bioDist, BiocStyle, Biostrings, colourpicker, data.table, DT, energy, ExperimentHub, ggplot2, gridExtra, pbapply, RColorBrewer, shiny, shinyBS, shinyjs, OmaDB, plyr, xml2, zoo Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 36aeadf79a2571110b12e11cc2696288 NeedsCompilation: no Title: PhyloProfile Description: PhyloProfile is a tool for exploring complex phylogenetic profiles. Phylogenetic profiles, presence/absence patterns of genes over a set of species, are commonly used to trace the functional and evolutionary history of genes across species and time. With PhyloProfile we can enrich regular phylogenetic profiles with further data like sequence/structure similarity, to make phylogenetic profiling more meaningful. Besides the interactive visualisation powered by R-Shiny, the package offers a set of further analysis features to gain insights like the gene age estimation or core gene identification. biocViews: Software, Visualization, DataRepresentation, MultipleComparison, FunctionalPrediction Author: Vinh Tran [aut, cre], Bastian Greshake Tzovaras [aut], Ingo Ebersberger [aut], Carla Mölbert [ctb] Maintainer: Vinh Tran URL: https://github.com/BIONF/PhyloProfile/ VignetteBuilder: knitr BugReports: https://github.com/BIONF/PhyloProfile/issues git_url: https://git.bioconductor.org/packages/PhyloProfile git_branch: RELEASE_3_12 git_last_commit: 5f97c5a git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-26 source.ver: src/contrib/PhyloProfile_1.4.11.tar.gz win.binary.ver: bin/windows/contrib/4.0/PhyloProfile_1.4.11.zip mac.binary.ver: bin/macosx/contrib/4.0/PhyloProfile_1.4.11.tgz vignettes: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.html vignetteTitles: PhyloProfile hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhyloProfile/inst/doc/PhyloProfile-vignette.R dependencyCount: 135 Package: phyloseq Version: 1.34.0 Depends: R (>= 3.3.0) Imports: ade4 (>= 1.7.4), ape (>= 5.0), Biobase (>= 2.36.2), BiocGenerics (>= 0.22.0), biomformat (>= 1.0.0), Biostrings (>= 2.40.0), cluster (>= 2.0.4), data.table (>= 1.10.4), foreach (>= 1.4.3), ggplot2 (>= 2.1.0), igraph (>= 1.0.1), methods (>= 3.3.0), multtest (>= 2.28.0), plyr (>= 1.8.3), reshape2 (>= 1.4.1), scales (>= 0.4.0), vegan (>= 2.5) Suggests: BiocStyle (>= 2.4), DESeq2 (>= 1.16.1), genefilter (>= 1.58), knitr (>= 1.16), magrittr (>= 1.5), metagenomeSeq (>= 1.14), rmarkdown (>= 1.6), testthat (>= 1.0.2) Enhances: doParallel (>= 1.0.10) License: AGPL-3 MD5sum: d037d4bee296637596dcc21a375dabc4 NeedsCompilation: no Title: Handling and analysis of high-throughput microbiome census data Description: phyloseq provides a set of classes and tools to facilitate the import, storage, analysis, and graphical display of microbiome census data. biocViews: ImmunoOncology, Sequencing, Microbiome, Metagenomics, Clustering, Classification, MultipleComparison, GeneticVariability Author: Paul J. McMurdie , Susan Holmes , with contributions from Gregory Jordan and Scott Chamberlain Maintainer: Paul J. McMurdie URL: http://dx.plos.org/10.1371/journal.pone.0061217 VignetteBuilder: knitr BugReports: https://github.com/joey711/phyloseq/issues git_url: https://git.bioconductor.org/packages/phyloseq git_branch: RELEASE_3_12 git_last_commit: cbed93e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/phyloseq_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/phyloseq_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/phyloseq_1.34.0.tgz vignettes: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-FAQ.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html vignetteTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phyloseq/inst/doc/phyloseq-analysis.R, vignettes/phyloseq/inst/doc/phyloseq-basics.R, vignettes/phyloseq/inst/doc/phyloseq-FAQ.R, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.R dependsOnMe: microbiome, SIAMCAT, phyloseqGraphTest importsMe: ANCOMBC, combi, metavizr, microbiomeDASim, MicrobiotaProcess, PathoStat, PERFect, RCM, reconsi, RPA, SPsimSeq, HMP2Data, adaptiveGPCA, corncob, HTSSIP, microbial, mixKernel, SigTree, treeDA suggestsMe: decontam, metagenomeFeatures, MMUPHin, philr, curatedMetagenomicData, HMP16SData, metacoder, microeco, PLNmodels dependencyCount: 75 Package: Pi Version: 2.2.1 Depends: igraph, dnet, ggplot2, graphics Imports: Matrix, ggbio, GenomicRanges, GenomeInfoDb, supraHex, scales, grDevices, ggrepel, ROCR, randomForest, glmnet, Gviz, lattice, caret, plot3D, stats, methods, MASS, IRanges, BiocGenerics, dplyr, tidyr, ggnetwork, osfr, RCircos, purrr, readr, tibble Suggests: foreach, doParallel, BiocStyle, knitr, rmarkdown, png, GGally, gridExtra, ggforce, fgsea, RColorBrewer, ggpubr, rtracklayer License: GPL-3 MD5sum: 8c5fe5b12963ba6866428e8fe179996d NeedsCompilation: no Title: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level Description: Priority index or Pi is developed as a genomic-led target prioritisation system. It integrates functional genomic predictors, knowledge of network connectivity and immune ontologies to prioritise potential drug targets at the gene and pathway level. biocViews: Software, Genetics, GraphAndNetwork, Pathways, GeneExpression, GeneTarget, GenomeWideAssociation, LinkageDisequilibrium, Network, HiC Author: Hai Fang, the ULTRA-DD Consortium, Julian C Knight Maintainer: Hai Fang URL: http://pi314.r-forge.r-project.org VignetteBuilder: knitr BugReports: https://github.com/hfang-bristol/Pi/issues git_url: https://git.bioconductor.org/packages/Pi git_branch: RELEASE_3_12 git_last_commit: 1d30730 git_last_commit_date: 2020-10-27 Date/Publication: 2020-11-24 source.ver: src/contrib/Pi_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/Pi_2.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/Pi_2.2.1.tgz vignettes: vignettes/Pi/inst/doc/Pi_vignettes.html vignetteTitles: Pi User Manual (R/Bioconductor package) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pi/inst/doc/Pi_vignettes.R dependencyCount: 199 Package: piano Version: 2.6.0 Depends: R (>= 3.5) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray, fgsea, shiny, DT, htmlwidgets, shinyjs, shinydashboard, visNetwork, scales, grDevices, graphics, stats, utils, methods Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi, knitr, rmarkdown, BiocStyle License: GPL (>=2) MD5sum: 2afe6447e92a40849b561d442019d31f NeedsCompilation: no Title: Platform for integrative analysis of omics data Description: Piano performs gene set analysis using various statistical methods, from different gene level statistics and a wide range of gene-set collections. Furthermore, the Piano package contains functions for combining the results of multiple runs of gene set analyses. biocViews: Microarray, Preprocessing, QualityControl, DifferentialExpression, Visualization, GeneExpression, GeneSetEnrichment, Pathways Author: Leif Varemo Wigge and Intawat Nookaew Maintainer: Leif Varemo Wigge URL: http://www.sysbio.se/piano VignetteBuilder: knitr BugReports: https://github.com/varemo/piano/issues git_url: https://git.bioconductor.org/packages/piano git_branch: RELEASE_3_12 git_last_commit: 09ab305 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/piano_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/piano_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/piano_2.6.0.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.html vignetteTitles: Piano - Platform for Integrative Analysis of Omics data, Running gene-set anaysis with piano hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R, vignettes/piano/inst/doc/Running_gene-set_analysis_with_piano.R importsMe: CoreGx suggestsMe: BloodCancerMultiOmics2017 dependencyCount: 92 Package: pickgene Version: 1.62.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: b503af602af8a13dc46869eb9dd9d166 NeedsCompilation: no Title: Adaptive Gene Picking for Microarray Expression Data Analysis Description: Functions to Analyze Microarray (Gene Expression) Data. biocViews: Microarray, DifferentialExpression Author: Brian S. Yandell Maintainer: Brian S. Yandell URL: http://www.stat.wisc.edu/~yandell/statgen git_url: https://git.bioconductor.org/packages/pickgene git_branch: RELEASE_3_12 git_last_commit: 0ab8f1f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pickgene_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pickgene_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pickgene_1.62.0.tgz vignettes: vignettes/pickgene/inst/doc/pickgene.pdf vignetteTitles: Adaptive Gene Picking for Microarray Expression Data Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: PICS Version: 2.34.0 Depends: R (>= 3.0.0) Imports: utils, stats, graphics, grDevices, methods, IRanges, GenomicRanges, Rsamtools, GenomicAlignments Suggests: rtracklayer, parallel, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: ef140114e4a6713f55d9c8ccf2dd3bfa NeedsCompilation: yes Title: Probabilistic inference of ChIP-seq Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, Visualization, Sequencing, ChIPseq Author: Xuekui Zhang , Raphael Gottardo Maintainer: Renan Sauteraud URL: https://github.com/SRenan/PICS VignetteBuilder: knitr BugReports: https://github.com/SRenan/PICS/issues git_url: https://git.bioconductor.org/packages/PICS git_branch: RELEASE_3_12 git_last_commit: b7ee719 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PICS_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PICS_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PICS_2.34.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.html vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING dependencyCount: 38 Package: Pigengene Version: 1.16.0 Depends: R (>= 3.5.0), graph Imports: bnlearn (>= 4.4.1), C50 (>= 0.1.2), MASS, matrixStats, partykit, Rgraphviz, WGCNA, GO.db, impute, preprocessCore, grDevices, graphics, stats, utils, parallel, pheatmap (>= 1.0.8), dplyr, gdata Suggests: org.Hs.eg.db (>= 3.7.0), org.Mm.eg.db (>= 3.7.0), biomaRt (>= 2.30.0), knitr, BiocStyle, AnnotationDbi, energy License: GPL (>=2) MD5sum: f112f7ef4cabb8d32282f1172ea75fb6 NeedsCompilation: no Title: Infers biological signatures from gene expression data Description: Pigengene package provides an efficient way to infer biological signatures from gene expression profiles. The signatures are independent from the underlying platform, e.g., the input can be microarray or RNA Seq data. It can even infer the signatures using data from one platform, and evaluate them on the other. Pigengene identifies the modules (clusters) of highly coexpressed genes using coexpression network analysis, summarizes the biological information of each module in an eigengene, learns a Bayesian network that models the probabilistic dependencies between modules, and builds a decision tree based on the expression of eigengenes. biocViews: GeneExpression, RNASeq, NetworkInference, Network, GraphAndNetwork, BiomedicalInformatics, SystemsBiology, Transcriptomics, Classification, Clustering, DecisionTree, DimensionReduction, PrincipalComponent, Microarray, Normalization, ImmunoOncology Author: Habil Zare, Amir Foroushani, Rupesh Agrahari, and Meghan Short Maintainer: Habil Zare VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pigengene git_branch: RELEASE_3_12 git_last_commit: 39ec5b3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Pigengene_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Pigengene_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Pigengene_1.16.0.tgz vignettes: vignettes/Pigengene/inst/doc/Pigengene_inference.pdf vignetteTitles: Pigengene: Computing and using eigengenes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pigengene/inst/doc/Pigengene_inference.R dependencyCount: 115 Package: PING Version: 2.34.0 Depends: R(>= 3.5.0) Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, GenomicRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: bff7dda36bf5f5dec92af0f087b39449 NeedsCompilation: yes Title: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data Description: Probabilistic inference of ChIP-Seq using an empirical Bayes mixture model approach. biocViews: Clustering, StatisticalMethod, Visualization, Sequencing Author: Xuekui Zhang , Raphael Gottardo , Sangsoon Woo Maintainer: Renan Sauteraud git_url: https://git.bioconductor.org/packages/PING git_branch: RELEASE_3_12 git_last_commit: e862317 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PING_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PING_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PING_2.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 156 Package: pipeComp Version: 1.0.0 Depends: R (>= 4.0) Imports: BiocParallel, S4Vectors, ComplexHeatmap, SingleCellExperiment, SummarizedExperiment, Seurat, matrixStats, Matrix, cluster, aricode, methods, utils, dplyr, grid, scales, scran, viridisLite, clue, randomcoloR, ggplot2, cowplot, intrinsicDimension, scater, knitr, reshape2, stats, Rtsne, uwot, circlize, RColorBrewer Suggests: BiocStyle License: GPL MD5sum: 76810003cd06f803a435c788e185f1c3 NeedsCompilation: no Title: pipeComp pipeline benchmarking framework Description: A simple framework to facilitate the comparison of pipelines involving various steps and parameters. The `pipelineDefinition` class represents pipelines as, minimally, a set of functions consecutively executed on the output of the previous one, and optionally accompanied by step-wise evaluation and aggregation functions. Given such an object, a set of alternative parameters/methods, and benchmark datasets, the `runPipeline` function then proceeds through all combinations arguments, avoiding recomputing the same step twice and compiling evaluations on the fly to avoid storing potentially large intermediate data. biocViews: GeneExpression, Transcriptomics, Clustering, DataRepresentation Author: Pierre-Luc Germain [cre, aut] (), Anthony Sonrel [aut] (), Mark D. Robinson [aut, fnd] () Maintainer: Pierre-Luc Germain URL: https://doi.org/10.1186/s13059-020-02136-7 VignetteBuilder: knitr BugReports: https://github.com/plger/pipeComp git_url: https://git.bioconductor.org/packages/pipeComp git_branch: RELEASE_3_12 git_last_commit: 6539449 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pipeComp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pipeComp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pipeComp_1.0.0.tgz vignettes: vignettes/pipeComp/inst/doc/pipeComp_dea.html, vignettes/pipeComp/inst/doc/pipeComp_scRNA.html, vignettes/pipeComp/inst/doc/pipeComp.html vignetteTitles: pipeComp_dea, pipeComp_scRNA, pipeComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pipeComp/inst/doc/pipeComp_dea.R, vignettes/pipeComp/inst/doc/pipeComp_scRNA.R, vignettes/pipeComp/inst/doc/pipeComp.R dependencyCount: 200 Package: pipeFrame Version: 1.6.0 Depends: R (>= 3.6.1), Imports: BSgenome, digest, visNetwork, magrittr, methods, Biostrings, GenomeInfoDb, parallel, stats, utils Suggests: BiocManager, knitr, rtracklayer, testthat License: GPL-3 MD5sum: a07bdf6ffe82214800050f4ffc3f0e48 NeedsCompilation: no Title: Pipeline framework for bioinformatics in R Description: pipeFrame is an R package for building a componentized bioinformatics pipeline. Each step in this pipeline is wrapped in the framework, so the connection among steps is created seamlessly and automatically. Users could focus more on fine-tuning arguments rather than spending a lot of time on transforming file format, passing task outputs to task inputs or installing the dependencies. Componentized step elements can be customized into other new pipelines flexibly as well. This pipeline can be split into several important functional steps, so it is much easier for users to understand the complex arguments from each step rather than parameter combination from the whole pipeline. At the same time, componentized pipeline can restart at the breakpoint and avoid rerunning the whole pipeline, which may save a lot of time for users on pipeline tuning or such issues as power off or process other interrupts. biocViews: Software, Infrastructure, WorkflowStep Author: Zheng Wei, Shining Ma Maintainer: Zheng Wei URL: https://github.com/wzthu/pipeFrame VignetteBuilder: knitr BugReports: https://github.com/wzthu/pipeFrame/issues git_url: https://git.bioconductor.org/packages/pipeFrame git_branch: RELEASE_3_12 git_last_commit: 952b4e0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pipeFrame_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pipeFrame_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pipeFrame_1.6.0.tgz vignettes: vignettes/pipeFrame/inst/doc/pipeFrame.html vignetteTitles: An Introduction to pipeFrame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pipeFrame/inst/doc/pipeFrame.R dependsOnMe: enrichTF, esATAC dependencyCount: 50 Package: pkgDepTools Version: 1.56.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocManager License: GPL-2 MD5sum: fedb69866c986cf60e211b0947e3a8b4 NeedsCompilation: no Title: Package Dependency Tools Description: This package provides tools for computing and analyzing dependency relationships among R packages. It provides tools for building a graph-based representation of the dependencies among all packages in a list of CRAN-style package repositories. There are also utilities for computing installation order of a given package. If the RCurl package is available, an estimate of the download size required to install a given package and its dependencies can be obtained. biocViews: Infrastructure, GraphAndNetwork Author: Seth Falcon [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/pkgDepTools git_branch: RELEASE_3_12 git_last_commit: f2b0394 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pkgDepTools_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pkgDepTools_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pkgDepTools_1.56.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pkgDepTools/inst/doc/pkgDepTools.R dependencyCount: 10 Package: plethy Version: 1.28.0 Depends: R (>= 3.1.0), methods, DBI (>= 0.5-1), RSQLite (>= 1.1), BiocGenerics, S4Vectors Imports: Streamer, IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: 01ee5fb31e9d179ed971e44fb3382a88 NeedsCompilation: no Title: R framework for exploration and analysis of respirometry data Description: This package provides the infrastructure and tools to import, query and perform basic analysis of whole body plethysmography and metabolism data. Currently support is limited to data derived from Buxco respirometry instruments as exported by their FinePointe software. biocViews: DataImport, biocViews, Infastructure, DataRepresentation,TimeCourse Author: Daniel Bottomly [aut, cre], Marty Ferris [ctb], Beth Wilmot [aut], Shannon McWeeney [aut] Maintainer: Daniel Bottomly git_url: https://git.bioconductor.org/packages/plethy git_branch: RELEASE_3_12 git_last_commit: e1f0d1b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/plethy_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/plethy_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/plethy_1.28.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plethy/inst/doc/plethy.R dependencyCount: 63 Package: plgem Version: 1.62.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS, methods License: GPL-2 MD5sum: 52372299161e21a0224f98f244c40a5d NeedsCompilation: no Title: Detect differential expression in microarray and proteomics datasets with the Power Law Global Error Model (PLGEM) Description: The Power Law Global Error Model (PLGEM) has been shown to faithfully model the variance-versus-mean dependence that exists in a variety of genome-wide datasets, including microarray and proteomics data. The use of PLGEM has been shown to improve the detection of differentially expressed genes or proteins in these datasets. biocViews: ImmunoOncology, Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it git_url: https://git.bioconductor.org/packages/plgem git_branch: RELEASE_3_12 git_last_commit: 42d15c7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/plgem_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/plgem_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/plgem_1.62.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plgem/inst/doc/plgem.R importsMe: INSPEcT dependencyCount: 9 Package: plier Version: 1.60.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 6f80df698ad3430efd67d6ea77dd9771 NeedsCompilation: yes Title: Implements the Affymetrix PLIER algorithm Description: The PLIER (Probe Logarithmic Error Intensity Estimate) method produces an improved signal by accounting for experimentally observed patterns in probe behavior and handling error at the appropriately at low and high signal values. biocViews: Software Author: Affymetrix Inc., Crispin J Miller, PICR Maintainer: Crispin Miller git_url: https://git.bioconductor.org/packages/plier git_branch: RELEASE_3_12 git_last_commit: 38b29af git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/plier_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/plier_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/plier_1.60.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano dependencyCount: 13 Package: PloGO2 Version: 1.2.0 Depends: R (>= 4.0), GO.db, GOstats Imports: lattice, httr, openxlsx, xtable License: GPL-2 MD5sum: 5a182194621e43050395a306948cc37c NeedsCompilation: no Title: Plot Gene Ontology and KEGG pathway Annotation and Abundance Description: Functions for enrichment analysis and plotting gene ontology or KEGG pathway information for multiple data subsets at the same time. It also enables encorporating multiple conditions and abundance data. biocViews: Annotation, Clustering, GO, GeneSetEnrichment, KEGG, MultipleComparison, Pathways, Software, Visualization Author: Dana Pascovici, Jemma Wu Maintainer: Jemma Wu , Dana Pascovici git_url: https://git.bioconductor.org/packages/PloGO2 git_branch: RELEASE_3_12 git_last_commit: 4803e37 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PloGO2_1.2.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/PloGO2_1.2.0.tgz vignettes: vignettes/PloGO2/inst/doc/PloGO2_vignette.pdf, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PloGO2/inst/doc/PloGO2_vignette.R, vignettes/PloGO2/inst/doc/PloGO2_with_WGNCA_vignette.R dependencyCount: 59 Package: plotGrouper Version: 1.8.0 Depends: R (>= 3.5) Imports: ggplot2 (>= 3.0.0), dplyr (>= 0.7.6), tidyr (>= 0.2.0), tibble (>= 1.4.2), stringr (>= 1.3.1), readr (>= 1.1.1), readxl (>= 1.1.0), scales (>= 1.0.0), stats, grid, gridExtra (>= 2.3), egg (>= 0.4.0), gtable (>= 0.2.0), ggpubr (>= 0.1.8), shiny (>= 1.1.0), shinythemes (>= 1.1.1), colourpicker (>= 1.0), magrittr (>= 1.5), Hmisc (>= 4.1.1), rlang (>= 0.2.2) Suggests: knitr, htmltools, BiocStyle, rmarkdown, testthat License: GPL-3 MD5sum: 6f3d4a7a1aa2c2e1eee9479988f80987 NeedsCompilation: no Title: Shiny app GUI wrapper for ggplot with built-in statistical analysis Description: A shiny app-based GUI wrapper for ggplot with built-in statistical analysis. Import data from file and use dropdown menus and checkboxes to specify the plotting variables, graph type, and look of your plots. Once created, plots can be saved independently or stored in a report that can be saved as a pdf. If new data are added to the file, the report can be refreshed to include new data. Statistical tests can be selected and added to the graphs. Analysis of flow cytometry data is especially integrated with plotGrouper. Count data can be transformed to return the absolute number of cells in a sample (this feature requires inclusion of the number of beads per sample and information about any dilution performed). biocViews: ImmunoOncology, FlowCytometry, GraphAndNetwork, StatisticalMethod, DataImport, GUI, MultipleComparison Author: John D. Gagnon [aut, cre] Maintainer: John D. Gagnon URL: https://jdgagnon.github.io/plotGrouper/ VignetteBuilder: knitr BugReports: https://github.com/jdgagnon/plotGrouper/issues git_url: https://git.bioconductor.org/packages/plotGrouper git_branch: RELEASE_3_12 git_last_commit: 56ef563 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/plotGrouper_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/plotGrouper_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/plotGrouper_1.8.0.tgz vignettes: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.html vignetteTitles: plotGrouper hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plotGrouper/inst/doc/plotGrouper-vignette.R dependencyCount: 139 Package: PLPE Version: 1.50.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 8d0f90fcc6318e4bda30c799fbc83bff NeedsCompilation: no Title: Local Pooled Error Test for Differential Expression with Paired High-throughput Data Description: This package performs tests for paired high-throughput data. biocViews: Proteomics, Microarray, DifferentialExpression Author: HyungJun Cho and Jae K. Lee Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/PLPE git_branch: RELEASE_3_12 git_last_commit: be40491 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PLPE_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PLPE_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PLPE_1.50.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PLPE/inst/doc/PLPE.R dependencyCount: 10 Package: plyranges Version: 1.10.0 Depends: R (>= 3.5), BiocGenerics, IRanges (>= 2.12.0), GenomicRanges (>= 1.28.4) Imports: methods, dplyr, rlang (>= 0.2.0), magrittr, tidyselect (>= 1.0.0), rtracklayer, GenomicAlignments, GenomeInfoDb, Rsamtools, S4Vectors (>= 0.23.10), utils Suggests: knitr, BiocStyle, rmarkdown, testthat (>= 2.1.0), HelloRanges, HelloRangesData, BSgenome.Hsapiens.UCSC.hg19, pasillaBamSubset, covr, ggplot2 License: Artistic-2.0 MD5sum: dc817c1dc0ab5563f611e983e9d83a3b NeedsCompilation: no Title: A fluent interface for manipulating GenomicRanges Description: A dplyr-like interface for interacting with the common Bioconductor classes Ranges and GenomicRanges. By providing a grammatical and consistent way of manipulating these classes their accessiblity for new Bioconductor users is hopefully increased. biocViews: Infrastructure, DataRepresentation, WorkflowStep, Coverage Author: Stuart Lee [aut, cre] (), Michael Lawrence [aut, ctb], Dianne Cook [aut, ctb], Spencer Nystrom [ctb] () Maintainer: Stuart Lee VignetteBuilder: knitr BugReports: https://github.com/sa-lee/plyranges git_url: https://git.bioconductor.org/packages/plyranges git_branch: RELEASE_3_12 git_last_commit: 5856b8c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/plyranges_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/plyranges_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/plyranges_1.10.0.tgz vignettes: vignettes/plyranges/inst/doc/an-introduction.html vignetteTitles: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plyranges/inst/doc/an-introduction.R importsMe: BUSpaRse, dasper, methylCC, multicrispr, nearBynding, fluentGenomics suggestsMe: StructuralVariantAnnotation dependencyCount: 57 Package: pmm Version: 1.22.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: 0575e882a5fa0ed6fce40702bc6bb376 NeedsCompilation: no Title: Parallel Mixed Model Description: The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines. biocViews: SystemsBiology, Regression Author: Anna Drewek Maintainer: Anna Drewek git_url: https://git.bioconductor.org/packages/pmm git_branch: RELEASE_3_12 git_last_commit: 526ba35 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pmm_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pmm_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pmm_1.22.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmm/inst/doc/pmm-package.R dependencyCount: 19 Package: pmp Version: 1.2.1 Depends: R (>= 4.0) Imports: stats, impute, pcaMethods, missForest, ggplot2, methods, SummarizedExperiment, S4Vectors, matrixStats, grDevices, reshape2, utils Suggests: testthat, covr, knitr, rmarkdown, BiocStyle, gridExtra, magick License: GPL-3 MD5sum: 9702f486ce25f57a60e7fffa32846245 NeedsCompilation: no Title: Peak Matrix Processing and signal batch correction for metabolomics datasets Description: Methods and tools for (pre-)processing of metabolomics datasets (i.e. peak matrices), including filtering, normalisation, missing value imputation, scaling, and signal drift and batch effect correction methods. Filtering methods are based on: the fraction of missing values (across samples or features); Relative Standard Deviation (RSD) calculated from the Quality Control (QC) samples; the blank samples. Normalisation methods include Probabilistic Quotient Normalisation (PQN) and normalisation to total signal intensity. A unified user interface for several commonly used missing value imputation algorithms is also provided. Supported methods are: k-nearest neighbours (knn), random forests (rf), Bayesian PCA missing value estimator (bpca), mean or median value of the given feature and a constant small value. The generalised logarithm (glog) transformation algorithm is available to stabilise the variance across low and high intensity mass spectral features. Finally, this package provides an implementation of the Quality Control-Robust Spline Correction (QCRSC) algorithm for signal drift and batch effect correction of mass spectrometry-based datasets. biocViews: MassSpectrometry, Metabolomics, Software, QualityControl, BatchEffect Author: Andris Jankevics and Ralf Johannes Maria Weber Maintainer: Andris Jankevics VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pmp git_branch: RELEASE_3_12 git_last_commit: 7819432 git_last_commit_date: 2021-03-29 Date/Publication: 2021-03-31 source.ver: src/contrib/pmp_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/pmp_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/pmp_1.2.1.tgz vignettes: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.html, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.html, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.html vignetteTitles: Peak Matrix Processing for metabolomics datasets, Signal drift and batch effect correction and mass spectral quality assessment, Signal drift and batch effect correction for mass spectrometry hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pmp/inst/doc/pmp_vignette_peak_matrix_processing_for_metabolomics_datasets.R, vignettes/pmp/inst/doc/pmp_vignette_sbc_spectral_quality_assessment.R, vignettes/pmp/inst/doc/pmp_vignette_signal_batch_correction_mass_spectrometry.R suggestsMe: structToolbox dependencyCount: 69 Package: podkat Version: 1.22.0 Depends: R (>= 3.2.0), methods, Rsamtools (>= 1.99.1), GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rhtslib (>= 1.15.3) Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, SummarizedExperiment, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 687cc17fbf6fc4cb34416ec396974b0e NeedsCompilation: yes Title: Position-Dependent Kernel Association Test Description: This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results. biocViews: Genetics, WholeGenome, Annotation, VariantAnnotation, Sequencing, DataImport Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/podkat/ https://github.com/UBod/podkat SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/podkat git_branch: RELEASE_3_12 git_last_commit: 64f1344 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/podkat_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/podkat_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/podkat_1.22.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/podkat/inst/doc/podkat.R dependencyCount: 42 Package: pogos Version: 1.10.0 Depends: R (>= 3.5.0), rjson (>= 0.2.15), httr (>= 1.3.1) Imports: methods, S4Vectors, utils, shiny, ontoProc, ggplot2, graphics Suggests: knitr, DT, ontologyPlot, testthat License: Artistic-2.0 MD5sum: fce0b024498d256c0874287b2cbde297 NeedsCompilation: no Title: PharmacOGenomics Ontology Support Description: Provide simple utilities for querying bhklab PharmacoDB, modeling API outputs, and integrating to cell and compound ontologies. biocViews: Pharmacogenomics, PooledScreens, ImmunoOncology Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pogos git_branch: RELEASE_3_12 git_last_commit: 8940a25 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pogos_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pogos_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pogos_1.10.0.tgz vignettes: vignettes/pogos/inst/doc/pogos.html vignetteTitles: pogos -- simple interface to bhklab PharmacoDB with emphasis on ontology hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pogos/inst/doc/pogos.R suggestsMe: BiocOncoTK dependencyCount: 97 Package: polyester Version: 1.26.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma, zlibbioc Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 296c8fa55feab0f5621c999c9f89abef NeedsCompilation: no Title: Simulate RNA-seq reads Description: This package can be used to simulate RNA-seq reads from differential expression experiments with replicates. The reads can then be aligned and used to perform comparisons of methods for differential expression. biocViews: Sequencing, DifferentialExpression Author: Alyssa C. Frazee, Andrew E. Jaffe, Rory Kirchner, Jeffrey T. Leek Maintainer: Jack Fu , Jeff Leek VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/polyester git_branch: RELEASE_3_12 git_last_commit: a241ea8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/polyester_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/polyester_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/polyester_1.26.0.tgz vignettes: vignettes/polyester/inst/doc/polyester.html vignetteTitles: The Polyester package for simulating RNA-seq reads hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R dependencyCount: 17 Package: Polyfit Version: 1.24.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) MD5sum: cab4556bcb69f908265962e59f2962e9 NeedsCompilation: no Title: Add-on to DESeq to improve p-values and q-values Description: Polyfit is an add-on to the packages DESeq which ensures the p-value distribution is uniform over the interval [0, 1] for data satisfying the null hypothesis of no differential expression, and uses an adpated Storey-Tibshiran method to calculate q-values. biocViews: ImmunoOncology, DifferentialExpression, Sequencing, RNASeq, GeneExpression Author: Conrad Burden Maintainer: Conrad Burden git_url: https://git.bioconductor.org/packages/Polyfit git_branch: RELEASE_3_12 git_last_commit: 68c011d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Polyfit_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Polyfit_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Polyfit_1.24.0.tgz vignettes: vignettes/Polyfit/inst/doc/polyfit.pdf vignetteTitles: Polyfit hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Polyfit/inst/doc/polyfit.R dependencyCount: 1 Package: POMA Version: 1.0.0 Depends: R (>= 4.0) Imports: Biobase, broom, caret, clisymbols, ComplexHeatmap, crayon, dplyr, e1071, ggcorrplot, ggplot2, ggraph, ggrepel, glasso (>= 1.11), glmnet, impute, knitr, limma, magrittr, mixOmics, MSnbase (>= 2.12), patchwork, plotly, qpdf, randomForest, RankProd (>= 3.14), reshape2, rmarkdown, tibble, tidyr, vegan Suggests: BiocStyle, covr, tidyverse, testthat (>= 2.3.2) License: GPL-3 MD5sum: 6a8f6a71d54c45e5b003a40e19e6464c NeedsCompilation: no Title: User-friendly Workflow for Pre-processing and Statistical Analysis of Mass Spectrometry Data Description: POMA introduces a structured, reproducible and easy-to-use workflow for the visualization, pre-processing, exploratory and statistical analysis of mass spectrometry data. The main aim of POMA is to enable a flexible data cleaning and statistical analysis processes in one comprehensible and user-friendly R package. This package also has a Shiny app version that implements all POMA functions. See https://github.com/pcastellanoescuder/POMAShiny. biocViews: MassSpectrometry, Metabolomics, Proteomics, Software, Visualization, Preprocessing, Normalization, ReportWriting Author: Pol Castellano-Escuder [aut, cre] (), Raúl González-Domínguez [aut] (), Cristina Andrés-Lacueva [aut] (), Alex Sánchez-Pla [aut] () Maintainer: Pol Castellano-Escuder URL: https://github.com/pcastellanoescuder/POMA VignetteBuilder: knitr BugReports: https://github.com/pcastellanoescuder/POMA/issues git_url: https://git.bioconductor.org/packages/POMA git_branch: RELEASE_3_12 git_last_commit: 801ae7b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/POMA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/POMA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/POMA_1.0.0.tgz vignettes: vignettes/POMA/inst/doc/POMA-demo.html, vignettes/POMA/inst/doc/POMA-eda.html, vignettes/POMA/inst/doc/POMA-normalization.html vignetteTitles: POMA Workflow, POMA EDA Example, POMA Normalization Methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POMA/inst/doc/POMA-demo.R, vignettes/POMA/inst/doc/POMA-eda.R, vignettes/POMA/inst/doc/POMA-normalization.R dependencyCount: 169 Package: POST Version: 1.14.0 Depends: R (>= 3.4.0) Imports: stats, CompQuadForm, Matrix, survival, Biobase, GSEABase License: GPL (>= 2) MD5sum: c891a17ed3db3c380042e0e9f2478bbc NeedsCompilation: no Title: Projection onto Orthogonal Space Testing for High Dimensional Data Description: Perform orthogonal projection of high dimensional data of a set, and statistical modeling of phenotye with projected vectors as predictor. biocViews: Microarray, GeneExpression Author: Xueyuan Cao and Stanley.pounds Maintainer: Xueyuan Cao git_url: https://git.bioconductor.org/packages/POST git_branch: RELEASE_3_12 git_last_commit: b350210 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/POST_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/POST_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/POST_1.14.0.tgz vignettes: vignettes/POST/inst/doc/POST.pdf vignetteTitles: An introduction to POST hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/POST/inst/doc/POST.R dependencyCount: 47 Package: PoTRA Version: 1.6.0 Depends: R (>= 3.6.0), stats, BiocGenerics, org.Hs.eg.db, igraph, graph, graphite Suggests: BiocStyle, knitr, rmarkdown, colr, metap, repmis License: LGPL MD5sum: 48e2c1da5e46d1e9adc616df4430294d NeedsCompilation: no Title: PoTRA: Pathways of Topological Rank Analysis Description: The PoTRA analysis is based on topological ranks of genes in biological pathways. PoTRA can be used to detect pathways involved in disease (Li, Liu & Dinu, 2018). We use PageRank to measure the relative topological ranks of genes in each biological pathway, then select hub genes for each pathway, and use Fishers Exact test to determine if the number of hub genes in each pathway is altered from normal to cancer (Li, Liu & Dinu, 2018). Alternatively, if the distribution of topological ranks of gene in a pathway is altered between normal and cancer, this pathway might also be involved in cancer (Li, Liu & Dinu, 2018). Hence, we use the Kolmogorov–Smirnov test to detect pathways that have an altered distribution of topological ranks of genes between two phenotypes (Li, Liu & Dinu, 2018). PoTRA can be used with the KEGG, Biocarta, Reactome, NCI, SMPDB and PharmGKB databases from the devel graphite library. biocViews: GraphAndNetwork, StatisticalMethod, GeneExpression, DifferentialExpression, Pathways, Reactome, Network, KEGG, BioCarta Author: Chaoxing Li, Li Liu and Valentin Dinu Maintainer: Valentin Dinu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PoTRA git_branch: RELEASE_3_12 git_last_commit: 8260972 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PoTRA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PoTRA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PoTRA_1.6.0.tgz vignettes: vignettes/PoTRA/inst/doc/PoTRA.html vignetteTitles: Pathways of Topological Rank Analysis (PoTRA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PoTRA/inst/doc/PoTRA.R dependencyCount: 47 Package: powerTCR Version: 1.10.3 Imports: cubature, doParallel, evmix, foreach, magrittr, methods, parallel, purrr, stats, truncdist, vegan, VGAM Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 888f5c5492d4d7a8d948f789c4cef039 NeedsCompilation: no Title: Model-Based Comparative Analysis of the TCR Repertoire Description: This package provides a model for the clone size distribution of the TCR repertoire. Further, it permits comparative analysis of TCR repertoire libraries based on theoretical model fits. biocViews: Software, Clustering, BiomedicalInformatics Author: Hillary Koch Maintainer: Hillary Koch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/powerTCR git_branch: RELEASE_3_12 git_last_commit: fd1a8ea git_last_commit_date: 2021-03-15 Date/Publication: 2021-03-16 source.ver: src/contrib/powerTCR_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/powerTCR_1.10.3.zip mac.binary.ver: bin/macosx/contrib/4.0/powerTCR_1.10.3.tgz vignettes: vignettes/powerTCR/inst/doc/powerTCR.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/powerTCR/inst/doc/powerTCR.R importsMe: scRepertoire dependencyCount: 32 Package: PPInfer Version: 1.16.0 Depends: biomaRt, fgsea, kernlab, ggplot2, igraph, STRINGdb, yeastExpData License: Artistic-2.0 MD5sum: a4bcb2d223494ccf50926331ccc96d43 NeedsCompilation: no Title: Inferring functionally related proteins using protein interaction networks Description: Interactions between proteins occur in many, if not most, biological processes. Most proteins perform their functions in networks associated with other proteins and other biomolecules. This fact has motivated the development of a variety of experimental methods for the identification of protein interactions. This variety has in turn ushered in the development of numerous different computational approaches for modeling and predicting protein interactions. Sometimes an experiment is aimed at identifying proteins closely related to some interesting proteins. A network based statistical learning method is used to infer the putative functions of proteins from the known functions of its neighboring proteins on a PPI network. This package identifies such proteins often involved in the same or similar biological functions. biocViews: Software, StatisticalMethod, Network, GraphAndNetwork, GeneSetEnrichment, NetworkEnrichment, Pathways Author: Dongmin Jung, Xijin Ge Maintainer: Dongmin Jung git_url: https://git.bioconductor.org/packages/PPInfer git_branch: RELEASE_3_12 git_last_commit: 899c700 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PPInfer_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PPInfer_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PPInfer_1.16.0.tgz vignettes: vignettes/PPInfer/inst/doc/PPInfer.pdf vignetteTitles: User manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PPInfer/inst/doc/PPInfer.R dependsOnMe: gsean dependencyCount: 109 Package: ppiStats Version: 1.56.0 Depends: ScISI (>= 1.13.2), lattice, ppiData (>= 0.1.19) Imports: Biobase, Category, graph, graphics, grDevices, lattice, methods, RColorBrewer, stats Suggests: yeastExpData, xtable License: Artistic-2.0 MD5sum: 325ca31bccae5875162b0480a98465f3 NeedsCompilation: no Title: Protein-Protein Interaction Statistical Package Description: Tools for the analysis of protein interaction data. biocViews: Proteomics, GraphAndNetwork, Network, NetworkInference Author: T. Chiang and D. Scholtens with contributions from W. Huber and L. Wang Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/ppiStats git_branch: RELEASE_3_12 git_last_commit: 236d5a5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ppiStats_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ppiStats_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ppiStats_1.56.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ppiStats/inst/doc/ppiStats.R dependsOnMe: PCpheno suggestsMe: BiocCaseStudies, RpsiXML, ppiData dependencyCount: 59 Package: pqsfinder Version: 2.6.0 Depends: Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH (>= 1.69.0) Suggests: BiocStyle, knitr, Gviz, rtracklayer, ggplot2, BSgenome.Hsapiens.UCSC.hg38, testthat, stringr, stringi License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 76e2bb1bdf18ca2d549a06dee9d7642c NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: Pqsfinder detects DNA and RNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, pqsfinder is able to detect G4s folded from imperfect G-runs containing bulges or mismatches or G4s having long loops. Pqsfinder also assigns an integer score to each hit that was fitted on G4 sequencing data and corresponds to expected stability of the folded G4. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Dominika Labudova, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon URL: https://pqsfinder.fi.muni.cz SystemRequirements: GNU make, C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pqsfinder git_branch: RELEASE_3_12 git_last_commit: 144084f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pqsfinder_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pqsfinder_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pqsfinder_2.6.0.tgz vignettes: vignettes/pqsfinder/inst/doc/pqsfinder.html vignetteTitles: pqsfinder: User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R dependencyCount: 22 Package: pram Version: 1.6.0 Depends: R (>= 3.6) Imports: methods, BiocParallel, tools, utils, data.table (>= 1.11.8), GenomicAlignments (>= 1.16.0), rtracklayer (>= 1.40.6), BiocGenerics (>= 0.26.0), GenomeInfoDb (>= 1.16.0), GenomicRanges (>= 1.32.0), IRanges (>= 2.14.12), Rsamtools (>= 1.32.3), S4Vectors (>= 0.18.3) Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL (>= 3) MD5sum: d590ce8b8b9149428d725005ee3f54e8 NeedsCompilation: no Title: Pooling RNA-seq datasets for assembling transcript models Description: Publicly available RNA-seq data is routinely used for retrospective analysis to elucidate new biology. Novel transcript discovery enabled by large collections of RNA-seq datasets has emerged as one of such analysis. To increase the power of transcript discovery from large collections of RNA-seq datasets, we developed a new R package named Pooling RNA-seq and Assembling Models (PRAM), which builds transcript models in intergenic regions from pooled RNA-seq datasets. This package includes functions for defining intergenic regions, extracting and pooling related RNA-seq alignments, predicting, selected, and evaluating transcript models. biocViews: Software, Technology, Sequencing, RNASeq Author: Peng Liu [aut, cre], Colin N. Dewey [aut], Sündüz Keleş [aut] Maintainer: Peng Liu URL: https://github.com/pliu55/pram VignetteBuilder: knitr BugReports: https://github.com/pliu55/pram/issues git_url: https://git.bioconductor.org/packages/pram git_branch: RELEASE_3_12 git_last_commit: 2a91c54 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pram_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pram_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pram_1.6.0.tgz vignettes: vignettes/pram/inst/doc/pram.pdf vignetteTitles: Pooling RNA-seq and Assembling Models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pram/inst/doc/pram.R dependencyCount: 41 Package: prebs Version: 1.30.0 Depends: R (>= 2.14.0), GenomicAlignments, affy, RPA Imports: parallel, methods, stats, GenomicRanges (>= 1.13.3), IRanges, Biobase, GenomeInfoDb, S4Vectors Suggests: prebsdata, hgu133plus2cdf, hgu133plus2probe License: Artistic-2.0 MD5sum: 24e9907e1bd63e118d6d43d5ca76a6b5 NeedsCompilation: no Title: Probe region expression estimation for RNA-seq data for improved microarray comparability Description: The prebs package aims at making RNA-sequencing (RNA-seq) data more comparable to microarray data. The comparability is achieved by summarizing sequencing-based expressions of probe regions using a modified version of RMA algorithm. The pipeline takes mapped reads in BAM format as an input and produces either gene expressions or original microarray probe set expressions as an output. biocViews: ImmunoOncology, Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela git_url: https://git.bioconductor.org/packages/prebs git_branch: RELEASE_3_12 git_last_commit: 24d9c3a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/prebs_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/prebs_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/prebs_1.30.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prebs/inst/doc/prebs.R dependencyCount: 100 Package: preciseTAD Version: 1.0.0 Depends: R (>= 4.0.0) Imports: S4Vectors, IRanges, GenomicRanges, randomForest, ModelMetrics, e1071, PRROC, pROC, caret, DMwR, utils, cluster, dbscan, doSNOW, foreach, pbapply, stats, parallel Suggests: knitr, rmarkdown, testthat, BiocCheck, BiocManager, BiocStyle License: MIT + file LICENSE MD5sum: 7168293cd3aff86ed10280286fb754d0 NeedsCompilation: no Title: preciseTAD: A machine learning framework for precise TAD boundary prediction Description: preciseTAD provides functions to predict the location of boundaries of topologically associated domains (TADs) and chromatin loops at base-level resolution. As an input, it takes BED-formatted genomic coordinates of domain boundaries detected from low-resolution Hi-C data, and coordinates of high-resolution genomic annotations from ENCODE or other consortia. preciseTAD employs several feature engineering strategies and resampling techniques to address class imbalance, and trains an optimized random forest model for predicting low-resolution domain boundaries. Translated on a base-level, preciseTAD predicts the probability for each base to be a boundary. Density-based clustering and scalable partitioning techniques are used to detect precise boundary regions and summit points. Compared with low-resolution boundaries, preciseTAD boundaries are highly enriched for CTCF, RAD21, SMC3, and ZNF143 signal and more conserved across cell lines. The pre-trained model can accurately predict boundaries in another cell line using CTCF, RAD21, SMC3, and ZNF143 annotation data for this cell line. biocViews: Software, HiC, Sequencing, Clustering, Classification, FunctionalGenomics, FeatureExtraction Author: Spiro Stilianoudakis [aut, cre], Mikhail Dozmorov [aut] Maintainer: Spiro Stilianoudakis URL: https://github.com/dozmorovlab/preciseTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/preciseTAD/issues git_url: https://git.bioconductor.org/packages/preciseTAD git_branch: RELEASE_3_12 git_last_commit: dfde728 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/preciseTAD_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/preciseTAD_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/preciseTAD_1.0.0.tgz vignettes: vignettes/preciseTAD/inst/doc/preciseTAD.html vignetteTitles: preciseTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/preciseTAD/inst/doc/preciseTAD.R dependencyCount: 93 Package: PrecisionTrialDrawer Version: 1.6.0 Depends: R (>= 3.6) Imports: graphics, grDevices, stats, utils, methods, cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, BiocParallel, magrittr, biomaRt, XML, httr, jsonlite, ggplot2, ggrepel, grid, S4Vectors, IRanges, GenomicRanges, LowMACAAnnotation, googleVis, shiny, shinyBS, DT, brglm, matrixStats Suggests: BiocStyle, knitr, rmarkdown, dplyr License: GPL-3 MD5sum: 29168a38ba1d3737edfb4c8ce5b2de56 NeedsCompilation: no Title: A Tool to Analyze and Design NGS Based Custom Gene Panels Description: A suite of methods to design umbrella and basket trials for precision oncology. biocViews: SomaticMutation, WholeGenome, Sequencing, DataImport, GeneExpression Author: Giorgio Melloni, Alessandro Guida, Luca Mazzarella Maintainer: Giorgio Melloni VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PrecisionTrialDrawer git_branch: RELEASE_3_12 git_last_commit: 4d8e877 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PrecisionTrialDrawer_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PrecisionTrialDrawer_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PrecisionTrialDrawer_1.6.0.tgz vignettes: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.html vignetteTitles: Bioconductor style for HTML documents hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PrecisionTrialDrawer/inst/doc/PrecisionTrialDrawer.R dependencyCount: 124 Package: PREDA Version: 1.36.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 90ad6e955580635e4f1dfce8e9f3307d NeedsCompilation: no Title: Position Related Data Analysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari git_url: https://git.bioconductor.org/packages/PREDA git_branch: RELEASE_3_12 git_last_commit: bf51167 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PREDA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PREDA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PREDA_1.36.0.tgz vignettes: vignettes/PREDA/inst/doc/PREDAclasses.pdf, vignettes/PREDA/inst/doc/PREDAtutorial.pdf vignetteTitles: PREDA S4-classes, PREDA tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PREDA/inst/doc/PREDAtutorial.R dependsOnMe: PREDAsampledata dependencyCount: 48 Package: predictionet Version: 1.36.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: da3d89ef7adc87d65fe99f71654c5892 NeedsCompilation: yes Title: Inference for predictive networks designed for (but not limited to) genomic data Description: This package contains a set of functions related to network inference combining genomic data and prior information extracted from biomedical literature and structured biological databases. The main function is able to generate networks using Bayesian or regression-based inference methods; while the former is limited to < 100 of variables, the latter may infer networks with hundreds of variables. Several statistics at the edge and node levels have been implemented (edge stability, predictive ability of each node, ...) in order to help the user to focus on high quality subnetworks. Ultimately, this package is used in the 'Predictive Networks' web application developed by the Dana-Farber Cancer Institute in collaboration with Entagen. biocViews: GraphAndNetwork, NetworkInference Author: Benjamin Haibe-Kains, Catharina Olsen, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Catharina Olsen URL: http://compbio.dfci.harvard.edu, http://www.ulb.ac.be/di/mlg git_url: https://git.bioconductor.org/packages/predictionet git_branch: RELEASE_3_12 git_last_commit: 751da77 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/predictionet_1.36.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/predictionet_1.36.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/predictionet/inst/doc/predictionet.R dependencyCount: 24 Package: preprocessCore Version: 1.52.1 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: f4f1f26997a137423b2e2d1547acbf0b NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines. biocViews: Infrastructure Author: Ben Bolstad Maintainer: Ben Bolstad URL: https://github.com/bmbolstad/preprocessCore git_url: https://git.bioconductor.org/packages/preprocessCore git_branch: RELEASE_3_12 git_last_commit: 91de4ab git_last_commit_date: 2020-11-24 Date/Publication: 2021-01-08 source.ver: src/contrib/preprocessCore_1.52.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/preprocessCore_1.52.1.zip mac.binary.ver: bin/macosx/contrib/4.0/preprocessCore_1.52.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, cqn, crlmm, RefPlus, SCATE importsMe: affy, bnbc, cn.farms, EMDomics, ExiMiR, fastLiquidAssociation, frma, frmaTools, hipathia, iCheck, ImmuneSpaceR, InPAS, lumi, MADSEQ, MBCB, MBQN, MEDIPS, mimager, minfi, MSnbase, MSPrep, MSstats, NormalyzerDE, oligo, PECA, PhosR, Pigengene, proBatch, qPLEXanalyzer, sesame, soGGi, tidybulk, yarn, ADAPTS, cinaR, FARDEEP, HEMDAG, lilikoi, MetaIntegrator, MiDA, noise, noisyr, RAMClustR, SMDIC, WGCNA suggestsMe: MsCoreUtils, multiClust, QFeatures, splatter, aroma.affymetrix, aroma.core, glycanr, wrMisc, wrTopDownFrag linksToMe: affy, affyPLM, crlmm, oligo dependencyCount: 1 Package: primirTSS Version: 1.8.0 Depends: R (>= 3.5.0) Imports: GenomicRanges (>= 1.32.2), S4Vectors (>= 0.18.2), rtracklayer (>= 1.40.3), dplyr (>= 0.7.6), stringr (>= 1.3.1), tidyr (>= 0.8.1), Biostrings (>= 2.48.0), purrr (>= 0.2.5), BSgenome.Hsapiens.UCSC.hg38 (>= 1.4.1), phastCons100way.UCSC.hg38 (>= 3.7.1), GenomicScores (>= 1.4.1), shiny (>= 1.0.5), Gviz (>= 1.24.0), BiocGenerics (>= 0.26.0), IRanges (>= 2.14.10), TFBSTools (>= 1.18.0), JASPAR2018 (>= 1.1.1), tibble (>= 1.4.2), R.utils (>= 2.6.0), stats, utils Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 1083222b71170da3ffaa9ed5bb7a89e1 NeedsCompilation: no Title: Prediction of pri-miRNA Transcription Start Site Description: A fast, convenient tool to identify the TSSs of miRNAs by integrating the data of H3K4me3 and Pol II as well as combining the conservation level and sequence feature, provided within both command-line and graphical interfaces, which achieves a better performance than the previous non-cell-specific methods on miRNA TSSs. biocViews: ImmunoOncology, Sequencing, RNASeq, Genetics, Preprocessing, Transcription, GeneRegulation Author: Pumin Li [aut, cre], Qi Xu [aut], Jie Li [aut], Jin Wang [aut] Maintainer: Pumin Li URL: https://github.com/ipumin/primirTSS VignetteBuilder: knitr BugReports: http://github.com/ipumin/primirTSS/issues git_url: https://git.bioconductor.org/packages/primirTSS git_branch: RELEASE_3_12 git_last_commit: 47273c4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/primirTSS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/primirTSS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/primirTSS_1.8.0.tgz vignettes: vignettes/primirTSS/inst/doc/primirTSS.html vignetteTitles: primirTSS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/primirTSS/inst/doc/primirTSS.R dependencyCount: 184 Package: PrInCE Version: 1.6.0 Depends: R (>= 3.6.0) Imports: purrr (>= 0.2.4), dplyr (>= 0.7.4), tidyr (>= 0.8.99), forecast (>= 8.2), progress (>= 1.1.2), Hmisc (>= 4.0), naivebayes (>= 0.9.1), robustbase (>= 0.92-7), ranger (>= 0.8.0), LiblineaR (>= 2.10-8), speedglm (>= 0.3-2), tester (>= 0.1.7), magrittr (>= 1.5), Biobase (>= 2.40.0), MSnbase (>= 2.8.3), stats, utils, methods, Rdpack (>= 0.7) Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: 676799a757169c39b244dc0025e8243d NeedsCompilation: no Title: Predicting Interactomes from Co-Elution Description: PrInCE (Predicting Interactomes from Co-Elution) uses a naive Bayes classifier trained on dataset-derived features to recover protein-protein interactions from co-elution chromatogram profiles. This package contains the R implementation of PrInCE. biocViews: Proteomics, SystemsBiology, NetworkInference Author: Michael Skinnider [aut, trl, cre], R. Greg Stacey [ctb], Nichollas Scott [ctb], Anders Kristensen [ctb], Leonard Foster [aut, led] Maintainer: Michael Skinnider VignetteBuilder: knitr BugReports: https://github.com/fosterlab/PrInCE/issues git_url: https://git.bioconductor.org/packages/PrInCE git_branch: RELEASE_3_12 git_last_commit: 82a1b68 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PrInCE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PrInCE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PrInCE_1.6.0.tgz vignettes: vignettes/PrInCE/inst/doc/intro-to-prince.html vignetteTitles: Interactome reconstruction from co-elution data with PrInCE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PrInCE/inst/doc/intro-to-prince.R dependencyCount: 137 Package: proActiv Version: 1.0.0 Depends: R (>= 4.0.0) Imports: dplyr, GenomicRanges, GenomicFeatures, GenomicAlignments, GenomeInfoDb, IRanges, S4Vectors, methods, rlang, SummarizedExperiment, AnnotationDbi, DESeq2, data.table, tibble, Gviz, BiocParallel Suggests: testthat, mockery, BSgenome, BiocManager, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, DEXSeq, Rtsne, ggplot2, tidyr, vdiffr License: MIT + file LICENSE MD5sum: 3cc9c3fb06e9f5b34375f52be63f4f7e NeedsCompilation: no Title: Estimate Promoter Activity from RNA-Seq data Description: Most human genes have multiple promoters that control the expression of different isoforms. The use of these alternative promoters enables the regulation of isoform expression pre-transcriptionally. Alternative promoters have been found to be important in a wide number of cell types and diseases. proActiv is an R package that enables the analysis of promoters from RNA-seq data. proActiv uses aligned reads as input, and generates counts and normalized promoter activity estimates for each annotated promoter. In particular, proActiv accepts junction files from TopHat2 or STAR or BAM files as inputs. These estimates can then be used to identify which promoter is active, which promoter is inactive, and which promoters change their activity across conditions. biocViews: RNASeq, GeneExpression, Transcription, AlternativeSplicing, GeneRegulation, DifferentialSplicing, FunctionalGenomics, Epigenetics, Transcriptomics, Preprocessing Author: Deniz Demircioglu [aut] (), Jonathan Göke [aut], Joseph Lee [cre] Maintainer: Joseph Lee URL: https://github.com/GoekeLab/proActiv VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/proActiv git_branch: RELEASE_3_12 git_last_commit: a9bc3ac git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/proActiv_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/proActiv_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/proActiv_1.0.0.tgz vignettes: vignettes/proActiv/inst/doc/proActiv.html vignetteTitles: Identifying Active and Alternative Promoters from RNA-Seq data with proActiv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/proActiv/inst/doc/proActiv.R dependencyCount: 145 Package: proBAMr Version: 1.24.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: a7ea67453432a9f2615017d28593be1c NeedsCompilation: no Title: Generating SAM file for PSMs in shotgun proteomics data Description: Mapping PSMs back to genome. The package builds SAM file from shotgun proteomics data The package also provides function to prepare annotation from GTF file. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang git_url: https://git.bioconductor.org/packages/proBAMr git_branch: RELEASE_3_12 git_last_commit: 8ea7d6e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/proBAMr_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/proBAMr_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/proBAMr_1.24.0.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBAMr/inst/doc/proBAMr.R dependencyCount: 88 Package: proBatch Version: 1.6.0 Depends: R (>= 3.6) Imports: Biobase, corrplot, dplyr, data.table, ggfortify, ggplot2, grDevices, lazyeval, lubridate, magrittr, pheatmap, preprocessCore, purrr, pvca, RColorBrewer, reshape2, rlang, scales, stats, sva, tidyr, tibble, tools, utils, viridis, wesanderson, WGCNA Suggests: knitr, rmarkdown, devtools, ggpubr, gtable, gridExtra, roxygen2, testthat (>= 2.1.0), spelling License: GPL-3 MD5sum: fd6575e4906fe62359e099db137fe8fa NeedsCompilation: no Title: Tools for Diagnostics and Corrections of Batch Effects in Proteomics Description: These tools facilitate batch effects analysis and correction in high-throughput experiments. It was developed primarily for mass-spectrometry proteomics (DIA/SWATH), but could also be applicable to most omic data with minor adaptations. The package contains functions for diagnostics (proteome/genome-wide and feature-level), correction (normalization and batch effects correction) and quality control. Non-linear fitting based approaches were also included to deal with complex, mass spectrometry-specific signal drifts. biocViews: BatchEffect, Normalization, Preprocessing, Software, MassSpectrometry,Proteomics, QualityControl Author: Jelena Cuklina , Chloe H. Lee , Patrick Pedrioli Maintainer: Chloe H. Lee URL: https://github.com/symbioticMe/proBatch VignetteBuilder: knitr BugReports: https://github.com/symbioticMe/proBatch/issues git_url: https://git.bioconductor.org/packages/proBatch git_branch: RELEASE_3_12 git_last_commit: cece206 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/proBatch_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/proBatch_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/proBatch_1.6.0.tgz vignettes: vignettes/proBatch/inst/doc/proBatch.pdf vignetteTitles: proBatch package overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proBatch/inst/doc/proBatch.R dependencyCount: 143 Package: PROcess Version: 1.66.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: 0ae36f822d6b8948b718ed5325441a37 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li git_url: https://git.bioconductor.org/packages/PROcess git_branch: RELEASE_3_12 git_last_commit: aa6a12e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PROcess_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PROcess_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PROcess_1.66.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROcess/inst/doc/howtoprocess.R dependencyCount: 11 Package: procoil Version: 2.18.0 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: ab8b28352e0ac4b137246014e6548a8c NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The package allows for predicting whether a coiled coil sequence (amino acid sequence plus heptad register) is more likely to form a dimer or more likely to form a trimer. Additionally to the prediction itself, a prediction profile is computed which allows for determining the strengths to which the individual residues are indicative for either class. Prediction profiles can also be visualized as curves or heatmaps. biocViews: Proteomics, Classification, SupportVectorMachine Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/procoil git_branch: RELEASE_3_12 git_last_commit: bd35bcf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/procoil_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/procoil_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/procoil_2.18.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/procoil/inst/doc/procoil.R dependencyCount: 27 Package: proDA Version: 1.4.0 Imports: stats, utils, methods, BiocGenerics, SummarizedExperiment, S4Vectors, extraDistr Suggests: testthat (>= 2.1.0), MSnbase, dplyr, stringr, readr, tidyr, tibble, limma, DEP, numDeriv, pheatmap, knitr, rmarkdown License: GPL-3 MD5sum: f21b41c7a4f4fafcf649ad62f2018482 NeedsCompilation: no Title: Differential Abundance Analysis of Label-Free Mass Spectrometry Data Description: Account for missing values in label-free mass spectrometry data without imputation. The package implements a probabilistic dropout model that ensures that the information from observed and missing values are properly combined. It adds empirical Bayesian priors to increase power to detect differentially abundant proteins. biocViews: Proteomics, MassSpectrometry, DifferentialExpression, Bayesian, Regression, Software, Normalization, QualityControl Author: Constantin Ahlmann-Eltze [aut, cre] (), Simon Anders [ths] () Maintainer: Constantin Ahlmann-Eltze URL: https://github.com/const-ae/proDA VignetteBuilder: knitr BugReports: https://github.com/const-ae/proDA/issues git_url: https://git.bioconductor.org/packages/proDA git_branch: RELEASE_3_12 git_last_commit: bbc0207 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/proDA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/proDA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/proDA_1.4.0.tgz vignettes: vignettes/proDA/inst/doc/data-import.html, vignettes/proDA/inst/doc/Introduction.html vignetteTitles: Data Import, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proDA/inst/doc/data-import.R, vignettes/proDA/inst/doc/Introduction.R suggestsMe: protti dependencyCount: 28 Package: proFIA Version: 1.16.3 Depends: R (>= 2.5.0), xcms Imports: stats, graphics, utils, grDevices, methods, pracma, Biobase, minpack.lm, BiocParallel, missForest, ropls Suggests: BiocGenerics, plasFIA, knitr, License: CeCILL Archs: i386, x64 MD5sum: 25772fdba3d1dcbe6b327f98eed5a3c7 NeedsCompilation: yes Title: Preprocessing of FIA-HRMS data Description: Flow Injection Analysis coupled to High-Resolution Mass Spectrometry is a promising approach for high-throughput metabolomics. FIA- HRMS data, however, cannot be pre-processed with current software tools which rely on liquid chromatography separation, or handle low resolution data only. Here we present the proFIA package, which implements a new methodology to pre-process FIA-HRMS raw data (netCDF, mzData, mzXML, and mzML) including noise modelling and injection peak reconstruction, and generate the peak table. The workflow includes noise modelling, band detection and filtering then signal matching and missing value imputation. The peak table can then be exported as a .tsv file for further analysis. Visualisations to assess the quality of the data and of the signal made are easely produced. biocViews: MassSpectrometry, Metabolomics, Lipidomics, Preprocessing, PeakDetection, Proteomics Author: Alexis Delabriere and Etienne Thevenot. Maintainer: Alexis Delabriere VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/proFIA git_branch: RELEASE_3_12 git_last_commit: 05b5c61 git_last_commit_date: 2021-03-19 Date/Publication: 2021-03-19 source.ver: src/contrib/proFIA_1.16.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/proFIA_1.16.3.zip mac.binary.ver: bin/macosx/contrib/4.0/proFIA_1.16.3.tgz vignettes: vignettes/proFIA/inst/doc/proFIA-vignette.html vignetteTitles: processing FIA-HRMS data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proFIA/inst/doc/proFIA-vignette.R dependsOnMe: plasFIA dependencyCount: 102 Package: profileplyr Version: 1.6.0 Depends: R (>= 3.6), BiocGenerics, SummarizedExperiment Imports: GenomicRanges, stats, soGGi, methods, utils, S4Vectors, R.utils, dplyr, magrittr, tidyr, IRanges, rjson, ChIPseeker,GenomicFeatures,TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene,TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene,org.Hs.eg.db,org.Mm.eg.db,rGREAT, pheatmap, ggplot2, EnrichedHeatmap, ComplexHeatmap, grid, circlize, BiocParallel, rtracklayer, GenomeInfoDb, grDevices, rlang, Cairo, tiff Suggests: BiocStyle, testthat, knitr, rmarkdown, png, Rsamtools License: GPL (>= 3) MD5sum: d43562c13044d309000ff3a5d7f9b682 NeedsCompilation: no Title: Visualization and annotation of read signal over genomic ranges with profileplyr Description: Quick and straightforward visualization of read signal over genomic intervals is key for generating hypotheses from sequencing data sets (e.g. ChIP-seq, ATAC-seq, bisulfite/methyl-seq). Many tools both inside and outside of R and Bioconductor are available to explore these types of data, and they typically start with a bigWig or BAM file and end with some representation of the signal (e.g. heatmap). profileplyr leverages many Bioconductor tools to allow for both flexibility and additional functionality in workflows that end with visualization of the read signal. biocViews: ChIPSeq, DataImport, Sequencing, ChipOnChip, Coverage Author: Tom Carroll and Doug Barrows Maintainer: Tom Carroll , Doug Barrows VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileplyr git_branch: RELEASE_3_12 git_last_commit: 6b87625 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/profileplyr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/profileplyr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/profileplyr_1.6.0.tgz vignettes: vignettes/profileplyr/inst/doc/profileplyr.html vignetteTitles: Visualization and annotation of read signal over genomic ranges with profileplyr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/profileplyr/inst/doc/profileplyr.R dependencyCount: 171 Package: profileScoreDist Version: 1.18.0 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE Archs: i386, x64 MD5sum: ead1e35e86da33fe1e4843b812b726c5 NeedsCompilation: yes Title: Profile score distributions Description: Regularization and score distributions for position count matrices. biocViews: Software, GeneRegulation, StatisticalMethod Author: Paal O. Westermark Maintainer: Paal O. Westermark VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/profileScoreDist git_branch: RELEASE_3_12 git_last_commit: 274fc93 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/profileScoreDist_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/profileScoreDist_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/profileScoreDist_1.18.0.tgz vignettes: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.pdf vignetteTitles: Using profileScoreDist hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/profileScoreDist/inst/doc/profileScoreDist-vignette.R dependencyCount: 7 Package: progeny Version: 1.12.0 Depends: R (>= 3.6.0) Imports: Biobase, stats, dplyr, tidyr, ggplot2, ggrepel, gridExtra Suggests: airway, biomaRt, BiocFileCache, broom, Seurat, SingleCellExperiment, DESeq2, BiocStyle, knitr, readr, readxl, pheatmap, tibble, testthat (>= 2.1.0) License: Apache License (== 2.0) | file LICENSE MD5sum: dd7909b4ada47a780565a7eef9e67797 NeedsCompilation: no Title: Pathway RespOnsive GENes for activity inference from gene expression Description: This package provides a function to infer pathway activity from gene expression using PROGENy. It contains the linear model we inferred in the publication "Perturbation-response genes reveal signaling footprints in cancer gene expression". biocViews: SystemsBiology, GeneExpression, FunctionalPrediction, GeneRegulation Author: Michael Schubert [aut], Alberto Valdeolivas [cre, ctb] (), Christian H. Holland [ctb] (), Igor Bulanov [ctb], Aurélien Dugourd [ctb] Maintainer: Alberto Valdeolivas URL: https://github.com/saezlab/progeny VignetteBuilder: knitr BugReports: https://github.com/saezlab/progeny/issues git_url: https://git.bioconductor.org/packages/progeny git_branch: RELEASE_3_12 git_last_commit: 703f786 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/progeny_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/progeny_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/progeny_1.12.0.tgz vignettes: vignettes/progeny/inst/doc/progenyBulk.html, vignettes/progeny/inst/doc/ProgenySingleCell.html vignetteTitles: PROGENy pathway signatures: Application to Bulk transcriptomics, Applying PROGENy on single-cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/progeny/inst/doc/progenyBulk.R, vignettes/progeny/inst/doc/ProgenySingleCell.R dependencyCount: 50 Package: projectR Version: 1.6.0 Imports: methods, cluster, stats, limma, CoGAPS, NMF, ROCR, ggalluvial, RColorBrewer, dplyr, reshape2, viridis, scales, ggplot2 Suggests: BiocStyle, gridExtra, grid, testthat, devtools, knitr, rmarkdown, ComplexHeatmap License: GPL (==2) MD5sum: d7496316bf79118cad7afd17b7a0caff NeedsCompilation: no Title: Functions for the projection of weights from PCA, CoGAPS, NMF, correlation, and clustering Description: Functions for the projection of data into the spaces defined by PCA, CoGAPS, NMF, correlation, and clustering. biocViews: FunctionalPrediction, GeneRegulation, BiologicalQuestion, Software Author: Gaurav Sharma, Genevieve Stein-O'Brien Maintainer: Genevieve Stein-O'Brien URL: https://github.com/genesofeve/projectR/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/projectR/ git_url: https://git.bioconductor.org/packages/projectR git_branch: RELEASE_3_12 git_last_commit: 8f57326 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/projectR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/projectR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/projectR_1.6.0.tgz vignettes: vignettes/projectR/inst/doc/projectR.pdf vignetteTitles: projectR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/projectR/inst/doc/projectR.R dependencyCount: 102 Package: pRoloc Version: 1.30.0 Depends: R (>= 2.15), MSnbase (>= 1.19.20), MLInterfaces (>= 1.67.10), methods, Rcpp (>= 0.10.3), BiocParallel Imports: stats4, Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, hexbin, BiocGenerics, stats, dendextend, RColorBrewer, scales, MASS, knitr, mvtnorm, LaplacesDemon, coda, mixtools, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, rmarkdown, pRolocdata (>= 1.9.4), roxygen2, synapter, xtable, rgl, BiocStyle (>= 2.5.19), hpar (>= 1.15.3), dplyr, akima, fields, vegan, GO.db, AnnotationDbi, Rtsne (>= 0.13), nipals, reshape License: GPL-2 Archs: i386, x64 MD5sum: e8158e9cf615a1b77e50f30ceef11154 NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: The pRoloc package implements machine learning and visualisation methods for the analysis and interogation of quantitiative mass spectrometry data to reliably infer protein sub-cellular localisation. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto, Oliver Crook and Lisa M. Breckels with contributions from Thomas Burger and Samuel Wieczorek Maintainer: Laurent Gatto URL: https://github.com/lgatto/pRoloc VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/lgatto/pRoloc/issues git_url: https://git.bioconductor.org/packages/pRoloc git_branch: RELEASE_3_12 git_last_commit: 9a69c5a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pRoloc_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pRoloc_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pRoloc_1.30.0.tgz vignettes: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.html, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.html, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.html, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.html, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.html vignetteTitles: Using pRoloc for spatial proteomics data analysis, Machine learning techniques available in pRoloc, Bayesian spatial proteomics with pRoloc, Annotating spatial proteomics data, A transfer learning algorithm for spatial proteomics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/v01-pRoloc-tutorial.R, vignettes/pRoloc/inst/doc/v02-pRoloc-ml.R, vignettes/pRoloc/inst/doc/v03-pRoloc-bayesian.R, vignettes/pRoloc/inst/doc/v04-pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/v05-pRoloc-transfer-learning.R dependsOnMe: pRolocGUI, proteomics suggestsMe: MSnbase, pRolocdata, RforProteomics dependencyCount: 190 Package: pRolocGUI Version: 2.0.0 Depends: methods, R (>= 3.1.0), pRoloc (>= 1.27.6), Biobase, MSnbase (>= 2.1.11) Imports: shiny (>= 0.9.1), scales, dplyr, DT (>= 0.1.40), graphics, utils, ggplot2, shinydashboardPlus, colourpicker, shinyhelper, shinyWidgets, shinyjs, colorspace, shinydashboard, stats, grDevices, grid, BiocGenerics Suggests: pRolocdata, knitr, BiocStyle (>= 2.5.19), rmarkdown License: GPL-2 MD5sum: f232cac342b8771fa52b5d1c647c3e0b NeedsCompilation: no Title: Interactive visualisation of spatial proteomics data Description: The package pRolocGUI comprises functions to interactively visualise organelle (spatial) proteomics data on the basis of pRoloc, pRolocdata and shiny. biocViews: Proteomics, Visualization, GUI Author: Lisa Breckels [aut], Thomas Naake [aut], Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues git_url: https://git.bioconductor.org/packages/pRolocGUI git_branch: RELEASE_3_12 git_last_commit: f187f56 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pRolocGUI_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pRolocGUI_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pRolocGUI_2.0.0.tgz vignettes: vignettes/pRolocGUI/inst/doc/pRolocGUI.html vignetteTitles: pRolocGUI - Interactive visualisation of spatial proteomics data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R dependencyCount: 202 Package: PROMISE Version: 1.42.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: db588e8d9d446661b73749438d601e31 NeedsCompilation: no Title: PRojection Onto the Most Interesting Statistical Evidence Description: A general tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables as described in Pounds et. al. (2009) Bioinformatics 25: 2013-2019 biocViews: Microarray, OneChannel, MultipleComparison, GeneExpression Author: Stan Pounds , Xueyuan Cao Maintainer: Stan Pounds , Xueyuan Cao git_url: https://git.bioconductor.org/packages/PROMISE git_branch: RELEASE_3_12 git_last_commit: 1fcdcb2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PROMISE_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PROMISE_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PROMISE_1.42.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROMISE/inst/doc/PROMISE.R dependsOnMe: CCPROMISE dependencyCount: 40 Package: PROPER Version: 1.22.0 Depends: R (>= 3.3) Imports: edgeR Suggests: BiocStyle,DESeq,DSS,knitr License: GPL MD5sum: f020c7e5270be309297f94ed9ac420b1 NeedsCompilation: no Title: PROspective Power Evaluation for RNAseq Description: This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPER git_branch: RELEASE_3_12 git_last_commit: 7986f27 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PROPER_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PROPER_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PROPER_1.22.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPER/inst/doc/PROPER.R dependencyCount: 11 Package: PROPS Version: 1.12.0 Imports: bnlearn, reshape2, sva, stats, utils, Biobase Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 47b2aabe65552385058ffb3be2119ff9 NeedsCompilation: no Title: PRObabilistic Pathway Score (PROPS) Description: This package calculates probabilistic pathway scores using gene expression data. Gene expression values are aggregated into pathway-based scores using Bayesian network representations of biological pathways. biocViews: Classification, Bayesian, GeneExpression Author: Lichy Han Maintainer: Lichy Han VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PROPS git_branch: RELEASE_3_12 git_last_commit: 862289c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PROPS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PROPS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PROPS_1.12.0.tgz vignettes: vignettes/PROPS/inst/doc/props.html vignetteTitles: PRObabilistic Pathway Scores (PROPS) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PROPS/inst/doc/props.R dependencyCount: 65 Package: Prostar Version: 1.22.10 Depends: R (>= 4.0.3) Imports: DAPAR (>= 1.22.8), DAPARdata (>= 1.20.0), rhandsontable, data.table, shinyjs, DT, shiny, shinyBS, shinyAce, highcharter, htmlwidgets, webshot, R.utils, shinythemes, XML,later, rclipboard, shinycssloaders, future, promises, colourpicker, BiocManager, shinyjqui,shinyTree, shinyWidgets, sass, tibble Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 5a1a5f36bd008e4fe961147024d0ebb7 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: Proteomics, MassSpectrometry, Normalization, Preprocessing, ImmunoOncology, R.utils, GO, GUI Author: Samuel Wieczorek [cre,aut], Thomas Burger [aut], Enora Fremy [aut] Maintainer: Samuel Wieczorek URL: http://www.prostar-proteomics.org/ BugReports: https://github.com/samWieczorek/Prostar/issues git_url: https://git.bioconductor.org/packages/Prostar git_branch: RELEASE_3_12 git_last_commit: 64b8130 git_last_commit_date: 2021-04-30 Date/Publication: 2021-04-30 source.ver: src/contrib/Prostar_1.22.10.tar.gz win.binary.ver: bin/windows/contrib/4.0/Prostar_1.22.10.zip mac.binary.ver: bin/macosx/contrib/4.0/Prostar_1.22.10.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar user manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_UserManual.R Package: proteinProfiles Version: 1.30.0 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: bcd285133f61822075219b60787d34ac NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring git_url: https://git.bioconductor.org/packages/proteinProfiles git_branch: RELEASE_3_12 git_last_commit: 0027ee7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/proteinProfiles_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/proteinProfiles_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/proteinProfiles_1.30.0.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteinProfiles/inst/doc/proteinProfiles.R dependencyCount: 2 Package: ProteomicsAnnotationHubData Version: 1.20.0 Depends: AnnotationHub (>= 2.1.45), AnnotationHubData, Imports: mzR (>= 2.3.2), MSnbase, Biostrings, GenomeInfoDb, utils, Biobase, BiocManager, RCurl Suggests: knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 MD5sum: 0d5a218da540f5285212969f5f35429b NeedsCompilation: no Title: Transform public proteomics data resources into Bioconductor Data Structures Description: These recipes convert a variety and a growing number of public proteomics data sets into easily-used standard Bioconductor data structures. biocViews: DataImport, Proteomics Author: Gatto Laurent [aut, cre], Sonali Arora [aut] Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProteomicsAnnotationHubData VignetteBuilder: knitr BugReports: https://github.com/lgatto/ProteomicsAnnotationHubData/issues git_url: https://git.bioconductor.org/packages/ProteomicsAnnotationHubData git_branch: RELEASE_3_12 git_last_commit: b1494af git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ProteomicsAnnotationHubData_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ProteomicsAnnotationHubData_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ProteomicsAnnotationHubData_1.20.0.tgz vignettes: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html vignetteTitles: Proteomics Data in Annotation Hub hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R dependencyCount: 151 Package: ProteoMM Version: 1.8.0 Depends: R (>= 3.5) Imports: gdata, biomaRt, ggplot2, ggrepel, gtools, stats, matrixStats, graphics Suggests: BiocStyle, knitr, rmarkdown License: MIT MD5sum: 38853a0f7f47002ff5176d9df97a1b19 NeedsCompilation: no Title: Multi-Dataset Model-based Differential Expression Proteomics Analysis Platform Description: ProteoMM is a statistical method to perform model-based peptide-level differential expression analysis of single or multiple datasets. For multiple datasets ProteoMM produces a single fold change and p-value for each protein across multiple datasets. ProteoMM provides functionality for normalization, missing value imputation and differential expression. Model-based peptide-level imputation and differential expression analysis component of package follows the analysis described in “A statistical framework for protein quantitation in bottom-up MS based proteomics" (Karpievitch et al. Bioinformatics 2009). EigenMS normalisation is implemented as described in "Normalization of peak intensities in bottom-up MS-based proteomics using singular value decomposition." (Karpievitch et al. Bioinformatics 2009). biocViews: ImmunoOncology, MassSpectrometry, Proteomics, Normalization, DifferentialExpression Author: Yuliya V Karpievitch, Tim Stuart and Sufyaan Mohamed Maintainer: Yuliya V Karpievitch VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ProteoMM git_branch: RELEASE_3_12 git_last_commit: 3aecd00 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ProteoMM_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ProteoMM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ProteoMM_1.8.0.tgz vignettes: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.html vignetteTitles: Multi-Dataset Model-based Differential Expression Proteomics Platform hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteoMM/inst/doc/ProteoMM_vignette.R dependencyCount: 83 Package: ProtGenerics Version: 1.22.0 Depends: methods License: Artistic-2.0 MD5sum: 49d614083211df743116d297a3ff80d0 NeedsCompilation: no Title: S4 generic functions for Bioconductor proteomics infrastructure Description: S4 generic functions needed by Bioconductor proteomics packages. biocViews: Infrastructure, Proteomics, MassSpectrometry Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/ProtGenerics git_url: https://git.bioconductor.org/packages/ProtGenerics git_branch: RELEASE_3_12 git_last_commit: 2bb3011 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ProtGenerics_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ProtGenerics_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ProtGenerics_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, Spectra, tofsims, topdownr importsMe: ensembldb, matter, MSGFplus, MSnID, mzID, mzR, QFeatures, xcms dependencyCount: 1 Package: PSEA Version: 1.24.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: 78fba04b50f8ab356eee0868fd32da2e NeedsCompilation: no Title: Population-Specific Expression Analysis. Description: Deconvolution of gene expression data by Population-Specific Expression Analysis (PSEA). biocViews: Software Author: Alexandre Kuhn Maintainer: Alexandre Kuhn git_url: https://git.bioconductor.org/packages/PSEA git_branch: RELEASE_3_12 git_last_commit: a7032a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PSEA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PSEA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PSEA_1.24.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.pdf, vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Deconvolution of RNA mixtures in Nature Methods paper, PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA_RNAmixtures.R, vignettes/PSEA/inst/doc/PSEA.R dependencyCount: 9 Package: psichomics Version: 1.16.0 Depends: R (>= 4.0), shiny (>= 1.5.0), shinyBS Imports: AnnotationDbi, AnnotationHub, BiocFileCache, cluster, colourpicker, data.table, digest, dplyr, DT (>= 0.2), edgeR, fastICA, fastmatch, ggplot2, ggrepel, graphics, grDevices, highcharter (>= 0.5.0), htmltools, httr, jsonlite, limma, pairsD3, plyr, Rcpp (>= 0.12.14), recount, Rfast, R.utils, reshape2, shinyjs, stringr, stats, SummarizedExperiment, survival, tools, utils, XML, xtable, methods, org.Hs.eg.db LinkingTo: Rcpp Suggests: testthat, knitr, parallel, devtools, rmarkdown, gplots, covr, car, rstudioapi, spelling License: MIT + file LICENSE Archs: i386, x64 MD5sum: 0caf5f92cc09903071f2c37953959618 NeedsCompilation: yes Title: Graphical Interface for Alternative Splicing Quantification, Analysis and Visualisation Description: Interactive R package with an intuitive Shiny-based graphical interface for alternative splicing quantification and integrative analyses of alternative splicing and gene expression based on The Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression project (GTEx), Sequence Read Archive (SRA) and user-provided data. The tool interactively performs survival, dimensionality reduction and median- and variance-based differential splicing and gene expression analyses that benefit from the incorporation of clinical and molecular sample-associated features (such as tumour stage or survival). Interactive visual access to genomic mapping and functional annotation of selected alternative splicing events is also included. biocViews: Sequencing, RNASeq, AlternativeSplicing, DifferentialSplicing, Transcription, GUI, PrincipalComponent, Survival, BiomedicalInformatics, Transcriptomics, ImmunoOncology, Visualization, MultipleComparison, GeneExpression, DifferentialExpression Author: Nuno Saraiva-Agostinho [aut, cre] (), Nuno Luís Barbosa-Morais [aut, led, ths] (), André Falcão [ths], Lina Gallego Paez [ctb], Marie Bordone [ctb], Teresa Maia [ctb], Mariana Ferreira [ctb], Ana Carolina Leote [ctb], Bernardo de Almeida [ctb] Maintainer: Nuno Saraiva-Agostinho URL: https://nuno-agostinho.github.io/psichomics/, https://github.com/nuno-agostinho/psichomics/ VignetteBuilder: knitr BugReports: https://github.com/nuno-agostinho/psichomics/issues git_url: https://git.bioconductor.org/packages/psichomics git_branch: RELEASE_3_12 git_last_commit: 7b7d873 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/psichomics_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/psichomics_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/psichomics_1.16.0.tgz vignettes: vignettes/psichomics/inst/doc/AS_events_preparation.html, vignettes/psichomics/inst/doc/CLI_tutorial.html, vignettes/psichomics/inst/doc/custom_data.html, vignettes/psichomics/inst/doc/GUI_tutorial.html vignetteTitles: Preparing an Alternative Splicing Annotation for psichomics, Case study: command-line interface (CLI) tutorial, Loading SRA,, VAST-TOOLS and user-provided RNA-seq data, Case study: visual interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psichomics/inst/doc/AS_events_preparation.R, vignettes/psichomics/inst/doc/CLI_tutorial.R, vignettes/psichomics/inst/doc/custom_data.R, vignettes/psichomics/inst/doc/GUI_tutorial.R dependencyCount: 198 Package: PSICQUIC Version: 1.28.0 Depends: R (>= 3.2.0), methods, IRanges, biomaRt (>= 2.34.1), BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 2bfe6b11c1014bbcffbf81afb12ca5c8 NeedsCompilation: no Title: Proteomics Standard Initiative Common QUery InterfaCe Description: PSICQUIC is a project within the HUPO Proteomics Standard Initiative (HUPO-PSI). It standardises programmatic access to molecular interaction databases. biocViews: DataImport, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon git_url: https://git.bioconductor.org/packages/PSICQUIC git_branch: RELEASE_3_12 git_last_commit: 60e58d6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PSICQUIC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PSICQUIC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PSICQUIC_1.28.0.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSICQUIC/inst/doc/PSICQUIC.R dependencyCount: 64 Package: psygenet2r Version: 1.22.5 Depends: R (>= 3.4) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO, Biobase, labeling, GO.db Suggests: testthat, knitr License: MIT + file LICENSE MD5sum: ad587d21391521c50d5bae9aa2b69b2c NeedsCompilation: no Title: psygenet2r - An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders Description: Package to retrieve data from PsyGeNET database (www.psygenet.org) and to perform comorbidity studies with PsyGeNET's and user's data. biocViews: Software, BiomedicalInformatics, Genetics, Infrastructure, DataImport, DataRepresentation Author: Alba Gutierrez-Sacristan [aut, cre], Carles Hernandez-Ferrer [aut], Jaun R. Gonzalez [aut], Laura I. Furlong [aut] Maintainer: Alba Gutierrez-Sacristan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/psygenet2r git_branch: RELEASE_3_12 git_last_commit: c203eaf git_last_commit_date: 2021-01-28 Date/Publication: 2021-01-29 source.ver: src/contrib/psygenet2r_1.22.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/psygenet2r_1.22.5.zip mac.binary.ver: bin/macosx/contrib/4.0/psygenet2r_1.22.5.tgz vignettes: vignettes/psygenet2r/inst/doc/case_study.html, vignettes/psygenet2r/inst/doc/general_overview.html vignetteTitles: psygenet2r: Case study on GWAS on bipolar disorder, psygenet2r: An R package for querying PsyGeNET and to perform comorbidity studies in psychiatric disorders hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/psygenet2r/inst/doc/case_study.R, vignettes/psygenet2r/inst/doc/general_overview.R dependencyCount: 93 Package: PubScore Version: 1.2.0 Depends: R (>= 4.0.0) Imports: ggplot2, igraph, ggrepel,rentrez, progress, graphics, dplyr, utils, methods, intergraph, network, sna Suggests: FCBF, plotly, SummarizedExperiment, SingleCellExperiment, knitr, rmarkdown, testthat (>= 2.1.0), BiocManager, biomaRt License: MIT + file LICENSE MD5sum: 896657908be6e3f179123bedc2367e70 NeedsCompilation: no Title: Automatic calculation of literature relevance of genes Description: Calculates the importance score for a given gene. The importance score is the total counts of articles in the pubmed database that are a result for that gene AND each term of a list. biocViews: GeneSetEnrichment, GeneExpression, SystemsBiology, Genetics, Epigenetics, BiomedicalInformatics, Visualization, SingleCell Author: Tiago Lubiana [aut, cre], Helder Nakaya [aut, ths] Maintainer: Tiago Lubiana VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PubScore git_branch: RELEASE_3_12 git_last_commit: 45bf079 git_last_commit_date: 2020-10-27 Date/Publication: 2020-11-09 source.ver: src/contrib/PubScore_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PubScore_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PubScore_1.2.0.tgz vignettes: vignettes/PubScore/inst/doc/PubScore_vignette.html vignetteTitles: FCBF : Fast Correlation Based Filter for Feature Selection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PubScore/inst/doc/PubScore_vignette.R dependencyCount: 65 Package: pulsedSilac Version: 1.4.0 Depends: R (>= 3.6.0) Imports: robustbase, methods, R.utils, taRifx, S4Vectors, SummarizedExperiment, ggplot2, ggridges, stats, utils, UpSetR, cowplot, grid, MuMIn Suggests: testthat (>= 2.1.0), knitr, rmarkdown, gridExtra License: GPL-3 MD5sum: 8eab0c7a177572f991cf5b730924bb2d NeedsCompilation: no Title: Analysis of pulsed-SILAC quantitative proteomics data Description: This package provides several tools for pulsed-SILAC data analysis. Functions are provided to organize the data, calculate isotope ratios, isotope fractions, model protein turnover, compare turnover models, estimate cell growth and estimate isotope recycling. Several visualization tools are also included to do basic data exploration, quality control, condition comparison, individual model inspection and model comparison. biocViews: Proteomics Author: Marc Pagès-Gallego, Tobias B. Dansen Maintainer: Marc Pagès-Gallego VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pulsedSilac git_branch: RELEASE_3_12 git_last_commit: 9ca6c3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pulsedSilac_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pulsedSilac_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pulsedSilac_1.4.0.tgz vignettes: vignettes/pulsedSilac/inst/doc/pulsedsilac.html vignetteTitles: Pulsed-SILAC data analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pulsedSilac/inst/doc/pulsedsilac.R dependencyCount: 72 Package: puma Version: 3.32.0 Depends: R (>= 3.2.0), oligo (>= 1.32.0),graphics,grDevices, methods, stats, utils, mclust, oligoClasses Imports: Biobase (>= 2.5.5), affy (>= 1.46.0), affyio, oligoClasses Suggests: pumadata, affydata, snow, limma, ROCR,annotate License: LGPL Archs: i386, x64 MD5sum: 38e15367137b2e62e2422374a690051f NeedsCompilation: yes Title: Propagating Uncertainty in Microarray Analysis(including Affymetrix tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) Description: Most analyses of Affymetrix GeneChip data (including tranditional 3' arrays and exon arrays and Human Transcriptome Array 2.0) are based on point estimates of expression levels and ignore the uncertainty of such estimates. By propagating uncertainty to downstream analyses we can improve results from microarray analyses. For the first time, the puma package makes a suite of uncertainty propagation methods available to a general audience. In additon to calculte gene expression from Affymetrix 3' arrays, puma also provides methods to process exon arrays and produces gene and isoform expression for alternative splicing study. puma also offers improvements in terms of scope and speed of execution over previously available uncertainty propagation methods. Included are summarisation, differential expression detection, clustering and PCA methods, together with useful plotting functions. biocViews: Microarray, OneChannel, Preprocessing, DifferentialExpression, Clustering, ExonArray, GeneExpression, mRNAMicroarray, ChipOnChip, AlternativeSplicing, DifferentialSplicing, Bayesian, TwoChannel, DataImport, HTA2.0 Author: Richard D. Pearson, Xuejun Liu, Magnus Rattray, Marta Milo, Neil D. Lawrence, Guido Sanguinetti, Li Zhang Maintainer: Xuejun Liu URL: http://umber.sbs.man.ac.uk/resources/puma git_url: https://git.bioconductor.org/packages/puma git_branch: RELEASE_3_12 git_last_commit: 7b61bfc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/puma_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/puma_3.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/puma_3.32.0.tgz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/puma/inst/doc/puma.R suggestsMe: tigre dependencyCount: 56 Package: PureCN Version: 1.20.0 Depends: R (>= 3.5.0), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb, GenomicFeatures, Rsamtools, Biostrings, BiocGenerics, rtracklayer, ggplot2, gridExtra, futile.logger, VGAM, tools, methods, rhdf5, Matrix Suggests: BiocParallel, BiocStyle, PSCBS, TxDb.Hsapiens.UCSC.hg19.knownGene, copynumber, covr, knitr, optparse, org.Hs.eg.db, jsonlite, rmarkdown, testthat Enhances: genomicsdb License: Artistic-2.0 MD5sum: c7da3a7daaa80f68215795c42bc08096 NeedsCompilation: no Title: Copy number calling and SNV classification using targeted short read sequencing Description: This package estimates tumor purity, copy number, and loss of heterozygosity (LOH), and classifies single nucleotide variants (SNVs) by somatic status and clonality. PureCN is designed for targeted short read sequencing data, integrates well with standard somatic variant detection and copy number pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection, Coverage, ImmunoOncology Author: Markus Riester [aut, cre] (), Angad P. Singh [aut] Maintainer: Markus Riester URL: https://github.com/lima1/PureCN VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PureCN git_branch: RELEASE_3_12 git_last_commit: 582f4fa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PureCN_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PureCN_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PureCN_1.20.0.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf, vignettes/PureCN/inst/doc/Quick.html vignetteTitles: Overview of the PureCN R package, Best practices,, quick start and command line usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R, vignettes/PureCN/inst/doc/Quick.R dependencyCount: 112 Package: pvac Version: 1.38.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: 87624260c099c80629f10887ef19cb86 NeedsCompilation: no Title: PCA-based gene filtering for Affymetrix arrays Description: The package contains the function for filtering genes by the proportion of variation accounted for by the first principal component (PVAC). biocViews: Microarray, OneChannel, QualityControl Author: Jun Lu and Pierre R. Bushel Maintainer: Jun Lu , Pierre R. Bushel git_url: https://git.bioconductor.org/packages/pvac git_branch: RELEASE_3_12 git_last_commit: 662d905 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pvac_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pvac_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pvac_1.38.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvac/inst/doc/pvac.R dependencyCount: 13 Package: pvca Version: 1.30.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: c2eacd9702fa030640f46f35f9507dd5 NeedsCompilation: no Title: Principal Variance Component Analysis (PVCA) Description: This package contains the function to assess the batch sourcs by fitting all "sources" as random effects including two-way interaction terms in the Mixed Model(depends on lme4 package) to selected principal components, which were obtained from the original data correlation matrix. This package accompanies the book "Batch Effects and Noise in Microarray Experiements, chapter 12. biocViews: Microarray, BatchEffect Author: Pierre Bushel Maintainer: Jianying LI git_url: https://git.bioconductor.org/packages/pvca git_branch: RELEASE_3_12 git_last_commit: 228ab24 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pvca_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pvca_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pvca_1.30.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pvca/inst/doc/pvca.R importsMe: proBatch, ExpressionNormalizationWorkflow, statVisual dependencyCount: 55 Package: Pviz Version: 1.24.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: e1b3707dc8116d86e3c8b2ab59029d01 NeedsCompilation: no Title: Peptide Annotation and Data Visualization using Gviz Description: Pviz adapts the Gviz package for protein sequences and data. biocViews: Visualization, Proteomics, Microarray Author: Renan Sauteraud, Mike Jiang, Raphael Gottardo Maintainer: Renan Sauteraud VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Pviz git_branch: RELEASE_3_12 git_last_commit: 57e92dd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Pviz_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Pviz_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Pviz_1.24.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pviz/inst/doc/Pviz.R suggestsMe: pepStat dependencyCount: 138 Package: PWMEnrich Version: 4.26.0 Depends: R (>= 3.5.0), methods, grid, BiocGenerics, Biostrings Imports: seqLogo, gdata, evd, S4Vectors Suggests: MotifDb, BSgenome, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 958f37bf6557dc4f787efb51aece0ac0 NeedsCompilation: no Title: PWM enrichment analysis Description: A toolkit of high-level functions for DNA motif scanning and enrichment analysis built upon Biostrings. The main functionality is PWM enrichment analysis of already known PWMs (e.g. from databases such as MotifDb), but the package also implements high-level functions for PWM scanning and visualisation. The package does not perform "de novo" motif discovery, but is instead focused on using motifs that are either experimentally derived or computationally constructed by other tools. biocViews: MotifAnnotation, SequenceMatching, Software Author: Robert Stojnic, Diego Diez Maintainer: Diego Diez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/PWMEnrich git_branch: RELEASE_3_12 git_last_commit: 5544f38 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/PWMEnrich_4.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/PWMEnrich_4.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/PWMEnrich_4.26.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PWMEnrich/inst/doc/PWMEnrich.R dependsOnMe: PWMEnrich.Dmelanogaster.background, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background suggestsMe: rTRM dependencyCount: 20 Package: pwOmics Version: 1.22.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges, graph, grDevices, stats, utils Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: 36b7c5d1f80f4daf36e44dcc86fbf22f NeedsCompilation: no Title: Pathway-based data integration of omics data Description: pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and phosphoproteins. A separate downstream analysis of phosphoproteomic data including pathway identification, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and potential proteomic regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration. In addition, it provides functions to identify individual signaling axes based on data integration. biocViews: SystemsBiology, Transcription, GeneTarget, GeneSignaling Author: Astrid Wachter Maintainer: Maren Sitte git_url: https://git.bioconductor.org/packages/pwOmics git_branch: RELEASE_3_12 git_last_commit: ae99184 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pwOmics_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pwOmics_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pwOmics_1.22.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 112 Package: pwrEWAS Version: 1.4.0 Depends: shinyBS, foreach Imports: doParallel, abind, truncnorm, CpGassoc, shiny, ggplot2, parallel, shinyWidgets, BiocManager, doSNOW, limma, genefilter, stats, grDevices, methods, utils, graphics, pwrEWAS.data Suggests: knitr, RUnit, BiocGenerics, rmarkdown License: Artistic-2.0 MD5sum: 58ac9efcc14fe99abb7e237f2b866b12 NeedsCompilation: no Title: A user-friendly tool for comprehensive power estimation for epigenome wide association studies (EWAS) Description: pwrEWAS is a user-friendly tool to assists researchers in the design and planning of EWAS to help circumvent under- and overpowered studies. biocViews: DNAMethylation, Microarray, DifferentialMethylation, TissueMicroarray Author: Stefan Graw Maintainer: Stefan Graw VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/pwrEWAS git_branch: RELEASE_3_12 git_last_commit: 436662d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/pwrEWAS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/pwrEWAS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/pwrEWAS_1.4.0.tgz vignettes: vignettes/pwrEWAS/inst/doc/pwrEWAS.pdf vignetteTitles: pwrEWAS User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwrEWAS/inst/doc/pwrEWAS.R dependencyCount: 111 Package: qckitfastq Version: 1.6.0 Imports: magrittr, ggplot2, dplyr, seqTools, zlibbioc, data.table, reshape2, grDevices, graphics, stats, utils, Rcpp, rlang, RSeqAn LinkingTo: Rcpp, RSeqAn Suggests: knitr, rmarkdown, kableExtra, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 38b274f57ce99990e00e1b79ea50bf82 NeedsCompilation: yes Title: FASTQ Quality Control Description: Assessment of FASTQ file format with multiple metrics including quality score, sequence content, overrepresented sequence and Kmers. biocViews: Software,QualityControl,Sequencing Author: Wenyue Xing [aut], August Guang [aut, cre] Maintainer: August Guang SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qckitfastq git_branch: RELEASE_3_12 git_last_commit: 38d9c8e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qckitfastq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qckitfastq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qckitfastq_1.6.0.tgz vignettes: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.pdf vignetteTitles: Quality control analysis and visualization using qckitfastq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qckitfastq/inst/doc/vignette-qckitfastq.R dependencyCount: 52 Package: qcmetrics Version: 1.28.0 Depends: R (>= 3.3) Imports: Biobase, methods, knitr, tools, Nozzle.R1, xtable, pander, S4Vectors Suggests: affy, MSnbase, ggplot2, lattice, yaqcaffy, MAQCsubsetAFX, RforProteomics, AnnotationDbi, mzR, hgu133plus2cdf, BiocStyle License: GPL-2 MD5sum: 7a7a994c0b2f41dc75993430e269ebbb NeedsCompilation: no Title: A Framework for Quality Control Description: The package provides a framework for generic quality control of data. It permits to create, manage and visualise individual or sets of quality control metrics and generate quality control reports in various formats. biocViews: ImmunoOncology, Software, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualization, ReportWriting Author: Laurent Gatto [aut, cre] Maintainer: Laurent Gatto URL: https://github.com/lgatto/qcmetrics VignetteBuilder: knitr BugReports: https://github.com/lgatto/qcmetrics/issues git_url: https://git.bioconductor.org/packages/qcmetrics git_branch: RELEASE_3_12 git_last_commit: 4b2848c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qcmetrics_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qcmetrics_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qcmetrics_1.28.0.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.pdf, vignettes/qcmetrics/inst/doc/vig-index.html vignetteTitles: The 'qcmetrics' infrastructure for quality control and reporting, Index file for the qcmetrics package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R, vignettes/qcmetrics/inst/doc/vig-index.R importsMe: MSstatsQC dependencyCount: 27 Package: QDNAseq Version: 1.26.0 Depends: R (>= 3.1.0) Imports: graphics, methods, stats, utils, Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), GenomicRanges (>= 1.20), IRanges (>= 2.2), matrixStats (>= 0.54.0), R.utils (>= 2.9.0), Rsamtools (>= 1.20), future (>= 1.14.0), future.apply (>= 1.3.0) Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.20), GenomeInfoDb (>= 1.6.0), R.cache (>= 0.13.0), QDNAseq.hg19, QDNAseq.mm10 License: GPL MD5sum: 1189ed07826fb67cd169b0314a6a7cb5 NeedsCompilation: no Title: Quantitative DNA Sequencing for Chromosomal Aberrations Description: Quantitative DNA sequencing for chromosomal aberrations. The genome is divided into non-overlapping fixed-sized bins, number of sequence reads in each counted, adjusted with a simultaneous two-dimensional loess correction for sequence mappability and GC content, and filtered to remove spurious regions in the genome. Downstream steps of segmentation and calling are also implemented via packages DNAcopy and CGHcall, respectively. biocViews: CopyNumberVariation, DNASeq, Genetics, GenomeAnnotation, Preprocessing, QualityControl, Sequencing Author: Ilari Scheinin [aut], Daoud Sie [aut, cre], Henrik Bengtsson [aut], Erik van Dijk [ctb] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues git_url: https://git.bioconductor.org/packages/QDNAseq git_branch: RELEASE_3_12 git_last_commit: 312b6c8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/QDNAseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/QDNAseq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/QDNAseq_1.26.0.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QDNAseq/inst/doc/QDNAseq.R dependsOnMe: GeneBreak, QDNAseq.hg19, QDNAseq.mm10 importsMe: ACE, biscuiteer, HiCcompare dependencyCount: 48 Package: QFeatures Version: 1.0.0 Depends: R (>= 4.0), MultiAssayExperiment Imports: methods, stats, utils, S4Vectors, IRanges, SummarizedExperiment, BiocGenerics, ProtGenerics (>= 1.19.3), AnnotationFilter, lazyeval, Biobase, MsCoreUtils (>= 1.1.2), Suggests: SingleCellExperiment, msdata, ggplot2, gplots, dplyr, limma, magrittr, DT, shiny, shinydashboard, testthat, knitr, BiocStyle, rmarkdown, vsn, preprocessCore, matrixStats, imputeLCMD, pcaMethods, impute, norm License: Artistic-2.0 MD5sum: d5616b1f832f75e2f5218fd5121283db NeedsCompilation: no Title: Quantitative features for mass spectrometry data Description: The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assay levels (such as peptide spectrum matches, peptides and proteins) in a coherent and tractable format. biocViews: Infrastructure, MassSpectrometry, Proteomics, Metabolomics Author: Laurent Gatto [aut, cre] (), Christophe Vanderaa [aut] () Maintainer: Laurent Gatto URL: https://github.com/RforMassSpectrometry/QFeatures VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/QFeatures/issues git_url: https://git.bioconductor.org/packages/QFeatures git_branch: RELEASE_3_12 git_last_commit: 8d382cf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/QFeatures_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/QFeatures_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/QFeatures_1.0.0.tgz vignettes: vignettes/QFeatures/inst/doc/Processing.html, vignettes/QFeatures/inst/doc/QFeatures.html vignetteTitles: Processing quantitative proteomics data with QFeatures, Quantitative features for mass spectrometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QFeatures/inst/doc/Processing.R, vignettes/QFeatures/inst/doc/QFeatures.R dependsOnMe: scp dependencyCount: 53 Package: qpcrNorm Version: 1.48.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: 06f41cf37304064ad8b33e998081ea26 NeedsCompilation: no Title: Data-driven normalization strategies for high-throughput qPCR data. Description: The package contains functions to perform normalization of high-throughput qPCR data. Basic functions for processing raw Ct data plus functions to generate diagnostic plots are also available. biocViews: Preprocessing, GeneExpression Author: Jessica Mar Maintainer: Jessica Mar git_url: https://git.bioconductor.org/packages/qpcrNorm git_branch: RELEASE_3_12 git_last_commit: 74eca3d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qpcrNorm_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qpcrNorm_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qpcrNorm_1.48.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpcrNorm/inst/doc/qpcrNorm.R suggestsMe: EasyqpcR dependencyCount: 14 Package: qpgraph Version: 2.24.3 Depends: R (>= 3.5) Imports: methods, parallel, Matrix (>= 1.0), grid, annotate, graph (>= 1.45.1), Biobase, S4Vectors, BiocParallel, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, mvtnorm, qtl, Rgraphviz Suggests: RUnit, BiocGenerics, BiocStyle, genefilter, org.EcK12.eg.db, rlecuyer, snow, Category, GOstats License: GPL (>= 2) Archs: i386, x64 MD5sum: e74430f21a8945add47ebe63348691bc NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Estimate gene and eQTL networks from high-throughput expression and genotyping assays. biocViews: Microarray, GeneExpression, Transcription, Pathways, NetworkInference, GraphAndNetwork, GeneRegulation, Genetics, GeneticVariability, SNP, Software Author: Robert Castelo [aut, cre], Alberto Roverato [aut] Maintainer: Robert Castelo URL: https://github.com/rcastelo/qpgraph BugReports: https://github.com/rcastelo/rcastelo/issues git_url: https://git.bioconductor.org/packages/qpgraph git_branch: RELEASE_3_12 git_last_commit: 38302eb git_last_commit_date: 2021-01-08 Date/Publication: 2021-01-08 source.ver: src/contrib/qpgraph_2.24.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/qpgraph_2.24.3.zip mac.binary.ver: bin/macosx/contrib/4.0/qpgraph_2.24.3.tgz vignettes: vignettes/qpgraph/inst/doc/BasicUsersGuide.pdf, vignettes/qpgraph/inst/doc/eQTLnetworks.pdf, vignettes/qpgraph/inst/doc/qpgraphSimulate.pdf, vignettes/qpgraph/inst/doc/qpTxRegNet.pdf vignetteTitles: BasicUsersGuide.pdf, Estimate eQTL networks using qpgraph, Simulating molecular regulatory networks using qpgraph, Reverse-engineer transcriptional regulatory networks using qpgraph hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qpgraph/inst/doc/eQTLnetworks.R, vignettes/qpgraph/inst/doc/qpgraphSimulate.R, vignettes/qpgraph/inst/doc/qpTxRegNet.R importsMe: clipper, simPATHy, topologyGSA dependencyCount: 94 Package: qPLEXanalyzer Version: 1.8.2 Depends: R (>= 4.0), Biobase, MSnbase Imports: assertthat, BiocGenerics, Biostrings, dplyr (>= 1.0.0), ggdendro, ggplot2, graphics, grDevices, IRanges, limma, magrittr, preprocessCore, purrr, RColorBrewer, readr, rlang, scales, stats, stringr, tibble, tidyr, tidyselect, utils Suggests: gridExtra, knitr, qPLEXdata, testthat, UniProt.ws, vdiffr License: GPL-2 MD5sum: 70acb3c7dfadacceef534c6d34a93650 NeedsCompilation: no Title: Tools for qPLEX-RIME data analysis Description: Tools for quantitative proteomics data analysis generated from qPLEX-RIME method. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, Normalization, Preprocessing, QualityControl, DataImport Author: Matthew Eldridge [aut], Kamal Kishore [aut], Ashley Sawle [aut, cre] Maintainer: Ashley Sawle VignetteBuilder: knitr BugReports: https://github.com/crukci-bioinformatics/qPLEXanalyzer/issues git_url: https://git.bioconductor.org/packages/qPLEXanalyzer git_branch: RELEASE_3_12 git_last_commit: 8bdf172 git_last_commit_date: 2021-02-01 Date/Publication: 2021-02-01 source.ver: src/contrib/qPLEXanalyzer_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/qPLEXanalyzer_1.8.2.zip mac.binary.ver: bin/macosx/contrib/4.0/qPLEXanalyzer_1.8.2.tgz vignettes: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.html vignetteTitles: qPLEXanalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qPLEXanalyzer/inst/doc/qPLEXanalyzer.R dependsOnMe: qPLEXdata dependencyCount: 90 Package: qrqc Version: 1.44.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rhtslib (>= 1.15.3) License: GPL (>=2) Archs: i386, x64 MD5sum: 3ae3ae709c373bf6b596d6fa8d026c0b NeedsCompilation: yes Title: Quick Read Quality Control Description: Quickly scans reads and gathers statistics on base and quality frequencies, read length, k-mers by position, and frequent sequences. Produces graphical output of statistics for use in quality control pipelines, and an optional HTML quality report. S4 SequenceSummary objects allow specific tests and functionality to be written around the data collected. biocViews: Sequencing, QualityControl, DataImport, Preprocessing, Visualization Author: Vince Buffalo Maintainer: Vince Buffalo URL: http://github.com/vsbuffalo/qrqc SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/qrqc git_branch: RELEASE_3_12 git_last_commit: b90bb78 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qrqc_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qrqc_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qrqc_1.44.0.tgz vignettes: vignettes/qrqc/inst/doc/qrqc.pdf vignetteTitles: Using the qrqc package to gather information about sequence qualities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qrqc/inst/doc/qrqc.R dependencyCount: 153 Package: qsea Version: 1.16.0 Depends: R (>= 3.5) Imports: Biostrings, graphics, gtools, methods, stats, utils, HMMcopy, rtracklayer, BSgenome, GenomicRanges, Rsamtools, IRanges, limma, GenomeInfoDb, BiocGenerics, grDevices, zoo, BiocParallel, KernSmooth, MASS Suggests: BSgenome.Hsapiens.UCSC.hg19, MEDIPSData, testthat, BiocStyle, knitr, rmarkdown, BiocManager License: GPL (>=2) Archs: i386, x64 MD5sum: a5561c3529dd309a9431dd645aba550e NeedsCompilation: yes Title: IP-seq data analysis and vizualization Description: qsea (quantitative sequencing enrichment analysis) was developed as the successor of the MEDIPS package for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-seq). However, qsea provides several functionalities for the analysis of other kinds of quantitative sequencing data (e.g. ChIP-seq, MBD-seq, CMS-seq and others) including calculation of differential enrichment between groups of samples. biocViews: Sequencing, DNAMethylation, CpGIsland, ChIPSeq, Preprocessing, Normalization, QualityControl, Visualization, CopyNumberVariation, ChipOnChip, DifferentialMethylation Author: Matthias Lienhard, Lukas Chavez, Ralf Herwig Maintainer: Matthias Lienhard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsea git_branch: RELEASE_3_12 git_last_commit: d0e6ac0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qsea_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qsea_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qsea_1.16.0.tgz vignettes: vignettes/qsea/inst/doc/qsea_tutorial.html vignetteTitles: qsea hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsea/inst/doc/qsea_tutorial.R dependencyCount: 48 Package: qsmooth Version: 1.6.0 Depends: R (>= 4.0) Imports: SummarizedExperiment, utils, sva, stats, methods, graphics Suggests: bodymapRat, quantro, knitr, rmarkdown, BiocStyle, testthat License: CC BY 4.0 MD5sum: 48242974a0d1e7d2fa846bcca5200c83 NeedsCompilation: no Title: Smooth quantile normalization Description: Smooth quantile normalization is a generalization of quantile normalization, which is average of the two types of assumptions about the data generation process: quantile normalization and quantile normalization between groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing, RNASeq, BatchEffect Author: Stephanie C. Hicks [aut, cre] (), Kwame Okrah [aut], Hector Corrada Bravo [aut] (), Rafael Irizarry [aut] () Maintainer: Stephanie C. Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qsmooth git_branch: RELEASE_3_12 git_last_commit: 06aebf4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qsmooth_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qsmooth_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qsmooth_1.6.0.tgz vignettes: vignettes/qsmooth/inst/doc/qsmooth.html vignetteTitles: The qsmooth user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qsmooth/inst/doc/qsmooth.R dependencyCount: 69 Package: QSutils Version: 1.8.0 Depends: R (>= 3.5), Biostrings, BiocGenerics,methods Imports: ape, stats, psych Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: file LICENSE MD5sum: fdbe81aa9a673885fe2690a6b34a7594 NeedsCompilation: no Title: Quasispecies Diversity Description: Set of utility functions for viral quasispecies analysis with NGS data. Most functions are equally useful for metagenomic studies. There are three main types: (1) data manipulation and exploration—functions useful for converting reads to haplotypes and frequencies, repairing reads, intersecting strand haplotypes, and visualizing haplotype alignments. (2) diversity indices—functions to compute diversity and entropy, in which incidence, abundance, and functional indices are considered. (3) data simulation—functions useful for generating random viral quasispecies data. biocViews: Software, Genetics, DNASeq, GeneticVariability, Sequencing, Alignment, SequenceMatching, DataImport Author: Mercedes Guerrero-Murillo [cre, aut] (), Josep Gregori i Font [aut] () Maintainer: Mercedes Guerrero-Murillo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QSutils git_branch: RELEASE_3_12 git_last_commit: fc24f54 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/QSutils_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/QSutils_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/QSutils_1.8.0.tgz vignettes: vignettes/QSutils/inst/doc/QSUtils-Alignment.html, vignettes/QSutils/inst/doc/QSutils-Diversity.html, vignettes/QSutils/inst/doc/QSutils-Simulation.html vignetteTitles: QSUtils-Alignment, QSutils-Diversity, QSutils-Simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QSutils/inst/doc/QSUtils-Alignment.R, vignettes/QSutils/inst/doc/QSutils-Diversity.R, vignettes/QSutils/inst/doc/QSutils-Simulation.R dependencyCount: 23 Package: Qtlizer Version: 1.4.0 Depends: R (>= 3.6.0) Imports: httr, curl, GenomicRanges, stringi Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 447d680553e3755ee57d6dd599e3a7e3 NeedsCompilation: no Title: Comprehensive QTL annotation of GWAS results Description: This R package provides access to the Qtlizer web server. Qtlizer annotates lists of common small variants (mainly SNPs) and genes in humans with associated changes in gene expression using the most comprehensive database of published quantitative trait loci (QTLs). biocViews: GenomeWideAssociation, SNP, Genetics, LinkageDisequilibrium Author: Matthias Munz [aut, cre] (), Julia Remes [aut] Maintainer: Matthias Munz VignetteBuilder: knitr BugReports: https://github.com/matmu/Qtlizer/issues git_url: https://git.bioconductor.org/packages/Qtlizer git_branch: RELEASE_3_12 git_last_commit: 3abb0b4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Qtlizer_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Qtlizer_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Qtlizer_1.4.0.tgz vignettes: vignettes/Qtlizer/inst/doc/Qtlizer.html vignetteTitles: Qtlizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Qtlizer/inst/doc/Qtlizer.R dependencyCount: 26 Package: quantro Version: 1.24.0 Depends: R (>= 4.0) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: efde63e5420e222cd1e4e74538966a2c NeedsCompilation: no Title: A test for when to use quantile normalization Description: A data-driven test for the assumptions of quantile normalization using raw data such as objects that inherit eSets (e.g. ExpressionSet, MethylSet). Group level information about each sample (such as Tumor / Normal status) must also be provided because the test assesses if there are global differences in the distributions between the user-defined groups. biocViews: Normalization, Preprocessing, MultipleComparison, Microarray, Sequencing Author: Stephanie Hicks [aut, cre] (), Rafael Irizarry [aut] () Maintainer: Stephanie Hicks VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/quantro git_branch: RELEASE_3_12 git_last_commit: c7c0180 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/quantro_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/quantro_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/quantro_1.24.0.tgz vignettes: vignettes/quantro/inst/doc/quantro.html vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro.R importsMe: yarn suggestsMe: qsmooth dependencyCount: 141 Package: quantsmooth Version: 1.56.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 966a33a83de7061885f8e45c6ad9c535 NeedsCompilation: no Title: Quantile smoothing and genomic visualization of array data Description: Implements quantile smoothing as introduced in: Quantile smoothing of array CGH data; Eilers PH, de Menezes RX; Bioinformatics. 2005 Apr 1;21(7):1146-53. biocViews: Visualization, CopyNumberVariation Author: Jan Oosting, Paul Eilers, Renee Menezes Maintainer: Jan Oosting git_url: https://git.bioconductor.org/packages/quantsmooth git_branch: RELEASE_3_12 git_last_commit: 6635ed8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/quantsmooth_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/quantsmooth_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/quantsmooth_1.56.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantsmooth/inst/doc/quantsmooth.R dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA dependencyCount: 15 Package: QuartPAC Version: 1.22.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 23f14d4d8cac6a576455f9541120c453 NeedsCompilation: no Title: Identification of mutational clusters in protein quaternary structures. Description: Identifies clustering of somatic mutations in proteins over the entire quaternary structure. biocViews: Clustering, Proteomics, SomaticMutation Author: Gregory Ryslik, Yuwei Cheng, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/QuartPAC git_branch: RELEASE_3_12 git_last_commit: a952cff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/QuartPAC_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/QuartPAC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/QuartPAC_1.22.0.tgz vignettes: vignettes/QuartPAC/inst/doc/QuartPAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuartPAC/inst/doc/QuartPAC.R dependencyCount: 39 Package: QuasR Version: 1.30.0 Depends: R (>= 4.0), parallel, GenomicRanges, Rbowtie Imports: methods, grDevices, graphics, utils, BiocGenerics, S4Vectors, IRanges, BiocManager, Biobase, Biostrings, BSgenome, Rsamtools, GenomicFeatures, ShortRead, GenomicAlignments, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles, Rhisat2, AnnotationDbi LinkingTo: Rhtslib Suggests: Gviz, BiocStyle, knitr, rmarkdown, covr, testthat License: GPL-2 Archs: x64 MD5sum: 5b6157ae70b4df1b8c5c4f033c64b2c6 NeedsCompilation: yes Title: Quantify and Annotate Short Reads in R Description: This package provides a framework for the quantification and analysis of Short Reads. It covers a complete workflow starting from raw sequence reads, over creation of alignments and quality control plots, to the quantification of genomic regions of interest. biocViews: Genetics, Preprocessing, Sequencing, ChIPSeq, RNASeq, MethylSeq, Coverage, Alignment, QualityControl, ImmunoOncology Author: Anita Lerch, Charlotte Soneson, Dimos Gaiditzis and Michael Stadler Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuasR git_branch: RELEASE_3_12 git_last_commit: 1b9d992 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/QuasR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/QuasR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/QuasR_1.30.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.html vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R suggestsMe: eisaR dependencyCount: 103 Package: QuaternaryProd Version: 1.24.0 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3), dplyr, yaml (>= 2.1.18) LinkingTo: Rcpp Suggests: knitr License: GPL (>=3) Archs: i386, x64 MD5sum: ac2fb01fe6039ec89a328e4902d22ba2 NeedsCompilation: yes Title: Computes the Quaternary Dot Product Scoring Statistic for Signed and Unsigned Causal Graphs Description: QuaternaryProd is an R package that performs causal reasoning on biological networks, including publicly available networks such as STRINGdb. QuaternaryProd is an open-source alternative to commercial products such as Inginuity Pathway Analysis. For a given a set of differentially expressed genes, QuaternaryProd computes the significance of upstream regulators in the network by performing causal reasoning using the Quaternary Dot Product Scoring Statistic (Quaternary Statistic), Ternary Dot product Scoring Statistic (Ternary Statistic) and Fisher's exact test (Enrichment test). The Quaternary Statistic handles signed, unsigned and ambiguous edges in the network. Ambiguity arises when the direction of causality is unknown, or when the source node (e.g., a protein) has edges with conflicting signs for the same target gene. On the other hand, the Ternary Statistic provides causal reasoning using the signed and unambiguous edges only. The Vignette provides more details on the Quaternary Statistic and illustrates an example of how to perform causal reasoning using STRINGdb. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/QuaternaryProd git_branch: RELEASE_3_12 git_last_commit: b94d3a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/QuaternaryProd_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/QuaternaryProd_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/QuaternaryProd_1.24.0.tgz vignettes: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.pdf vignetteTitles: QuaternaryProdVignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuaternaryProd/inst/doc/QuaternaryProdVignette.R dependencyCount: 23 Package: QUBIC Version: 1.18.0 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, fields, knitr, rmarkdown Enhances: RColorBrewer License: CC BY-NC-ND 4.0 + file LICENSE Archs: i386, x64 MD5sum: 360dcc5bcc2b0e455070df1d7d7280a2 NeedsCompilation: yes Title: An R package for qualitative biclustering in support of gene co-expression analyses Description: The core function of this R package is to provide the implementation of the well-cited and well-reviewed QUBIC algorithm, aiming to deliver an effective and efficient biclustering capability. This package also includes the following related functions: (i) a qualitative representation of the input gene expression data, through a well-designed discretization way considering the underlying data property, which can be directly used in other biclustering programs; (ii) visualization of identified biclusters using heatmap in support of overall expression pattern analysis; (iii) bicluster-based co-expression network elucidation and visualization, where different correlation coefficient scores between a pair of genes are provided; and (iv) a generalize output format of biclusters and corresponding network can be freely downloaded so that a user can easily do following comprehensive functional enrichment analysis (e.g. DAVID) and advanced network visualization (e.g. Cytoscape). biocViews: StatisticalMethod, Microarray, DifferentialExpression, MultipleComparison, Clustering, Visualization, GeneExpression, Network Author: Yu Zhang [aut, cre], Qin Ma [aut] Maintainer: Yu Zhang URL: http://github.com/zy26/QUBIC SystemRequirements: C++11, Rtools (>= 3.1) VignetteBuilder: knitr BugReports: http://github.com/zy26/QUBIC/issues git_url: https://git.bioconductor.org/packages/QUBIC git_branch: RELEASE_3_12 git_last_commit: 71ab9d2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/QUBIC_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/QUBIC_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/QUBIC_1.18.0.tgz vignettes: vignettes/QUBIC/inst/doc/qubic_vignette.pdf vignetteTitles: QUBIC Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/QUBIC/inst/doc/qubic_vignette.R suggestsMe: runibic dependencyCount: 53 Package: qusage Version: 2.24.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, emmeans, fftw License: GPL (>= 2) MD5sum: 01357e564aeea79cae9b2dd6c165f53f NeedsCompilation: no Title: qusage: Quantitative Set Analysis for Gene Expression Description: This package is an implementation the Quantitative Set Analysis for Gene Expression (QuSAGE) method described in (Yaari G. et al, Nucl Acids Res, 2013). This is a novel Gene Set Enrichment-type test, which is designed to provide a faster, more accurate, and easier to understand test for gene expression studies. qusage accounts for inter-gene correlations using the Variance Inflation Factor technique proposed by Wu et al. (Nucleic Acids Res, 2012). In addition, rather than simply evaluating the deviation from a null hypothesis with a single number (a P value), qusage quantifies gene set activity with a complete probability density function (PDF). From this PDF, P values and confidence intervals can be easily extracted. Preserving the PDF also allows for post-hoc analysis (e.g., pair-wise comparisons of gene set activity) while maintaining statistical traceability. Finally, while qusage is compatible with individual gene statistics from existing methods (e.g., LIMMA), a Welch-based method is implemented that is shown to improve specificity. The QuSAGE package also includes a mixed effects model implementation, as described in (Turner JA et al, BMC Bioinformatics, 2015), and a meta-analysis framework as described in (Meng H, et al. PLoS Comput Biol. 2019). For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software, ImmunoOncology Author: Christopher Bolen and Gur Yaari, with contributions from Juilee Thakar, Hailong Meng, Jacob Turner, Derek Blankenship, and Steven Kleinstein Maintainer: Christopher Bolen URL: http://clip.med.yale.edu/qusage git_url: https://git.bioconductor.org/packages/qusage git_branch: RELEASE_3_12 git_last_commit: b39cc81 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qusage_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qusage_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qusage_2.24.0.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qusage/inst/doc/qusage.R dependsOnMe: DrInsight importsMe: mExplorer suggestsMe: SigCheck dependencyCount: 20 Package: qvalue Version: 2.22.0 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: e7d1ed30c0a0bb7364ddb256f06f610d NeedsCompilation: no Title: Q-value estimation for false discovery rate control Description: This package takes a list of p-values resulting from the simultaneous testing of many hypotheses and estimates their q-values and local FDR values. The q-value of a test measures the proportion of false positives incurred (called the false discovery rate) when that particular test is called significant. The local FDR measures the posterior probability the null hypothesis is true given the test's p-value. Various plots are automatically generated, allowing one to make sensible significance cut-offs. Several mathematical results have recently been shown on the conservative accuracy of the estimated q-values from this software. The software can be applied to problems in genomics, brain imaging, astrophysics, and data mining. biocViews: MultipleComparisons Author: John D. Storey [aut, cre], Andrew J. Bass [aut], Alan Dabney [aut], David Robinson [aut], Gregory Warnes [ctb] Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/qvalue git_branch: RELEASE_3_12 git_last_commit: b4bde81 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/qvalue_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/qvalue_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/qvalue_2.22.0.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qvalue/inst/doc/qvalue.R dependsOnMe: anota, CancerMutationAnalysis, DEGseq, DrugVsDisease, metaseqR, r3Cseq, webbioc, BonEV, cp4p, isva importsMe: Anaquin, anota, clusterProfiler, derfinder, DOSE, edge, epihet, erccdashboard, EventPointer, fishpond, metaseqR2, methylKit, MOMA, msmsTests, MWASTools, netresponse, normr, OPWeight, PAST, RNAsense, Rnits, SDAMS, sights, signatureSearch, SSPA, subSeq, synapter, trigger, webbioc, IHWpaper, AEenrich, armada, cancerGI, DGEobj.utils, fdrDiscreteNull, glmmSeq, groupedSurv, jaccard, jackstraw, NBPSeq, SeqFeatR, ssizeRNA suggestsMe: biobroom, LBE, maanova, PREDA, RnBeads, SummarizedBenchmark, swfdr, RNAinteractMAPK, BootstrapQTL, CpGassoc, dartR, matR, mutoss, seqgendiff, wrMisc dependencyCount: 44 Package: R3CPET Version: 1.22.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges (>= 1.31.8), ggbio LinkingTo: Rcpp Suggests: BiocStyle, knitr, TxDb.Hsapiens.UCSC.hg19.knownGene, biovizBase, biomaRt, AnnotationDbi, org.Hs.eg.db, shiny, ChIPpeakAnno License: GPL (>=2) Archs: i386, x64 MD5sum: bf37ae56d8118be311498ecf0b10153f NeedsCompilation: yes Title: 3CPET: Finding Co-factor Complexes in Chia-PET experiment using a Hierarchical Dirichlet Process Description: The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results. biocViews: NetworkInference, GenePrediction, Bayesian, GraphAndNetwork, Network, GeneExpression, HiC Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues git_url: https://git.bioconductor.org/packages/R3CPET git_branch: RELEASE_3_12 git_last_commit: bae520d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/R3CPET_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/R3CPET_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/R3CPET_1.22.0.tgz vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R3CPET/inst/doc/R3CPET.R dependencyCount: 153 Package: r3Cseq Version: 1.36.0 Depends: GenomicRanges, Rsamtools, rtracklayer, VGAM, qvalue Imports: methods, GenomeInfoDb, IRanges, Biostrings, data.table, sqldf, RColorBrewer Suggests: BSgenome.Mmusculus.UCSC.mm9.masked, BSgenome.Mmusculus.UCSC.mm10.masked, BSgenome.Hsapiens.UCSC.hg18.masked, BSgenome.Hsapiens.UCSC.hg19.masked, BSgenome.Rnorvegicus.UCSC.rn5.masked License: GPL-3 MD5sum: bffcb7fde70646cc6256b4856c751a43 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is used for the analysis of long-range chromatin interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC WIMM Centre for Computational Biology, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea or URL: http://r3cseq.genereg.net,https://github.com/supatt-lab/r3Cseq/ git_url: https://git.bioconductor.org/packages/r3Cseq git_branch: RELEASE_3_12 git_last_commit: 5903521 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/r3Cseq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/r3Cseq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/r3Cseq_1.36.0.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/r3Cseq/inst/doc/r3Cseq.R dependencyCount: 90 Package: R453Plus1Toolbox Version: 1.40.0 Depends: R (>= 2.12.0), methods, VariantAnnotation (>= 1.25.11), Biostrings (>= 2.47.6), Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector, GenomicRanges (>= 1.31.8), SummarizedExperiment, biomaRt, BSgenome (>= 1.47.3), Rsamtools, ShortRead (>= 1.37.1) Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 Archs: i386, x64 MD5sum: 9bb54c145c2b1b27dfa617e0f6ae047b NeedsCompilation: yes Title: A package for importing and analyzing data from Roche's Genome Sequencer System Description: The R453Plus1 Toolbox comprises useful functions for the analysis of data generated by Roche's 454 sequencing platform. It adds functions for quality assurance as well as for annotation and visualization of detected variants, complementing the software tools shipped by Roche with their product. Further, a pipeline for the detection of structural variants is provided. biocViews: Sequencing, Infrastructure, DataImport, DataRepresentation, Visualization, QualityControl, ReportWriting Author: Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert Maintainer: Hans-Ulrich Klein git_url: https://git.bioconductor.org/packages/R453Plus1Toolbox git_branch: RELEASE_3_12 git_last_commit: 4524058 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/R453Plus1Toolbox_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/R453Plus1Toolbox_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/R453Plus1Toolbox_1.40.0.tgz vignettes: vignettes/R453Plus1Toolbox/inst/doc/vignette.pdf vignetteTitles: A package for importing and analyzing data from Roche's Genome Sequencer System hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R453Plus1Toolbox/inst/doc/vignette.R dependencyCount: 99 Package: R4RNA Version: 1.18.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: db9794bae0c22b4ffe1a0cdf7d7686d9 NeedsCompilation: no Title: An R package for RNA visualization and analysis Description: A package for RNA basepair analysis, including the visualization of basepairs as arc diagrams for easy comparison and annotation of sequence and structure. Arc diagrams can additionally be projected onto multiple sequence alignments to assess basepair conservation and covariation, with numerical methods for computing statistics for each. biocViews: Alignment, MultipleSequenceAlignment, Preprocessing, Visualization, DataImport, DataRepresentation, MultipleComparison Author: Daniel Lai, Irmtraud Meyer Maintainer: Daniel Lai URL: http://www.e-rna.org/r-chie/ git_url: https://git.bioconductor.org/packages/R4RNA git_branch: RELEASE_3_12 git_last_commit: c4de6f4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/R4RNA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/R4RNA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/R4RNA_1.18.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R suggestsMe: rfaRm dependencyCount: 15 Package: RadioGx Version: 1.0.0 Depends: R (>= 4.0), CoreGx Imports: SummarizedExperiment, S4Vectors, Biobase, parallel, BiocParallel, RColorBrewer, caTools, magicaxis, methods, reshape2, scales, grDevices, graphics, stats, utils, assertthat, matrixStats, downloader Suggests: rmarkdown, BiocStyle, knitr, pander License: GPL-3 MD5sum: 5536ed23b2c3a744ddcfb1118b747da2 NeedsCompilation: no Title: Analysis of Large-Scale Radio-Genomic Data Description: Computational tool box for radio-genomic analysis which integrates radio-response data, radio-biological modelling and comprehensive cell line annotations for hundreds of cancer cell lines. The 'RadioSet' class enables creation and manipulation of standardized datasets including information about cancer cells lines, radio-response assays and dose-response indicators. Included methods allow fitting and plotting dose-response data using established radio-biological models along with quality control to validate results. Additional functions related to fitting and plotting dose response curves, quantifying statistical correlation and calculating area under the curve (AUC) or survival fraction (SF) are included. For more details please see the included documentation, references, as well as: Manem, V. et al (2018) . biocViews: Software, Pharmacogenetics, QualityControl, Survival, Pharmacogenomics, Classification Author: Venkata Manem [aut], Petr Smirnov [aut], Ian Smith [aut], Meghan Lambie [aut], Christopher Eeles [aut], Scott Bratman [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RadioGx git_branch: RELEASE_3_12 git_last_commit: 54fe743 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RadioGx_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RadioGx_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RadioGx_1.0.0.tgz vignettes: vignettes/RadioGx/inst/doc/RadioGx.html vignetteTitles: RadioGx: An R Package for Analysis of Large Radiogenomic Datasets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RadioGx/inst/doc/RadioGx.R dependencyCount: 124 Package: RaggedExperiment Version: 1.14.2 Depends: R (>= 3.6.0), GenomicRanges (>= 1.37.17) Imports: BiocGenerics, GenomeInfoDb, IRanges, Matrix, MatrixGenerics, methods, S4Vectors, stats, SummarizedExperiment Suggests: BiocStyle, knitr, rmarkdown, testthat, MultiAssayExperiment License: Artistic-2.0 MD5sum: dd9a54464e645f30ee8e52870d5e09c1 NeedsCompilation: no Title: Representation of Sparse Experiments and Assays Across Samples Description: This package provides a flexible representation of copy number, mutation, and other data that fit into the ragged array schema for genomic location data. The basic representation of such data provides a rectangular flat table interface to the user with range information in the rows and samples/specimen in the columns. biocViews: Infrastructure, DataRepresentation Author: Martin Morgan [aut, cre], Marcel Ramos [aut] Maintainer: Martin Morgan VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/RaggedExperiment/issues git_url: https://git.bioconductor.org/packages/RaggedExperiment git_branch: RELEASE_3_12 git_last_commit: 5940392 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/RaggedExperiment_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/RaggedExperiment_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.0/RaggedExperiment_1.14.2.tgz vignettes: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.html vignetteTitles: RaggedExperiment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RaggedExperiment/inst/doc/RaggedExperiment.R dependsOnMe: CNVRanger, TxRegInfra importsMe: cBioPortalData, omicsPrint, RTCGAToolbox, TCGAutils suggestsMe: MultiAssayExperiment, MultiDataSet, curatedTCGAData dependencyCount: 26 Package: rain Version: 1.24.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: 49221106b48fbbe2c9039958fb0a5d32 NeedsCompilation: no Title: Rhythmicity Analysis Incorporating Non-parametric Methods Description: This package uses non-parametric methods to detect rhythms in time series. It deals with outliers, missing values and is optimized for time series comprising 10-100 measurements. As it does not assume expect any distinct waveform it is optimal or detecting oscillating behavior (e.g. circadian or cell cycle) in e.g. genome- or proteome-wide biological measurements such as: micro arrays, proteome mass spectrometry, or metabolome measurements. biocViews: TimeCourse, Genetics, SystemsBiology, Proteomics, Microarray, MultipleComparison Author: Paul F. Thaben, Pål O. Westermark Maintainer: Paul F. Thaben git_url: https://git.bioconductor.org/packages/rain git_branch: RELEASE_3_12 git_last_commit: 55cddbc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rain_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rain_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rain_1.24.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rain/inst/doc/rain.R dependencyCount: 17 Package: rama Version: 1.64.0 Depends: R(>= 2.5.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: c4bdb82c49d3a0fb81654db01e3af35a NeedsCompilation: yes Title: Robust Analysis of MicroArrays Description: Robust estimation of cDNA microarray intensities with replicates. The package uses a Bayesian hierarchical model for the robust estimation. Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Raphael Gottardo Maintainer: Raphael Gottardo git_url: https://git.bioconductor.org/packages/rama git_branch: RELEASE_3_12 git_last_commit: 4db830b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rama_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rama_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rama_1.64.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rama/inst/doc/rama.R dependsOnMe: bridge dependencyCount: 0 Package: ramwas Version: 1.14.0 Depends: R (>= 3.3.0), methods, filematrix Imports: graphics, stats, utils, digest, glmnet, KernSmooth, grDevices, GenomicAlignments, Rsamtools, parallel, biomaRt, Biostrings, BiocGenerics Suggests: knitr, rmarkdown, pander, BiocStyle, BSgenome.Ecoli.NCBI.20080805 License: LGPL-3 Archs: i386, x64 MD5sum: 91faa6f6589c9fec4193dbab43806997 NeedsCompilation: yes Title: Fast Methylome-Wide Association Study Pipeline for Enrichment Platforms Description: A complete toolset for methylome-wide association studies (MWAS). It is specifically designed for data from enrichment based methylation assays, but can be applied to other data as well. The analysis pipeline includes seven steps: (1) scanning aligned reads from BAM files, (2) calculation of quality control measures, (3) creation of methylation score (coverage) matrix, (4) principal component analysis for capturing batch effects and detection of outliers, (5) association analysis with respect to phenotypes of interest while correcting for top PCs and known covariates, (6) annotation of significant findings, and (7) multi-marker analysis (methylation risk score) using elastic net. Additionally, RaMWAS include tools for joint analysis of methlyation and genotype data. This work is published in Bioinformatics, Shabalin et al. (2018) . biocViews: DNAMethylation, Sequencing, QualityControl, Coverage, Preprocessing, Normalization, BatchEffect, PrincipalComponent, DifferentialMethylation, Visualization Author: Andrey A Shabalin [aut, cre] (), Shaunna L Clark [aut], Mohammad W Hattab [aut], Karolina A Aberg [aut], Edwin J C G van den Oord [aut] Maintainer: Andrey A Shabalin URL: https://bioconductor.org/packages/ramwas/ VignetteBuilder: knitr BugReports: https://github.com/andreyshabalin/ramwas/issues git_url: https://git.bioconductor.org/packages/ramwas git_branch: RELEASE_3_12 git_last_commit: b13659e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ramwas_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ramwas_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ramwas_1.14.0.tgz vignettes: vignettes/ramwas/inst/doc/RW1_intro.html, vignettes/ramwas/inst/doc/RW2_CpG_sets.html, vignettes/ramwas/inst/doc/RW3_BAM_QCs.html, vignettes/ramwas/inst/doc/RW4_SNPs.html, vignettes/ramwas/inst/doc/RW5a_matrix.html, vignettes/ramwas/inst/doc/RW5c_matrix.html, vignettes/ramwas/inst/doc/RW6_param.html vignetteTitles: 1. Overview, 2. CpG sets, 3. BAM Quality Control Measures, 4. Joint Analysis of Methylation and Genotype Data, 5.a. Analyzing Illumina Methylation Array Data, 5.c. Analyzing data from other sources, 6. RaMWAS parameters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ramwas/inst/doc/RW1_intro.R, vignettes/ramwas/inst/doc/RW2_CpG_sets.R, vignettes/ramwas/inst/doc/RW3_BAM_QCs.R, vignettes/ramwas/inst/doc/RW4_SNPs.R, vignettes/ramwas/inst/doc/RW5a_matrix.R, vignettes/ramwas/inst/doc/RW5c_matrix.R, vignettes/ramwas/inst/doc/RW6_param.R dependencyCount: 96 Package: RandomWalkRestartMH Version: 1.10.0 Depends: R(>= 3.5.0) Imports: igraph, Matrix, dnet, methods Suggests: BiocStyle, testthat License: GPL (>= 2) MD5sum: 774a15f9f800140e4a66e44548eb29a6 NeedsCompilation: no Title: Random walk with restart on multiplex and heterogeneous Networks Description: This package performs Random Walk with Restart on multiplex and heterogeneous networks. It is described in the following article: "Random Walk With Restart On Multiplex And Heterogeneous Biological Networks". https://www.biorxiv.org/content/early/2017/08/30/134734 . biocViews: GenePrediction, NetworkInference, SomaticMutation, BiomedicalInformatics, MathematicalBiology, SystemsBiology, GraphAndNetwork, Pathways, BioCarta, KEGG, Reactome, Network Author: Alberto Valdeolivas Urbelz Maintainer: Alberto Valdeolivas Urbelz URL: https://www.biorxiv.org/content/early/2017/08/30/134734 git_url: https://git.bioconductor.org/packages/RandomWalkRestartMH git_branch: RELEASE_3_12 git_last_commit: f2ebe4d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RandomWalkRestartMH_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RandomWalkRestartMH_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RandomWalkRestartMH_1.10.0.tgz vignettes: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.pdf vignetteTitles: Random Walk with Restart on Multiplex and Heterogeneous Networks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RandomWalkRestartMH/inst/doc/RandomWalkRestartMH1.R dependencyCount: 48 Package: randPack Version: 1.36.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: ae7fa3681c3533c708618454bea1d0a8 NeedsCompilation: no Title: Randomization routines for Clinical Trials Description: A suite of classes and functions for randomizing patients in clinical trials. biocViews: StatisticalMethod Author: Vincent Carey and Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/randPack git_branch: RELEASE_3_12 git_last_commit: 8ed4fc7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/randPack_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/randPack_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/randPack_1.36.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randPack/inst/doc/randPack.R dependencyCount: 7 Package: randRotation Version: 1.2.2 Imports: methods, graphics, utils, stats, Rdpack (>= 0.7) Suggests: knitr, BiocParallel, lme4, nlme, rmarkdown, BiocStyle, testthat (>= 2.1.0), limma, sva License: GPL-3 MD5sum: 5416f0418cfc25e74184a5c67375cd88 NeedsCompilation: no Title: Random Rotation Methods for High Dimensional Data with Batch Structure Description: A collection of methods for performing random rotations on high-dimensional, normally distributed data (e.g. microarray or RNA-seq data) with batch structure. The random rotation approach allows exact testing of dependent test statistics with linear models following arbitrary batch effect correction methods. biocViews: Software, Sequencing, BatchEffect, BiomedicalInformatics, RNASeq, Preprocessing, Microarray, DifferentialExpression, GeneExpression, Genetics, MicroRNAArray, Normalization, StatisticalMethod Author: Peter Hettegger [aut, cre] () Maintainer: Peter Hettegger URL: https://github.com/phettegger/randRotation VignetteBuilder: knitr BugReports: https://github.com/phettegger/randRotation/issues git_url: https://git.bioconductor.org/packages/randRotation git_branch: RELEASE_3_12 git_last_commit: a0000a9 git_last_commit_date: 2021-02-08 Date/Publication: 2021-04-13 source.ver: src/contrib/randRotation_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/randRotation_1.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/randRotation_1.2.2.tgz vignettes: vignettes/randRotation/inst/doc/randRotationIntro.pdf vignetteTitles: Random Rotation Package Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/randRotation/inst/doc/randRotationIntro.R dependencyCount: 7 Package: RankProd Version: 3.16.0 Depends: R (>= 3.2.1), stats, methods, Rmpfr, gmp Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: 653f34908d2367c5c32544b0a214978a NeedsCompilation: no Title: Rank Product method for identifying differentially expressed genes with application in meta-analysis Description: Non-parametric method for identifying differentially expressed (up- or down- regulated) genes based on the estimated percentage of false predictions (pfp). The method can combine data sets from different origins (meta-analysis) to increase the power of the identification. biocViews: DifferentialExpression, StatisticalMethod, Software, ResearchField, Metabolomics, Lipidomics, Proteomics, SystemsBiology, GeneExpression, Microarray, GeneSignaling Author: Francesco Del Carratore , Andris Jankevics Fangxin Hong , Ben Wittner , Rainer Breitling , and Florian Battke Maintainer: Francesco Del Carratore git_url: https://git.bioconductor.org/packages/RankProd git_branch: RELEASE_3_12 git_last_commit: 2b79b07 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RankProd_3.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RankProd_3.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RankProd_3.16.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RankProd/inst/doc/RankProd.R dependsOnMe: RNAither, tRanslatome importsMe: POMA, synlet, INCATome, sigQC dependencyCount: 6 Package: RareVariantVis Version: 2.18.0 Depends: BiocGenerics, VariantAnnotation, googleVis, GenomicFeatures Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, gtools, BSgenome, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, phastCons100way.UCSC.hg19, SummarizedExperiment, GenomicScores Suggests: knitr License: Artistic-2.0 MD5sum: 55d36a7b2120f771ceb0337090418042 NeedsCompilation: no Title: A suite for analysis of rare genomic variants in whole genome sequencing data Description: Second version of RareVariantVis package aims to provide comprehensive information about rare variants for your genome data. It annotates, filters and presents genomic variants (especially rare ones) in a global, per chromosome way. For discovered rare variants CRISPR guide RNAs are designed, so the user can plan further functional studies. Large structural variants, including copy number variants are also supported. Package accepts variants directly from variant caller - for example GATK or Speedseq. Output of package are lists of variants together with adequate visualization. Visualization of variants is performed in two ways - standard that outputs png figures and interactive that uses JavaScript d3 package. Interactive visualization allows to analyze trio/family data, for example in search for causative variants in rare Mendelian diseases, in point-and-click interface. The package includes homozygous region caller and allows to analyse whole human genomes in less than 30 minutes on a desktop computer. RareVariantVis disclosed novel causes of several rare monogenic disorders, including one with non-coding causative variant - keratolythic winter erythema. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Adam Gudys and Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RareVariantVis git_branch: RELEASE_3_12 git_last_commit: ceca9d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RareVariantVis_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RareVariantVis_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RareVariantVis_2.18.0.tgz vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RareVariantVis/inst/doc/RareVariantsVis.R dependencyCount: 123 Package: Rariant Version: 1.25.0 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, ggbio, ggplot2, exomeCopy, SomaticSignatures, Rsamtools, shiny, VGAM, dplyr, reshape2 Suggests: h5vcData, testthat, knitr, optparse, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: f5611c5291450dbbdc793ea09eae1d80 NeedsCompilation: no Title: Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies Description: The 'Rariant' package identifies single nucleotide variants from sequencing data based on the difference of binomially distributed mismatch rates between matched samples. biocViews: Sequencing, StatisticalMethod, GenomicVariation, SomaticMutation, VariantDetection, Visualization Author: Julian Gehring, Simon Anders, Bernd Klaus Maintainer: Julian Gehring URL: https://github.com/juliangehring/Rariant VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/Rariant git_branch: master git_last_commit: 5921e69 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/Rariant_1.25.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rariant_1.25.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rariant_1.25.0.tgz vignettes: vignettes/Rariant/inst/doc/Rariant-vignette.html vignetteTitles: Rariant hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rariant/inst/doc/Rariant-vignette.R dependencyCount: 174 Package: RbcBook1 Version: 1.58.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: 1f907ab42c5c15ea2c254c3b77fd7822 NeedsCompilation: no Title: Support for Springer monograph on Bioconductor Description: tools for building book biocViews: Software Author: Vince Carey and Wolfgang Huber Maintainer: Vince Carey URL: http://www.biostat.harvard.edu/~carey git_url: https://git.bioconductor.org/packages/RbcBook1 git_branch: RELEASE_3_12 git_last_commit: 9195d6f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RbcBook1_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RbcBook1_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RbcBook1_1.58.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RbcBook1/inst/doc/RbcBook1.R dependencyCount: 11 Package: RBGL Version: 1.66.0 Depends: graph, methods Imports: methods LinkingTo: BH Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 892861d0bb053df568585f03f5eafbce NeedsCompilation: yes Title: An interface to the BOOST graph library Description: A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library. biocViews: GraphAndNetwork, Network Author: Vince Carey , Li Long , R. Gentleman Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RBGL git_branch: RELEASE_3_12 git_last_commit: bf0c111 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RBGL_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RBGL_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RBGL_1.66.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBGL/inst/doc/RBGL.R dependsOnMe: apComplex, BioNet, CellNOptR, pkgDepTools, RpsiXML, archeofrag, PerfMeas, QuACN, RSeed, SubpathwayLNCE importsMe: alpine, BiocPkgTools, biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, CytoML, DEGraph, DEsubs, EventPointer, flowWorkspace, GAPGOM, GeneAnswers, GOSim, GOstats, MIGSA, NCIgraph, OrganismDbi, pkgDepTools, predictionet, RDAVIDWebService, Streamer, ToPASeq, VariantFiltering, gRbase, HEMDAG, netgwas, pcalg, rags2ridges, RANKS, SID, wiseR suggestsMe: BiocCaseStudies, DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools, yeastExpData, gRc, maGUI dependencyCount: 9 Package: RBioinf Version: 1.50.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 1dc91e9b9da2ef4680b0f203fd9b9877 NeedsCompilation: yes Title: RBioinf Description: Functions and datasets and examples to accompany the monograph R For Bioinformatics. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl, Classification, Clustering, MultipleComparison, Annotation Author: Robert Gentleman Maintainer: Robert Gentleman git_url: https://git.bioconductor.org/packages/RBioinf git_branch: RELEASE_3_12 git_last_commit: 47b0999 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RBioinf_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RBioinf_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RBioinf_1.50.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBioinf/inst/doc/RBioinf.R dependencyCount: 8 Package: rBiopaxParser Version: 2.30.0 Depends: R (>= 4.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, RBGL, igraph License: GPL (>= 2) MD5sum: 2b0296313100cc16a04ff108ddb28387 NeedsCompilation: no Title: Parses BioPax files and represents them in R Description: Parses BioPAX files and represents them in R, at the moment BioPAX level 2 and level 3 are supported. biocViews: DataRepresentation Author: Frank Kramer Maintainer: Frank Kramer URL: https://github.com/frankkramer-lab/rBiopaxParser git_url: https://git.bioconductor.org/packages/rBiopaxParser git_branch: RELEASE_3_12 git_last_commit: 8c0f805 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rBiopaxParser_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rBiopaxParser_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rBiopaxParser_2.30.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.R importsMe: pwOmics suggestsMe: AnnotationHub, NetPathMiner dependencyCount: 4 Package: RBM Version: 1.22.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: 1166f5581afaf3f061f191c899865d86 NeedsCompilation: no Title: RBM: a R package for microarray and RNA-Seq data analysis Description: Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets. biocViews: Microarray, DifferentialExpression Author: Dongmei Li and Chin-Yuan Liang Maintainer: Dongmei Li git_url: https://git.bioconductor.org/packages/RBM git_branch: RELEASE_3_12 git_last_commit: f15d3de git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RBM_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RBM_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RBM_1.22.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RBM/inst/doc/RBM.R dependencyCount: 7 Package: Rbowtie Version: 1.30.0 Suggests: parallel, BiocStyle, knitr, rmarkdown License: Artistic-2.0 | file LICENSE Archs: x64 MD5sum: 4bc513afbcdfcf0354377fffad908e6e NeedsCompilation: yes Title: R bowtie wrapper Description: This package provides an R wrapper around the popular bowtie short read aligner and around SpliceMap, a de novo splice junction discovery and alignment tool. The package is used by the QuasR bioconductor package. We recommend to use the QuasR package instead of using Rbowtie directly. biocViews: Sequencing, Alignment Author: Florian Hahne, Anita Lerch, Michael B Stadler Maintainer: Michael Stadler SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie git_branch: RELEASE_3_12 git_last_commit: bceee87 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rbowtie_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rbowtie_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rbowtie_1.30.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.html vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR importsMe: MACPET, multicrispr suggestsMe: eisaR dependencyCount: 0 Package: Rbowtie2 Version: 1.12.0 Depends: R (>= 3.5) Suggests: knitr License: GPL (>= 3) Archs: x64 MD5sum: 2ab1ff7884140aa3596f54a637f81c4e NeedsCompilation: yes Title: An R Wrapper for Bowtie2 and AdapterRemoval Description: This package provides an R wrapper of the popular bowtie2 sequencing reads aligner and AdapterRemoval, a convenient tool for rapid adapter trimming, identification, and read merging. biocViews: Sequencing, Alignment, Preprocessing Author: Zheng Wei, Wei Zhang Maintainer: Zheng Wei SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rbowtie2 git_branch: RELEASE_3_12 git_last_commit: f1e2bee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rbowtie2_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rbowtie2_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rbowtie2_1.12.0.tgz vignettes: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.html vignetteTitles: An Introduction to Rbowtie2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rbowtie2/inst/doc/Rbowtie2-Introduction.R importsMe: esATAC, UMI4Cats dependencyCount: 0 Package: rbsurv Version: 2.48.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 33bf7ee5111836326ff337742ba84211 NeedsCompilation: no Title: Robust likelihood-based survival modeling with microarray data Description: This package selects genes associated with survival. biocViews: Microarray Author: HyungJun Cho , Sukwoo Kim , Soo-heang Eo , Jaewoo Kang Maintainer: Soo-heang Eo URL: http://www.korea.ac.kr/~stat2242/ git_url: https://git.bioconductor.org/packages/rbsurv git_branch: RELEASE_3_12 git_last_commit: ed16daf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rbsurv_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rbsurv_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rbsurv_2.48.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rbsurv/inst/doc/rbsurv.R dependencyCount: 13 Package: Rcade Version: 1.32.0 Depends: R (>= 3.5.0), methods, GenomicRanges, Rsamtools, baySeq Imports: utils, grDevices, stats, graphics, rgl, plotrix, S4Vectors (>= 0.23.19), IRanges, GenomeInfoDb, GenomicAlignments Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: a25cb2af603904bba905a27b249c050c NeedsCompilation: no Title: R-based analysis of ChIP-seq And Differential Expression - a tool for integrating a count-based ChIP-seq analysis with differential expression summary data Description: Rcade (which stands for "R-based analysis of ChIP-seq And Differential Expression") is a tool for integrating ChIP-seq data with differential expression summary data, through a Bayesian framework. A key application is in identifing the genes targeted by a transcription factor of interest - that is, we collect genes that are associated with a ChIP-seq peak, and differential expression under some perturbation related to that TF. biocViews: DifferentialExpression, GeneExpression, Transcription, ChIPSeq, Sequencing, Genetics Author: Jonathan Cairns Maintainer: Jonathan Cairns git_url: https://git.bioconductor.org/packages/Rcade git_branch: RELEASE_3_12 git_last_commit: 41f4e17 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rcade_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rcade_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rcade_1.32.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcade/inst/doc/Rcade.R dependencyCount: 90 Package: RCAS Version: 1.16.0 Depends: R (>= 3.3.0), plotly (>= 4.5.2), DT (>= 0.2), data.table, Imports: GenomicRanges, IRanges, BSgenome, BSgenome.Hsapiens.UCSC.hg19, GenomeInfoDb (>= 1.12.0), Biostrings, rtracklayer, GenomicFeatures, rmarkdown (>= 0.9.5), genomation (>= 1.5.5), knitr (>= 1.12.3), BiocGenerics, S4Vectors, plotrix, pbapply, RSQLite, proxy, pheatmap, ggplot2, cowplot, ggseqlogo, utils, ranger, gprofiler2 Suggests: testthat, covr License: Artistic-2.0 MD5sum: 5b16988b7ab333caae759d6233acdc5a NeedsCompilation: no Title: RNA Centric Annotation System Description: RCAS is an R/Bioconductor package designed as a generic reporting tool for the functional analysis of transcriptome-wide regions of interest detected by high-throughput experiments. Such transcriptomic regions could be, for instance, signal peaks detected by CLIP-Seq analysis for protein-RNA interaction sites, RNA modification sites (alias the epitranscriptome), CAGE-tag locations, or any other collection of query regions at the level of the transcriptome. RCAS produces in-depth annotation summaries and coverage profiles based on the distribution of the query regions with respect to transcript features (exons, introns, 5'/3' UTR regions, exon-intron boundaries, promoter regions). Moreover, RCAS can carry out functional enrichment analyses and discriminative motif discovery. biocViews: Software, GeneTarget, MotifAnnotation, MotifDiscovery, GO, Transcriptomics, GenomeAnnotation, GeneSetEnrichment, Coverage Author: Bora Uyar [aut, cre], Dilmurat Yusuf [aut], Ricardo Wurmus [aut], Altuna Akalin [aut] Maintainer: Bora Uyar SystemRequirements: pandoc (>= 1.12.3) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCAS git_branch: RELEASE_3_12 git_last_commit: ece9ca7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RCAS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RCAS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RCAS_1.16.0.tgz vignettes: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.html, vignettes/RCAS/inst/doc/RCAS.vignette.html vignetteTitles: How to do meta-analysis of multiple samples, Introduction - single sample analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCAS/inst/doc/RCAS.metaAnalysis.vignette.R, vignettes/RCAS/inst/doc/RCAS.vignette.R dependencyCount: 145 Package: RCASPAR Version: 1.36.0 License: GPL (>=3) MD5sum: 626f9735e86b82a4947b3c99f54e559e NeedsCompilation: no Title: A package for survival time prediction based on a piecewise baseline hazard Cox regression model. Description: The package is the R-version of the C-based software \bold{CASPAR} (Kaderali,2006: \url{http://bioinformatics.oxfordjournals.org/content/22/12/1495}). It is meant to help predict survival times in the presence of high-dimensional explanatory covariates. The model is a piecewise baseline hazard Cox regression model with an Lq-norm based prior that selects for the most important regression coefficients, and in turn the most relevant covariates for survival analysis. It was primarily tried on gene expression and aCGH data, but can be used on any other type of high-dimensional data and in disciplines other than biology and medicine. biocViews: aCGH, GeneExpression, Genetics, Proteomics, Visualization Author: Douaa Mugahid, Lars Kaderali Maintainer: Douaa Mugahid , Lars Kaderali git_url: https://git.bioconductor.org/packages/RCASPAR git_branch: RELEASE_3_12 git_last_commit: 1b5a84b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RCASPAR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RCASPAR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RCASPAR_1.36.0.tgz vignettes: vignettes/RCASPAR/inst/doc/RCASPAR.pdf vignetteTitles: RCASPAR: Software for high-dimentional-data driven survival time prediction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCASPAR/inst/doc/RCASPAR.R dependencyCount: 0 Package: rcellminer Version: 2.12.1 Depends: R (>= 3.2), Biobase, rcellminerData (>= 2.0.0) Imports: stringr, gplots, ggplot2, methods, stats, utils, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite, heatmaply, glmnet, foreach, doSNOW, parallel License: LGPL-3 + file LICENSE MD5sum: a239fbbdd2fe2a0055b16b24ce72d7de NeedsCompilation: no Title: rcellminer: Molecular Profiles, Drug Response, and Chemical Structures for the NCI-60 Cell Lines Description: The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/ cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data. biocViews: aCGH, CellBasedAssays, CopyNumberVariation, GeneExpression, Pharmacogenomics, Pharmacogenetics, miRNA, Cheminformatics, Visualization, Software, SystemsBiology Author: Augustin Luna, Vinodh Rajapakse, Fabricio Sousa Maintainer: Augustin Luna , Vinodh Rajapakse , Fathi Elloumi URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rcellminer git_branch: RELEASE_3_12 git_last_commit: 8d70149 git_last_commit_date: 2020-11-24 Date/Publication: 2020-11-24 source.ver: src/contrib/rcellminer_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/rcellminer_2.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/rcellminer_2.12.1.tgz vignettes: vignettes/rcellminer/inst/doc/rcellminerUsage.html vignetteTitles: Using rcellminer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R suggestsMe: rcellminerData dependencyCount: 69 Package: rCGH Version: 1.20.0 Depends: R (>= 3.4),methods,stats,utils,graphics Imports: plyr,DNAcopy,lattice,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene,TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi, parallel,IRanges,grDevices,aCGH Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: c5869b9a597f7c3f27188ea04e847362 NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through commercial or custom aCGH arrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, Affymetrix SNP6.0 and cytoScanHD probeset.txt, cychp.txt, and cnchp.txt files exported from ChAS or Affymetrix Power Tools. rCGH also supports custom arrays, provided data complies with the expected format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to profiles segmentation and gene annotations. This package also provides several visualization functions (static or interactive) which facilitate individual profiles interpretation. Input files can be in compressed format, e.g. .bz2 or .gz. biocViews: aCGH,CopyNumberVariation,Preprocessing,FeatureExtraction Author: Frederic Commo [aut, cre] Maintainer: Frederic Commo URL: https://github.com/fredcommo/rCGH VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rCGH git_branch: RELEASE_3_12 git_last_commit: 35225d2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rCGH_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rCGH_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rCGH_1.20.0.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rCGH/inst/doc/rCGH.R dependencyCount: 132 Package: RchyOptimyx Version: 2.30.0 Depends: R (>= 2.10) Imports: Rgraphviz, sfsmisc, graphics, methods, graph, grDevices, flowType (>= 2.0.0) Suggests: flowCore License: Artistic-2.0 Archs: i386, x64 MD5sum: 8777fb50d67a67cdaafd77081320545a NeedsCompilation: yes Title: Optimyzed Cellular Hierarchies for Flow Cytometry Description: Constructs a hierarchy of cells using flow cytometry for maximization of an external variable (e.g., a clinical outcome or a cytokine response). biocViews: FlowCytometry Author: Adrin Jalali, Nima Aghaeepour Maintainer: Adrin Jalali , Nima Aghaeepour git_url: https://git.bioconductor.org/packages/RchyOptimyx git_branch: RELEASE_3_12 git_last_commit: 68c269c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RchyOptimyx_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RchyOptimyx_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RchyOptimyx_2.30.0.tgz vignettes: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.R dependencyCount: 14 Package: RcisTarget Version: 1.10.0 Depends: R (>= 3.4) Imports: AUCell (>= 1.1.6), BiocGenerics, data.table, feather, graphics, GSEABase, methods, R.utils, stats, SummarizedExperiment, utils Suggests: Biobase, BiocStyle, BiocParallel, doParallel, DT, tibble, foreach, igraph, knitr, RcisTarget.hg19.motifDBs.cisbpOnly.500bp, rmarkdown, testthat, visNetwork, arrow Enhances: doMC, doRNG, zoo License: GPL-3 MD5sum: fb4093902366fa3131c0e0f8bd15ba15 NeedsCompilation: no Title: RcisTarget: Identify transcription factor binding motifs enriched on a gene list Description: RcisTarget identifies transcription factor binding motifs (TFBS) over-represented on a gene list. In a first step, RcisTarget selects DNA motifs that are significantly over-represented in the surroundings of the transcription start site (TSS) of the genes in the gene-set. This is achieved by using a database that contains genome-wide cross-species rankings for each motif. The motifs that are then annotated to TFs and those that have a high Normalized Enrichment Score (NES) are retained. Finally, for each motif and gene-set, RcisTarget predicts the candidate target genes (i.e. genes in the gene-set that are ranked above the leading edge). biocViews: GeneRegulation, MotifAnnotation, Transcriptomics, Transcription, GeneSetEnrichment, GeneTarget Author: Sara Aibar, Gert Hulselmans, Stein Aerts. Laboratory of Computational Biology. VIB-KU Leuven Center for Brain & Disease Research. Leuven, Belgium Maintainer: Sara Aibar URL: http://scenic.aertslab.org VignetteBuilder: knitr BugReports: https://github.com/aertslab/RcisTarget/issues git_url: https://git.bioconductor.org/packages/RcisTarget git_branch: RELEASE_3_12 git_last_commit: 15f2d94 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RcisTarget_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RcisTarget_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RcisTarget_1.10.0.tgz vignettes: vignettes/RcisTarget/inst/doc/RcisTarget.html vignetteTitles: RcisTarget: Transcription factor binding motif enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcisTarget/inst/doc/RcisTarget.R dependencyCount: 91 Package: RCM Version: 1.6.0 Depends: R (>= 3.6.0) Imports: RColorBrewer, alabama, edgeR, reshape2, tseries, VGAM, ggplot2 (>= 2.2.1.9000), nleqslv, phyloseq, tensor, MASS, stats, grDevices, graphics, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 468fe988a4e92f8a3ffaefc70f860dd8 NeedsCompilation: no Title: Fit row-column association models with the negative binomial distribution for the microbiome Description: Combine ideas of log-linear analysis of contingency table, flexible response function estimation and empirical Bayes dispersion estimation for explorative visualization of microbiome datasets. The package includes unconstrained as well as constrained analysis. biocViews: Metagenomics, DimensionReduction, Microbiome, Visualization Author: Stijn Hawinkel Maintainer: Joris Meys URL: https://bioconductor.org/packages/release/bioc/vignettes/RCM/inst/doc/RCMvignette.html/ VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/RCM/issues git_url: https://git.bioconductor.org/packages/RCM git_branch: RELEASE_3_12 git_last_commit: 14befb4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RCM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RCM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RCM_1.6.0.tgz vignettes: vignettes/RCM/inst/doc/RCMvignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCM/inst/doc/RCMvignette.R dependencyCount: 91 Package: Rcpi Version: 1.26.0 Depends: R (>= 3.0.2) Imports: stats, utils, methods, RCurl, rjson, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR, rcdk (>= 3.3.8) Suggests: knitr, rmarkdown, RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 | file LICENSE MD5sum: edce794c288db2adce6051cb467ff1a8 NeedsCompilation: no Title: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery Description: Rcpi offers a molecular informatics toolkit with a comprehensive integration of bioinformatics and chemoinformatics tools for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, SystemsBiology Author: Nan Xiao [aut, cre], Dong-Sheng Cao [aut], Qing-Song Xu [aut] Maintainer: Nan Xiao URL: https://nanx.me/Rcpi/, https://github.com/nanxstats/Rcpi VignetteBuilder: knitr BugReports: https://github.com/nanxstats/Rcpi/issues git_url: https://git.bioconductor.org/packages/Rcpi git_branch: RELEASE_3_12 git_last_commit: 01c47f5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rcpi_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rcpi_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rcpi_1.26.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.html, vignettes/Rcpi/inst/doc/Rcpi.html vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform for Drug Discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcpi/inst/doc/Rcpi.R dependencyCount: 90 Package: Rcwl Version: 1.6.0 Depends: R (>= 3.6), yaml, methods, S4Vectors Imports: utils, stats, BiocParallel, batchtools, DiagrammeR, shiny, R.utils, codetools Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-2 | file LICENSE MD5sum: e7632f0f2160bcd8a047424f0bf009b4 NeedsCompilation: no Title: An R interface to the Common Workflow Language Description: The Common Workflow Language (CWL) is an open standard for development of data analysis workflows that is portable and scalable across different tools and working environments. Rcwl provides a simple way to wrap command line tools and build CWL data analysis pipelines programmatically within R. It increases the ease of usage, development, and maintenance of CWL pipelines. biocViews: Software, WorkflowStep, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut] Maintainer: Qiang Hu SystemRequirements: python (>= 2.7), cwltool (>= 1.0.2018) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rcwl git_branch: RELEASE_3_12 git_last_commit: 54e6062 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rcwl_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/Rcwl_1.6.0.tgz vignettes: vignettes/Rcwl/inst/doc/Rcwl.html vignetteTitles: User Guide for Rcwl hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rcwl/inst/doc/Rcwl.R dependsOnMe: RcwlPipelines dependencyCount: 102 Package: RcwlPipelines Version: 1.6.2 Depends: R (>= 3.6), Rcwl, BiocFileCache Imports: rappdirs, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle, dplyr License: GPL-2 MD5sum: 6788b4705551831dcfc67b6e947f6938 NeedsCompilation: no Title: Bioinformatics pipelines based on Rcwl Description: A collection of Bioinformatics tools and pipelines based on R and the Common Workflow Language. biocViews: Software, WorkflowStep, Alignment, Preprocessing, QualityControl, DNASeq, RNASeq, DataImport, ImmunoOncology Author: Qiang Hu [aut, cre], Qian Liu [aut], Shuang Gao [aut] Maintainer: Qiang Hu SystemRequirements: nodejs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RcwlPipelines git_branch: RELEASE_3_12 git_last_commit: 350f433 git_last_commit_date: 2021-03-05 Date/Publication: 2021-03-05 source.ver: src/contrib/RcwlPipelines_1.6.2.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/RcwlPipelines_1.6.2.tgz vignettes: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.html vignetteTitles: Rcwl Pipelines hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RcwlPipelines/inst/doc/RcwlPipelines.R dependencyCount: 118 Package: RCy3 Version: 2.10.2 Imports: httr, methods, RJSONIO, XML, utils, BiocGenerics, igraph, stats, graph, R.utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 66f35b5d6a5981997a5dad03b59f36fa NeedsCompilation: no Title: Functions to Access and Control Cytoscape Description: Vizualize, analyze and explore networks using Cytoscape via R. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Alex Pico [aut, cre] (), Tanja Muetze [aut], Paul Shannon [aut], Ruth Isserlin [ctb], Shraddha Pai [ctb], Julia Gustavsen [ctb], Georgi Kolishovski [ctb] Maintainer: Alex Pico URL: https://github.com/cytoscape/RCy3 SystemRequirements: Cytoscape (>= 3.7.1), CyREST (>= 3.8.0) VignetteBuilder: knitr BugReports: https://github.com/cytoscape/RCy3/issues git_url: https://git.bioconductor.org/packages/RCy3 git_branch: RELEASE_3_12 git_last_commit: 85dc193 git_last_commit_date: 2020-11-18 Date/Publication: 2020-11-19 source.ver: src/contrib/RCy3_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/RCy3_2.10.2.zip mac.binary.ver: bin/macosx/contrib/4.0/RCy3_2.10.2.tgz vignettes: vignettes/RCy3/inst/doc/Cancer-networks-and-data.html, vignettes/RCy3/inst/doc/Custom-Graphics.html, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.html, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.html, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.html, vignettes/RCy3/inst/doc/Filtering-Networks.html, vignettes/RCy3/inst/doc/Group-nodes.html, vignettes/RCy3/inst/doc/Identifier-mapping.html, vignettes/RCy3/inst/doc/Importing-data.html, vignettes/RCy3/inst/doc/Network-functions-and-visualization.html, vignettes/RCy3/inst/doc/Overview-of-RCy3.html, vignettes/RCy3/inst/doc/Phylogenetic-trees.html, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.html vignetteTitles: 06. Cancer networks and data ~40 min, 11. Custom Graphics and Labels ~10 min, 03. Cytoscape and graphNEL ~5 min, 02. Cytoscape and igraph ~5 min, 09. Cytoscape and NDEx ~20 min, 12. Filtering Networks ~10 min, 10. Group nodes ~15 min, 07. Identifier mapping ~20 min, 04. Importing data ~5 min, 05. Network functions and visualization ~15 min, 01. Overview of RCy3 ~25 min, 13. Phylogenetic Trees ~3 min, 08. Upgrading existing scripts ~15 min hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCy3/inst/doc/Cancer-networks-and-data.R, vignettes/RCy3/inst/doc/Custom-Graphics.R, vignettes/RCy3/inst/doc/Cytoscape-and-graphNEL.R, vignettes/RCy3/inst/doc/Cytoscape-and-iGraph.R, vignettes/RCy3/inst/doc/Cytoscape-and-NDEx.R, vignettes/RCy3/inst/doc/Filtering-Networks.R, vignettes/RCy3/inst/doc/Group-nodes.R, vignettes/RCy3/inst/doc/Identifier-mapping.R, vignettes/RCy3/inst/doc/Importing-data.R, vignettes/RCy3/inst/doc/Network-functions-and-visualization.R, vignettes/RCy3/inst/doc/Overview-of-RCy3.R, vignettes/RCy3/inst/doc/Phylogenetic-trees.R, vignettes/RCy3/inst/doc/Upgrading-existing-scripts.R importsMe: categoryCompare, CeTF, MOGAMUN, NCIgraph, regutools, TimiRGeN, transomics2cytoscape, lilikoi, ScriptMapR suggestsMe: graphite, rScudo, sparsebnUtils dependencyCount: 29 Package: RCyjs Version: 2.12.0 Depends: R (>= 3.5.0), BrowserViz (>= 2.7.18), graph (>= 1.56.0) Imports: methods, httpuv (>= 1.5.0), BiocGenerics, base64enc, utils Suggests: RUnit, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 422548c0cc47d5231ac12596ee676bf2 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js, using websockets. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RCyjs git_branch: RELEASE_3_12 git_last_commit: bc3444a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RCyjs_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RCyjs_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RCyjs_2.12.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.html vignetteTitles: "RCyjs: programmatic access to the web browser-based network viewer,, cytoscape.js" hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R dependencyCount: 18 Package: RDAVIDWebService Version: 1.28.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: f24b3f71ca083b04f64919f73dadd465 NeedsCompilation: no Title: An R Package for retrieving data from DAVID into R objects using Web Services API. Description: Tools for retrieving data from the Database for Annotation, Visualization and Integrated Discovery (DAVID) using Web Services into R objects. This package offers the main functionalities of DAVID website including: i) user friendly connectivity to upload gene/background list/s, change gene/background position, select current specie/s, select annotations, etc. ii) Reports of the submitted Gene List, Annotation Category Summary, Gene/Term Clusters, Functional Annotation Chart, Functional Annotation Table biocViews: Visualization, DifferentialExpression, GraphAndNetwork Author: Cristobal Fresno and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar, http://david.abcc.ncifcrf.gov/ git_url: https://git.bioconductor.org/packages/RDAVIDWebService git_branch: RELEASE_3_12 git_last_commit: 93ce9ea git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RDAVIDWebService_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RDAVIDWebService_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RDAVIDWebService_1.28.0.tgz vignettes: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.pdf vignetteTitles: RDAVIDWebService: a versatile R interface to DAVID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDAVIDWebService/inst/doc/RDavidWS-vignette.R dependsOnMe: CompGO suggestsMe: FGNet, IntramiRExploreR dependencyCount: 80 Package: rDGIdb Version: 1.16.0 Imports: jsonlite,httr,methods,graphics Suggests: BiocStyle,knitr,testthat License: MIT + file LICENSE MD5sum: 1b1b1b6a60a38c73ac170adbdf2805c2 NeedsCompilation: no Title: R Wrapper for DGIdb Description: The rDGIdb package provides a wrapper for the Drug Gene Interaction Database (DGIdb). For simplicity, the wrapper query function and output resembles the user interface and results format provided on the DGIdb website (https://www.dgidb.org/). biocViews: Software,ResearchField,Pharmacogenetics,Pharmacogenomics, FunctionalGenomics,WorkflowStep,Annotation Author: Thomas Thurnherr, Franziska Singer, Daniel J. Stekhoven, and Niko Beerenwinkel Maintainer: Lars Bosshard VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rDGIdb git_branch: RELEASE_3_12 git_last_commit: 01ef1f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rDGIdb_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rDGIdb_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rDGIdb_1.16.0.tgz vignettes: vignettes/rDGIdb/inst/doc/vignette.pdf vignetteTitles: Query DGIdb using R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rDGIdb/inst/doc/vignette.R dependencyCount: 11 Package: Rdisop Version: 1.50.0 Depends: R (>= 2.0.0), Rcpp LinkingTo: Rcpp Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: db9ef10f4ded454637c2a5e808d725c8 NeedsCompilation: yes Title: Decomposition of Isotopic Patterns Description: Identification of metabolites using high precision mass spectrometry. MS Peaks are used to derive a ranked list of sum formulae, alternatively for a given sum formula the theoretical isotope distribution can be calculated to search in MS peak lists. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Anton Pervukhin , Steffen Neumann Maintainer: Steffen Neumann URL: https://github.com/sneumann/Rdisop SystemRequirements: None BugReports: https://github.com/sneumann/Rdisop/issues/new git_url: https://git.bioconductor.org/packages/Rdisop git_branch: RELEASE_3_12 git_last_commit: ef7cede git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rdisop_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rdisop_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rdisop_1.50.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE importsMe: enviGCMS, HiResTEC, InterpretMSSpectrum, MetaDBparse suggestsMe: adductomicsR, MSnbase, RforProteomics dependencyCount: 3 Package: RDRToolbox Version: 1.40.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: db206719be9e981dea6aa35a95d7472b NeedsCompilation: no Title: A package for nonlinear dimension reduction with Isomap and LLE. Description: A package for nonlinear dimension reduction using the Isomap and LLE algorithm. It also includes a routine for computing the Davis-Bouldin-Index for cluster validation, a plotting tool and a data generator for microarray gene expression data and for the Swiss Roll dataset. biocViews: DimensionReduction, FeatureExtraction, Visualization, Clustering, Microarray Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RDRToolbox git_branch: RELEASE_3_12 git_last_commit: d14be28 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RDRToolbox_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RDRToolbox_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RDRToolbox_1.40.0.tgz vignettes: vignettes/RDRToolbox/inst/doc/vignette.pdf vignetteTitles: A package for nonlinear dimension reduction with Isomap and LLE. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RDRToolbox/inst/doc/vignette.R suggestsMe: loon, loon.tourr dependencyCount: 54 Package: ReactomeGSA Version: 1.4.2 Imports: jsonlite, httr, progress, ggplot2, methods, gplots, RColorBrewer Suggests: testthat, knitr, rmarkdown, ReactomeGSA.data, Biobase, devtools Enhances: limma, edgeR, Seurat (>= 3.0), scater License: MIT + file LICENSE MD5sum: 10402cce8ce8735b5de7e037bd380363 NeedsCompilation: no Title: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis Description: The ReactomeGSA packages uses Reactome's online analysis service to perform a multi-omics gene set analysis. The main advantage of this package is, that the retrieved results can be visualized using REACTOME's powerful webapplication. Since Reactome's analysis service also uses R to perfrom the actual gene set analysis you will get similar results when using the same packages (such as limma and edgeR) locally. Therefore, if you only require a gene set analysis, different packages are more suited. biocViews: GeneSetEnrichment, Proteomics, Transcriptomics, SystemsBiology, GeneExpression, Reactome Author: Johannes Griss [aut, cre] () Maintainer: Johannes Griss URL: https://github.com/reactome/ReactomeGSA VignetteBuilder: knitr BugReports: https://github.com/reactome/ReactomeGSA/issues git_url: https://git.bioconductor.org/packages/ReactomeGSA git_branch: RELEASE_3_12 git_last_commit: 5752d7f git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/ReactomeGSA_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/ReactomeGSA_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/ReactomeGSA_1.4.2.tgz vignettes: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.html, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.html vignetteTitles: Analysing single-cell RNAseq data, Using the ReactomeGSA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ReactomeGSA/inst/doc/analysing-scRNAseq.R, vignettes/ReactomeGSA/inst/doc/using-reactomegsa.R dependsOnMe: ReactomeGSA.data dependencyCount: 54 Package: ReactomePA Version: 1.34.0 Depends: R (>= 3.4.0) Imports: AnnotationDbi, DOSE (>= 3.5.1), enrichplot, ggplot2, ggraph, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr, org.Hs.eg.db, prettydoc, testthat License: GPL-2 MD5sum: 67a19ba73f137add585299ca06645f21 NeedsCompilation: no Title: Reactome Pathway Analysis Description: This package provides functions for pathway analysis based on REACTOME pathway database. It implements enrichment analysis, gene set enrichment analysis and several functions for visualization. biocViews: Pathways, Visualization, Annotation, MultipleComparison, GeneSetEnrichment, Reactome Author: Guangchuang Yu [aut, cre], Vladislav Petyuk [ctb] Maintainer: Guangchuang Yu URL: https://yulab-smu.top/biomedical-knowledge-mining-book/ VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues git_url: https://git.bioconductor.org/packages/ReactomePA git_branch: RELEASE_3_12 git_last_commit: 9a94de4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ReactomePA_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ReactomePA_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ReactomePA_1.34.0.tgz vignettes: vignettes/ReactomePA/inst/doc/ReactomePA.html vignetteTitles: An R package for Reactome Pathway Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R dependsOnMe: maEndToEnd importsMe: bioCancer, epihet, miRspongeR, scTensor suggestsMe: ChIPseeker, CINdex, clusterProfiler, cola, scGPS dependencyCount: 112 Package: ReadqPCR Version: 1.36.0 Depends: R(>= 2.14.0), Biobase, methods Suggests: qpcR License: LGPL-3 MD5sum: 7bac90eca2420385ea44efa65695efb2 NeedsCompilation: no Title: Read qPCR data Description: The package provides functions to read raw RT-qPCR data of different platforms. biocViews: DataImport, MicrotitrePlateAssay, GeneExpression, qPCR Author: James Perkins, Matthias Kohl, Nor Izayu Abdul Rahman Maintainer: James Perkins URL: http://www.bioconductor.org/packages/release/bioc/html/ReadqPCR.html git_url: https://git.bioconductor.org/packages/ReadqPCR git_branch: RELEASE_3_12 git_last_commit: 22361ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ReadqPCR_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ReadqPCR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ReadqPCR_1.36.0.tgz vignettes: vignettes/ReadqPCR/inst/doc/ReadqPCR.pdf vignetteTitles: Functions to load RT-qPCR data into R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReadqPCR/inst/doc/ReadqPCR.R dependsOnMe: NormqPCR dependencyCount: 7 Package: REBET Version: 1.8.0 Depends: ASSET Imports: stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 Archs: i386, x64 MD5sum: a077f2e6aec14a58f3da88d3f08eaa05 NeedsCompilation: yes Title: The subREgion-based BurdEn Test (REBET) Description: There is an increasing focus to investigate the association between rare variants and diseases. The REBET package implements the subREgion-based BurdEn Test which is a powerful burden test that simultaneously identifies susceptibility loci and sub-regions. biocViews: Software, VariantAnnotation, SNP Author: Bill Wheeler [cre], Bin Zhu [aut], Lisa Mirabello [ctb], Nilanjan Chatterjee [ctb] Maintainer: Bill Wheeler git_url: https://git.bioconductor.org/packages/REBET git_branch: RELEASE_3_12 git_last_commit: b9689ad git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/REBET_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/REBET_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/REBET_1.8.0.tgz vignettes: vignettes/REBET/inst/doc/vignette.pdf vignetteTitles: REBET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REBET/inst/doc/vignette.R dependencyCount: 16 Package: rebook Version: 1.0.0 Imports: utils, methods, knitr, callr, rmarkdown, CodeDepends, BiocStyle Suggests: testthat, igraph, BiocManager License: GPL-3 MD5sum: d21e2528720a4a086795bf74d288effe NeedsCompilation: no Title: Re-using Content in Bioconductor Books Description: Provides utilities to re-use content across chapters of a Bioconductor book. This is mostly based on functionality developed while writing the OSCA book, but generalized for potential use in other large books with heavy compute. Also contains some functions to assist book deployment. biocViews: Software, Infrastructure, ReportWriting Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rebook git_branch: RELEASE_3_12 git_last_commit: 93381c3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rebook_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rebook_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rebook_1.0.0.tgz vignettes: vignettes/rebook/inst/doc/userguide.html vignetteTitles: Reusing book content hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rebook/inst/doc/userguide.R dependencyCount: 38 Package: receptLoss Version: 1.2.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, magrittr, tidyr, SummarizedExperiment Suggests: knitr, rmarkdown, testthat (>= 2.1.0), here License: GPL-3 + file LICENSE MD5sum: 933f8c75c3ccf24a92facbf41d9426f6 NeedsCompilation: no Title: Unsupervised Identification of Genes with Expression Loss in Subsets of Tumors Description: receptLoss identifies genes whose expression is lost in subsets of tumors relative to normal tissue. It is particularly well-suited in cases where the number of normal tissue samples is small, as the distribution of gene expression in normal tissue samples is approximated by a Gaussian. Originally designed for identifying nuclear hormone receptor expression loss but can be applied transcriptome wide as well. biocViews: GeneExpression, StatisticalMethod Author: Daniel Pique, John Greally, Jessica Mar Maintainer: Daniel Pique VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/receptLoss git_branch: RELEASE_3_12 git_last_commit: f76666a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/receptLoss_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/receptLoss_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/receptLoss_1.2.0.tgz vignettes: vignettes/receptLoss/inst/doc/receptLoss.html vignetteTitles: receptLoss hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/receptLoss/inst/doc/receptLoss.R dependencyCount: 62 Package: reconsi Version: 1.2.0 Imports: phyloseq, KernSmooth, reshape2, ggplot2, stats, methods, graphics, grDevices, matrixStats Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 9b910abd67cdb8253d2c28d710a88bbb NeedsCompilation: no Title: Resampling Collapsed Null Distributions for Simultaneous Inference Description: Improves simultaneous inference under dependence of tests by estimating a collapsed null distribution through resampling. Accounting for the dependence between tests increases the power while reducing the variability of the false discovery proportion. This dependence is common in genomics applications, e.g. when combining flow cytometry measurements with microbiome sequence counts. biocViews: Metagenomics, Microbiome, MultipleComparison, FlowCytometry Author: Stijn Hawinkel Maintainer: Joris Meys VignetteBuilder: knitr BugReports: https://github.com/CenterForStatistics-UGent/reconsi/issues git_url: https://git.bioconductor.org/packages/reconsi git_branch: RELEASE_3_12 git_last_commit: e83e3fa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/reconsi_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/reconsi_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/reconsi_1.2.0.tgz vignettes: vignettes/reconsi/inst/doc/reconsiVignette.html vignetteTitles: Manual for the RCM pacakage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reconsi/inst/doc/reconsiVignette.R dependencyCount: 78 Package: recount Version: 1.16.1 Depends: R (>= 3.3.0), SummarizedExperiment Imports: BiocParallel, derfinder, downloader, GEOquery, GenomeInfoDb, GenomicRanges, IRanges, methods, RCurl, rentrez, rtracklayer (>= 1.35.3), S4Vectors, stats, utils Suggests: AnnotationDbi, BiocManager, BiocStyle (>= 2.5.19), DESeq2, sessioninfo, EnsDb.Hsapiens.v79, GenomicFeatures, knitr (>= 1.6), org.Hs.eg.db, RefManageR, regionReport (>= 1.9.4), rmarkdown (>= 0.9.5), testthat (>= 2.1.0), covr, pheatmap License: Artistic-2.0 MD5sum: c262c8455edb208d23f879f47326e5de NeedsCompilation: no Title: Explore and download data from the recount project Description: Explore and download data from the recount project available at https://jhubiostatistics.shinyapps.io/recount/. Using the recount package you can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level, the raw counts, the phenotype metadata used, the urls to the sample coverage bigWig files or the mean coverage bigWig file for a particular study. The RangedSummarizedExperiment objects can be used by different packages for performing differential expression analysis. Using http://bioconductor.org/packages/derfinder you can perform annotation-agnostic differential expression analyses with the data from the recount project as described at http://www.nature.com/nbt/journal/v35/n4/full/nbt.3838.html. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport, ImmunoOncology Author: Leonardo Collado-Torres [aut, cre] (), Abhinav Nellore [ctb], Andrew E. Jaffe [ctb] (), Margaret A. Taub [ctb], Kai Kammers [ctb], Shannon E. Ellis [ctb] (), Kasper Daniel Hansen [ctb] (), Ben Langmead [ctb] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/recount VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/recount/ git_url: https://git.bioconductor.org/packages/recount git_branch: RELEASE_3_12 git_last_commit: 4772744 git_last_commit_date: 2020-12-18 Date/Publication: 2020-12-18 source.ver: src/contrib/recount_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/recount_1.16.1.zip mac.binary.ver: bin/macosx/contrib/4.0/recount_1.16.1.tgz vignettes: vignettes/recount/inst/doc/recount-quickstart.html, vignettes/recount/inst/doc/SRP009615-results.html vignetteTitles: recount quick start guide, Basic DESeq2 results exploration hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount/inst/doc/recount-quickstart.R, vignettes/recount/inst/doc/SRP009615-results.R importsMe: psichomics, RNAAgeCalc, recountWorkflow suggestsMe: dasper, recount3 dependencyCount: 152 Package: recount3 Version: 1.0.7 Depends: SummarizedExperiment Imports: BiocFileCache, methods, rtracklayer, S4Vectors, utils, RCurl, data.table, R.utils, Matrix, GenomicRanges, sessioninfo, tools Suggests: BiocStyle, covr, knitcitations, knitr, RefManageR, rmarkdown, testthat, pryr, interactiveDisplayBase, recount License: Artistic-2.0 MD5sum: 0bac936b14fbadb193dfec9bb564c5d5 NeedsCompilation: no Title: Explore and download data from the recount3 project Description: The recount3 package enables access to a large amount of uniformly processed RNA-seq data from human and mouse. You can download RangedSummarizedExperiment objects at the gene, exon or exon-exon junctions level with sample metadata and QC statistics. In addition we provide access to sample coverage BigWig files. biocViews: Coverage, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Leonardo Collado-Torres [aut, cre] () Maintainer: Leonardo Collado-Torres URL: https://github.com/LieberInstitute/recount3 VignetteBuilder: knitr BugReports: https://github.com/LieberInstitute/recount3/issues git_url: https://git.bioconductor.org/packages/recount3 git_branch: RELEASE_3_12 git_last_commit: 66048cf git_last_commit_date: 2021-02-10 Date/Publication: 2021-02-11 source.ver: src/contrib/recount3_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/4.0/recount3_1.0.7.zip mac.binary.ver: bin/macosx/contrib/4.0/recount3_1.0.7.tgz vignettes: vignettes/recount3/inst/doc/recount3-quickstart.html vignetteTitles: recount3 quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recount3/inst/doc/recount3-quickstart.R dependencyCount: 84 Package: recountmethylation Version: 1.0.0 Depends: R (>= 4.0.0) Imports: minfi, HDF5Array, rhdf5, S4Vectors, utils, methods, RCurl, R.utils, BiocFileCache Suggests: knitr, testthat, ggplot2, gridExtra, rmarkdown, BiocStyle, GenomicRanges, limma, ExperimentHub, AnnotationHub License: Artistic-2.0 MD5sum: 33b9ca3af77bc4c329d300b0ba74bba8 NeedsCompilation: no Title: Access and Analyze DNA Methylation Array Databases Description: Access cross-study compilations of DNA methylation array databases. Database files can be downloaded and accessed using provided functions. Background about database file types (HDF5 and HDF5-SummarizedExperiment), SummarizedExperiment classes, and examples for data handling, validation, and analyses, can be found in the package vignettes. Note the disclaimer on package load, and consult the main manuscript for further info. biocViews: DNAMethylation, Epigenetics, Microarray, MethylationArray, ExperimentHub Author: Sean K Maden [cre, aut] (), Reid F Thompson [aut] (), Kasper D Hansen [aut] (), Abhinav Nellore [aut] () Maintainer: Sean K Maden URL: https://github.com/metamaden/recountmethylation VignetteBuilder: knitr BugReports: https://github.com/metamaden/recountmethylation/issues git_url: https://git.bioconductor.org/packages/recountmethylation git_branch: RELEASE_3_12 git_last_commit: 8c2ff8d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/recountmethylation_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/recountmethylation_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/recountmethylation_1.0.0.tgz vignettes: vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.pdf, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.html vignetteTitles: Data Analyses, recountmethylation User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recountmethylation/inst/doc/recountmethylation_data_analyses.R, vignettes/recountmethylation/inst/doc/recountmethylation_users_guide.R dependencyCount: 133 Package: recoup Version: 1.18.0 Depends: R (>= 4.0.0), GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, circlize, GenomeInfoDb, GenomicFeatures, graphics, grDevices, httr, methods, parallel, RSQLite, Rsamtools, rtracklayer, S4Vectors, stats, stringr, utils Suggests: grid, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocManager, BSgenome, RMySQL License: GPL (>= 3) MD5sum: 567b8a0e672a3050d1a969a17d4cb456 NeedsCompilation: no Title: An R package for the creation of complex genomic profile plots Description: recoup calculates and plots signal profiles created from short sequence reads derived from Next Generation Sequencing technologies. The profiles provided are either sumarized curve profiles or heatmap profiles. Currently, recoup supports genomic profile plots for reads derived from ChIP-Seq and RNA-Seq experiments. The package uses ggplot2 and ComplexHeatmap graphics facilities for curve and heatmap coverage profiles respectively. biocViews: ImmunoOncology, Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage, ATACSeq, ChipOnChip, Alignment, DataImport Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/recoup git_branch: RELEASE_3_12 git_last_commit: 71261f1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/recoup_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/recoup_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/recoup_1.18.0.tgz vignettes: vignettes/recoup/inst/doc/recoup_intro.html vignetteTitles: Introduction to the recoup package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R dependencyCount: 113 Package: RedeR Version: 1.38.0 Depends: R (>= 3.3.3), methods Imports: igraph Suggests: pvclust, BiocStyle, knitr, rmarkdown License: GPL (>= 2) MD5sum: f7dcf77d4624df9b1b56e311adb9820c NeedsCompilation: no Title: Interactive visualization and manipulation of nested networks Description: RedeR is an R-based package combined with a stand-alone Java application for interactive visualization and manipulation of modular structures, nested networks and multiple levels of hierarchical associations. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Mauro Castro, Xin Wang, Florian Markowetz Maintainer: Mauro Castro URL: http://genomebiology.com/2012/13/4/R29 SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RedeR git_branch: RELEASE_3_12 git_last_commit: 9c5ee11 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RedeR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RedeR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RedeR_1.38.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.html vignetteTitles: "RedeR: hierarchical network representation" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R dependsOnMe: Fletcher2013b, dc3net importsMe: PANR, RTN, transcriptogramer, TreeAndLeaf dependencyCount: 11 Package: REDseq Version: 1.36.0 Depends: R (>= 3.5.0), BiocGenerics, BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: AnnotationDbi, graphics, IRanges (>= 1.13.5), stats, utils License: GPL (>=2) MD5sum: 459df2f8ebe0358fffbbb4635d9e9b79 NeedsCompilation: no Title: Analysis of high-throughput sequencing data processed by restriction enzyme digestion Description: The package includes functions to build restriction enzyme cut site (RECS) map, distribute mapped sequences on the map with five different approaches, find enriched/depleted RECSs for a sample, and identify differentially enriched/depleted RECSs between samples. biocViews: Sequencing, SequenceMatching, Preprocessing Author: Lihua Julie Zhu and Thomas Fazzio Maintainer: Lihua Julie Zhu git_url: https://git.bioconductor.org/packages/REDseq git_branch: RELEASE_3_12 git_last_commit: 89a4802 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/REDseq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/REDseq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/REDseq_1.36.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REDseq/inst/doc/REDseq.R dependencyCount: 118 Package: RefPlus Version: 1.60.0 Depends: R (>= 2.8.0), Biobase (>= 2.1.0), affy (>= 1.20.0), affyPLM (>= 1.18.0), preprocessCore (>= 1.4.0) Suggests: affydata License: GPL (>= 2) MD5sum: 9bc456cf98a28942aa700d9d946aab68 NeedsCompilation: no Title: A function set for the Extrapolation Strategy (RMA+) and Extrapolation Averaging (RMA++) methods. Description: The package contains functions for pre-processing Affymetrix data using the RMA+ and the RMA++ methods. biocViews: Microarray, OneChannel, Preprocessing Author: Kai-Ming Chang , Chris Harbron , Marie C South Maintainer: Kai-Ming Chang git_url: https://git.bioconductor.org/packages/RefPlus git_branch: RELEASE_3_12 git_last_commit: a13e64f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RefPlus_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RefPlus_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RefPlus_1.60.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefPlus/inst/doc/RefPlus.R dependencyCount: 23 Package: RegEnrich Version: 1.0.1 Depends: R (>= 4.0.0), S4Vectors, dplyr, tibble, BiocSet, SummarizedExperiment Imports: randomForest, fgsea, DOSE, BiocParallel, DESeq2, limma, WGCNA, ggplot2 (>= 2.2.0), methods, reshape2, magrittr Suggests: GEOquery, rmarkdown, knitr, BiocManager, testthat License: GPL (>= 2) MD5sum: b2e5537ad3b4099f4f4e2dc13144cb32 NeedsCompilation: no Title: Gene regulator enrichment analysis Description: This package is a pipeline to identify the key gene regulators in a biological process, for example in cell differentiation and in cell development after stimulation. There are four major steps in this pipeline: (1) differential expression analysis; (2) regulator-target network inference; (3) enrichment analysis; and (4) regulators scoring and ranking. biocViews: GeneExpression, Transcriptomics, RNASeq, TwoChannel, Transcription, GeneTarget, NetworkEnrichment, DifferentialExpression, Network, NetworkInference, GeneSetEnrichment, FunctionalPrediction Author: Weiyang Tao [cre, aut], Aridaman Pandit [aut] Maintainer: Weiyang Tao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RegEnrich git_branch: RELEASE_3_12 git_last_commit: bd81547 git_last_commit_date: 2021-03-04 Date/Publication: 2021-03-05 source.ver: src/contrib/RegEnrich_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/RegEnrich_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/RegEnrich_1.0.1.tgz vignettes: vignettes/RegEnrich/inst/doc/RegEnrich.html vignetteTitles: Gene regulator enrichment with RegEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RegEnrich/inst/doc/RegEnrich.R dependencyCount: 148 Package: regioneR Version: 1.22.0 Depends: GenomicRanges Imports: memoise, GenomicRanges, IRanges, BSgenome, Biostrings, rtracklayer, parallel, graphics, stats, utils, methods, GenomeInfoDb, S4Vectors, tools Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 79c5921f5b4612f8edb8e0013b7760e7 NeedsCompilation: no Title: Association analysis of genomic regions based on permutation tests Description: regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features. biocViews: Genetics, ChIPSeq, DNASeq, MethylSeq, CopyNumberVariation Author: Anna Diez-Villanueva , Roberto Malinverni and Bernat Gel Maintainer: Bernat Gel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/regioneR git_branch: RELEASE_3_12 git_last_commit: fb28e1a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/regioneR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/regioneR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/regioneR_1.22.0.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.html vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R dependsOnMe: karyoploteR importsMe: annotatr, ChIPpeakAnno, CNVfilteR, CopyNumberPlots, karyoploteR, RIPAT, UMI4Cats suggestsMe: CNVRanger dependencyCount: 45 Package: regionReport Version: 1.24.2 Depends: R(>= 3.2) Imports: BiocStyle (>= 2.5.19), derfinder (>= 1.2.5), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitr (>= 1.6), knitrBootstrap (>= 0.9.0), methods, RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment, utils Suggests: BiocManager, biovizBase, bumphunter (>= 1.7.6), derfinderPlot (>= 1.3.2), sessioninfo, DT, edgeR, ggbio (>= 1.35.2), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 MD5sum: 44355f4d16ba03ac39627fa812e2efe9 NeedsCompilation: no Title: Generate HTML or PDF reports for a set of genomic regions or DESeq2/edgeR results Description: Generate HTML or PDF reports to explore a set of regions such as the results from annotation-agnostic expression analysis of RNA-seq data at base-pair resolution performed by derfinder. You can also create reports for DESeq2 or edgeR results. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage, ReportWriting, DifferentialMethylation, DifferentialPeakCalling, ImmunoOncology, QualityControl Author: Leonardo Collado-Torres [aut, cre] (), Andrew E. Jaffe [aut] (), Jeffrey T. Leek [aut, ths] () Maintainer: Leonardo Collado-Torres URL: https://github.com/leekgroup/regionReport VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regionReport/ git_url: https://git.bioconductor.org/packages/regionReport git_branch: RELEASE_3_12 git_last_commit: 4eb94a2 git_last_commit_date: 2020-12-18 Date/Publication: 2020-12-18 source.ver: src/contrib/regionReport_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/regionReport_1.24.2.zip mac.binary.ver: bin/macosx/contrib/4.0/regionReport_1.24.2.tgz vignettes: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/regionReport.html vignetteTitles: Example report using bumphunter results, Introduction to regionReport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/regionReport.R importsMe: recountWorkflow suggestsMe: recount dependencyCount: 161 Package: regsplice Version: 1.16.0 Imports: glmnet, SummarizedExperiment, S4Vectors, limma, edgeR, stats, pbapply, utils, methods Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: fe7194cf80498b50cfc43fb705f8b794 NeedsCompilation: no Title: L1-regularization based methods for detection of differential splicing Description: Statistical methods for detection of differential splicing (differential exon usage) in RNA-seq and exon microarray data, using L1-regularization (lasso) to improve power. biocViews: ImmunoOncology, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, RNASeq, Microarray, ExonArray, ExperimentalDesign, Software Author: Lukas M. Weber [aut, cre] Maintainer: Lukas M. Weber URL: https://github.com/lmweber/regsplice VignetteBuilder: knitr BugReports: https://github.com/lmweber/regsplice/issues git_url: https://git.bioconductor.org/packages/regsplice git_branch: RELEASE_3_12 git_last_commit: 110cc93 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/regsplice_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/regsplice_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/regsplice_1.16.0.tgz vignettes: vignettes/regsplice/inst/doc/regsplice-workflow.html vignetteTitles: regsplice workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/regsplice/inst/doc/regsplice-workflow.R dependencyCount: 38 Package: regutools Version: 1.2.3 Depends: R (>= 4.0) Imports: AnnotationDbi, AnnotationHub, Biostrings, DBI, GenomicRanges, Gviz, IRanges, RCy3, RSQLite, S4Vectors, methods, stats, utils, BiocFileCache Suggests: BiocStyle, knitr, RefManageR, rmarkdown, sessioninfo, testthat (>= 2.1.0), covr License: Artistic-2.0 MD5sum: ad0a5388062fc7b393a8822b8935b0e2 NeedsCompilation: no Title: regutools: an R package for data extraction from RegulonDB Description: RegulonDB has collected, harmonized and centralized data from hundreds of experiments for nearly two decades and is considered a point of reference for transcriptional regulation in Escherichia coli K12. Here, we present the regutools R package to facilitate programmatic access to RegulonDB data in computational biology. regutools provides researchers with the possibility of writing reproducible workflows with automated queries to RegulonDB. The regutools package serves as a bridge between RegulonDB data and the Bioconductor ecosystem by reusing the data structures and statistical methods powered by other Bioconductor packages. We demonstrate the integration of regutools with Bioconductor by analyzing transcription factor DNA binding sites and transcriptional regulatory networks from RegulonDB. We anticipate that regutools will serve as a useful building block in our progress to further our understanding of gene regulatory networks. biocViews: GeneRegulation, GeneExpression, SystemsBiology, Network,NetworkInference,Visualization, Transcription Author: Joselyn Chavez [aut, cre] (), Carmina Barberena-Jonas [aut] (), Jesus E. Sotelo-Fonseca [aut] (), Jose Alquicira-Hernandez [ctb] (), Heladia Salgado [ctb] (), Leonardo Collado-Torres [aut] (), Alejandro Reyes [aut] () Maintainer: Joselyn Chavez URL: https://github.com/ComunidadBioInfo/regutools VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/regutools git_url: https://git.bioconductor.org/packages/regutools git_branch: RELEASE_3_12 git_last_commit: de4a10a git_last_commit_date: 2020-12-18 Date/Publication: 2020-12-18 source.ver: src/contrib/regutools_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/regutools_1.2.3.zip mac.binary.ver: bin/macosx/contrib/4.0/regutools_1.2.3.tgz vignettes: vignettes/regutools/inst/doc/regutools.html vignetteTitles: regutools: an R package for data extraction from RegulonDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regutools/inst/doc/regutools.R dependencyCount: 162 Package: REMP Version: 1.14.0 Depends: R (>= 3.6), SummarizedExperiment(>= 1.1.6), minfi (>= 1.22.0) Imports: readr, rtracklayer, graphics, stats, utils, methods, settings, BiocGenerics, S4Vectors, Biostrings, GenomicRanges, IRanges, GenomeInfoDb, BiocParallel, doParallel, parallel, foreach, caret, kernlab, ranger, BSgenome, AnnotationHub, org.Hs.eg.db, impute, iterators Suggests: IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, minfiDataEPIC License: GPL-3 MD5sum: 495fa22b2a84771b2ad9b532ec6a6be2 NeedsCompilation: no Title: Repetitive Element Methylation Prediction Description: Machine learning-based tools to predict DNA methylation of locus-specific repetitive elements (RE) by learning surrounding genetic and epigenetic information. These tools provide genomewide and single-base resolution of DNA methylation prediction on RE that are difficult to measure using array-based or sequencing-based platforms, which enables epigenome-wide association study (EWAS) and differentially methylated region (DMR) analysis on RE. biocViews: DNAMethylation, Microarray, MethylationArray, Sequencing, GenomeWideAssociation, Epigenetics, Preprocessing, MultiChannel, TwoChannel, DifferentialMethylation, QualityControl, DataImport Author: Yinan Zheng [aut, cre], Lei Liu [aut], Wei Zhang [aut], Warren Kibbe [aut], Lifang Hou [aut, cph] Maintainer: Yinan Zheng URL: https://github.com/YinanZheng/REMP BugReports: https://github.com/YinanZheng/REMP/issues git_url: https://git.bioconductor.org/packages/REMP git_branch: RELEASE_3_12 git_last_commit: cdf19ff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/REMP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/REMP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/REMP_1.14.0.tgz vignettes: vignettes/REMP/inst/doc/REMP.pdf vignetteTitles: An Introduction to the REMP Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/REMP/inst/doc/REMP.R dependencyCount: 186 Package: Repitools Version: 1.36.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome (>= 1.47.3), gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) Archs: i386, x64 MD5sum: 69980a18fd552d846f4abf1f75a7655d NeedsCompilation: yes Title: Epigenomic tools Description: Tools for the analysis of enrichment-based epigenomic data. Features include summarization and visualization of epigenomic data across promoters according to gene expression context, finding regions of differential methylation/binding, BayMeth for quantifying methylation etc. biocViews: DNAMethylation, GeneExpression, MethylSeq Author: Mark Robinson , Dario Strbenac , Aaron Statham , Andrea Riebler Maintainer: Mark Robinson git_url: https://git.bioconductor.org/packages/Repitools git_branch: RELEASE_3_12 git_last_commit: 60617aa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Repitools_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Repitools_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Repitools_1.36.0.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Repitools/inst/doc/Repitools_vignette.R dependencyCount: 110 Package: ReportingTools Version: 2.30.2 Depends: methods, knitr, utils Imports: Biobase,hwriter,Category,GOstats,limma(>= 3.17.5),lattice,AnnotationDbi,edgeR, annotate,PFAM.db, GSEABase, BiocGenerics(>= 0.1.6), grid, XML, R.utils, DESeq2(>= 1.3.41), ggplot2, ggbio, IRanges Suggests: RUnit, ALL, hgu95av2.db, org.Mm.eg.db, shiny, pasilla, org.Sc.sgd.db License: Artistic-2.0 MD5sum: d75ce77967dc294a369b664bb52b8806 NeedsCompilation: no Title: Tools for making reports in various formats Description: The ReportingTools software package enables users to easily display reports of analysis results generated from sources such as microarray and sequencing data. The package allows users to create HTML pages that may be viewed on a web browser such as Safari, or in other formats readable by programs such as Excel. Users can generate tables with sortable and filterable columns, make and display plots, and link table entries to other data sources such as NCBI or larger plots within the HTML page. Using the package, users can also produce a table of contents page to link various reports together for a particular project that can be viewed in a web browser. For more examples, please visit our site: http:// research-pub.gene.com/ReportingTools. biocViews: ImmunoOncology, Software, Visualization, Microarray, RNASeq, GO, DataRepresentation, GeneSetEnrichment Author: Jason A. Hackney, Melanie Huntley, Jessica L. Larson, Christina Chaivorapol, Gabriel Becker, and Josh Kaminker Maintainer: Jason A. Hackney , Gabriel Becker , Jessica L. Larson VignetteBuilder: utils, knitr git_url: https://git.bioconductor.org/packages/ReportingTools git_branch: RELEASE_3_12 git_last_commit: d0b7f53 git_last_commit_date: 2021-03-08 Date/Publication: 2021-03-08 source.ver: src/contrib/ReportingTools_2.30.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/ReportingTools_2.30.2.zip mac.binary.ver: bin/macosx/contrib/4.0/ReportingTools_2.30.2.tgz vignettes: vignettes/ReportingTools/inst/doc/basicReportingTools.pdf, vignettes/ReportingTools/inst/doc/microarrayAnalysis.pdf, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.pdf, vignettes/ReportingTools/inst/doc/shiny.pdf, vignettes/ReportingTools/inst/doc/knitr.html vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny, Knitr and ReportingTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReportingTools/inst/doc/basicReportingTools.R, vignettes/ReportingTools/inst/doc/knitr.R, vignettes/ReportingTools/inst/doc/microarrayAnalysis.R, vignettes/ReportingTools/inst/doc/rnaseqAnalysis.R, vignettes/ReportingTools/inst/doc/shiny.R dependsOnMe: rnaseqGene importsMe: affycoretools suggestsMe: cpvSNP, EnrichmentBrowser, GSEABase, npGSEA dependencyCount: 169 Package: RepViz Version: 1.6.0 Depends: R (>= 3.5.1), GenomicRanges (>= 1.30.0), Rsamtools (>= 1.34.1), IRanges (>= 2.14.0), biomaRt (>= 2.36.0), S4Vectors (>= 0.18.0), graphics, grDevices, utils Suggests: knitr, testthat License: GPL-3 MD5sum: b87270b280a79664741238c6b1636177 NeedsCompilation: no Title: Replicate oriented Visualization of a genomic region Description: RepViz enables the view of a genomic region in a simple and efficient way. RepViz allows simultaneous viewing of both intra- and intergroup variation in sequencing counts of the studied conditions, as well as their comparison to the output features (e.g. identified peaks) from user selected data analysis methods.The RepViz tool is primarily designed for chromatin data such as ChIP-seq and ATAC-seq, but can also be used with other sequencing data such as RNA-seq, or combinations of different types of genomic data. biocViews: WorkflowStep, Visualization, Sequencing, ChIPSeq, ATACSeq, Software, Coverage, GenomicVariation Author: Thomas Faux, Kalle Rytkönen, Asta Laiho, Laura L. Elo Maintainer: Thomas Faux, Asta Laiho VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RepViz git_branch: RELEASE_3_12 git_last_commit: 10854f8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RepViz_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RepViz_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RepViz_1.6.0.tgz vignettes: vignettes/RepViz/inst/doc/RepViz.html vignetteTitles: RepViz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RepViz/inst/doc/RepViz.R dependencyCount: 78 Package: ReQON Version: 1.36.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: dd4d74ad5687b48478f6595f37df408c NeedsCompilation: no Title: Recalibrating Quality Of Nucleotides Description: Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format. biocViews: Sequencing, HighThroughputSequencing, Preprocessing, QualityControl Author: Christopher Cabanski, Keary Cavin, Chris Bizon Maintainer: Christopher Cabanski SystemRequirements: Java version >= 1.6 git_url: https://git.bioconductor.org/packages/ReQON git_branch: RELEASE_3_12 git_last_commit: 1ab211a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ReQON_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ReQON_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ReQON_1.36.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReQON/inst/doc/ReQON.R dependencyCount: 31 Package: ResidualMatrix Version: 1.0.0 Imports: methods, Matrix, S4Vectors, DelayedArray Suggests: testthat, BiocStyle, knitr, rmarkdown, BiocSingular License: GPL-3 MD5sum: 257bffca34f0cefaa361df149dd313cc NeedsCompilation: no Title: Creating a DelayedMatrix of Regression Residuals Description: Provides delayed computation of a matrix of residuals after fitting a linear model to each column of an input matrix. Also supports partial computation of residuals where selected factors are to be preserved in the output matrix. Implements a number of efficient methods for operating on the delayed matrix of residuals, most notably matrix multiplication and calculation of row/column sums or means. biocViews: Software, DataRepresentation, Regression, BatchEffect, ExperimentalDesign Author: Aaron Lun [aut, cre, cph] Maintainer: Aaron Lun URL: https://github.com/LTLA/ResidualMatrix VignetteBuilder: knitr BugReports: https://github.com/LTLA/ResidualMatrix/issues git_url: https://git.bioconductor.org/packages/ResidualMatrix git_branch: RELEASE_3_12 git_last_commit: 120f14f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ResidualMatrix_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ResidualMatrix_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ResidualMatrix_1.0.0.tgz vignettes: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.html vignetteTitles: Using the ResidualMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ResidualMatrix/inst/doc/ResidualMatrix.R importsMe: batchelor suggestsMe: BiocSingular dependencyCount: 16 Package: restfulSE Version: 1.12.0 Depends: R (>= 3.6), SummarizedExperiment,DelayedArray Imports: utils, stats, methods, S4Vectors, Biobase,reshape2, AnnotationDbi, DBI, GO.db, rhdf5client, dplyr (>= 0.7.1), magrittr, bigrquery, ExperimentHub, AnnotationHub, rlang Suggests: knitr, testthat, Rtsne, org.Mm.eg.db, org.Hs.eg.db, BiocStyle, restfulSEData License: Artistic-2.0 MD5sum: 836ccfc851760291da4d154230a2c9c9 NeedsCompilation: no Title: Access matrix-like HDF5 server content or BigQuery content through a SummarizedExperiment interface Description: This package provides functions and classes to interface with remote data stores by operating on SummarizedExperiment-like objects. biocViews: Infrastructure, SingleCell, Transcriptomics, Sequencing, Coverage Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/restfulSE git_branch: RELEASE_3_12 git_last_commit: 0466453 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/restfulSE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/restfulSE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/restfulSE_1.12.0.tgz vignettes: vignettes/restfulSE/inst/doc/restfulSE.pdf vignetteTitles: restfulSE -- experiments with SE interface to remote HDF5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/restfulSE/inst/doc/restfulSE.R dependsOnMe: tenXplore suggestsMe: BiocOncoTK, BiocSklearn dependencyCount: 105 Package: rexposome Version: 1.12.4 Depends: R (>= 3.5), Biobase Imports: methods, utils, stats, lsr, FactoMineR, stringr, circlize, corrplot, ggplot2, reshape2, pryr, S4Vectors, imputeLCMD, scatterplot3d, glmnet, gridExtra, grid, Hmisc, gplots, gtools, scales, lme4, grDevices, graphics, ggrepel, mice Suggests: mclust, flexmix, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 66e9ba38bf482f3ea6abf5bdfa489452 NeedsCompilation: no Title: Exposome exploration and outcome data analysis Description: Package that allows to explore the exposome and to perform association analyses between exposures and health outcomes. biocViews: Software, BiologicalQuestion, Infrastructure, DataImport, DataRepresentation, BiomedicalInformatics, ExperimentalDesign, MultipleComparison, Classification, Clustering Author: Carles Hernandez-Ferrer [aut, cre], Juan R. Gonzalez [aut], Xavier Escribà-Montagut [aut] Maintainer: Xavier Escribà Montagut VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rexposome git_branch: RELEASE_3_12 git_last_commit: c47928c git_last_commit_date: 2021-03-11 Date/Publication: 2021-03-11 source.ver: src/contrib/rexposome_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/rexposome_1.12.4.zip mac.binary.ver: bin/macosx/contrib/4.0/rexposome_1.12.4.tgz vignettes: vignettes/rexposome/inst/doc/exposome_data_analysis.html, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.html vignetteTitles: Exposome Data Analysis, Dealing with Multiple Imputations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rexposome/inst/doc/exposome_data_analysis.R, vignettes/rexposome/inst/doc/mutiple_imputation_data_analysis.R importsMe: omicRexposome suggestsMe: brgedata dependencyCount: 154 Package: rfaRm Version: 1.2.1 Imports: httr, stringi, rsvg, magick, data.table, Biostrings, utils, rvest, xml2, IRanges, S4Vectors Suggests: R4RNA, treeio, knitr, BiocStyle, rmarkdown, BiocGenerics License: GPL-3 MD5sum: a88e30882d5f9af960eeb8a9d22c145d NeedsCompilation: no Title: An R interface to the Rfam database Description: rfaRm provides a client interface to the Rfam database of RNA families. Data that can be retrieved include RNA families, secondary structure images, covariance models, sequences within each family, alignments leading to the identification of a family and secondary structures in the dot-bracket format. biocViews: FunctionalGenomics, DataImport, ThirdPartyClient, Visualization, MultipleSequenceAlignment Author: Lara Selles Vidal, Rafael Ayala, Guy-Bart Stan, Rodrigo Ledesma-Amaro Maintainer: Lara Selles Vidal , Rafael Ayala VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rfaRm git_branch: RELEASE_3_12 git_last_commit: 312b840 git_last_commit_date: 2021-03-15 Date/Publication: 2021-03-15 source.ver: src/contrib/rfaRm_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/rfaRm_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/rfaRm_1.2.1.tgz vignettes: vignettes/rfaRm/inst/doc/rfaRm.html vignetteTitles: rfaRm hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfaRm/inst/doc/rfaRm.R dependencyCount: 44 Package: Rfastp Version: 1.0.0 Imports: Rcpp, rjson, ggplot2, reshape2 LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: BiocStyle, testthat, knitr, rmarkdown License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 18ca256b458754d0a32acc3f548e8e30 NeedsCompilation: yes Title: An Ultra-Fast and All-in-One Fastq Preprocessor (Quality Control, Adapter, low quality and polyX trimming) and UMI Sequence Parsing). Description: Rfastp is an R wrapper of fastp developed in c++. fastp performs quality control for fastq files. including low quality bases trimming, polyX trimming, adapter auto-detection and trimming, paired-end reads merging, UMI sequence/id handling. Rfastp can concatenate multiple files into one file (like shell command cat) and accept multiple files as input. biocViews: QualityControl, Sequencing, Preprocessing, Software Author: Wei Wang [aut] (), Ji-Dung Luo [ctb] (), Thomas Carroll [cre, aut] () Maintainer: Thomas Carroll SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rfastp git_branch: RELEASE_3_12 git_last_commit: e6fb642 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rfastp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rfastp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rfastp_1.0.0.tgz vignettes: vignettes/Rfastp/inst/doc/Rfastp.html vignetteTitles: Rfastp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rfastp/inst/doc/Rfastp.R dependencyCount: 47 Package: rfPred Version: 1.28.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) MD5sum: cb9d820ee3937ddb8819c1312b2b20bb NeedsCompilation: yes Title: Assign rfPred functional prediction scores to a missense variants list Description: Based on external numerous data files where rfPred scores are pre-calculated on all genomic positions of the human exome, the package gives rfPred scores to missense variants identified by the chromosome, the position (hg19 version), the referent and alternative nucleotids and the uniprot identifier of the protein. Note that for using the package, the user has to be connected on the Internet or to download the TabixFile and index (approximately 3.3 Go). biocViews: Software, Annotation, Classification Author: Fabienne Jabot-Hanin, Hugo Varet and Jean-Philippe Jais Maintainer: Hugo Varet URL: http://www.sbim.fr/rfPred git_url: https://git.bioconductor.org/packages/rfPred git_branch: RELEASE_3_12 git_last_commit: 522f851 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rfPred_1.28.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/rfPred_1.28.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rfPred/inst/doc/vignette.R dependencyCount: 30 Package: rGADEM Version: 2.38.0 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, GenomicRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: 0fe31f1d807cb826fe274b6295141b61 NeedsCompilation: yes Title: de novo motif discovery Description: rGADEM is an efficient de novo motif discovery tool for large-scale genomic sequence data. It is an open-source R package, which is based on the GADEM software. biocViews: Microarray, ChIPchip, Sequencing, ChIPSeq, MotifDiscovery Author: Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li Maintainer: Arnaud Droit git_url: https://git.bioconductor.org/packages/rGADEM git_branch: RELEASE_3_12 git_last_commit: 84e3b59 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rGADEM_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rGADEM_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rGADEM_2.38.0.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGADEM/inst/doc/rGADEM.R importsMe: TCGAWorkflow dependencyCount: 42 Package: RGalaxy Version: 1.34.0 Depends: XML, methods, tools, optparse Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, AnnotationDbi, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: 09e330601f1eff9ff1aa9170db23568a NeedsCompilation: no Title: Make an R function available in the Galaxy web platform Description: Given an R function and its manual page, make the documented function available in Galaxy. biocViews: Infrastructure Author: Dan Tenenbaum Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGalaxy git_branch: RELEASE_3_12 git_last_commit: 0c5b560 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RGalaxy_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RGalaxy_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RGalaxy_1.34.0.tgz vignettes: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html vignetteTitles: Introduction to RGalaxy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R dependencyCount: 39 Package: Rgin Version: 1.10.0 Depends: R (>= 3.5) LinkingTo: RcppEigen (>= 0.3.3.5.0) Suggests: knitr, rmarkdown License: MIT + file LICENSE Archs: i386, x64 MD5sum: 2db5b91d1ee81e2803c1592f62e1ed51 NeedsCompilation: yes Title: gin in R Description: C++ implementation of SConES. biocViews: Software, GenomeWideAssociation, SNP, GeneticVariability, Genetics, FeatureExtraction, GraphAndNetwork, Network Author: Hector Climente-Gonzalez [aut, cre], Dominik Gerhard Grimm [aut], Chloe-Agathe Azencott [aut] Maintainer: Hector Climente-Gonzalez VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rgin git_branch: RELEASE_3_12 git_last_commit: 880c928 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rgin_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rgin_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rgin_1.10.0.tgz vignettes: vignettes/Rgin/inst/doc/Rgin-UsingCppLibraries.html vignetteTitles: Using Rgin C++ libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE linksToMe: martini dependencyCount: 10 Package: RGMQL Version: 1.10.0 Depends: R(>= 3.4.2), RGMQLlib Imports: httr, rJava, GenomicRanges, rtracklayer, data.table, utils, plyr, xml2, methods, S4Vectors, dplyr, stats, glue, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 962c97bf673544ec670cb68f0e55a5f9 NeedsCompilation: no Title: GenoMetric Query Language for R/Bioconductor Description: This package brings the GenoMetric Query Language (GMQL) functionalities into the R environment. GMQL is a high-level, declarative language to manage heterogeneous genomic datasets for biomedical purposes, using simple queries to process genomic regions and their metadata and properties. GMQL adopts algorithms efficiently designed for big data using cloud-computing technologies (like Apache Hadoop and Spark) allowing GMQL to run on modern infrastructures, in order to achieve scalability and high performance. It allows to create, manipulate and extract genomic data from different data sources both locally and remotely. Our RGMQL functions allow complex queries and processing leveraging on the R idiomatic paradigm. The RGMQL package also provides a rich set of ancillary classes that allow sophisticated input/output management and sorting, such as: ASC, DESC, BAG, MIN, MAX, SUM, AVG, MEDIAN, STD, Q1, Q2, Q3 (and many others). Note that many RGMQL functions are not directly executed in R environment, but are deferred until real execution is issued. biocViews: Software, Infrastructure, DataImport, Network, ImmunoOncology, SingleCell Author: Simone Pallotta, Marco Masseroli Maintainer: Simone Pallotta URL: http://www.bioinformatics.deib.polimi.it/genomic_computing/GMQL/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGMQL git_branch: RELEASE_3_12 git_last_commit: 61091e1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RGMQL_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RGMQL_1.10.0.zip vignettes: vignettes/RGMQL/inst/doc/RGMQL-vignette.pdf vignetteTitles: RGMQL: GenoMetric Query Language for R/Bioconductor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGMQL/inst/doc/RGMQL-vignette.R dependencyCount: 70 Package: RGraph2js Version: 1.18.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: 4475e6ee7471ff10c22c5e3f2915e652 NeedsCompilation: no Title: Convert a Graph into a D3js Script Description: Generator of web pages which display interactive network/graph visualizations with D3js, jQuery and Raphael. biocViews: Visualization, Network, GraphAndNetwork, ThirdPartyClient Author: Stephane Cano [aut, cre], Sylvain Gubian [aut], Florian Martin [aut] Maintainer: Stephane Cano SystemRequirements: jQuery, jQueryUI, qTip2, D3js and Raphael are required Javascript libraries made available via the online CDNJS service (http://cdnjs.cloudflare.com). git_url: https://git.bioconductor.org/packages/RGraph2js git_branch: RELEASE_3_12 git_last_commit: 975f2ab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RGraph2js_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RGraph2js_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RGraph2js_1.18.0.tgz vignettes: vignettes/RGraph2js/inst/doc/RGraph2js.pdf vignetteTitles: RGraph2js hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGraph2js/inst/doc/RGraph2js.R dependencyCount: 11 Package: Rgraphviz Version: 2.34.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: b487eef658cc88ebac4e6ee59c36d036 NeedsCompilation: yes Title: Provides plotting capabilities for R graph objects Description: Interfaces R with the AT and T graphviz library for plotting R graph objects from the graph package. biocViews: GraphAndNetwork, Visualization Author: Kasper Daniel Hansen [cre, aut], Jeff Gentry [aut], Li Long [aut], Robert Gentleman [aut], Seth Falcon [aut], Florian Hahne [aut], Deepayan Sarkar [aut] Maintainer: Kasper Daniel Hansen SystemRequirements: optionally Graphviz (>= 2.16) git_url: https://git.bioconductor.org/packages/Rgraphviz git_branch: RELEASE_3_12 git_last_commit: 9746623 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rgraphviz_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rgraphviz_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rgraphviz_2.34.0.tgz vignettes: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.pdf, vignettes/Rgraphviz/inst/doc/Rgraphviz.pdf vignetteTitles: A New Interface to Plot Graphs Using Rgraphviz, How To Plot A Graph Using Rgraphviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rgraphviz/inst/doc/newRgraphvizInterface.R, vignettes/Rgraphviz/inst/doc/Rgraphviz.R dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, MineICA, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, maEndToEnd, abn, dlsem, geneNetBP, gridGraphviz, GUIProfiler, hasseDiagram importsMe: apComplex, biocGraph, BiocOncoTK, chimeraviz, CompGO, CytoML, DEGraph, EnrichmentBrowser, flowWorkspace, GeneNetworkBuilder, GOstats, hyperdraw, MIGSA, mirIntegrator, mnem, OncoSimulR, ontoProc, paircompviz, pathview, Pigengene, qpgraph, RchyOptimyx, SplicingGraphs, trackViewer, TRONCO, BiDAG, bnpa, ceg, CePa, classGraph, cogmapr, dnet, gRain, gRbase, gRim, hmma, hpoPlot, IMaGES, maGUI, MetaClean, ontologyPlot, stablespec, wiseR suggestsMe: a4, altcdfenvs, annotate, BiocCaseStudies, Category, CNORfeeder, CNORfuzzy, DEGraph, flowCore, geneplotter, GlobalAncova, globaltest, GSEABase, KEGGgraph, MLP, NCIgraph, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RDAVIDWebService, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, ViSEAGO, vtpnet, NCIgraphData, SNAData, arulesViz, BayesNetBP, bnclassify, bnlearn, bnstruct, bsub, ChoR, CodeDepends, gbutils, GeneNet, gRc, HEMDAG, iTOP, kpcalg, kst, lava, loon, MCDA, msSurv, multiplex, ParallelPC, pcalg, psych, relations, rEMM, rPref, RSeed, SCCI, sisal, SourceSet, textplot, tm, topologyGSA, unifDAG, zenplots dependencyCount: 10 Package: rGREAT Version: 1.22.0 Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils, stats Suggests: testthat (>= 0.3), knitr, circlize (>= 0.4.8), rmarkdown License: MIT + file LICENSE MD5sum: 93817e4b920bc97baa8f7b07aeaf46b2 NeedsCompilation: no Title: Client for GREAT Analysis Description: This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user's input and automatically retrieving results from GREAT web server. biocViews: GeneSetEnrichment, GO, Pathways, Software, Sequencing, WholeGenome, GenomeAnnotation, Coverage Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/rGREAT, http://great.stanford.edu/public/html/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rGREAT git_branch: RELEASE_3_12 git_last_commit: af8fdc3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rGREAT_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rGREAT_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rGREAT_1.22.0.tgz vignettes: vignettes/rGREAT/inst/doc/rGREAT.html vignetteTitles: Analyze with GREAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R suggestsMe: TADCompare dependencyCount: 22 Package: RGSEA Version: 1.24.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: e232eb6f154bbac217a8d888ca8f317b NeedsCompilation: no Title: Random Gene Set Enrichment Analysis Description: Combining bootstrap aggregating and Gene set enrichment analysis (GSEA), RGSEA is a classfication algorithm with high robustness and no over-fitting problem. It performs well especially for the data generated from different exprements. biocViews: GeneSetEnrichment, StatisticalMethod, Classification Author: Chengcheng Ma Maintainer: Chengcheng Ma VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RGSEA git_branch: RELEASE_3_12 git_last_commit: 4f3d173 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RGSEA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RGSEA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RGSEA_1.24.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGSEA/inst/doc/RGSEA.R dependencyCount: 6 Package: rgsepd Version: 1.22.0 Depends: R (>= 4.0.0), DESeq2, goseq (>= 1.28) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi Suggests: boot, tools, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: da3fee06505f2938598173428710a5b4 NeedsCompilation: no Title: Gene Set Enrichment / Projection Displays Description: R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at transcript IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group. biocViews: ImmunoOncology, Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rgsepd git_branch: RELEASE_3_12 git_last_commit: 3c6fade git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rgsepd_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rgsepd_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rgsepd_1.22.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R dependencyCount: 121 Package: rhdf5 Version: 2.34.0 Depends: R (>= 3.5.0), methods Imports: Rhdf5lib (>= 1.11.0), rhdf5filters LinkingTo: Rhdf5lib Suggests: bit64, BiocStyle, knitr, rmarkdown, testthat, microbenchmark, dplyr, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: 28e2fd759161e9fe67c4b34ccf1e76d5 NeedsCompilation: yes Title: R Interface to HDF5 Description: This package provides an interface between HDF5 and R. HDF5's main features are the ability to store and access very large and/or complex datasets and a wide variety of metadata on mass storage (disk) through a completely portable file format. The rhdf5 package is thus suited for the exchange of large and/or complex datasets between R and other software package, and for letting R applications work on datasets that are larger than the available RAM. biocViews: Infrastructure, DataImport Author: Bernd Fischer [aut], Mike Smith [aut, cre] (), Gregoire Pau [aut], Martin Morgan [ctb], Daniel van Twisk [ctb] Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5/issues git_url: https://git.bioconductor.org/packages/rhdf5 git_branch: RELEASE_3_12 git_last_commit: ec861b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rhdf5_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rhdf5_2.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rhdf5_2.34.0.tgz vignettes: vignettes/rhdf5/inst/doc/practical_tips.html, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.html, vignettes/rhdf5/inst/doc/rhdf5.html vignetteTitles: rhdf5 Practical Tips, Reading HDF5 Files In The Cloud, rhdf5 - HDF5 interface for R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/practical_tips.R, vignettes/rhdf5/inst/doc/rhdf5_cloud_reading.R, vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GenoGAM, GSCA, HDF5Array, HiCBricks, LoomExperiment importsMe: BayesSpace, BgeeCall, biomformat, bnbc, bsseq, CiteFuse, cmapR, CoGAPS, CopyNumberPlots, cTRAP, diffHic, DropletUtils, EventPointer, FRASER, GenomicScores, gep2pep, h5vc, HiCcompare, IONiseR, MOFA, MOFA2, phantasus, PureCN, recountmethylation, ribor, scCB2, scone, signatureSearch, slinky, MafH5.gnomAD.r3.0.GRCh38, DmelSGI, MethylSeqData, signatureSearchData, bioRad, NEONiso, ondisc, smapr suggestsMe: edgeR, slalom, Spectra, SummarizedExperiment, tximport, zellkonverter, antaresProcessing, antaresRead, antaresViz, conos, hadron, io, isoreader, MplusAutomation, neonstore, neonUtilities, rbiom, SignacX dependencyCount: 3 Package: rhdf5client Version: 1.12.0 Depends: R (>= 3.6), methods, DelayedArray Imports: S4Vectors, httr, R6, rjson, utils Suggests: knitr, testthat, BiocStyle, DT, reticulate License: Artistic-2.0 Archs: i386, x64 MD5sum: 4caf7a3501fd0aadb18cde2d7b4efeb2 NeedsCompilation: yes Title: Access HDF5 content from h5serv Description: Provides functionality for reading data from h5serv server from within R. biocViews: DataImport, Software Author: Samuela Pollack [aut], Shweta Gopaulakrishnan [aut], Vincent Carey [cre, aut] Maintainer: Vincent Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rhdf5client git_branch: RELEASE_3_12 git_last_commit: 4216a18 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rhdf5client_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rhdf5client_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rhdf5client_1.12.0.tgz vignettes: vignettes/rhdf5client/inst/doc/delayed-array.html vignetteTitles: HSDSArray DelayedArray backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5client/inst/doc/delayed-array.R importsMe: restfulSE dependencyCount: 26 Package: rhdf5filters Version: 1.2.1 LinkingTo: Rhdf5lib Suggests: BiocStyle, knitr, rmarkdown, testthat (>= 2.1.0) License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 2c33526ef4e8757742f290442bc2792b NeedsCompilation: yes Title: HDF5 Compression Filters Description: Provides a collection of compression filters for use with HDF5 datasets. biocViews: Infrastructure, DataImport Author: Mike Smith [aut, cre] () Maintainer: Mike Smith URL: https://github.com/grimbough/rhdf5filters SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/rhdf5filters git_url: https://git.bioconductor.org/packages/rhdf5filters git_branch: RELEASE_3_12 git_last_commit: 04dcf6e git_last_commit_date: 2021-05-03 Date/Publication: 2021-05-03 source.ver: src/contrib/rhdf5filters_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/rhdf5filters_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/rhdf5filters_1.2.1.tgz vignettes: vignettes/rhdf5filters/inst/doc/rhdf5filters.html vignetteTitles: HDF5 Compression Filters hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rhdf5filters/inst/doc/rhdf5filters.R importsMe: rhdf5 dependencyCount: 1 Package: Rhdf5lib Version: 1.12.1 Depends: R (>= 3.3.0) Suggests: BiocStyle, knitr, rmarkdown, tinytest License: Artistic-2.0 Archs: i386, x64 MD5sum: 026aece8e805bc6b618705f208165a92 NeedsCompilation: yes Title: hdf5 library as an R package Description: Provides C and C++ hdf5 libraries. biocViews: Infrastructure Author: Mike Smith Maintainer: Mike Smith URL: https://github.com/grimbough/Rhdf5lib SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/grimbough/Rhdf5lib git_url: https://git.bioconductor.org/packages/Rhdf5lib git_branch: RELEASE_3_12 git_last_commit: cf464f4 git_last_commit_date: 2021-01-26 Date/Publication: 2021-01-26 source.ver: src/contrib/Rhdf5lib_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rhdf5lib_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/Rhdf5lib_1.12.1.tgz vignettes: vignettes/Rhdf5lib/inst/doc/downloadHDF5.html, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.html vignetteTitles: Creating this HDF5 distribution, Linking to Rhdf5lib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhdf5lib/inst/doc/downloadHDF5.R, vignettes/Rhdf5lib/inst/doc/Rhdf5lib.R importsMe: rhdf5 suggestsMe: mbkmeans linksToMe: DropletUtils, HDF5Array, mbkmeans, mzR, ncdfFlow, rhdf5, rhdf5filters, ondisc dependencyCount: 0 Package: Rhisat2 Version: 1.6.0 Depends: R (>= 3.6) Imports: GenomicFeatures, SGSeq, GenomicRanges, methods, utils Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: x64 MD5sum: fedd705d38d6e045dc89b3ed70804b32 NeedsCompilation: yes Title: R Wrapper for HISAT2 Aligner Description: An R interface to the HISAT2 spliced short-read aligner by Kim et al. (2015). The package contains wrapper functions to create a genome index and to perform the read alignment to the generated index. biocViews: Alignment, Sequencing, SplicedAlignment Author: Charlotte Soneson [aut, cre] () Maintainer: Charlotte Soneson URL: https://github.com/fmicompbio/Rhisat2 SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/fmicompbio/Rhisat2/issues git_url: https://git.bioconductor.org/packages/Rhisat2 git_branch: RELEASE_3_12 git_last_commit: b93a9b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rhisat2_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rhisat2_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rhisat2_1.6.0.tgz vignettes: vignettes/Rhisat2/inst/doc/Rhisat2.html vignetteTitles: Rhisat2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rhisat2/inst/doc/Rhisat2.R importsMe: QuasR dependencyCount: 91 Package: Rhtslib Version: 1.22.0 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) Archs: i386, x64 MD5sum: 519615e896fd6647af00b6f04d50bce6 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.7 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib"). biocViews: DataImport, Sequencing Author: Nathaniel Hayden [led, aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rhtslib, http://www.htslib.org/ SystemRequirements: libbz2 & liblzma & libcurl (with header files), GNU make VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/Rhtslib/issues git_url: https://git.bioconductor.org/packages/Rhtslib git_branch: RELEASE_3_12 git_last_commit: 899b79f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rhtslib_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rhtslib_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rhtslib_1.22.0.tgz vignettes: vignettes/Rhtslib/inst/doc/Rhtslib.html vignetteTitles: Motivation and use of Rhtslib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R importsMe: deepSNV, diffHic, scPipe linksToMe: ArrayExpressHTS, bamsignals, BitSeq, csaw, deepSNV, DiffBind, diffHic, h5vc, methylKit, podkat, qrqc, QuasR, Rfastp, Rsamtools, scPipe, seqbias, ShortRead, TransView, VariantAnnotation, jackalope dependencyCount: 1 Package: RiboProfiling Version: 1.20.0 Depends: R (>= 3.2.2), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape2, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer, data.table, sqldf Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat, SummarizedExperiment License: GPL-3 MD5sum: 2fb425f39c9fc3d98123fc0e33c27d26 NeedsCompilation: no Title: Ribosome Profiling Data Analysis: from BAM to Data Representation and Interpretation Description: Starting with a BAM file, this package provides the necessary functions for quality assessment, read start position recalibration, the counting of reads on CDS, 3'UTR, and 5'UTR, plotting of count data: pairs, log fold-change, codon frequency and coverage assessment, principal component analysis on codon coverage. biocViews: RiboSeq, Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RiboProfiling git_branch: RELEASE_3_12 git_last_commit: 0037574 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RiboProfiling_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RiboProfiling_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RiboProfiling_1.20.0.tgz vignettes: vignettes/RiboProfiling/inst/doc/RiboProfiling.pdf vignetteTitles: Analysing Ribo-Seq data with the "RiboProfiling" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RiboProfiling/inst/doc/RiboProfiling.R dependencyCount: 153 Package: ribor Version: 1.2.0 Depends: R (>= 3.6.0) Imports: dplyr, ggplot2, hash, methods, rhdf5, rlang, stats, S4Vectors, tidyr, tools, yaml Suggests: testthat, knitr, rmarkdown License: GPL-3 MD5sum: 338778ca9b3013407ff19bb182df48bb NeedsCompilation: no Title: An R Interface for Ribo Files Description: The ribor package provides an R Interface for .ribo files. It provides functionality to read the .ribo file, which is of HDF5 format, and performs common analyses on its contents. biocViews: Software, Infrastructure Author: Michael Geng [cre], Hakan Ozadam [aut], Can Cenik [aut] Maintainer: Michael Geng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribor git_branch: RELEASE_3_12 git_last_commit: c9ed535 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ribor_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ribor_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ribor_1.2.0.tgz vignettes: vignettes/ribor/inst/doc/ribor.html vignetteTitles: A Walkthrough of RiboR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ribor/inst/doc/ribor.R dependencyCount: 54 Package: riboSeqR Version: 1.24.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Imports: Rsamtools, IRanges, baySeq, GenomeInfoDb, seqLogo Suggests: BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 79587442b2efe52c0a662296eeea785f NeedsCompilation: no Title: Analysis of sequencing data from ribosome profiling experiments Description: Plotting functions, frameshift detection and parsing of sequencing data from ribosome profiling experiments. biocViews: Sequencing,Genetics,Visualization,RiboSeq Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/riboSeqR git_branch: RELEASE_3_12 git_last_commit: ac2e64e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/riboSeqR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/riboSeqR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/riboSeqR_1.24.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/riboSeqR/inst/doc/riboSeqR.R dependencyCount: 38 Package: ribosomeProfilingQC Version: 1.2.1 Depends: R (>= 4.0), GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, BSgenome, EDASeq, GenomicAlignments, GenomicFeatures, GenomeInfoDb, IRanges, methods, motifStack, rtracklayer, Rsamtools, RUVSeq, Rsubread, S4Vectors, XVector, ggplot2, ggfittext, scales, ggrepel, utils, cluster, stats, graphics, grid Suggests: RUnit, BiocStyle, knitr, BSgenome.Drerio.UCSC.danRer10, edgeR, limma, testthat License: GPL (>=3) + file LICENSE MD5sum: 4a214333c2c05ee66e417f3eded3f7af NeedsCompilation: no Title: Ribosome Profiling Quality Control Description: Ribo-Seq (also named ribosome profiling or footprinting) measures translatome (unlike RNA-Seq, which sequences the transcriptome) by direct quantification of the ribosome-protected fragments (RPFs). This package provides the tools for quality assessment of ribosome profiling. In addition, it can preprocess Ribo-Seq data for subsequent differential analysis. biocViews: RiboSeq, Sequencing, GeneRegulation, QualityControl, Visualization, Coverage Author: Jianhong Ou [aut, cre] (), Mariah Hoye [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ribosomeProfilingQC git_branch: RELEASE_3_12 git_last_commit: 5c7f59f git_last_commit_date: 2021-03-10 Date/Publication: 2021-03-11 source.ver: src/contrib/ribosomeProfilingQC_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ribosomeProfilingQC_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ribosomeProfilingQC_1.2.1.tgz vignettes: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.html vignetteTitles: ribosomeProfilingQC Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ribosomeProfilingQC/inst/doc/ribosomeProfilingQC.R dependencyCount: 132 Package: RImmPort Version: 1.18.0 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 1271130280879752d92ec29f21cd86bd NeedsCompilation: no Title: RImmPort: Enabling Ready-for-analysis Immunology Research Data Description: The RImmPort package simplifies access to ImmPort data for analysis in the R environment. It provides a standards-based interface to the ImmPort study data that is in a proprietary format. biocViews: BiomedicalInformatics, DataImport, DataRepresentation Author: Ravi Shankar Maintainer: Zicheng Hu , Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RImmPort git_branch: RELEASE_3_12 git_last_commit: 16640aa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RImmPort_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RImmPort_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RImmPort_1.18.0.tgz vignettes: vignettes/RImmPort/inst/doc/RImmPort_Article.pdf, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.pdf vignetteTitles: RImmPort: Enabling ready-for-analysis immunology research data, RImmPort: Quick Start Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RImmPort/inst/doc/RImmPort_Article.R, vignettes/RImmPort/inst/doc/RImmPort_QuickStart.R dependencyCount: 43 Package: Ringo Version: 1.54.0 Depends: methods, Biobase (>= 1.14.1), RColorBrewer, limma, Matrix, grid, lattice Imports: BiocGenerics (>= 0.1.11), genefilter, limma, vsn, stats4 Suggests: rtracklayer (>= 1.3.1), mclust, topGO (>= 1.15.0) License: Artistic-2.0 Archs: i386, x64 MD5sum: c9958d8f07f743b002523093f6014e5c NeedsCompilation: yes Title: R Investigation of ChIP-chip Oligoarrays Description: The package Ringo facilitates the primary analysis of ChIP-chip data. The main functionalities of the package are data read-in, quality assessment, data visualisation and identification of genomic regions showing enrichment in ChIP-chip. The package has functions to deal with two-color oligonucleotide microarrays from NimbleGen used in ChIP-chip projects, but also contains more general functions for ChIP-chip data analysis, given that the data is supplied as RGList (raw) or ExpressionSet (pre- processed). The package employs functions from various other packages of the Bioconductor project and provides additional ChIP-chip-specific and NimbleGen-specific functionalities. biocViews: Microarray,TwoChannel,DataImport,QualityControl,Preprocessing Author: Joern Toedling, Oleg Sklyar, Tammo Krueger, Matt Ritchie, Wolfgang Huber Maintainer: J. Toedling git_url: https://git.bioconductor.org/packages/Ringo git_branch: RELEASE_3_12 git_last_commit: be3a6c8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Ringo_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Ringo_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Ringo_1.54.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Ringo/inst/doc/Ringo.R dependsOnMe: SimBindProfiles, ccTutorial importsMe: Repitools dependencyCount: 75 Package: RIPAT Version: 1.0.0 Depends: R (>= 4.0) Imports: biomaRt (>= 2.38.0), GenomicRanges (>= 1.34.0), ggplot2 (>= 3.1.0), grDevices (>= 3.5.3), IRanges (>= 2.16.0), karyoploteR (>= 1.6.3), openxlsx (>= 4.1.4), plyr (>= 1.8.4), regioneR (>= 1.12.0), rtracklayer (>= 1.42.2), stats (>= 3.5.3), stringr (>= 1.3.1), utils (>= 3.5.3) Suggests: knitr (>= 1.28) License: Artistic-2.0 MD5sum: 2df0689915470f06933db9692b32526f NeedsCompilation: no Title: Retroviral Integration Pattern Analysis Tool (RIPAT) Description: RIPAT is developed as an R package for retroviral integration sites annotation and distribution analysis. RIPAT needs local alignment results from BLAST and BLAT. Specific input format is depicted in RIPAT manual. RIPAT provides RV integration pattern analysis result as forms of R objects, excel file with multiple sheets and plots. biocViews: Annotation Author: Min-Jeong Baek [aut, cre] Maintainer: Min-Jeong Baek URL: https://github.com/bioinfo16/RIPAT/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RIPAT git_branch: RELEASE_3_12 git_last_commit: 3f0561e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RIPAT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RIPAT_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RIPAT_1.0.0.tgz vignettes: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.html vignetteTitles: RIPAT : Retroviral Integration Pattern Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPAT/inst/doc/RIPAT_manual_v0.99.8.R dependencyCount: 144 Package: Risa Version: 1.32.0 Depends: R (>= 2.0.9), Biobase (>= 2.4.0), methods, Rcpp (>= 0.9.13), biocViews, affy Imports: xcms Suggests: faahKO (>= 1.2.11) License: LGPL MD5sum: a31e1a16558edad1deef99cd517a5503 NeedsCompilation: no Title: Converting experimental metadata from ISA-tab into Bioconductor data structures Description: The Investigation / Study / Assay (ISA) tab-delimited format is a general purpose framework with which to collect and communicate complex metadata (i.e. sample characteristics, technologies used, type of measurements made) from experiments employing a combination of technologies, spanning from traditional approaches to high-throughput techniques. Risa allows to access metadata/data in ISA-Tab format and build Bioconductor data structures. Currently, data generated from microarray, flow cytometry and metabolomics-based (i.e. mass spectrometry) assays are supported. The package is extendable and efforts are undergoing to support metadata associated to proteomics assays. biocViews: Annotation, DataImport, MassSpectrometry Author: Alejandra Gonzalez-Beltran, Audrey Kauffmann, Steffen Neumann, Gabriella Rustici, ISA Team Maintainer: Alejandra Gonzalez-Beltran URL: http://www.isa-tools.org/ BugReports: https://github.com/ISA-tools/Risa/issues git_url: https://git.bioconductor.org/packages/Risa git_branch: RELEASE_3_12 git_last_commit: 5112a59 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Risa_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Risa_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Risa_1.32.0.tgz vignettes: vignettes/Risa/inst/doc/Risa.pdf vignetteTitles: Risa: converts experimental metadata from ISA-tab into Bioconductor data structures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Risa/inst/doc/Risa.R suggestsMe: mtbls2 dependencyCount: 96 Package: RITAN Version: 1.14.0 Depends: R (>= 3.4), Imports: graphics, stats, utils, grid, gridExtra, reshape2, gplots, ggplot2, plotrix, RColorBrewer, STRINGdb, MCL, linkcomm, dynamicTreeCut, gsubfn, hash, png, sqldf, igraph, BgeeDB, knitr, RITANdata Suggests: rmarkdown License: file LICENSE MD5sum: 3de01c405135a071afe1664d67317aca NeedsCompilation: no Title: Rapid Integration of Term Annotation and Network resources Description: Tools for comprehensive gene set enrichment and extraction of multi-resource high confidence subnetworks. RITAN facilitates bioinformatic tasks for enabling network biology research. biocViews: QualityControl, Network, NetworkEnrichment, NetworkInference, GeneSetEnrichment, FunctionalGenomics Author: Michael Zimmermann Maintainer: Michael Zimmermann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RITAN git_branch: RELEASE_3_12 git_last_commit: 73d26c8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RITAN_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RITAN_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RITAN_1.14.0.tgz vignettes: vignettes/RITAN/inst/doc/choosing_resources.html, vignettes/RITAN/inst/doc/enrichment.html, vignettes/RITAN/inst/doc/multi_tissue_analysis.html, vignettes/RITAN/inst/doc/resource_relationships.html, vignettes/RITAN/inst/doc/subnetworks.html vignetteTitles: Choosing Resources, Enrichment Vignette, Multi-Tissue Analysis, Relationships Among Resources, Network Biology Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RITAN/inst/doc/choosing_resources.R, vignettes/RITAN/inst/doc/enrichment.R, vignettes/RITAN/inst/doc/multi_tissue_analysis.R, vignettes/RITAN/inst/doc/resource_relationships.R, vignettes/RITAN/inst/doc/subnetworks.R dependencyCount: 102 Package: RIVER Version: 1.14.0 Depends: R (>= 3.3.2) Imports: glmnet, pROC, ggplot2, graphics, stats, Biobase, methods, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools License: GPL (>= 2) MD5sum: 56a828243af05875d33410e615cc99af NeedsCompilation: no Title: R package for RIVER (RNA-Informed Variant Effect on Regulation) Description: An implementation of a probabilistic modeling framework that jointly analyzes personal genome and transcriptome data to estimate the probability that a variant has regulatory impact in that individual. It is based on a generative model that assumes that genomic annotations, such as the location of a variant with respect to regulatory elements, determine the prior probability that variant is a functional regulatory variant, which is an unobserved variable. The functional regulatory variant status then influences whether nearby genes are likely to display outlier levels of gene expression in that person. See the RIVER website for more information, documentation and examples. biocViews: GeneExpression, GeneticVariability, SNP, Transcription, FunctionalPrediction, GeneRegulation, GenomicVariation, BiomedicalInformatics, FunctionalGenomics, Genetics, SystemsBiology, Transcriptomics, Bayesian, Clustering, TranscriptomeVariant, Regression Author: Yungil Kim [aut, cre], Alexis Battle [aut] Maintainer: Yungil Kim URL: https://github.com/ipw012/RIVER VignetteBuilder: knitr BugReports: https://github.com/ipw012/RIVER/issues git_url: https://git.bioconductor.org/packages/RIVER git_branch: RELEASE_3_12 git_last_commit: 3aec9fb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RIVER_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RIVER_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RIVER_1.14.0.tgz vignettes: vignettes/RIVER/inst/doc/RIVER.html vignetteTitles: RIVER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIVER/inst/doc/RIVER.R dependencyCount: 50 Package: RJMCMCNucleosomes Version: 1.14.0 Depends: R (>= 3.4), IRanges, GenomicRanges Imports: Rcpp (>= 0.12.5), consensusSeekeR, BiocGenerics, GenomeInfoDb, S4Vectors (>= 0.23.10), BiocParallel, stats, graphics, methods, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, nucleoSim, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 1a49f1ff5ce1e81f88fc14dd033bdda7 NeedsCompilation: yes Title: Bayesian hierarchical model for genome-wide nucleosome positioning with high-throughput short-read data (MNase-Seq) Description: This package does nucleosome positioning using informative Multinomial-Dirichlet prior in a t-mixture with reversible jump estimation of nucleosome positions for genome-wide profiling. biocViews: BiologicalQuestion, ChIPSeq, NucleosomePositioning, Software, StatisticalMethod, Bayesian, Sequencing, Coverage Author: Pascal Belleau [aut], Rawane Samb [aut], Astrid Deschênes [cre, aut], Khader Khadraoui [aut], Lajmi Lakhal-Chaieb [aut], Arnaud Droit [aut] Maintainer: Astrid Deschênes URL: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes SystemRequirements: Rcpp VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/RJMCMCNucleosomes/issues git_url: https://git.bioconductor.org/packages/RJMCMCNucleosomes git_branch: RELEASE_3_12 git_last_commit: 1638992 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RJMCMCNucleosomes_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RJMCMCNucleosomes_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RJMCMCNucleosomes_1.14.0.tgz vignettes: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.html vignetteTitles: Nucleosome Positioning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RJMCMCNucleosomes/inst/doc/RJMCMCNucleosomes.R dependencyCount: 46 Package: RLMM Version: 1.52.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 003f5d1756cf2d0ac5ff69c76d48196d NeedsCompilation: no Title: A Genotype Calling Algorithm for Affymetrix SNP Arrays Description: A classification algorithm, based on a multi-chip, multi-SNP approach for Affymetrix SNP arrays. Using a large training sample where the genotype labels are known, this aglorithm will obtain more accurate classification results on new data. RLMM is based on a robust, linear model and uses the Mahalanobis distance for classification. The chip-to-chip non-biological variation is removed through normalization. This model-based algorithm captures the similarities across genotype groups and probes, as well as thousands other SNPs for accurate classification. NOTE: 100K-Xba only at for now. biocViews: Microarray, OneChannel, SNP, GeneticVariability Author: Nusrat Rabbee , Gary Wong Maintainer: Nusrat Rabbee URL: http://www.stat.berkeley.edu/users/nrabbee/RLMM SystemRequirements: Internal files Xba.CQV, Xba.regions (or other regions file) git_url: https://git.bioconductor.org/packages/RLMM git_branch: RELEASE_3_12 git_last_commit: 037dc72 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RLMM_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RLMM_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RLMM_1.52.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RLMM/inst/doc/RLMM.R dependencyCount: 6 Package: Rmagpie Version: 1.46.0 Depends: R (>= 2.6.1), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), e1071, graphics, grDevices, kernlab, methods, pamr, stats, utils Suggests: xtable License: GPL (>= 3) MD5sum: 15c89384bac556c2a0bc3449807b061b NeedsCompilation: no Title: MicroArray Gene-expression-based Program In Error rate estimation Description: Microarray Classification is designed for both biologists and statisticians. It offers the ability to train a classifier on a labelled microarray dataset and to then use that classifier to predict the class of new observations. A range of modern classifiers are available, including support vector machines (SVMs), nearest shrunken centroids (NSCs)... Advanced methods are provided to estimate the predictive error rate and to report the subset of genes which appear essential in discriminating between classes. biocViews: Microarray, Classification Author: Camille Maumet , with contributions from C. Ambroise J. Zhu Maintainer: Camille Maumet URL: http://www.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rmagpie git_branch: RELEASE_3_12 git_last_commit: 80c2f92 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rmagpie_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rmagpie_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rmagpie_1.46.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmagpie/inst/doc/Magpie_examples.R dependencyCount: 20 Package: RMassBank Version: 3.0.0 Depends: Rcpp Imports: XML,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase,httr, enviPat,assertthat Suggests: BiocStyle,gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, RUnit, knitr License: Artistic-2.0 MD5sum: cde5bba4be8a70abcd66b2a14ce63ef1 NeedsCompilation: no Title: Workflow to process tandem MS files and build MassBank records Description: Workflow to process tandem MS files and build MassBank records. Functions include automated extraction of tandem MS spectra, formula assignment to tandem MS fragments, recalibration of tandem MS spectra with assigned fragments, spectrum cleanup, automated retrieval of compound information from Internet databases, and export to MassBank records. biocViews: ImmunoOncology, Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RMassBank git_branch: RELEASE_3_12 git_last_commit: e514334 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RMassBank_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RMassBank_3.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RMassBank_3.0.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.html, vignettes/RMassBank/inst/doc/RMassBankNonstandard.html, vignettes/RMassBank/inst/doc/RMassBankXCMS.html vignetteTitles: RMassBank: The workflow by example, RMassBank: Non-standard usage, RMassBank for XCMS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R suggestsMe: RMassBankData dependencyCount: 92 Package: rmelting Version: 1.6.0 Depends: R (>= 3.6) Imports: Rdpack, rJava (>= 0.5-0) Suggests: readxl, knitr, rmarkdown, reshape2, pander, testthat License: GPL-2 | GPL-3 MD5sum: 0321b7817b4584c0588d2219a83567b8 NeedsCompilation: no Title: R Interface to MELTING 5 Description: R interface to the MELTING 5 program () to compute melting temperatures of nucleic acid duplexes along with other thermodynamic parameters. biocViews: BiomedicalInformatics, Cheminformatics, Author: J. Aravind [aut, cre] (), G. K. Krishna [aut], Bob Rudis [ctb] (melting5jars), Nicolas Le Novère [ctb] (MELTING 5 Java Library), Marine Dumousseau [ctb] (MELTING 5 Java Library), William John Gowers [ctb] (MELTING 5 Java Library) Maintainer: J. Aravind URL: https://github.com/aravind-j/rmelting, https://aravind-j.github.io/PGRdup/ SystemRequirements: Java VignetteBuilder: knitr BugReports: https://github.com/aravind-j/rmelting/issues git_url: https://git.bioconductor.org/packages/rmelting git_branch: RELEASE_3_12 git_last_commit: 9ec4027 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rmelting_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rmelting_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rmelting_1.6.0.tgz vignettes: vignettes/rmelting/inst/doc/Tutorial.pdf vignetteTitles: Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 6 Package: RmiR Version: 1.46.0 Depends: R (>= 2.7.0), RmiR.Hs.miRNA, RSVGTipsDevice Imports: DBI, methods, stats Suggests: hgug4112a.db,org.Hs.eg.db License: Artistic-2.0 MD5sum: ab5a86a592b4a26c522324b09a4a7fc9 NeedsCompilation: no Title: Package to work with miRNAs and miRNA targets with R Description: Useful functions to merge microRNA and respective targets using differents databases biocViews: Software,GeneExpression,Microarray,TimeCourse,Visualization Author: Francesco Favero Maintainer: Francesco Favero git_url: https://git.bioconductor.org/packages/RmiR git_branch: RELEASE_3_12 git_last_commit: 6facff2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RmiR_1.46.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/RmiR_1.46.0.tgz vignettes: vignettes/RmiR/inst/doc/RmiR.pdf vignetteTitles: RmiR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RmiR/inst/doc/RmiR.R dependencyCount: 28 Package: Rmmquant Version: 1.8.1 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, S4Vectors, GenomicRanges, SummarizedExperiment, devtools, TBX20BamSubset, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, DESeq2, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: 045d606652048138db6dd211d6f02a0b NeedsCompilation: yes Title: RNA-Seq multi-mapping Reads Quantification Tool Description: RNA-Seq is currently used routinely, and it provides accurate information on gene transcription. However, the method cannot accurately estimate duplicated genes expression. Several strategies have been previously used, but all of them provide biased results. With Rmmquant, if a read maps at different positions, the tool detects that the corresponding genes are duplicated; it merges the genes and creates a merged gene. The counts of ambiguous reads is then based on the input genes and the merged genes. Rmmquant is a drop-in replacement of the widely used tools findOverlaps and featureCounts that handles multi-mapping reads in an unabiased way. biocViews: GeneExpression, Transcription Author: Zytnicki Matthias [aut, cre] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rmmquant git_branch: RELEASE_3_12 git_last_commit: d8d5abd git_last_commit_date: 2021-01-05 Date/Publication: 2021-01-05 source.ver: src/contrib/Rmmquant_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rmmquant_1.8.1.zip mac.binary.ver: bin/macosx/contrib/4.0/Rmmquant_1.8.1.tgz vignettes: vignettes/Rmmquant/inst/doc/Rmmquant.html vignetteTitles: The Rmmquant package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rmmquant/inst/doc/Rmmquant.R dependencyCount: 160 Package: RNAAgeCalc Version: 1.2.0 Depends: R (>= 3.6) Imports: ggplot2, recount, impute, AnnotationDbi, org.Hs.eg.db, stats, SummarizedExperiment, methods Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 76460e7462fddf262eddd81509db053a NeedsCompilation: no Title: A multi-tissue transcriptional age calculator Description: It has been shown that both DNA methylation and RNA transcription are linked to chronological age and age related diseases. Several estimators have been developed to predict human aging from DNA level and RNA level. Most of the human transcriptional age predictor are based on microarray data and limited to only a few tissues. To date, transcriptional studies on aging using RNASeq data from different human tissues is limited. The aim of this package is to provide a tool for across-tissue and tissue-specific transcriptional age calculation based on GTEx RNASeq data. biocViews: RNASeq,GeneExpression Author: Xu Ren [aut, cre], Pei Fen Kuan [aut] Maintainer: Xu Ren URL: https://github.com/reese3928/RNAAgeCalc VignetteBuilder: knitr BugReports: https://github.com/reese3928/RNAAgeCalc/issues git_url: https://git.bioconductor.org/packages/RNAAgeCalc git_branch: RELEASE_3_12 git_last_commit: 67fb1d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAAgeCalc_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAAgeCalc_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAAgeCalc_1.2.0.tgz vignettes: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.html vignetteTitles: RNAAgeCalc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAAgeCalc/inst/doc/RNAAge-vignette.R dependencyCount: 155 Package: RNAdecay Version: 1.10.0 Depends: R (>= 3.5) Imports: stats, grDevices, grid, ggplot2, gplots, utils, TMB, nloptr, scales Suggests: parallel, knitr, reshape2, rmarkdown License: GPL-2 MD5sum: 98b61bacaef2694c686396058aef6938 NeedsCompilation: yes Title: Maximum Likelihood Decay Modeling of RNA Degradation Data Description: RNA degradation is monitored through measurement of RNA abundance after inhibiting RNA synthesis. This package has functions and example scripts to facilitate (1) data normalization, (2) data modeling using constant decay rate or time-dependent decay rate models, (3) the evaluation of treatment or genotype effects, and (4) plotting of the data and models. Data Normalization: functions and scripts make easy the normalization to the initial (T0) RNA abundance, as well as a method to correct for artificial inflation of Reads per Million (RPM) abundance in global assessments as the total size of the RNA pool decreases. Modeling: Normalized data is then modeled using maximum likelihood to fit parameters. For making treatment or genotype comparisons (up to four), the modeling step models all possible treatment effects on each gene by repeating the modeling with constraints on the model parameters (i.e., the decay rate of treatments A and B are modeled once with them being equal and again allowing them to both vary independently). Model Selection: The AICc value is calculated for each model, and the model with the lowest AICc is chosen. Modeling results of selected models are then compiled into a single data frame. Graphical Plotting: functions are provided to easily visualize decay data model, or half-life distributions using ggplot2 package functions. biocViews: ImmunoOncology, Software, GeneExpression, GeneRegulation, DifferentialExpression, Transcription, Transcriptomics, TimeCourse, Regression, RNASeq, Normalization, WorkflowStep Author: Reed Sorenson [aut, cre], Katrina Johnson [aut], Frederick Adler [aut], Leslie Sieburth [aut] Maintainer: Reed Sorenson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RNAdecay git_branch: RELEASE_3_12 git_last_commit: 91f0751 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAdecay_1.10.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/RNAdecay_1.10.0.tgz vignettes: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.html vignetteTitles: RNAdecay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAdecay/inst/doc/RNAdecay_workflow.R dependencyCount: 47 Package: rnaEditr Version: 1.0.0 Depends: R (>= 4.0) Imports: GenomicRanges, IRanges, BiocGenerics, GenomeInfoDb, bumphunter, S4Vectors, stats, survival, logistf, plyr, corrplot Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 19a93b3f0521e30e354753279d3bc3c5 NeedsCompilation: no Title: Statistical analysis of RNA editing sites and hyper-editing regions Description: RNAeditr analyzes site-specific RNA editing events, as well as hyper-editing regions. The editing frequencies can be tested against binary, continuous or survival outcomes. Multiple covariate variables as well as interaction effects can also be incorporated in the statistical models. biocViews: GeneTarget, Epigenetics, DimensionReduction, FeatureExtraction, Regression, Survival, RNASeq Author: Lanyu Zhang [aut, cre], Gabriel Odom [aut], Tiago Silva [aut], Lissette Gomez [aut], Lily Wang [aut] Maintainer: Lanyu Zhang URL: https://github.com/TransBioInfoLab/rnaEditr VignetteBuilder: knitr BugReports: https://github.com/TransBioInfoLab/rnaEditr/issues git_url: https://git.bioconductor.org/packages/rnaEditr git_branch: RELEASE_3_12 git_last_commit: bc58dff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rnaEditr_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rnaEditr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rnaEditr_1.0.0.tgz vignettes: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.html vignetteTitles: Introduction to rnaEditr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaEditr/inst/doc/introduction_to_rnaEditr.R dependencyCount: 111 Package: RNAinteract Version: 1.38.0 Depends: R (>= 2.12.0), abind, locfit, Biobase Imports: RColorBrewer, ICS, ICSNP, cellHTS2, geneplotter, gplots, grid, hwriter, lattice, latticeExtra, limma, methods, splots (>= 1.13.12) License: Artistic-2.0 MD5sum: dca642246aba71fa099fe21a573e883a NeedsCompilation: no Title: Estimate Pairwise Interactions from multidimensional features Description: RNAinteract estimates genetic interactions from multi-dimensional read-outs like features extracted from images. The screen is assumed to be performed in multi-well plates or similar designs. Starting from a list of features (e.g. cell number, area, fluorescence intensity) per well, genetic interactions are estimated. The packages provides functions for reporting interacting gene pairs, plotting heatmaps and double RNAi plots. An HTML report can be written for quality control and analysis. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer Maintainer: Bernd Fischer git_url: https://git.bioconductor.org/packages/RNAinteract git_branch: RELEASE_3_12 git_last_commit: e6318ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAinteract_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAinteract_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAinteract_1.38.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAinteract/inst/doc/RNAinteract.R dependsOnMe: RNAinteractMAPK dependencyCount: 102 Package: RNAither Version: 2.38.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: 3ea77d6fc60a4e23a4dbcc8b694d6002 NeedsCompilation: no Title: Statistical analysis of high-throughput RNAi screens Description: RNAither analyzes cell-based RNAi screens, and includes quality assessment, customizable normalization and statistical tests, leading to lists of significant genes and biological processes. biocViews: CellBasedAssays, QualityControl, Preprocessing, Visualization, Annotation, GO, ImmunoOncology Author: Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Maintainer: Lars Kaderali git_url: https://git.bioconductor.org/packages/RNAither git_branch: RELEASE_3_12 git_last_commit: c2ad330 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAither_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAither_2.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAither_2.38.0.tgz vignettes: vignettes/RNAither/inst/doc/vignetteRNAither.pdf vignetteTitles: RNAither,, an automated pipeline for the statistical analysis of high-throughput RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAither/inst/doc/vignetteRNAither.R dependencyCount: 124 Package: RNAmodR Version: 1.4.2 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomicRanges, Modstrings Imports: methods, stats, grDevices, matrixStats, BiocGenerics, Biostrings (>= 2.57.2), BiocParallel, GenomicFeatures, GenomicAlignments, GenomeInfoDb, rtracklayer, Rsamtools, BSgenome, RColorBrewer, colorRamps, ggplot2, Gviz (>= 1.31.0), reshape2, graphics, ROCR Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data License: Artistic-2.0 MD5sum: 2e08e601a4f79e45f76e8ac12e6d6317 NeedsCompilation: no Title: Detection of post-transcriptional modifications in high throughput sequencing data Description: RNAmodR provides classes and workflows for loading/aggregation data from high througput sequencing aimed at detecting post-transcriptional modifications through analysis of specific patterns. In addition, utilities are provided to validate and visualize the results. The RNAmodR package provides a core functionality from which specific analysis strategies can be easily implemented as a seperate package. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR/issues git_url: https://git.bioconductor.org/packages/RNAmodR git_branch: RELEASE_3_12 git_last_commit: facd587 git_last_commit_date: 2020-12-12 Date/Publication: 2020-12-13 source.ver: src/contrib/RNAmodR_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAmodR_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAmodR_1.4.2.tgz vignettes: vignettes/RNAmodR/inst/doc/RNAmodR.creation.html, vignettes/RNAmodR/inst/doc/RNAmodR.html vignetteTitles: RNAmodR - creating new classes for a new detection strategy, RNAmodR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR/inst/doc/RNAmodR.creation.R, vignettes/RNAmodR/inst/doc/RNAmodR.R dependsOnMe: RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq dependencyCount: 147 Package: RNAmodR.AlkAnilineSeq Version: 1.4.0 Depends: R (>= 3.6), RNAmodR Imports: methods, S4Vectors, IRanges, BiocGenerics, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, Biostrings, RNAmodR.Data License: Artistic-2.0 MD5sum: 37954df0293112318d1e532517eff03e NeedsCompilation: no Title: Detection of m7G, m3C and D modification by AlkAnilineSeq Description: RNAmodR.AlkAnilineSeq implements the detection of m7G, m3C and D modifications on RNA from experimental data generated with the AlkAnilineSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.AlkAnilineSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.AlkAnilineSeq git_branch: RELEASE_3_12 git_last_commit: a2e2b37 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAmodR.AlkAnilineSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAmodR.AlkAnilineSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAmodR.AlkAnilineSeq_1.4.0.tgz vignettes: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.html vignetteTitles: RNAmodR.AlkAnilineSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.AlkAnilineSeq/inst/doc/RNAmodR.AlkAnilineSeq.R suggestsMe: RNAmodR.ML dependencyCount: 148 Package: RNAmodR.ML Version: 1.4.0 Depends: R (>= 3.6), RNAmodR Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, stats, ranger Suggests: BiocStyle, knitr, rmarkdown, testthat, RNAmodR.Data, RNAmodR.AlkAnilineSeq, GenomicFeatures, Rsamtools, rtracklayer, keras License: Artistic-2.0 MD5sum: bf68c427716221ba5e13c53ab2fd1ab9 NeedsCompilation: no Title: Detecting patterns of post-transcriptional modifications using machine learning Description: RNAmodR.ML extend the functionality of the RNAmodR package and classical detection strategies towards detection through machine learning models. RNAmodR.ML provides classes, functions and an example workflow to establish a detection stratedy, which can be packaged. biocViews: Software, Infrastructure, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.ML VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.ML/issues git_url: https://git.bioconductor.org/packages/RNAmodR.ML git_branch: RELEASE_3_12 git_last_commit: 7196e32 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAmodR.ML_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAmodR.ML_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAmodR.ML_1.4.0.tgz vignettes: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.html vignetteTitles: RNAmodR.ML hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.ML/inst/doc/RNAmodR.ML.R dependencyCount: 150 Package: RNAmodR.RiboMethSeq Version: 1.4.0 Depends: R (>= 3.6), RNAmodR Imports: methods, S4Vectors, BiocGenerics, IRanges, GenomicRanges, Gviz Suggests: BiocStyle, knitr, rmarkdown, testthat, rtracklayer, RNAmodR.Data License: Artistic-2.0 MD5sum: db899d1753cb0747d21444f59233bf6a NeedsCompilation: no Title: Detection of 2'-O methylations by RiboMethSeq Description: RNAmodR.RiboMethSeq implements the detection of 2'-O methylations on RNA from experimental data generated with the RiboMethSeq protocol. The package builds on the core functionality of the RNAmodR package to detect specific patterns of the modifications in high throughput sequencing data. biocViews: Software, WorkflowStep, Visualization, Sequencing Author: Felix G.M. Ernst [aut, cre] (), Denis L.J. Lafontaine [ctb, fnd] Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/RNAmodR.RiboMethSeq VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/RNAmodR.RiboMethSeq/issues git_url: https://git.bioconductor.org/packages/RNAmodR.RiboMethSeq git_branch: RELEASE_3_12 git_last_commit: 3de0d69 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAmodR.RiboMethSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAmodR.RiboMethSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAmodR.RiboMethSeq_1.4.0.tgz vignettes: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.html vignetteTitles: RNAmodR.RiboMethSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAmodR.RiboMethSeq/inst/doc/RNAmodR.RiboMethSeq.R dependencyCount: 148 Package: RNAprobR Version: 1.22.0 Depends: R (>= 3.1.1), GenomicFeatures(>= 1.16.3), plyr(>= 1.8.1), BiocGenerics(>= 0.10.0) Imports: Biostrings(>= 2.32.1), GenomicRanges(>= 1.16.4), IRanges(>= 2.10.5), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2), GenomicAlignments(>= 1.5.12), S4Vectors(>= 0.14.7), graphics, stats, utils Suggests: BiocStyle License: GPL (>=2) MD5sum: f6a00caaf88d5dd3c2406e383315e393 NeedsCompilation: no Title: An R package for analysis of massive parallel sequencing based RNA structure probing data Description: This package facilitates analysis of Next Generation Sequencing data for which positional information with a single nucleotide resolution is a key. It allows for applying different types of relevant normalizations, data visualization and export in a table or UCSC compatible bedgraph file. biocViews: Coverage, Normalization, Sequencing, GenomeAnnotation Author: Lukasz Jan Kielpinski [aut], Nikos Sidiropoulos [cre, aut], Jeppe Vinther [aut] Maintainer: Nikos Sidiropoulos git_url: https://git.bioconductor.org/packages/RNAprobR git_branch: RELEASE_3_12 git_last_commit: a1d0885 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAprobR_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAprobR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAprobR_1.22.0.tgz vignettes: vignettes/RNAprobR/inst/doc/RNAprobR.pdf vignetteTitles: RNAprobR: An R package for analysis of the massive parallel sequencing based methods of RNA structure probing hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAprobR/inst/doc/RNAprobR.R dependencyCount: 89 Package: RNAsense Version: 1.4.0 Depends: R (>= 3.6) Imports: ggplot2, parallel, NBPSeq, qvalue, SummarizedExperiment, stats, utils, methods Suggests: knitr, rmarkdown License: GPL-3 MD5sum: f6d85038bb87f17bd4ed164baf19b9d4 NeedsCompilation: no Title: Analysis of Time-Resolved RNA-Seq Data Description: RNA-sense tool compares RNA-seq time curves in two experimental conditions, i.e. wild-type and mutant, and works in three steps. At Step 1, it builds expression profile for each transcript in one condition (i.e. wild-type) and tests if the transcript abundance grows or decays significantly. Dynamic transcripts are then sorted to non-overlapping groups (time profiles) by the time point of switch up or down. At Step 2, RNA-sense outputs the groups of differentially expressed transcripts, which are up- or downregulated in the mutant compared to the wild-type at each time point. At Step 3, Correlations (Fisher's exact test) between the outputs of Step 1 (switch up- and switch down- time profile groups) and the outputs of Step2 (differentially expressed transcript groups) are calculated. The results of the correlation analysis are printed as two-dimensional color plot, with time profiles and differential expression groups at y- and x-axis, respectively, and facilitates the biological interpretation of the data. biocViews: RNASeq, GeneExpression, DifferentialExpression Author: Marcus Rosenblatt [cre], Gao Meijang [aut], Helge Hass [aut], Daria Onichtchouk [aut] Maintainer: Marcus Rosenblatt VignetteBuilder: knitr BugReports: https://github.com/marcusrosenblatt/RNAsense git_url: https://git.bioconductor.org/packages/RNAsense git_branch: RELEASE_3_12 git_last_commit: 954f1e7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNAsense_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNAsense_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNAsense_1.4.0.tgz vignettes: vignettes/RNAsense/inst/doc/example.html vignetteTitles: Put the title of your vignette here hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNAsense/inst/doc/example.R dependencyCount: 63 Package: rnaseqcomp Version: 1.20.0 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: fd5dfae5e77dfa678a2cec22d372e4fd NeedsCompilation: no Title: Benchmarks for RNA-seq Quantification Pipelines Description: Several quantitative and visualized benchmarks for RNA-seq quantification pipelines. Two-condition quantifications for genes, transcripts, junctions or exons by each pipeline with necessary meta information should be organized into numeric matrices in order to proceed the evaluation. biocViews: RNASeq, Visualization, QualityControl Author: Mingxiang Teng and Rafael A. Irizarry Maintainer: Mingxiang Teng URL: https://github.com/tengmx/rnaseqcomp VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rnaseqcomp git_branch: RELEASE_3_12 git_last_commit: 184a274 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rnaseqcomp_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rnaseqcomp_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rnaseqcomp_1.20.0.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.html vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R suggestsMe: SummarizedBenchmark dependencyCount: 2 Package: rnaSeqMap Version: 2.48.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Archs: i386, x64 MD5sum: 7b922e6ec129d79364302f528934aeb3 NeedsCompilation: yes Title: rnaSeq secondary analyses Description: The rnaSeqMap library provides classes and functions to analyze the RNA-sequencing data using the coverage profiles in multiple samples at a time biocViews: ImmunoOncology, Annotation, ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, SAGE, Visualization Author: Anna Lesniewska ; Michal Okoniewski Maintainer: Michal Okoniewski git_url: https://git.bioconductor.org/packages/rnaSeqMap git_branch: RELEASE_3_12 git_last_commit: a8c515e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rnaSeqMap_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rnaSeqMap_2.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rnaSeqMap_2.48.0.tgz vignettes: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.pdf vignetteTitles: rnaSeqMap primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.R dependencyCount: 44 Package: RNASeqPower Version: 1.30.0 License: LGPL (>=2) MD5sum: 23722aecd623560c3747bf6fc6dd09ed NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: ImmunoOncology, RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau git_url: https://git.bioconductor.org/packages/RNASeqPower git_branch: RELEASE_3_12 git_last_commit: 1fb5fc5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNASeqPower_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RNASeqPower_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RNASeqPower_1.30.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqPower/inst/doc/samplesize.R importsMe: DGEobj.utils dependencyCount: 0 Package: RNASeqR Version: 1.8.0 Depends: R(>= 3.5.0), ggplot2, pathview, edgeR, methods Imports: Rsamtools, tools, reticulate, ballgown, gridExtra, rafalib, FactoMineR, factoextra, corrplot, PerformanceAnalytics, reshape2, DESeq2, systemPipeR, systemPipeRdata, clusterProfiler, org.Hs.eg.db, org.Sc.sgd.db, stringr, pheatmap, grDevices, graphics, stats, utils, DOSE, Biostrings, parallel Suggests: knitr, png, grid, RNASeqRData License: Artistic-2.0 MD5sum: 0324ad9a786f65dba9c4ddd17ae02ba6 NeedsCompilation: no Title: RNASeqR: an R package for automated two-group RNA-Seq analysis workflow Description: This R package is designed for case-control RNA-Seq analysis (two-group). There are six steps: "RNASeqRParam S4 Object Creation", "Environment Setup", "Quality Assessment", "Reads Alignment & Quantification", "Gene-level Differential Analyses" and "Functional Analyses". Each step corresponds to a function in this package. After running functions in order, a basic RNASeq analysis would be done easily. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, GeneExpression, GeneSetEnrichment, Alignment, QualityControl, DifferentialExpression, FunctionalPrediction, ExperimentalDesign, GO, KEGG, Visualization, Normalization, Pathways, Clustering, ImmunoOncology Author: Kuan-Hao Chao Maintainer: Kuan-Hao Chao URL: https://github.com/HowardChao/RNASeqR SystemRequirements: RNASeqR only support Linux and macOS. Window is not supported. Python2 is highly recommended. If your machine is Python3, make sure '2to3' command is available. VignetteBuilder: knitr BugReports: https://github.com/HowardChao/RNASeqR/issues git_url: https://git.bioconductor.org/packages/RNASeqR git_branch: RELEASE_3_12 git_last_commit: 07483cd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RNASeqR_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/RNASeqR_1.8.0.tgz vignettes: vignettes/RNASeqR/inst/doc/RNASeqR.html vignetteTitles: RNA-Seq analysis based on one independent variable hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RNASeqR/inst/doc/RNASeqR.R dependencyCount: 243 Package: RnaSeqSampleSize Version: 2.0.0 Depends: R (>= 4.0.0), RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,methods,grDevices, graphics, stats, utils,Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: b1a300142c776cc5eafa67c9e8a08f4b NeedsCompilation: yes Title: RnaSeqSampleSize Description: RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data. It controls FDR for multiple testing and utilizes the average read count and dispersion distributions from real data to estimate a more reliable sample size. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. biocViews: ImmunoOncology, ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao Developer [aut, cre], Chung-I Li Developer [aut], Yan Guo Developer [aut], Quanhu Sheng Developer [aut], Yu Shyr Developer [aut] Maintainer: Shilin Zhao Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RnaSeqSampleSize git_branch: RELEASE_3_12 git_last_commit: 8998a44 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RnaSeqSampleSize_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RnaSeqSampleSize_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RnaSeqSampleSize_2.0.0.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.R dependencyCount: 75 Package: RnBeads Version: 2.8.1 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS, RnBeads.hg19, RnBeads.mm9, XML, annotate, biomaRt, foreach, doParallel, ggbio, isva, mclust, mgcv, minfi, nlme, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, quadprog, rtracklayer, qvalue, sva, wateRmelon, wordcloud, qvalue, argparse, glmnet, GLAD, IlluminaHumanMethylation450kanno.ilmn12.hg19, scales, missMethyl, impute, shiny, shinyjs, plotrix, hexbin, RUnit, MethylSeekR License: GPL-3 MD5sum: b2ccca6b02cb7b8d18c0b29593ca47b2 NeedsCompilation: no Title: RnBeads Description: RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale. biocViews: DNAMethylation, MethylationArray, MethylSeq, Epigenetics, QualityControl, Preprocessing, BatchEffect, DifferentialMethylation, Sequencing, CpGIsland, ImmunoOncology, TwoChannel, DataImport Author: Yassen Assenov [aut], Pavlo Lutsik [aut], Michael Scherer [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller git_url: https://git.bioconductor.org/packages/RnBeads git_branch: RELEASE_3_12 git_last_commit: b87f536 git_last_commit_date: 2021-03-01 Date/Publication: 2021-03-01 source.ver: src/contrib/RnBeads_2.8.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/RnBeads_2.8.1.zip mac.binary.ver: bin/macosx/contrib/4.0/RnBeads_2.8.1.tgz vignettes: vignettes/RnBeads/inst/doc/RnBeads_Annotations.pdf, vignettes/RnBeads/inst/doc/RnBeads.pdf vignetteTitles: RnBeads Annotation, Comprehensive DNA Methylation Analysis with RnBeads hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RnBeads/inst/doc/RnBeads_Annotations.R, vignettes/RnBeads/inst/doc/RnBeads.R suggestsMe: RnBeads.hg19, RnBeads.hg38, RnBeads.mm10, RnBeads.mm9, RnBeads.rn5 dependencyCount: 156 Package: Rnits Version: 1.24.0 Depends: R (>= 3.6.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: be3753ae4e57a598609a9354e218e2a1 NeedsCompilation: no Title: R Normalization and Inference of Time Series data Description: R/Bioconductor package for normalization, curve registration and inference in time course gene expression data. biocViews: GeneExpression, Microarray, TimeCourse, DifferentialExpression, Normalization Author: Dipen P. Sangurdekar Maintainer: Dipen P. Sangurdekar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Rnits git_branch: RELEASE_3_12 git_last_commit: 9e68973 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rnits_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rnits_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rnits_1.24.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rnits/inst/doc/Rnits-vignette.R dependencyCount: 56 Package: roar Version: 1.26.0 Depends: R (>= 3.0.1) Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, GenomicAlignments (>= 0.99.4), rtracklayer, GenomeInfoDb Suggests: RNAseqData.HNRNPC.bam.chr14, testthat License: GPL-3 MD5sum: 2a9406198ffdbfa4accdce0a30c1781c NeedsCompilation: no Title: Identify differential APA usage from RNA-seq alignments Description: Identify preferential usage of APA sites, comparing two biological conditions, starting from known alternative sites and alignments obtained from standard RNA-seq experiments. biocViews: Sequencing, HighThroughputSequencing, RNAseq, Transcription Author: Elena Grassi Maintainer: Elena Grassi URL: https://github.com/vodkatad/roar/ git_url: https://git.bioconductor.org/packages/roar git_branch: RELEASE_3_12 git_last_commit: 42dac5f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/roar_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/roar_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/roar_1.26.0.tgz vignettes: vignettes/roar/inst/doc/roar.pdf vignetteTitles: Identify differential APA usage from RNA-seq alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/roar/inst/doc/roar.R importsMe: XBSeq dependencyCount: 40 Package: ROC Version: 1.66.0 Depends: R (>= 1.9.0), utils, methods Imports: knitr Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 84a4db47ba1e76216afe5345a26cae72 NeedsCompilation: yes Title: utilities for ROC, with microarray focus Description: Provide utilities for ROC, with microarray focus. biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ROC git_branch: RELEASE_3_12 git_last_commit: 62701ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ROC_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ROC_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ROC_1.66.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.html vignetteTitles: Notes on ROC package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst, rMisbeta suggestsMe: genefilter dependencyCount: 15 Package: ROCpAI Version: 1.2.0 Depends: boot, SummarizedExperiment, fission, knitr, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 47173ce5fc0359a8f96e7b3451716925 NeedsCompilation: no Title: Receiver Operating Characteristic Partial Area Indexes for evaluating classifiers Description: The package analyzes the Curve ROC, identificates it among different types of Curve ROC and calculates the area under de curve through the method that is most accuracy. This package is able to standarizate proper and improper pAUC. biocViews: Software, StatisticalMethod, Classification Author: Juan-Pedro Garcia [aut, cre], Manuel Franco [aut], Juana-María Vivo [aut] Maintainer: Juan-Pedro Garcia VignetteBuilder: knitr BugReports: https://github.com/juanpegarcia/ROCpAI/tree/master/issues git_url: https://git.bioconductor.org/packages/ROCpAI git_branch: RELEASE_3_12 git_last_commit: 51f4b3c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ROCpAI_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ROCpAI_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ROCpAI_1.2.0.tgz vignettes: vignettes/ROCpAI/inst/doc/vignettes.html vignetteTitles: ROC Partial Area Indexes for evaluating classifiers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROCpAI/inst/doc/vignettes.R dependencyCount: 39 Package: rols Version: 2.18.3 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase, BiocGenerics (>= 0.23.1) Suggests: GO.db, knitr (>= 1.1.0), BiocStyle (>= 2.5.19), testthat, lubridate, DT, rmarkdown, License: GPL-2 MD5sum: bde09dc2b050508f49357bf8a1289e7a NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: The rols package is an interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: ImmunoOncology, Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto [aut, cre], Tiage Chedraoui Silva [ctb] Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues git_url: https://git.bioconductor.org/packages/rols git_branch: RELEASE_3_12 git_last_commit: 844e811 git_last_commit_date: 2021-03-30 Date/Publication: 2021-03-31 source.ver: src/contrib/rols_2.18.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/rols_2.18.3.zip mac.binary.ver: bin/macosx/contrib/4.0/rols_2.18.3.tgz vignettes: vignettes/rols/inst/doc/rols.html vignetteTitles: An R interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R dependsOnMe: proteomics importsMe: spatialHeatmap suggestsMe: MSnbase, RforProteomics dependencyCount: 27 Package: ROntoTools Version: 2.18.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 41259278d2b24c60a779c6675b7c7248 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis. biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sahar Ansari and Sorin Draghici Maintainer: Calin Voichita git_url: https://git.bioconductor.org/packages/ROntoTools git_branch: RELEASE_3_12 git_last_commit: a8db5c7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ROntoTools_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ROntoTools_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ROntoTools_2.18.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROntoTools/inst/doc/rontotools.R dependsOnMe: BLMA dependencyCount: 33 Package: ropls Version: 1.22.0 Depends: Biobase Imports: graphics, grDevices, methods, MultiDataSet, stats Suggests: BiocGenerics, BiocStyle, knitr, multtest, omicade4, rmarkdown, testthat License: CeCILL MD5sum: c2fb17407e99f255551160e72f16217e NeedsCompilation: no Title: PCA, PLS(-DA) and OPLS(-DA) for multivariate analysis and feature selection of omics data Description: Latent variable modeling with Principal Component Analysis(PCA) and Partial Least Squares (PLS) are powerful methods for visualization, regression, classification, and feature selection of omics data where the number of variables exceeds the number of samples and with multicollinearity among variables. Orthogonal Partial Least Squares (OPLS) enables to separately model the variation correlated (predictive) to the factor of interest and the uncorrelated (orthogonal) variation. While performing similarly to PLS, OPLS facilitates interpretation. Successful applications of these chemometrics techniques include spectroscopic data such as Raman spectroscopy, nuclear magnetic resonance (NMR), mass spectrometry (MS) in metabolomics and proteomics, but also transcriptomics data. In addition to scores, loadings and weights plots, the package provides metrics and graphics to determine the optimal number of components (e.g. with the R2 and Q2 coefficients), check the validity of the model by permutation testing, detect outliers, and perform feature selection (e.g. with Variable Importance in Projection or regression coefficients). The package can be accessed via a user interface on the Workflow4Metabolomics.org online resource for computational metabolomics (built upon the Galaxy environment). biocViews: Regression, Classification, PrincipalComponent, Transcriptomics, Proteomics, Metabolomics, Lipidomics, MassSpectrometry, ImmunoOncology Author: Etienne A. Thevenot Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.1021/acs.jproteome.5b00354 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ropls git_branch: RELEASE_3_12 git_last_commit: 78847f9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ropls_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ropls_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ropls_1.22.0.tgz vignettes: vignettes/ropls/inst/doc/ropls-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls-vignette.R dependsOnMe: biosigner importsMe: ASICS, lipidr, MultiBaC, proFIA, MetabolomicsBasics suggestsMe: structToolbox dependencyCount: 62 Package: ROSeq Version: 1.2.10 Depends: R (>= 4.0) Imports: pbmcapply, edgeR, limma Suggests: knitr, rmarkdown, testthat, RUnit, BiocGenerics License: GPL-3 MD5sum: 5a30465f445445488f7c7e55dbfd70ec NeedsCompilation: no Title: Modeling expression ranks for noise-tolerant differential expression analysis of scRNA-Seq data Description: ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. ROSeq takes filtered and normalized read matrix and cell-annotation/condition as input and determines the differentially expressed genes between the contrasting groups of single cells. One of the input parameters is the number of cores to be used. biocViews: GeneExpression, DifferentialExpression, SingleCell Author: Krishan Gupta [aut, cre], Manan Lalit [aut], Aditya Biswas [aut], Abhik Ghosh [aut], Debarka Sengupta [aut] Maintainer: Krishan Gupta URL: https://github.com/krishan57gupta/ROSeq VignetteBuilder: knitr BugReports: https://github.com/krishan57gupta/ROSeq/issues git_url: https://git.bioconductor.org/packages/ROSeq git_branch: RELEASE_3_12 git_last_commit: 9540c4e git_last_commit_date: 2021-02-16 Date/Publication: 2021-02-16 source.ver: src/contrib/ROSeq_1.2.10.tar.gz win.binary.ver: bin/windows/contrib/4.0/ROSeq_1.2.10.zip mac.binary.ver: bin/macosx/contrib/4.0/ROSeq_1.2.10.tgz vignettes: vignettes/ROSeq/inst/doc/ROSeq.html vignetteTitles: ROSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ROSeq/inst/doc/ROSeq.R dependencyCount: 13 Package: ROTS Version: 1.18.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 2c3d576b0da40fe49e41ba875bb76780 NeedsCompilation: yes Title: Reproducibility-Optimized Test Statistic Description: Calculates the Reproducibility-Optimized Test Statistic (ROTS) for differential testing in omics data. biocViews: Software, GeneExpression, DifferentialExpression, Microarray, RNASeq, Proteomics, ImmunoOncology Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Tomi Suomi git_url: https://git.bioconductor.org/packages/ROTS git_branch: RELEASE_3_12 git_last_commit: 1d4e206 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ROTS_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ROTS_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ROTS_1.18.0.tgz vignettes: vignettes/ROTS/inst/doc/ROTS.pdf vignetteTitles: ROTS: Reproducibility Optimized Test Statistic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROTS/inst/doc/ROTS.R importsMe: PECA suggestsMe: wrProteo dependencyCount: 8 Package: RPA Version: 1.46.0 Depends: R (>= 3.1.1), affy, BiocGenerics, methods Imports: phyloseq Suggests: affydata, knitr, parallel License: BSD_2_clause + file LICENSE MD5sum: 4cba4f315e06e89dc0838c34a01ec44c NeedsCompilation: no Title: RPA: Robust Probabilistic Averaging for probe-level analysis Description: Probabilistic analysis of probe reliability and differential gene expression on short oligonucleotide arrays. biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti [aut, cre] Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA VignetteBuilder: knitr BugReports: https://github.com/antagomir/RPA git_url: https://git.bioconductor.org/packages/RPA git_branch: RELEASE_3_12 git_last_commit: 2024292 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RPA_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RPA_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RPA_1.46.0.tgz vignettes: vignettes/RPA/inst/doc/RPA.html vignetteTitles: RPA R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs dependencyCount: 80 Package: RProtoBufLib Version: 2.2.0 License: BSD_3_clause Archs: i386, x64 MD5sum: d4ef6d88873e2d63103742d90c35296c NeedsCompilation: yes Title: C++ headers and static libraries of Protocol buffers Description: This package provides the headers and static library of Protocol buffers for other R packages to compile and link against. biocViews: Infrastructure Author: Mike Jiang Maintainer: Mike Jiang , Jake Wagner SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RProtoBufLib git_branch: RELEASE_3_12 git_last_commit: 28d99c5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RProtoBufLib_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RProtoBufLib_2.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RProtoBufLib_2.2.0.tgz vignettes: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.html vignetteTitles: Using RProtoBufLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/RProtoBufLib/inst/doc/UsingRProtoBufLib.R importsMe: cytolib, flowWorkspace linksToMe: cytolib, CytoML, flowCore, flowWorkspace dependencyCount: 0 Package: RpsiXML Version: 2.32.0 Depends: methods, annotate (>= 1.21.0), graph (>= 1.21.0), Biobase, RBGL (>= 1.17.0), XML (>= 2.4.0), hypergraph (>= 1.15.2), AnnotationDbi Suggests: org.Hs.eg.db, org.Mm.eg.db, org.Dm.eg.db, org.Rn.eg.db, org.Sc.sgd.db,hom.Hs.inp.db, hom.Mm.inp.db, hom.Dm.inp.db, hom.Rn.inp.db, hom.Sc.inp.db,Rgraphviz, ppiStats, ScISI License: LGPL-3 MD5sum: 749f10d91ae3f88401b56ee581a323b0 NeedsCompilation: no Title: R interface to PSI-MI 2.5 files Description: Queries, data structure and interface to visualization of interaction datasets. This package inplements the PSI-MI 2.5 standard and supports up to now 8 databases. Further databases supporting PSI-MI 2.5 standard will be added continuously. biocViews: Infrastructure, Proteomics Author: Jitao David Zhang, Stefan Wiemann, Marc Carlson, with contributions from Tony Chiang Maintainer: Jitao David Zhang URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/RpsiXML git_branch: RELEASE_3_12 git_last_commit: b9fbebe git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RpsiXML_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RpsiXML_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RpsiXML_2.32.0.tgz vignettes: vignettes/RpsiXML/inst/doc/RpsiXML.pdf, vignettes/RpsiXML/inst/doc/RpsiXMLApp.pdf vignetteTitles: Reading PSI-25 XML files, Application Examples of RpsiXML package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RpsiXML/inst/doc/RpsiXML.R, vignettes/RpsiXML/inst/doc/RpsiXMLApp.R dependsOnMe: ScISI importsMe: ScISI dependencyCount: 42 Package: rpx Version: 1.26.2 Depends: methods Imports: BiocFileCache, rappdirs, xml2, RCurl, utils Suggests: Biostrings, BiocStyle, testthat, knitr License: GPL-2 MD5sum: f1116f1eff9250834f4b1a645b0ca94a NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: The rpx package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: ImmunoOncology, Proteomics, MassSpectrometry, DataImport, ThirdPartyClient Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/rpx VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues git_url: https://git.bioconductor.org/packages/rpx git_branch: RELEASE_3_12 git_last_commit: f45782b git_last_commit_date: 2021-03-12 Date/Publication: 2021-03-13 source.ver: src/contrib/rpx_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/rpx_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.0/rpx_1.26.2.tgz vignettes: vignettes/rpx/inst/doc/rpx.html vignetteTitles: An R interface to the ProteomeXchange repository hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R dependsOnMe: proteomics importsMe: MBQN suggestsMe: MSnbase, RforProteomics dependencyCount: 49 Package: Rqc Version: 1.24.0 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics (>= 0.25.1), Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 8d9700db247960b4e8f323cb6987afcf NeedsCompilation: yes Title: Quality Control Tool for High-Throughput Sequencing Data Description: Rqc is an optimised tool designed for quality control and assessment of high-throughput sequencing data. It performs parallel processing of entire files and produces a report which contains a set of high-resolution graphics. biocViews: Sequencing, QualityControl, DataImport Author: Welliton Souza, Benilton Carvalho Maintainer: Welliton Souza URL: https://github.com/labbcb/Rqc VignetteBuilder: knitr BugReports: https://github.com/labbcb/Rqc/issues git_url: https://git.bioconductor.org/packages/Rqc git_branch: RELEASE_3_12 git_last_commit: bf3c098 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rqc_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rqc_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rqc_1.24.0.tgz vignettes: vignettes/Rqc/inst/doc/Rqc.html vignetteTitles: Using Rqc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R dependencyCount: 158 Package: rqt Version: 1.16.0 Depends: R (>= 3.4), SummarizedExperiment Imports: stats,Matrix,ropls,methods,car,RUnit,metap,CompQuadForm,glmnet,utils,pls Suggests: BiocStyle, knitr, rmarkdown License: GPL MD5sum: 6acbe1094885cb99b60c31aa4e258e02 NeedsCompilation: no Title: rqt: utilities for gene-level meta-analysis Description: Despite the recent advances of modern GWAS methods, it still remains an important problem of addressing calculation an effect size and corresponding p-value for the whole gene rather than for single variant. The R- package rqt offers gene-level GWAS meta-analysis. For more information, see: "Gene-set association tests for next-generation sequencing data" by Lee et al (2016), Bioinformatics, 32(17), i611-i619, . biocViews: GenomeWideAssociation, Regression, Survival, PrincipalComponent, StatisticalMethod, Sequencing Author: I. Y. Zhbannikov, K. G. Arbeev, A. I. Yashin. Maintainer: Ilya Y. Zhbannikov URL: https://github.com/izhbannikov/rqt VignetteBuilder: knitr BugReports: https://github.com/izhbannikov/rqt/issues git_url: https://git.bioconductor.org/packages/rqt git_branch: RELEASE_3_12 git_last_commit: ea2c0e3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rqt_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rqt_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rqt_1.16.0.tgz vignettes: vignettes/rqt/inst/doc/rqt-vignette.html vignetteTitles: Tutorial for rqt package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqt/inst/doc/rqt-vignette.R dependencyCount: 141 Package: rqubic Version: 1.36.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Archs: i386, x64 MD5sum: 2713a2462cc5862bd4750f4978d1a04e NeedsCompilation: yes Title: Qualitative biclustering algorithm for expression data analysis in R Description: This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data. biocViews: Clustering Author: Jitao David Zhang [aut, cre, ctb] () Maintainer: Jitao David Zhang git_url: https://git.bioconductor.org/packages/rqubic git_branch: RELEASE_3_12 git_last_commit: c3c678c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rqubic_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rqubic_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rqubic_1.36.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rqubic/inst/doc/rqubic.R importsMe: miRSM suggestsMe: RcmdrPlugin.BiclustGUI dependencyCount: 53 Package: rRDP Version: 1.24.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: c7d996ef2431b34dfa296b30e369231f NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome, ImmunoOncology Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java git_url: https://git.bioconductor.org/packages/rRDP git_branch: RELEASE_3_12 git_last_commit: aeefb99 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rRDP_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rRDP_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rRDP_1.24.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rRDP/inst/doc/rRDP.R dependsOnMe: rRDPData dependencyCount: 15 Package: RRHO Version: 1.30.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: c80efa88868a55ccb9ac290a35b1aceb NeedsCompilation: no Title: Inference on agreement between ordered lists Description: The package is aimed at inference on the amount of agreement in two sorted lists using the Rank-Rank Hypergeometric Overlap test. biocViews: Genetics, SequenceMatching, Microarray, Transcription Author: Jonathan Rosenblatt and Jason Stein Maintainer: Jonathan Rosenblatt git_url: https://git.bioconductor.org/packages/RRHO git_branch: RELEASE_3_12 git_last_commit: b4aaf15 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RRHO_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RRHO_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RRHO_1.30.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RRHO/inst/doc/RRHO.R dependencyCount: 7 Package: rrvgo Version: 1.2.0 Imports: GOSemSim, AnnotationDbi, GO.db, pheatmap, ggplot2, ggrepel, treemap, tm, wordcloud, shiny, grDevices, grid, stats, methods Suggests: knitr, rmarkdown, BiocStyle, testthat (>= 2.1.0), shinydashboard, DT, plotly, heatmaply, magrittr, utils, clusterProfiler, DOSE, slam, org.Ag.eg.db, org.At.tair.db, org.Bt.eg.db, org.Ce.eg.db, org.Cf.eg.db, org.Dm.eg.db, org.Dr.eg.db, org.EcK12.eg.db, org.EcSakai.eg.db, org.Gg.eg.db, org.Hs.eg.db, org.Mm.eg.db, org.Mmu.eg.db, org.Pf.plasmo.db, org.Pt.eg.db, org.Rn.eg.db, org.Sc.sgd.db, org.Ss.eg.db, org.Xl.eg.db License: GPL-3 MD5sum: b652386168f2067a491b4e7a9c7f4cf7 NeedsCompilation: no Title: Reduce + Visualize GO Description: Reduce and visualize lists of Gene Ontology terms by identifying redudance based on semantic similarity. biocViews: Annotation, Clustering, GO, Network, Pathways, Software Author: Sergi Sayols [aut, cre] Maintainer: Sergi Sayols URL: https://www.bioconductor.org/packages/rrvgo, https://ssayols.github.io/rrvgo/index.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rrvgo git_branch: RELEASE_3_12 git_last_commit: ed38a76 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rrvgo_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rrvgo_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rrvgo_1.2.0.tgz vignettes: vignettes/rrvgo/inst/doc/rrvgo.html vignetteTitles: Using rrvgo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rrvgo/inst/doc/rrvgo.R dependencyCount: 86 Package: Rsamtools Version: 2.6.0 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.31.8), Biostrings (>= 2.47.6) Imports: utils, BiocGenerics (>= 0.25.1), S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), XVector (>= 0.19.7), zlibbioc, bitops, BiocParallel, stats LinkingTo: Rhtslib (>= 1.17.7), S4Vectors, IRanges, XVector, Biostrings Suggests: GenomicAlignments, ShortRead (>= 1.19.10), GenomicFeatures, TxDb.Dmelanogaster.UCSC.dm3.ensGene, KEGG.db, TxDb.Hsapiens.UCSC.hg18.knownGene, RNAseqData.HNRNPC.bam.chr14, BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: 2cae68a5e330ab856cce47e286adf826 NeedsCompilation: yes Title: Binary alignment (BAM), FASTA, variant call (BCF), and tabix file import Description: This package provides an interface to the 'samtools', 'bcftools', and 'tabix' utilities for manipulating SAM (Sequence Alignment / Map), FASTA, binary variant call (BCF) and compressed indexed tab-delimited (tabix) files. biocViews: DataImport, Sequencing, Coverage, Alignment, QualityControl Author: Martin Morgan, Hervé Pagès, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/Rsamtools SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q BugReports: https://github.com/Bioconductor/Rsamtools/issues git_url: https://git.bioconductor.org/packages/Rsamtools git_branch: RELEASE_3_12 git_last_commit: f2aea06 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rsamtools_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rsamtools_2.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rsamtools_2.6.0.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf vignetteTitles: An introduction to Rsamtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R dependsOnMe: ArrayExpressHTS, BitSeq, chimera, CODEX, contiBAIT, CoverageView, esATAC, exomeCopy, FRASER, GenomicAlignments, GenomicFiles, girafe, gmapR, HelloRanges, IntEREst, MEDIPS, methylPipe, MMDiff2, podkat, r3Cseq, Rcade, RepViz, ReQON, rfPred, rnaSeqMap, SCOPE, SGSeq, ShortRead, SICtools, SNPhood, systemPipeR, TarSeqQC, TEQC, VariantAnnotation, wavClusteR, leeBamViews, TBX20BamSubset, sequencing, Brundle importsMe: AllelicImbalance, alpine, AneuFinder, annmap, AnnotationHubData, appreci8R, ArrayExpressHTS, ASpediaFI, ASpli, ATACseqQC, BadRegionFinder, bambu, BBCAnalyzer, biovizBase, biscuiteer, breakpointR, BRGenomics, BSgenome, CAGEr, casper, cellbaseR, CexoR, cfDNAPro, chimeraviz, ChIPexoQual, ChIPpeakAnno, ChIPQC, ChIPSeqSpike, ChromSCape, chromstaR, chromVAR, cn.mops, CNVfilteR, CNVPanelizer, CNVrd2, compEpiTools, consensusDE, CopyNumberPlots, CopywriteR, CrispRVariants, csaw, CSSQ, customProDB, DAMEfinder, DegNorm, derfinder, DEXSeq, DiffBind, diffHic, EDASeq, ensembldb, epigenomix, eudysbiome, FilterFFPE, FourCSeq, FunChIP, FunciSNP, gcapc, GeneGeneInteR, GenoGAM, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, GGtools, gmoviz, GOTHiC, GreyListChIP, GUIDEseq, Gviz, h5vc, HTSeqGenie, icetea, IMAS, INSPEcT, karyoploteR, ldblock, MACPET, MADSEQ, MDTS, metagene, metagene2, metaseqR2, methylKit, MMAPPR2, mosaics, motifmatchr, msgbsR, NADfinder, NanoMethViz, nearBynding, ngsReports, nucleR, ORFik, panelcn.mops, PGA, PICS, plyranges, pram, PureCN, QDNAseq, qsea, QuasR, R453Plus1Toolbox, ramwas, Rariant, recoup, Repitools, RiboProfiling, riboSeqR, ribosomeProfilingQC, RNAmodR, RNAprobR, RNASeqR, Rqc, rtracklayer, scruff, segmentSeq, seqplots, seqsetvis, SimFFPE, soGGi, SplicingGraphs, srnadiff, strandCheckR, TCseq, TFutils, tracktables, trackViewer, transcriptR, tRNAscanImport, TSRchitect, TVTB, UMI4Cats, uncoverappLib, VariantFiltering, VariantTools, vasp, VaSP, VCFArray, VplotR, chipseqDBData, LungCancerLines, MMAPPR2data, BinQuasi, ExomeDepth, hoardeR, kibior, MicroSEC, NIPTeR, noisyr, PlasmaMutationDetector, pulseTD, RAPIDR, Signac, spp, VALERIE, viromeBrowser suggestsMe: AnnotationHub, APAlyzer, bamsignals, BaseSpaceR, BiocGenerics, BiocParallel, biomvRCNS, Chicago, epivizrChart, gage, GenomeInfoDb, GenomicDataCommons, GenomicFeatures, GenomicRanges, gQTLstats, gwascat, IRanges, metaseqR, omicsPrint, profileplyr, RNAmodR.ML, SeqArray, seqbias, SigFuge, similaRpeak, Streamer, TFutils, GeuvadisTranscriptExpr, NanoporeRNASeq, parathyroidSE, chipseqDB, csawUsersGuide, polyRAD, seqmagick dependencyCount: 28 Package: rsbml Version: 2.48.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: ca1483ff4f8db766e3e2892a05182a66 NeedsCompilation: yes Title: R support for SBML, using libsbml Description: Links R to libsbml for SBML parsing, validating output, provides an S4 SBML DOM, converts SBML to R graph objects. Optionally links to the SBML ODE Solver Library (SOSLib) for simulating models. biocViews: GraphAndNetwork, Pathways, Network Author: Michael Lawrence Maintainer: Michael Lawrence URL: http://www.sbml.org SystemRequirements: libsbml (==5.10.2) git_url: https://git.bioconductor.org/packages/rsbml git_branch: RELEASE_3_12 git_last_commit: ccd9226 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rsbml_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rsbml_2.48.0.zip vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR, seeds dependencyCount: 8 Package: rScudo Version: 1.6.0 Depends: R (>= 3.6) Imports: methods, stats, igraph, stringr, grDevices, Biobase, S4Vectors, SummarizedExperiment, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, ALL, RCy3, caret, e1071, parallel, doParallel License: GPL-3 MD5sum: 67a5b75103fabb047d9e7f49676614d0 NeedsCompilation: no Title: Signature-based Clustering for Diagnostic Purposes Description: SCUDO (Signature-based Clustering for Diagnostic Purposes) is a rank-based method for the analysis of gene expression profiles for diagnostic and classification purposes. It is based on the identification of sample-specific gene signatures composed of the most up- and down-regulated genes for that sample. Starting from gene expression data, functions in this package identify sample-specific gene signatures and use them to build a graph of samples. In this graph samples are joined by edges if they have a similar expression profile, according to a pre-computed similarity matrix. The similarity between the expression profiles of two samples is computed using a method similar to GSEA. The graph of samples can then be used to perform community clustering or to perform supervised classification of samples in a testing set. biocViews: GeneExpression, DifferentialExpression, BiomedicalInformatics, Classification, Clustering, GraphAndNetwork, Network, Proteomics, Transcriptomics, SystemsBiology, FeatureExtraction Author: Matteo Ciciani [aut, cre], Thomas Cantore [aut], Enrica Colasurdo [ctb], Mario Lauria [ctb] Maintainer: Matteo Ciciani URL: https://github.com/Matteo-Ciciani/scudo VignetteBuilder: knitr BugReports: https://github.com/Matteo-Ciciani/scudo/issues git_url: https://git.bioconductor.org/packages/rScudo git_branch: RELEASE_3_12 git_last_commit: c0987fd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rScudo_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rScudo_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rScudo_1.6.0.tgz vignettes: vignettes/rScudo/inst/doc/rScudo-vignette.html vignetteTitles: Signature-based Clustering for Diagnostic Purposes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rScudo/inst/doc/rScudo-vignette.R dependencyCount: 32 Package: rsemmed Version: 1.0.0 Depends: R (>= 4.0), igraph Imports: methods, magrittr, stringr, dplyr Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 69dbeea4e86ce71010f1c78ae4ca4022 NeedsCompilation: no Title: An interface to the Semantic MEDLINE database Description: A programmatic interface to the Semantic MEDLINE database. It provides functions for searching the database for concepts and finding paths between concepts. Path searching can also be tailored to user specifications, such as placing restrictions on concept types and the type of link between concepts. It also provides functions for summarizing and visualizing those paths. biocViews: Software, Annotation, Pathways, SystemsBiology Author: Leslie Myint [aut, cre] () Maintainer: Leslie Myint URL: https://github.com/lmyint/rsemmed VignetteBuilder: knitr BugReports: https://github.com/lmyint/rsemmed/issues git_url: https://git.bioconductor.org/packages/rsemmed git_branch: RELEASE_3_12 git_last_commit: a0d8916 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rsemmed_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rsemmed_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rsemmed_1.0.0.tgz vignettes: vignettes/rsemmed/inst/doc/rsemmed_user_guide.html vignetteTitles: rsemmed User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsemmed/inst/doc/rsemmed_user_guide.R dependencyCount: 30 Package: RSeqAn Version: 1.10.0 Imports: Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: BSD_3_clause + file LICENSE MD5sum: 738b4a17090b521c549d4c985cb2113b NeedsCompilation: yes Title: R SeqAn Description: Headers and some wrapper functions from the SeqAn C++ library for ease of usage in R. biocViews: Infrastructure, Software Author: August Guang [aut, cre] Maintainer: August Guang VignetteBuilder: knitr BugReports: https://github.com/compbiocore/RSeqAn/issues git_url: https://git.bioconductor.org/packages/RSeqAn git_branch: RELEASE_3_12 git_last_commit: 3a9f639 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RSeqAn_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RSeqAn_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RSeqAn_1.10.0.tgz vignettes: vignettes/RSeqAn/inst/doc/first_example.html vignetteTitles: Introduction to Using RSeqAn hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RSeqAn/inst/doc/first_example.R importsMe: qckitfastq linksToMe: qckitfastq dependencyCount: 3 Package: Rsubread Version: 2.4.3 Imports: grDevices, stats, utils, Matrix License: GPL (>=3) Archs: i386, x64 MD5sum: 52fcb3496bc528ad63ab5ef3dd769fb5 NeedsCompilation: yes Title: Mapping, quantification and variant analysis of sequencing data Description: Alignment, quantification and analysis of RNA sequencing data (including both bulk RNA-seq and scRNA-seq) and DNA sequenicng data (including ATAC-seq, ChIP-seq, WGS, WES etc). Includes functionality for read mapping, read counting, SNP calling, structural variant detection and gene fusion discovery. Can be applied to all major sequencing techologies and to both short and long sequence reads. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, SingleCell, GeneExpression, GeneRegulation, Genetics, ImmunoOncology, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, GeneFusionDetection, IndelDetection, VariantAnnotation, VariantDetection, MultipleSequenceAlignment Author: Wei Shi, Yang Liao and Gordon K Smyth with contributions from Jenny Dai Maintainer: Wei Shi , Yang Liao and Gordon K Smyth URL: http://bioconductor.org/packages/Rsubread git_url: https://git.bioconductor.org/packages/Rsubread git_branch: RELEASE_3_12 git_last_commit: 04818c6 git_last_commit_date: 2021-03-15 Date/Publication: 2021-03-16 source.ver: src/contrib/Rsubread_2.4.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rsubread_2.4.3.zip mac.binary.ver: bin/macosx/contrib/4.0/Rsubread_2.4.3.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf, vignettes/Rsubread/inst/doc/SubreadUsersGuide.pdf vignetteTitles: Rsubread Vignette, SubreadUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R dependsOnMe: ExCluster importsMe: APAlyzer, dupRadar, FRASER, ribosomeProfilingQC, scruff suggestsMe: icetea, scPipe, singleCellTK, tidybulk dependencyCount: 8 Package: RSVSim Version: 1.30.0 Depends: R (>= 3.0.0), Biostrings, GenomicRanges Imports: methods, IRanges, ShortRead Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome.Hsapiens.UCSC.hg19.masked, MASS, rtracklayer License: LGPL-3 MD5sum: bf8d083809095d0a030eb559e83f6548 NeedsCompilation: no Title: RSVSim: an R/Bioconductor package for the simulation of structural variations Description: RSVSim is a package for the simulation of deletions, insertions, inversion, tandem-duplications and translocations of various sizes in any genome available as FASTA-file or BSgenome data package. SV breakpoints can be placed uniformly accross the whole genome, with a bias towards repeat regions and regions of high homology (for hg19) or at user-supplied coordinates. biocViews: Sequencing Author: Christoph Bartenhagen Maintainer: Christoph Bartenhagen git_url: https://git.bioconductor.org/packages/RSVSim git_branch: RELEASE_3_12 git_last_commit: cfe4d5f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RSVSim_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RSVSim_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RSVSim_1.30.0.tgz vignettes: vignettes/RSVSim/inst/doc/vignette.pdf vignetteTitles: RSVSim: an R/Bioconductor package for the simulation of structural variations hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RSVSim/inst/doc/vignette.R dependencyCount: 44 Package: rSWeeP Version: 1.2.0 Depends: R (>= 4.0) Imports: pracma, stats Suggests: Biostrings, methods, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: 9fbde720c6a75c024c3dc3bc080e1d19 NeedsCompilation: no Title: Functions to creation of low dimensional comparative matrices of Amino Acid Sequence occurrences Description: The SWeeP method was developed to favor the analizes between amino acids sequences and to assist alignment free phylogenetic studies. This method is based on the concept of sparse words, which is applied in the scan of biological sequences and its the conversion in a matrix of ocurrences. Aiming the generation of low dimensional matrices of Amino Acid Sequence occurrences. biocViews: Software,StatisticalMethod,SupportVectorMachine,Technology,Sequencing,Genetics, Alignment Author: Danrley R. Fernandes [com, cre, aut] Maintainer: Danrley R. Fernandes VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/rSWeeP git_branch: RELEASE_3_12 git_last_commit: 82a9567 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rSWeeP_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rSWeeP_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rSWeeP_1.2.0.tgz vignettes: vignettes/rSWeeP/inst/doc/rSWeeP.html vignetteTitles: rSWeeP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSWeeP/inst/doc/rSWeeP.R dependencyCount: 5 Package: RTCA Version: 1.42.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: aa690d293b855708a297b05d7c58ddcb NeedsCompilation: no Title: Open-source toolkit to analyse data from xCELLigence System (RTCA) Description: Import, analyze and visualize data from Roche(R) xCELLigence RTCA systems. The package imports real-time cell electrical impedance data into R. As an alternative to commercial software shipped along the system, the Bioconductor package RTCA provides several unique transformation (normalization) strategies and various visualization tools. biocViews: ImmunoOncology, CellBasedAssays, Infrastructure, Visualization, TimeCourse Author: Jitao David Zhang Maintainer: Jitao David Zhang URL: http://code.google.com/p/xcelligence/,http://www.xcelligence.roche.com/,http://www.nextbiomotif.com/Home/scientific-programming git_url: https://git.bioconductor.org/packages/RTCA git_branch: RELEASE_3_12 git_last_commit: 22a85fb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RTCA_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RTCA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RTCA_1.42.0.tgz vignettes: vignettes/RTCA/inst/doc/aboutRTCA.pdf, vignettes/RTCA/inst/doc/RTCAtransformation.pdf vignetteTitles: Introduction to Data Analysis of the Roche xCELLigence System with RTCA Package, RTCAtransformation: Discussion of transformation methods of RTCA data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTCA/inst/doc/aboutRTCA.R, vignettes/RTCA/inst/doc/RTCAtransformation.R dependencyCount: 9 Package: RTCGA Version: 1.20.0 Depends: R (>= 3.3.0) Imports: XML, assertthat, stringi, rvest, data.table, xml2, dplyr, purrr, survival, survminer, ggplot2, ggthemes, viridis, knitr, scales Suggests: devtools, testthat, pander, Biobase, GenomicRanges, IRanges, S4Vectors, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations, RTCGA.RPPA, RTCGA.mRNA, RTCGA.miRNASeq, RTCGA.methylation, RTCGA.CNV, RTCGA.PANCAN12, magrittr, tidyr License: GPL-2 MD5sum: 8f7d044cdaf986efc9c4602f36477684 NeedsCompilation: no Title: The Cancer Genome Atlas Data Integration Description: The Cancer Genome Atlas (TCGA) Data Portal provides a platform for researchers to search, download, and analyze data sets generated by TCGA. It contains clinical information, genomic characterization data, and high level sequence analysis of the tumor genomes. The key is to understand genomics to improve cancer care. RTCGA package offers download and integration of the variety and volume of TCGA data using patient barcode key, what enables easier data possession. This may have an benefcial infuence on impact on development of science and improvement of patients' treatment. Furthermore, RTCGA package transforms TCGA data to tidy form which is convenient to use. biocViews: ImmunoOncology, Software, DataImport, DataRepresentation, Preprocessing, RNASeq Author: Marcin Kosinski , Przemyslaw Biecek Maintainer: Marcin Kosinski URL: https://rtcga.github.io/RTCGA VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues git_url: https://git.bioconductor.org/packages/RTCGA git_branch: RELEASE_3_12 git_last_commit: a59c025 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RTCGA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RTCGA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RTCGA_1.20.0.tgz vignettes: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html vignetteTitles: Integrating TCGA Data - RTCGA Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RTCGA.clinical, RTCGA.CNV, RTCGA.methylation, RTCGA.miRNASeq, RTCGA.mRNA, RTCGA.mutations, RTCGA.PANCAN12, RTCGA.rnaseq, RTCGA.RPPA dependencyCount: 132 Package: RTCGAToolbox Version: 2.20.0 Depends: R (>= 3.5.0) Imports: BiocGenerics, data.table, DelayedArray, GenomicRanges, GenomeInfoDb, httr, limma, methods, RaggedExperiment, RCircos, RCurl, RJSONIO, S4Vectors (>= 0.23.10), stats, stringr, SummarizedExperiment, survival, TCGAutils (>= 1.9.4), XML Suggests: BiocStyle, Homo.sapiens, knitr, readr, rmarkdown License: file LICENSE MD5sum: cb41e32ecedbb39cc0ce6f7f7437b802 NeedsCompilation: no Title: A new tool for exporting TCGA Firehose data Description: Managing data from large scale projects such as The Cancer Genome Atlas (TCGA) for further analysis is an important and time consuming step for research projects. Several efforts, such as Firehose project, make TCGA pre-processed data publicly available via web services and data portals but it requires managing, downloading and preparing the data for following steps. We developed an open source and extensible R based data client for Firehose pre-processed data and demonstrated its use with sample case studies. Results showed that RTCGAToolbox could improve data management for researchers who are interested with TCGA data. In addition, it can be integrated with other analysis pipelines for following data analysis. biocViews: DifferentialExpression, GeneExpression, Sequencing Author: Mehmet Samur [aut], Marcel Ramos [aut, cre], Ludwig Geistlinger [ctb] Maintainer: Marcel Ramos URL: http://mksamur.github.io/RTCGAToolbox/ VignetteBuilder: knitr BugReports: https://github.com/mksamur/RTCGAToolbox/issues git_url: https://git.bioconductor.org/packages/RTCGAToolbox git_branch: RELEASE_3_12 git_last_commit: 224bf4c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RTCGAToolbox_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RTCGAToolbox_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RTCGAToolbox_2.20.0.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-deprecated.html, vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html vignetteTitles: RTCGAToolbox Deprecated Functions, RTCGAToolbox Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-deprecated.R, vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R importsMe: cBioPortalData, TCGAWorkflow suggestsMe: TCGAutils dependencyCount: 104 Package: RTN Version: 2.14.1 Depends: R (>= 3.6.3), methods, Imports: RedeR, minet, viper, mixtools, snow, stats, limma, data.table, IRanges, igraph, S4Vectors, SummarizedExperiment, car, pwr, pheatmap, grDevices, graphics, utils Suggests: RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 719e0242748e6a0418a70735a3d33b64 NeedsCompilation: no Title: RTN: Reconstruction of Transcriptional regulatory Networks and analysis of regulons Description: A transcriptional regulatory network (TRN) consists of a collection of transcription factors (TFs) and the regulated target genes. TFs are regulators that recognize specific DNA sequences and guide the expression of the genome, either activating or repressing the expression the target genes. The set of genes controlled by the same TF forms a regulon. This package provides classes and methods for the reconstruction of TRNs and analysis of regulons. biocViews: Transcription, Network, NetworkInference, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment, GeneticVariability Author: Clarice Groeneveld [ctb], Gordon Robertson [ctb], Xin Wang [aut], Michael Fletcher [aut], Florian Markowetz [aut], Kerstin Meyer [aut], and Mauro Castro [aut] Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTN git_branch: RELEASE_3_12 git_last_commit: dc65d5c git_last_commit_date: 2020-11-10 Date/Publication: 2020-11-10 source.ver: src/contrib/RTN_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/RTN_2.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/RTN_2.14.1.tgz vignettes: vignettes/RTN/inst/doc/RTN.html vignetteTitles: "RTN: reconstruction of transcriptional regulatory networks and analysis of regulons."" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R dependsOnMe: RTNduals, RTNsurvival, Fletcher2013b suggestsMe: geneplast dependencyCount: 123 Package: RTNduals Version: 1.14.1 Depends: R(>= 3.6.3), RTN(>= 2.14.1), methods Imports: graphics, grDevices, stats, utils Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 201fede26de3678a4421f168b76a871f NeedsCompilation: no Title: Analysis of co-regulation and inference of 'dual regulons' Description: RTNduals is a tool that searches for possible co-regulatory loops between regulon pairs generated by the RTN package. It compares the shared targets in order to infer 'dual regulons', a new concept that tests whether regulators can co-operate or compete in influencing targets. biocViews: GeneRegulation, GeneExpression, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Vinicius S. Chagas, Clarice S. Groeneveld, Gordon Robertson, Kerstin B. Meyer, Mauro A. A. Castro Maintainer: Mauro Castro , Clarice Groeneveld VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNduals git_branch: RELEASE_3_12 git_last_commit: 0f1531e git_last_commit_date: 2020-11-10 Date/Publication: 2020-11-10 source.ver: src/contrib/RTNduals_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/RTNduals_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/RTNduals_1.14.1.tgz vignettes: vignettes/RTNduals/inst/doc/RTNduals.html vignetteTitles: "RTNduals: analysis of co-regulation and inference of dual regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNduals/inst/doc/RTNduals.R dependsOnMe: RTNsurvival dependencyCount: 124 Package: RTNsurvival Version: 1.14.1 Depends: R(>= 3.6.3), RTN(>= 2.14.1), RTNduals(>= 1.14.1), methods Imports: survival, RColorBrewer, grDevices, graphics, stats, utils, scales, data.table, egg, ggplot2, pheatmap, dunn.test Suggests: Fletcher2013b, knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2fdbe490ea258a552904ff8283f70a66 NeedsCompilation: no Title: Survival analysis using transcriptional networks inferred by the RTN package Description: RTNsurvival is a tool for integrating regulons generated by the RTN package with survival information. For a given regulon, the 2-tailed GSEA approach computes a differential Enrichment Score (dES) for each individual sample, and the dES distribution of all samples is then used to assess the survival statistics for the cohort. There are two main survival analysis workflows: a Cox Proportional Hazards approach used to model regulons as predictors of survival time, and a Kaplan-Meier analysis assessing the stratification of a cohort based on the regulon activity. All plots can be fine-tuned to the user's specifications. biocViews: NetworkEnrichment, Survival, GeneRegulation, GeneSetEnrichment, NetworkInference, GraphAndNetwork Author: Clarice S. Groeneveld, Vinicius S. Chagas, Mauro A. A. Castro Maintainer: Clarice Groeneveld , Mauro A. A. Castro VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RTNsurvival git_branch: RELEASE_3_12 git_last_commit: ec16902 git_last_commit_date: 2020-11-10 Date/Publication: 2020-11-10 source.ver: src/contrib/RTNsurvival_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/RTNsurvival_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/RTNsurvival_1.14.1.tgz vignettes: vignettes/RTNsurvival/inst/doc/RTNsurvival.html vignetteTitles: "RTNsurvival: multivariate survival analysis using transcriptional networks and regulons." hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTNsurvival/inst/doc/RTNsurvival.R dependencyCount: 132 Package: RTopper Version: 1.36.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: 2883b60b681b19bdb318157891587295 NeedsCompilation: no Title: This package is designed to perform Gene Set Analysis across multiple genomic platforms Description: the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms. biocViews: Microarray Author: Luigi Marchionni , Svitlana Tyekucheva Maintainer: Luigi Marchionni git_url: https://git.bioconductor.org/packages/RTopper git_branch: RELEASE_3_12 git_last_commit: 99d7d0a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RTopper_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RTopper_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RTopper_1.36.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTopper/inst/doc/RTopper.R dependencyCount: 17 Package: Rtpca Version: 1.0.0 Depends: R (>= 4.0.0), stats, dplyr, tidyr Imports: Biobase, methods, ggplot2, pROC, fdrtool, splines, utils, tibble Suggests: knitr, BiocStyle, TPP, testthat License: GPL-3 MD5sum: f8a88052ade06e230e92e440444ca8e4 NeedsCompilation: no Title: Thermal proximity co-aggregation with R Description: R package for performing thermal proximity co-aggregation analysis with thermal proteome profiling datasets to analyse protein complex assembly and (differential) protein-protein interactions across conditions. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], André Mateus [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/Rtpca git_branch: RELEASE_3_12 git_last_commit: cc51235 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rtpca_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rtpca_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rtpca_1.0.0.tgz vignettes: vignettes/Rtpca/inst/doc/Rtpca.html vignetteTitles: Introduction to Rtpca hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtpca/inst/doc/Rtpca.R dependencyCount: 51 Package: rtracklayer Version: 1.50.0 Depends: R (>= 3.3), methods, GenomicRanges (>= 1.37.2) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.35.3), S4Vectors (>= 0.23.18), IRanges (>= 2.13.13), XVector (>= 0.19.7), GenomeInfoDb (>= 1.15.2), Biostrings (>= 2.47.6), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6), tools LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, GenomicFeatures, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 4cfe973ff5d2be86eb1cd0cd88287b01 NeedsCompilation: yes Title: R interface to genome annotation files and the UCSC genome browser Description: Extensible framework for interacting with multiple genome browsers (currently UCSC built-in) and manipulating annotation tracks in various formats (currently GFF, BED, bedGraph, BED15, WIG, BigWig and 2bit built-in). The user may export/import tracks to/from the supported browsers, as well as query and modify the browser state, such as the current viewport. biocViews: Annotation,Visualization,DataImport Author: Michael Lawrence, Vince Carey, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/rtracklayer git_branch: RELEASE_3_12 git_last_commit: d2e61f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rtracklayer_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rtracklayer_1.49.5.zip mac.binary.ver: bin/macosx/contrib/4.0/rtracklayer_1.50.0.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BRGenomics, BSgenome, CAGEfightR, ChIPSeqSpike, CoverageView, CSSQ, cummeRbund, ExCluster, geneXtendeR, GenomicFiles, groHMM, HelloRanges, LoomExperiment, MethylSeekR, r3Cseq, StructuralVariantAnnotation, EatonEtAlChIPseq, liftOver, sequencing, HiCfeat importsMe: ALPS, AnnotationHubData, annotatr, APAlyzer, ASpediaFI, ATACseqQC, ballgown, BgeeCall, biscuiteer, BiSeq, branchpointer, BSgenome, CAGEr, casper, CexoR, chipenrich, ChIPpeakAnno, ChIPseeker, ChromHeatMap, ChromSCape, chromswitch, circRNAprofiler, CNEr, coMET, CompGO, consensusSeekeR, contiBAIT, conumee, customProDB, DeepBlueR, derfinder, DEScan2, diffHic, diffloop, DMCFB, DMCHMM, dmrseq, eisaR, ELMER, ENCODExplorer, enrichTF, ensembldb, erma, esATAC, fcScan, FourCSeq, FunciSNP, genbankr, geneAttribution, genomation, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, GGtools, gmapR, gmoviz, GOTHiC, gQTLBase, GreyListChIP, Gviz, hiAnnotator, HiTC, HTSeqGenie, icetea, igvR, INSPEcT, IsoformSwitchAnalyzeR, karyoploteR, MACPET, MADSEQ, maser, MEDIPS, metagene, metagene2, metaseqR2, methrix, methyAnalysis, methylKit, motifbreakR, MotifDb, multicrispr, NADfinder, nanotatoR, nearBynding, normr, OMICsPCA, ORFik, PAST, periodicDNA, PGA, plyranges, pram, primirTSS, proBAMr, profileplyr, PureCN, qsea, QuasR, RCAS, recount, recount3, recoup, regioneR, REMP, Repitools, RGMQL, RiboProfiling, ribosomeProfilingQC, RIPAT, RNAmodR, RNAprobR, roar, scPipe, scruff, seqCAT, seqplots, seqsetvis, sevenC, SGSeq, SigsPack, soGGi, srnadiff, TFBSTools, trackViewer, transcriptR, tRNAscanImport, TSRchitect, VariantAnnotation, VariantTools, wavClusteR, wiggleplotr, GenomicState, chipenrich.data, DMRcatedata, geneLenDataBase, SingscoreAMLMutations, crispRdesignR, GALLO, kibior, PlasmaMutationDetector suggestsMe: alpine, AnnotationHub, BiocFileCache, biovizBase, bsseq, cicero, CINdex, compEpiTools, CrispRVariants, DAMEfinder, dasper, epivizrChart, epivizrData, geneXtendeR, GenomicAlignments, GenomicRanges, goseq, InPAS, interactiveDisplay, megadepth, metaseqR, methylumi, miRBaseConverter, MutationalPatterns, OrganismDbi, Pi, PICS, PING, pipeFrame, pqsfinder, R453Plus1Toolbox, rGADEM, Ringo, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RnBeads, RSVSim, signeR, similaRpeak, TAPseq, TCGAutils, triplex, tRNAdbImport, TVTB, EpiTxDb.Hs.hg38, FDb.FANTOM4.promoters.hg19, dsQTL, GeuvadisTranscriptExpr, nanotubes, PasillaTranscriptExpr, chipseqDB, csawUsersGuide, gkmSVM, LDheatmap, RTIGER, Seurat, Signac dependencyCount: 39 Package: Rtreemix Version: 1.52.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: i386, x64 MD5sum: 15e5eb557c2d2034d3584de627a9821b NeedsCompilation: yes Title: Rtreemix: Mutagenetic trees mixture models. Description: Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. biocViews: StatisticalMethod Author: Jasmina Bogojeska Maintainer: Jasmina Bogojeska git_url: https://git.bioconductor.org/packages/Rtreemix git_branch: RELEASE_3_12 git_last_commit: 10fe162 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Rtreemix_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Rtreemix_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Rtreemix_1.52.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rtreemix/inst/doc/Rtreemix.R dependencyCount: 74 Package: rTRM Version: 1.28.0 Depends: R (>= 2.10), igraph (>= 1.0) Imports: AnnotationDbi, DBI, RSQLite Suggests: RUnit, BiocGenerics, MotifDb, graph, PWMEnrich, biomaRt, knitr, Biostrings, BSgenome.Mmusculus.UCSC.mm8.masked, org.Hs.eg.db, org.Mm.eg.db, ggplot2 License: GPL-3 MD5sum: 1b104754a0db1ca993c3695352f71054 NeedsCompilation: no Title: Identification of transcriptional regulatory modules from PPI networks Description: rTRM identifies transcriptional regulatory modules (TRMs) from protein-protein interaction networks. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRM VignetteBuilder: knitr BugReports: https://github.com/ddiez/rTRM/issues git_url: https://git.bioconductor.org/packages/rTRM git_branch: RELEASE_3_12 git_last_commit: 2908ebc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rTRM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rTRM_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rTRM_1.28.0.tgz vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRM/inst/doc/rTRM_Introduction.R importsMe: rTRMui dependencyCount: 32 Package: rTRMui Version: 1.28.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 44ee25f7523d3f67412dea0fb9964d43 NeedsCompilation: no Title: A shiny user interface for rTRM Description: This package provides a web interface to compute transcriptional regulatory modules with rTRM. biocViews: Transcription, Network, GeneRegulation, GraphAndNetwork, GUI Author: Diego Diez Maintainer: Diego Diez URL: https://github.com/ddiez/rTRMui BugReports: https://github.com/ddiez/rTRMui/issues git_url: https://git.bioconductor.org/packages/rTRMui git_branch: RELEASE_3_12 git_last_commit: ea680aa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/rTRMui_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/rTRMui_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/rTRMui_1.28.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rTRMui/inst/doc/rTRMui.R dependencyCount: 84 Package: runibic Version: 1.12.0 Depends: R (>= 3.4.0), biclust, SummarizedExperiment Imports: Rcpp (>= 0.12.12), testthat, methods LinkingTo: Rcpp Suggests: knitr, rmarkdown, GEOquery, affy, airway, QUBIC License: MIT + file LICENSE Archs: i386, x64 MD5sum: f525688e6df5baed3e076b1256a8093c NeedsCompilation: yes Title: runibic: row-based biclustering algorithm for analysis of gene expression data in R Description: This package implements UbiBic algorithm in R. This biclustering algorithm for analysis of gene expression data was introduced by Zhenjia Wang et al. in 2016. It is currently considered the most promising biclustering method for identification of meaningful structures in complex and noisy data. biocViews: Microarray, Clustering, GeneExpression, Sequencing, Coverage Author: Patryk Orzechowski, Artur Pańszczyk Maintainer: Patryk Orzechowski URL: http://github.com/athril/runibic SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: http://github.com/athril/runibic/issues git_url: https://git.bioconductor.org/packages/runibic git_branch: RELEASE_3_12 git_last_commit: bf1efa2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/runibic_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/runibic_1.12.0.zip vignettes: vignettes/runibic/inst/doc/runibic.html vignetteTitles: runibic: UniBic in R Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependencyCount: 83 Package: RUVcorr Version: 1.22.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2, graphics Suggests: knitr, hgu133a2.db License: GPL-2 MD5sum: 8d84303b5289dead1e5c8710dbacc99d NeedsCompilation: no Title: Removal of unwanted variation for gene-gene correlations and related analysis Description: RUVcorr allows to apply global removal of unwanted variation (ridged version of RUV) to real and simulated gene expression data. biocViews: GeneExpression, Normalization Author: Saskia Freytag Maintainer: Saskia Freytag VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RUVcorr git_branch: RELEASE_3_12 git_last_commit: 714b1a0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RUVcorr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RUVcorr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RUVcorr_1.22.0.tgz vignettes: vignettes/RUVcorr/inst/doc/Vignette.html vignetteTitles: Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/Vignette.R dependencyCount: 35 Package: RUVnormalize Version: 1.24.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: d00fd2b0695b5579f82aa27cceacd333 NeedsCompilation: no Title: RUV for normalization of expression array data Description: RUVnormalize is meant to remove unwanted variation from gene expression data when the factor of interest is not defined, e.g., to clean up a dataset for general use or to do any kind of unsupervised analysis. biocViews: StatisticalMethod, Normalization Author: Laurent Jacob Maintainer: Laurent Jacob git_url: https://git.bioconductor.org/packages/RUVnormalize git_branch: RELEASE_3_12 git_last_commit: 9c60e83 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RUVnormalize_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RUVnormalize_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RUVnormalize_1.24.0.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVnormalize/inst/doc/RUVnormalize.R dependencyCount: 8 Package: RUVSeq Version: 1.24.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 50c0be0489576b6c100b736749167b43 NeedsCompilation: no Title: Remove Unwanted Variation from RNA-Seq Data Description: This package implements the remove unwanted variation (RUV) methods of Risso et al. (2014) for the normalization of RNA-Seq read counts between samples. biocViews: ImmunoOncology, DifferentialExpression, Preprocessing, RNASeq, Software Author: Davide Risso [aut, cre, cph], Sandrine Dudoit [aut], Lorena Pantano [ctb], Kamil Slowikowski [ctb] Maintainer: Davide Risso URL: https://github.com/drisso/RUVSeq VignetteBuilder: knitr BugReports: https://github.com/drisso/RUVSeq/issues git_url: https://git.bioconductor.org/packages/RUVSeq git_branch: RELEASE_3_12 git_last_commit: eab5505 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RUVSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RUVSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RUVSeq_1.24.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVSeq/inst/doc/RUVSeq.R dependsOnMe: rnaseqGene importsMe: consensusDE, ribosomeProfilingQC, scone suggestsMe: DEScan2 dependencyCount: 104 Package: RVS Version: 1.12.0 Depends: R (>= 3.5.0) Imports: GENLIB, gRain, snpStats, kinship2, methods, stats, utils Suggests: knitr, testthat, rmarkdown, BiocStyle, VariantAnnotation License: GPL-2 MD5sum: 938fcbc8054633704e3a8ba5d6716d15 NeedsCompilation: no Title: Computes estimates of the probability of related individuals sharing a rare variant Description: Rare Variant Sharing (RVS) implements tests of association and linkage between rare genetic variant genotypes and a dichotomous phenotype, e.g. a disease status, in family samples. The tests are based on probabilities of rare variant sharing by relatives under the null hypothesis of absence of linkage and association between the rare variants and the phenotype and apply to single variants or multiple variants in a region (e.g. gene-based test). biocViews: ImmunoOncology, Genetics, GenomeWideAssociation, VariantDetection, ExomeSeq, WholeGenome Author: Alexandre Bureau, Ingo Ruczinski, Samuel Younkin, Thomas Sherman Maintainer: Thomas Sherman VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/RVS git_branch: RELEASE_3_12 git_last_commit: a28e783 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/RVS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/RVS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/RVS_1.12.0.tgz vignettes: vignettes/RVS/inst/doc/RVS.html vignetteTitles: The RVS Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RVS/inst/doc/RVS.R dependencyCount: 35 Package: rWikiPathways Version: 1.10.2 Imports: httr, utils, XML, rjson, data.table, tidyr, RCurl Suggests: testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENSE MD5sum: ffb8950bb03e978f54ca88d8b69c51d6 NeedsCompilation: no Title: rWikiPathways - R client library for the WikiPathways API Description: Use this package to interface with the WikiPathways API. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network, Metabolomics Author: Egon Willighagen [aut, cre] (), Alex Pico [aut] () Maintainer: Egon Willighagen URL: https://github.com/wikipathways/rwikipathways VignetteBuilder: knitr BugReports: https://github.com/wikipathways/rwikipathways/issues git_url: https://git.bioconductor.org/packages/rWikiPathways git_branch: RELEASE_3_12 git_last_commit: 8b32421 git_last_commit_date: 2021-04-24 Date/Publication: 2021-04-25 source.ver: src/contrib/rWikiPathways_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/rWikiPathways_1.10.2.zip mac.binary.ver: bin/macosx/contrib/4.0/rWikiPathways_1.10.2.tgz vignettes: vignettes/rWikiPathways/inst/doc/Overview.html, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.html, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.html vignetteTitles: 1. Overview, 4. Pathway Analysis, 2. rWikiPathways and BridgeDbR, 3. rWikiPathways and RCy3 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rWikiPathways/inst/doc/Overview.R, vignettes/rWikiPathways/inst/doc/Pathway-Analysis.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-BridgeDbR.R, vignettes/rWikiPathways/inst/doc/rWikiPathways-and-RCy3.R importsMe: famat, TimiRGeN, RVA suggestsMe: TRONCO dependencyCount: 36 Package: S4Vectors Version: 0.28.1 Depends: R (>= 4.0.0), methods, utils, stats, stats4, BiocGenerics (>= 0.36.0) Suggests: IRanges, GenomicRanges, SummarizedExperiment, Matrix, DelayedArray, ShortRead, graph, data.table, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: c90b69bd53cac8c07a3cf3bc474cd30a NeedsCompilation: yes Title: Foundation of vector-like and list-like containers in Bioconductor Description: The S4Vectors package defines the Vector and List virtual classes and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as concrete subclasses of Vector or List. In addition, a few low-level concrete subclasses of general interest (e.g. DataFrame, Rle, and Hits) are implemented in the S4Vectors package itself (many more are implemented in the IRanges package and in other Bioconductor infrastructure packages). biocViews: Infrastructure, DataRepresentation Author: H. Pagès, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/S4Vectors BugReports: https://github.com/Bioconductor/S4Vectors/issues git_url: https://git.bioconductor.org/packages/S4Vectors git_branch: RELEASE_3_12 git_last_commit: 994cb7e git_last_commit_date: 2020-12-08 Date/Publication: 2020-12-09 source.ver: src/contrib/S4Vectors_0.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/S4Vectors_0.28.1.zip mac.binary.ver: bin/macosx/contrib/4.0/S4Vectors_0.28.1.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf, vignettes/S4Vectors/inst/doc/S4QuickOverview.pdf, vignettes/S4Vectors/inst/doc/S4VectorsOverview.pdf vignetteTitles: Rle Tips and Tricks, A quick overview of the S4 class system, An Overview of the S4Vectors package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R, vignettes/S4Vectors/inst/doc/S4QuickOverview.R, vignettes/S4Vectors/inst/doc/S4VectorsOverview.R dependsOnMe: altcdfenvs, AnnotationHubData, ATACseqQC, bambu, Biostrings, BiSeq, BRGenomics, BSgenome, bumphunter, Cardinal, CellMapper, CexoR, chimeraviz, ChIPpeakAnno, chipseq, ChIPseqR, ClassifyR, CODEX, coseq, CSAR, CSSQ, DelayedArray, DelayedDataFrame, DESeq2, DEXSeq, DirichletMultinomial, DMCFB, DMCHMM, DMRcaller, epigenomix, epihet, ExperimentHubData, ExpressionAtlas, fCCAC, GA4GHclient, GenoGAM, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicScores, GenomicTuples, girafe, groHMM, Gviz, HelloRanges, InPAS, InTAD, IntEREst, IRanges, LoomExperiment, MotifDb, MSnbase, NADfinder, NBAMSeq, OTUbase, padma, plethy, Rcwl, RegEnrich, RepViz, RNAmodR, RnBeads, scDataviz, segmentSeq, SeqGate, Spectra, SQLDataFrame, Structstrings, SummarizedBenchmark, TimeSeriesExperiment, topdownr, TreeSummarizedExperiment, triplex, VariantExperiment, VariantTools, vulcan, XVector, pd.ag, pd.aragene.1.0.st, pd.aragene.1.1.st, pd.ath1.121501, pd.barley1, pd.bovgene.1.0.st, pd.bovgene.1.1.st, 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pd.ht.hg.u133.plus.pm, pd.ht.hg.u133a, pd.ht.mg.430a, pd.hta.2.0, pd.hu6800, pd.huex.1.0.st.v2, pd.hugene.1.0.st.v1, pd.hugene.1.1.st.v1, pd.hugene.2.0.st, pd.hugene.2.1.st, pd.maize, pd.mapping250k.nsp, pd.mapping250k.sty, pd.mapping50k.hind240, pd.mapping50k.xba240, pd.margene.1.0.st, pd.margene.1.1.st, pd.medgene.1.0.st, pd.medgene.1.1.st, pd.medicago, pd.mg.u74a, pd.mg.u74av2, pd.mg.u74b, pd.mg.u74bv2, pd.mg.u74c, pd.mg.u74cv2, pd.mirna.1.0, pd.mirna.2.0, pd.mirna.3.0, pd.mirna.4.0, pd.moe430a, pd.moe430b, pd.moex.1.0.st.v1, pd.mogene.1.0.st.v1, pd.mogene.1.1.st.v1, pd.mogene.2.0.st, pd.mogene.2.1.st, pd.mouse430.2, pd.mouse430a.2, pd.mta.1.0, pd.mu11ksuba, pd.mu11ksubb, pd.nugo.hs1a520180, pd.nugo.mm1a520177, pd.ovigene.1.0.st, pd.ovigene.1.1.st, pd.pae.g1a, pd.plasmodium.anopheles, pd.poplar, pd.porcine, pd.porgene.1.0.st, pd.porgene.1.1.st, pd.rabgene.1.0.st, pd.rabgene.1.1.st, pd.rae230a, pd.rae230b, pd.raex.1.0.st.v1, pd.ragene.1.0.st.v1, pd.ragene.1.1.st.v1, pd.ragene.2.0.st, pd.ragene.2.1.st, pd.rat230.2, pd.rcngene.1.0.st, pd.rcngene.1.1.st, pd.rg.u34a, pd.rg.u34b, pd.rg.u34c, pd.rhegene.1.0.st, pd.rhegene.1.1.st, pd.rhesus, pd.rice, pd.rjpgene.1.0.st, pd.rjpgene.1.1.st, pd.rn.u34, pd.rta.1.0, pd.rusgene.1.0.st, pd.rusgene.1.1.st, pd.s.aureus, pd.soybean, pd.soygene.1.0.st, pd.soygene.1.1.st, pd.sugar.cane, pd.tomato, pd.u133.x3p, pd.vitis.vinifera, pd.wheat, pd.x.laevis.2, pd.x.tropicalis, pd.xenopus.laevis, pd.yeast.2, pd.yg.s98, pd.zebgene.1.0.st, pd.zebgene.1.1.st, pd.zebrafish, SNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, FlowSorted.Blood.EPIC, eQTL, generegulation, pagoo importsMe: ADImpute, affycoretools, aggregateBioVar, ALDEx2, AllelicImbalance, alpine, AlpsNMR, amplican, AneuFinder, animalcules, AnnotationDbi, AnnotationForge, AnnotationHub, annotatr, appreci8R, ASpediaFI, ASpli, AUCell, BadRegionFinder, ballgown, BASiCS, batchelor, BayesSpace, BiocIO, BiocNeighbors, BiocOncoTK, BiocSet, BiocSingular, biotmle, biovizBase, biscuiteer, BiSeq, BitSeq, bluster, bnbc, BPRMeth, BrainSABER, branchpointer, breakpointR, BSgenome, bsseq, BUSpaRse, CAGEfightR, CAGEr, casper, CATALYST, cBioPortalData, ccfindR, celaref, celda, CellaRepertorium, CeTF, CHETAH, ChIC, chipenrich, ChIPexoQual, ChIPQC, ChIPseeker, ChIPSeqSpike, ChromSCape, chromstaR, chromswitch, chromVAR, cicero, circRNAprofiler, CiteFuse, cleaver, CluMSID, clusterExperiment, clustifyr, cn.mops, CNEr, CNVPanelizer, CNVRanger, COCOA, CoGAPS, coMET, compEpiTools, ComplexHeatmap, consensusDE, consensusSeekeR, contiBAIT, copynumber, CopywriteR, CoreGx, CoverageView, CRISPRseek, CrispRVariants, csaw, cummeRbund, customProDB, cydar, cytomapper, DAMEfinder, dasper, debrowser, DECIPHER, decompTumor2Sig, DEFormats, DegNorm, DEGreport, DelayedMatrixStats, derfinder, derfinderHelper, derfinderPlot, DEScan2, DEWSeq, DiffBind, diffcyt, diffHic, diffloop, DiscoRhythm, dittoSeq, DMRcate, dmrseq, doseR, DRIMSeq, DropletUtils, eegc, eisaR, ELMER, ENCODExplorer, EnrichmentBrowser, enrichTF, ensembldb, ensemblVEP, EpiTxDb, epivizr, epivizrData, epivizrStandalone, erma, esATAC, EventPointer, ExperimentHub, ExploreModelMatrix, FastqCleaner, fastseg, FilterFFPE, FindMyFriends, fishpond, flowCore, FRASER, FunciSNP, GA4GHshiny, gcapc, GDSArray, genbankr, GeneRegionScan, GENESIS, GeneTonic, genomation, genomeIntervals, GenomicAlignments, GenomicDataCommons, GenomicFiles, GenomicInteractions, GenomicOZone, genoset, GGBase, ggbio, GGtools, Glimma, gmapR, gmoviz, GOpro, GOTHiC, gQTLBase, gQTLstats, GRmetrics, GSEABenchmarkeR, GSVA, GUIDEseq, gwascat, h5vc, HCAExplorer, HDF5Array, HiCBricks, HiCcompare, HiLDA, hipathia, hmdbQuery, HTSeqGenie, HumanTranscriptomeCompendium, icetea, ideal, ILoReg, IMAS, INSPEcT, InteractionSet, InterMineR, iSEE, iSEEu, isomiRs, IVAS, ivygapSE, karyoploteR, kebabs, lionessR, lipidr, loci2path, LOLA, MACPET, MADSEQ, marr, martini, MAST, mbkmeans, mCSEA, MEAL, meshr, MesKit, metabCombiner, metagenomeFeatures, metaseqR2, MetCirc, MethCP, methInheritSim, MethReg, methylCC, methylInheritance, methylKit, methylPipe, methylSig, methylumi, methyvim, mimager, minfi, MinimumDistance, MIRA, MiRaGE, missMethyl, missRows, MMAPPR2, MMDiff2, Modstrings, mosaics, MOSim, motifbreakR, motifmatchr, mpra, msa, MsCoreUtils, msgbsR, MSPrep, MultiAssayExperiment, MultiDataSet, muscat, musicatk, MutationalPatterns, mygene, myvariant, NanoMethViz, ncRNAtools, nearBynding, nucleoSim, nucleR, oligoClasses, ontoProc, openPrimeR, ORFik, Organism.dplyr, OrganismDbi, OUTRIDER, packFinder, PAIRADISE, panelcn.mops, PAST, pcaExplorer, pdInfoBuilder, periodicDNA, PGA, PharmacoGx, phemd, PING, pipeComp, plyranges, pmp, pogos, polyester, pqsfinder, pram, prebs, preciseTAD, PrecisionTrialDrawer, primirTSS, proActiv, procoil, proDA, profileplyr, pulsedSilac, PureCN, PWMEnrich, qcmetrics, QFeatures, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RadioGx, RaggedExperiment, RareVariantVis, Rariant, Rcade, RCAS, recount, recount3, recountmethylation, recoup, regioneR, regionReport, regsplice, regutools, REMP, Repitools, ResidualMatrix, restfulSE, rexposome, rfaRm, RGMQL, rhdf5client, RiboProfiling, ribor, ribosomeProfilingQC, RJMCMCNucleosomes, Rmmquant, rnaEditr, RNAmodR.AlkAnilineSeq, RNAmodR.ML, RNAmodR.RiboMethSeq, RNAprobR, roar, Rqc, Rsamtools, rScudo, RTCGAToolbox, RTN, rtracklayer, SC3, scater, scClassify, scDblFinder, scDD, scds, scHOT, scmap, scMerge, SCnorm, SCOPE, scp, scPipe, scran, scruff, scTensor, scTGIF, scuttle, SeqArray, seqCAT, seqplots, seqsetvis, SeqSQC, SeqVarTools, sesame, SEtools, sevenbridges, sevenC, SGSeq, ShortRead, SingleCellExperiment, singleCellTK, SingleR, singscore, skewr, SMITE, SNPhood, soGGi, SomaticSignatures, Spaniel, SpatialExperiment, spicyR, splatter, SplicingGraphs, SPLINTER, sRACIPE, srnadiff, STAN, strandCheckR, struct, SummarizedExperiment, SynExtend, TAPseq, TarSeqQC, TBSignatureProfiler, TCGAbiolinks, TCGAutils, TFBSTools, TFHAZ, tidySingleCellExperiment, tidySummarizedExperiment, TileDBArray, TnT, ToxicoGx, trackViewer, tradeSeq, transcriptR, TransView, Trendy, tRNA, tRNAdbImport, tRNAscanImport, TSCAN, tscR, TSRchitect, TVTB, twoddpcr, tximeta, TxRegInfra, Ularcirc, UMI4Cats, universalmotif, VanillaICE, VariantAnnotation, VariantFiltering, vasp, VaSP, VCFArray, velociraptor, VplotR, wavClusteR, weitrix, wiggleplotr, XCIR, xcms, XVector, yamss, zellkonverter, fitCons.UCSC.hg19, MafDb.1Kgenomes.phase1.GRCh38, MafDb.1Kgenomes.phase1.hs37d5, MafDb.1Kgenomes.phase3.GRCh38, MafDb.1Kgenomes.phase3.hs37d5, MafDb.ExAC.r1.0.GRCh38, MafDb.ExAC.r1.0.hs37d5, MafDb.ExAC.r1.0.nonTCGA.GRCh38, MafDb.ExAC.r1.0.nonTCGA.hs37d5, MafDb.gnomAD.r2.1.GRCh38, MafDb.gnomAD.r2.1.hs37d5, MafDb.gnomAD.r3.0.GRCh38, MafDb.gnomADex.r2.1.GRCh38, MafDb.gnomADex.r2.1.hs37d5, MafDb.TOPMed.freeze5.hg19, MafDb.TOPMed.freeze5.hg38, MafH5.gnomAD.r3.0.GRCh38, phastCons100way.UCSC.hg19, phastCons100way.UCSC.hg38, phastCons7way.UCSC.hg38, SNPlocs.Hsapiens.dbSNP.20101109, SNPlocs.Hsapiens.dbSNP141.GRCh38, SNPlocs.Hsapiens.dbSNP144.GRCh37, SNPlocs.Hsapiens.dbSNP144.GRCh38, SNPlocs.Hsapiens.dbSNP149.GRCh38, SNPlocs.Hsapiens.dbSNP150.GRCh38, SNPlocs.Hsapiens.dbSNP151.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP144.GRCh37, XtraSNPlocs.Hsapiens.dbSNP144.GRCh38, celldex, cgdv17, chipenrich.data, chipseqDBData, curatedMetagenomicData, curatedTCGAData, DropletTestFiles, HighlyReplicatedRNASeq, HMP16SData, HMP2Data, leeBamViews, MetaGxPancreas, MethylSeqData, MouseGastrulationData, pd.atdschip.tiling, scRNAseq, SingleCellMultiModal, SomaticCancerAlterations, spatialLIBD, ActiveDriverWGS, BinQuasi, crispRdesignR, driveR, genBaRcode, geno2proteo, hoardeR, LoopRig, microbial, NIPTeR, PlasmaMutationDetector, pulseTD, restfulr, rsolr, Signac suggestsMe: BiocGenerics, dearseq, epivizrChart, globalSeq, GWASTools, GWENA, RTCGA, StructuralVariantAnnotation, TFEA.ChIP, TFutils, tidybulk, alternativeSplicingEvents.hg19, alternativeSplicingEvents.hg38, curatedAdipoChIP, curatedAdipoRNA, cancerTiming, GeoTcgaData, gkmSVM, polyRAD, rliger, Seurat, tcgsaseq, valr linksToMe: Biostrings, CNEr, DECIPHER, DelayedArray, GenomicAlignments, GenomicRanges, HDF5Array, IRanges, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, Structstrings, triplex, VariantAnnotation, VariantFiltering, XVector dependencyCount: 7 Package: safe Version: 3.30.0 Depends: R (>= 2.4.0), AnnotationDbi, Biobase, methods, SparseM Suggests: GO.db, PFAM.db, reactome.db, hgu133a.db, breastCancerUPP, survival, foreach, doRNG, Rgraphviz, GOstats License: GPL (>= 2) MD5sum: 69509f71697678cc36d785e5510b9590 NeedsCompilation: no Title: Significance Analysis of Function and Expression Description: SAFE is a resampling-based method for testing functional categories in gene expression experiments. SAFE can be applied to 2-sample and multi-class comparisons, or simple linear regressions. Other experimental designs can also be accommodated through user-defined functions. biocViews: DifferentialExpression, Pathways, GeneSetEnrichment, StatisticalMethod, Software Author: William T. Barry Maintainer: Ludwig Geistlinger git_url: https://git.bioconductor.org/packages/safe git_branch: RELEASE_3_12 git_last_commit: 07021b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/safe_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/safe_3.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/safe_3.30.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/safe/inst/doc/SAFEmanual3.R dependsOnMe: PCGSE importsMe: EGSEA, EnrichmentBrowser dependencyCount: 27 Package: sagenhaft Version: 1.60.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, stats, utils License: GPL (>= 2) MD5sum: 692abdf647860dc1c01269142bbb245d NeedsCompilation: no Title: Collection of functions for reading and comparing SAGE libraries Description: This package implements several functions useful for analysis of gene expression data by sequencing tags as done in SAGE (Serial Analysis of Gene Expressen) data, i.e. extraction of a SAGE library from sequence files, sequence error correction, library comparison. Sequencing error correction is implementing using an Expectation Maximization Algorithm based on a Mixture Model of tag counts. biocViews: SAGE Author: Tim Beissbarth , with contributions from Gordon Smyth Maintainer: Tim Beissbarth URL: http://www.bioinf.med.uni-goettingen.de git_url: https://git.bioconductor.org/packages/sagenhaft git_branch: RELEASE_3_12 git_last_commit: b32f5aa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sagenhaft_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sagenhaft_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sagenhaft_1.60.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sagenhaft/inst/doc/SAGEnhaft.R dependencyCount: 5 Package: SAGx Version: 1.64.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Archs: i386, x64 MD5sum: 4af6b72b841befddc0995393964da22f NeedsCompilation: yes Title: Statistical Analysis of the GeneChip Description: A package for retrieval, preparation and analysis of data from the Affymetrix GeneChip. In particular the issue of identifying differentially expressed genes is addressed. biocViews: Microarray, OneChannel, Preprocessing, DataImport, DifferentialExpression, Clustering, MultipleComparison, GeneExpression, GeneSetEnrichment, Pathways, Regression, KEGG Author: Per Broberg Maintainer: Per Broberg, URL: http://home.swipnet.se/pibroberg/expression_hemsida1.html git_url: https://git.bioconductor.org/packages/SAGx git_branch: RELEASE_3_12 git_last_commit: ed2c638 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SAGx_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SAGx_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SAGx_1.64.0.tgz vignettes: vignettes/SAGx/inst/doc/samroc-ex.pdf vignetteTitles: samroc - example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAGx/inst/doc/samroc-ex.R dependencyCount: 16 Package: SAIGEgds Version: 1.4.2 Depends: R (>= 3.5.0), gdsfmt (>= 1.20.0), SeqArray (>= 1.30.0), Rcpp Imports: methods, stats, utils, RcppParallel, SPAtest (>= 3.0.0) LinkingTo: Rcpp, RcppArmadillo, RcppParallel (>= 5.0.0) Suggests: parallel, crayon, RUnit, knitr, markdown, rmarkdown, BiocGenerics, SNPRelate License: GPL-3 Archs: i386, x64 MD5sum: cd6d1962ef16fe454a5e0cd49ac71274 NeedsCompilation: yes Title: Scalable Implementation of Generalized mixed models using GDS files in Phenome-Wide Association Studies Description: Scalable implementation of generalized mixed models with highly optimized C++ implementation and integration with Genomic Data Structure (GDS) files. It is designed for single variant tests in large-scale phenome-wide association studies (PheWAS) with millions of variants and samples, controlling for sample structure and case-control imbalance. The implementation is based on the original SAIGE R package (v0.29.4.4 for single variant tests, Zhou et al. 2018). SAIGEgds also implements some of the SPAtest functions in C to speed up the calculation of Saddlepoint approximation. Benchmarks show that SAIGEgds is 5 to 6 times faster than the original SAIGE R package. biocViews: Software, Genetics, StatisticalMethod Author: Xiuwen Zheng [aut, cre] (), Wei Zhou [ctb] (the original author of the SAIGE R package), J. Wade Davis [ctb] Maintainer: Xiuwen Zheng URL: https://github.com/AbbVie-ComputationalGenomics/SAIGEgds SystemRequirements: C++11, GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SAIGEgds git_branch: RELEASE_3_12 git_last_commit: 631315f git_last_commit_date: 2021-04-30 Date/Publication: 2021-05-01 source.ver: src/contrib/SAIGEgds_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/SAIGEgds_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SAIGEgds_1.4.2.tgz vignettes: vignettes/SAIGEgds/inst/doc/SAIGEgds.html vignetteTitles: SAIGEgds Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SAIGEgds/inst/doc/SAIGEgds.R dependencyCount: 26 Package: samExploreR Version: 1.13.0 Depends: ggplot2,Rsubread,RNAseqData.HNRNPC.bam.chr14,edgeR,R (>= 3.4.0) Imports: grDevices, stats, graphics Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL-3 MD5sum: 94c261b23acc7d850b593b85aac8ed5c NeedsCompilation: no Title: samExploreR package: high-performance read summarisation to count vectors with avaliability of sequencing depth reduction simulation Description: This R package is designed for subsampling procedure to simulate sequencing experiments with reduced sequencing depth. This package can be used to anlayze data generated from all major sequencing platforms such as Illumina GA, HiSeq, MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM Proton sequencers. It supports multiple operating systems incluidng Linux, Mac OS X, FreeBSD and Solaris. Was developed with usage of Rsubread. biocViews: ImmunoOncology, Sequencing, SequenceMatching, RNASeq, ChIPSeq, DNASeq, WholeGenome, GeneTarget, Alignment, GeneExpression, GeneticVariability, GeneRegulation, Preprocessing, GenomeAnnotation, Software Author: Alexey Stupnikov, Shailesh Tripathi and Frank Emmert-Streib Maintainer: shailesh tripathi git_url: https://git.bioconductor.org/packages/samExploreR git_branch: master git_last_commit: 4f8e793 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/samExploreR_1.13.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/samExploreR_1.13.0.tgz vignettes: vignettes/samExploreR/inst/doc/Manual.pdf vignetteTitles: samExploreR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/samExploreR/inst/doc/Manual.R dependencyCount: 44 Package: sampleClassifier Version: 1.14.2 Depends: R (>= 4.0), MGFM, MGFR, annotate Imports: e1071, ggplot2, stats, utils Suggests: sampleClassifierData, BiocStyle, hgu133a.db, hgu133plus2.db License: Artistic-2.0 MD5sum: c95ce51ab6deba366452a8a674c0827b NeedsCompilation: no Title: Sample Classifier Description: The package is designed to classify microarray RNA-seq gene expression profiles. biocViews: ImmunoOncology, Classification, Microarray, RNASeq, GeneExpression Author: Khadija El Amrani [aut, cre] Maintainer: Khadija El Amrani git_url: https://git.bioconductor.org/packages/sampleClassifier git_branch: RELEASE_3_12 git_last_commit: 567967b git_last_commit_date: 2021-01-19 Date/Publication: 2021-01-19 source.ver: src/contrib/sampleClassifier_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/sampleClassifier_1.14.2.zip mac.binary.ver: bin/macosx/contrib/4.0/sampleClassifier_1.14.2.tgz vignettes: vignettes/sampleClassifier/inst/doc/sampleClassifier.pdf vignetteTitles: sampleClassifier Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sampleClassifier/inst/doc/sampleClassifier.R dependencyCount: 86 Package: SamSPECTRAL Version: 1.44.0 Depends: R (>= 3.3.3) Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 7cd7c0aaa36f08534a9ef2db6ef2f491 NeedsCompilation: yes Title: Identifies cell population in flow cytometry data. Description: Samples large data such that spectral clustering is possible while preserving density information in edge weights. More specifically, given a matrix of coordinates as input, SamSPECTRAL first builds the communities to sample the data points. Then, it builds a graph and after weighting the edges by conductance computation, the graph is passed to a classic spectral clustering algorithm to find the spectral clusters. The last stage of SamSPECTRAL is to combine the spectral clusters. The resulting "connected components" estimate biological cell populations in the data. See the vignette for more details on how to use this package, some illustrations, and simple examples. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV, ImmunoOncology Author: Habil Zare and Parisa Shooshtari Maintainer: Habil git_url: https://git.bioconductor.org/packages/SamSPECTRAL git_branch: RELEASE_3_12 git_last_commit: d58c918 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SamSPECTRAL_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SamSPECTRAL_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SamSPECTRAL_1.44.0.tgz vignettes: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.pdf vignetteTitles: A modified spectral clustering method for clustering Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SamSPECTRAL/inst/doc/Clustering_by_SamSPECTRAL.R importsMe: ddPCRclust dependencyCount: 1 Package: sangeranalyseR Version: 1.0.0 Depends: R (>= 3.6.0), stringr, ape, Biostrings, DECIPHER, parallel, reshape2, phangorn, sangerseqR, gridExtra, shiny, shinydashboard, shinyjs, data.table, plotly, DT, zeallot, excelR, shinycssloaders, ggdendro, shinyWidgets, openxlsx, tools, rmarkdown, kableExtra, seqinr, BiocStyle, logger Suggests: knitr, testthat (>= 2.1.0) License: GPL-2 MD5sum: c3ca1d4e64acb434236a5b8a5b96f080 NeedsCompilation: no Title: sangeranalyseR: a suite of functions for the analysis of Sanger sequence data in R Description: This package builds on sangerseqR to allow users to create contigs from collections of Sanger sequencing reads. It provides a wide range of options for a number of commonly-performed actions including read trimming, detecting secondary peaks, and detecting indels using a reference sequence. All parameters can be adjusted interactively either in R or in the associated Shiny applications. There is extensive online documentation, and the package can outputs detailed HTML reports, including chromatograms. biocViews: Genetics, Alignment, Sequencing, SangerSeq, Preprocessing, QualityControl, Visualization, GUI Author: Rob Lanfear , Kuan-Hao Chao Maintainer: Kuan-Hao Chao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangeranalyseR git_branch: RELEASE_3_12 git_last_commit: d1a14ae git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sangeranalyseR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sangeranalyseR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sangeranalyseR_1.0.0.tgz vignettes: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.html vignetteTitles: sangeranalyseR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sangeranalyseR/inst/doc/sangeranalyseR.R dependencyCount: 142 Package: sangerseqR Version: 1.26.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 2d18ffa71e567d92155c973ca8a512a3 NeedsCompilation: no Title: Tools for Sanger Sequencing Data in R Description: This package contains several tools for analyzing Sanger Sequencing data files in R, including reading .scf and .ab1 files, making basecalls and plotting chromatograms. biocViews: Sequencing, SNP, Visualization Author: Jonathon T. Hill, Bradley Demarest Maintainer: Jonathon Hill VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sangerseqR git_branch: RELEASE_3_12 git_last_commit: 58e603d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sangerseqR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sangerseqR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sangerseqR_1.26.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.R dependsOnMe: sangeranalyseR suggestsMe: CrispRVariants, bold dependencyCount: 42 Package: sapFinder Version: 1.28.0 Depends: R (>= 3.0.0),rTANDEM (>= 1.3.5) Imports: pheatmap,Rcpp (>= 0.10.6),graphics,grDevices,stats, utils LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-2 Archs: x64 MD5sum: 5924c16135f91750f0ee26699ff26bd5 NeedsCompilation: yes Title: A package for variant peptides detection and visualization in shotgun proteomics. Description: sapFinder is developed to automate (1) variation-associated database construction, (2) database searching, (3) post-processing, (4) HTML-based report generation in shotgun proteomics. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, SNP, RNASeq, Visualization, ReportWriting Author: Shaohang Xu, Bo Wen Maintainer: Shaohang Xu , Bo Wen PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/sapFinder git_branch: RELEASE_3_12 git_last_commit: e960481 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sapFinder_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sapFinder_1.28.0.zip vignettes: vignettes/sapFinder/inst/doc/sapFinder.pdf vignetteTitles: sapFinder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sapFinder/inst/doc/sapFinder.R dependencyCount: 21 Package: sarks Version: 1.2.0 Depends: R (>= 4.0) Imports: rJava, Biostrings, IRanges, utils, stats, cluster, binom Suggests: RUnit, BiocGenerics, ggplot2 License: BSD_3_clause + file LICENSE MD5sum: 1df1293567a9a6bfaff6550d943e9fe1 NeedsCompilation: no Title: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains Description: Suffix Array Kernel Smoothing (see https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797), or SArKS, identifies sequence motifs whose presence correlates with numeric scores (such as differential expression statistics) assigned to the sequences (such as gene promoters). SArKS smooths over sequence similarity, quantified by location within a suffix array based on the full set of input sequences. A second round of smoothing over spatial proximity within sequences reveals multi-motif domains. Discovered motifs can then be merged or extended based on adjacency within MMDs. False positive rates are estimated and controlled by permutation testing. biocViews: MotifDiscovery, GeneRegulation, GeneExpression, Transcriptomics, RNASeq, DifferentialExpression, FeatureExtraction Author: Dennis Wylie [aut, cre] () Maintainer: Dennis Wylie URL: https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797, https://github.com/denniscwylie/sarks SystemRequirements: Java (>= 1.8) BugReports: https://github.com/denniscwylie/sarks/issues git_url: https://git.bioconductor.org/packages/sarks git_branch: RELEASE_3_12 git_last_commit: 6ee5a80 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sarks_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sarks_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sarks_1.2.0.tgz vignettes: vignettes/sarks/inst/doc/sarks-vignette.pdf vignetteTitles: sarks-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sarks/inst/doc/sarks-vignette.R dependencyCount: 18 Package: savR Version: 1.28.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: 7bde190036722dc4de229ab11560f6b1 NeedsCompilation: no Title: Parse and analyze Illumina SAV files Description: Parse Illumina Sequence Analysis Viewer (SAV) files, access data, and generate QC plots. biocViews: Sequencing Author: R. Brent Calder Maintainer: R. Brent Calder URL: https://github.com/bcalder/savR BugReports: https://github.com/bcalder/savR/issues git_url: https://git.bioconductor.org/packages/savR git_branch: RELEASE_3_12 git_last_commit: b051b0b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/savR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/savR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/savR_1.28.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/savR/inst/doc/savR.R dependencyCount: 46 Package: SBGNview Version: 1.4.1 Depends: R (>= 3.6), pathview, SBGNview.data Imports: Rdpack, grDevices, methods, stats, utils, xml2, rsvg, igraph, rmarkdown, knitr, SummarizedExperiment, AnnotationDbi, httr, KEGGREST, bookdown Suggests: testthat, gage License: AGPL-3 MD5sum: bc6e25c3d34ce30768f7ead8d823166f NeedsCompilation: no Title: "SBGNview: Data Analysis, Integration and Visualization on SBGN Pathways" Description: SBGNview is a tool set for pathway based data visalization, integration and analysis. SBGNview is similar and complementary to the widely used Pathview, with the following key features: 1. Pathway definition by the widely adopted Systems Biology Graphical Notation (SBGN); 2. Supports multiple major pathway databases beyond KEGG (Reactome, MetaCyc, SMPDB, PANTHER, METACROP) and user defined pathways; 3. Covers 5,200 reference pathways and over 3,000 species by default; 4. Extensive graphics controls, including glyph and edge attributes, graph layout and sub-pathway highlight; 5. SBGN pathway data manipulation, processing, extraction and analysis. biocViews: GeneTarget, Pathways, GraphAndNetwork, Visualization, GeneSetEnrichment, DifferentialExpression, GeneExpression, Microarray, RNASeq, Genetics, Metabolomics, Proteomics, SystemsBiology, Sequencing, GeneTarget Author: Xiaoxi Dong*, Kovidh Vegesna*, Weijun Luo Maintainer: Weijun Luo URL: https://github.com/datapplab/SBGNview VignetteBuilder: knitr BugReports: https://github.com/datapplab/SBGNview/issues git_url: https://git.bioconductor.org/packages/SBGNview git_branch: RELEASE_3_12 git_last_commit: 260f865 git_last_commit_date: 2021-02-28 Date/Publication: 2021-03-01 source.ver: src/contrib/SBGNview_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/SBGNview_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.0/SBGNview_1.4.1.tgz vignettes: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.html, vignettes/SBGNview/inst/doc/SBGNview.quick.start.html, vignettes/SBGNview/inst/doc/SBGNview.Vignette.html vignetteTitles: Pathway analysis using SBGNview gene set, Quick start SBGNview, SBGNview functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBGNview/inst/doc/pathway.enrichment.analysis.R, vignettes/SBGNview/inst/doc/SBGNview.quick.start.R, vignettes/SBGNview/inst/doc/SBGNview.Vignette.R dependencyCount: 81 Package: SBMLR Version: 1.86.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: b64751edd010e079263aea8110837005 NeedsCompilation: no Title: SBML-R Interface and Analysis Tools Description: This package contains a systems biology markup language (SBML) interface to R. biocViews: GraphAndNetwork, Pathways, Network Author: Tomas Radivoyevitch, Vishak Venkateswaran Maintainer: Tomas Radivoyevitch URL: http://epbi-radivot.cwru.edu/SBMLR/SBMLR.html git_url: https://git.bioconductor.org/packages/SBMLR git_branch: RELEASE_3_12 git_last_commit: 1b53a3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SBMLR_1.86.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SBMLR_1.86.0.zip vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SBMLR/inst/doc/quick-start.R dependencyCount: 7 Package: SC3 Version: 1.18.0 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), ROCR, robustbase, rrcov, cluster, WriteXLS, Rcpp (>= 0.11.1), SummarizedExperiment, SingleCellExperiment, BiocGenerics, S4Vectors LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown, mclust, scater License: GPL-3 Archs: i386, x64 MD5sum: acc43d2595516022da5aa719a63d7409 NeedsCompilation: yes Title: Single-Cell Consensus Clustering Description: A tool for unsupervised clustering and analysis of single cell RNA-Seq data. biocViews: ImmunoOncology, SingleCell, Software, Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/sc3/ git_url: https://git.bioconductor.org/packages/SC3 git_branch: RELEASE_3_12 git_last_commit: 9065446 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SC3_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SC3_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SC3_1.18.0.tgz vignettes: vignettes/SC3/inst/doc/SC3.html vignetteTitles: SC3 package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SC3/inst/doc/SC3.R dependencyCount: 99 Package: Scale4C Version: 1.12.0 Depends: R (>= 3.4), smoothie, GenomicRanges, IRanges, SummarizedExperiment Imports: methods, grDevices, graphics, utils License: LGPL-3 MD5sum: 0932064fd3fda1303a3b55003fc52d4a NeedsCompilation: no Title: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data Description: Scale4C is an R/Bioconductor package for scale-space transformation and visualization of 4C-seq data. The scale-space transformation is a multi-scale visualization technique to transform a 2D signal (e.g. 4C-seq reads on a genomic interval of choice) into a tesselation in the scale space (2D, genomic position x scale factor) by applying different smoothing kernels (Gauss, with increasing sigma). This transformation allows for explorative analysis and comparisons of the data's structure with other samples. biocViews: Visualization, QualityControl, DataImport, Sequencing, Coverage Author: Carolin Walter Maintainer: Carolin Walter git_url: https://git.bioconductor.org/packages/Scale4C git_branch: RELEASE_3_12 git_last_commit: 17a82a6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Scale4C_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Scale4C_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Scale4C_1.12.0.tgz vignettes: vignettes/Scale4C/inst/doc/vignette.pdf vignetteTitles: Scale4C: an R/Bioconductor package for scale-space transformation of 4C-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Scale4C/inst/doc/vignette.R dependencyCount: 27 Package: scAlign Version: 1.4.0 Depends: R (>= 3.5), SingleCellExperiment (>= 1.4), Seurat (>= 2.3.4), tensorflow, purrr, irlba, Rtsne, ggplot2, methods, utils, FNN Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 4c28d2b3f84c0f35f7eb6efb750cf0f6 NeedsCompilation: no Title: An alignment and integration method for single cell genomics Description: An unsupervised deep learning method for data alignment, integration and estimation of per-cell differences in -omic data (e.g. gene expression) across datasets (conditions, tissues, species). See Johansen and Quon (2019) for more details. biocViews: SingleCell, Transcriptomics, DimensionReduction, NeuralNetwork Author: Nelson Johansen [aut, cre], Gerald Quon [aut] Maintainer: Nelson Johansen URL: https://github.com/quon-titative-biology/scAlign SystemRequirements: python (< 3.7), tensorflow VignetteBuilder: knitr BugReports: https://github.com/quon-titative-biology/scAlign/issues git_url: https://git.bioconductor.org/packages/scAlign git_branch: RELEASE_3_12 git_last_commit: 0309fa7 git_last_commit_date: 2020-10-27 Date/Publication: 2021-04-21 source.ver: src/contrib/scAlign_1.4.0.tar.gz vignettes: vignettes/scAlign/inst/doc/scAlign.pdf vignetteTitles: alignment_tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scAlign/inst/doc/scAlign.R dependencyCount: 160 Package: SCAN.UPC Version: 2.32.0 Depends: R (>= 2.14.0), Biobase (>= 2.6.0), oligo, Biostrings, GEOquery, affy, affyio, foreach, sva Imports: utils, methods, MASS, tools, IRanges Suggests: pd.hg.u95a License: MIT MD5sum: eb1828be7f93dd88cd962d0edfb4a92e NeedsCompilation: no Title: Single-channel array normalization (SCAN) and Universal exPression Codes (UPC) Description: SCAN is a microarray normalization method to facilitate personalized-medicine workflows. Rather than processing microarray samples as groups, which can introduce biases and present logistical challenges, SCAN normalizes each sample individually by modeling and removing probe- and array-specific background noise using only data from within each array. SCAN can be applied to one-channel (e.g., Affymetrix) or two-channel (e.g., Agilent) microarrays. The Universal exPression Codes (UPC) method is an extension of SCAN that estimates whether a given gene/transcript is active above background levels in a given sample. The UPC method can be applied to one-channel or two-channel microarrays as well as to RNA-Seq read counts. Because UPC values are represented on the same scale and have an identical interpretation for each platform, they can be used for cross-platform data integration. biocViews: ImmunoOncology, Software, Microarray, Preprocessing, RNASeq, TwoChannel, OneChannel Author: Stephen R. Piccolo and Andrea H. Bild and W. Evan Johnson Maintainer: Stephen R. Piccolo URL: http://bioconductor.org, http://jlab.bu.edu/software/scan-upc git_url: https://git.bioconductor.org/packages/SCAN.UPC git_branch: RELEASE_3_12 git_last_commit: ecb2414 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SCAN.UPC_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SCAN.UPC_2.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SCAN.UPC_2.32.0.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.R dependencyCount: 101 Package: SCANVIS Version: 1.4.0 Depends: R (>= 3.6) Imports: IRanges,plotrix,RCurl,rtracklayer Suggests: knitr, rmarkdown License: file LICENSE MD5sum: 2d5fbade5cfa8c29811a6543af0372cb NeedsCompilation: no Title: SCANVIS - a tool for SCoring, ANnotating and VISualizing splice junctions Description: SCANVIS is a set of annotation-dependent tools for analyzing splice junctions and their read support as predetermined by an alignment tool of choice (for example, STAR aligner). SCANVIS assesses each junction's relative read support (RRS) by relating to the context of local split reads aligning to annotated transcripts. SCANVIS also annotates each splice junction by indicating whether the junction is supported by annotation or not, and if not, what type of junction it is (e.g. exon skipping, alternative 5' or 3' events, Novel Exons). Unannotated junctions are also futher annotated by indicating whether it induces a frame shift or not. SCANVIS includes a visualization function to generate static sashimi-style plots depicting relative read support and number of split reads using arc thickness and arc heights, making it easy for users to spot well-supported junctions. These plots also clearly delineate unannotated junctions from annotated ones using designated color schemes, and users can also highlight splice junctions of choice. Variants and/or a read profile are also incoroporated into the plot if the user supplies variants in bed format and/or the BAM file. One further feature of the visualization function is that users can submit multiple samples of a certain disease or cohort to generate a single plot - this occurs via a "merge" function wherein junction details over multiple samples are merged to generate a single sashimi plot, which is useful when contrasting cohorots (eg. disease vs control). biocViews: Software,ResearchField,Transcriptomics,WorkflowStep,Annotation,Visualization Author: Phaedra Agius Maintainer: Phaedra Agius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCANVIS git_branch: RELEASE_3_12 git_last_commit: 0a98a3b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SCANVIS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SCANVIS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SCANVIS_1.4.0.tgz vignettes: vignettes/SCANVIS/inst/doc/runningSCANVIS.pdf vignetteTitles: SCANVIS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCANVIS/inst/doc/runningSCANVIS.R dependencyCount: 41 Package: SCATE Version: 1.0.0 Depends: parallel, preprocessCore, splines, splines2, xgboost, SCATEData, Rtsne, mclust Imports: utils, stats, GenomicAlignments, GenomicRanges Suggests: rmarkdown, ggplot2, knitr License: MIT + file LICENSE MD5sum: d2749ebe898e83a8a06c2bcf8151a23f NeedsCompilation: no Title: SCATE: Single-cell ATAC-seq Signal Extraction and Enhancement Description: SCATE is a software tool for extracting and enhancing the sparse and discrete Single-cell ATAC-seq Signal. Single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) is the state-of-the-art technology for analyzing genome-wide regulatory landscapes in single cells. Single-cell ATAC-seq data are sparse and noisy, and analyzing such data is challenging. Existing computational methods cannot accurately reconstruct activities of individual cis-regulatory elements (CREs) in individual cells or rare cell subpopulations. SCATE was developed to adaptively integrate information from co-activated CREs, similar cells, and publicly available regulome data and substantially increase the accuracy for estimating activities of individual CREs. We demonstrate that SCATE can be used to better reconstruct the regulatory landscape of a heterogeneous sample. biocViews: ExperimentHub, ExperimentData, Genome, SequencingData, SingleCellData, SNPData Author: Zhicheng Ji [aut], Weiqiang Zhou [aut], Wenpin Hou [cre, aut] (), Hongkai Ji [aut] Maintainer: Wenpin Hou VignetteBuilder: knitr BugReports: https://github.com/Winnie09/SCATE/issues git_url: https://git.bioconductor.org/packages/SCATE git_branch: RELEASE_3_12 git_last_commit: 00a484b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SCATE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SCATE_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SCATE_1.0.0.tgz vignettes: vignettes/SCATE/inst/doc/SCATE.html vignetteTitles: 1. SCATE package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SCATE/inst/doc/SCATE.R dependencyCount: 112 Package: scater Version: 1.18.6 Depends: SingleCellExperiment, ggplot2 Imports: stats, utils, methods, grid, gridExtra, Matrix, BiocGenerics, S4Vectors, SummarizedExperiment, DelayedArray, DelayedMatrixStats, BiocNeighbors, BiocSingular, BiocParallel, scuttle, rlang, ggbeeswarm, viridis Suggests: BiocStyle, biomaRt, cowplot, destiny, knitr, scRNAseq, robustbase, rmarkdown, Rtsne, uwot, NMF, testthat, pheatmap, Biobase License: GPL-3 MD5sum: 24edeb577da40121460438717f283e98 NeedsCompilation: no Title: Single-Cell Analysis Toolkit for Gene Expression Data in R Description: A collection of tools for doing various analyses of single-cell RNA-seq gene expression data, with a focus on quality control and visualization. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage Author: Davis McCarthy [aut], Kieran Campbell [aut], Aaron Lun [aut, ctb], Quin Wills [aut], Vladimir Kiselev [ctb], Alan O'Callaghan [ctb, cre] Maintainer: Alan O'Callaghan URL: http://bioconductor.org/packages/scater/ VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/scater git_branch: RELEASE_3_12 git_last_commit: 813ccd0 git_last_commit_date: 2021-02-25 Date/Publication: 2021-02-26 source.ver: src/contrib/scater_1.18.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/scater_1.18.6.zip mac.binary.ver: bin/macosx/contrib/4.0/scater_1.18.6.tgz vignettes: vignettes/scater/inst/doc/overview.html vignetteTitles: Overview of scater functionality hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/overview.R dependsOnMe: netSmooth importsMe: BayesSpace, CATALYST, celda, CellMixS, ChromSCape, distinct, muscat, netDx, peco, pipeComp, scDblFinder, scPipe, singleCellTK, Spaniel, splatter, spatialLIBD suggestsMe: batchelor, bluster, CellaRepertorium, CellTrails, CiteFuse, ExperimentSubset, fcoex, iSEE, iSEEu, M3Drop, MAST, mbkmeans, monocle, Nebulosa, SC3, scds, schex, scHOT, scMerge, scp, scran, scRepertoire, SingleR, slalom, snifter, SummarizedBenchmark, tidySingleCellExperiment, velociraptor, waddR, DuoClustering2018, HCAData, muscData, TabulaMurisData, simpleSingleCell, bcTSNE dependencyCount: 83 Package: scBFA Version: 1.4.0 Depends: R (>= 3.6) Imports: SingleCellExperiment, SummarizedExperiment, Seurat, MASS, zinbwave, stats, copula, ggplot2, DESeq2, utils, grid, methods, Matrix Suggests: knitr, rmarkdown, testthat, Rtsne License: GPL-3 + file LICENSE MD5sum: 897bf642e9c90421ec5c99e570fa75d0 NeedsCompilation: no Title: A dimensionality reduction tool using gene detection pattern to mitigate noisy expression profile of scRNA-seq Description: This package is designed to model gene detection pattern of scRNA-seq through a binary factor analysis model. This model allows user to pass into a cell level covariate matrix X and gene level covariate matrix Q to account for nuisance variance(e.g batch effect), and it will output a low dimensional embedding matrix for downstream analysis. biocViews: SingleCell, Transcriptomics, DimensionReduction,GeneExpression, ATACSeq, BatchEffect, KEGG, QualityControl Author: Ruoxin Li [aut, cre], Gerald Quon [aut] Maintainer: Ruoxin Li URL: https://github.com/ucdavis/quon-titative-biology/BFA VignetteBuilder: knitr BugReports: https://github.com/ucdavis/quon-titative-biology/BFA/issues git_url: https://git.bioconductor.org/packages/scBFA git_branch: RELEASE_3_12 git_last_commit: 5575b66 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scBFA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scBFA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scBFA_1.4.0.tgz vignettes: vignettes/scBFA/inst/doc/vignette.html vignetteTitles: Gene Detection Analysis for scRNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scBFA/inst/doc/vignette.R dependencyCount: 185 Package: SCBN Version: 1.8.0 Depends: R (>= 3.5.0) Imports: stats Suggests: knitr,rmarkdown License: GPL-2 MD5sum: a3ce76b14bbe53acc0a33752b98652d8 NeedsCompilation: no Title: A statistical normalization method and differential expression analysis for RNA-seq data between different species Description: This package provides a scale based normalization (SCBN) method to identify genes with differential expression between different species. It takes into account the available knowledge of conserved orthologous genes and the hypothesis testing framework to detect differentially expressed orthologous genes. The method on this package are described in the article 'A statistical normalization method and differential expression analysis for RNA-seq data between different species' by Yan Zhou, Jiadi Zhu, Tiejun Tong, Junhui Wang, Bingqing Lin, Jun Zhang (2018, pending publication). biocViews: DifferentialExpression, GeneExpression, Normalization Author: Yan Zhou Maintainer: Yan Zhou <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCBN git_branch: RELEASE_3_12 git_last_commit: 490d99f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SCBN_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SCBN_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SCBN_1.8.0.tgz vignettes: vignettes/SCBN/inst/doc/SCBN.html vignetteTitles: SCBN Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCBN/inst/doc/SCBN.R dependencyCount: 1 Package: scCB2 Version: 1.0.0 Depends: R (>= 3.6.0) Imports: SingleCellExperiment, SummarizedExperiment, Matrix, methods, utils, stats, edgeR, rhdf5, parallel, DropletUtils, doParallel, iterators, foreach, Seurat Suggests: testthat (>= 2.1.0), KernSmooth, beachmat, knitr, BiocStyle, rmarkdown License: GPL-3 MD5sum: a8310eed9bc53d4044927605bd5349f7 NeedsCompilation: yes Title: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data Description: scCB2 is an R package implementing CB2 for distinguishing real cells from empty droplets in droplet-based single cell RNA-seq experiments (especially for 10x Chromium). It is based on clustering similar barcodes and calculating Monte-Carlo p-value for each cluster to test against background distribution. This cluster-level test outperforms single-barcode-level tests in dealing with low count barcodes and homogeneous sequencing library, while keeping FDR well controlled. biocViews: DataImport, RNASeq, SingleCell, Sequencing, GeneExpression, Transcriptomics, Preprocessing, Clustering Author: Zijian Ni [aut, cre], Shuyang Chen [ctb], Christina Kendziorski [ctb] Maintainer: Zijian Ni URL: https://github.com/zijianni/scCB2 SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/zijianni/scCB2/issues git_url: https://git.bioconductor.org/packages/scCB2 git_branch: RELEASE_3_12 git_last_commit: 8c6771d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scCB2_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scCB2_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scCB2_1.0.0.tgz vignettes: vignettes/scCB2/inst/doc/scCB2.html vignetteTitles: CB2 improves power of cell detection in droplet-based single-cell RNA sequencing data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scCB2/inst/doc/scCB2.R dependencyCount: 177 Package: scClassify Version: 1.2.0 Depends: R (>= 4.0) Imports: S4Vectors, limma, ggraph, igraph, methods, cluster, minpack.lm, mixtools, BiocParallel, proxy, proxyC, Matrix, ggplot2, hopach, diptest, mgcv, stats, graphics, statmod Suggests: knitr, rmarkdown, BiocStyle, pkgdown License: GPL-3 MD5sum: 20c2eeb2b40e896427eca5de552ce7b9 NeedsCompilation: no Title: scClassify: single-cell Hierarchical Classification Description: scClassify is a multiscale classification framework for single-cell RNA-seq data based on ensemble learning and cell type hierarchies, enabling sample size estimation required for accurate cell type classification and joint classification of cells using multiple references. biocViews: SingleCell, GeneExpression, Classification Author: Yingxin Lin Maintainer: Yingxin Lin VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scClassify/issues git_url: https://git.bioconductor.org/packages/scClassify git_branch: RELEASE_3_12 git_last_commit: bae755d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scClassify_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scClassify_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scClassify_1.2.0.tgz vignettes: vignettes/scClassify/inst/doc/pretrainedModel.html, vignettes/scClassify/inst/doc/scClassify.html vignetteTitles: pretrainedModel, scClassify hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scClassify/inst/doc/pretrainedModel.R, vignettes/scClassify/inst/doc/scClassify.R dependencyCount: 83 Package: scDataviz Version: 1.0.0 Depends: R (>= 4.0), S4Vectors, SingleCellExperiment, Imports: ggplot2, ggrepel, flowCore, umap, Seurat, reshape2, scales, RColorBrewer, corrplot, stats, grDevices, graphics, utils, MASS, matrixStats, methods Suggests: PCAtools, cowplot, BiocGenerics, knitr, kableExtra License: GPL-3 MD5sum: c1acebf791f7b1edf81fe15a7bff94cb NeedsCompilation: no Title: scDataviz: single cell dataviz and downstream analyses Description: In the single cell World, which includes flow cytometry, mass cytometry, single-cell RNA-seq (scRNA-seq), and others, there is a need to improve data visualisation and to bring analysis capabilities to researchers even from non-technical backgrounds. scDataviz attempts to fit into this space, while also catering for advanced users. Additonally, due to the way that scDataviz is designed, which is based on SingleCellExperiment, it has a 'plug and play' feel, and immediately lends itself as flexibile and compatibile with studies that go beyond scDataviz. Finally, the graphics in scDataviz are generated via the ggplot engine, which means that users can 'add on' features to these with ease. biocViews: SingleCell, ImmunoOncology, RNASeq, GeneExpression, Transcription, FlowCytometry, MassSpectrometry, DataImport Author: Kevin Blighe [aut, cre] Maintainer: Kevin Blighe URL: https://github.com/kevinblighe/scDataviz VignetteBuilder: knitr BugReports: https://github.com/kevinblighe/scDataviz/issues git_url: https://git.bioconductor.org/packages/scDataviz git_branch: RELEASE_3_12 git_last_commit: d20acde git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scDataviz_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scDataviz_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scDataviz_1.0.0.tgz vignettes: vignettes/scDataviz/inst/doc/scDataviz.html vignetteTitles: scDataviz: single cell dataviz and downstream analyses hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDataviz/inst/doc/scDataviz.R dependencyCount: 160 Package: scDblFinder Version: 1.4.0 Depends: R (>= 4.0) Imports: igraph, Matrix, BiocGenerics, BiocParallel, BiocNeighbors, BiocSingular, S4Vectors, SummarizedExperiment, SingleCellExperiment, scran, scater, scuttle, bluster, methods, DelayedArray, xgboost, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, scRNAseq, bluster License: GPL-3 MD5sum: 6a323e13629a59af2dbe164d99dcec44 NeedsCompilation: no Title: scDblFinder Description: The scDblFinder package gathers various methods for the detection and handling of doublets/multiplets in single-cell RNA sequencing data (i.e. multiple cells captured within the same droplet or reaction volume). It includes methods formerly found in the scran package, and the new fast and comprehensive scDblFinder method. biocViews: Preprocessing, SingleCell, RNASeq Author: Pierre-Luc Germain [cre, aut] (), Aaron Lun [ctb] Maintainer: Pierre-Luc Germain URL: https://github.com/plger/scDblFinder VignetteBuilder: knitr BugReports: https://github.com/plger/scDblFinder/issues git_url: https://git.bioconductor.org/packages/scDblFinder git_branch: RELEASE_3_12 git_last_commit: b3dd92a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scDblFinder_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scDblFinder_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scDblFinder_1.4.0.tgz vignettes: vignettes/scDblFinder/inst/doc/1_introduction.html, vignettes/scDblFinder/inst/doc/2_scDblFinder.html, vignettes/scDblFinder/inst/doc/3_findDoubletClusters.html, vignettes/scDblFinder/inst/doc/4_computeDoubletDensity.html, vignettes/scDblFinder/inst/doc/5_recoverDoublets.html vignetteTitles: 1_introduction, 2_scDblFinder, 3_findDoubletClusters, 4_computeDoubletDensity, 5_recoverDoublets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scDblFinder/inst/doc/1_introduction.R, vignettes/scDblFinder/inst/doc/2_scDblFinder.R, vignettes/scDblFinder/inst/doc/3_findDoubletClusters.R, vignettes/scDblFinder/inst/doc/4_computeDoubletDensity.R importsMe: singleCellTK dependencyCount: 96 Package: scDD Version: 1.14.0 Depends: R (>= 3.4) Imports: fields, mclust, BiocParallel, outliers, ggplot2, EBSeq, arm, SingleCellExperiment, SummarizedExperiment, grDevices, graphics, stats, S4Vectors, scran Suggests: BiocStyle, knitr, gridExtra License: GPL-2 MD5sum: 053c63c32a58a93f6bcdc535b5d3378b NeedsCompilation: yes Title: Mixture modeling of single-cell RNA-seq data to identify genes with differential distributions Description: This package implements a method to analyze single-cell RNA- seq Data utilizing flexible Dirichlet Process mixture models. Genes with differential distributions of expression are classified into several interesting patterns of differences between two conditions. The package also includes functions for simulating data with these patterns from negative binomial distributions. biocViews: ImmunoOncology, Bayesian, Clustering, RNASeq, SingleCell, MultipleComparison, Visualization, DifferentialExpression Author: Keegan Korthauer [cre, aut] () Maintainer: Keegan Korthauer URL: https://github.com/kdkorthauer/scDD VignetteBuilder: knitr BugReports: https://github.com/kdkorthauer/scDD/issues git_url: https://git.bioconductor.org/packages/scDD git_branch: RELEASE_3_12 git_last_commit: 07bb642 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scDD_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scDD_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scDD_1.14.0.tgz vignettes: vignettes/scDD/inst/doc/scDD.pdf vignetteTitles: scDD Quickstart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scDD/inst/doc/scDD.R suggestsMe: splatter dependencyCount: 149 Package: scde Version: 2.18.0 Depends: R (>= 3.0.0), flexmix Imports: Rcpp (>= 0.10.4), RcppArmadillo (>= 0.5.400.2.0), mgcv, Rook, rjson, MASS, Cairo, RColorBrewer, edgeR, quantreg, methods, nnet, RMTstat, extRemes, pcaMethods, BiocParallel, parallel LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, cba, fastcluster, WGCNA, GO.db, org.Hs.eg.db, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: cd7aed0ff63008fd309d67f05e29d587 NeedsCompilation: yes Title: Single Cell Differential Expression Description: The scde package implements a set of statistical methods for analyzing single-cell RNA-seq data. scde fits individual error models for single-cell RNA-seq measurements. These models can then be used for assessment of differential expression between groups of cells, as well as other types of analysis. The scde package also contains the pagoda framework which applies pathway and gene set overdispersion analysis to identify and characterize putative cell subpopulations based on transcriptional signatures. The overall approach to the differential expression analysis is detailed in the following publication: "Bayesian approach to single-cell differential expression analysis" (Kharchenko PV, Silberstein L, Scadden DT, Nature Methods, doi: 10.1038/nmeth.2967). The overall approach to subpopulation identification and characterization is detailed in the following pre-print: "Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis" (Fan J, Salathia N, Liu R, Kaeser G, Yung Y, Herman J, Kaper F, Fan JB, Zhang K, Chun J, and Kharchenko PV, Nature Methods, doi:10.1038/nmeth.3734). biocViews: ImmunoOncology, RNASeq, StatisticalMethod, DifferentialExpression, Bayesian, Transcription, Software Author: Peter Kharchenko [aut, cre], Jean Fan [aut] Maintainer: Jean Fan URL: http://pklab.med.harvard.edu/scde VignetteBuilder: knitr BugReports: https://github.com/hms-dbmi/scde/issues git_url: https://git.bioconductor.org/packages/scde git_branch: RELEASE_3_12 git_last_commit: 34da197 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scde_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scde_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scde_2.18.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pagoda2 dependencyCount: 47 Package: scds Version: 1.6.0 Depends: R (>= 3.6.0) Imports: Matrix, S4Vectors, SingleCellExperiment, SummarizedExperiment, xgboost, methods, stats, dplyr, pROC Suggests: BiocStyle, knitr, rsvd, Rtsne, scater, cowplot License: MIT + file LICENSE MD5sum: a12e5054ddf18e96270abb6a6243528b NeedsCompilation: no Title: In-Silico Annotation of Doublets for Single Cell RNA Sequencing Data Description: In single cell RNA sequencing (scRNA-seq) data combinations of cells are sometimes considered a single cell (doublets). The scds package provides methods to annotate doublets in scRNA-seq data computationally. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Transcriptomics, GeneExpression, Sequencing, Software, Classification Author: Dennis Kostka [aut, cre], Bais Abha [aut] Maintainer: Dennis Kostka VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scds git_branch: RELEASE_3_12 git_last_commit: c30d3cd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scds_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scds_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scds_1.6.0.tgz vignettes: vignettes/scds/inst/doc/scds.html vignetteTitles: Introduction to the scds package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scds/inst/doc/scds.R importsMe: singleCellTK suggestsMe: ExperimentSubset, muscData dependencyCount: 51 Package: SCFA Version: 1.0.0 Depends: R (>= 4.0) Imports: matrixStats, keras, tensorflow, BiocParallel, igraph, Matrix, cluster, clusterCrit, psych, glmnet, RhpcBLASctl, stats, utils, methods, survival Suggests: knitr License: LGPL MD5sum: 791789515f445542eda9cf1ac0f6f9fc NeedsCompilation: no Title: SCFA: Subtyping via Consensus Factor Analysis Description: Subtyping via Consensus Factor Analysis (SCFA) can efficiently remove noisy signals from consistent molecular patterns in multi-omics data. SCFA first uses an autoencoder to select only important features and then repeatedly performs factor analysis to represent the data with different numbers of factors. Using these representations, it can reliably identify cancer subtypes and accurately predict risk scores of patients. biocViews: Survival, Clustering, Classification Author: Duc Tran [aut, cre], Hung Nguyen [aut], Tin Nguyen [fnd] Maintainer: Duc Tran URL: https://github.com/duct317/SCFA VignetteBuilder: knitr BugReports: https://github.com/duct317/SCFA/issues git_url: https://git.bioconductor.org/packages/SCFA git_branch: RELEASE_3_12 git_last_commit: 5b7b596 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SCFA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SCFA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SCFA_1.0.0.tgz vignettes: vignettes/SCFA/inst/doc/Example.html vignetteTitles: SCFA package manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCFA/inst/doc/Example.R dependencyCount: 59 Package: scFeatureFilter Version: 1.10.0 Depends: R (>= 3.6) Imports: dplyr (>= 0.7.3), ggplot2 (>= 2.1.0), magrittr (>= 1.5), rlang (>= 0.1.2), tibble (>= 1.3.4), stats, methods Suggests: testthat, knitr, rmarkdown, BiocStyle, SingleCellExperiment, SummarizedExperiment, scRNAseq, cowplot License: MIT + file LICENSE MD5sum: a4ddd8b398e4d665342387fd480a45fb NeedsCompilation: no Title: A correlation-based method for quality filtering of single-cell RNAseq data Description: An R implementation of the correlation-based method developed in the Joshi laboratory to analyse and filter processed single-cell RNAseq data. It returns a filtered version of the data containing only genes expression values unaffected by systematic noise. biocViews: ImmunoOncology, SingleCell, RNASeq, Preprocessing, GeneExpression Author: Angeles Arzalluz-Luque [aut], Guillaume Devailly [aut, cre], Anagha Joshi [aut] Maintainer: Guillaume Devailly VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scFeatureFilter git_branch: RELEASE_3_12 git_last_commit: 40bc316 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scFeatureFilter_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scFeatureFilter_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scFeatureFilter_1.10.0.tgz vignettes: vignettes/scFeatureFilter/inst/doc/Introduction.html vignetteTitles: Introduction to scFeatureFilter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scFeatureFilter/inst/doc/Introduction.R dependencyCount: 42 Package: scGPS Version: 1.4.0 Depends: R (>= 3.6), SummarizedExperiment, dynamicTreeCut, SingleCellExperiment Imports: glmnet (> 2.0), caret (>= 6.0), ggplot2 (>= 2.2.1), fastcluster, dplyr, Rcpp, RcppArmadillo, RcppParallel, grDevices, graphics, stats, utils, DESeq, locfit LinkingTo: Rcpp, RcppArmadillo, RcppParallel Suggests: Matrix (>= 1.2), testthat, knitr, parallel, rmarkdown, RColorBrewer, ReactomePA, clusterProfiler, cowplot, org.Hs.eg.db, reshape2, xlsx, dendextend, networkD3, Rtsne, BiocParallel, e1071, WGCNA, devtools, DOSE License: GPL-3 Archs: i386, x64 MD5sum: 099ebb979b8757da6f09ad65ead809b2 NeedsCompilation: yes Title: A complete analysis of single cell subpopulations, from identifying subpopulations to analysing their relationship (scGPS = single cell Global Predictions of Subpopulation) Description: The package implements two main algorithms to answer two key questions: a SCORE (Stable Clustering at Optimal REsolution) to find subpopulations, followed by scGPS to investigate the relationships between subpopulations. biocViews: SingleCell, Clustering, DataImport, Sequencing, Coverage Author: Quan Nguyen [aut, cre], Michael Thompson [aut], Anne Senabouth [aut] Maintainer: Quan Nguyen SystemRequirements: GNU make VignetteBuilder: knitr BugReports: https://github.com/IMB-Computational-Genomics-Lab/scGPS/issues git_url: https://git.bioconductor.org/packages/scGPS git_branch: RELEASE_3_12 git_last_commit: fe892ef git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scGPS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scGPS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scGPS_1.4.0.tgz vignettes: vignettes/scGPS/inst/doc/vignette.html vignetteTitles: single cell Global fate Potential of Subpopulations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scGPS/inst/doc/vignette.R dependencyCount: 97 Package: schex Version: 1.4.0 Depends: SingleCellExperiment (>= 1.7.4), Seurat, ggplot2 (>= 3.2.1), shiny Imports: hexbin, stats, methods, cluster, dplyr, entropy, ggforce, scales, grid, concaveman Suggests: ggrepel, knitr, rmarkdown, testthat (>= 2.1.0), covr, TENxPBMCData, scater, shinydashboard, iSEE, igraph, scran License: GPL-3 MD5sum: 76dcb9af0922299bcc7863340bbb656c NeedsCompilation: no Title: Hexbin plots for single cell omics data Description: Builds hexbin plots for variables and dimension reduction stored in single cell omics data such as SingleCellExperiment and SeuratObject. The ideas used in this package are based on the excellent work of Dan Carr, Nicholas Lewin-Koh, Martin Maechler and Thomas Lumley. biocViews: Software, Sequencing, SingleCell, DimensionReduction, Visualization Author: Saskia Freytag Maintainer: Saskia Freytag URL: https://github.com/SaskiaFreytag/schex VignetteBuilder: knitr BugReports: https://github.com/SaskiaFreytag/schex/issues git_url: https://git.bioconductor.org/packages/schex git_branch: RELEASE_3_12 git_last_commit: 9457aae git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/schex_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/schex_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/schex_1.4.0.tgz vignettes: vignettes/schex/inst/doc/multi_modal_schex.html, vignettes/schex/inst/doc/picking_the_right_resolution.html, vignettes/schex/inst/doc/Seurat_schex.html, vignettes/schex/inst/doc/shiny_schex.html, vignettes/schex/inst/doc/using_schex.html vignetteTitles: multi_modal_schex, picking_the_right_resolution, Seurat_schex, shiny_schhex, using_schex hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/schex/inst/doc/multi_modal_schex.R, vignettes/schex/inst/doc/picking_the_right_resolution.R, vignettes/schex/inst/doc/Seurat_schex.R, vignettes/schex/inst/doc/shiny_schex.R, vignettes/schex/inst/doc/using_schex.R importsMe: scTensor, scTGIF dependencyCount: 166 Package: scHOT Version: 1.2.0 Depends: R (>= 4.0) Imports: S4Vectors (>= 0.24.3), SingleCellExperiment, Matrix, SummarizedExperiment, IRanges, methods, stats, BiocParallel, reshape, ggplot2, igraph, grDevices, ggforce, graphics Suggests: knitr, rmarkdown, scater, scattermore, scales, matrixStats, deldir License: GPL-3 MD5sum: 5199f93313fca0e9b0f0548ebbcca880 NeedsCompilation: no Title: single-cell higher order testing Description: Single cell Higher Order Testing (scHOT) is an R package that facilitates testing changes in higher order structure of gene expression along either a developmental trajectory or across space. scHOT is general and modular in nature, can be run in multiple data contexts such as along a continuous trajectory, between discrete groups, and over spatial orientations; as well as accommodate any higher order measurement such as variability or correlation. scHOT meaningfully adds to first order effect testing, such as differential expression, and provides a framework for interrogating higher order interactions from single cell data. biocViews: GeneExpression, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Shila Ghazanfar [aut, cre], Yingxin Lin [aut] Maintainer: Shila Ghazanfar VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scHOT git_branch: RELEASE_3_12 git_last_commit: 56cf7fb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scHOT_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scHOT_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scHOT_1.2.0.tgz vignettes: vignettes/scHOT/inst/doc/scHOT.html vignetteTitles: Getting started: scHOT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scHOT/inst/doc/scHOT.R dependencyCount: 74 Package: ScISI Version: 1.62.0 Depends: R (>= 2.10), GO.db, RpsiXML, annotate, apComplex Imports: AnnotationDbi, GO.db, RpsiXML, annotate, methods, org.Sc.sgd.db, utils Suggests: ppiData, xtable License: LGPL MD5sum: 744c66609ecc3f32861749660c782c3f NeedsCompilation: no Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang git_url: https://git.bioconductor.org/packages/ScISI git_branch: RELEASE_3_12 git_last_commit: 7fafccc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ScISI_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ScISI_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ScISI_1.62.0.tgz vignettes: vignettes/ScISI/inst/doc/vignette.pdf vignetteTitles: ScISI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ScISI/inst/doc/vignette.R dependsOnMe: PCpheno, ppiStats, SLGI importsMe: PCpheno, SLGI suggestsMe: RpsiXML dependencyCount: 49 Package: scMAGeCK Version: 1.2.0 Imports: Seurat, stats, utils Suggests: knitr, rmarkdown License: BSD_2_clause MD5sum: 2ea9f9ffe71231886b5f45d8aa78efb6 NeedsCompilation: yes Title: Identify genes associated with multiple expression phenotypes in single-cell CRISPR screening data Description: scMAGeCK is a computational model to identify genes associated with multiple expression phenotypes from CRISPR screening coupled with single-cell RNA sequencing data (CROP-seq) biocViews: CRISPR, SingleCell, RNASeq, PooledScreens, Transcriptomics, GeneExpression, Regression Author: Wei Li, Xiaolong Cheng Maintainer: Xiaolong Cheng VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scMAGeCK git_branch: RELEASE_3_12 git_last_commit: d710baa git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scMAGeCK_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scMAGeCK_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scMAGeCK_1.2.0.tgz vignettes: vignettes/scMAGeCK/inst/doc/scMAGeCK.html vignetteTitles: scMAGeCK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMAGeCK/inst/doc/scMAGeCK.R dependencyCount: 138 Package: scmap Version: 1.12.0 Depends: R(>= 3.4) Imports: Biobase, SingleCellExperiment, SummarizedExperiment, BiocGenerics, S4Vectors, dplyr, reshape2, matrixStats, proxy, utils, googleVis, ggplot2, methods, stats, e1071, randomForest, Rcpp (>= 0.12.12) LinkingTo: Rcpp, RcppArmadillo Suggests: knitr, rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: 219e2e094873e2eadaeea41d2da903db NeedsCompilation: yes Title: A tool for unsupervised projection of single cell RNA-seq data Description: Single-cell RNA-seq (scRNA-seq) is widely used to investigate the composition of complex tissues since the technology allows researchers to define cell-types using unsupervised clustering of the transcriptome. However, due to differences in experimental methods and computational analyses, it is often challenging to directly compare the cells identified in two different experiments. scmap is a method for projecting cells from a scRNA-seq experiment on to the cell-types or individual cells identified in a different experiment. biocViews: ImmunoOncology, SingleCell, Software, Classification, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, Transcription, Sequencing, Preprocessing, GeneExpression, DataImport Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/scmap VignetteBuilder: knitr BugReports: https://support.bioconductor.org/t/scmap/ git_url: https://git.bioconductor.org/packages/scmap git_branch: RELEASE_3_12 git_last_commit: d4afc32 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scmap_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scmap_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scmap_1.12.0.tgz vignettes: vignettes/scmap/inst/doc/scmap.html vignetteTitles: `scmap` package vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scmap/inst/doc/scmap.R dependencyCount: 73 Package: scMerge Version: 1.6.0 Depends: R (>= 3.6.0) Imports: BiocParallel, BiocSingular, cluster, DelayedArray, DelayedMatrixStats, distr, igraph, M3Drop (>= 1.9.4), parallel, pdist, proxy, ruv, S4Vectors (>= 0.23.19), SingleCellExperiment (>= 1.7.3), SummarizedExperiment Suggests: BiocStyle, covr, HDF5Array, knitr, Matrix, rmarkdown, scales, scater, testthat, badger License: GPL-3 MD5sum: bd79355234f34cd5bff402d4677a95f7 NeedsCompilation: no Title: scMerge: Merging multiple batches of scRNA-seq data Description: Like all gene expression data, single-cell RNA-seq (scRNA-Seq) data suffers from batch effects and other unwanted variations that makes accurate biological interpretations difficult. The scMerge method leverages factor analysis, stably expressed genes (SEGs) and (pseudo-) replicates to remove unwanted variations and merge multiple scRNA-Seq data. This package contains all the necessary functions in the scMerge pipeline, including the identification of SEGs, replication-identification methods, and merging of scRNA-Seq data. biocViews: BatchEffect, GeneExpression, Normalization, RNASeq, Sequencing, SingleCell, Software, Transcriptomics Author: Yingxin Lin [aut, cre], Kevin Wang [aut], Sydney Bioinformatics and Biometrics Group [fnd] Maintainer: Yingxin Lin URL: https://github.com/SydneyBioX/scMerge VignetteBuilder: knitr BugReports: https://github.com/SydneyBioX/scMerge/issues git_url: https://git.bioconductor.org/packages/scMerge git_branch: RELEASE_3_12 git_last_commit: 4d04879 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scMerge_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scMerge_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scMerge_1.6.0.tgz vignettes: vignettes/scMerge/inst/doc/scMerge.html vignetteTitles: scMerge hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scMerge/inst/doc/scMerge.R importsMe: singleCellTK dependencyCount: 123 Package: scmeth Version: 1.10.0 Depends: R (>= 3.5.0) Imports: knitr, rmarkdown, bsseq, AnnotationHub, GenomicRanges, reshape2, stats, utils, BSgenome, DelayedArray (>= 0.5.15), annotatr, SummarizedExperiment (>= 1.5.6), GenomeInfoDb, Biostrings, DT, HDF5Array (>= 1.7.5) Suggests: BSgenome.Mmusculus.UCSC.mm10, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, org.Hs.eg.db, Biobase, BiocGenerics, ggplot2, ggthemes License: GPL-2 MD5sum: aa2647b8c41b672011df49b5137dd65e NeedsCompilation: no Title: Functions to conduct quality control analysis in methylation data Description: Functions to analyze methylation data can be found here. Some functions are relevant for single cell methylation data but most other functions can be used for any methylation data. Highlight of this workflow is the comprehensive quality control report. biocViews: DNAMethylation, QualityControl, Preprocessing, SingleCell, ImmunoOncology Author: Divy Kangeyan Maintainer: Divy Kangeyan VignetteBuilder: knitr BugReports: https://github.com/aryeelab/scmeth/issues git_url: https://git.bioconductor.org/packages/scmeth git_branch: RELEASE_3_12 git_last_commit: cb60498 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scmeth_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scmeth_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scmeth_1.10.0.tgz vignettes: vignettes/scmeth/inst/doc/my-vignette.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scmeth/inst/doc/my-vignette.R suggestsMe: biscuiteer dependencyCount: 156 Package: SCnorm Version: 1.12.1 Depends: R (>= 3.4.0), Imports: SingleCellExperiment, SummarizedExperiment, stats, methods, graphics, grDevices, parallel, quantreg, cluster, moments, data.table, BiocParallel, S4Vectors, ggplot2, forcats, BiocGenerics Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL (>= 2) MD5sum: 75d3026b56e643fae323176194bdfb89 NeedsCompilation: no Title: Normalization of single cell RNA-seq data Description: This package implements SCnorm — a method to normalize single-cell RNA-seq data. biocViews: Normalization, RNASeq, SingleCell, ImmunoOncology Author: Rhonda Bacher Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/SCnorm VignetteBuilder: knitr BugReports: https://github.com/rhondabacher/SCnorm/issues git_url: https://git.bioconductor.org/packages/SCnorm git_branch: RELEASE_3_12 git_last_commit: 7371f80 git_last_commit_date: 2021-04-21 Date/Publication: 2021-04-22 source.ver: src/contrib/SCnorm_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/SCnorm_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/SCnorm_1.12.1.tgz vignettes: vignettes/SCnorm/inst/doc/SCnorm.pdf vignetteTitles: SCnorm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCnorm/inst/doc/SCnorm.R dependencyCount: 74 Package: scone Version: 1.14.0 Depends: R (>= 3.4), methods, SummarizedExperiment Imports: graphics, stats, utils, aroma.light, BiocParallel, class, cluster, compositions, diptest, edgeR, fpc, gplots, grDevices, hexbin, limma, matrixStats, mixtools, RColorBrewer, boot, rhdf5, RUVSeq, rARPACK Suggests: BiocStyle, DT, ggplot2, knitr, miniUI, NMF, plotly, reshape2, rmarkdown, scran, scRNAseq, shiny, testthat, visNetwork, doParallel, BatchJobs License: Artistic-2.0 MD5sum: bbeecc9f72669c64af81737f9afa36ba NeedsCompilation: no Title: Single Cell Overview of Normalized Expression data Description: SCONE is an R package for comparing and ranking the performance of different normalization schemes for single-cell RNA-seq and other high-throughput analyses. biocViews: ImmunoOncology, Normalization, Preprocessing, QualityControl, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell, Coverage Author: Michael Cole [aut, cph], Davide Risso [aut, cre, cph] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/YosefLab/scone/issues git_url: https://git.bioconductor.org/packages/scone git_branch: RELEASE_3_12 git_last_commit: 3fa8d6b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scone_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scone_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scone_1.14.0.tgz vignettes: vignettes/scone/inst/doc/sconeTutorial.html vignetteTitles: scone Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scone/inst/doc/sconeTutorial.R dependencyCount: 137 Package: Sconify Version: 1.10.0 Depends: R (>= 3.5) Imports: tibble, dplyr, FNN, flowCore, Rtsne, ggplot2, magrittr, utils, stats, readr Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 19e99a8fe6112746ef9204c5272bbff4 NeedsCompilation: no Title: A toolkit for performing KNN-based statistics for flow and mass cytometry data Description: This package does k-nearest neighbor based statistics and visualizations with flow and mass cytometery data. This gives tSNE maps"fold change" functionality and provides a data quality metric by assessing manifold overlap between fcs files expected to be the same. Other applications using this package include imputation, marker redundancy, and testing the relative information loss of lower dimension embeddings compared to the original manifold. biocViews: ImmunoOncology, SingleCell, FlowCytometry, Software, MultipleComparison, Visualization Author: Tyler J Burns Maintainer: Tyler J Burns VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Sconify git_branch: RELEASE_3_12 git_last_commit: f0c0734 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Sconify_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Sconify_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Sconify_1.10.0.tgz vignettes: vignettes/Sconify/inst/doc/DataQuality.html, vignettes/Sconify/inst/doc/FindingIdealK.html, vignettes/Sconify/inst/doc/Step1.PreProcessing.html, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.html, vignettes/Sconify/inst/doc/Step3.PostProcessing.html vignetteTitles: Data Quality, Finding Ideal K, How to process FCS files for downstream use in R, General Scone Analysis, Final Post-Processing Steps for Scone hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sconify/inst/doc/DataQuality.R, vignettes/Sconify/inst/doc/FindingIdealK.R, vignettes/Sconify/inst/doc/Step1.PreProcessing.R, vignettes/Sconify/inst/doc/Step2.TheSconeWorkflow.R, vignettes/Sconify/inst/doc/Step3.PostProcessing.R dependencyCount: 62 Package: SCOPE Version: 1.2.0 Depends: R (>= 3.6.0), GenomicRanges, IRanges, Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Imports: stats, grDevices, graphics, utils, DescTools, RColorBrewer, gplots, foreach, parallel, doParallel, DNAcopy, BSgenome, Biostrings, BiocGenerics, S4Vectors Suggests: knitr, rmarkdown, WGSmapp, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Mmusculus.UCSC.mm10, testthat (>= 2.1.0) License: GPL-2 MD5sum: daac9fdc5645eb3f90ed0721c67aa2ac NeedsCompilation: no Title: A normalization and copy number estimation method for single-cell DNA sequencing Description: Whole genome single-cell DNA sequencing (scDNA-seq) enables characterization of copy number profiles at the cellular level. This circumvents the averaging effects associated with bulk-tissue sequencing and has increased resolution yet decreased ambiguity in deconvolving cancer subclones and elucidating cancer evolutionary history. ScDNA-seq data is, however, sparse, noisy, and highly variable even within a homogeneous cell population, due to the biases and artifacts that are introduced during the library preparation and sequencing procedure. Here, we propose SCOPE, a normalization and copy number estimation method for scDNA-seq data. The distinguishing features of SCOPE include: (i) utilization of cell-specific Gini coefficients for quality controls and for identification of normal/diploid cells, which are further used as negative control samples in a Poisson latent factor model for normalization; (ii) modeling of GC content bias using an expectation-maximization algorithm embedded in the Poisson generalized linear models, which accounts for the different copy number states along the genome; (iii) a cross-sample iterative segmentation procedure to identify breakpoints that are shared across cells from the same genetic background. biocViews: SingleCell, Normalization, CopyNumberVariation, Sequencing, WholeGenome, Coverage, Alignment, QualityControl, DataImport, DNASeq Author: Rujin Wang, Danyu Lin, Yuchao Jiang Maintainer: Rujin Wang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SCOPE git_branch: RELEASE_3_12 git_last_commit: b63bbf0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SCOPE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SCOPE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SCOPE_1.2.0.tgz vignettes: vignettes/SCOPE/inst/doc/SCOPE_vignette.html vignetteTitles: SCOPE: Single-cell Copy Number Estimation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SCOPE/inst/doc/SCOPE_vignette.R dependencyCount: 68 Package: scoreInvHap Version: 1.12.1 Depends: R (>= 3.6.0) Imports: Biostrings, methods, snpStats, VariantAnnotation, GenomicRanges, BiocParallel, graphics, SummarizedExperiment Suggests: testthat, knitr, BiocStyle, rmarkdown License: file LICENSE MD5sum: ff5a3bdae7f9b58e1ba4abf15b46bb50 NeedsCompilation: no Title: Get inversion status in predefined regions Description: scoreInvHap can get the samples' inversion status of known inversions. scoreInvHap uses SNP data as input and requires the following information about the inversion: genotype frequencies in the different haplotypes, R2 between the region SNPs and inversion status and heterozygote genotypes in the reference. The package include this data for 21 inversions. biocViews: SNP, Genetics, GenomicVariation Author: Carlos Ruiz [aut, cre], Dolors Pelegrí-Sisó [aut], Juan R. Gonzalez [aut] Maintainer: Carlos Ruiz VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scoreInvHap git_branch: RELEASE_3_12 git_last_commit: a604ad1 git_last_commit_date: 2021-02-04 Date/Publication: 2021-02-04 source.ver: src/contrib/scoreInvHap_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/scoreInvHap_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/scoreInvHap_1.12.1.tgz vignettes: vignettes/scoreInvHap/inst/doc/scoreInvHap.html vignetteTitles: Inversion genotyping with scoreInvHap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scoreInvHap/inst/doc/scoreInvHap.R dependencyCount: 93 Package: scp Version: 1.0.0 Depends: R (>= 4.0), QFeatures Imports: methods, stats, utils, SingleCellExperiment, SummarizedExperiment, MultiAssayExperiment, S4Vectors, dplyr, magrittr, rlang Suggests: testthat, knitr, BiocStyle, rmarkdown, patchwork, ggplot2, matrixStats, impute, scater, sva, uwot License: Artistic-2.0 MD5sum: 87549a1b622e4a38095bed0b787021dd NeedsCompilation: no Title: Mass Spectrometry-Based Single-Cell Proteomics Data Analysis Description: Utility functions for manipulating, processing, and analyzing mass spectrometry-based single-cell proteomics (SCP) data. The package is an extension to the 'QFeatures' package designed for SCP applications. biocViews: GeneExpression, Proteomics, SingleCell, MassSpectrometry, Preprocessing, CellBasedAssays Author: Christophe Vanderaa [aut, cre] (), Laurent Gatto [aut] () Maintainer: Christophe Vanderaa URL: https://UCLouvain-CBIO.github.io/scp VignetteBuilder: knitr BugReports: https://github.com/UCLouvain-CBIO/scp/issues git_url: https://git.bioconductor.org/packages/scp git_branch: RELEASE_3_12 git_last_commit: 8a942fb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scp_1.0.0.tgz vignettes: vignettes/scp/inst/doc/scp.html vignetteTitles: Single Cell Proteomics data processing and analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scp/inst/doc/scp.R dependencyCount: 55 Package: scPCA Version: 1.4.0 Depends: R (>= 3.6) Imports: stats, methods, assertthat, tibble, dplyr, purrr, stringr, Rdpack, matrixStats, BiocParallel, elasticnet, sparsepca, cluster, kernlab, origami, RSpectra, coop, Matrix, DelayedArray Suggests: DelayedMatrixStats, sparseMatrixStats, testthat (>= 2.1.0), covr, knitr, rmarkdown, BiocStyle, ggplot2, ggpubr, splatter, SingleCellExperiment, microbenchmark License: MIT + file LICENSE MD5sum: 5d87d19f018a0ae1757a00a206a9f0b1 NeedsCompilation: no Title: Sparse Contrastive Principal Component Analysis Description: A toolbox for sparse contrastive principal component analysis (scPCA) of high-dimensional biological data. scPCA combines the stability and interpretability of sparse PCA with contrastive PCA's ability to disentangle biological signal from unwanted variation through the use of control data. Also implements and extends cPCA. biocViews: PrincipalComponent, GeneExpression, DifferentialExpression, Sequencing, Microarray, RNASeq Author: Philippe Boileau [aut, cre, cph] (), Nima Hejazi [aut] (), Sandrine Dudoit [ctb, ths] () Maintainer: Philippe Boileau URL: https://github.com/PhilBoileau/scPCA VignetteBuilder: knitr BugReports: https://github.com/PhilBoileau/scPCA/issues git_url: https://git.bioconductor.org/packages/scPCA git_branch: RELEASE_3_12 git_last_commit: 776cd89 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scPCA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scPCA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scPCA_1.4.0.tgz vignettes: vignettes/scPCA/inst/doc/scpca_intro.html vignetteTitles: Sparse contrastive principal component analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scPCA/inst/doc/scpca_intro.R dependencyCount: 67 Package: scPipe Version: 1.12.0 Depends: R (>= 3.4), ggplot2, methods, SingleCellExperiment Imports: Rhtslib, biomaRt, GGally, MASS, mclust, Rcpp (>= 0.11.3), reshape, BiocGenerics, robustbase, scales, utils, stats, S4Vectors, SummarizedExperiment, AnnotationDbi, org.Hs.eg.db, org.Mm.eg.db, stringr, rtracklayer, hash, dplyr, GenomicRanges, magrittr, glue (>= 1.3.0), rlang, scater (>= 1.11.0) LinkingTo: Rcpp, Rhtslib (>= 1.13.1), zlibbioc, testthat Suggests: Rsubread, knitr, rmarkdown, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: c1c909da8ac4381a966cb545f110dbb1 NeedsCompilation: yes Title: pipeline for single cell RNA-seq data analysis Description: A preprocessing pipeline for single cell RNA-seq data that starts from the fastq files and produces a gene count matrix with associated quality control information. It can process fastq data generated by CEL-seq, MARS-seq, Drop-seq, Chromium 10x and SMART-seq protocols. biocViews: ImmunoOncology, Software, Sequencing, RNASeq, GeneExpression, SingleCell, Visualization, SequenceMatching, Preprocessing, QualityControl, GenomeAnnotation Author: Luyi Tian Maintainer: Luyi Tian URL: https://github.com/LuyiTian/scPipe SystemRequirements: C++11, GNU make VignetteBuilder: knitr BugReports: https://github.com/LuyiTian/scPipe git_url: https://git.bioconductor.org/packages/scPipe git_branch: RELEASE_3_12 git_last_commit: 8acde69 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scPipe_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scPipe_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scPipe_1.12.0.tgz vignettes: vignettes/scPipe/inst/doc/scPipe_tutorial.html vignetteTitles: scPipe: flexible data preprocessing pipeline for 3' end scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scPipe/inst/doc/scPipe_tutorial.R dependencyCount: 148 Package: scran Version: 1.18.7 Depends: SingleCellExperiment Imports: SummarizedExperiment, S4Vectors, BiocGenerics, BiocParallel, Rcpp, stats, methods, utils, Matrix, scuttle, edgeR, limma, BiocNeighbors, igraph, statmod, DelayedArray, DelayedMatrixStats, BiocSingular, bluster, dqrng, beachmat LinkingTo: Rcpp, beachmat, BH, dqrng, scuttle Suggests: testthat, BiocStyle, knitr, rmarkdown, HDF5Array, scRNAseq, dynamicTreeCut, DESeq2, monocle, Biobase, pheatmap, scater License: GPL-3 Archs: i386, x64 MD5sum: 4e892927c7aa941d1c3161ce1894883c NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: Implements miscellaneous functions for interpretation of single-cell RNA-seq data. Methods are provided for assignment of cell cycle phase, detection of highly variable and significantly correlated genes, identification of marker genes, and other common tasks in routine single-cell analysis workflows. biocViews: ImmunoOncology, Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics, SingleCell, Visualization, BatchEffect, Clustering Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scran git_branch: RELEASE_3_12 git_last_commit: 5849abc git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/scran_1.18.7.tar.gz win.binary.ver: bin/windows/contrib/4.0/scran_1.18.7.zip mac.binary.ver: bin/macosx/contrib/4.0/scran_1.18.7.tgz vignettes: vignettes/scran/inst/doc/scran.html vignetteTitles: Using scran to analyze scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R importsMe: BASiCS, BayesSpace, celda, ChromSCape, CiteFuse, msImpute, pipeComp, scDblFinder, scDD, SingleCellSignalR, singleCellTK suggestsMe: batchelor, bluster, CellTrails, clusterExperiment, ExperimentSubset, fcoex, glmGamPoi, iSEEu, Nebulosa, PCAtools, schex, scone, SingleR, snifter, splatter, tidySingleCellExperiment, TSCAN, velociraptor, HCAData, SingleCellMultiModal, TabulaMurisData, simpleSingleCell dependencyCount: 58 Package: scRecover Version: 1.6.0 Depends: R (>= 3.4.0) Imports: stats, utils, methods, graphics, doParallel, foreach, parallel, penalized, kernlab, rsvd, Matrix (>= 1.2-14), MASS (>= 7.3-45), pscl (>= 1.4.9), bbmle (>= 1.0.18), gamlss (>= 4.4-0), preseqR (>= 4.0.0), SAVER (>= 1.1.1), Rmagic (>= 1.3.0), BiocParallel (>= 1.12.0) Suggests: knitr, rmarkdown, SingleCellExperiment, testthat License: GPL MD5sum: f321eeed61df40f2696ab16874f5347e NeedsCompilation: no Title: scRecover for imputation of single-cell RNA-seq data Description: scRecover is an R package for imputation of single-cell RNA-seq (scRNA-seq) data. It will detect and impute dropout values in a scRNA-seq raw read counts matrix while keeping the real zeros unchanged, since there are both dropout zeros and real zeros in scRNA-seq data. By combination with scImpute, SAVER and MAGIC, scRecover not only detects dropout and real zeros at higher accuracy, but also improve the downstream clustering and visualization results. biocViews: GeneExpression, SingleCell, RNASeq, Transcriptomics, Sequencing, Preprocessing, Software Author: Zhun Miao, Xuegong Zhang Maintainer: Zhun Miao URL: https://miaozhun.github.io/scRecover VignetteBuilder: knitr BugReports: https://github.com/miaozhun/scRecover/issues git_url: https://git.bioconductor.org/packages/scRecover git_branch: RELEASE_3_12 git_last_commit: 07112e2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scRecover_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scRecover_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scRecover_1.6.0.tgz vignettes: vignettes/scRecover/inst/doc/scRecover.html vignetteTitles: scRecover hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRecover/inst/doc/scRecover.R dependencyCount: 76 Package: scRepertoire Version: 1.0.0 Depends: dplyr, ggalluvial, ggplot2, reshape2, R (>= 4.0) Imports: Biostrings, RColorBrewer, colorRamps, ggdendro, ggfittext, stringr, vegan, powerTCR, SummarizedExperiment, SingleCellExperiment, Seurat Suggests: knitr, rmarkdown, BiocStyle, scater, circlize, scales License: Apache License 2.0 MD5sum: c641917f3bc0f2f915dc8d3b93dfb99d NeedsCompilation: no Title: A toolkit for single-cell immune receptor profiling Description: scRepertoire was built to process data derived from the 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with the popular Seurat and SingleCellExperiment R packages. biocViews: Software, ImmunoOncology, SingleCell, Classification, Annotation, Sequencing Author: Nick Borcherding [aut, cre] Maintainer: Nick Borcherding VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scRepertoire git_branch: RELEASE_3_12 git_last_commit: 4a17abd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scRepertoire_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scRepertoire_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scRepertoire_1.0.0.tgz vignettes: vignettes/scRepertoire/inst/doc/vignette.html vignetteTitles: Using scRepertoire hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scRepertoire/inst/doc/vignette.R dependencyCount: 172 Package: scruff Version: 1.8.3 Depends: R (>= 3.5.0) Imports: data.table, GenomicAlignments, GenomicFeatures, GenomicRanges, Rsamtools, ShortRead, parallel, plyr, BiocGenerics, BiocParallel, S4Vectors, AnnotationDbi, Biostrings, methods, ggplot2, ggthemes, scales, GenomeInfoDb, stringdist, ggbio, rtracklayer, SingleCellExperiment, SummarizedExperiment, Rsubread Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 0e1ab404db3efb4555974052cda710d3 NeedsCompilation: no Title: Single Cell RNA-Seq UMI Filtering Facilitator (scruff) Description: A pipeline which processes single cell RNA-seq (scRNA-seq) reads from CEL-seq and CEL-seq2 protocols. Demultiplex scRNA-seq FASTQ files, align reads to reference genome using Rsubread, and generate UMI filtered count matrix. Also provide visualizations of read alignments and pre- and post-alignment QC metrics. biocViews: Software, Technology, Sequencing, Alignment, RNASeq, SingleCell, WorkflowStep, Preprocessing, QualityControl, Visualization, ImmunoOncology Author: Zhe Wang [aut, cre], Junming Hu [aut], Joshua Campbell [aut] Maintainer: Zhe Wang VignetteBuilder: knitr BugReports: https://github.com/campbio/scruff/issues git_url: https://git.bioconductor.org/packages/scruff git_branch: RELEASE_3_12 git_last_commit: 371fab0 git_last_commit_date: 2021-02-11 Date/Publication: 2021-02-11 source.ver: src/contrib/scruff_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/scruff_1.8.3.zip mac.binary.ver: bin/macosx/contrib/4.0/scruff_1.8.3.tgz vignettes: vignettes/scruff/inst/doc/scruff.html vignetteTitles: Process Single Cell RNA-Seq reads using scruff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/scruff/inst/doc/scruff.R dependencyCount: 155 Package: scry Version: 1.2.0 Depends: R (>= 4.0), stats, methods Imports: DelayedArray, glmpca (>= 0.2.0), HDF5Array, Matrix, SingleCellExperiment, SummarizedExperiment, BiocSingular Suggests: BiocGenerics, covr, DuoClustering2018, ggplot2, knitr, rmarkdown, TENxPBMCData, testthat License: Artistic-2.0 MD5sum: 6b2ac0461d310a3a9900b7e6af7a9f35 NeedsCompilation: no Title: Small-Count Analysis Methods for High-Dimensional Data Description: Many modern biological datasets consist of small counts that are not well fit by standard linear-Gaussian methods such as principal component analysis. This package provides implementations of count-based feature selection and dimension reduction algorithms. These methods can be used to facilitate unsupervised analysis of any high-dimensional data such as single-cell RNA-seq. biocViews: DimensionReduction, GeneExpression, Normalization, PrincipalComponent, RNASeq, Software, Sequencing, SingleCell, Transcriptomics Author: Kelly Street [aut, cre], F. William Townes [aut, cph], Davide Risso [aut], Stephanie Hicks [aut] Maintainer: Kelly Street URL: https://bioconductor.org/packages/scry.html VignetteBuilder: knitr BugReports: https://github.com/kstreet13/scry/issues git_url: https://git.bioconductor.org/packages/scry git_branch: RELEASE_3_12 git_last_commit: 1a47b34 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scry_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scry_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scry_1.2.0.tgz vignettes: vignettes/scry/inst/doc/bigdata.html, vignettes/scry/inst/doc/scry.html vignetteTitles: Scry Methods For Larger Datasets, Overview of Scry Methods hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scry/inst/doc/bigdata.R, vignettes/scry/inst/doc/scry.R dependencyCount: 45 Package: scTensor Version: 2.0.0 Depends: R (>= 3.5.0) Imports: methods, RSQLite, igraph, S4Vectors, plotly, reactome.db, AnnotationDbi, SummarizedExperiment, SingleCellExperiment, nnTensor, rTensor, abind, plotrix, heatmaply, tagcloud, rmarkdown, BiocStyle, knitr, AnnotationHub, MeSHDbi, grDevices, graphics, stats, utils, outliers, Category, meshr, GOstats, ReactomePA, DOSE, crayon, checkmate, BiocManager, visNetwork, schex, ggplot2 Suggests: testthat, LRBase.Hsa.eg.db, LRBase.Mmu.eg.db, LRBaseDbi, Seurat, scTGIF, Homo.sapiens License: Artistic-2.0 MD5sum: 759efab6569d55d6d1a071f5c3bd39d9 NeedsCompilation: no Title: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition Description: The algorithm is based on the non-negative tucker decomposition (NTD2) of nnTensor. biocViews: DimensionReduction, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre], Kozo Nishida [aut] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTensor git_branch: RELEASE_3_12 git_last_commit: 0fafdd3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scTensor_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scTensor_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scTensor_2.0.0.tgz vignettes: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.html, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.html, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.html, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.html, vignettes/scTensor/inst/doc/scTensor.html vignetteTitles: scTensor: 1. Data format and ID conversion, scTensor: 2. Interpretation of HTML report, scTensor: 3. Simulation of CCI, scTensor: 4. Reanalysis of the results of scTensor, scTensor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTensor/inst/doc/scTensor_1_Data_format_ID_Conversion.R, vignettes/scTensor/inst/doc/scTensor_2_Report_Interpretation.R, vignettes/scTensor/inst/doc/scTensor_3_CCI_Simulation.R, vignettes/scTensor/inst/doc/scTensor_4_Reanalysis.R, vignettes/scTensor/inst/doc/scTensor.R dependencyCount: 288 Package: scTGIF Version: 1.4.0 Depends: R (>= 3.6.0) Imports: GSEABase, Biobase, SingleCellExperiment, BiocStyle, plotly, tagcloud, rmarkdown, Rcpp, grDevices, graphics, utils, knitr, S4Vectors, SummarizedExperiment, RColorBrewer, nnTensor, methods, scales, msigdbr, schex, tibble, ggplot2, igraph Suggests: testthat License: Artistic-2.0 MD5sum: 3f2a2789a1c1c7c81d7f78d3ce9633ae NeedsCompilation: no Title: Cell type annotation for unannotated single-cell RNA-Seq data Description: scTGIF connects the cells and the related gene functions without cell type label. biocViews: DimensionReduction, QualityControl, SingleCell, Software, GeneExpression Author: Koki Tsuyuzaki [aut, cre] Maintainer: Koki Tsuyuzaki VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scTGIF git_branch: RELEASE_3_12 git_last_commit: f21027e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scTGIF_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scTGIF_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scTGIF_1.4.0.tgz vignettes: vignettes/scTGIF/inst/doc/scTGIF.html vignetteTitles: scTGIF hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTGIF/inst/doc/scTGIF.R suggestsMe: scTensor dependencyCount: 192 Package: scTHI Version: 1.2.0 Depends: R (>= 4.0) Imports: BiocParallel, Rtsne, grDevices, graphics, stats Suggests: scTHI.data, knitr, rmarkdown License: GPL-2 MD5sum: e61f7f17b44a36974bbe60bd0fe093c4 NeedsCompilation: no Title: Indentification of significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data Description: scTHI is an R package to identify active pairs of ligand-receptors from single cells in order to study,among others, tumor-host interactions. scTHI contains a set of signatures to classify cells from the tumor microenvironment. biocViews: Software,SingleCell Author: Francesca Pia Caruso [aut], Michele Ceccarelli [aut, cre] Maintainer: Michele Ceccarelli VignetteBuilder: knitr BugReports: https://github.com/miccec/scTHI/issues git_url: https://git.bioconductor.org/packages/scTHI git_branch: RELEASE_3_12 git_last_commit: 6e2dcad git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/scTHI_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/scTHI_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/scTHI_1.2.0.tgz vignettes: vignettes/scTHI/inst/doc/vignette.html vignetteTitles: Using scTHI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scTHI/inst/doc/vignette.R dependencyCount: 15 Package: scuttle Version: 1.0.4 Depends: SingleCellExperiment Imports: methods, utils, stats, Matrix, Rcpp, BiocGenerics, S4Vectors, BiocParallel, GenomicRanges, SummarizedExperiment, DelayedArray, DelayedMatrixStats, beachmat LinkingTo: Rcpp, beachmat Suggests: BiocStyle, knitr, scRNAseq, rmarkdown, testthat License: GPL-3 Archs: i386, x64 MD5sum: bf36544154a929f50351799e0fa8dc6a NeedsCompilation: yes Title: Single-Cell RNA-Seq Analysis Utilities Description: Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages. biocViews: ImmunoOncology, SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport Author: Aaron Lun [aut, cre], Davis McCarthy [aut] Maintainer: Aaron Lun SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/scuttle git_branch: RELEASE_3_12 git_last_commit: a827e27 git_last_commit_date: 2020-12-17 Date/Publication: 2020-12-17 source.ver: src/contrib/scuttle_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/scuttle_1.0.4.zip mac.binary.ver: bin/macosx/contrib/4.0/scuttle_1.0.4.tgz vignettes: vignettes/scuttle/inst/doc/overview.html vignetteTitles: Package overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scuttle/inst/doc/overview.R importsMe: batchelor, DropletUtils, scater, scDblFinder, scran, velociraptor suggestsMe: bluster, SingleR, snifter, splatter, TSCAN, HCAData linksToMe: DropletUtils, scran dependencyCount: 42 Package: SDAMS Version: 1.10.0 Depends: R(>= 3.5), SummarizedExperiment Imports: trust, qvalue, methods, stats, utils Suggests: testthat License: GPL MD5sum: dfa0209377474068ca31c17580159022 NeedsCompilation: no Title: Differential Abundant Analysis for Metabolomics, Proteomics and single-cell RNA sequencing Data Description: This Package utilizes a Semi-parametric Differential Abundance analysis (SDA) method for metabolomics and proteomics data from mass spectrometry as well as single cell RNA sequencing data. SDA is able to robustly handle non-normally distributed data and provides a clear quantification of the effect size. biocViews: ImmunoOncology, DifferentialExpression, Metabolomics, Proteomics, MassSpectrometry, SingleCell Author: Yuntong Li , Chi Wang , Li Chen Maintainer: Yuntong Li git_url: https://git.bioconductor.org/packages/SDAMS git_branch: RELEASE_3_12 git_last_commit: 6d0f475 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SDAMS_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SDAMS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SDAMS_1.10.0.tgz vignettes: vignettes/SDAMS/inst/doc/SDAMS.pdf vignetteTitles: SDAMS Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SDAMS/inst/doc/SDAMS.R dependencyCount: 63 Package: segmentSeq Version: 2.24.0 Depends: R (>= 3.0.0), methods, baySeq (>= 2.9.0), S4Vectors, parallel, GenomicRanges, ShortRead, stats Imports: Rsamtools, IRanges, GenomeInfoDb, graphics, grDevices, utils, abind Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: dec10829a978abae8f9117ed59250485 NeedsCompilation: no Title: Methods for identifying small RNA loci from high-throughput sequencing data Description: High-throughput sequencing technologies allow the production of large volumes of short sequences, which can be aligned to the genome to create a set of matches to the genome. By looking for regions of the genome which to which there are high densities of matches, we can infer a segmentation of the genome into regions of biological significance. The methods in this package allow the simultaneous segmentation of data from multiple samples, taking into account replicate data, in order to create a consensus segmentation. This has obvious applications in a number of classes of sequencing experiments, particularly in the discovery of small RNA loci and novel mRNA transcriptome discovery. biocViews: MultipleComparison, Sequencing, Alignment, DifferentialExpression, QualityControl, DataImport Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle git_url: https://git.bioconductor.org/packages/segmentSeq git_branch: RELEASE_3_12 git_last_commit: 1c3ee51 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/segmentSeq_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/segmentSeq_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/segmentSeq_2.24.0.tgz vignettes: vignettes/segmentSeq/inst/doc/methylationAnalysis.pdf, vignettes/segmentSeq/inst/doc/segmentSeq.pdf vignetteTitles: segmentsSeq: Methylation locus identification, segmentSeq: small RNA locus detection hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/segmentSeq/inst/doc/methylationAnalysis.R, vignettes/segmentSeq/inst/doc/segmentSeq.R dependencyCount: 50 Package: selectKSigs Version: 1.2.0 Depends: R(>= 3.6) Imports: HiLDA, magrittr, gtools, methods, Rcpp LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, dplyr, tidyr License: GPL-3 Archs: i386, x64 MD5sum: 47a4ee8fc9433c801020413a9d815e79 NeedsCompilation: yes Title: Selecting the number of mutational signatures using a perplexity-based measure and cross-validation Description: A package to suggest the number of mutational signatures in a collection of somatic mutations using calculating the cross-validated perplexity score. biocViews: Software, SomaticMutation, Sequencing, StatisticalMethod, Clustering Author: Zhi Yang [aut, cre], Yuichi Shiraishi [ctb] Maintainer: Zhi Yang URL: https://github.com/USCbiostats/selectKSigs VignetteBuilder: knitr BugReports: https://github.com/USCbiostats/HiLDA/selectKSigs git_url: https://git.bioconductor.org/packages/selectKSigs git_branch: RELEASE_3_12 git_last_commit: 039d1b5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/selectKSigs_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/selectKSigs_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/selectKSigs_1.2.0.tgz vignettes: vignettes/selectKSigs/inst/doc/selectKSigs.html vignetteTitles: An introduction to HiLDA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/selectKSigs/inst/doc/selectKSigs.R dependencyCount: 118 Package: SELEX Version: 1.22.0 Depends: rJava (>= 0.5-0), Biostrings (>= 2.26.0) Imports: stats, utils License: GPL (>=2) MD5sum: cab90a66001dffea6f38ed73804fd207 NeedsCompilation: no Title: Functions for analyzing SELEX-seq data Description: Tools for quantifying DNA binding specificities based on SELEX-seq data. biocViews: Software, MotifDiscovery, MotifAnnotation, GeneRegulation, Transcription Author: Chaitanya Rastogi, Dahong Liu, Lucas Melo, and Harmen J. Bussemaker Maintainer: Harmen J. Bussemaker URL: https://bussemakerlab.org/site/software/ SystemRequirements: Java (>= 1.5) git_url: https://git.bioconductor.org/packages/SELEX git_branch: RELEASE_3_12 git_last_commit: a7de1ab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SELEX_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SELEX_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SELEX_1.22.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SELEX/inst/doc/SELEX.R dependencyCount: 16 Package: SemDist Version: 1.24.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 363752b1bd1b9a14ca0c23f2f2a8c6f8 NeedsCompilation: no Title: Information Accretion-based Function Predictor Evaluation Description: This package implements methods to calculate information accretion for a given version of the gene ontology and uses this data to calculate remaining uncertainty, misinformation, and semantic similarity for given sets of predicted annotations and true annotations from a protein function predictor. biocViews: Classification, Annotation, GO, Software Author: Ian Gonzalez and Wyatt Clark Maintainer: Ian Gonzalez URL: http://github.com/iangonzalez/SemDist git_url: https://git.bioconductor.org/packages/SemDist git_branch: RELEASE_3_12 git_last_commit: 122c538 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SemDist_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SemDist_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SemDist_1.24.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SemDist/inst/doc/introduction.R dependencyCount: 39 Package: semisup Version: 1.14.0 Depends: R (>= 3.0.0) Imports: VGAM Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 55a57a14314e7da258964bde4eb46ca2 NeedsCompilation: no Title: Semi-Supervised Mixture Model Description: Implements a parametric semi-supervised mixture model. The permutation test detects markers with main or interactive effects, without distinguishing them. Possible applications include genome-wide association analysis and differential expression analysis. biocViews: SNP, GenomicVariation, SomaticMutation, Genetics, Classification, Clustering, DNASeq, Microarray, MultipleComparison Author: Armin Rauschenberger [aut, cre] Maintainer: Armin Rauschenberger URL: https://github.com/rauschenberger/semisup VignetteBuilder: knitr BugReports: https://github.com/rauschenberger/semisup/issues git_url: https://git.bioconductor.org/packages/semisup git_branch: RELEASE_3_12 git_last_commit: d008211 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/semisup_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/semisup_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/semisup_1.14.0.tgz vignettes: vignettes/semisup/inst/doc/semisup.pdf, vignettes/semisup/inst/doc/article.html, vignettes/semisup/inst/doc/vignette.html vignetteTitles: vignette source, article frame, vignette frame hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/semisup/inst/doc/semisup.R dependencyCount: 5 Package: SEPIRA Version: 1.10.0 Depends: R (>= 3.5.0) Imports: limma (>= 3.32.5), corpcor (>= 1.6.9), parallel (>= 3.3.1), stats Suggests: knitr, rmarkdown, testthat, igraph License: GPL-3 MD5sum: ec98d04385bc0f7e5c301c42ac96b189 NeedsCompilation: no Title: Systems EPigenomics Inference of Regulatory Activity Description: SEPIRA (Systems EPigenomics Inference of Regulatory Activity) is an algorithm that infers sample-specific transcription factor activity from the genome-wide expression or DNA methylation profile of the sample. biocViews: GeneExpression, Transcription, GeneRegulation, GeneTarget, NetworkInference, Network, Software Author: Yuting Chen [aut, cre], Andrew Teschendorff [aut] Maintainer: Yuting Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SEPIRA git_branch: RELEASE_3_12 git_last_commit: 354d03a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SEPIRA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SEPIRA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SEPIRA_1.10.0.tgz vignettes: vignettes/SEPIRA/inst/doc/SEPIRA.html vignetteTitles: Introduction to `SEPIRA` hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPIRA/inst/doc/SEPIRA.R dependencyCount: 8 Package: seq2pathway Version: 1.22.0 Depends: R (>= 3.5.0) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: 74f4ed6e8561fff1591b997e6d2fe5e6 NeedsCompilation: no Title: a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data Description: Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of "seq2gene" and "gene2path" components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments. biocViews: Software Author: Xinan Yang ; Bin Wang Maintainer: Xinan Yang with contribution from Jennifer Sun SystemRequirements: Python3 with packages future, builtins, past.utils, bisect, datetime, shutil, and functools git_url: https://git.bioconductor.org/packages/seq2pathway git_branch: RELEASE_3_12 git_last_commit: 01571d8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seq2pathway_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seq2pathway_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seq2pathway_1.22.0.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.R dependencyCount: 125 Package: SeqArray Version: 1.30.0 Depends: R (>= 3.5.0), gdsfmt (>= 1.23.5) Imports: methods, parallel, IRanges, GenomicRanges, GenomeInfoDb, Biostrings, S4Vectors LinkingTo: gdsfmt Suggests: Biobase, BiocGenerics, BiocParallel, RUnit, Rcpp, SNPRelate, digest, crayon, knitr, Rsamtools, VariantAnnotation License: GPL-3 Archs: i386, x64 MD5sum: ae0099d34540967262d94ccafbd2b8c6 NeedsCompilation: yes Title: Data management of large-scale whole-genome sequence variant calls Description: Data management of large-scale whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, DataRepresentation, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre] (), Stephanie Gogarten [aut], David Levine [ctb], Cathy Laurie [ctb] Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SeqArray VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SeqArray/issues git_url: https://git.bioconductor.org/packages/SeqArray git_branch: RELEASE_3_12 git_last_commit: 6a9e919 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SeqArray_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SeqArray_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SeqArray_1.30.0.tgz vignettes: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/SeqArray.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html vignetteTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/SeqArray.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R dependsOnMe: SAIGEgds, SeqVarTools importsMe: GDSArray, GENESIS, VariantExperiment, coxmeg, GMMAT, MAGEE suggestsMe: DelayedDataFrame, VCFArray dependencyCount: 21 Package: seqbias Version: 1.38.0 Depends: R (>= 3.0.2), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods LinkingTo: Rhtslib (>= 1.15.3) Suggests: Rsamtools, ggplot2 License: LGPL-3 MD5sum: ca03bfd9ae6dc3a221e5c2d84a9934ba NeedsCompilation: yes Title: Estimation of per-position bias in high-throughput sequencing data Description: This package implements a model of per-position sequencing bias in high-throughput sequencing data using a simple Bayesian network, the structure and parameters of which are trained on a set of aligned reads and a reference genome sequence. biocViews: Sequencing Author: Daniel Jones Maintainer: Daniel Jones SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/seqbias git_branch: RELEASE_3_12 git_last_commit: b5490ca git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqbias_1.38.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/seqbias_1.38.0.tgz vignettes: vignettes/seqbias/inst/doc/overview.pdf vignetteTitles: Assessing and Adjusting for Technical Bias in High Throughput Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqbias/inst/doc/overview.R dependsOnMe: ReQON dependencyCount: 21 Package: seqCAT Version: 1.12.0 Depends: R (>= 3.6), GenomicRanges (>= 1.26.4), VariantAnnotation(>= 1.20.3) Imports: dplyr (>= 0.5.0), GenomeInfoDb (>= 1.13.4), ggplot2 (>= 2.2.1), grid (>= 3.5.0), IRanges (>= 2.8.2), methods, rtracklayer, rlang, scales (>= 0.4.1), S4Vectors (>= 0.12.2), stats, SummarizedExperiment (>= 1.4.0), tidyr (>= 0.6.1), utils Suggests: knitr, BiocStyle, rmarkdown, testthat, BiocManager License: MIT + file LICENCE MD5sum: a8a350096d61741e86ef7451e92b235d NeedsCompilation: no Title: High Throughput Sequencing Cell Authentication Toolkit Description: The seqCAT package uses variant calling data (in the form of VCF files) from high throughput sequencing technologies to authenticate and validate the source, function and characteristics of biological samples used in scientific endeavours. biocViews: Coverage, GenomicVariation, Sequencing, VariantAnnotation Author: Erik Fasterius [aut, cre] Maintainer: Erik Fasterius VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqCAT git_branch: RELEASE_3_12 git_last_commit: 61ed7f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqCAT_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqCAT_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqCAT_1.12.0.tgz vignettes: vignettes/seqCAT/inst/doc/seqCAT.html vignetteTitles: seqCAT: The High Throughput Sequencing Cell Authentication Toolkit hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCAT/inst/doc/seqCAT.R dependencyCount: 107 Package: seqCNA Version: 1.36.0 Depends: R (>= 3.0), GLAD (>= 2.14), doSNOW (>= 1.0.5), adehabitatLT (>= 0.3.4), seqCNA.annot (>= 0.99), methods License: GPL-3 Archs: i386, x64 MD5sum: 1220c2d42392f924c77545ab9dd7ece3 NeedsCompilation: yes Title: Copy number analysis of high-throughput sequencing cancer data Description: Copy number analysis of high-throughput sequencing cancer data with fast summarization, extensive filtering and improved normalization biocViews: CopyNumberVariation, Genetics, Sequencing Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena SystemRequirements: samtools git_url: https://git.bioconductor.org/packages/seqCNA git_branch: RELEASE_3_12 git_last_commit: afece4e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqCNA_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqCNA_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqCNA_1.36.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R suggestsMe: Herper dependencyCount: 37 Package: seqcombo Version: 1.12.0 Depends: R (>= 3.4.0) Imports: Biostrings, cowplot, dplyr, ggplot2, grid, igraph, magrittr, methods, rvcheck, utils Suggests: emojifont, knitr, prettydoc, tibble License: Artistic-2.0 MD5sum: 4dc624d339de360bd278ce443c4e4418 NeedsCompilation: no Title: Visualization Tool for Sequence Recombination and Reassortment Description: Provides useful functions for visualizing sequence recombination and virus reassortment events. biocViews: Alignment, Software, Visualization Author: Guangchuang Yu [aut, cre] Maintainer: Guangchuang Yu VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/seqcombo/issues git_url: https://git.bioconductor.org/packages/seqcombo git_branch: RELEASE_3_12 git_last_commit: 169a03f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqcombo_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqcombo_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqcombo_1.12.0.tgz vignettes: vignettes/seqcombo/inst/doc/reassortment.html, vignettes/seqcombo/inst/doc/seqcombo.html vignetteTitles: Reassortment, seqcombo introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqcombo/inst/doc/reassortment.R, vignettes/seqcombo/inst/doc/seqcombo.R dependencyCount: 55 Package: SeqGate Version: 1.0.1 Depends: S4Vectors, SummarizedExperiment, GenomicRanges Imports: stats, methods, BiocManager Suggests: testthat, edgeR, BiocStyle, knitr, rmarkdown License: GPL (>= 2.0) MD5sum: 2df06c6f3740409bfecd9c67903fbe4d NeedsCompilation: no Title: Filtering of Lowly Expressed Features Description: Filtering of lowly expressed features (e.g. genes) is a common step before performing statistical analysis, but an arbitrary threshold is generally chosen. SeqGate implements a method that rationalize this step by the analysis of the distibution of counts in replicate samples. The gate is the threshold above which sequenced features can be considered as confidently quantified. biocViews: DifferentialExpression, GeneExpression, Transcriptomics, Sequencing, RNASeq Author: Christelle Reynès [aut], Stéphanie Rialle [aut, cre] Maintainer: Stéphanie Rialle VignetteBuilder: knitr BugReports: https://github.com/srialle/SeqGate/issues git_url: https://git.bioconductor.org/packages/SeqGate git_branch: RELEASE_3_12 git_last_commit: b0199a5 git_last_commit_date: 2021-01-22 Date/Publication: 2021-01-22 source.ver: src/contrib/SeqGate_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/SeqGate_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/SeqGate_1.0.1.tgz vignettes: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.html vignetteTitles: SeqGate: Filter lowly expressed features hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGate/inst/doc/Seqgate-html-vignette.R dependencyCount: 27 Package: SeqGSEA Version: 1.30.0 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) MD5sum: 233e21d321e04d335ec5f02877169dc7 NeedsCompilation: no Title: Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing Description: The package generally provides methods for gene set enrichment analysis of high-throughput RNA-Seq data by integrating differential expression and splicing. It uses negative binomial distribution to model read count data, which accounts for sequencing biases and biological variation. Based on permutation tests, statistical significance can also be achieved regarding each gene's differential expression and splicing, respectively. biocViews: Sequencing, RNASeq, GeneSetEnrichment, GeneExpression, DifferentialExpression, DifferentialSplicing, ImmunoOncology Author: Xi Wang Maintainer: Xi Wang git_url: https://git.bioconductor.org/packages/SeqGSEA git_branch: RELEASE_3_12 git_last_commit: 8c89028 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SeqGSEA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SeqGSEA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SeqGSEA_1.30.0.tgz vignettes: vignettes/SeqGSEA/inst/doc/SeqGSEA.pdf vignetteTitles: Gene set enrichment analysis of RNA-Seq data with the SeqGSEA package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqGSEA/inst/doc/SeqGSEA.R dependencyCount: 66 Package: seqLogo Version: 1.56.0 Depends: methods, grid Imports: stats4, grDevices Suggests: knitr, BiocStyle, rmarkdown, testthat License: LGPL (>= 2) MD5sum: 64fa4f79a7c7e191f84bb2dcd9dc570a NeedsCompilation: no Title: Sequence logos for DNA sequence alignments Description: seqLogo takes the position weight matrix of a DNA sequence motif and plots the corresponding sequence logo as introduced by Schneider and Stephens (1990). biocViews: SequenceMatching Author: Oliver Bembom [aut], Robert Ivanek [aut, cre] () Maintainer: Robert Ivanek VignetteBuilder: knitr BugReports: https://github.com/ivanek/seqLogo/issues git_url: https://git.bioconductor.org/packages/seqLogo git_branch: RELEASE_3_12 git_last_commit: d371619 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqLogo_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqLogo_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqLogo_1.56.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.html vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: rGADEM, generegulation importsMe: igvR, IntEREst, PWMEnrich, rGADEM, riboSeqR, SPLINTER, TFBSTools suggestsMe: BCRANK, DiffLogo, motifcounter, MotifDb, universalmotif, phangorn dependencyCount: 4 Package: seqPattern Version: 1.22.0 Depends: methods, R (>= 2.15.0) Imports: Biostrings, GenomicRanges, IRanges, KernSmooth, plotrix Suggests: BSgenome.Drerio.UCSC.danRer7, CAGEr, RUnit, BiocGenerics, BiocStyle Enhances: parallel License: GPL-3 MD5sum: a2e3f5f53c7fa617b5f87163205b7f25 NeedsCompilation: no Title: Visualising oligonucleotide patterns and motif occurrences across a set of sorted sequences Description: Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature. biocViews: Visualization, SequenceMatching Author: Vanja Haberle Maintainer: Vanja Haberle git_url: https://git.bioconductor.org/packages/seqPattern git_branch: RELEASE_3_12 git_last_commit: 65d8931 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqPattern_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqPattern_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqPattern_1.22.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqPattern/inst/doc/seqPattern.R importsMe: genomation dependencyCount: 22 Package: seqplots Version: 1.27.0 Depends: R (>= 3.2.0) Imports: methods, IRanges, BSgenome, digest, rtracklayer, GenomicRanges, Biostrings, shiny (>= 0.13.0), DBI, RSQLite, plotrix, fields, grid, kohonen, parallel, GenomeInfoDb, class, S4Vectors, ggplot2, reshape2, gridExtra, jsonlite, DT (>= 0.1.0), RColorBrewer, Rsamtools, GenomicAlignments, BiocManager Suggests: testthat, BiocStyle, knitr, rmarkdown, covr License: GPL-3 MD5sum: bc44d96f5bc1fa515829f6b709bbaa2b NeedsCompilation: no Title: An interactive tool for visualizing NGS signals and sequence motif densities along genomic features using average plots and heatmaps Description: SeqPlots is a tool for plotting next generation sequencing (NGS) based experiments' signal tracks, e.g. reads coverage from ChIP-seq, RNA-seq and DNA accessibility assays like DNase-seq and MNase-seq, over user specified genomic features, e.g. promoters, gene bodies, etc. It can also calculate sequence motif density profiles from reference genome. The data are visualized as average signal profile plot, with error estimates (standard error and 95% confidence interval) shown as fields, or as series of heatmaps that can be sorted and clustered using hierarchical clustering, k-means algorithm and self organising maps. Plots can be prepared using R programming language or web browser based graphical user interface (GUI) implemented using Shiny framework. The dual-purpose implementation allows running the software locally on desktop or deploying it on server. SeqPlots is useful for both for exploratory data analyses and preparing replicable, publication quality plots. Other features of the software include collaboration and data sharing capabilities, as well as ability to store pre-calculated result matrixes, that combine many sequencing experiments and in-silico generated tracks with multiple different features. These binaries can be further used to generate new combination plots on fly, run automated batch operations or share with colleagues, who can adjust their plotting parameters without loading actual tracks and recalculating numeric values. SeqPlots relays on Bioconductor packages, mainly on rtracklayer for data input and BSgenome packages for reference genome sequence and annotations. biocViews: ImmunoOncology, ChIPSeq, RNASeq, Sequencing, Software, Visualization Author: Przemyslaw Stempor [aut, cph, cre] Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr BugReports: http://github.com/przemol/seqplots/issues git_url: https://git.bioconductor.org/packages/seqplots git_branch: master git_last_commit: dfef1c4 git_last_commit_date: 2020-04-27 Date/Publication: 2020-04-27 source.ver: src/contrib/seqplots_1.27.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqplots_1.27.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqplots_1.27.0.tgz vignettes: vignettes/seqplots/inst/doc/QuickStart.html, vignettes/seqplots/inst/doc/SeqPlotsGUI.html vignetteTitles: SeqPlots Quick Start, SeqPlots GUI hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqplots/inst/doc/QuickStart.R, vignettes/seqplots/inst/doc/SeqPlotsGUI.R importsMe: ChIPSeqSpike dependencyCount: 114 Package: seqsetvis Version: 1.10.0 Depends: R (>= 3.6), ggplot2 Imports: data.table, eulerr, GenomeInfoDb, GenomicAlignments, GenomicRanges, grDevices, grid, IRanges, limma, methods, pbapply, pbmcapply, png, RColorBrewer, Rsamtools, rtracklayer, S4Vectors, stats Suggests: BiocFileCache, BiocManager, BiocStyle, ChIPpeakAnno, covr, cowplot, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: c8a0e242da0c7c4f7e0cf481dbc95f6c NeedsCompilation: no Title: Set Based Visualizations for Next-Gen Sequencing Data Description: seqsetvis enables the visualization and analysis of sets of genomic sites in next gen sequencing data. Although seqsetvis was designed for the comparison of mulitple ChIP-seq samples, this package is domain-agnostic and allows the processing of multiple genomic coordinate files (bed-like files) and signal files (bigwig files pileups from bam file). biocViews: Software, ChIPSeq, MultipleComparison, Sequencing, Visualization Author: Joseph R Boyd [aut, cre] Maintainer: Joseph R Boyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/seqsetvis git_branch: RELEASE_3_12 git_last_commit: 24f478a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqsetvis_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqsetvis_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqsetvis_1.10.0.tgz vignettes: vignettes/seqsetvis/inst/doc/seqsetvis_overview.html vignetteTitles: Overview and Use Cases hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/seqsetvis/inst/doc/seqsetvis_overview.R dependencyCount: 80 Package: SeqSQC Version: 1.12.0 Depends: R (>= 3.4), ExperimentHub (>= 1.3.7), SNPRelate (>= 1.10.2) Imports: e1071, GenomicRanges, gdsfmt, ggplot2, GGally, IRanges, methods, rbokeh, RColorBrewer, reshape2, rmarkdown, S4Vectors, stats, utils Suggests: BiocStyle, knitr, testthat License: GPL-3 MD5sum: bf17ca9bba8b23216d001f08bd46e1a8 NeedsCompilation: no Title: A bioconductor package for sample quality check with next generation sequencing data Description: The SeqSQC is designed to identify problematic samples in NGS data, including samples with gender mismatch, contamination, cryptic relatedness, and population outlier. biocViews: Experiment Data, Homo_sapiens_Data, Sequencing Data, Project1000genomes, Genome Author: Qian Liu [aut, cre] Maintainer: Qian Liu URL: https://github.com/Liubuntu/SeqSQC VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/SeqSQC/issues git_url: https://git.bioconductor.org/packages/SeqSQC git_branch: RELEASE_3_12 git_last_commit: 0a0a0f2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SeqSQC_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SeqSQC_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SeqSQC_1.12.0.tgz vignettes: vignettes/SeqSQC/inst/doc/vignette.html vignetteTitles: Sample Quality Check for Next-Generation Sequencing Data with SeqSQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqSQC/inst/doc/vignette.R dependencyCount: 135 Package: seqTools Version: 1.24.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: c84c760a7e27b82fb7ec79decade30d8 NeedsCompilation: yes Title: Analysis of nucleotide, sequence and quality content on fastq files Description: Analyze read length, phred scores and alphabet frequency and DNA k-mers on uncompressed and compressed fastq files. biocViews: QualityControl,Sequencing Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers git_url: https://git.bioconductor.org/packages/seqTools git_branch: RELEASE_3_12 git_last_commit: 8b5b88d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/seqTools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/seqTools_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/seqTools_1.24.0.tgz vignettes: vignettes/seqTools/inst/doc/seqTools_qual_report.pdf, vignettes/seqTools/inst/doc/seqTools.pdf vignetteTitles: seqTools_qual_report, Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqTools/inst/doc/seqTools_qual_report.R, vignettes/seqTools/inst/doc/seqTools.R importsMe: qckitfastq dependencyCount: 3 Package: SeqVarTools Version: 1.28.1 Depends: SeqArray Imports: grDevices, graphics, stats, methods, Biobase, BiocGenerics, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, logistf, Matrix, data.table, Suggests: BiocStyle, RUnit, stringr License: GPL-3 MD5sum: ec7ae0a81098cdc50c9ec5ce6b11ce62 NeedsCompilation: no Title: Tools for variant data Description: An interface to the fast-access storage format for VCF data provided in SeqArray, with tools for common operations and analysis. biocViews: SNP, GeneticVariability, Sequencing, Genetics Author: Stephanie M. Gogarten, Xiuwen Zheng, Adrienne Stilp Maintainer: Stephanie M. Gogarten URL: https://github.com/smgogarten/SeqVarTools git_url: https://git.bioconductor.org/packages/SeqVarTools git_branch: RELEASE_3_12 git_last_commit: 54a8fd7 git_last_commit_date: 2020-11-20 Date/Publication: 2020-11-20 source.ver: src/contrib/SeqVarTools_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/SeqVarTools_1.28.1.zip mac.binary.ver: bin/macosx/contrib/4.0/SeqVarTools_1.28.1.tgz vignettes: vignettes/SeqVarTools/inst/doc/Iterators.pdf, vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Iterators in SeqVarTools, Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/Iterators.R, vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS, VariantExperiment, GMMAT, MAGEE dependencyCount: 59 Package: sesame Version: 1.8.12 Depends: R (>= 4.0), sesameData, methods Imports: BiocParallel, grDevices, utils, stringr, tibble, illuminaio, MASS, GenomicRanges, IRanges, grid, preprocessCore, S4Vectors, randomForest, wheatmap, ggplot2, parallel, matrixStats, DNAcopy, stats, SummarizedExperiment Suggests: scales, knitr, rmarkdown, testthat, dplyr, tidyr, BiocStyle, IlluminaHumanMethylation450kmanifest, minfi, FlowSorted.CordBloodNorway.450k, FlowSorted.Blood.450k, HDF5Array License: MIT + file LICENSE MD5sum: f7a7747c60793c8da4bab7a65338a93f NeedsCompilation: no Title: SEnsible Step-wise Analysis of DNA MEthylation BeadChips Description: Tools For analyzing Illumina Infinium DNA methylation arrays.SeSAMe provides utilities to support analyses of multiple generations of Infinium DNA methylation BeadChips, including preprocessing, quality control, visualization and inference. SeSAMe features more accurate detection calling, intelligenet inference of ethnicity, sex and advanced quality control routines. biocViews: DNAMethylation, MethylationArray, Preprocessing, QualityControl Author: Wanding Zhou [aut, cre], Hui Shen [aut], Timothy Triche [ctb], Bret Barnes [ctb] Maintainer: Wanding Zhou URL: https://github.com/zwdzwd/sesame VignetteBuilder: knitr BugReports: https://github.com/zwdzwd/sesame/issues git_url: https://git.bioconductor.org/packages/sesame git_branch: RELEASE_3_12 git_last_commit: 37a1f75 git_last_commit_date: 2021-04-29 Date/Publication: 2021-05-01 source.ver: src/contrib/sesame_1.8.12.tar.gz win.binary.ver: bin/windows/contrib/4.0/sesame_1.8.10.zip mac.binary.ver: bin/macosx/contrib/4.0/sesame_1.8.12.tgz vignettes: vignettes/sesame/inst/doc/datasanitization.html, vignettes/sesame/inst/doc/largeData.html, vignettes/sesame/inst/doc/mammal.html, vignettes/sesame/inst/doc/minfi.html, vignettes/sesame/inst/doc/mouse.html, vignettes/sesame/inst/doc/QC.html, vignettes/sesame/inst/doc/sesame.html vignetteTitles: "4. data sanitization", 2. Large Data, 5. Horvath Mammal40 Array, 3. Minfi Interaction, 6. Mouse Array, 1. Quality Controls, "0. SeSAMe User Guide" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sesame/inst/doc/datasanitization.R, vignettes/sesame/inst/doc/largeData.R, vignettes/sesame/inst/doc/mammal.R, vignettes/sesame/inst/doc/minfi.R, vignettes/sesame/inst/doc/mouse.R, vignettes/sesame/inst/doc/QC.R, vignettes/sesame/inst/doc/sesame.R importsMe: TCGAbiolinksGUI suggestsMe: TCGAbiolinks, sesameData dependencyCount: 128 Package: SEtools Version: 1.4.0 Depends: R (>= 4.0) Imports: S4Vectors, SummarizedExperiment, data.table, pheatmap, seriation, ComplexHeatmap, circlize, methods, BiocParallel, randomcoloR, edgeR, openxlsx Suggests: BiocStyle, knitr, rmarkdown, ggplot2 License: GPL MD5sum: ebbe69d3fbf38e0c5a38c2071bf71e87 NeedsCompilation: no Title: SEtools: tools for working with SummarizedExperiment Description: This includes a set of tools for working with the SummarizedExperiment class, including merging, melting, aggregation and plotting functions. In particular, SEtools offers a simple interface for plotting complex heatmaps from SE objects. biocViews: GeneExpression, Visualization Author: Pierre-Luc Germain [cre, aut] () Maintainer: Pierre-Luc Germain VignetteBuilder: knitr BugReports: https://github.com/plger/SEtools git_url: https://git.bioconductor.org/packages/SEtools git_branch: RELEASE_3_12 git_last_commit: 035e0cc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SEtools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SEtools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SEtools_1.4.0.tgz vignettes: vignettes/SEtools/inst/doc/SEtools.html vignetteTitles: SEtools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEtools/inst/doc/SEtools.R dependencyCount: 104 Package: sevenbridges Version: 1.20.1 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, uuid, data.table Suggests: knitr, rmarkdown, testthat, readr License: Apache License 2.0 | file LICENSE MD5sum: 5739f1e52f46c8aaffc0ba01ea23c895 NeedsCompilation: no Title: Seven Bridges Platform API Client and Common Workflow Language Tool Builder in R Description: R client and utilities for Seven Bridges platform API, from Cancer Genomics Cloud to other Seven Bridges supported platforms. biocViews: Software, DataImport, ThirdPartyClient Author: Soner Koc [aut, cre], Nan Xiao [aut], Tengfei Yin [aut], Dusan Randjelovic [ctb], Emile Young [ctb], Seven Bridges Genomics [cph, fnd] Maintainer: Soner Koc URL: https://www.sevenbridges.com, https://sbg.github.io/sevenbridges-r/, https://github.com/sbg/sevenbridges-r VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues git_url: https://git.bioconductor.org/packages/sevenbridges git_branch: RELEASE_3_12 git_last_commit: 6d0ec68 git_last_commit_date: 2021-03-24 Date/Publication: 2021-03-24 source.ver: src/contrib/sevenbridges_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/sevenbridges_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.0/sevenbridges_1.20.1.tgz vignettes: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-datasets.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html vignetteTitles: Complete Guide for Seven Bridges API R Client, Describe and Execute CWL Tools/Workflows in R, Master Tutorial: Use R for Cancer Genomics Cloud, Find Data on CGC via Data Browser and Datasets API, Creating Your Docker Container and Command Line Interface (with docopt), IDE Container: RStudio Server,, Shiny Server,, and More hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sevenbridges/inst/doc/api.R, vignettes/sevenbridges/inst/doc/apps.R, vignettes/sevenbridges/inst/doc/bioc-workflow.R, vignettes/sevenbridges/inst/doc/cgc-datasets.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R dependencyCount: 27 Package: sevenC Version: 1.10.0 Depends: R (>= 3.5), InteractionSet (>= 1.2.0) Imports: rtracklayer (>= 1.34.1), BiocGenerics (>= 0.22.0), GenomeInfoDb (>= 1.12.2), GenomicRanges (>= 1.28.5), IRanges (>= 2.10.3), S4Vectors (>= 0.14.4), readr (>= 1.1.0), purrr (>= 0.2.2), data.table (>= 1.10.4), boot (>= 1.3-20), methods (>= 3.4.1) Suggests: testthat, BiocStyle, knitr, rmarkdown, GenomicInteractions, covr License: GPL-3 MD5sum: dd0576b0949ee76cc708f954fb9faa63 NeedsCompilation: no Title: Computational Chromosome Conformation Capture by Correlation of ChIP-seq at CTCF motifs Description: Chromatin looping is an essential feature of eukaryotic genomes and can bring regulatory sequences, such as enhancers or transcription factor binding sites, in the close physical proximity of regulated target genes. Here, we provide sevenC, an R package that uses protein binding signals from ChIP-seq and sequence motif information to predict chromatin looping events. Cross-linking of proteins that bind close to loop anchors result in ChIP-seq signals at both anchor loci. These signals are used at CTCF motif pairs together with their distance and orientation to each other to predict whether they interact or not. The resulting chromatin loops might be used to associate enhancers or transcription factor binding sites (e.g., ChIP-seq peaks) to regulated target genes. biocViews: DNA3DStructure, ChIPchip, Coverage, DataImport, Epigenetics, FunctionalGenomics, Classification, Regression, ChIPSeq, HiC, Annotation Author: Jonas Ibn-Salem [aut, cre] Maintainer: Jonas Ibn-Salem URL: https://github.com/ibn-salem/sevenC VignetteBuilder: knitr BugReports: https://github.com/ibn-salem/sevenC/issues git_url: https://git.bioconductor.org/packages/sevenC git_branch: RELEASE_3_12 git_last_commit: 5cd3589 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sevenC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sevenC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sevenC_1.10.0.tgz vignettes: vignettes/sevenC/inst/doc/sevenC.html vignetteTitles: Introduction to sevenC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sevenC/inst/doc/sevenC.R dependencyCount: 62 Package: SGSeq Version: 1.24.0 Depends: R (>= 4.0), IRanges (>= 2.13.15), GenomicRanges (>= 1.31.10), Rsamtools (>= 1.31.2), SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics (>= 0.31.5), Biostrings (>= 2.47.6), GenomicAlignments (>= 1.15.7), GenomicFeatures (>= 1.31.5), GenomeInfoDb, RUnit, S4Vectors (>= 0.23.19), grDevices, graphics, igraph, parallel, rtracklayer (>= 1.39.7), stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: e23d8b849220e8330a1c369c9aee2a95 NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: SGSeq is a software package for analyzing splice events from RNA-seq data. Input data are RNA-seq reads mapped to a reference genome in BAM format. Genes are represented as a splice graph, which can be obtained from existing annotation or predicted from the mapped sequence reads. Splice events are identified from the graph and are quantified locally using structurally compatible reads at the start or end of each splice variant. The software includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, ImmunoOncology, RNASeq, Transcription Author: Leonard Goldstein [cre, aut] Maintainer: Leonard Goldstein VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SGSeq git_branch: RELEASE_3_12 git_last_commit: d776538 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SGSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SGSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SGSeq_1.24.0.tgz vignettes: vignettes/SGSeq/inst/doc/SGSeq.html vignetteTitles: SGSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R dependsOnMe: EventPointer importsMe: Rhisat2 dependencyCount: 90 Package: SharedObject Version: 1.4.0 Depends: R (>= 3.6.0) Imports: Rcpp, methods, stats, BiocGenerics LinkingTo: BH, Rcpp Suggests: testthat, parallel, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: c9481cfdef3626c44abf5d2ab6c729ca NeedsCompilation: yes Title: Sharing R objects across multiple R processes without memory duplication Description: This package is developed for facilitating parallel computing in R. It is capable to create an R object in the shared memory space and share the data across multiple R processes. It avoids the overhead of memory dulplication and data transfer, which make sharing big data object across many clusters possible. biocViews: Infrastructure Author: Jiefei Wang [aut, cre] Maintainer: Jiefei Wang SystemRequirements: GNU make, C++11 VignetteBuilder: knitr BugReports: https://github.com/Jiefei-Wang/SharedObject/issues git_url: https://git.bioconductor.org/packages/SharedObject git_branch: RELEASE_3_12 git_last_commit: af4b1eb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SharedObject_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SharedObject_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SharedObject_1.4.0.tgz vignettes: vignettes/SharedObject/inst/doc/quick_start_guide.html vignetteTitles: quickStart hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SharedObject/inst/doc/quick_start_guide.R dependsOnMe: NewWave dependencyCount: 8 Package: shinyMethyl Version: 1.26.0 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.13.2), minfi (>= 1.18.2), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: 032193a61f18748de9f75ee42f8f26bb NeedsCompilation: no Title: Interactive visualization for Illumina methylation arrays Description: Interactive tool for visualizing Illumina methylation array data. Both the 450k and EPIC array are supported. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/shinyMethyl git_branch: RELEASE_3_12 git_last_commit: 1dab127 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/shinyMethyl_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/shinyMethyl_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/shinyMethyl_1.26.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/shinyMethyl/inst/doc/shinyMethyl.R dependencyCount: 143 Package: shinyTANDEM Version: 1.28.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: d14dd9fb0d5f3c772ee1878ca2f0b4ba NeedsCompilation: no Title: Provides a GUI for rTANDEM Description: This package provides a GUI interface for rTANDEM. The GUI is primarily designed to visualize rTANDEM result object or result xml files. But it will also provides an interface for creating parameter objects, launching searches or performing conversions between R objects and xml files. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Frederic Fournier , Arnaud Droit Maintainer: Frederic Fournier PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/shinyTANDEM git_branch: RELEASE_3_12 git_last_commit: 0602b16 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/shinyTANDEM_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/shinyTANDEM_1.28.0.zip vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 44 Package: ShortRead Version: 1.48.0 Depends: BiocGenerics (>= 0.23.3), BiocParallel, Biostrings (>= 2.47.6), Rsamtools (>= 1.31.2), GenomicAlignments (>= 1.15.6) Imports: Biobase, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.31.8), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib, zlibbioc Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 Archs: i386, x64 MD5sum: a1d99be87db91cbbcda1dcfead019d0f NeedsCompilation: yes Title: FASTQ input and manipulation Description: This package implements sampling, iteration, and input of FASTQ files. The package includes functions for filtering and trimming reads, and for generating a quality assessment report. Data are represented as DNAStringSet-derived objects, and easily manipulated for a diversity of purposes. The package also contains legacy support for early single-end, ungapped alignment formats. biocViews: DataImport, Sequencing, QualityControl Author: Martin Morgan, Michael Lawrence, Simon Anders Maintainer: Bioconductor Package Maintainer git_url: https://git.bioconductor.org/packages/ShortRead git_branch: RELEASE_3_12 git_last_commit: ba44cd2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ShortRead_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ShortRead_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ShortRead_1.48.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ShortRead/inst/doc/Overview.R dependsOnMe: chipseq, EDASeq, esATAC, girafe, HTSeqGenie, OTUbase, Rqc, segmentSeq, systemPipeR, EatonEtAlChIPseq, NestLink, sequencing, SimRAD, STRMPS importsMe: amplican, ArrayExpressHTS, basecallQC, BEAT, chipseq, ChIPseqR, ChIPsim, dada2, FastqCleaner, GOTHiC, icetea, IONiseR, MACPET, ngsReports, nucleR, QuasR, R453Plus1Toolbox, RSVSim, scruff, UMI4Cats, genBaRcode suggestsMe: BiocParallel, CSAR, DBChIP, GenomicAlignments, PING, Repitools, Rsamtools, S4Vectors, HiCDataLymphoblast, yeastRNASeq dependencyCount: 43 Package: SIAMCAT Version: 1.10.0 Depends: R (>= 3.6.0), mlr, phyloseq Imports: beanplot, glmnet, graphics, grDevices, grid, gridBase, gridExtra, LiblineaR, matrixStats, methods, ParamHelpers, pROC, PRROC, RColorBrewer, scales, stats, stringr, utils, infotheo, progress, corrplot Suggests: BiocStyle, optparse, testthat, knitr, rmarkdown License: GPL-3 MD5sum: e50165faa2bd70a4316c7fec78508923 NeedsCompilation: no Title: Statistical Inference of Associations between Microbial Communities And host phenoTypes Description: Pipeline for Statistical Inference of Associations between Microbial Communities And host phenoTypes (SIAMCAT). A primary goal of analyzing microbiome data is to determine changes in community composition that are associated with environmental factors. In particular, linking human microbiome composition to host phenotypes such as diseases has become an area of intense research. For this, robust statistical modeling and biomarker extraction toolkits are crucially needed. SIAMCAT provides a full pipeline supporting data preprocessing, statistical association testing, statistical modeling (LASSO logistic regression) including tools for evaluation and interpretation of these models (such as cross validation, parameter selection, ROC analysis and diagnostic model plots). biocViews: ImmunoOncology, Metagenomics, Classification, Microbiome, Sequencing, Preprocessing, Clustering, FeatureExtraction, GeneticVariability, MultipleComparison,Regression Author: Konrad Zych [aut] (), Jakob Wirbel [aut, cre] (), Georg Zeller [aut] (), Morgan Essex [ctb], Nicolai Karcher [ctb], Kersten Breuer [ctb] Maintainer: Jakob Wirbel VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIAMCAT git_branch: RELEASE_3_12 git_last_commit: ff95f83 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SIAMCAT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SIAMCAT_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SIAMCAT_1.10.0.tgz vignettes: vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.html, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.html vignetteTitles: SIAMCAT holdout testing vignette, SIAMCAT.input, SIAMCAT basic vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIAMCAT/inst/doc/SIAMCAT_holdout.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_read-in.R, vignettes/SIAMCAT/inst/doc/SIAMCAT_vignette.R dependencyCount: 95 Package: SICtools Version: 1.20.0 Depends: R (>= 3.0.0), methods, Rsamtools (>= 1.18.1), doParallel (>= 1.0.8), Biostrings (>= 2.32.1), stringr (>= 0.6.2), matrixStats (>= 0.10.0), plyr (>= 1.8.3), GenomicRanges (>= 1.22.4), IRanges (>= 2.4.8) Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: dd2c2c75be100711a3d583139ce59f03 NeedsCompilation: yes Title: Find SNV/Indel differences between two bam files with near relationship Description: This package is to find SNV/Indel differences between two bam files with near relationship in a way of pairwise comparison thourgh each base position across the genome region of interest. The difference is inferred by fisher test and euclidean distance, the input of which is the base count (A,T,G,C) in a given position and read counts for indels that span no less than 2bp on both sides of indel region. biocViews: Alignment, Sequencing, Coverage, SequenceMatching, QualityControl, DataImport, Software, SNP, VariantDetection Author: Xiaobin Xing, Wu Wei Maintainer: Xiaobin Xing VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SICtools git_branch: RELEASE_3_12 git_last_commit: f802269 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SICtools_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/SICtools_1.20.0.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: Using SICtools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SICtools/inst/doc/SICtools.R dependencyCount: 40 Package: SigCheck Version: 2.22.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: 599c8feb18b9b54a36d48197c81acd8c NeedsCompilation: no Title: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata Description: While gene signatures are frequently used to predict phenotypes (e.g. predict prognosis of cancer patients), it it not always clear how optimal or meaningful they are (cf David Venet, Jacques E. Dumont, and Vincent Detours' paper "Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome"). Based on suggestions in that paper, SigCheck accepts a data set (as an ExpressionSet) and a gene signature, and compares its performance on survival and/or classification tasks against a) random gene signatures of the same length; b) known, related and unrelated gene signatures; and c) permuted data and/or metadata. biocViews: GeneExpression, Classification, GeneSetEnrichment Author: Rory Stark and Justin Norden Maintainer: Rory Stark git_url: https://git.bioconductor.org/packages/SigCheck git_branch: RELEASE_3_12 git_last_commit: 8ab64bf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SigCheck_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SigCheck_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SigCheck_2.22.0.tgz vignettes: vignettes/SigCheck/inst/doc/SigCheck.pdf vignetteTitles: Checking gene expression signatures against random and known signatures with SigCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigCheck/inst/doc/SigCheck.R dependencyCount: 112 Package: sigFeature Version: 1.8.0 Depends: R (>= 3.5.0) Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods Suggests: RUnit, BiocGenerics, knitr License: GPL MD5sum: 2ac910a25763453993674c8e84b3a0da NeedsCompilation: no Title: sigFeature: Significant feature selection using SVM-RFE & t-statistic Description: This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy. biocViews: FeatureExtraction, GeneExpression, Microarray, Transcription, mRNAMicroarray, GenePrediction, Normalization, Classification, SupportVectorMachine Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] Maintainer: Pijush Das Developer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sigFeature git_branch: RELEASE_3_12 git_last_commit: dbdde92 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sigFeature_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sigFeature_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sigFeature_1.8.0.tgz vignettes: vignettes/sigFeature/inst/doc/vignettes.pdf vignetteTitles: sigFeature hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sigFeature/inst/doc/vignettes.R dependencyCount: 62 Package: SigFuge Version: 1.28.0 Depends: R (>= 3.1.1), GenomicRanges Imports: ggplot2, matlab, reshape, sigclust Suggests: org.Hs.eg.db, prebsdata, Rsamtools (>= 1.17.0), TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle License: GPL-3 MD5sum: 419a897ec2f3953e67128d7383dd9310 NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq, ImmunoOncology Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes git_url: https://git.bioconductor.org/packages/SigFuge git_branch: RELEASE_3_12 git_last_commit: 32a8707 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SigFuge_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SigFuge_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SigFuge_1.28.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigFuge/inst/doc/SigFuge.R dependencyCount: 56 Package: siggenes Version: 1.64.0 Depends: Biobase, multtest, splines, methods Imports: stats4, grDevices, graphics, stats, scrime (>= 1.2.5) Suggests: affy, annotate, genefilter, KernSmooth License: LGPL (>= 2) MD5sum: 0431e592232677003db6b90d447d7ad6 NeedsCompilation: no Title: Multiple Testing using SAM and Efron's Empirical Bayes Approaches Description: Identification of differentially expressed genes and estimation of the False Discovery Rate (FDR) using both the Significance Analysis of Microarrays (SAM) and the Empirical Bayes Analyses of Microarrays (EBAM). biocViews: MultipleComparison, Microarray, GeneExpression, SNP, ExonArray, DifferentialExpression Author: Holger Schwender Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/siggenes git_branch: RELEASE_3_12 git_last_commit: 3b528d3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/siggenes_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/siggenes_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/siggenes_1.64.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf, vignettes/siggenes/inst/doc/siggenesRnews.pdf, vignettes/siggenes/inst/doc/identify.sam.html, vignettes/siggenes/inst/doc/plot.ebam.html, vignettes/siggenes/inst/doc/plot.finda0.html, vignettes/siggenes/inst/doc/plot.sam.html, vignettes/siggenes/inst/doc/print.ebam.html, vignettes/siggenes/inst/doc/print.finda0.html, vignettes/siggenes/inst/doc/print.sam.html, vignettes/siggenes/inst/doc/summary.ebam.html, vignettes/siggenes/inst/doc/summary.sam.html vignetteTitles: siggenes Manual, siggenesRnews.pdf, identify.sam.html, plot.ebam.html, plot.finda0.html, plot.sam.html, print.ebam.html, print.finda0.html, print.sam.html, summary.ebam.html, summary.sam.html hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart importsMe: coexnet, minfi, trio, XDE, DeSousa2013, INCATome suggestsMe: GCSscore, logicFS dependencyCount: 17 Package: sights Version: 1.16.0 Depends: R(>= 3.3) Imports: MASS(>= 7.3), qvalue(>= 2.2), ggplot2(>= 2.0), reshape2(>= 1.4), lattice(>= 0.2), stats(>= 3.3) Suggests: testthat, knitr, rmarkdown, ggthemes, gridExtra, xlsx License: GPL-3 | file LICENSE MD5sum: 6018f25afb1d30c26262dd47554e5f3c NeedsCompilation: no Title: Statistics and dIagnostic Graphs for HTS Description: SIGHTS is a suite of normalization methods, statistical tests, and diagnostic graphical tools for high throughput screening (HTS) assays. HTS assays use microtitre plates to screen large libraries of compounds for their biological, chemical, or biochemical activity. biocViews: ImmunoOncology, CellBasedAssays, MicrotitrePlateAssay, Normalization, MultipleComparison, Preprocessing, QualityControl, BatchEffect, Visualization Author: Elika Garg [aut, cre], Carl Murie [aut], Heydar Ensha [ctb], Robert Nadon [aut] Maintainer: Elika Garg URL: https://eg-r.github.io/sights/ VignetteBuilder: knitr BugReports: https://github.com/eg-r/sights/issues git_url: https://git.bioconductor.org/packages/sights git_branch: RELEASE_3_12 git_last_commit: 90d3300 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sights_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sights_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sights_1.16.0.tgz vignettes: vignettes/sights/inst/doc/sights.html vignetteTitles: Using **SIGHTS** R-package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sights/inst/doc/sights.R dependencyCount: 45 Package: signatureSearch Version: 1.4.6 Depends: R(>= 3.6.0), Rcpp, SummarizedExperiment Imports: AnnotationDbi, ggplot2, data.table, ExperimentHub, HDF5Array, magrittr, RSQLite, dplyr, fgsea, scales, methods, qvalue, stats, utils, reshape2, visNetwork, BiocParallel, fastmatch, reactome.db, Matrix, clusterProfiler, readr, DOSE, rhdf5, GSEABase, DelayedArray LinkingTo: Rcpp Suggests: knitr, testthat, rmarkdown, BiocStyle, org.Hs.eg.db License: Artistic-2.0 Archs: i386, x64 MD5sum: 720f65d05422e439d8f6631749bed9b7 NeedsCompilation: yes Title: Environment for Gene Expression Searching Combined with Functional Enrichment Analysis Description: This package implements algorithms and data structures for performing gene expression signature (GES) searches, and subsequently interpreting the results functionally with specialized enrichment methods. biocViews: Software, GeneExpression, GO, KEGG, NetworkEnrichment, Sequencing, Coverage, DifferentialExpression Author: Yuzhu Duan [cre, aut], Thomas Girke [aut] Maintainer: Yuzhu Duan URL: https://github.com/yduan004/signatureSearch/ SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://github.com/yduan004/signatureSearch/issues git_url: https://git.bioconductor.org/packages/signatureSearch git_branch: RELEASE_3_12 git_last_commit: 0befcb1 git_last_commit_date: 2021-04-23 Date/Publication: 2021-04-23 source.ver: src/contrib/signatureSearch_1.4.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/signatureSearch_1.4.6.zip mac.binary.ver: bin/macosx/contrib/4.0/signatureSearch_1.4.6.tgz vignettes: vignettes/signatureSearch/inst/doc/signatureSearch.html vignetteTitles: signatureSearch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signatureSearch/inst/doc/signatureSearch.R importsMe: signatureSearchData dependencyCount: 158 Package: signeR Version: 1.16.0 Depends: VariantAnnotation, NMF Imports: BiocGenerics, Biostrings, class, graphics, grDevices, GenomeInfoDb, GenomicRanges, IRanges, nloptr, methods, stats, utils, PMCMR LinkingTo: Rcpp, RcppArmadillo (>= 0.7.100) Suggests: knitr, rtracklayer, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 Archs: i386, x64 MD5sum: 36f7efb8bed5af61cb2eecec68f71260 NeedsCompilation: yes Title: Empirical Bayesian approach to mutational signature discovery Description: The signeR package provides an empirical Bayesian approach to mutational signature discovery. It is designed to analyze single nucleotide variaton (SNV) counts in cancer genomes, but can also be applied to other features as well. Functionalities to characterize signatures or genome samples according to exposure patterns are also provided. biocViews: GenomicVariation, SomaticMutation, StatisticalMethod, Visualization Author: Rafael Rosales, Rodrigo Drummond, Renan Valieris, Israel Tojal da Silva Maintainer: Renan Valieris URL: https://github.com/rvalieris/signeR SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/signeR git_branch: RELEASE_3_12 git_last_commit: ca1c552 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/signeR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/signeR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/signeR_1.16.0.tgz vignettes: vignettes/signeR/inst/doc/signeR-vignette.html vignetteTitles: signeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/signeR/inst/doc/signeR-vignette.R dependencyCount: 124 Package: sigPathway Version: 1.58.0 Depends: R (>= 2.10) Suggests: hgu133a.db (>= 1.10.0), XML (>= 1.6-3), AnnotationDbi (>= 1.3.12) License: GPL-2 Archs: i386, x64 MD5sum: a196b48d10a9c16ae3cd2e1039c0652c NeedsCompilation: yes Title: Pathway Analysis Description: Conducts pathway analysis by calculating the NT_k and NE_k statistics as described in Tian et al. (2005) biocViews: DifferentialExpression, MultipleComparison Author: Weil Lai (optimized R and C code), Lu Tian and Peter Park (algorithm development and initial R code) Maintainer: Weil Lai URL: http://www.pnas.org/cgi/doi/10.1073/pnas.0506577102, http://www.chip.org/~ppark/Supplements/PNAS05.html git_url: https://git.bioconductor.org/packages/sigPathway git_branch: RELEASE_3_12 git_last_commit: 582d569 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sigPathway_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sigPathway_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sigPathway_1.58.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigPathway/inst/doc/sigPathway-vignette.R dependsOnMe: tRanslatome dependencyCount: 0 Package: SigsPack Version: 1.4.0 Depends: R (>= 3.6) Imports: quadprog (>= 1.5-5), methods, Biobase, BSgenome (>= 1.46.0), VariantAnnotation (>= 1.24.5), Biostrings, GenomeInfoDb, GenomicRanges, rtracklayer, SummarizedExperiment, graphics, stats, utils Suggests: IRanges, BSgenome.Hsapiens.UCSC.hg19, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: ad636de270a6fe270f4adec9f624e248 NeedsCompilation: no Title: Mutational Signature Estimation for Single Samples Description: Single sample estimation of exposure to mutational signatures. Exposures to known mutational signatures are estimated for single samples, based on quadratic programming algorithms. Bootstrapping the input mutational catalogues provides estimations on the stability of these exposures. The effect of the sequence composition of mutational context can be taken into account by normalising the catalogues. biocViews: SomaticMutation, SNP, VariantAnnotation, BiomedicalInformatics, DNASeq Author: Franziska Schumann Maintainer: Franziska Schumann URL: https://github.com/bihealth/SigsPack VignetteBuilder: knitr BugReports: https://github.com/bihealth/SigsPack/issues git_url: https://git.bioconductor.org/packages/SigsPack git_branch: RELEASE_3_12 git_last_commit: e3f4b5f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SigsPack_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SigsPack_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SigsPack_1.4.0.tgz vignettes: vignettes/SigsPack/inst/doc/SigsPack.html vignetteTitles: Introduction to SigsPack hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SigsPack/inst/doc/SigsPack.R dependencyCount: 91 Package: sigsquared Version: 1.22.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: a81fdaa170b2935eaf90fac593a91f68 NeedsCompilation: no Title: Gene signature generation for functionally validated signaling pathways Description: By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error. Author: UnJin Lee Maintainer: UnJin Lee git_url: https://git.bioconductor.org/packages/sigsquared git_branch: RELEASE_3_12 git_last_commit: 6ad9900 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sigsquared_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sigsquared_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sigsquared_1.22.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigsquared/inst/doc/sigsquared.R dependencyCount: 13 Package: SIM Version: 1.60.0 Depends: R (>= 3.5), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: 17df2f7d0607a366ef23dd7644934cfb NeedsCompilation: yes Title: Integrated Analysis on two human genomic datasets Description: Finds associations between two human genomic datasets. biocViews: Microarray, Visualization Author: Renee X. de Menezes and Judith M. Boer Maintainer: Renee X. de Menezes git_url: https://git.bioconductor.org/packages/SIM git_branch: RELEASE_3_12 git_last_commit: 775f25e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SIM_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SIM_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SIM_1.60.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIM/inst/doc/SIM.R dependencyCount: 52 Package: SIMAT Version: 1.22.0 Depends: R (>= 3.5.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2, grDevices, stats, utils Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: dc1bb59895ebf2eadb3b7eb79160b19d NeedsCompilation: no Title: GC-SIM-MS data processing and alaysis tool Description: This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user. biocViews: ImmunoOncology, Software, Metabolomics, MassSpectrometry Author: M. R. Nezami Ranjbar Maintainer: M. R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html git_url: https://git.bioconductor.org/packages/SIMAT git_branch: RELEASE_3_12 git_last_commit: 6d18e11 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SIMAT_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SIMAT_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SIMAT_1.22.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: SIMAT Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMAT/inst/doc/SIMAT-vignette.R dependencyCount: 52 Package: SimBindProfiles Version: 1.28.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: 4e5547fd9154239424a070a0fcd60987 NeedsCompilation: no Title: Similar Binding Profiles Description: SimBindProfiles identifies common and unique binding regions in genome tiling array data. This package does not rely on peak calling, but directly compares binding profiles processed on the same array platform. It implements a simple threshold approach, thus allowing retrieval of commonly and differentially bound regions between datasets as well as events of compensation and increased binding. biocViews: Microarray, Software Author: Bettina Fischer, Enrico Ferrero, Robert Stojnic, Steve Russell Maintainer: Bettina Fischer git_url: https://git.bioconductor.org/packages/SimBindProfiles git_branch: RELEASE_3_12 git_last_commit: 9493d44 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SimBindProfiles_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SimBindProfiles_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SimBindProfiles_1.28.0.tgz vignettes: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.pdf vignetteTitles: SimBindProfiles: Similar Binding Profiles,, identifies common and unique regions in array genome tiling array data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimBindProfiles/inst/doc/SimBindProfiles.R dependencyCount: 77 Package: SIMD Version: 1.8.0 Depends: R (>= 3.5.0) Imports: edgeR, statmod, methylMnM, stats, utils Suggests: BiocStyle, knitr,rmarkdown License: GPL-3 Archs: i386, x64 MD5sum: d17bb750aea923583f5effff57d2ef40 NeedsCompilation: yes Title: Statistical Inferences with MeDIP-seq Data (SIMD) to infer the methylation level for each CpG site Description: This package provides a inferential analysis method for detecting differentially expressed CpG sites in MeDIP-seq data. It uses statistical framework and EM algorithm, to identify differentially expressed CpG sites. The methods on this package are described in the article 'Methylation-level Inferences and Detection of Differential Methylation with Medip-seq Data' by Yan Zhou, Jiadi Zhu, Mingtao Zhao, Baoxue Zhang, Chunfu Jiang and Xiyan Yang (2018, pending publication). biocViews: ImmunoOncology, DifferentialMethylation,SingleCell, DifferentialExpression Author: Yan Zhou Maintainer: Jiadi Zhu <2160090406@email.szu.edu.cn> VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SIMD git_branch: RELEASE_3_12 git_last_commit: fa7dbdc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SIMD_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SIMD_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SIMD_1.8.0.tgz vignettes: vignettes/SIMD/inst/doc/SIMD.html vignetteTitles: SIMD Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SIMD/inst/doc/SIMD.R dependencyCount: 13 Package: SimFFPE Version: 1.2.0 Depends: Biostrings Imports: dplyr, foreach, doParallel, truncnorm, GenomicRanges, IRanges, Rsamtools, parallel, graphics, stats, utils, methods Suggests: BiocStyle License: LGPL-3 MD5sum: 2bb8677e10c18fa6c4d4afb0d19f9e1a NeedsCompilation: no Title: NGS Read Simulator for FFPE Tissue Description: This package simulates artifact chimeric reads specifically generated in next-generation sequencing (NGS) process of formalin-fixed paraffin-embedded (FFPE) tissue. biocViews: Sequencing, Alignment, MultipleComparison, SequenceMatching, DataImport Author: Lanying Wei Maintainer: Lanying Wei git_url: https://git.bioconductor.org/packages/SimFFPE git_branch: RELEASE_3_12 git_last_commit: 3d90ef3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SimFFPE_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SimFFPE_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SimFFPE_1.2.0.tgz vignettes: vignettes/SimFFPE/inst/doc/SimFFPE.pdf vignetteTitles: An introduction to SimFFPE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SimFFPE/inst/doc/SimFFPE.R dependencyCount: 51 Package: similaRpeak Version: 1.22.0 Depends: R6 (>= 2.0) Imports: stats Suggests: RUnit, BiocGenerics, knitr, Rsamtools, GenomicAlignments, rtracklayer, rmarkdown, BiocStyle License: Artistic-2.0 MD5sum: 88257fddc7b9230d46d6dd2e17002a1f NeedsCompilation: no Title: Metrics to estimate a level of similarity between two ChIP-Seq profiles Description: This package calculates metrics which assign a level of similarity between ChIP-Seq profiles. biocViews: BiologicalQuestion, ChIPSeq, Genetics, MultipleComparison, DifferentialExpression Author: Astrid Deschenes [cre, aut], Elsa Bernatchez [aut], Charles Joly Beauparlant [aut], Fabien Claude Lamaze [aut], Rawane Samb [aut], Pascal Belleau [aut], Arnaud Droit [aut] Maintainer: Astrid Deschenes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues git_url: https://git.bioconductor.org/packages/similaRpeak git_branch: RELEASE_3_12 git_last_commit: 3a928ff git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/similaRpeak_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/similaRpeak_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/similaRpeak_1.22.0.tgz vignettes: vignettes/similaRpeak/inst/doc/similaRpeak.html vignetteTitles: Similarity between two ChIP-Seq profiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R suggestsMe: metagene dependencyCount: 2 Package: SIMLR Version: 1.16.0 Depends: R (>= 4.0.0), Imports: parallel, Matrix, stats, methods, Rcpp, pracma, RcppAnnoy, RSpectra LinkingTo: Rcpp Suggests: BiocGenerics, BiocStyle, testthat, knitr, igraph License: file LICENSE Archs: i386, x64 MD5sum: ca55d7d0a40b2c5e759db915c709c846 NeedsCompilation: yes Title: Single-cell Interpretation via Multi-kernel LeaRning (SIMLR) Description: Single-cell RNA-seq technologies enable high throughput gene expression measurement of individual cells, and allow the discovery of heterogeneity within cell populations. Measurement of cell-to-cell gene expression similarity is critical for the identification, visualization and analysis of cell populations. However, single-cell data introduce challenges to conventional measures of gene expression similarity because of the high level of noise, outliers and dropouts. We develop a novel similarity-learning framework, SIMLR (Single-cell Interpretation via Multi-kernel LeaRning), which learns an appropriate distance metric from the data for dimension reduction, clustering and visualization. biocViews: ImmunoOncology, Clustering, GeneExpression, Sequencing, SingleCell Author: Daniele Ramazzotti [cre, aut] (), Bo Wang [aut], Luca De Sano [aut] (), Serafim Batzoglou [ctb] Maintainer: Luca De Sano URL: https://github.com/BatzoglouLabSU/SIMLR VignetteBuilder: knitr BugReports: https://github.com/BatzoglouLabSU/SIMLR git_url: https://git.bioconductor.org/packages/SIMLR git_branch: RELEASE_3_12 git_last_commit: 596a86f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SIMLR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SIMLR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SIMLR_1.16.0.tgz vignettes: vignettes/SIMLR/inst/doc/vignette.pdf vignetteTitles: Single-cell Interpretation via Multi-kernel LeaRning (\Biocpkg{SIMLR}) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SIMLR/inst/doc/vignette.R importsMe: SingleCellSignalR dependencyCount: 14 Package: simpleaffy Version: 2.66.0 Depends: R (>= 2.0.0), methods, utils, grDevices, graphics, stats, BiocGenerics (>= 0.1.12), Biobase, affy (>= 1.33.6), genefilter, gcrma Imports: methods, utils, grDevices, graphics, stats, BiocGenerics, Biobase, affy, genefilter, gcrma License: GPL (>= 2) Archs: i386, x64 MD5sum: b8325e829e57636c9efd15c1862cfef0 NeedsCompilation: yes Title: Very simple high level analysis of Affymetrix data Description: Provides high level functions for reading Affy .CEL files, phenotypic data, and then computing simple things with it, such as t-tests, fold changes and the like. Makes heavy use of the affy library. Also has some basic scatter plot functions and mechanisms for generating high resolution journal figures... biocViews: Microarray, OneChannel, QualityControl, Preprocessing, Transcription, DataImport, DifferentialExpression, Annotation, ReportWriting, Visualization Author: Crispin J Miller Maintainer: Crispin Miller URL: http://www.bioconductor.org, http://bioinformatics.picr.man.ac.uk/simpleaffy/ PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/simpleaffy git_branch: RELEASE_3_12 git_last_commit: 902db69 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/simpleaffy_2.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/simpleaffy_2.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/simpleaffy_2.66.0.tgz vignettes: vignettes/simpleaffy/inst/doc/simpleAffy.pdf vignetteTitles: simpleaffy primer hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simpleaffy/inst/doc/simpleAffy.R dependsOnMe: yaqcaffy importsMe: affyQCReport, arrayMvout suggestsMe: AffyExpress, ArrayTools, ELBOW, maGUI dependencyCount: 54 Package: simplifyEnrichment Version: 1.0.0 Depends: R (>= 3.6.0), BiocGenerics, grid Imports: GOSemSim, ComplexHeatmap (>= 2.5.4), circlize, GetoptLong, digest, tm, GO.db, org.Hs.eg.db, AnnotationDbi, slam, methods, clue, grDevices, graphics, stats, utils, proxyC, Matrix, cluster (>= 1.14.2) Suggests: knitr, ggplot2, cowplot, mclust, apcluster, MCL, dbscan, igraph, gridExtra, dynamicTreeCut, testthat, gridGraphics, clusterProfiler, msigdbr, DOSE, DO.db, reactome.db, flexclust, BiocManager License: MIT + file LICENSE MD5sum: 23b9c6d0266f728c21dfb1cf9c0c50ef NeedsCompilation: no Title: Simplify Functional Enrichment Results Description: A new method (binary cut) is proposed to effectively cluster GO terms into groups from the semantic similarity matrix. Summaries of GO terms in each cluster are visualized by word clouds. biocViews: Software, Visualization, GO, Clustering, GeneSetEnrichment Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/simplifyEnrichment, https://simplifyEnrichment.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/simplifyEnrichment git_branch: RELEASE_3_12 git_last_commit: 8e81880 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/simplifyEnrichment_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/simplifyEnrichment_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/simplifyEnrichment_1.0.0.tgz vignettes: vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.html vignetteTitles: Simplify Functional Enrichment Results hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/simplifyEnrichment/inst/doc/simplifyEnrichment.R dependencyCount: 57 Package: simulatorZ Version: 1.24.0 Depends: R (>= 3.5), Biobase, SummarizedExperiment, survival, CoxBoost, BiocGenerics Imports: graphics, stats, gbm, Hmisc, GenomicRanges, methods Suggests: RUnit, BiocStyle, curatedOvarianData, parathyroidSE License: Artistic-2.0 Archs: i386, x64 MD5sum: b9bdbb0615a387ccf5ef266d126bb785 NeedsCompilation: yes Title: Simulator for Collections of Independent Genomic Data Sets Description: simulatorZ is a package intended primarily to simulate collections of independent genomic data sets, as well as performing training and validation with predicting algorithms. It supports ExpressionSet and RangedSummarizedExperiment objects. biocViews: Survival Author: Yuqing Zhang, Christoph Bernau, Levi Waldron Maintainer: Yuqing Zhang URL: https://github.com/zhangyuqing/simulatorZ BugReports: https://github.com/zhangyuqing/simulatorZ git_url: https://git.bioconductor.org/packages/simulatorZ git_branch: RELEASE_3_12 git_last_commit: 7bb005a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/simulatorZ_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/simulatorZ_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/simulatorZ_1.24.0.tgz vignettes: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.pdf vignetteTitles: SimulatorZ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.R dependencyCount: 88 Package: sincell Version: 1.22.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) Archs: i386, x64 MD5sum: edf301c4f6cc9ee7343538bbbc4611d7 NeedsCompilation: yes Title: R package for the statistical assessment of cell state hierarchies from single-cell RNA-seq data Description: Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies. biocViews: ImmunoOncology, Sequencing, RNASeq, Clustering, GraphAndNetwork, Visualization, GeneExpression, GeneSetEnrichment, BiomedicalInformatics, CellBiology, FunctionalGenomics, SystemsBiology Author: Miguel Julia , Amalio Telenti , Antonio Rausell Maintainer: Miguel Julia , Antonio Rausell URL: http://bioconductor.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sincell git_branch: RELEASE_3_12 git_last_commit: d286cd9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sincell_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sincell_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sincell_1.22.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sincell/inst/doc/sincell-vignette.R importsMe: ctgGEM dependencyCount: 61 Package: SingleCellExperiment Version: 1.12.0 Depends: SummarizedExperiment Imports: methods, utils, stats, S4Vectors, BiocGenerics Suggests: testthat, BiocStyle, knitr, rmarkdown, Matrix, scRNAseq, Rtsne License: GPL-3 MD5sum: c1076a3b9486e27bedd3944beb172501 NeedsCompilation: no Title: S4 Classes for Single Cell Data Description: Defines a S4 class for storing data from single-cell experiments. This includes specialized methods to store and retrieve spike-in information, dimensionality reduction coordinates and size factors for each cell, along with the usual metadata for genes and libraries. biocViews: ImmunoOncology, DataRepresentation, DataImport, Infrastructure, SingleCell Author: Aaron Lun [aut, cph], Davide Risso [aut, cre, cph], Keegan Korthauer [ctb], Kevin Rue-Albrecht [ctb] Maintainer: Davide Risso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellExperiment git_branch: RELEASE_3_12 git_last_commit: 66063b7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SingleCellExperiment_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SingleCellExperiment_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SingleCellExperiment_1.12.0.tgz vignettes: vignettes/SingleCellExperiment/inst/doc/devel.html, vignettes/SingleCellExperiment/inst/doc/intro.html vignetteTitles: 2. Developing around the SingleCellExperiment class, 1. An introduction to the SingleCellExperiment class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellExperiment/inst/doc/devel.R, vignettes/SingleCellExperiment/inst/doc/intro.R dependsOnMe: BASiCS, batchelor, BayesSpace, CATALYST, CellBench, CellTrails, CHETAH, clusterExperiment, cydar, cytomapper, DropletUtils, ExperimentSubset, iSEE, LoomExperiment, MAST, scAlign, scater, scGPS, schex, scPipe, scran, scuttle, singleCellTK, Spaniel, SpatialExperiment, splatter, switchde, tidySingleCellExperiment, TreeSummarizedExperiment, zinbwave, HCAData, MouseGastrulationData, muscData, scRNAseq, spatialLIBD, TENxBrainData, TENxPBMCData, TMExplorer, DIscBIO importsMe: ADImpute, aggregateBioVar, bayNorm, BEARscc, ccfindR, celda, CellMixS, ChromSCape, CiteFuse, clustifyr, CoGAPS, corral, destiny, distinct, dittoSeq, escape, fcoex, HCAMatrixBrowser, HIPPO, ILoReg, infercnv, iSEEu, LineagePulse, mbkmeans, MetaNeighbor, muscat, Nebulosa, netSmooth, NewWave, peco, phemd, pipeComp, SC3, scBFA, scCB2, scDblFinder, scDD, scds, scHOT, scmap, scMerge, SCnorm, scp, scRepertoire, scruff, scry, scTensor, scTGIF, slalom, slingshot, SPsimSeq, tradeSeq, TSCAN, velociraptor, waddR, zellkonverter, SingleCellMultiModal, RCSL suggestsMe: CellaRepertorium, DEsingle, FCBF, fishpond, M3Drop, MOFA2, ontoProc, phenopath, progeny, PubScore, QFeatures, scFeatureFilter, scPCA, scRecover, SingleR, dorothea, DuoClustering2018, TabulaMurisData, simpleSingleCell, clustree, dyngen, Seurat, singleCellHaystack dependencyCount: 26 Package: SingleCellSignalR Version: 1.2.0 Depends: R (>= 4.0) Imports: BiocManager, circlize, limma, igraph, gplots, grDevices, edgeR, SIMLR, data.table, pheatmap, stats, Rtsne, graphics, stringr, foreach, multtest, scran, utils, Suggests: knitr, rmarkdown License: GPL-3 MD5sum: b50e4c5bb62ea5a4c45d28d535d4df25 NeedsCompilation: no Title: Cell Signalling Using Single Cell RNAseq Data Analysis Description: Allows single cell RNA seq data analysis, clustering, creates internal network and infers cell-cell interactions. biocViews: SingleCell, Network, Clustering, RNASeq, Classification Author: Simon Cabello-Aguilar [aut], Jacques Colinge [cre, aut] Maintainer: Jacques Colinge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SingleCellSignalR git_branch: RELEASE_3_12 git_last_commit: 666b92d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SingleCellSignalR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SingleCellSignalR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SingleCellSignalR_1.2.0.tgz vignettes: vignettes/SingleCellSignalR/inst/doc/UsersGuide.html vignetteTitles: my-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SingleCellSignalR/inst/doc/UsersGuide.R suggestsMe: tidySingleCellExperiment dependencyCount: 96 Package: singleCellTK Version: 2.0.0 Depends: R (>= 4.0), SummarizedExperiment, SingleCellExperiment, DelayedArray, Biobase Imports: ape, batchelor, BiocGenerics, BiocParallel, colourpicker, colorspace, cowplot, cluster, ComplexHeatmap, data.table, DelayedMatrixStats, DESeq2, dplyr, DT, ExperimentHub, fields, ggplot2, ggplotify, ggrepel, ggtree, gridExtra, GSVA (>= 1.26.0), GSVAdata, igraph, KernSmooth, limma, MAST, Matrix, matrixStats, methods, msigdbr, multtest, plotly, RColorBrewer, ROCR, Rtsne, S4Vectors, scater, scMerge (>= 1.2.0), scran, Seurat (>= 3.1.3), shiny, shinyFiles, shinyWidgets, shinyjs, shinyBS, shinyjqui, sva, reshape2, AnnotationDbi, shinyalert, circlize, enrichR, celda, shinycssloaders, shinythemes, uwot, DropletUtils, scds (>= 1.2.0), reticulate (>= 1.14), tools, withr, GSEABase, R.utils, zinbwave, scRNAseq (>= 2.0.2), TENxPBMCData, yaml, rmarkdown, magrittr, kableExtra, scDblFinder Suggests: testthat, Rsubread, BiocStyle, knitr, lintr, bladderbatch, xtable, spelling, org.Mm.eg.db, stringr License: MIT + file LICENSE MD5sum: 411176a5dead39107fb5303eec672328 NeedsCompilation: no Title: Comprehensive and Interactive Analysis of Single Cell RNA-Seq Data Description: Run common single cell analysis in the R console or directly through your browser. Includes many functions for import, quality control, normalization, batch correction, clustering, differential expression, and visualization.. biocViews: SingleCell, GeneExpression, DifferentialExpression, Alignment, Clustering, ImmunoOncology Author: David Jenkins [aut] (), Vidya Akavoor [aut], Salam Alabdullatif [aut], Shruthi Bandyadka [aut], Emma Briars [aut] (), Xinyun Cao [aut], Sebastian Carrasco Pro [aut], Tyler Faits [aut], Rui Hong [aut], Mohammed Muzamil Khan [aut], Yusuke Koga [aut, cre], Anastasia Leshchyk [aut], Irzam Sarfraz [aut], Yichen Wang [aut], Zhe Wang [aut], W. Evan Johnson [aut] (), Joshua David Campbell [aut] Maintainer: Yusuke Koga URL: https://compbiomed.github.io/sctk_docs/ VignetteBuilder: knitr BugReports: https://github.com/compbiomed/singleCellTK/issues git_url: https://git.bioconductor.org/packages/singleCellTK git_branch: RELEASE_3_12 git_last_commit: e97b135 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/singleCellTK_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/singleCellTK_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/singleCellTK_2.0.0.tgz vignettes: vignettes/singleCellTK/inst/doc/singleCellTK.html vignetteTitles: 1. Introduction to singleCellTK hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/singleCellTK/inst/doc/singleCellTK.R dependencyCount: 330 Package: SingleR Version: 1.4.1 Depends: SummarizedExperiment Imports: methods, Matrix, S4Vectors, DelayedArray, DelayedMatrixStats, BiocNeighbors, BiocParallel, BiocSingular, stats, utils, Rcpp, beachmat LinkingTo: Rcpp, beachmat Suggests: testthat, knitr, rmarkdown, BiocStyle, BiocGenerics, SingleCellExperiment, scuttle, scater, scran, scRNAseq, ggplot2, pheatmap, grDevices, gridExtra, viridis, celldex License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: f570c03d7c9f59c17eaff272723ad80b NeedsCompilation: yes Title: Reference-Based Single-Cell RNA-Seq Annotation Description: Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently. biocViews: Software, SingleCell, GeneExpression, Transcriptomics, Classification, Clustering, Annotation Author: Dvir Aran [aut, cph], Aaron Lun [ctb, cre], Daniel Bunis [ctb], Jared Andrews [ctb], Friederike Dündar [ctb] Maintainer: Aaron Lun URL: https://github.com/LTLA/SingleR SystemRequirements: C++11 VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/SingleR git_branch: RELEASE_3_12 git_last_commit: 5b9704c git_last_commit_date: 2021-02-02 Date/Publication: 2021-02-02 source.ver: src/contrib/SingleR_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/SingleR_1.4.1.zip mac.binary.ver: bin/macosx/contrib/4.0/SingleR_1.4.1.tgz vignettes: vignettes/SingleR/inst/doc/SingleR.html vignetteTitles: Annotating scRNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SingleR/inst/doc/SingleR.R suggestsMe: tidySingleCellExperiment, tidyseurat dependencyCount: 46 Package: singscore Version: 1.10.0 Depends: R (>= 3.6) Imports: methods, stats, graphics, ggplot2, grDevices, ggrepel, GSEABase, plotly, tidyr, plyr, magrittr, reshape, edgeR, RColorBrewer, Biobase, BiocParallel, SummarizedExperiment, matrixStats, reshape2, S4Vectors Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: a4869926d88524e1647ef6b4ac2f0839 NeedsCompilation: no Title: Rank-based single-sample gene set scoring method Description: A simple single-sample gene signature scoring method that uses rank-based statistics to analyze the sample's gene expression profile. It scores the expression activities of gene sets at a single-sample level. biocViews: Software, GeneExpression, GeneSetEnrichment Author: Ruqian Lyu [aut, ctb], Momeneh Foroutan [aut, ctb] (), Dharmesh D. Bhuva [aut, cre] () Maintainer: Dharmesh D. Bhuva URL: https://davislaboratory.github.io/singscore VignetteBuilder: knitr BugReports: https://github.com/DavisLaboratory/singscore/issues git_url: https://git.bioconductor.org/packages/singscore git_branch: RELEASE_3_12 git_last_commit: 485b0c1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/singscore_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/singscore_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/singscore_1.10.0.tgz vignettes: vignettes/singscore/inst/doc/singscore.html vignetteTitles: Single sample scoring hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/singscore/inst/doc/singscore.R importsMe: TBSignatureProfiler, SingscoreAMLMutations, clustermole dependencyCount: 111 Package: SISPA Version: 1.20.0 Depends: R (>= 3.5),genefilter,GSVA,changepoint Imports: data.table, plyr, ggplot2 Suggests: knitr License: GPL-2 MD5sum: 6c78ebb90dfe4d718f47d62e62ce17b5 NeedsCompilation: no Title: SISPA: Method for Sample Integrated Set Profile Analysis Description: Sample Integrated Set Profile Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. biocViews: GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi and Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SISPA git_branch: RELEASE_3_12 git_last_commit: aa0aff3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SISPA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SISPA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SISPA_1.20.0.tgz vignettes: vignettes/SISPA/inst/doc/SISPA.html vignetteTitles: SISPA:Method for Sample Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SISPA/inst/doc/SISPA.R dependencyCount: 93 Package: sitePath Version: 1.6.6 Depends: R (>= 4.0.0) Imports: RColorBrewer, Rcpp, ape, aplot, ggplot2, ggrepel, ggtree, graphics, grDevices, gridExtra, methods, parallel, seqinr, stats, tidytree, utils LinkingTo: Rcpp Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 20edc4a7dab4af91f59ea388762f2ef5 NeedsCompilation: yes Title: Detection of site fixation in molecular evolution Description: The package does hierarchical search for fixation mutations given multiple sequence alignment and phylogenetic tree. These fixation mutations can be specific to a phylogenetic lineages or shared by multiple lineages. The package also provides visualization of these mutations on the tree. biocViews: Alignment, MultipleSequenceAlignment, Phylogenetics, SNP, Software Author: Chengyang Ji [aut, cre, cph] (), Aiping Wu [ths] Maintainer: Chengyang Ji URL: https://wuaipinglab.github.io/sitePath/ VignetteBuilder: knitr BugReports: https://github.com/wuaipinglab/sitePath/issues git_url: https://git.bioconductor.org/packages/sitePath git_branch: RELEASE_3_12 git_last_commit: 192e516 git_last_commit_date: 2021-04-27 Date/Publication: 2021-04-27 source.ver: src/contrib/sitePath_1.6.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/sitePath_1.6.6.zip mac.binary.ver: bin/macosx/contrib/4.0/sitePath_1.6.6.tgz vignettes: vignettes/sitePath/inst/doc/sitePath.html vignetteTitles: An introduction to sitePath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sitePath/inst/doc/sitePath.R dependencyCount: 67 Package: sizepower Version: 1.60.0 Depends: stats License: LGPL MD5sum: 55adda1727c83e51e0b56991eaa37bb6 NeedsCompilation: no Title: Sample Size and Power Calculation in Micorarray Studies Description: This package has been prepared to assist users in computing either a sample size or power value for a microarray experimental study. The user is referred to the cited references for technical background on the methodology underpinning these calculations. This package provides support for five types of sample size and power calculations. These five types can be adapted in various ways to encompass many of the standard designs encountered in practice. biocViews: Microarray Author: Weiliang Qiu and Mei-Ling Ting Lee and George Alex Whitmore Maintainer: Weiliang Qiu git_url: https://git.bioconductor.org/packages/sizepower git_branch: RELEASE_3_12 git_last_commit: a3f3fdd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sizepower_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sizepower_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sizepower_1.60.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: Sample Size and Power Calculation in Microarray Studies Using the \Rpackage{sizepower} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sizepower/inst/doc/sizepower.R dependencyCount: 1 Package: skewr Version: 1.22.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, S4Vectors (>= 0.19.1), RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: ecb0e720a1d32dec5dcfd56571cf06e5 NeedsCompilation: no Title: Visualize Intensities Produced by Illumina's Human Methylation 450k BeadChip Description: The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the "rs" SNP probes and the probes associated with imprinted genes as series of 'tick' marks located above the x-axis. biocViews: DNAMethylation, TwoChannel, Preprocessing, QualityControl Author: Ryan Putney [cre, aut], Steven Eschrich [aut], Anders Berglund [aut] Maintainer: Ryan Putney VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/skewr git_branch: RELEASE_3_12 git_last_commit: 9bf5bfb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/skewr_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/skewr_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/skewr_1.22.0.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/skewr/inst/doc/skewr.R dependencyCount: 163 Package: slalom Version: 1.12.0 Depends: R (>= 3.4) Imports: Rcpp (>= 0.12.8), RcppArmadillo, BH, ggplot2, grid, GSEABase, methods, rsvd, SingleCellExperiment, SummarizedExperiment, stats LinkingTo: Rcpp, RcppArmadillo, BH Suggests: knitr, rhdf5, scater, testthat License: GPL-2 Archs: i386, x64 MD5sum: 1f0a005b6ddad4d676acbfd260f0e944 NeedsCompilation: yes Title: Factorial Latent Variable Modeling of Single-Cell RNA-Seq Data Description: slalom is a scalable modelling framework for single-cell RNA-seq data that uses gene set annotations to dissect single-cell transcriptome heterogeneity, thereby allowing to identify biological drivers of cell-to-cell variability and model confounding factors. biocViews: ImmunoOncology, SingleCell, RNASeq, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, Reactome Author: Florian Buettner [aut], Naruemon Pratanwanich [aut], Davis McCarthy [aut, cre], John Marioni [aut], Oliver Stegle [aut] Maintainer: Davis McCarthy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slalom git_branch: RELEASE_3_12 git_last_commit: af10976 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/slalom_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/slalom_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/slalom_1.12.0.tgz vignettes: vignettes/slalom/inst/doc/vignette.html vignetteTitles: Introduction to slalom hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slalom/inst/doc/vignette.R dependencyCount: 83 Package: SLGI Version: 1.50.0 Depends: R (>= 2.10), ScISI, lattice Imports: AnnotationDbi, Biobase, GO.db, ScISI, graphics, lattice, methods, stats, BiocGenerics Suggests: GO.db, org.Sc.sgd.db License: Artistic-2.0 MD5sum: 641231612d6d3517cfc18063b3fb7d91 NeedsCompilation: no Title: Synthetic Lethal Genetic Interaction Description: A variety of data files and functions for the analysis of genetic interactions biocViews: GraphAndNetwork, Proteomics, Genetics, Network Author: Nolwenn LeMeur, Zhen Jiang, Ting-Yuan Liu, Jess Mar and Robert Gentleman Maintainer: Nolwenn Le Meur git_url: https://git.bioconductor.org/packages/SLGI git_branch: RELEASE_3_12 git_last_commit: e380484 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SLGI_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SLGI_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SLGI_1.50.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLGI/inst/doc/SLGI.R dependsOnMe: PCpheno dependencyCount: 51 Package: slingshot Version: 1.8.0 Depends: R (>= 3.5), princurve (>= 2.0.4), stats Imports: ape, graphics, grDevices, igraph, matrixStats, methods, SingleCellExperiment, SummarizedExperiment Suggests: BiocGenerics, BiocStyle, clusterExperiment, knitr, mclust, RColorBrewer, rgl, rmarkdown, testthat, uwot, covr License: Artistic-2.0 MD5sum: ea3d29e3b4d394a7a43beac3f3abb018 NeedsCompilation: no Title: Tools for ordering single-cell sequencing Description: Provides functions for inferring continuous, branching lineage structures in low-dimensional data. Slingshot was designed to model developmental trajectories in single-cell RNA sequencing data and serve as a component in an analysis pipeline after dimensionality reduction and clustering. It is flexible enough to handle arbitrarily many branching events and allows for the incorporation of prior knowledge through supervised graph construction. biocViews: Clustering, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, Sequencing, SingleCell, Transcriptomics, Visualization Author: Kelly Street [aut, cre, cph], Davide Risso [aut], Diya Das [aut], Sandrine Dudoit [ths], Koen Van den Berge [ctb], Robrecht Cannoodt [ctb] (, rcannood) Maintainer: Kelly Street VignetteBuilder: knitr BugReports: https://github.com/kstreet13/slingshot/issues git_url: https://git.bioconductor.org/packages/slingshot git_branch: RELEASE_3_12 git_last_commit: cf8b399 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/slingshot_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/slingshot_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/slingshot_1.8.0.tgz vignettes: vignettes/slingshot/inst/doc/conditionsVignette.html, vignettes/slingshot/inst/doc/vignette.html vignetteTitles: Differential Topology, Slingshot hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/slingshot/inst/doc/conditionsVignette.R, vignettes/slingshot/inst/doc/vignette.R importsMe: tradeSeq dependencyCount: 34 Package: slinky Version: 1.8.0 Depends: R (>= 3.5.0) Imports: SummarizedExperiment, curl, dplyr, foreach, httr, stats, utils, methods, readr, rhdf5, jsonlite, tidyr Suggests: GeoDE, doParallel, testthat, knitr, rmarkdown, ggplot2, Rtsne, Biobase, BiocStyle License: MIT + file LICENSE MD5sum: 2852bc45687ca5c3e3dfa5befbe0af76 NeedsCompilation: no Title: Putting the fun in LINCS L1000 data analysis Description: Wrappers to query the L1000 metadata available via the clue.io REST API as well as helpers for dealing with LINCS gctx files, extracting data sets of interest, converting to SummarizedExperiment objects, and some facilities for performing streamlined differential expression analysis of these data sets. biocViews: DataImport, ThirdPartyClient, GeneExpression, DifferentialExpression, GeneSetEnrichment, PatternLogic Author: Eric J. Kort Maintainer: Eric J. Kort VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/slinky git_branch: RELEASE_3_12 git_last_commit: 6f345a6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/slinky_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/slinky_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/slinky_1.8.0.tgz vignettes: vignettes/slinky/inst/doc/LINCS-analysis.html vignetteTitles: "LINCS analysis with slinky" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/slinky/inst/doc/LINCS-analysis.R dependencyCount: 63 Package: SLqPCR Version: 1.56.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 1be0042c52bfb5f8e85cd466492df832 NeedsCompilation: no Title: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH Description: Functions for analysis of real-time quantitative PCR data at SIRS-Lab GmbH biocViews: MicrotitrePlateAssay, qPCR Author: Matthias Kohl Maintainer: Matthias Kohl git_url: https://git.bioconductor.org/packages/SLqPCR git_branch: RELEASE_3_12 git_last_commit: 3420049 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SLqPCR_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SLqPCR_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SLqPCR_1.56.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SLqPCR/inst/doc/SLqPCR.R suggestsMe: EasyqpcR dependencyCount: 1 Package: SMAD Version: 1.6.0 Depends: R (>= 3.6.0), RcppAlgos Imports: magrittr (>= 1.5), dplyr, stats, tidyr, utils, Rcpp (>= 1.0.0) LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: 0ecb977dd60c11e7cd26829ba8c4b56a NeedsCompilation: yes Title: Statistical Modelling of AP-MS Data (SMAD) Description: Assigning probability scores to prey proteins captured in affinity purification mass spectrometry (AP-MS) expriments to infer protein-protein interactions. The output would facilitate non-specific background removal as contaminants are commonly found in AP-MS data. biocViews: MassSpectrometry, Proteomics, Software Author: Qingzhou Zhang [aut, cre] Maintainer: Qingzhou Zhang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SMAD git_branch: RELEASE_3_12 git_last_commit: 63a4488 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SMAD_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SMAD_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SMAD_1.6.0.tgz vignettes: vignettes/SMAD/inst/doc/quickstart.html vignetteTitles: SMAD Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SMAD/inst/doc/quickstart.R dependencyCount: 28 Package: SMAP Version: 1.54.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: b4d615dc0e82dddbad82dcb3771ffd11 NeedsCompilation: yes Title: A Segmental Maximum A Posteriori Approach to Array-CGH Copy Number Profiling Description: Functions and classes for DNA copy number profiling of array-CGH data biocViews: Microarray, TwoChannel, CopyNumberVariation Author: Robin Andersson Maintainer: Robin Andersson git_url: https://git.bioconductor.org/packages/SMAP git_branch: RELEASE_3_12 git_last_commit: e85d3b7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SMAP_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SMAP_1.54.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SMAP_1.54.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMAP/inst/doc/SMAP.R dependencyCount: 1 Package: SMITE Version: 1.18.0 Depends: R (>= 3.3), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGGREST, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr License: GPL (>=2) MD5sum: bd1dee13c1cd735a112eae131874cdec NeedsCompilation: no Title: Significance-based Modules Integrating the Transcriptome and Epigenome Description: This package builds on the Epimods framework which facilitates finding weighted subnetworks ("modules") on Illumina Infinium 27k arrays using the SpinGlass algorithm, as implemented in the iGraph package. We have created a class of gene centric annotations associated with p-values and effect sizes and scores from any researchers prior statistical results to find functional modules. biocViews: ImmunoOncology, DifferentialMethylation, DifferentialExpression, SystemsBiology, NetworkEnrichment,GenomeAnnotation,Network, Sequencing, RNASeq, Coverage Author: Neil Ari Wijetunga, Andrew Damon Johnston, John Murray Greally Maintainer: Neil Ari Wijetunga , Andrew Damon Johnston URL: https://github.com/GreallyLab/SMITE VignetteBuilder: knitr BugReports: https://github.com/GreallyLab/SMITE/issues git_url: https://git.bioconductor.org/packages/SMITE git_branch: RELEASE_3_12 git_last_commit: 7c95087 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SMITE_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SMITE_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SMITE_1.18.0.tgz vignettes: vignettes/SMITE/inst/doc/SMITE.pdf vignetteTitles: SMITE Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SMITE/inst/doc/SMITE.R dependencyCount: 141 Package: SNAGEE Version: 1.30.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: 3fcf38b3a3721bf31827b5a6777e5958 NeedsCompilation: no Title: Signal-to-Noise applied to Gene Expression Experiments Description: Signal-to-Noise applied to Gene Expression Experiments. Signal-to-noise ratios can be used as a proxy for quality of gene expression studies and samples. The SNRs can be calculated on any gene expression data set as long as gene IDs are available, no access to the raw data files is necessary. This allows to flag problematic studies and samples in any public data set. biocViews: Microarray, OneChannel, TwoChannel, QualityControl Author: David Venet Maintainer: David Venet URL: http://bioconductor.org/ git_url: https://git.bioconductor.org/packages/SNAGEE git_branch: RELEASE_3_12 git_last_commit: e47867a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SNAGEE_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SNAGEE_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SNAGEE_1.30.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNAGEE/inst/doc/SNAGEE.R suggestsMe: SNAGEEdata dependencyCount: 1 Package: snapCGH Version: 1.60.0 Depends: R (>= 3.5.0) Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: 7399e388c8f1c17573cd4ae5ce89785e NeedsCompilation: yes Title: Segmentation, normalisation and processing of aCGH data Description: Methods for segmenting, normalising and processing aCGH data; including plotting functions for visualising raw and segmented data for individual and multiple arrays. biocViews: Microarray, CopyNumberVariation, TwoChannel, Preprocessing Author: Mike L. Smith, John C. Marioni, Steven McKinney, Thomas Hardcastle, Natalie P. Thorne Maintainer: John Marioni git_url: https://git.bioconductor.org/packages/snapCGH git_branch: RELEASE_3_12 git_last_commit: 03e3471 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/snapCGH_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/snapCGH_1.60.0.zip mac.binary.ver: bin/macosx/contrib/4.0/snapCGH_1.60.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snapCGH/inst/doc/snapCGHguide.R importsMe: ADaCGH2 suggestsMe: beadarraySNP dependencyCount: 88 Package: snapcount Version: 1.2.0 Depends: R (>= 4.0.0) Imports: R6, httr, rlang, purrr, jsonlite, assertthat, data.table, Matrix, magrittr, methods, stringr, stats, IRanges, GenomicRanges, SummarizedExperiment Suggests: BiocManager, bit64, covr, knitcitations, knitr (>= 1.6), JunctionSeq, devtools, BiocStyle (>= 2.5.19), rmarkdown (>= 0.9.5), testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: cd7ad986ff20519676a34f404c576191 NeedsCompilation: no Title: R/Bioconductor Package for interfacing with Snaptron for rapid querying of expression counts Description: snapcount is a client interface to the Snaptron webservices which support querying by gene name or genomic region. Results include raw expression counts derived from alignment of RNA-seq samples and/or various summarized measures of expression across one or more regions/genes per-sample (e.g. percent spliced in). biocViews: Coverage, GeneExpression, RNASeq, Sequencing, Software, DataImport Author: Rone Charles Maintainer: Rone Charles URL: https://github.com/langmead-lab/snapcount VignetteBuilder: knitr BugReports: https://github.com/langmead-lab/snapcount/issues git_url: https://git.bioconductor.org/packages/snapcount git_branch: RELEASE_3_12 git_last_commit: 17f8c8d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/snapcount_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/snapcount_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/snapcount_1.2.0.tgz vignettes: vignettes/snapcount/inst/doc/snapcount_vignette.html vignetteTitles: snapcount quick start guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/snapcount/inst/doc/snapcount_vignette.R dependencyCount: 42 Package: snifter Version: 1.0.0 Depends: R (>= 4.0.0) Imports: basilisk, reticulate, assertthat Suggests: knitr, rmarkdown, scRNAseq, BiocStyle, scater, scran, scuttle, ggplot2, testthat License: GPL-3 MD5sum: 4d13c0fb6654ff16c2b295d53ed5ca59 NeedsCompilation: no Title: R wrapper for the python openTSNE library Description: Provides an R wrapper for the implementation of FI-tSNE from the python package openTNSE. See Poličar et al. (2019) and the algorithm described by Linderman et al. (2018) . biocViews: DimensionReduction, Visualization, Software, SingleCell, Sequencing Author: Alan O'Callaghan [aut, cre], Aaron Lun [aut] Maintainer: Alan O'Callaghan VignetteBuilder: knitr BugReports: https://github.com/Alanocallaghan/snifter/issues git_url: https://git.bioconductor.org/packages/snifter git_branch: RELEASE_3_12 git_last_commit: 9f53809 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/snifter_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/snifter_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/snifter_1.0.0.tgz vignettes: vignettes/snifter/inst/doc/snifter.html vignetteTitles: Introduction to snifter hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snifter/inst/doc/snifter.R dependencyCount: 20 Package: snm Version: 1.38.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: e779db07684549308fa34e00f6829611 NeedsCompilation: no Title: Supervised Normalization of Microarrays Description: SNM is a modeling strategy especially designed for normalizing high-throughput genomic data. The underlying premise of our approach is that your data is a function of what we refer to as study-specific variables. These variables are either biological variables that represent the target of the statistical analysis, or adjustment variables that represent factors arising from the experimental or biological setting the data is drawn from. The SNM approach aims to simultaneously model all study-specific variables in order to more accurately characterize the biological or clinical variables of interest. biocViews: Microarray, OneChannel, TwoChannel, MultiChannel, DifferentialExpression, ExonArray, GeneExpression, Transcription, MultipleComparison, Preprocessing, QualityControl Author: Brig Mecham and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/snm git_branch: RELEASE_3_12 git_last_commit: b1d2381 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/snm_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/snm_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/snm_1.38.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snm/inst/doc/snm.R importsMe: edge, ExpressionNormalizationWorkflow dependencyCount: 20 Package: SNPediaR Version: 1.16.0 Depends: R (>= 3.0.0) Imports: RCurl, jsonlite Suggests: BiocStyle, knitr, rmarkdown, testthat License: GPL-2 MD5sum: 6e30be52e1ebbf50f3126d60b39eeb3f NeedsCompilation: no Title: Query data from SNPedia Description: SNPediaR provides some tools for downloading and parsing data from the SNPedia web site . The implemented functions allow users to import the wiki text available in SNPedia pages and to extract the most relevant information out of them. If some information in the downloaded pages is not automatically processed by the library functions, users can easily implement their own parsers to access it in an efficient way. biocViews: SNP, VariantAnnotation Author: David Montaner [aut, cre] Maintainer: David Montaner URL: https://github.com/genometra/SNPediaR VignetteBuilder: knitr BugReports: https://github.com/genometra/SNPediaR/issues git_url: https://git.bioconductor.org/packages/SNPediaR git_branch: RELEASE_3_12 git_last_commit: 6920830 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SNPediaR_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/SNPediaR_1.16.0.tgz vignettes: vignettes/SNPediaR/inst/doc/SNPediaR.html vignetteTitles: SNPediaR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPediaR/inst/doc/SNPediaR.R dependencyCount: 4 Package: SNPhood Version: 1.20.0 Depends: R (>= 3.1), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices, gridExtra, stats, grid, utils, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, pryr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: 71a654e52fe557c48853d7c0bb09ba2c NeedsCompilation: no Title: SNPhood: Investigate, quantify and visualise the epigenomic neighbourhood of SNPs using NGS data Description: To date, thousands of single nucleotide polymorphisms (SNPs) have been found to be associated with complex traits and diseases. However, the vast majority of these disease-associated SNPs lie in the non-coding part of the genome, and are likely to affect regulatory elements, such as enhancers and promoters, rather than function of a protein. Thus, to understand the molecular mechanisms underlying genetic traits and diseases, it becomes increasingly important to study the effect of a SNP on nearby molecular traits such as chromatin environment or transcription factor (TF) binding. Towards this aim, we developed SNPhood, a user-friendly *Bioconductor* R package to investigate and visualize the local neighborhood of a set of SNPs of interest for NGS data such as chromatin marks or transcription factor binding sites from ChIP-Seq or RNA- Seq experiments. SNPhood comprises a set of easy-to-use functions to extract, normalize and summarize reads for a genomic region, perform various data quality checks, normalize read counts using additional input files, and to cluster and visualize the regions according to the binding pattern. The regions around each SNP can be binned in a user-defined fashion to allow for analysis of very broad patterns as well as a detailed investigation of specific binding shapes. Furthermore, SNPhood supports the integration with genotype information to investigate and visualize genotype-specific binding patterns. Finally, SNPhood can be employed for determining, investigating, and visualizing allele-specific binding patterns around the SNPs of interest. biocViews: Software Author: Christian Arnold [aut, cre], Pooja Bhat [aut], Judith Zaugg [aut] Maintainer: Christian Arnold URL: https://bioconductor.org/packages/SNPhood VignetteBuilder: knitr BugReports: christian.arnold@embl.de git_url: https://git.bioconductor.org/packages/SNPhood git_branch: RELEASE_3_12 git_last_commit: 3ee61dc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SNPhood_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SNPhood_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SNPhood_1.20.0.tgz vignettes: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html vignetteTitles: Introduction and Methodological Details, Workflow example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R dependencyCount: 120 Package: SNPRelate Version: 1.24.0 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) Imports: methods LinkingTo: gdsfmt Suggests: parallel, Matrix, RUnit, knitr, MASS, BiocGenerics Enhances: SeqArray (>= 1.12.0) License: GPL-3 Archs: i386, x64 MD5sum: fd9ada5481b904389737ae14dc88a00e NeedsCompilation: yes Title: Parallel Computing Toolset for Relatedness and Principal Component Analysis of SNP Data Description: Genome-wide association studies (GWAS) are widely used to investigate the genetic basis of diseases and traits, but they pose many computational challenges. We developed an R package SNPRelate to provide a binary format for single-nucleotide polymorphism (SNP) data in GWAS utilizing CoreArray Genomic Data Structure (GDS) data files. The GDS format offers the efficient operations specifically designed for integers with two bits, since a SNP could occupy only two bits. SNPRelate is also designed to accelerate two key computations on SNP data using parallel computing for multi-core symmetric multiprocessing computer architectures: Principal Component Analysis (PCA) and relatedness analysis using Identity-By-Descent measures. The SNP GDS format is also used by the GWASTools package with the support of S4 classes and generic functions. The extended GDS format is implemented in the SeqArray package to support the storage of single nucleotide variations (SNVs), insertion/deletion polymorphism (indel) and structural variation calls in whole-genome and whole-exome variant data. biocViews: Infrastructure, Genetics, StatisticalMethod, PrincipalComponent Author: Xiuwen Zheng [aut, cre, cph] (), Stephanie Gogarten [ctb], Cathy Laurie [ctb], Bruce Weir [ctb, ths] () Maintainer: Xiuwen Zheng URL: http://github.com/zhengxwen/SNPRelate VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SNPRelate/issues git_url: https://git.bioconductor.org/packages/SNPRelate git_branch: RELEASE_3_12 git_last_commit: 419b13b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SNPRelate_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SNPRelate_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SNPRelate_1.24.0.tgz vignettes: vignettes/SNPRelate/inst/doc/SNPRelate.html vignetteTitles: SNPRelate Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelate.R dependsOnMe: SeqSQC importsMe: CNVRanger, GDSArray, GENESIS, gwasurvivr, VariantExperiment, coxmeg, dartR, EthSEQ, R.SamBada, simplePHENOTYPES suggestsMe: GWASTools, HIBAG, SAIGEgds, SeqArray dependencyCount: 2 Package: snpStats Version: 1.40.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: 5cff5c1e2fa65ee8b7deb314cec3965c NeedsCompilation: yes Title: SnpMatrix and XSnpMatrix classes and methods Description: Classes and statistical methods for large SNP association studies. This extends the earlier snpMatrix package, allowing for uncertainty in genotypes. biocViews: Microarray, SNP, GeneticVariability Author: David Clayton Maintainer: David Clayton git_url: https://git.bioconductor.org/packages/snpStats git_branch: RELEASE_3_12 git_last_commit: 5fcac6f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/snpStats_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/snpStats_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/snpStats_1.40.0.tgz vignettes: vignettes/snpStats/inst/doc/data-input-vignette.pdf, vignettes/snpStats/inst/doc/differences.pdf, vignettes/snpStats/inst/doc/Fst-vignette.pdf, vignettes/snpStats/inst/doc/imputation-vignette.pdf, vignettes/snpStats/inst/doc/ld-vignette.pdf, vignettes/snpStats/inst/doc/pca-vignette.pdf, vignettes/snpStats/inst/doc/snpStats-vignette.pdf, vignettes/snpStats/inst/doc/tdt-vignette.pdf vignetteTitles: Data input, snpMatrix-differences, Fst, Imputation and meta-analysis, LD statistics, Principal components analysis, snpStats introduction, TDT tests hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/snpStats/inst/doc/data-input-vignette.R, vignettes/snpStats/inst/doc/Fst-vignette.R, vignettes/snpStats/inst/doc/imputation-vignette.R, vignettes/snpStats/inst/doc/ld-vignette.R, vignettes/snpStats/inst/doc/pca-vignette.R, vignettes/snpStats/inst/doc/snpStats-vignette.R, vignettes/snpStats/inst/doc/tdt-vignette.R dependsOnMe: GGBase, GGdata, eQTL, snpStatsWriter importsMe: FunciSNP, GeneGeneInteR, GGtools, gQTLstats, gwascat, ldblock, martini, RVS, scoreInvHap, coloc, GenomicTools, GenomicTools.fileHandler, GWASbyCluster, LDheatmap, PhenotypeSimulator, snpEnrichment, TriadSim suggestsMe: crlmm, GenomicFiles, GWASTools, omicRexposome, omicsPrint, VariantAnnotation, adjclust, genio, pegas dependencyCount: 13 Package: soGGi Version: 1.22.0 Depends: R (>= 3.2.0), BiocGenerics, SummarizedExperiment Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) MD5sum: eb040003885210da3ea377cdd1b1a36c NeedsCompilation: no Title: Visualise ChIP-seq, MNase-seq and motif occurrence as aggregate plots Summarised Over Grouped Genomic Intervals Description: The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events. biocViews: Sequencing, ChIPSeq, Coverage Author: Gopuraja Dharmalingam, Doug Barrows, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/soGGi git_branch: RELEASE_3_12 git_last_commit: 7f949ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/soGGi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/soGGi_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/soGGi_1.22.0.tgz vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/soGGi/inst/doc/soggi.R importsMe: profileplyr dependencyCount: 81 Package: sojourner Version: 1.4.2 Imports: ggplot2,dplyr,reshape2,gridExtra,EBImage,MASS,R.matlab,Rcpp,fitdistrplus,mclust,minpack.lm,mixtools,mltools,nls2,plyr,sampSurf,scales,shiny,shinyjs,sp,truncnorm,utils,stats,pixmap,rlang,graphics,grDevices,grid,compiler,lattice Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 10f7f29a91993ff8d0c01f73392c446b NeedsCompilation: no Title: Statistical analysis of single molecule trajectories Description: Single molecule tracking has evolved as a novel new approach complementing genomic sequencing, it reports live biophysical properties of molecules being investigated besides properties relating their coding sequence; here we provided "sojourner" package, to address statistical and bioinformatic needs related to the analysis and comprehension of high throughput single molecule tracking data. biocViews: Technology, WorkflowStep Author: Sheng Liu [aut], Sun Jay Yoo [aut], Xiao Na Tang [aut], Young Soo Sung [aut], Carl Wu [aut], Anand Ranjan [ctb], Vu Nguyen [ctb], Sojourner Developer [cre] Maintainer: Sojourner Developer URL: https://github.com/sheng-liu/sojourner VignetteBuilder: knitr BugReports: https://github.com/sheng-liu/sojourner/issues git_url: https://git.bioconductor.org/packages/sojourner git_branch: RELEASE_3_12 git_last_commit: 8242813 git_last_commit_date: 2021-03-27 Date/Publication: 2021-03-28 source.ver: src/contrib/sojourner_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/sojourner_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/sojourner_1.4.2.tgz vignettes: vignettes/sojourner/inst/doc/sojourner-vignette.html vignetteTitles: Sojourner: an R package for statistical analysis of single molecule trajectories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sojourner/inst/doc/sojourner-vignette.R dependencyCount: 108 Package: SomaticSignatures Version: 2.26.0 Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges, NMF Imports: S4Vectors, IRanges, GenomeInfoDb, Biostrings, ggplot2, ggbio, reshape2, NMF, pcaMethods, Biobase, methods, proxy Suggests: testthat, knitr, parallel, BSgenome.Hsapiens.1000genomes.hs37d5, SomaticCancerAlterations, ggdendro, fastICA, sva License: MIT + file LICENSE MD5sum: a96ddb6aac20f6ad8029993713279e37 NeedsCompilation: no Title: Somatic Signatures Description: The SomaticSignatures package identifies mutational signatures of single nucleotide variants (SNVs). It provides a infrastructure related to the methodology described in Nik-Zainal (2012, Cell), with flexibility in the matrix decomposition algorithms. biocViews: Sequencing, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod Author: Julian Gehring Maintainer: Julian Gehring URL: https://github.com/juliangehring/SomaticSignatures VignetteBuilder: knitr BugReports: https://support.bioconductor.org git_url: https://git.bioconductor.org/packages/SomaticSignatures git_branch: RELEASE_3_12 git_last_commit: 9d4bed6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SomaticSignatures_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SomaticSignatures_2.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SomaticSignatures_2.26.0.tgz vignettes: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html vignetteTitles: SomaticSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R importsMe: Rariant, YAPSA dependencyCount: 161 Package: SpacePAC Version: 1.28.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: cd117f1d4831b77bd1131ae1f3c4ad5a NeedsCompilation: no Title: Identification of Mutational Clusters in 3D Protein Space via Simulation. Description: Identifies clustering of somatic mutations in proteins via a simulation approach while considering the protein's tertiary structure. biocViews: Clustering, Proteomics Author: Gregory Ryslik, Hongyu Zhao Maintainer: Gregory Ryslik git_url: https://git.bioconductor.org/packages/SpacePAC git_branch: RELEASE_3_12 git_last_commit: b18cd70 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SpacePAC_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SpacePAC_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SpacePAC_1.28.0.tgz vignettes: vignettes/SpacePAC/inst/doc/SpacePAC.pdf vignetteTitles: SpacePAC: Identifying mutational clusters in 3D protein space using simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpacePAC/inst/doc/SpacePAC.R dependsOnMe: QuartPAC dependencyCount: 27 Package: Spaniel Version: 1.4.0 Depends: R (>= 3.6), Seurat, SingleCellExperiment, SummarizedExperiment, dplyr Imports: methods, ggplot2, scater (>= 1.13.27), shiny, jpeg, magrittr, utils, S4Vectors Suggests: knitr, rmarkdown, testthat, devtools License: MIT + file LICENSE MD5sum: 0c3bb327c029501703d0d24c8c2e8518 NeedsCompilation: no Title: Spatial Transcriptomics Analysis Description: Spaniel includes a series of tools to aid the quality control and analysis of Spatial Transcriptomics data. The package contains functions to create either a Seurat object or SingleCellExperiment from a count matrix and spatial barcode file and provides a method of loading a histologial image into R. The spanielPlot function allows visualisation of metrics contained within the S4 object overlaid onto the image of the tissue. biocViews: SingleCell, RNASeq, QualityControl, Preprocessing, Normalization, Visualization, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure, Coverage, Clustering Author: Rachel Queen Maintainer: Rachel Queen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Spaniel git_branch: RELEASE_3_12 git_last_commit: 18f2c89 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Spaniel_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Spaniel_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Spaniel_1.4.0.tgz vignettes: vignettes/Spaniel/inst/doc/spaniel-vignette.html vignetteTitles: Using Spaniel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Spaniel/inst/doc/spaniel-vignette.R dependencyCount: 177 Package: sparseDOSSA Version: 1.14.0 Imports: stats, utils, optparse, MASS, tmvtnorm (>= 1.4.10), MCMCpack Suggests: knitr, BiocStyle, BiocGenerics, rmarkdown License: MIT + file LICENSE MD5sum: 2476480db558972aa29c5178120b2ea9 NeedsCompilation: no Title: Sparse Data Observations for Simulating Synthetic Abundance Description: The package is to provide a model based Bayesian method to characterize and simulate microbiome data. sparseDOSSA's model captures the marginal distribution of each microbial feature as a truncated, zero-inflated log-normal distribution, with parameters distributed as a parent log-normal distribution. The model can be effectively fit to reference microbial datasets in order to parameterize their microbes and communities, or to simulate synthetic datasets of similar population structure. Most importantly, it allows users to include both known feature-feature and feature-metadata correlation structures and thus provides a gold standard to enable benchmarking of statistical methods for metagenomic data analysis. biocViews: ImmunoOncology, Bayesian, Microbiome, Metagenomics, Software Author: Boyu Ren, Emma Schwager, Timothy Tickle, Curtis Huttenhower Maintainer: Boyu Ren, Emma Schwager , George Weingart VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparseDOSSA git_branch: RELEASE_3_12 git_last_commit: 6411c4f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sparseDOSSA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sparseDOSSA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sparseDOSSA_1.14.0.tgz vignettes: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.html vignetteTitles: Sparse Data Observations for the Simulation of Synthetic Abundances (sparseDOSSA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseDOSSA/inst/doc/sparsedossa-vignette.R dependencyCount: 27 Package: sparseMatrixStats Version: 1.2.1 Depends: MatrixGenerics Imports: Rcpp, Matrix, matrixStats, methods LinkingTo: Rcpp Suggests: testthat (>= 2.1.0), knitr, bench, rmarkdown, BiocStyle License: MIT + file LICENSE Archs: i386, x64 MD5sum: b9675c86131cbe54e22e080ed496f17c NeedsCompilation: yes Title: Summary Statistics for Rows and Columns of Sparse Matrices Description: High performance functions for row and column operations on sparse matrices. For example: col / rowMeans2, col / rowMedians, col / rowVars etc. Currently, the optimizations are limited to data in the column sparse format. This package is inspired by the matrixStats package by Henrik Bengtsson. biocViews: Infrastructure, Software, DataRepresentation Author: Constantin Ahlmann-Eltze [aut, cre] () Maintainer: Constantin Ahlmann-Eltze SystemRequirements: C++14 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparseMatrixStats git_branch: RELEASE_3_12 git_last_commit: 9726f3d git_last_commit_date: 2021-02-02 Date/Publication: 2021-02-02 source.ver: src/contrib/sparseMatrixStats_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/sparseMatrixStats_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/sparseMatrixStats_1.2.1.tgz vignettes: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.html vignetteTitles: sparseMatrixStats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sparseMatrixStats/inst/doc/sparseMatrixStats.R importsMe: DelayedMatrixStats suggestsMe: MatrixGenerics, scPCA dependencyCount: 11 Package: sparsenetgls Version: 1.8.0 Depends: R (>= 4.0.0), Matrix, MASS Imports: methods, glmnet, huge, stats, graphics, utils Suggests: testthat, lme4, BiocStyle, knitr, rmarkdown, roxygen2 (>= 5.0.0) License: GPL-3 MD5sum: f7dd41ee35ea6dd708eccd356b94722a NeedsCompilation: no Title: Using Gaussian graphical structue learning estimation in generalized least squared regression for multivariate normal regression Description: The package provides methods of combining the graph structure learning and generalized least squares regression to improve the regression estimation. The main function sparsenetgls() provides solutions for multivariate regression with Gaussian distributed dependant variables and explanatory variables utlizing multiple well-known graph structure learning approaches to estimating the precision matrix, and uses a penalized variance covariance matrix with a distance tuning parameter of the graph structure in deriving the sandwich estimators in generalized least squares (gls) regression. This package also provides functions for assessing a Gaussian graphical model which uses the penalized approach. It uses Receiver Operative Characteristics curve as a visualization tool in the assessment. biocViews: ImmunoOncology, GraphAndNetwork,Regression,Metabolomics,CopyNumberVariation,MassSpectrometry,Proteomics,Software,Visualization Author: Irene Zeng [aut, cre], Thomas Lumley [ctb] Maintainer: Irene Zeng SystemRequirements: GNU make VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sparsenetgls git_branch: RELEASE_3_12 git_last_commit: 12d69dc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sparsenetgls_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sparsenetgls_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sparsenetgls_1.8.0.tgz vignettes: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.html vignetteTitles: Introduction to sparsenetgls hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sparsenetgls/inst/doc/vignettes_sparsenetgls.R dependencyCount: 22 Package: SparseSignatures Version: 2.0.0 Depends: R (>= 4.0.0), NMF Imports: nnlasso, nnls, parallel, data.table, Biostrings, GenomicRanges, IRanges, BSgenome, GenomeInfoDb, ggplot2, gridExtra, reshape2 Suggests: BiocGenerics, BSgenome.Hsapiens.1000genomes.hs37d5, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: 854dd95bb0efc5cbeb8890a0f8e06bfe NeedsCompilation: no Title: SparseSignatures Description: Point mutations occurring in a genome can be divided into 96 categories based on the base being mutated, the base it is mutated into and its two flanking bases. Therefore, for any patient, it is possible to represent all the point mutations occurring in that patient's tumor as a vector of length 96, where each element represents the count of mutations for a given category in the patient. A mutational signature represents the pattern of mutations produced by a mutagen or mutagenic process inside the cell. Each signature can also be represented by a vector of length 96, where each element represents the probability that this particular mutagenic process generates a mutation of the 96 above mentioned categories. In this R package, we provide a set of functions to extract and visualize the mutational signatures that best explain the mutation counts of a large number of patients. biocViews: BiomedicalInformatics, SomaticMutation Author: Daniele Ramazzotti [cre, aut] (), Avantika Lal [aut], Keli Liu [ctb], Luca De Sano [aut] (), Robert Tibshirani [ctb], Arend Sidow [aut] Maintainer: Luca De Sano URL: https://github.com/danro9685/SparseSignatures VignetteBuilder: knitr BugReports: https://github.com/danro9685/SparseSignatures git_url: https://git.bioconductor.org/packages/SparseSignatures git_branch: RELEASE_3_12 git_last_commit: 39c8170 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SparseSignatures_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SparseSignatures_2.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SparseSignatures_2.0.0.tgz vignettes: vignettes/SparseSignatures/inst/doc/vignette.pdf vignetteTitles: SparseSignatures hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SparseSignatures/inst/doc/vignette.R dependencyCount: 92 Package: SpatialCPie Version: 1.6.0 Depends: R (>= 3.6) Imports: colorspace (>= 1.3-2), data.table (>= 1.12.2), digest (>= 0.6.21), dplyr (>= 0.7.6), ggforce (>= 0.3.0), ggiraph (>= 0.5.0), ggplot2 (>= 3.0.0), ggrepel (>= 0.8.0), grid (>= 3.5.1), igraph (>= 1.2.2), lpSolve (>= 5.6.13), methods (>= 3.5.0), purrr (>= 0.2.5), readr (>= 1.1.1), rlang (>= 0.2.2), shiny (>= 1.1.0), shinycssloaders (>= 0.2.0), shinyjs (>= 1.0), shinyWidgets (>= 0.4.8), stats (>= 3.6.0), SummarizedExperiment (>= 1.10.1), tibble (>= 1.4.2), tidyr (>= 0.8.1), tidyselect (>= 0.2.4), tools (>= 3.6.0), utils (>= 3.5.0), zeallot (>= 0.1.0) Suggests: BiocStyle (>= 2.8.2), jpeg (>= 0.1-8), knitr (>= 1.20), rmarkdown (>= 1.10), testthat (>= 2.0.0) License: MIT + file LICENSE MD5sum: 790ff97d39521aa4ab03759f5a8c9453 NeedsCompilation: no Title: Cluster analysis of Spatial Transcriptomics data Description: SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues. biocViews: Transcriptomics, Clustering, RNASeq, Software Author: Joseph Bergenstraahle [aut, cre] Maintainer: Joseph Bergenstraahle VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SpatialCPie git_branch: RELEASE_3_12 git_last_commit: 032432f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SpatialCPie_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SpatialCPie_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SpatialCPie_1.6.0.tgz vignettes: vignettes/SpatialCPie/inst/doc/SpatialCPie.html vignetteTitles: SpatialCPie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialCPie/inst/doc/SpatialCPie.R dependencyCount: 102 Package: SpatialDecon Version: 1.0.0 Depends: R (>= 4.0.0) Imports: logNormReg, grDevices, stats, utils, graphics, Suggests: testthat, knitr, rmarkdown License: GPL-3 + file LICENSE MD5sum: df1729fdfc7375ee3436ac28a3ea5490 NeedsCompilation: no Title: Deconvolution of mixed cells from spatial and/or bulk gene expression data Description: Using spatial or bulk gene expression data, estimates abundance of mixed cell types within each observation. Based on "Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data", Danaher (2020). Designed for use with the NanoString GeoMx platform, but applicable to any gene expression data. biocViews: ImmunoOncology, FeatureExtraction, GeneExpression, Transcriptomics Author: Patrick Danaher [aut, cre] Maintainer: Patrick Danaher VignetteBuilder: knitr BugReports: https://github.com/Nanostring-Biostats/SpatialDecon/issues git_url: https://git.bioconductor.org/packages/SpatialDecon git_branch: RELEASE_3_12 git_last_commit: 43581d0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SpatialDecon_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SpatialDecon_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SpatialDecon_1.0.0.tgz vignettes: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.html vignetteTitles: SpatialDecon_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpatialDecon/inst/doc/SpatialDecon_vignette.R dependencyCount: 5 Package: SpatialExperiment Version: 1.0.0 Depends: R (>= 4.0.0), methods, SingleCellExperiment Imports: S4Vectors Suggests: testthat, knitr, rjson, Matrix License: GPL-3 MD5sum: 5e7f3e9a52130a08cd486a01b2040db1 NeedsCompilation: no Title: S4 Class for Spatial Experiments handling Description: Defines S4 classes for storing data for spatial experiments. Main examples are reported by using seqFISH and 10x-Visium Spatial Gene Expression data. This includes specialized methods for storing, retrieving spatial coordinates, 10x dedicated parameters and their handling. biocViews: DataRepresentation, DataImport, Infrastructure, SingleCell, ImmunoOncology Author: Dario Righelli [aut, cre], Davide Risso [aut] Maintainer: Dario Righelli VignetteBuilder: knitr BugReports: https://github.com/drighelli/SpatialExperiment/issues git_url: https://git.bioconductor.org/packages/SpatialExperiment git_branch: RELEASE_3_12 git_last_commit: 16dd582 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SpatialExperiment_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SpatialExperiment_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SpatialExperiment_1.0.0.tgz vignettes: vignettes/SpatialExperiment/inst/doc/MouseCoronalVisiumAnalysis.html, vignettes/SpatialExperiment/inst/doc/seqFISHAnalysis.html vignetteTitles: 10x-Visum Spatial Data Analysis, seqFISH Spatial Data Workflow hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpatialExperiment/inst/doc/MouseCoronalVisiumAnalysis.R, vignettes/SpatialExperiment/inst/doc/seqFISHAnalysis.R importsMe: SingleCellMultiModal, spatialLIBD dependencyCount: 27 Package: spatialHeatmap Version: 1.0.0 Imports: av, DESeq2, edgeR, WGCNA, flashClust, htmlwidgets, genefilter, ggplot2, ggdendro, grImport, grid, gridExtra, gplots, igraph, rsvg, shiny, dynamicTreeCut, grDevices, graphics, ggplotify, plotly, rols, stats, SummarizedExperiment, shinydashboard, utils, visNetwork, methods, xml2, yaml Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, data.table, ExpressionAtlas, DT, reshape2, Biobase, GEOquery, shinyWidgets License: Artistic-2.0 MD5sum: 0b12c1350efe0696534f4ad20ba432ba NeedsCompilation: no Title: spatialHeatmap Description: The spatialHeatmap package provides functionalities for visualizing cell-, tissue- and organ-specific data of biological assays by coloring the corresponding spatial features defined in anatomical images according to a numeric color key. biocViews: Visualization, Microarray, Sequencing, GeneExpression, DataRepresentation, Network, Clustering, GraphAndNetwork, CellBasedAssays, ATACSeq, DNASeq, TissueMicroarray, SingleCell, CellBiology, GeneTarget Author: Jianhai Zhang [aut, trl, cre], Jordan Hayes [aut], Le Zhang [aut], Bing Yang [aut], Wolf Frommer [aut], Julia Bailey-Serres [aut], Thomas Girke [aut] Maintainer: Jianhai Zhang URL: https://github.com/jianhaizhang/spatialHeatmap VignetteBuilder: knitr BugReports: https://github.com/jianhaizhang/spatialHeatmap/issues git_url: https://git.bioconductor.org/packages/spatialHeatmap git_branch: RELEASE_3_12 git_last_commit: c4cba0e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/spatialHeatmap_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/spatialHeatmap_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/spatialHeatmap_1.0.0.tgz vignettes: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.html vignetteTitles: spatialHeatmap: Visualizing Spatial Assays in Anatomical Images and Network Graphs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spatialHeatmap/inst/doc/spatialHeatmap.R dependencyCount: 170 Package: specL Version: 1.24.0 Depends: R (>= 3.6), DBI (>= 0.5), methods (>= 3.3), protViz (>= 0.5), RSQLite (>= 1.1), seqinr (>= 3.3) Suggests: BiocGenerics, BiocStyle (>= 2.2), knitr (>= 1.15), rmarkdown, RUnit (>= 0.4) License: GPL-3 MD5sum: 9617cbfb984483641ff28401e465d235 NeedsCompilation: no Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: provides a functions for generating spectra libraries that can be used for MRM SRM MS workflows in proteomics. The package provides a BiblioSpec reader, a function which can add the protein information using a FASTA formatted amino acid file, and an export method for using the created library in the Spectronaut software. The package is developed, tested and used at the Functional Genomics Center Zurich . biocViews: MassSpectrometry, Proteomics Author: Christian Panse [aut, cre] (), Jonas Grossmann [aut] (), Christian Trachsel [aut], Witold E. Wolski [ctb] Maintainer: Christian Panse URL: http://bioconductor.org/packages/specL/ VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues git_url: https://git.bioconductor.org/packages/specL git_branch: RELEASE_3_12 git_last_commit: 07fefbb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/specL_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/specL_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/specL_1.24.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf, vignettes/specL/inst/doc/report.html vignetteTitles: Introduction to specL, Automatic Workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/report.R, vignettes/specL/inst/doc/specL.R suggestsMe: msqc1, NestLink dependencyCount: 35 Package: SpeCond Version: 1.44.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: ecc52bf0b3c71d2e4100fd47c1e007e4 NeedsCompilation: no Title: Condition specific detection from expression data Description: This package performs a gene expression data analysis to detect condition-specific genes. Such genes are significantly up- or down-regulated in a small number of conditions. It does so by fitting a mixture of normal distributions to the expression values. Conditions can be environmental conditions, different tissues, organs or any other sources that you wish to compare in terms of gene expression. biocViews: Microarray, DifferentialExpression, MultipleComparison, Clustering, ReportWriting Author: Florence Cavalli Maintainer: Florence Cavalli git_url: https://git.bioconductor.org/packages/SpeCond git_branch: RELEASE_3_12 git_last_commit: 96f352b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SpeCond_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SpeCond_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SpeCond_1.44.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpeCond/inst/doc/SpeCond.R dependencyCount: 16 Package: Spectra Version: 1.0.5 Depends: R (>= 4.0.0), S4Vectors, BiocParallel, ProtGenerics (>= 1.17.4) Imports: methods, IRanges, MsCoreUtils (>= 1.1.5), graphics, grDevices, stats, tools, utils, fs Suggests: testthat, knitr (>= 1.1.0), msdata (>= 0.19.3), roxygen2, BiocStyle (>= 2.5.19), mzR (>= 2.19.6), rhdf5 (>= 2.32.0), rmarkdown, vdiffr, magrittr License: Artistic-2.0 MD5sum: e976faaa0e434ab8a6a250de214698f6 NeedsCompilation: no Title: Spectra Infrastructure for Mass Spectrometry Data Description: The Spectra package defines an efficient infrastructure for storing and handling mass spectrometry spectra and functionality to subset, process, visualize and compare spectra data. It provides different implementations (backends) to store mass spectrometry data. These comprise backends tuned for fast data access and processing and backends for very large data sets ensuring a small memory footprint. biocViews: Infrastructure, Proteomics, MassSpectrometry, Metabolomics Author: RforMassSpectrometry Package Maintainer [cre], Laurent Gatto [aut] (), Johannes Rainer [aut] (), Sebastian Gibb [aut] () Maintainer: RforMassSpectrometry Package Maintainer URL: https://github.com/RforMassSpectrometry/Spectra VignetteBuilder: knitr BugReports: https://github.com/RforMassSpectrometry/Spectra/issues git_url: https://git.bioconductor.org/packages/Spectra git_branch: RELEASE_3_12 git_last_commit: b1c93af git_last_commit_date: 2020-11-25 Date/Publication: 2020-11-25 source.ver: src/contrib/Spectra_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/Spectra_1.0.5.zip mac.binary.ver: bin/macosx/contrib/4.0/Spectra_1.0.5.tgz vignettes: vignettes/Spectra/inst/doc/Spectra.html vignetteTitles: Description and usage of Spectra object hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Spectra/inst/doc/Spectra.R dependencyCount: 23 Package: SpectralTAD Version: 1.6.0 Depends: R (>= 3.6) Imports: dplyr, PRIMME, cluster, Matrix, parallel, BiocParallel, magrittr, HiCcompare, GenomicRanges Suggests: BiocCheck, BiocManager, BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr License: MIT + file LICENSE MD5sum: ccdbf25d260a11aa05b92dfeda2b406e NeedsCompilation: no Title: SpectralTAD: Hierarchical TAD detection using spectral clustering Description: SpectralTAD is an R package designed to identify Topologically Associated Domains (TADs) from Hi-C contact matrices. It uses a modified version of spectral clustering that uses a sliding window to quickly detect TADs. The function works on a range of different formats of contact matrices and returns a bed file of TAD coordinates. The method does not require users to adjust any parameters to work and gives them control over the number of hierarchical levels to be returned. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Kellen Cresswell , John Stansfield , Mikhail Dozmorov Maintainer: Kellen Cresswell URL: https://github.com/dozmorovlab/SpectralTAD VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/SpectralTAD/issues git_url: https://git.bioconductor.org/packages/SpectralTAD git_branch: RELEASE_3_12 git_last_commit: 2643c90 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SpectralTAD_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SpectralTAD_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SpectralTAD_1.6.0.tgz vignettes: vignettes/SpectralTAD/inst/doc/SpectralTAD.html vignetteTitles: SpectralTAD hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SpectralTAD/inst/doc/SpectralTAD.R suggestsMe: TADCompare dependencyCount: 100 Package: SPEM Version: 1.30.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 1b6e4a792e662285f2fda818d2caa747 NeedsCompilation: no Title: S-system parameter estimation method Description: This package can optimize the parameter in S-system models given time series data biocViews: Network, NetworkInference, Software Author: Xinyi YANG Developer, Jennifer E. DENT Developer and Christine NARDINI Supervisor Maintainer: Xinyi YANG git_url: https://git.bioconductor.org/packages/SPEM git_branch: RELEASE_3_12 git_last_commit: 6b2eb64 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SPEM_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SPEM_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SPEM_1.30.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPEM/inst/doc/SPEM-package.R importsMe: TMixClust dependencyCount: 9 Package: SPIA Version: 2.42.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: d796521707516ab7340e7884d15eb61a NeedsCompilation: no Title: Signaling Pathway Impact Analysis (SPIA) using combined evidence of pathway over-representation and unusual signaling perturbations Description: This package implements the Signaling Pathway Impact Analysis (SPIA) which uses the information form a list of differentially expressed genes and their log fold changes together with signaling pathways topology, in order to identify the pathways most relevant to the condition under the study. biocViews: Microarray, GraphAndNetwork Author: Adi Laurentiu Tarca , Purvesh Kathri and Sorin Draghici Maintainer: Adi Laurentiu Tarca URL: http://bioinformatics.oxfordjournals.org/cgi/reprint/btn577v1 git_url: https://git.bioconductor.org/packages/SPIA git_branch: RELEASE_3_12 git_last_commit: 00f0b65 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SPIA_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SPIA_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SPIA_2.42.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SPIA/inst/doc/SPIA.R importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph dependencyCount: 12 Package: spicyR Version: 1.2.1 Depends: R (>= 4.0.0) Imports: ggplot2, concaveman, BiocParallel, spatstat.core, spatstat.geom, lmerTest, BiocGenerics, S4Vectors, lme4, methods, mgcv, pheatmap, rlang, grDevices, IRanges, stats, data.table, dplyr, tidyr Suggests: BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: 34955022988229822bbe8da022b4a157 NeedsCompilation: no Title: Spatial analysis of in situ cytometry data Description: spicyR provides a series of functions to aid in the analysis of both immunofluorescence and mass cytometry imaging data as well as other assays that can deeply phenotype individual cells and their spatial location. biocViews: SingleCell, CellBasedAssays Author: Nicolas Canete [aut], Ellis Patrick [aut, cre] Maintainer: Ellis Patrick VignetteBuilder: knitr BugReports: https://github.com/ellispatrick/spicyR/issues git_url: https://git.bioconductor.org/packages/spicyR git_branch: RELEASE_3_12 git_last_commit: ba2dade git_last_commit_date: 2021-03-15 Date/Publication: 2021-03-16 source.ver: src/contrib/spicyR_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/spicyR_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/spicyR_1.2.1.tgz vignettes: vignettes/spicyR/inst/doc/segmentedCells.html, vignettes/spicyR/inst/doc/spicy.html vignetteTitles: "Introduction to SegmentedCells", "Introduction to spicy" hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spicyR/inst/doc/segmentedCells.R, vignettes/spicyR/inst/doc/spicy.R dependencyCount: 91 Package: SpidermiR Version: 1.20.0 Depends: R (>= 3.0.0) Imports: httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots, grDevices, lattice, latticeExtra, visNetwork, TCGAbiolinks, gdata, MAGeCKFlute,networkD3 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: c4949dbd290b6a52d29a6655c59f646e NeedsCompilation: no Title: SpidermiR: An R/Bioconductor package for integrative network analysis with miRNA data Description: The aims of SpidermiR are : i) facilitate the network open-access data retrieval from GeneMania data, ii) prepare the data using the appropriate gene nomenclature, iii) integration of miRNA data in a specific network, iv) provide different standard analyses and v) allow the user to visualize the results. In more detail, the package provides multiple methods for query, prepare and download network data (GeneMania), and the integration with validated and predicted miRNA data (mirWalk, miRTarBase, miRandola, Miranda, PicTar and TargetScan). Furthermore, we also present a statistical test to identify pharmaco-mir relationships using the gene-drug interactions derived by DGIdb and MATADOR database. biocViews: GeneRegulation, miRNA, Network Author: Claudia Cava, Antonio Colaprico, Alex Graudenzi, Gloria Bertoli, Tiago C. Silva, Catharina Olsen, Houtan Noushmehr, Gianluca Bontempi, Giancarlo Mauri, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/SpidermiR VignetteBuilder: knitr BugReports: https://github.com/claudiacava/SpidermiR/issues git_url: https://git.bioconductor.org/packages/SpidermiR git_branch: RELEASE_3_12 git_last_commit: f71b8e0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SpidermiR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SpidermiR_1.19.3.zip mac.binary.ver: bin/macosx/contrib/4.0/SpidermiR_1.20.0.tgz vignettes: vignettes/SpidermiR/inst/doc/SpidermiR.html vignetteTitles: Working with SpidermiR package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpidermiR/inst/doc/SpidermiR.R importsMe: StarBioTrek dependencyCount: 165 Package: spikeLI Version: 2.50.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: dd536ac76f51aa5c838897de0bdf7814 NeedsCompilation: no Title: Affymetrix Spike-in Langmuir Isotherm Data Analysis Tool Description: SpikeLI is a package that performs the analysis of the Affymetrix spike-in data using the Langmuir Isotherm. The aim of this package is to show the advantages of a physical-chemistry based analysis of the Affymetrix microarray data compared to the traditional methods. The spike-in (or Latin square) data for the HGU95 and HGU133 chipsets have been downloaded from the Affymetrix web site. The model used in the spikeLI package is described in details in E. Carlon and T. Heim, Physica A 362, 433 (2006). biocViews: Microarray, QualityControl Author: Delphine Baillon, Paul Leclercq , Sarah Ternisien, Thomas Heim, Enrico Carlon Maintainer: Enrico Carlon git_url: https://git.bioconductor.org/packages/spikeLI git_branch: RELEASE_3_12 git_last_commit: b5a7e11 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/spikeLI_2.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/spikeLI_2.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/spikeLI_2.50.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 4 Package: spkTools Version: 1.46.0 Depends: R (>= 2.7.0), Biobase (>= 2.5.5) Imports: Biobase (>= 2.5.5), graphics, grDevices, gtools, methods, RColorBrewer, stats, utils Suggests: xtable License: GPL (>= 2) MD5sum: 42341448c2e208f49960aac96e7f2da6 NeedsCompilation: no Title: Methods for Spike-in Arrays Description: The package contains functions that can be used to compare expression measures on different array platforms. biocViews: Software, Technology, Microarray Author: Matthew N McCall , Rafael A Irizarry Maintainer: Matthew N McCall URL: http://bioconductor.org git_url: https://git.bioconductor.org/packages/spkTools git_branch: RELEASE_3_12 git_last_commit: 18e3313 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/spkTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/spkTools_1.46.0.zip mac.binary.ver: bin/macosx/contrib/4.0/spkTools_1.46.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spkTools/inst/doc/spkDoc.R dependencyCount: 10 Package: splatter Version: 1.14.1 Depends: R (>= 4.0), SingleCellExperiment Imports: BiocGenerics, BiocParallel, checkmate (>= 2.0.0), edgeR, fitdistrplus, ggplot2, locfit, matrixStats, methods, scales, scater (>= 1.15.16), stats, SummarizedExperiment, utils, crayon, S4Vectors, grDevices Suggests: BiocStyle, covr, cowplot, magick, knitr, limSolve, lme4, progress, pscl, testthat, preprocessCore, rmarkdown, scDD, scran, mfa, phenopath, BASiCS (>= 1.7.10), zinbwave, SparseDC, BiocManager, spelling, igraph, scuttle, BiocSingular, VariantAnnotation, Biostrings, GenomeInfoDb, GenomicRanges, IRanges License: GPL-3 + file LICENSE MD5sum: 490f149c8e9f51d72f169abdbc55bdcd NeedsCompilation: no Title: Simple Simulation of Single-cell RNA Sequencing Data Description: Splatter is a package for the simulation of single-cell RNA sequencing count data. It provides a simple interface for creating complex simulations that are reproducible and well-documented. Parameters can be estimated from real data and functions are provided for comparing real and simulated datasets. biocViews: SingleCell, RNASeq, Transcriptomics, GeneExpression, Sequencing, Software, ImmunoOncology Author: Luke Zappia [aut, cre] (), Belinda Phipson [aut] (), Christina Azodi [ctb] (), Alicia Oshlack [aut] () Maintainer: Luke Zappia URL: https://github.com/Oshlack/splatter VignetteBuilder: knitr BugReports: https://github.com/Oshlack/splatter/issues git_url: https://git.bioconductor.org/packages/splatter git_branch: RELEASE_3_12 git_last_commit: 8c83847 git_last_commit_date: 2020-12-01 Date/Publication: 2020-12-01 source.ver: src/contrib/splatter_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/splatter_1.14.1.zip mac.binary.ver: bin/macosx/contrib/4.0/splatter_1.14.1.tgz vignettes: vignettes/splatter/inst/doc/splat_params.html, vignettes/splatter/inst/doc/splatPop.html, vignettes/splatter/inst/doc/splatter.html vignetteTitles: Splat simulation parameters, splatPop simulation, An introduction to the Splatter package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/splatter/inst/doc/splat_params.R, vignettes/splatter/inst/doc/splatPop.R, vignettes/splatter/inst/doc/splatter.R suggestsMe: NewWave, scPCA, SummarizedBenchmark, bcTSNE dependencyCount: 91 Package: SplicingGraphs Version: 1.30.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, graphics, igraph, BiocGenerics, S4Vectors (>= 0.17.5), BiocParallel, IRanges (>= 2.21.2), GenomeInfoDb, GenomicRanges (>= 1.23.21), GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 4839d61ac8a92eba15200313fd1a108a NeedsCompilation: no Title: Create, manipulate, visualize splicing graphs, and assign RNA-seq reads to them Description: This package allows the user to create, manipulate, and visualize splicing graphs and their bubbles based on a gene model for a given organism. Additionally it allows the user to assign RNA-seq reads to the edges of a set of splicing graphs, and to summarize them in different ways. biocViews: Genetics, Annotation, DataRepresentation, Visualization, Sequencing, RNASeq, GeneExpression, AlternativeSplicing, Transcription, ImmunoOncology Author: D. Bindreither, M. Carlson, M. Morgan, H. Pagès Maintainer: H. Pagès URL: https://bioconductor.org/packages/SplicingGraphs BugReports: https://github.com/Bioconductor/SplicingGraphs/issues git_url: https://git.bioconductor.org/packages/SplicingGraphs git_branch: RELEASE_3_12 git_last_commit: b093fb0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SplicingGraphs_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SplicingGraphs_1.29.1.zip mac.binary.ver: bin/macosx/contrib/4.0/SplicingGraphs_1.30.0.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.R dependencyCount: 91 Package: splineTimeR Version: 1.18.0 Depends: R (>= 3.3), Biobase, igraph, limma, GSEABase, gtools, splines, GeneNet (>= 1.2.13), longitudinal (>= 1.1.12), FIs Suggests: knitr License: GPL-3 MD5sum: 56e828e75db6bb4858db853e80ddc142 NeedsCompilation: no Title: Time-course differential gene expression data analysis using spline regression models followed by gene association network reconstruction Description: This package provides functions for differential gene expression analysis of gene expression time-course data. Natural cubic spline regression models are used. Identified genes may further be used for pathway enrichment analysis and/or the reconstruction of time dependent gene regulatory association networks. biocViews: GeneExpression, DifferentialExpression, TimeCourse, Regression, GeneSetEnrichment, NetworkEnrichment, NetworkInference, GraphAndNetwork Author: Agata Michna Maintainer: Herbert Braselmann , Martin Selmansberger VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/splineTimeR git_branch: RELEASE_3_12 git_last_commit: 5fe2f7a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/splineTimeR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/splineTimeR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/splineTimeR_1.18.0.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R importsMe: rmRNAseq dependencyCount: 54 Package: SPLINTER Version: 1.16.0 Depends: R (>= 3.6.0), grDevices, stats Imports: graphics, ggplot2, seqLogo, Biostrings, biomaRt, GenomicAlignments, GenomicRanges, GenomicFeatures, Gviz, IRanges, S4Vectors, GenomeInfoDb, utils, plyr,stringr, methods, BSgenome.Mmusculus.UCSC.mm9, googleVis Suggests: BiocStyle, knitr, rmarkdown License: GPL-2 MD5sum: cead69f08d3afb1fec257b915db54d07 NeedsCompilation: no Title: Splice Interpreter of Transcripts Description: Provides tools to analyze alternative splicing sites, interpret outcomes based on sequence information, select and design primers for site validiation and give visual representation of the event to guide downstream experiments. biocViews: ImmunoOncology, GeneExpression, RNASeq, Visualization, AlternativeSplicing Author: Diana Low [aut, cre] Maintainer: Diana Low URL: https://github.com/dianalow/SPLINTER/ VignetteBuilder: knitr BugReports: https://github.com/dianalow/SPLINTER/issues git_url: https://git.bioconductor.org/packages/SPLINTER git_branch: RELEASE_3_12 git_last_commit: 0cd3dd0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SPLINTER_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SPLINTER_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SPLINTER_1.16.0.tgz vignettes: vignettes/SPLINTER/inst/doc/vignette.pdf vignetteTitles: SPLINTER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPLINTER/inst/doc/vignette.R dependencyCount: 142 Package: splots Version: 1.56.0 Imports: grid, RColorBrewer License: LGPL MD5sum: dcd056023043626b7b74fe34e1ee20db NeedsCompilation: no Title: Visualization of high-throughput assays in microtitre plate or slide format Description: The splots package provides the plotScreen function for visualising data in microtitre plate or slide format. biocViews: Visualization, Sequencing, MicrotitrePlateAssay Author: Wolfgang Huber, Oleg Sklyar Maintainer: Wolfgang Huber git_url: https://git.bioconductor.org/packages/splots git_branch: RELEASE_3_12 git_last_commit: 23c8478 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/splots_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/splots_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/splots_1.56.0.tgz vignettes: vignettes/splots/inst/doc/splotsHOWTO.pdf vignetteTitles: Visualization of data from assays in microtitre plate or slide format hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splots/inst/doc/splotsHOWTO.R dependsOnMe: cellHTS2, HD2013SGI importsMe: RNAinteract, RNAither dependencyCount: 2 Package: SPONGE Version: 1.12.0 Depends: R (>= 3.4) Imports: Biobase, stats, ppcor, logging, foreach, doRNG, data.table, MASS, expm, gRbase, glmnet, igraph, iterators, Suggests: testthat, knitr, rmarkdown, visNetwork, ggplot2, ggrepel, gridExtra, digest, doParallel, bigmemory License: GPL (>=3) MD5sum: 690c298f36464db480571b99436d04c7 NeedsCompilation: no Title: Sparse Partial Correlations On Gene Expression Description: This package provides methods to efficiently detect competitive endogeneous RNA interactions between two genes. Such interactions are mediated by one or several miRNAs such that both gene and miRNA expression data for a larger number of samples is needed as input. biocViews: GeneExpression, Transcription, GeneRegulation, NetworkInference, Transcriptomics, SystemsBiology, Regression Author: Markus List, Azim Dehghani Amirabad, Dennis Kostka, Marcel H. Schulz Maintainer: Markus List VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPONGE git_branch: RELEASE_3_12 git_last_commit: 33265cf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SPONGE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SPONGE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SPONGE_1.12.0.tgz vignettes: vignettes/SPONGE/inst/doc/SPONGE.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPONGE/inst/doc/SPONGE.R dependencyCount: 38 Package: spqn Version: 1.2.0 Depends: R (>= 4.0), ggplot2, ggridges, SummarizedExperiment, BiocGenerics Imports: graphics, stats, utils, matrixStats Suggests: BiocStyle, knitr, rmarkdown, tools, spqnData (>= 0.99.3), RUnit License: Artistic-2.0 MD5sum: 04e3012ca8d92bfd30f2b5e9d9eb7597 NeedsCompilation: no Title: Spatial quantile normalization Description: The spqn package implements spatial quantile normalization (SpQN). This method was developed to remove a mean-correlation relationship in correlation matrices built from gene expression data. It can serve as pre-processing step prior to a co-expression analysis. biocViews: NetworkInference, GraphAndNetwork, Normalization Author: Yi Wang [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Yi Wang URL: https://github.com/hansenlab/spqn VignetteBuilder: knitr BugReports: https://github.com/hansenlab/spqn/issues git_url: https://git.bioconductor.org/packages/spqn git_branch: RELEASE_3_12 git_last_commit: d4ffed5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/spqn_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/spqn_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/spqn_1.2.0.tgz vignettes: vignettes/spqn/inst/doc/spqn.html vignetteTitles: spqn User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spqn/inst/doc/spqn.R dependencyCount: 59 Package: SPsimSeq Version: 1.0.0 Depends: R (>= 4.0) Imports: stats, methods, SingleCellExperiment, fitdistrplus, graphics, edgeR, Hmisc, WGCNA, limma, mvtnorm, phyloseq, utils Suggests: knitr, rmarkdown, LSD, testthat, BiocStyle License: GPL-2 MD5sum: 361983eaa41bdb7e1319a271d396893a NeedsCompilation: no Title: Semi-parametric simulation tool for bulk and single-cell RNA sequencing data Description: SPsimSeq uses a specially designed exponential family for density estimation to constructs the distribution of gene expression levels from a given real RNA sequencing data (single-cell or bulk), and subsequently simulates a new dataset from the estimated marginal distributions using Gaussian-copulas to retain the dependence between genes. It allows simulation of multiple groups and batches with any required sample size and library size. biocViews: GeneExpression, RNASeq, SingleCell, Sequencing, DNASeq Author: Alemu Takele Assefa [aut], Olivier Thas [ths], Joris Meys [cre], Stijn Hawinkel [aut] Maintainer: Joris Meys URL: https://github.com/CenterForStatistics-UGent/SPsimSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SPsimSeq git_branch: RELEASE_3_12 git_last_commit: 1123f18 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SPsimSeq_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SPsimSeq_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SPsimSeq_1.0.0.tgz vignettes: vignettes/SPsimSeq/inst/doc/SPsimSeq.html vignetteTitles: Manual for the SPsimSeq package: semi-parametric simulation for bulk and single cell RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SPsimSeq/inst/doc/SPsimSeq.R dependencyCount: 132 Package: SQLDataFrame Version: 1.4.2 Depends: R (>= 3.6), dplyr (>= 0.8.0.1), dbplyr (>= 1.4.0), S4Vectors Imports: DBI, lazyeval, methods, tools, stats, BiocGenerics, RSQLite, tibble Suggests: RMySQL, bigrquery, testthat, knitr, rmarkdown, DelayedArray License: GPL-3 MD5sum: 99c434e39dfd6ed478999cedb9011cf3 NeedsCompilation: no Title: Representation of SQL database in DataFrame metaphor Description: SQLDataFrame is developed to lazily represent and efficiently analyze SQL-based tables in _R_. SQLDataFrame supports common and familiar 'DataFrame' operations such as '[' subsetting, rbind, cbind, etc.. The internal implementation is based on the widely adopted dplyr grammar and SQL commands. In-memory datasets or plain text files (.txt, .csv, etc.) could also be easily converted into SQLDataFrames objects (which generates a new database on-disk). biocViews: Infrastructure, DataRepresentation Author: Qian Liu [aut, cre] (), Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/SQLDataFrame VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SQLDataFrame/issues git_url: https://git.bioconductor.org/packages/SQLDataFrame git_branch: RELEASE_3_12 git_last_commit: 5849521 git_last_commit_date: 2020-11-23 Date/Publication: 2020-11-27 source.ver: src/contrib/SQLDataFrame_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/SQLDataFrame_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/SQLDataFrame_1.4.2.tgz vignettes: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.html, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.html vignetteTitles: SQLDataFrame Internal Implementation, SQLDataFrame: Lazy representation of SQL database in DataFrame metaphor hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQLDataFrame/inst/doc/SQLDataFrame-internal.R, vignettes/SQLDataFrame/inst/doc/SQLDataFrame.R dependencyCount: 42 Package: SQUADD Version: 1.40.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 8d517d8e7802fef5370a9a842073cd24 NeedsCompilation: no Title: Add-on of the SQUAD Software Description: This package SQUADD is a SQUAD add-on. It permits to generate SQUAD simulation matrix, prediction Heat-Map and Correlation Circle from PCA analysis. biocViews: GraphAndNetwork, Network, Visualization Author: Martial Sankar, supervised by Christian Hardtke and Ioannis Xenarios Maintainer: Martial Sankar URL: http://www.unil.ch/dbmv/page21142_en.html git_url: https://git.bioconductor.org/packages/SQUADD git_branch: RELEASE_3_12 git_last_commit: 0d290dd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SQUADD_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SQUADD_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SQUADD_1.40.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD ERK exemple, SQUADD HOW-TO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R dependencyCount: 6 Package: sRACIPE Version: 1.6.0 Depends: R (>= 3.6.0),SummarizedExperiment, methods, Rcpp Imports: ggplot2, reshape2, MASS, RColorBrewer, gridExtra,visNetwork, gplots, umap, htmlwidgets, S4Vectors, BiocGenerics, grDevices, stats, utils, graphics LinkingTo: Rcpp Suggests: knitr, BiocStyle, rmarkdown, tinytest, doFuture License: MIT + file LICENSE Archs: i386, x64 MD5sum: 441b16d7996040bee5f5dbdb2eb29766 NeedsCompilation: yes Title: Systems biology tool to simulate gene regulatory circuits Description: sRACIPE implements a randomization-based method for gene circuit modeling. It allows us to study the effect of both the gene expression noise and the parametric variation on any gene regulatory circuit (GRC) using only its topology, and simulates an ensemble of models with random kinetic parameters at multiple noise levels. Statistical analysis of the generated gene expressions reveals the basin of attraction and stability of various phenotypic states and their changes associated with intrinsic and extrinsic noises. sRACIPE provides a holistic picture to evaluate the effects of both the stochastic nature of cellular processes and the parametric variation. biocViews: ResearchField, SystemsBiology, MathematicalBiology, GeneExpression, GeneRegulation, GeneTarget Author: Vivek Kohar [aut, cre] (), Mingyang Lu [aut] Maintainer: Vivek Kohar URL: https://vivekkohar.github.io/sRACIPE/, https://github.com/vivekkohar/sRACIPE, https://geneex.jax.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sRACIPE git_branch: RELEASE_3_12 git_last_commit: 15f5583 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sRACIPE_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sRACIPE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sRACIPE_1.6.0.tgz vignettes: vignettes/sRACIPE/inst/doc/sRACIPE.html vignetteTitles: A systems biology tool for gene regulatory circuit simulation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/sRACIPE/inst/doc/sRACIPE.R dependencyCount: 81 Package: SRAdb Version: 1.52.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: cb20bc48cca452c38f5795304fc3b766 NeedsCompilation: no Title: A compilation of metadata from NCBI SRA and tools Description: The Sequence Read Archive (SRA) is the largest public repository of sequencing data from the next generation of sequencing platforms including Roche 454 GS System, Illumina Genome Analyzer, Applied Biosystems SOLiD System, Helicos Heliscope, and others. However, finding data of interest can be challenging using current tools. SRAdb is an attempt to make access to the metadata associated with submission, study, sample, experiment and run much more feasible. This is accomplished by parsing all the NCBI SRA metadata into a SQLite database that can be stored and queried locally. Fulltext search in the package make querying metadata very flexible and powerful. fastq and sra files can be downloaded for doing alignment locally. Beside ftp protocol, the SRAdb has funcitons supporting fastp protocol (ascp from Aspera Connect) for faster downloading large data files over long distance. The SQLite database is updated regularly as new data is added to SRA and can be downloaded at will for the most up-to-date metadata. biocViews: Infrastructure, Sequencing, DataImport Author: Jack Zhu and Sean Davis Maintainer: Jack Zhu URL: http://gbnci.abcc.ncifcrf.gov/sra/ BugReports: https://github.com/seandavi/SRAdb/issues/new git_url: https://git.bioconductor.org/packages/SRAdb git_branch: RELEASE_3_12 git_last_commit: 14b7f25 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SRAdb_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SRAdb_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SRAdb_1.52.0.tgz vignettes: vignettes/SRAdb/inst/doc/SRAdb.pdf vignetteTitles: Using SRAdb to Query the Sequence Read Archive hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SRAdb/inst/doc/SRAdb.R suggestsMe: parathyroidSE dependencyCount: 57 Package: SRGnet Version: 1.16.0 Depends: R (>= 3.3.1), EBcoexpress, MASS, igraph, pvclust (>= 2.0-0), gbm (>= 2.1.1), limma, DMwR (>= 0.4.1), matrixStats, Hmisc Suggests: knitr, rmarkdown License: GPL-2 MD5sum: dc9e45a0485997d47c3565eb108b3f81 NeedsCompilation: no Title: SRGnet: An R package for studying synergistic response to gene mutations from transcriptomics data Description: We developed SRGnet to analyze synergistic regulatory mechanisms in transcriptome profiles that act to enhance the overall cell response to combination of mutations, drugs or environmental exposure. This package can be used to identify regulatory modules downstream of synergistic response genes, prioritize synergistic regulatory genes that may be potential intervention targets, and contextualize gene perturbation experiments. biocViews: Software, StatisticalMethod, Regression Author: Isar Nassiri [aut, cre], Matthew McCall [aut, cre] Maintainer: Isar Nassiri VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SRGnet git_branch: RELEASE_3_12 git_last_commit: 11e7be7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SRGnet_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SRGnet_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SRGnet_1.16.0.tgz vignettes: vignettes/SRGnet/inst/doc/vignette.html vignetteTitles: SRGnet An R package for studying synergistic response to gene mutations from transcriptomics data \ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 83 Package: srnadiff Version: 1.10.1 Depends: R (>= 3.6) Imports: Rcpp (>= 0.12.8), methods, devtools, S4Vectors, GenomeInfoDb, rtracklayer, SummarizedExperiment, IRanges, GenomicRanges, DESeq2, Rsamtools, GenomicFeatures, GenomicAlignments, grDevices, Gviz, BiocParallel, BiocManager, BiocStyle LinkingTo: Rcpp Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: ff50de9577a8e907ddd0d9c5575a6e52 NeedsCompilation: yes Title: Finding differentially expressed unannotated genomic regions from RNA-seq data Description: srnadiff is a package that finds differently expressed regions from RNA-seq data at base-resolution level without relying on existing annotation. To do so, the package implements the identify-then-annotate methodology that builds on the idea of combining two pipelines approachs differential expressed regions detection and differential expression quantification. biocViews: ImmunoOncology, GeneExpression, Coverage, SmallRNA, Epigenetics, StatisticalMethod, Preprocessing, DifferentialExpression Author: Zytnicki Matthias [aut, cre], Gonzalez Ignacio [aut] Maintainer: Zytnicki Matthias SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/srnadiff git_branch: RELEASE_3_12 git_last_commit: b06f511 git_last_commit_date: 2020-10-13 Date/Publication: 2021-01-05 source.ver: src/contrib/srnadiff_1.10.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/srnadiff_1.10.1.tgz vignettes: vignettes/srnadiff/inst/doc/srnadiff.html vignetteTitles: The srnadiff package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/srnadiff/inst/doc/srnadiff.R dependencyCount: 182 Package: sscore Version: 1.62.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: 9e9f745fcbff1188eb1ad995ce58473c NeedsCompilation: no Title: S-Score Algorithm for Affymetrix Oligonucleotide Microarrays Description: This package contains an implementation of the S-Score algorithm as described by Zhang et al (2002). biocViews: DifferentialExpression Author: Richard Kennedy , based on C++ code from Li Zhang and Borland Delphi code from Robnet Kerns . Maintainer: Richard Kennedy URL: http://home.att.net/~richard-kennedy/professional.html git_url: https://git.bioconductor.org/packages/sscore git_branch: RELEASE_3_12 git_last_commit: c7472a4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sscore_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sscore_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sscore_1.62.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sscore/inst/doc/sscore.R dependencyCount: 13 Package: sscu Version: 2.20.0 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: 5bc1ad92f287b2afe4855124cae6ce7f NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package calculates the indexes for selective stength in codon usage in bacteria species. (1) The package can calculate the strength of selected codon usage bias (sscu, also named as s_index) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on four pairs of codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. (2) The package can detect the strength of translational accuracy selection by Akashi's test. The test tabulating all codons into four categories with the feature as conserved/variable amino acids and optimal/non-optimal codons. (3) Optimal codon lists (selected codons) can be calculated by either op_highly function (by using the highly expressed genes compared with all genes to identify optimal codons), or op_corre_CodonW/op_corre_NCprime function (by correlative method developed by Hershberg & Petrov). Users will have a list of optimal codons for further analysis, such as input to the Akashi's test. (4) The detailed codon usage information, such as RSCU value, number of optimal codons in the highly/all gene set, as well as the genomic gc3 value, can be calculate by the optimal_codon_statistics and genomic_gc3 function. (5) Furthermore, we added one test function low_frequency_op in the package. The function try to find the low frequency optimal codons, among all the optimal codons identified by the op_highly function. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/sscu git_branch: RELEASE_3_12 git_last_commit: 820100b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sscu_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sscu_2.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sscu_2.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 33 Package: sSeq Version: 1.28.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: fa2cf7f545d5b442933363816b74a4ca NeedsCompilation: no Title: Shrinkage estimation of dispersion in Negative Binomial models for RNA-seq experiments with small sample size Description: The purpose of this package is to discover the genes that are differentially expressed between two conditions in RNA-seq experiments. Gene expression is measured in counts of transcripts and modeled with the Negative Binomial (NB) distribution using a shrinkage approach for dispersion estimation. The method of moment (MM) estimates for dispersion are shrunk towards an estimated target, which minimizes the average squared difference between the shrinkage estimates and the initial estimates. The exact per-gene probability under the NB model is calculated, and used to test the hypothesis that the expected expression of a gene in two conditions identically follow a NB distribution. biocViews: ImmunoOncology, RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu git_url: https://git.bioconductor.org/packages/sSeq git_branch: RELEASE_3_12 git_last_commit: 401f680 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sSeq_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sSeq_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sSeq_1.28.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sSeq/inst/doc/sSeq.R importsMe: MLSeq dependencyCount: 3 Package: ssize Version: 1.64.0 Depends: gdata, xtable License: LGPL MD5sum: 83950b00080d0ad470d71deeca261f3f NeedsCompilation: no Title: Estimate Microarray Sample Size Description: Functions for computing and displaying sample size information for gene expression arrays. biocViews: Microarray, DifferentialExpression Author: Gregory R. Warnes, Peng Liu, and Fasheng Li Maintainer: Gregory R. Warnes git_url: https://git.bioconductor.org/packages/ssize git_branch: RELEASE_3_12 git_last_commit: d74a7f3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ssize_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ssize_1.64.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ssize_1.64.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: Sample Size Estimation for Microarray Experiments Using the \code{ssize} package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssize/inst/doc/ssize.R importsMe: maGUI dependencyCount: 6 Package: SSPA Version: 2.30.0 Depends: R (>= 2.12), methods Imports: graphics, stats, qvalue, lattice, limma Suggests: BiocStyle, knitr, rmarkdown, genefilter, edgeR, DESeq License: GPL (>= 2) Archs: i386, x64 MD5sum: fb0a35358e78b7caefd919c8a799f214 NeedsCompilation: yes Title: General Sample Size and Power Analysis for Microarray and Next-Generation Sequencing Data Description: General Sample size and power analysis for microarray and next-generation sequencing data. biocViews: ImmunoOncology, GeneExpression, RNASeq, Microarray, StatisticalMethod Author: Maarten van Iterson Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SSPA git_branch: RELEASE_3_12 git_last_commit: 3fa684d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SSPA_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SSPA_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SSPA_2.30.0.tgz vignettes: vignettes/SSPA/inst/doc/SSPA.html vignetteTitles: Power and sample size analysis,, Microarray data,, RNAseq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SSPA/inst/doc/SSPA.R dependencyCount: 46 Package: ssPATHS Version: 1.4.0 Depends: R (>= 3.5.0), SummarizedExperiment Imports: ROCR, dml, MESS Suggests: ggplot2, testthat (>= 2.1.0) License: MIT + file LICENSE MD5sum: 001502c05f8a738fe50f495e02976f9c NeedsCompilation: no Title: ssPATHS: Single Sample PATHway Score Description: This package generates pathway scores from expression data for single samples after training on a reference cohort. The score is generated by taking the expression of a gene set (pathway) from a reference cohort and performing linear discriminant analysis to distinguish samples in the cohort that have the pathway augmented and not. The separating hyperplane is then used to score new samples. biocViews: Software, GeneExpression, BiomedicalInformatics, RNASeq, Pathways, Transcriptomics, DimensionReduction, Classification Author: Natalie R. Davidson Maintainer: Natalie R. Davidson git_url: https://git.bioconductor.org/packages/ssPATHS git_branch: RELEASE_3_12 git_last_commit: ff97ecc git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ssPATHS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ssPATHS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ssPATHS_1.4.0.tgz vignettes: vignettes/ssPATHS/inst/doc/ssPATHS.pdf vignetteTitles: Using ssPATHS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ssPATHS/inst/doc/ssPATHS.R dependencyCount: 105 Package: ssrch Version: 1.6.0 Depends: R (>= 3.6), methods Imports: shiny, DT, utils Suggests: knitr, testthat License: Artistic-2.0 MD5sum: 58cbf2304727f0116fe1d018d075d500 NeedsCompilation: no Title: a simple search engine Description: Demonstrate tokenization and a search gadget for collections of CSV files. biocViews: Infrastructure Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssrch git_branch: RELEASE_3_12 git_last_commit: aee7e03 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ssrch_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ssrch_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ssrch_1.6.0.tgz vignettes: vignettes/ssrch/inst/doc/ssrch.html vignetteTitles: ssrch: small search engine hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssrch/inst/doc/ssrch.R importsMe: HumanTranscriptomeCompendium dependencyCount: 39 Package: ssviz Version: 1.24.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer,stats Suggests: knitr License: GPL-2 MD5sum: 34534ff2535bd7f5a90b8f440d7dcee9 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: ImmunoOncology, Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ssviz git_branch: RELEASE_3_12 git_last_commit: 63dc1b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ssviz_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ssviz_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ssviz_1.24.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ssviz/inst/doc/ssviz.R dependencyCount: 64 Package: stageR Version: 1.12.0 Depends: R (>= 3.4), SummarizedExperiment Imports: methods, stats Suggests: knitr, rmarkdown, BiocStyle, methods, Biobase, edgeR, limma, DEXSeq, testthat License: GNU General Public License version 3 MD5sum: 0ccdf9d0919d7ced4b72477045c8c533 NeedsCompilation: no Title: stageR: stage-wise analysis of high throughput gene expression data in R Description: The stageR package allows automated stage-wise analysis of high-throughput gene expression data. The method is published in Genome Biology at https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1277-0 biocViews: Software, StatisticalMethod Author: Koen Van den Berge and Lieven Clement Maintainer: Koen Van den Berge VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/stageR git_branch: RELEASE_3_12 git_last_commit: 1f3fac2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/stageR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/stageR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/stageR_1.12.0.tgz vignettes: vignettes/stageR/inst/doc/stageRVignette.html vignetteTitles: stageR: stage-wise analysis of high-throughput gene expression data in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stageR/inst/doc/stageRVignette.R dependsOnMe: rnaseqDTU suggestsMe: MethReg dependencyCount: 26 Package: STAN Version: 2.18.0 Depends: methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 0345efa8ccdfc00fc5dc32bd5ac06cdb NeedsCompilation: yes Title: The Genomic STate ANnotation Package Description: Genome segmentation with hidden Markov models has become a useful tool to annotate genomic elements, such as promoters and enhancers. STAN (genomic STate ANnotation) implements (bidirectional) hidden Markov models (HMMs) using a variety of different probability distributions, which can model a wide range of current genomic data (e.g. continuous, discrete, binary). STAN de novo learns and annotates the genome into a given number of 'genomic states'. The 'genomic states' may for instance reflect distinct genome-associated protein complexes (e.g. 'transcription states') or describe recurring patterns of chromatin features (referred to as 'chromatin states'). Unlike other tools, STAN also allows for the integration of strand-specific (e.g. RNA) and non-strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing, ChIPSeq, RNASeq, ChipOnChip, Transcription, ImmunoOncology Author: Benedikt Zacher, Julia Ertl, Rafael Campos-Martin, Julien Gagneur, Achim Tresch Maintainer: Rafael Campos-Martin VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STAN git_branch: RELEASE_3_12 git_last_commit: efe18e3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/STAN_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/STAN_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/STAN_2.18.0.tgz vignettes: vignettes/STAN/inst/doc/STAN-knitr.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN-knitr.R dependencyCount: 141 Package: staRank Version: 1.32.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: 22b5738c5be97c356739f7f1525effac NeedsCompilation: no Title: Stability Ranking Description: Detecting all relevant variables from a data set is challenging, especially when only few samples are available and data is noisy. Stability ranking provides improved variable rankings of increased robustness using resampling or subsampling. biocViews: ImmunoOncology, MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg git_url: https://git.bioconductor.org/packages/staRank git_branch: RELEASE_3_12 git_last_commit: df7ec31 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/staRank_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/staRank_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/staRank_1.32.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/staRank/inst/doc/staRank.R dependencyCount: 85 Package: StarBioTrek Version: 1.16.0 Depends: R (>= 3.3) Imports: SpidermiR, graphite, AnnotationDbi, e1071, ROCR, MLmetrics, grDevices, igraph, reshape2, ggplot2 Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2, qgraph, png, grid License: GPL (>= 3) MD5sum: 2631459a5edaabc1119b682e721f10e9 NeedsCompilation: no Title: StarBioTrek Description: This tool StarBioTrek presents some methodologies to measure pathway activity and cross-talk among pathways integrating also the information of network data. biocViews: GeneRegulation, Network, Pathways, KEGG Author: Claudia Cava, Isabella Castiglioni Maintainer: Claudia Cava URL: https://github.com/claudiacava/StarBioTrek VignetteBuilder: knitr BugReports: https://github.com/claudiacava/StarBioTrek/issues git_url: https://git.bioconductor.org/packages/StarBioTrek git_branch: RELEASE_3_12 git_last_commit: 50f1212 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/StarBioTrek_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/StarBioTrek_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/StarBioTrek_1.16.0.tgz vignettes: vignettes/StarBioTrek/inst/doc/StarBioTrek.html vignetteTitles: Vignette Title hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StarBioTrek/inst/doc/StarBioTrek.R dependencyCount: 175 Package: STATegRa Version: 1.26.0 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, stats, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: bb6da25cc52d66462455b24741796dfa NeedsCompilation: no Title: Classes and methods for multi-omics data integration Description: Classes and tools for multi-omics data integration. biocViews: Software, StatisticalMethod, Clustering, DimensionReduction, PrincipalComponent Author: STATegra Consortia Maintainer: David Gomez-Cabrero , Núria Planell VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/STATegRa git_branch: RELEASE_3_12 git_last_commit: 064ea6c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/STATegRa_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/STATegRa_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/STATegRa_1.26.0.tgz vignettes: vignettes/STATegRa/inst/doc/STATegRa.html vignetteTitles: STATegRa User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R dependencyCount: 60 Package: statTarget Version: 1.20.0 Depends: R (>= 3.6.0) Imports: randomForest,plyr,pdist,ROC,utils,grDevices,graphics,rrcov,stats, pls,impute Suggests: testthat, BiocStyle, knitr, rmarkdown, gWidgets2,gWidgets2RGtk2,RGtk2 License: LGPL (>= 3) MD5sum: afbe3504c98804bcf5b9b6882e48eae0 NeedsCompilation: no Title: Statistical Analysis of Molecular Profiles Description: A streamlined tool provides a graphical user interface for quality control based signal drift correction (QC-RFSC), integration of data from multi-batch MS-based experiments, and the comprehensive statistical analysis in metabolomics and proteomics. biocViews: ImmunoOncology, Metabolomics, Proteomics, Machine Learning, Lipidomics, MassSpectrometry, QualityControl, Normalization, QC-RFSC, QC-RLSC, ComBat, DifferentialExpression, BatchEffect, Visualization, MultipleComparison,Preprocessing, GUI, Software Author: Hemi Luan Maintainer: Hemi Luan URL: https://stattarget.github.io VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/statTarget git_branch: RELEASE_3_12 git_last_commit: cb5ef0e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/statTarget_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/statTarget_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/statTarget_1.20.0.tgz vignettes: vignettes/statTarget/inst/doc/Combat.html, vignettes/statTarget/inst/doc/pathway_analysis.html, vignettes/statTarget/inst/doc/statTarget.html vignetteTitles: QC_free approach with Combat method, statTarget2 for pathway analysis, statTarget2 On using the Graphical User Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/statTarget/inst/doc/Combat.R, vignettes/statTarget/inst/doc/pathway_analysis.R, vignettes/statTarget/inst/doc/statTarget.R dependencyCount: 32 Package: stepNorm Version: 1.62.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 9f538b024077d17b4bb7173fff2d85b3 NeedsCompilation: no Title: Stepwise normalization functions for cDNA microarrays Description: Stepwise normalization functions for cDNA microarray data. biocViews: Microarray, TwoChannel, Preprocessing Author: Yuanyuan Xiao , Yee Hwa (Jean) Yang Maintainer: Yuanyuan Xiao URL: http://www.biostat.ucsf.edu/jean/ git_url: https://git.bioconductor.org/packages/stepNorm git_branch: RELEASE_3_12 git_last_commit: 15fdd6e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/stepNorm_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/stepNorm_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/stepNorm_1.62.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 8 Package: strandCheckR Version: 1.8.0 Imports: dplyr, magrittr, GenomeInfoDb, GenomicAlignments, GenomicRanges, IRanges, Rsamtools, S4Vectors, grid, BiocGenerics, ggplot2, reshape2, stats, gridExtra, TxDb.Hsapiens.UCSC.hg38.knownGene, methods, stringr Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) MD5sum: bc63914c8d99d60c5cd66a31d6c560d9 NeedsCompilation: no Title: Calculate strandness information of a bam file Description: This package aims to quantify and remove putative double strand DNA from a strand-specific RNA sample. There are also options and methods to plot the positive/negative proportions of all sliding windows, which allow users to have an idea of how much the sample was contaminated and the appropriate threshold to be used for filtering. biocViews: RNASeq, Alignment, QualityControl, Coverage, ImmunoOncology Author: Thu-Hien To [aut, cre], Steve Pederson [aut] Maintainer: Thu-Hien To VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/strandCheckR git_branch: RELEASE_3_12 git_last_commit: 6d8767e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/strandCheckR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/strandCheckR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/strandCheckR_1.8.0.tgz vignettes: vignettes/strandCheckR/inst/doc/strandCheckR.html vignetteTitles: An Introduction To strandCheckR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/strandCheckR/inst/doc/strandCheckR.R dependencyCount: 107 Package: Streamer Version: 1.36.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: aa591d924add71a6c4e912accede201c NeedsCompilation: yes Title: Enabling stream processing of large files Description: Large data files can be difficult to work with in R, where data generally resides in memory. This package encourages a style of programming where data is 'streamed' from disk into R via a `producer' and through a series of `consumers' that, typically reduce the original data to a manageable size. The package provides useful Producer and Consumer stream components for operations such as data input, sampling, indexing, and transformation; see package?Streamer for details. biocViews: Infrastructure, DataImport Author: Martin Morgan, Nishant Gopalakrishnan Maintainer: Martin Morgan git_url: https://git.bioconductor.org/packages/Streamer git_branch: RELEASE_3_12 git_last_commit: ee88eca git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Streamer_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Streamer_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Streamer_1.36.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Streamer/inst/doc/Streamer.R importsMe: plethy dependencyCount: 10 Package: STRINGdb Version: 2.2.2 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 0def31229f71e845d00a0fc1fd9c9ab2 NeedsCompilation: no Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins database) Description: The STRINGdb package provides a R interface to the STRING protein-protein interactions database (https://www.string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Damian Szklarczyk git_url: https://git.bioconductor.org/packages/STRINGdb git_branch: RELEASE_3_12 git_last_commit: 506db6a git_last_commit_date: 2021-01-19 Date/Publication: 2021-03-08 source.ver: src/contrib/STRINGdb_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/STRINGdb_2.2.2.zip mac.binary.ver: bin/macosx/contrib/4.0/STRINGdb_2.2.2.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STRINGdb/inst/doc/STRINGdb.R dependsOnMe: PPInfer importsMe: coexnet, glmSparseNet, IMMAN, pwOmics, RITAN, XINA suggestsMe: epiNEM, GeneNetworkBuilder, netSmooth, PCAN, protti dependencyCount: 40 Package: STROMA4 Version: 1.14.0 Depends: R (>= 3.4), Biobase, BiocParallel, cluster, matrixStats, stats, graphics, utils Suggests: breastCancerMAINZ License: GPL-3 MD5sum: 6745287bd7f8562a48795386447cc82e NeedsCompilation: no Title: Assign Properties to TNBC Patients Description: This package estimates four stromal properties identified in TNBC patients in each patient of a gene expression datasets. These stromal property assignments can be combined to subtype patients. These four stromal properties were identified in Triple negative breast cancer (TNBC) patients and represent the presence of different cells in the stroma: T-cells (T), B-cells (B), stromal infiltrating epithelial cells (E), and desmoplasia (D). Additionally this package can also be used to estimate generative properties for the Lehmann subtypes, an alternative TNBC subtyping scheme (PMID: 21633166). biocViews: ImmunoOncology, GeneExpression, BiomedicalInformatics, Classification, Microarray, RNASeq, Software Author: Sadiq Saleh [aut, cre], Michael Hallett [aut] Maintainer: Sadiq Saleh git_url: https://git.bioconductor.org/packages/STROMA4 git_branch: RELEASE_3_12 git_last_commit: 69aed88 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/STROMA4_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/STROMA4_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/STROMA4_1.14.0.tgz vignettes: vignettes/STROMA4/inst/doc/STROMA4-vignette.pdf vignetteTitles: Using the STROMA4 package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STROMA4/inst/doc/STROMA4-vignette.R dependencyCount: 17 Package: struct Version: 1.2.0 Depends: R (>= 4.0) Imports: methods, ontologyIndex, datasets, graphics, stats, utils, knitr, SummarizedExperiment, S4Vectors Suggests: testthat, rstudioapi, rmarkdown, covr, BiocStyle, openxlsx, ggplot2, magick License: GPL-3 MD5sum: be3a69720a83615cf82b8f77d0e2a652 NeedsCompilation: no Title: Statistics in R Using Class-based Templates Description: Defines and includes a set of class-based templates for developing and implementing data processing and analysis workflows, with a strong emphasis on statistics and machine learning. The templates can be used and where needed extended to 'wrap' tools and methods from other packages into a common standardised structure to allow for effective and fast integration. Model objects can be combined into sequences, and sequences nested in iterators using overloaded operators to simplify and improve readability of the code. STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for methods, inputs and outputs wrapped using the class-based templates. biocViews: WorkflowStep Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/struct git_branch: RELEASE_3_12 git_last_commit: 47c7614 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/struct_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/struct_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/struct_1.2.0.tgz vignettes: vignettes/struct/inst/doc/struct_templates_and_helper_functions.html vignetteTitles: Introduction to STRUCT - STatistics in R using Class-based Templates hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/struct/inst/doc/struct_templates_and_helper_functions.R dependsOnMe: structToolbox importsMe: metabolomicsWorkbenchR dependencyCount: 39 Package: Structstrings Version: 1.6.1 Depends: R (>= 4.0), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), Biostrings (>= 2.57.2) Imports: methods, BiocGenerics, XVector, stringr, stringi, crayon, grDevices LinkingTo: IRanges, S4Vectors Suggests: testthat, knitr, rmarkdown, tRNAscanImport, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: bea592b2637b1e6fa72010e3d2eccb49 NeedsCompilation: yes Title: Implementation of the dot bracket annotations with Biostrings Description: The Structstrings package implements the widely used dot bracket annotation for storing base pairing information in structured RNA. Structstrings uses the infrastructure provided by the Biostrings package and derives the DotBracketString and related classes from the BString class. From these, base pair tables can be produced for in depth analysis. In addition, the loop indices of the base pairs can be retrieved as well. For better efficiency, information conversion is implemented in C, inspired to a large extend by the ViennaRNA package. biocViews: DataImport, DataRepresentation, Infrastructure, Sequencing, Software, Alignment, SequenceMatching Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/Structstrings VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/Structstrings/issues git_url: https://git.bioconductor.org/packages/Structstrings git_branch: RELEASE_3_12 git_last_commit: 47bf2ec git_last_commit_date: 2020-12-09 Date/Publication: 2020-12-09 source.ver: src/contrib/Structstrings_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/Structstrings_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/Structstrings_1.6.1.tgz vignettes: vignettes/Structstrings/inst/doc/Structstrings.html vignetteTitles: Structstrings hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Structstrings/inst/doc/Structstrings.R dependsOnMe: tRNA, tRNAdbImport importsMe: tRNAscanImport dependencyCount: 19 Package: structToolbox Version: 1.2.0 Depends: R (>= 4.0), struct (>= 1.1.2) Imports: ggplot2, ggthemes, grid, gridExtra, methods, scales, sp, stats, utils Suggests: agricolae, BiocFileCache, BiocStyle, car, covr, cowplot, e1071, emmeans, ggdendro, knitr, magick, nlme, openxlsx, pls, pmp, reshape2, ropls, rmarkdown, Rtsne, testthat License: GPL-3 MD5sum: 7591d2e8c9dd35562937e187cc38bf93 NeedsCompilation: no Title: Data processing & analysis tools for Metabolomics and other omics Description: An extensive set of data (pre-)processing and analysis methods and tools for metabolomics and other omics, with a strong emphasis on statistics and machine learning. This toolbox allows the user to build extensive and standardised workflows for data analysis. The methods and tools have been implemented using class-based templates provided by the struct (Statistics in R Using Class-based Templates) package. The toolbox includes pre-processing methods (e.g. signal drift and batch correction, normalisation, missing value imputation and scaling), univariate (e.g. ttest, various forms of ANOVA, Kruskal–Wallis test and more) and multivariate statistical methods (e.g. PCA and PLS, including cross-validation and permutation testing) as well as machine learning methods (e.g. Support Vector Machines). The STATistics Ontology (STATO) has been integrated and implemented to provide standardised definitions for the different methods, inputs and outputs. biocViews: WorkflowStep, Metabolomics Author: Gavin Rhys Lloyd [aut, cre], Ralf Johannes Maria Weber [aut] Maintainer: Gavin Rhys Lloyd VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/structToolbox git_branch: RELEASE_3_12 git_last_commit: dea6388 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/structToolbox_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/structToolbox_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/structToolbox_1.2.0.tgz vignettes: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.html vignetteTitles: Data analysis of metabolomics and other omics datasets using the structToolbox hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/structToolbox/inst/doc/data_analysis_omics_using_the_structtoolbox.R dependencyCount: 72 Package: StructuralVariantAnnotation Version: 1.6.0 Depends: GenomicRanges, rtracklayer, VariantAnnotation, BiocGenerics, R (>= 3.6.0) Imports: assertthat, Biostrings, stringr, dplyr, methods, rlang Suggests: BSgenome.Hsapiens.UCSC.hg19, ggplot2, devtools, testthat, roxygen2, covr, knitr, plyranges, ggbio, biovizBase, circlize, tictoc, GenomeInfoDb, IRanges, S4Vectors, SummarizedExperiment License: GPL-3 MD5sum: 857caa7e423916e2325a9fd3515b58ca NeedsCompilation: no Title: Variant annotations for structural variants Description: StructuralVariantAnnotation contains useful helper functions for dealing with structural variants in VCF format. The packages contains functions for parsing VCFs from a number of popular callers as well as functions for dealing with breakpoints involving two separate genomic loci encoded as GRanges objects. biocViews: DataImport, Sequencing, Annotation, Genetics, VariantAnnotation Author: Daniel Cameron [aut, cre] (), Ruining Dong [aut] () Maintainer: Daniel Cameron VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/StructuralVariantAnnotation git_branch: RELEASE_3_12 git_last_commit: e585636 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/StructuralVariantAnnotation_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/StructuralVariantAnnotation_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/StructuralVariantAnnotation_1.6.0.tgz vignettes: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.html vignetteTitles: Structural Variant Annotation Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/StructuralVariantAnnotation/inst/doc/vignettes.R dependencyCount: 90 Package: SubCellBarCode Version: 1.6.0 Depends: R (>= 3.6) Imports: Rtsne, scatterplot3d, caret, e1071, ggplot2, gridExtra, networkD3, ggrepel, graphics, stats, org.Hs.eg.db, AnnotationDbi Suggests: knitr, rmarkdown, BiocStyle License: GPL-2 MD5sum: 2da9c1d4940e0e02b89c6695bcb69e2c NeedsCompilation: no Title: SubCellBarCode: Integrated workflow for robust mapping and visualizing whole human spatial proteome Description: Mass-Spectrometry based spatial proteomics have enabled the proteome-wide mapping of protein subcellular localization (Orre et al. 2019, Molecular Cell). SubCellBarCode R package robustly classifies proteins into corresponding subcellular localization. biocViews: Proteomics, MassSpectrometry, Classification Author: Taner Arslan Maintainer: Taner Arslan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SubCellBarCode git_branch: RELEASE_3_12 git_last_commit: 41ba24b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SubCellBarCode_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SubCellBarCode_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SubCellBarCode_1.6.0.tgz vignettes: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.html vignetteTitles: SubCellBarCode R Markdown vignettes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SubCellBarCode/inst/doc/SubCellBarCode.R dependencyCount: 101 Package: subSeq Version: 1.20.0 Depends: R (>= 3.2) Imports: data.table, dplyr, tidyr, ggplot2, magrittr, qvalue (>= 1.99), digest, Biobase Suggests: limma, edgeR, DESeq2, DEXSeq (>= 1.9.7), testthat, knitr License: MIT + file LICENSE MD5sum: c0d0fee704788fd28385638218080634 NeedsCompilation: no Title: Subsampling of high-throughput sequencing count data Description: Subsampling of high throughput sequencing count data for use in experiment design and analysis. biocViews: ImmunoOncology, Sequencing, Transcription, RNASeq, GeneExpression, DifferentialExpression Author: David Robinson, John D. Storey, with contributions from Andrew J. Bass Maintainer: Andrew J. Bass , John D. Storey URL: http://github.com/StoreyLab/subSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/subSeq git_branch: RELEASE_3_12 git_last_commit: e789db2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/subSeq_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/subSeq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/subSeq_1.20.0.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/subSeq/inst/doc/subSeq.R dependencyCount: 55 Package: SummarizedBenchmark Version: 2.8.0 Depends: R (>= 3.6), tidyr, SummarizedExperiment, S4Vectors, BiocGenerics, methods, UpSetR, rlang, stringr, utils, BiocParallel, ggplot2, mclust, dplyr, digest, sessioninfo, crayon, tibble Suggests: iCOBRA, BiocStyle, knitr, magrittr, IHW, qvalue, testthat, DESeq2, edgeR, limma, tximport, readr, scRNAseq, splatter, scater, rnaseqcomp, biomaRt License: GPL (>= 3) MD5sum: 03f9213d17c19d1bd60e261a12f46f0e NeedsCompilation: no Title: Classes and methods for performing benchmark comparisons Description: This package defines the BenchDesign and SummarizedBenchmark classes for building, executing, and evaluating benchmark experiments of computational methods. The SummarizedBenchmark class extends the RangedSummarizedExperiment object, and is designed to provide infrastructure to store and compare the results of applying different methods to a shared data set. This class provides an integrated interface to store metadata such as method parameters and software versions as well as ground truths (when these are available) and evaluation metrics. biocViews: Software, Infrastructure Author: Alejandro Reyes [aut] (), Patrick Kimes [aut, cre] () Maintainer: Patrick Kimes URL: https://github.com/areyesq89/SummarizedBenchmark, http://bioconductor.org/packages/SummarizedBenchmark/ VignetteBuilder: knitr BugReports: https://github.com/areyesq89/SummarizedBenchmark/issues git_url: https://git.bioconductor.org/packages/SummarizedBenchmark git_branch: RELEASE_3_12 git_last_commit: 580cff9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SummarizedBenchmark_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SummarizedBenchmark_2.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SummarizedBenchmark_2.8.0.tgz vignettes: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.html, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.html, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.html, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.html, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.html vignetteTitles: Case Study: Benchmarking non-R Methods, Case Study: Single-Cell RNA-Seq Simulation, Feature: Error Handling, Feature: Iterative Benchmarking, Feature: Parallelization, SummarizedBenchmark: Class Details, SummarizedBenchmark: Full Case Study, SummarizedBenchmark: Introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedBenchmark/inst/doc/CaseStudy-RNAseqQuantification.R, vignettes/SummarizedBenchmark/inst/doc/CaseStudy-SingleCellSimulation.R, vignettes/SummarizedBenchmark/inst/doc/Feature-ErrorHandling.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Iterative.R, vignettes/SummarizedBenchmark/inst/doc/Feature-Parallel.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-ClassDetails.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-FullCaseStudy.R, vignettes/SummarizedBenchmark/inst/doc/SummarizedBenchmark-Introduction.R suggestsMe: benchmarkfdrData2019 dependencyCount: 77 Package: SummarizedExperiment Version: 1.20.0 Depends: R (>= 3.2), methods, MatrixGenerics (>= 1.1.3), GenomicRanges (>= 1.41.5), Biobase Imports: utils, stats, tools, Matrix, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), GenomeInfoDb (>= 1.13.1), DelayedArray (>= 0.15.10) Suggests: HDF5Array (>= 1.7.5), annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, jsonlite, rhdf5, airway, BiocStyle, knitr, rmarkdown, RUnit, testthat, digest License: Artistic-2.0 MD5sum: f5c2d170d29ab485aa9a5c61b9986a1e NeedsCompilation: no Title: SummarizedExperiment container Description: The SummarizedExperiment container contains one or more assays, each represented by a matrix-like object of numeric or other mode. The rows typically represent genomic ranges of interest and the columns represent samples. biocViews: Genetics, Infrastructure, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Martin Morgan, Valerie Obenchain, Jim Hester, Hervé Pagès Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/SummarizedExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/SummarizedExperiment/issues git_url: https://git.bioconductor.org/packages/SummarizedExperiment git_branch: RELEASE_3_12 git_last_commit: 874aa87 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SummarizedExperiment_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SummarizedExperiment_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SummarizedExperiment_1.20.0.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/Extensions.html, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html vignetteTitles: 2. Extending the SummarizedExperiment class, 1. SummarizedExperiment for Coordinating Experimental Assays,, Samples,, and Regions of Interest hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/Extensions.R, vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R dependsOnMe: AffiXcan, AllelicImbalance, ASpediaFI, bambu, BDMMAcorrect, BiocSklearn, BiSeq, bnbc, BrainSABER, bsseq, CAGEfightR, celaref, clusterExperiment, coseq, csaw, CSSQ, DaMiRseq, deco, deepSNV, DeMixT, DESeq2, DEXSeq, DiffBind, diffcoexp, diffHic, divergence, DMCFB, DMCHMM, DMRcate, EnrichmentBrowser, epigenomix, evaluomeR, EventPointer, ExperimentSubset, ExpressionAtlas, FRASER, GenoGAM, GenomicAlignments, GenomicFiles, genoset, GRmetrics, GSEABenchmarkeR, HelloRanges, hipathia, IgGeneUsage, InteractionSet, IntEREst, iSEE, isomiRs, ivygapSE, lefser, lipidr, LoomExperiment, made4, MBASED, methrix, methylPipe, minfi, miRmine, mpra, MultiAssayExperiment, NADfinder, NBAMSeq, NewWave, OUTRIDER, padma, profileplyr, recount, recount3, RegEnrich, REMP, ROCpAI, rqt, runibic, Scale4C, scGPS, scone, SDAMS, SeqGate, SGSeq, signatureSearch, simulatorZ, SingleCellExperiment, singleCellTK, SingleR, soGGi, Spaniel, spqn, ssPATHS, stageR, SummarizedBenchmark, survtype, tidySummarizedExperiment, TimeSeriesExperiment, TissueEnrich, TNBC.CMS, UMI4Cats, VanillaICE, VariantAnnotation, VariantExperiment, velociraptor, weitrix, yamss, zinbwave, airway, benchmarkfdrData2019, bodymapRat, celldex, curatedAdipoChIP, curatedAdipoRNA, DREAM4, dsQTL, fission, FlowSorted.Blood.EPIC, FlowSorted.CordBloodCombined.450k, geuvPack, HDCytoData, HighlyReplicatedRNASeq, HMP16SData, MetaGxOvarian, MetaGxPancreas, MethylSeqData, microRNAome, MouseGastrulationData, parathyroidSE, restfulSEData, sampleClassifierData, spqnData, timecoursedata, DRomics importsMe: ADAM, ADImpute, aggregateBioVar, ALDEx2, alpine, AlpsNMR, animalcules, anota2seq, APAlyzer, apeglm, appreci8R, ASICS, AUCell, BASiCS, batchelor, BayesSpace, bayNorm, BBCAnalyzer, bigPint, BiocOncoTK, biotmle, biovizBase, biscuiteer, BiSeq, blacksheepr, BRGenomics, BUMHMM, BUScorrect, CAGEr, CATALYST, cBioPortalData, ccfindR, celda, CellMixS, CellTrails, CeTF, CHETAH, ChIPpeakAnno, ChromSCape, chromVAR, CiteFuse, clustifyr, cmapR, CNVfilteR, CNVRanger, coexnet, CoGAPS, combi, compartmap, consensusDE, CopyNumberPlots, CoreGx, corral, countsimQC, cydar, cytomapper, DAMEfinder, dasper, debCAM, debrowser, DEComplexDisease, decompTumor2Sig, DEFormats, DEGreport, deltaCaptureC, DEP, DEScan2, destiny, DEWSeq, diffcyt, DiscoRhythm, distinct, dittoSeq, DominoEffect, doppelgangR, doseR, DropletUtils, Dune, eisaR, ELMER, ensemblVEP, epivizrData, erma, FCBF, fcScan, fishpond, FourCSeq, GARS, gCrisprTools, GeneTonic, GenomicDataCommons, getDEE2, GGBase, ggbio, Glimma, glmGamPoi, glmSparseNet, gQTLBase, gQTLstats, GreyListChIP, gscreend, GSVA, gwasurvivr, GWENA, HTSeqGenie, HumanTranscriptomeCompendium, hummingbird, iasva, icetea, ideal, ILoReg, infercnv, INSPEcT, InterMineR, iSEEu, iteremoval, LACE, LineagePulse, lionessR, MADSEQ, marr, MAST, mbkmeans, MBQN, mCSEA, MEAL, MEAT, MEB, metabolomicsWorkbenchR, MetaNeighbor, metaseqR2, MethReg, methyAnalysis, MethylAid, methylumi, methyvim, MinimumDistance, miRSM, missMethyl, MLSeq, MoonlightR, motifbreakR, motifmatchr, MPRAnalyze, msgbsR, MSPrep, MultiDataSet, multiOmicsViz, muscat, musicatk, MWASTools, NanoMethViz, Nebulosa, netSmooth, NormalyzerDE, oligoClasses, omicRexposome, OmicsLonDA, omicsPrint, oncomix, ORFik, OVESEG, PAIRADISE, pcaExplorer, peco, PharmacoGx, phemd, phenopath, pipeComp, pmp, proActiv, proDA, psichomics, pulsedSilac, PureCN, QFeatures, qsmooth, R453Plus1Toolbox, RadioGx, RaggedExperiment, RareVariantVis, RcisTarget, receptLoss, regionReport, regsplice, rgsepd, Rmmquant, RNAAgeCalc, RNAsense, roar, rScudo, RTCGAToolbox, RTN, SBGNview, SC3, scater, scBFA, scCB2, scDblFinder, scDD, scds, scHOT, scmap, scMerge, scmeth, SCnorm, scoreInvHap, scp, scPipe, scran, scRepertoire, scruff, scry, scTensor, scTGIF, scuttle, seqCAT, sesame, SEtools, sigFeature, SigsPack, singscore, slalom, slingshot, slinky, snapcount, SNPhood, SpatialCPie, spatialHeatmap, splatter, srnadiff, struct, switchde, systemPipeR, TBSignatureProfiler, TCGAbiolinks, TCGAbiolinksGUI, TCGAutils, TCseq, tenXplore, tidybulk, TOAST, tomoda, ToPASeq, ToxicoGx, tradeSeq, TreeSummarizedExperiment, Trendy, TSCAN, tscR, TSRchitect, TTMap, TVTB, tximeta, TxRegInfra, VariantFiltering, vidger, wpm, xcms, zellkonverter, zFPKM, BloodCancerMultiOmics2017, brgedata, CLLmethylation, COSMIC.67, curatedTCGAData, FieldEffectCrc, HMP2Data, IHWpaper, MetaGxBreast, scRNAseq, SingleCellMultiModal, spatialLIBD, TCGAWorkflowData, yriMulti, fluentGenomics, SingscoreAMLMutations, TCGAWorkflow, BinQuasi, HeritSeq, microbial, PlasmaMutationDetector, pulseTD suggestsMe: AnnotationHub, biobroom, BiocPkgTools, dcanr, dearseq, DelayedArray, edgeR, EnMCB, epivizr, epivizrChart, esetVis, GENIE3, GenomicRanges, globalSeq, gsean, HDF5Array, Informeasure, interactiveDisplay, MatrixGenerics, MOFA2, MSnbase, pathwayPCA, podkat, PubScore, RiboProfiling, S4Vectors, scFeatureFilter, semisup, StructuralVariantAnnotation, systemPipeShiny, TFutils, biotmleData, curatedAdipoArray, dorothea, DuoClustering2018, methyvimData, pathprintGEOData, RforProteomics, SBGNview.data, tissueTreg, CAGEWorkflow, clustree, conos, dyngen, polyRAD, RaceID, seqgendiff, Seurat, Signac, singleCellHaystack dependencyCount: 25 Package: supraHex Version: 1.28.2 Depends: R (>= 3.6), hexbin Imports: ape, MASS, grDevices, graphics, stats, readr, tibble, tidyr, dplyr, stringr, purrr, magrittr, igraph, methods License: GPL-2 MD5sum: 18eb04b3639ff8591a360267b4699bf2 NeedsCompilation: no Title: supraHex: a supra-hexagonal map for analysing tabular omics data Description: A supra-hexagonal map is a giant hexagon on a 2-dimensional grid seamlessly consisting of smaller hexagons. It is supposed to train, analyse and visualise a high-dimensional omics input data. The supraHex is able to carry out gene clustering/meta-clustering and sample correlation, plus intuitive visualisations to facilitate exploratory analysis. More importantly, it allows for overlaying additional data onto the trained map to explore relations between input and additional data. So with supraHex, it is also possible to carry out multilayer omics data comparisons. Newly added utilities are advanced heatmap visualisation and tree-based analysis of sample relationships. Uniquely to this package, users can ultrafastly understand any tabular omics data, both scientifically and artistically, especially in a sample-specific fashion but without loss of information on large genes. biocViews: Software, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://suprahex.r-forge.r-project.org git_url: https://git.bioconductor.org/packages/supraHex git_branch: RELEASE_3_12 git_last_commit: 73c6b44 git_last_commit_date: 2021-04-29 Date/Publication: 2021-04-29 source.ver: src/contrib/supraHex_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/supraHex_1.28.2.zip mac.binary.ver: bin/macosx/contrib/4.0/supraHex_1.28.2.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/supraHex/inst/doc/supraHex_vignettes.R dependsOnMe: dnet importsMe: Pi suggestsMe: TCGAbiolinks dependencyCount: 42 Package: survcomp Version: 1.40.0 Depends: survival, prodlim, R (>= 3.4) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta, stats, graphics Suggests: Hmisc, CPE, clinfun, xtable, Biobase, BiocManager License: Artistic-2.0 Archs: i386, x64 MD5sum: 87b733be4432db2515cbd2b886ae46b6 NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: Assessment and Comparison for Performance of Risk Prediction (Survival) Models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush, Samuel Branders, Zhaleh Safikhani Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ git_url: https://git.bioconductor.org/packages/survcomp git_branch: RELEASE_3_12 git_last_commit: 25b364b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/survcomp_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/survcomp_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/survcomp_1.40.0.tgz vignettes: vignettes/survcomp/inst/doc/survcomp.pdf vignetteTitles: SurvComp: a package for performance assessment and comparison for survival analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survcomp/inst/doc/survcomp.R dependsOnMe: genefu importsMe: metaseqR2, pencal, plsRcox, SIGN suggestsMe: glmSparseNet, GSgalgoR, metaseqR, breastCancerMAINZ, breastCancerNKI, breastCancerTRANSBIG, breastCancerUNT, breastCancerUPP, breastCancerVDX dependencyCount: 25 Package: survtype Version: 1.6.1 Depends: SummarizedExperiment, pheatmap, survival, survminer, clustvarsel, stats, utils Suggests: maftools, scales, knitr, rmarkdown License: Artistic-2.0 MD5sum: eb39cb0d9bdf67d72fa34654ac741a1e NeedsCompilation: no Title: Subtype Identification with Survival Data Description: Subtypes are defined as groups of samples that have distinct molecular and clinical features. Genomic data can be analyzed for discovering patient subtypes, associated with clinical data, especially for survival information. This package is aimed to identify subtypes that are both clinically relevant and biologically meaningful. biocViews: Software, StatisticalMethod, GeneExpression, Survival, Clustering, Sequencing, Coverage Author: Dongmin Jung Maintainer: Dongmin Jung VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/survtype git_branch: RELEASE_3_12 git_last_commit: 595b6bb git_last_commit_date: 2021-04-20 Date/Publication: 2021-04-20 source.ver: src/contrib/survtype_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/survtype_1.6.1.zip mac.binary.ver: bin/macosx/contrib/4.0/survtype_1.6.1.tgz vignettes: vignettes/survtype/inst/doc/survtype.html vignetteTitles: survtype hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/survtype/inst/doc/survtype.R dependencyCount: 147 Package: Sushi Version: 1.28.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: eb7ece539fed0a47216c7aa51a6f357e NeedsCompilation: no Title: Tools for visualizing genomics data Description: Flexible, quantitative, and integrative genomic visualizations for publication-quality multi-panel figures biocViews: DataRepresentation, Visualization, Genetics, Sequencing, Infrastructure, HiC Author: Douglas H Phanstiel Maintainer: Douglas H Phanstiel git_url: https://git.bioconductor.org/packages/Sushi git_branch: RELEASE_3_12 git_last_commit: 9e8298e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Sushi_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Sushi_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Sushi_1.28.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Sushi/inst/doc/Sushi.R importsMe: diffloop, Ularcirc, vasp, VaSP dependencyCount: 64 Package: sva Version: 3.38.0 Depends: R (>= 3.2), mgcv, genefilter, BiocParallel Imports: matrixStats, stats, graphics, utils, limma, edgeR Suggests: pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: a61d4a99e94a5e3041cd71687b66c31a NeedsCompilation: yes Title: Surrogate Variable Analysis Description: The sva package contains functions for removing batch effects and other unwanted variation in high-throughput experiment. Specifically, the sva package contains functions for the identifying and building surrogate variables for high-dimensional data sets. Surrogate variables are covariates constructed directly from high-dimensional data (like gene expression/RNA sequencing/methylation/brain imaging data) that can be used in subsequent analyses to adjust for unknown, unmodeled, or latent sources of noise. The sva package can be used to remove artifacts in three ways: (1) identifying and estimating surrogate variables for unknown sources of variation in high-throughput experiments (Leek and Storey 2007 PLoS Genetics,2008 PNAS), (2) directly removing known batch effects using ComBat (Johnson et al. 2007 Biostatistics) and (3) removing batch effects with known control probes (Leek 2014 biorXiv). Removing batch effects and using surrogate variables in differential expression analysis have been shown to reduce dependence, stabilize error rate estimates, and improve reproducibility, see (Leek and Storey 2007 PLoS Genetics, 2008 PNAS or Leek et al. 2011 Nat. Reviews Genetics). biocViews: ImmunoOncology, Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , Yuqing Zhang , John D. Storey , Leonardo Collado Torres Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson git_url: https://git.bioconductor.org/packages/sva git_branch: RELEASE_3_12 git_last_commit: 5ded8ba git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/sva_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/sva_3.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/sva_3.38.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sva/inst/doc/sva.R dependsOnMe: SCAN.UPC, rnaseqGene, bapred, leapp, SmartSVA importsMe: ASSIGN, ballgown, BatchQC, bnbc, crossmeta, CytoTree, DaMiRseq, debrowser, doppelgangR, edge, flowSpy, KnowSeq, MSPrep, omicRexposome, PAA, proBatch, PROPS, qsmooth, singleCellTK, trigger, DeSousa2013, ExpressionNormalizationWorkflow, cate, cinaR, DGEobj.utils, dSVA, seqgendiff suggestsMe: Harman, iasva, MAGeCKFlute, randRotation, RnBeads, scp, SomaticSignatures, TCGAbiolinks, tidybulk, curatedBladderData, curatedCRCData, curatedOvarianData, FieldEffectCrc, CAGEWorkflow, SuperLearner dependencyCount: 58 Package: SWATH2stats Version: 1.20.1 Depends: R(>= 2.10.0) Imports: data.table, reshape2, ggplot2, stats, grDevices, graphics, utils, biomaRt, methods Suggests: testthat, knitr, rmarkdown Enhances: MSstats, PECA, aLFQ License: GPL-3 MD5sum: 802d9d783d0e61804869e856168e3fae NeedsCompilation: no Title: Transform and Filter SWATH Data for Statistical Packages Description: This package is intended to transform SWATH data from the OpenSWATH software into a format readable by other statistics packages while performing filtering, annotation and FDR estimation. biocViews: Proteomics, Annotation, ExperimentalDesign, Preprocessing, MassSpectrometry, ImmunoOncology Author: Peter Blattmann [aut, cre] Moritz Heusel [aut] Ruedi Aebersold [aut] Maintainer: Peter Blattmann URL: https://peterblattmann.github.io/SWATH2stats/ VignetteBuilder: knitr BugReports: https://github.com/peterblattmann/SWATH2stats git_url: https://git.bioconductor.org/packages/SWATH2stats git_branch: RELEASE_3_12 git_last_commit: c27aa06 git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/SWATH2stats_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/SWATH2stats_1.20.1.zip mac.binary.ver: bin/macosx/contrib/4.0/SWATH2stats_1.20.1.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.pdf, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: SWATH2stats example script, SWATH2stats package Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SWATH2stats/inst/doc/SWATH2stats_example_script.R, vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.R dependencyCount: 82 Package: SwathXtend Version: 2.12.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: d3cdd18bcfee339eb9a3b15f99378dc9 NeedsCompilation: no Title: SWATH extended library generation and statistical data analysis Description: Contains utility functions for integrating spectral libraries for SWATH and statistical data analysis for SWATH generated data. biocViews: Software Author: J WU and D Pascovici Maintainer: Jemma Wu git_url: https://git.bioconductor.org/packages/SwathXtend git_branch: RELEASE_3_12 git_last_commit: 352f3af git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SwathXtend_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SwathXtend_2.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SwathXtend_2.12.0.tgz vignettes: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.pdf vignetteTitles: SwathXtend hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwathXtend/inst/doc/SwathXtend_vignette.R dependencyCount: 21 Package: swfdr Version: 1.16.0 Depends: R (>= 3.4) Imports: methods, splines, stats4, stats Suggests: dplyr, ggplot2, BiocStyle, knitr, qvalue, reshape2, rmarkdown, testthat License: GPL (>= 3) MD5sum: f2e9d476df194525f6e6133b931878d6 NeedsCompilation: no Title: Science-wise false discovery rate and proportion of true null hypotheses estimation Description: This package allows users to estimate the science-wise false discovery rate from Jager and Leek, "Empirical estimates suggest most published medical research is true," 2013, Biostatistics, using an EM approach due to the presence of rounding and censoring. It also allows users to estimate the false discovery rate conditional on covariates, using a regression framework, as per Boca and Leek, "A direct approach to estimating false discovery rates conditional on covariates," 2018, PeerJ. biocViews: MultipleComparison, StatisticalMethod, Software Author: Jeffrey T. Leek, Leah Jager, Simina M. Boca, Tomasz Konopka Maintainer: Simina M. Boca , Jeffrey T. Leek URL: https://github.com/leekgroup/swfdr VignetteBuilder: knitr BugReports: https://github.com/leekgroup/swfdr/issues git_url: https://git.bioconductor.org/packages/swfdr git_branch: RELEASE_3_12 git_last_commit: e384fe1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/swfdr_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/swfdr_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/swfdr_1.16.0.tgz vignettes: vignettes/swfdr/inst/doc/swfdrQ.pdf, vignettes/swfdr/inst/doc/swfdrTutorial.pdf vignetteTitles: Computing covariate-adjusted q-values, Tutorial for swfdr package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/swfdr/inst/doc/swfdrQ.R, vignettes/swfdr/inst/doc/swfdrTutorial.R dependencyCount: 4 Package: SwimR Version: 1.28.0 Depends: R (>= 3.0.0), methods, gplots (>= 2.10.1), heatmap.plus (>= 1.3), signal (>= 0.7), R2HTML (>= 2.2.1) Imports: methods License: LGPL-2 MD5sum: f739003eb56d735b71c66969f1c6184e NeedsCompilation: no Title: SwimR: A Suite of Analytical Tools for Quantification of C. elegans Swimming Behavior Description: SwimR is an R-based suite that calculates, analyses, and plots the frequency of C. elegans swimming behavior over time. It places a particular emphasis on identifying paralysis and quantifying the kinetic elements of paralysis during swimming. Data is input to SwipR from a custom built program that fits a 5 point morphometric spine to videos of single worms swimming in a buffer called Worm Tracker. biocViews: Visualization Author: Jing Wang , Andrew Hardaway and Bing Zhang Maintainer: Randy Blakely git_url: https://git.bioconductor.org/packages/SwimR git_branch: RELEASE_3_12 git_last_commit: 63b051a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SwimR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SwimR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SwimR_1.28.0.tgz vignettes: vignettes/SwimR/inst/doc/SwimR.pdf vignetteTitles: SwimR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SwimR/inst/doc/SwimR.R dependencyCount: 14 Package: switchBox Version: 1.26.0 Depends: R (>= 2.13.1), pROC, gplots License: GPL-2 Archs: i386, x64 MD5sum: 0b120de7b393fd922ef42ebae0b8daf3 NeedsCompilation: yes Title: Utilities to train and validate classifiers based on pair switching using the K-Top-Scoring-Pair (KTSP) algorithm Description: The package offer different classifiers based on comparisons of pair of features (TSP), using various decision rules (e.g., majority wins principle). biocViews: Software, StatisticalMethod, Classification Author: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara Maintainer: Bahman Afsari , Luigi Marchionni , Wikum Dinalankara git_url: https://git.bioconductor.org/packages/switchBox git_branch: RELEASE_3_12 git_last_commit: 0e8c14c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/switchBox_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/switchBox_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/switchBox_1.26.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchBox/inst/doc/switchBox.R suggestsMe: multiclassPairs dependencyCount: 11 Package: switchde Version: 1.16.0 Depends: R (>= 3.4), SingleCellExperiment Imports: SummarizedExperiment, dplyr, ggplot2, methods, stats Suggests: knitr, rmarkdown, BiocStyle, testthat, numDeriv, tidyr License: GPL (>= 2) MD5sum: f13df11cc84d9a7168f1401a23d9c09f NeedsCompilation: no Title: Switch-like differential expression across single-cell trajectories Description: Inference and detection of switch-like differential expression across single-cell RNA-seq trajectories. biocViews: ImmunoOncology, Software, Transcriptomics, GeneExpression, RNASeq, Regression, DifferentialExpression, SingleCell Author: Kieran Campbell [aut, cre] Maintainer: Kieran Campbell URL: https://github.com/kieranrcampbell/switchde VignetteBuilder: knitr BugReports: https://github.com/kieranrcampbell/switchde git_url: https://git.bioconductor.org/packages/switchde git_branch: RELEASE_3_12 git_last_commit: f2c6b64 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/switchde_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/switchde_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/switchde_1.16.0.tgz vignettes: vignettes/switchde/inst/doc/switchde_vignette.html vignetteTitles: An overview of the switchde package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/switchde/inst/doc/switchde_vignette.R dependencyCount: 61 Package: synapter Version: 2.14.0 Depends: R (>= 3.1.0), methods, MSnbase (>= 2.1.2) Imports: RColorBrewer, lattice, qvalue, multtest, utils, tools, Biobase, knitr, Biostrings, cleaver (>= 1.3.3), readr (>= 0.2), rmarkdown (>= 1.0) Suggests: synapterdata (>= 1.13.2), xtable, testthat (>= 0.8), BRAIN, BiocStyle License: GPL-2 MD5sum: f8dd54cec486e078460a538b80bcb1c9 NeedsCompilation: no Title: Label-free data analysis pipeline for optimal identification and quantitation Description: The synapter package provides functionality to reanalyse label-free proteomics data acquired on a Synapt G2 mass spectrometer. One or several runs, possibly processed with additional ion mobility separation to increase identification accuracy can be combined to other quantitation files to maximise identification and quantitation accuracy. biocViews: ImmunoOncology, MassSpectrometry, Proteomics, QualityControl Author: Laurent Gatto, Nick J. Bond, Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto and Sebastian Gibb URL: https://lgatto.github.io/synapter/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synapter git_branch: RELEASE_3_12 git_last_commit: 8cbfb2c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/synapter_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/synapter_2.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/synapter_2.14.0.tgz vignettes: vignettes/synapter/inst/doc/fragmentmatching.html, vignettes/synapter/inst/doc/synapter.html, vignettes/synapter/inst/doc/synapter2.html vignetteTitles: Fragment matching using 'synapter', Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation, Synapter2 and synergise2 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/fragmentmatching.R, vignettes/synapter/inst/doc/synapter.R, vignettes/synapter/inst/doc/synapter2.R dependsOnMe: synapterdata suggestsMe: pRoloc, RforProteomics dependencyCount: 100 Package: synergyfinder Version: 2.4.16 Depends: R (>= 4.0.0), drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Imports: drc (>= 3.0-1), reshape2 (>= 1.4.4), tidyverse (>= 1.3.0), dplyr (>= 1.0.3), tidyr (>= 1.1.2), purrr (>= 0.3.4), furrr (>= 0.2.2), ggplot2 (>= 3.3.3), ggforce (>= 0.3.2), grid (>= 4.0.2), vegan (>= 2.5-7), gstat (>= 2.0-6), sp (>= 1.4-5), methods (>= 4.0.2), SpatialExtremes (>= 2.0-9), ggrepel (>= 0.9.1), kriging (>= 1.1), plotly (>= 4.9.3), stringr (>= 1.4.0), future (>= 1.21.0), mice (>= 3.13.0), lattice (>= 0.20-41), nleqslv (>= 3.3.2), stats (>= 4.0.2), graphics (>= 4.0.2), grDevices (>= 4.0.2), magrittr (>= 2.0.1), pbapply (>= 1.4-3), metR (>= 0.9.1) Suggests: knitr, rmarkdown License: Mozilla Public License 2.0 MD5sum: 47108814cc90230e604c7a6cd1406bfe NeedsCompilation: no Title: Calculate and Visualize Synergy Scores for Drug Combinations Description: Efficient implementations for analyzing pre-clinical multiple drug combination datasets. 1. Synergy scores valuculation via all the popular models, including HSA, Loewe, Bliss and ZIP; 2. Drug Sensitivity Score (CSS) and Relitave Inhibition (RI) for drug sensitivity evaluation; 3. Visualization for drug combination matrices and scores. Based on this package, we also provide a web application (http://synergyfinder.org/) for users who prefer more friendly user interface. biocViews: Software, StatisticalMethod Author: Shuyu Zheng [aut, cre], Jing Tang [aut] Maintainer: Shuyu Zheng URL: http://synergyfinder.org/ VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synergyfinder git_branch: RELEASE_3_12 git_last_commit: 5d572fb git_last_commit_date: 2021-04-21 Date/Publication: 2021-04-21 source.ver: src/contrib/synergyfinder_2.4.16.tar.gz win.binary.ver: bin/windows/contrib/4.0/synergyfinder_2.4.16.zip mac.binary.ver: bin/macosx/contrib/4.0/synergyfinder_2.4.16.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 182 Package: SynExtend Version: 1.2.0 Depends: R (>= 4.0.0), DECIPHER (>= 2.14.0), igraph (>= 1.2.4.1) Imports: methods, Biostrings, S4Vectors, IRanges, utils, stats Suggests: knitr License: GPL-3 MD5sum: bf30200a3ab05ae0e560ad85b5dd0251 NeedsCompilation: no Title: Tools for Working With Synteny Objects Description: Shared order between genomic sequences provide a great deal of information. Synteny objects produced by the R package DECIPHER provides quantitative information about that shared order. SynExtend provides tools for extracting information from Synteny objects. biocViews: Genetics, Clustering, ComparativeGenomics, DataImport Author: Nicholas Cooley [aut, cre] (), Adelle Fernando [ctb], Erik Wright [aut] Maintainer: Nicholas Cooley VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/SynExtend git_branch: RELEASE_3_12 git_last_commit: 5786318 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SynExtend_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SynExtend_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SynExtend_1.2.0.tgz vignettes: vignettes/SynExtend/inst/doc/UsingSynExtend.html vignetteTitles: ExploreModelMatrix hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynExtend/inst/doc/UsingSynExtend.R dependencyCount: 36 Package: synlet Version: 1.20.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat License: GPL-3 MD5sum: cd60e01eddc64ab355d488a46c8e3447 NeedsCompilation: no Title: Hits Selection for Synthetic Lethal RNAi Screen Data Description: Select hits from synthetic lethal RNAi screen data. For example, there are two identical celllines except one gene is knocked-down in one cellline. The interest is to find genes that lead to stronger lethal effect when they are knocked-down further by siRNA. Quality control and various visualisation tools are implemented. Four different algorithms could be used to pick up the interesting hits. This package is designed based on 384 wells plates, but may apply to other platforms with proper configuration. biocViews: ImmunoOncology, CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao Maintainer: Chunxuan Shao VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/synlet git_branch: RELEASE_3_12 git_last_commit: 83ffa0c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/synlet_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/synlet_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/synlet_1.20.0.tgz vignettes: vignettes/synlet/inst/doc/synlet-vignette.html vignetteTitles: A working Demo for synlet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R dependencyCount: 75 Package: SynMut Version: 1.6.0 Imports: seqinr, methods, Biostrings, stringr, BiocGenerics Suggests: BiocManager, knitr, rmarkdown, testthat, devtools, prettydoc, glue License: GPL-2 MD5sum: 14f9d30c047092f72e597ee4c35d553d NeedsCompilation: no Title: SynMut: Designing Synonymously Mutated Sequences with Different Genomic Signatures Description: There are increasing demands on designing virus mutants with specific dinucleotide or codon composition. This tool can take both dinucleotide preference and/or codon usage bias into account while designing mutants. It is a powerful tool for in silico designs of DNA sequence mutants. biocViews: SequenceMatching, ExperimentalDesign, Preprocessing Author: Haogao Gu [aut, cre], Leo L.M. Poon [led] Maintainer: Haogao Gu URL: https://github.com/Koohoko/SynMut VignetteBuilder: knitr BugReports: https://github.com/Koohoko/SynMut/issues git_url: https://git.bioconductor.org/packages/SynMut git_branch: RELEASE_3_12 git_last_commit: 5f24d78 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/SynMut_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/SynMut_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/SynMut_1.6.0.tgz vignettes: vignettes/SynMut/inst/doc/SynMut.html vignetteTitles: SynMut: Designing Synonymous Mutants for DNA Sequences hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SynMut/inst/doc/SynMut.R dependencyCount: 36 Package: systemPipeR Version: 1.24.6 Depends: Rsamtools (>= 1.31.2), Biostrings, ShortRead (>= 1.37.1), methods Imports: GenomicRanges, GenomicFeatures (>= 1.31.3), SummarizedExperiment, VariantAnnotation (>= 1.25.11), rjson, ggplot2, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, batchtools, yaml, stringr, assertthat, magrittr, DOT, rsvg, IRanges Suggests: BiocGenerics, ape, RUnit, BiocStyle, knitr, rmarkdown, biomaRt, BiocParallel, BiocManager, systemPipeRdata, GenomicAlignments, grid License: Artistic-2.0 MD5sum: 3ff5f813c8a79d934106b7250f5568b1 NeedsCompilation: no Title: systemPipeR: NGS workflow and report generation environment Description: R package for building and running automated end-to-end analysis workflows for a wide range of next generation sequence (NGS) applications such as RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Important features include a uniform workflow interface across different NGS applications, automated report generation, and support for running both R and command-line software, such as NGS aligners or peak/variant callers, on local computers or compute clusters. Efficient handling of complex sample sets and experimental designs is facilitated by a consistently implemented sample annotation infrastructure. Instructions for using systemPipeR are given in the Overview Vignette (HTML). The remaining Vignettes, linked below, are workflow templates for common NGS use cases. biocViews: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, RiboSeq, ChIPSeq, MethylSeq, SNP, GeneExpression, Coverage, GeneSetEnrichment, Alignment, QualityControl, ImmunoOncology, ReportWriting, Workflow Author: Thomas Girke Maintainer: Thomas Girke URL: https://girke.bioinformatics.ucr.edu/systemPipeR/ SystemRequirements: systemPipeR can be used to run external command-line software (e.g. short read aligners), but the corresponding tool needs to be installed on a system. VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/systemPipeR git_branch: RELEASE_3_12 git_last_commit: a9dc042 git_last_commit_date: 2021-05-01 Date/Publication: 2021-05-01 source.ver: src/contrib/systemPipeR_1.24.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/systemPipeR_1.24.6.zip mac.binary.ver: bin/macosx/contrib/4.0/systemPipeR_1.24.6.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.html, vignettes/systemPipeR/inst/doc/systemPipeR.html vignetteTitles: systemPipeR: Workflows collection, systemPipeR: Workflow design and reporting generation environment hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeR_workflows.R, vignettes/systemPipeR/inst/doc/systemPipeR.R importsMe: DiffBind, RNASeqR suggestsMe: systemPipeShiny, systemPipeRdata dependencyCount: 141 Package: systemPipeShiny Version: 1.0.20 Depends: R (>= 4.0.0), shiny (>= 1.5.0), spsUtil, spsComps (>= 0.1.1), drawer Imports: DT, assertthat, bsplus, crayon, dplyr, ggplot2, glue, magrittr, methods, plotly, rlang, rstudioapi, shinyAce, shinyFiles, shinyWidgets, shinydashboard, shinydashboardPlus (>= 2.0.0), shinyjqui, shinyjs, shinytoastr, stringr, stats, styler, tibble, utils, vroom (>= 1.3.1), yaml Suggests: testthat, BiocStyle, knitr, rmarkdown, systemPipeR, systemPipeRdata, networkD3, rhandsontable, zip, callr, pushbar, fs, openssl, readr, R.utils, DOT, shinyTree, DESeq2, SummarizedExperiment, glmpca, pheatmap, grid, ape, ggtree, Rtsne, UpSetR, tidyr, esquisse, cicerone License: GPL (>= 3) MD5sum: ca5696a6a6396ed24b9eac63d1296988 NeedsCompilation: no Title: systemPipeShiny: An Interactive Framework for Workflow Management and Visualization Description: systemPipeShiny (SPS) extends the widely used systemPipeR (SPR) workflow environment with a versatile graphical user interface provided by a Shiny App. This allows non-R users, such as experimentalists, to run many systemPipeR’s workflow designs, control, and visualization functionalities interactively without requiring knowledge of R. Most importantly, SPS has been designed as a general purpose framework for interacting with other R packages in an intuitive manner. Like most Shiny Apps, SPS can be used on both local computers as well as centralized server-based deployments that can be accessed remotely as a public web service for using SPR’s functionalities with community and/or private data. The framework can integrate many core packages from the R/Bioconductor ecosystem. Examples of SPS’ current functionalities include: (a) interactive creation of experimental designs and metadata using an easy to use tabular editor or file uploader; (b) visualization of workflow topologies combined with auto-generation of R Markdown preview for interactively designed workflows; (d) access to a wide range of data processing routines; (e) and an extendable set of visualization functionalities. Complex visual results can be managed on a 'Canvas Workbench’ allowing users to organize and to compare plots in an efficient manner combined with a session snapshot feature to continue work at a later time. The present suite of pre-configured visualization examples. The modular design of SPR makes it easy to design custom functions without any knowledge of Shiny, as well as extending the environment in the future with contributions from the community. biocViews: Infrastructure, DataImport, Sequencing, QualityControl, ReportWriting, ExperimentalDesign, Clustering Author: Le Zhang [aut, cre], Daniela Cassol [aut], Ponmathi Ramasamy [aut], Jianhai Zhang [aut], Gordon Mosher [aut], Thomas Girke [aut] Maintainer: Le Zhang URL: https://github.com/systemPipeR/systemPipeShiny VignetteBuilder: knitr BugReports: https://github.com/systemPipeR/systemPipeShiny/issues git_url: https://git.bioconductor.org/packages/systemPipeShiny git_branch: RELEASE_3_12 git_last_commit: 51f0dac git_last_commit_date: 2021-03-15 Date/Publication: 2021-03-15 source.ver: src/contrib/systemPipeShiny_1.0.20.tar.gz win.binary.ver: bin/windows/contrib/4.0/systemPipeShiny_1.0.20.zip mac.binary.ver: bin/macosx/contrib/4.0/systemPipeShiny_1.0.20.tgz vignettes: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.html vignetteTitles: systemPipeShiny hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeShiny/inst/doc/systemPipeShiny.R dependencyCount: 116 Package: TADCompare Version: 1.0.0 Depends: R (>= 4.0) Imports: dplyr, PRIMME, cluster, Matrix, magrittr, HiCcompare, ggplot2, tidyr, ggpubr, RColorBrewer, reshape2, cowplot Suggests: BiocStyle, knitr, rmarkdown, microbenchmark, testthat, covr, pheatmap, rGREAT, SpectralTAD License: MIT + file LICENSE MD5sum: 0a5dff2aa249933cd86269cdfcaf2ef8 NeedsCompilation: no Title: TADCompare: Identification and characterization of differential TADs Description: TADCompare is an R package designed to identify and characterize differential Topologically Associated Domains (TADs) between multiple Hi-C contact matrices. It contains functions for finding differential TADs between two datasets, finding differential TADs over time and identifying consensus TADs across multiple matrices. It takes all of the main types of HiC input and returns simple, comprehensive, easy to analyze results. biocViews: Software, HiC, Sequencing, FeatureExtraction, Clustering Author: Kellen Cresswell , Mikhail Dozmorov Maintainer: Kellen Cresswell VignetteBuilder: knitr BugReports: https://github.com/dozmorovlab/TADCompare/issues git_url: https://git.bioconductor.org/packages/TADCompare git_branch: RELEASE_3_12 git_last_commit: da4ed3d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TADCompare_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TADCompare_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TADCompare_1.0.0.tgz vignettes: vignettes/TADCompare/inst/doc/Input_Data.html, vignettes/TADCompare/inst/doc/Ontology_Analysis.html, vignettes/TADCompare/inst/doc/TADCompare.html vignetteTitles: Input data formats, Gene Ontology Enrichment Analysis, TAD comparison between two conditions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TADCompare/inst/doc/Input_Data.R, vignettes/TADCompare/inst/doc/Ontology_Analysis.R, vignettes/TADCompare/inst/doc/TADCompare.R dependencyCount: 157 Package: TAPseq Version: 1.2.0 Depends: R (>= 4.0) Imports: methods, GenomicAlignments, GenomicRanges, IRanges, BiocGenerics, S4Vectors (>= 0.20.1), GenomeInfoDb, BSgenome, GenomicFeatures, Biostrings, dplyr, tidyr, BiocParallel Suggests: testthat, BSgenome.Hsapiens.UCSC.hg38, knitr, rmarkdown, ggplot2, Seurat, glmnet, cowplot, Matrix, rtracklayer License: MIT + file LICENSE MD5sum: 76c2e1ba273ab0ead1f4458c5eab184a NeedsCompilation: no Title: Targeted scRNA-seq primer design for TAP-seq Description: Design primers for targeted single-cell RNA-seq used by TAP-seq. Create sequence templates for target gene panels and design gene-specific primers using Primer3. Potential off-targets can be estimated with BLAST. Requires working installations of Primer3 and BLASTn. biocViews: SingleCell, Sequencing, Technology, CRISPR, PooledScreens Author: Andreas Gschwind [aut, cre] (), Lars Velten [aut] (), Lars Steinmetz [aut] Maintainer: Andreas Gschwind URL: https://github.com/argschwind/TAPseq SystemRequirements: Primer3 (>= 2.5.0), BLAST+ (>=2.6.0) VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TAPseq git_branch: RELEASE_3_12 git_last_commit: ab2957a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TAPseq_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TAPseq_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TAPseq_1.2.0.tgz vignettes: vignettes/TAPseq/inst/doc/tapseq_primer_design.html, vignettes/TAPseq/inst/doc/tapseq_target_genes.html vignetteTitles: TAP-seq primer design workflow, Select target genes for TAP-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TAPseq/inst/doc/tapseq_primer_design.R, vignettes/TAPseq/inst/doc/tapseq_target_genes.R dependencyCount: 91 Package: target Version: 1.4.0 Depends: R (>= 3.6) Imports: BiocGenerics, GenomicRanges, IRanges, matrixStats, methods, stats, graphics, shiny Suggests: testthat (>= 2.1.0), knitr, rmarkdown, shinytest, shinyBS, covr License: GPL-3 MD5sum: 935deb72670ab566e02ff3acc26ddf4d NeedsCompilation: no Title: Predict Combined Function of Transcription Factors Description: Implement the BETA algorithm for infering direct target genes from DNA-binding and perturbation expression data Wang et al. (2013) . Extend the algorithm to predict the combined function of two DNA-binding elements from comprable binding and expression data. biocViews: Software, StatisticalMethod, Transcription Author: Mahmoud Ahmed [aut, cre] Maintainer: Mahmoud Ahmed URL: https://github.com/MahShaaban/target VignetteBuilder: knitr BugReports: https://github.com/MahShaaban/target/issues git_url: https://git.bioconductor.org/packages/target git_branch: RELEASE_3_12 git_last_commit: b6298bd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/target_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/target_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/target_1.4.0.tgz vignettes: vignettes/target/inst/doc/extend-target.html, vignettes/target/inst/doc/target.html vignetteTitles: Using target to predict combined binding, Using the target package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/target/inst/doc/extend-target.R, vignettes/target/inst/doc/target.R dependencyCount: 47 Package: TargetScore Version: 1.28.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: 143502b455f795d43055a9ba259e8ae4 NeedsCompilation: no Title: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information Description: Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html git_url: https://git.bioconductor.org/packages/TargetScore git_branch: RELEASE_3_12 git_last_commit: 88c2b1e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TargetScore_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TargetScore_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TargetScore_1.28.0.tgz vignettes: vignettes/TargetScore/inst/doc/TargetScore.pdf vignetteTitles: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetScore/inst/doc/TargetScore.R suggestsMe: TargetScoreData dependencyCount: 9 Package: TargetSearch Version: 1.46.3 Depends: ncdf4 Imports: graphics, grDevices, methods, stats, utils, assertthat Suggests: TargetSearchData, BiocStyle, knitr, tinytest License: GPL (>= 2) Archs: i386, x64 MD5sum: 7b1c971db0d1ee63d8a3bfeb6d413fd4 NeedsCompilation: yes Title: A package for the analysis of GC-MS metabolite profiling data Description: This packages provides a targeted pre-processing method for GC-MS data. biocViews: MassSpectrometry, Preprocessing, DecisionTree, ImmunoOncology Author: Alvaro Cuadros-Inostroza , Jan Lisec, Henning Redestig, Matt Hannah Maintainer: Alvaro Cuadros-Inostroza URL: https://github.com/acinostroza/TargetSearch VignetteBuilder: knitr BugReports: https://github.com/acinostroza/TargetSearch/issues git_url: https://git.bioconductor.org/packages/TargetSearch git_branch: RELEASE_3_12 git_last_commit: 8c2e91b git_last_commit_date: 2021-03-10 Date/Publication: 2021-03-10 source.ver: src/contrib/TargetSearch_1.46.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/TargetSearch_1.46.3.zip mac.binary.ver: bin/macosx/contrib/4.0/TargetSearch_1.46.3.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction extra, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RetentionIndexCorrection.R, vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R dependencyCount: 8 Package: TarSeqQC Version: 1.20.0 Depends: R (>= 3.5.1), methods, GenomicRanges, Rsamtools (>= 1.9.2), ggplot2, plyr, openxlsx Imports: grDevices, stats, utils, S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, Biostrings, cowplot, graphics, GenomicAlignments, Hmisc Suggests: BiocManager, RUnit License: GPL (>=2) MD5sum: 4bbf823a42fca4fc889aa75a0ef5457e NeedsCompilation: no Title: TARgeted SEQuencing Experiment Quality Control Description: The package allows the representation of targeted experiment in R. This is based on current packages and incorporates functions to do a quality control over this kind of experiments and a fast exploration of the sequenced regions. An xlsx file is generated as output. biocViews: Software, Sequencing, TargetedResequencing, QualityControl, Visualization, Coverage, Alignment, DataImport Author: Gabriela A. Merino, Cristobal Fresno, Yanina Murua, Andrea S. Llera and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar git_url: https://git.bioconductor.org/packages/TarSeqQC git_branch: RELEASE_3_12 git_last_commit: 96ddeb9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TarSeqQC_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/TarSeqQC_1.20.0.tgz vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality Control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.R dependencyCount: 103 Package: TBSignatureProfiler Version: 1.2.0 Depends: R (>= 4.0.0) Imports: ASSIGN (>= 1.23.1), GSVA, SummarizedExperiment, S4Vectors, methods, BiocParallel, ComplexHeatmap, RColorBrewer, ggplot2, reshape2, circlize, glmnet, ROCit, bioDist, readr, boot, DESeq2, caret, ggfortify, e1071, DT, edgeR, singscore, gdata, shiny Suggests: testthat, spelling, lintr, covr, knitr, rmarkdown, BiocStyle License: MIT + file LICENSE MD5sum: ab96c901083889db28fd1a668c771140 NeedsCompilation: no Title: Profile RA-Seq Data Using TB Pathway Signatures Description: Signatures of TB progression, TB disease, and other TB disease states have been created. This package makes it easy to profile RNA-Seq data using these signatures and common signature profiling tools including ASSIGN, GSVA, and ssGSEA. biocViews: GeneExpression, DifferentialExpression Author: David Jenkins [aut, cre], Yue Zhao [aut], W. Evan Johnson [aut], Aubrey Odom [aut], Christian Love [aut] Maintainer: David Jenkins URL: https://github.com/compbiomed/TBSignatureProfiler VignetteBuilder: knitr BugReports: https://github.com/compbiomed/TBSignatureProfiler/issues git_url: https://git.bioconductor.org/packages/TBSignatureProfiler git_branch: RELEASE_3_12 git_last_commit: 16ccdd3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TBSignatureProfiler_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TBSignatureProfiler_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TBSignatureProfiler_1.2.0.tgz vignettes: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.html vignetteTitles: "Introduction to the TBSignatureProfiler" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TBSignatureProfiler/inst/doc/tbspVignette.R dependencyCount: 178 Package: TCC Version: 1.30.0 Depends: R (>= 3.0), methods, DESeq2, edgeR, baySeq, ROC Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: bc8328a01c8d44306cb023a2cebdae72 NeedsCompilation: no Title: TCC: Differential expression analysis for tag count data with robust normalization strategies Description: This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages. biocViews: ImmunoOncology, Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama git_url: https://git.bioconductor.org/packages/TCC git_branch: RELEASE_3_12 git_last_commit: 40c62a0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TCC_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TCC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TCC_1.30.0.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCC/inst/doc/TCC.R suggestsMe: compcodeR dependencyCount: 103 Package: TCGAbiolinks Version: 2.18.0 Depends: R (>= 4.0) Imports: downloader (>= 0.4), grDevices, biomaRt, dplyr, graphics, tibble, GenomicRanges, XML (>= 3.98.0), data.table, jsonlite (>= 1.0.0), plyr, knitr, methods, ggplot2, stringr (>= 1.0.0), IRanges, rvest (>= 0.3.0), stats, utils, S4Vectors, R.utils, SummarizedExperiment (>= 1.4.0), TCGAbiolinksGUI.data, readr, tools, tidyr, purrr, xml2, httr (>= 1.2.1) Suggests: jpeg, png, BiocStyle, rmarkdown, devtools, maftools, parmigene, c3net, minet, dnet, Biobase, affy, testthat, sesame, pathview, clusterProfiler, ComplexHeatmap, circlize, ConsensusClusterPlus, igraph, supraHex, limma, edgeR, sva, EDASeq, survminer, genefilter, gridExtra, survival, doParallel, parallel, ggrepel (>= 0.6.3), scales, grid License: GPL (>= 3) MD5sum: fe386cee73e48b931a72e47edf3f0608 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with GDC data Description: The aim of TCGAbiolinks is : i) facilitate the GDC open-access data retrieval, ii) prepare the data using the appropriate pre-processing strategies, iii) provide the means to carry out different standard analyses and iv) to easily reproduce earlier research results. In more detail, the package provides multiple methods for analysis (e.g., differential expression analysis, identifying differentially methylated regions) and methods for visualization (e.g., survival plots, volcano plots, starburst plots) in order to easily develop complete analysis pipelines. biocViews: DNAMethylation, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Pathways, Network, Sequencing, Survival, Software Author: Antonio Colaprico, Tiago Chedraoui Silva, Catharina Olsen, Luciano Garofano, Davide Garolini, Claudia Cava, Thais Sabedot, Tathiane Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Tiago Chedraoui Silva , Antonio Colaprico URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues git_url: https://git.bioconductor.org/packages/TCGAbiolinks git_branch: RELEASE_3_12 git_last_commit: df6b579 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TCGAbiolinks_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TCGAbiolinks_2.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TCGAbiolinks_2.18.0.tgz vignettes: vignettes/TCGAbiolinks/inst/doc/analysis.html, vignettes/TCGAbiolinks/inst/doc/casestudy.html, vignettes/TCGAbiolinks/inst/doc/classifiers.html, vignettes/TCGAbiolinks/inst/doc/clinical.html, vignettes/TCGAbiolinks/inst/doc/download_prepare.html, vignettes/TCGAbiolinks/inst/doc/extension.html, vignettes/TCGAbiolinks/inst/doc/gui.html, vignettes/TCGAbiolinks/inst/doc/index.html, vignettes/TCGAbiolinks/inst/doc/mutation.html, vignettes/TCGAbiolinks/inst/doc/query.html, vignettes/TCGAbiolinks/inst/doc/subtypes.html vignetteTitles: 7. Analyzing and visualizing TCGA data, 8. Case Studies, 10. Classifiers, "4. Clinical data", "3. Downloading and preparing files for analysis", "10. TCGAbiolinks_Extension", "9. Graphical User Interface (GUI)", "1. Introduction", "5. Mutation data", "2. Searching GDC database", 6. Compilation of TCGA molecular subtypes hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/analysis.R, vignettes/TCGAbiolinks/inst/doc/casestudy.R, vignettes/TCGAbiolinks/inst/doc/classifiers.R, vignettes/TCGAbiolinks/inst/doc/clinical.R, vignettes/TCGAbiolinks/inst/doc/download_prepare.R, vignettes/TCGAbiolinks/inst/doc/extension.R, vignettes/TCGAbiolinks/inst/doc/gui.R, vignettes/TCGAbiolinks/inst/doc/index.R, vignettes/TCGAbiolinks/inst/doc/mutation.R, vignettes/TCGAbiolinks/inst/doc/query.R, vignettes/TCGAbiolinks/inst/doc/subtypes.R dependsOnMe: MAFDash importsMe: ELMER, MoonlightR, SpidermiR, TCGAbiolinksGUI, SingscoreAMLMutations, TCGAWorkflow suggestsMe: Rediscover dependencyCount: 110 Package: TCGAbiolinksGUI Version: 1.16.0 Depends: R (>= 3.3.1), shinydashboard (>= 0.5.3), TCGAbiolinksGUI.data Imports: shiny (>= 0.14.1), downloader (>= 0.4), grid, DT, plotly, readr, maftools, stringr (>= 1.1.0), SummarizedExperiment, ggrepel, data.table, caret, shinyFiles (>= 0.6.2), ggplot2 (>= 2.1.0), pathview, ELMER (>= 2.0.0), clusterProfiler, parallel, TCGAbiolinks (>= 2.5.5), shinyjs (>= 0.7), colourpicker, sesame, shinyBS (>= 0.61) Suggests: testthat, dplyr, knitr, roxygen2, devtools, rvest, xml2, BiocStyle, animation, pander License: GPL (>= 3) MD5sum: 5408bc45c18b818f306088a6a45dac28 NeedsCompilation: no Title: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data" Description: "TCGAbiolinksGUI: A Graphical User Interface to analyze cancer molecular and clinical data. A demo version of GUI is found in https://tcgabiolinksgui.shinyapps.io/tcgabiolinks/" biocViews: Genetics, GUI, DNAMethylation, StatisticalMethod, DifferentialMethylation, GeneRegulation, GeneExpression, MethylationArray, DifferentialExpression, Sequencing, Pathways, Network, DNASeq Author: Tiago Chedraoui Silva , Antonio Colaprico , Catharina Olsen , Michele Ceccarelli, Gianluca Bontempi , Benjamin P. Berman , Houtan Noushmehr Maintainer: Tiago C. Silva VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TCGAbiolinksGUI git_branch: RELEASE_3_12 git_last_commit: 215515b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TCGAbiolinksGUI_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TCGAbiolinksGUI_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TCGAbiolinksGUI_1.16.0.tgz vignettes: vignettes/TCGAbiolinksGUI/inst/doc/analysis.html, vignettes/TCGAbiolinksGUI/inst/doc/Cases.html, vignettes/TCGAbiolinksGUI/inst/doc/data.html, vignettes/TCGAbiolinksGUI/inst/doc/index.html, vignettes/TCGAbiolinksGUI/inst/doc/integrative.html vignetteTitles: "3. Analysis menu", "5. Cases study", "2. Data menu", "1. Introduction", "4. Integrative analysis menu" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinksGUI/inst/doc/data.R, vignettes/TCGAbiolinksGUI/inst/doc/index.R dependencyCount: 277 Package: TCGAutils Version: 1.10.1 Depends: R (>= 4.0.0) Imports: AnnotationDbi, BiocGenerics, GenomeInfoDb, GenomicFeatures, GenomicRanges, GenomicDataCommons, IRanges, methods, MultiAssayExperiment, RaggedExperiment (>= 1.5.7), rvest, S4Vectors, stats, stringr, SummarizedExperiment, utils, xml2 Suggests: BiocFileCache, BiocStyle, curatedTCGAData, ComplexHeatmap, devtools, dplyr, IlluminaHumanMethylation450kanno.ilmn12.hg19, impute, knitr, magrittr, mirbase.db, org.Hs.eg.db, RColorBrewer, readr, rmarkdown, RTCGAToolbox (>= 2.17.4), rtracklayer, R.utils, testthat, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: b32a47801fded6742d49583d387afd0e NeedsCompilation: no Title: TCGA utility functions for data management Description: A suite of helper functions for checking and manipulating TCGA data including data obtained from the curatedTCGAData experiment package. These functions aim to simplify and make working with TCGA data more manageable. biocViews: Software, WorkflowStep, Preprocessing Author: Marcel Ramos [aut, cre], Lucas Schiffer [aut], Sean Davis [ctb], Levi Waldron [aut] Maintainer: Marcel Ramos VignetteBuilder: knitr BugReports: https://github.com/waldronlab/TCGAutils/issues git_url: https://git.bioconductor.org/packages/TCGAutils git_branch: RELEASE_3_12 git_last_commit: d619ddd git_last_commit_date: 2021-04-16 Date/Publication: 2021-04-16 source.ver: src/contrib/TCGAutils_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/TCGAutils_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/TCGAutils_1.10.1.tgz vignettes: vignettes/TCGAutils/inst/doc/TCGAutils.html vignetteTitles: TCGAutils Essentials hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAutils/inst/doc/TCGAutils.R importsMe: cBioPortalData, RTCGAToolbox suggestsMe: CNVRanger, glmSparseNet, netDx, curatedTCGAData dependencyCount: 97 Package: TCseq Version: 1.14.0 Depends: R (>= 3.4) Imports: edgeR, BiocGenerics, reshape2, GenomicRanges, IRanges, SummarizedExperiment, GenomicAlignments, Rsamtools, e1071, cluster, ggplot2, grid, grDevices, stats, utils, methods, locfit Suggests: testthat License: GPL (>= 2) MD5sum: b820e36cd770a6c2898920f3b35a0930 NeedsCompilation: no Title: Time course sequencing data analysis Description: Quantitative and differential analysis of epigenomic and transcriptomic time course sequencing data, clustering analysis and visualization of temporal patterns of time course data. biocViews: Epigenetics, TimeCourse, Sequencing, ChIPSeq, RNASeq, DifferentialExpression, Clustering, Visualization Author: Mengjun Wu , Lei Gu Maintainer: Mengjun Wu git_url: https://git.bioconductor.org/packages/TCseq git_branch: RELEASE_3_12 git_last_commit: 3a210f7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TCseq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TCseq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TCseq_1.14.0.tgz vignettes: vignettes/TCseq/inst/doc/TCseq.pdf vignetteTitles: TCseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCseq/inst/doc/TCseq.R dependencyCount: 79 Package: TDARACNE Version: 1.40.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: 172d68bedbb727a40eb3d399f829e803 NeedsCompilation: no Title: Network reverse engineering from time course data. Description: To infer gene networks from time-series measurements is a current challenge into bioinformatics research area. In order to detect dependencies between genes at different time delays, we propose an approach to infer gene regulatory networks from time-series measurements starting from a well known algorithm based on information theory. The proposed algorithm is expected to be useful in reconstruction of small biological directed networks from time course data. biocViews: Microarray, TimeCourse Author: Zoppoli P.,Morganella S., Ceccarelli M. Maintainer: Zoppoli Pietro git_url: https://git.bioconductor.org/packages/TDARACNE git_branch: RELEASE_3_12 git_last_commit: c8e5b94 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TDARACNE_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TDARACNE_1.40.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TDARACNE_1.40.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TDARACNE/inst/doc/TDARACNE.R dependencyCount: 14 Package: tenXplore Version: 1.12.0 Depends: R (>= 3.4), shiny, restfulSE (>= 0.99.12) Imports: methods, ontoProc (>= 0.99.7), SummarizedExperiment, AnnotationDbi, matrixStats, org.Mm.eg.db, stats, utils Suggests: org.Hs.eg.db, testthat, knitr License: Artistic-2.0 MD5sum: 14ceb7f4ad3906f3045d95e1c4cadc04 NeedsCompilation: no Title: ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics Description: Perform ontological exploration of scRNA-seq of 1.3 million mouse neurons from 10x genomics. biocViews: ImmunoOncology, DimensionReduction, PrincipalComponent, Transcriptomics, SingleCell Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tenXplore git_branch: RELEASE_3_12 git_last_commit: c1bb861 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tenXplore_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tenXplore_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tenXplore_1.12.0.tgz vignettes: vignettes/tenXplore/inst/doc/tenXplore.html vignetteTitles: tenXplore: ontology for scRNA-seq,, applied to 10x 1.3 million neurons hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tenXplore/inst/doc/tenXplore.R dependencyCount: 114 Package: TEQC Version: 4.12.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 8c7a1a73706ce4881a133dda2ad719cb NeedsCompilation: no Title: Quality control for target capture experiments Description: Target capture experiments combine hybridization-based (in solution or on microarrays) capture and enrichment of genomic regions of interest (e.g. the exome) with high throughput sequencing of the captured DNA fragments. This package provides functionalities for assessing and visualizing the quality of the target enrichment process, like specificity and sensitivity of the capture, per-target read coverage and so on. biocViews: QualityControl, Microarray, Sequencing, Genetics Author: M. Hummel, S. Bonnin, E. Lowy, G. Roma Maintainer: Manuela Hummel git_url: https://git.bioconductor.org/packages/TEQC git_branch: RELEASE_3_12 git_last_commit: ec0c464 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TEQC_4.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TEQC_4.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TEQC_4.12.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TEQC/inst/doc/TEQC.R dependencyCount: 31 Package: ternarynet Version: 1.34.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: c23a7709a46449a66610027e649795ff NeedsCompilation: yes Title: Ternary Network Estimation Description: A computational Bayesian approach to ternary gene regulatory network estimation from gene perturbation experiments. biocViews: Software, CellBiology, GraphAndNetwork Author: Matthew N. McCall , Anthony Almudevar , David Burton , Harry Stern Maintainer: Matthew N. McCall git_url: https://git.bioconductor.org/packages/ternarynet git_branch: RELEASE_3_12 git_last_commit: 021b6fd git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ternarynet_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ternarynet_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ternarynet_1.34.0.tgz vignettes: vignettes/ternarynet/inst/doc/ternarynet.pdf vignetteTitles: ternarynet: A Computational Bayesian Approach to Ternary Network Estimation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ternarynet/inst/doc/ternarynet.R dependencyCount: 11 Package: TFARM Version: 1.12.0 Depends: R (>= 3.4) Imports: arules, fields, GenomicRanges, graphics, stringr, methods, stats, gplots Suggests: BiocStyle, knitr, plyr License: Artistic-2.0 MD5sum: 90ba2fdeb960c3e63d6155215a93a091 NeedsCompilation: no Title: Transcription Factors Association Rules Miner Description: It searches for relevant associations of transcription factors with a transcription factor target, in specific genomic regions. It also allows to evaluate the Importance Index distribution of transcription factors (and combinations of transcription factors) in association rules. biocViews: BiologicalQuestion, Infrastructure, StatisticalMethod, Transcription Author: Liuba Nausicaa Martino, Alice Parodi, Gaia Ceddia, Piercesare Secchi, Stefano Campaner, Marco Masseroli Maintainer: Liuba Nausicaa Martino VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFARM git_branch: RELEASE_3_12 git_last_commit: e96b7f4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TFARM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TFARM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TFARM_1.12.0.tgz vignettes: vignettes/TFARM/inst/doc/TFARM.pdf vignetteTitles: Transcription Factor Association Rule Miner hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFARM/inst/doc/TFARM.R dependencyCount: 34 Package: TFBSTools Version: 1.28.0 Depends: R (>= 3.2.2) Imports: Biobase(>= 2.28), Biostrings(>= 2.36.4), BiocGenerics(>= 0.14.0), BiocParallel(>= 1.2.21), BSgenome(>= 1.36.3), caTools(>= 1.17.1), CNEr(>= 1.4.0), DirichletMultinomial(>= 1.10.0), GenomeInfoDb(>= 1.6.1), GenomicRanges(>= 1.20.6), gtools(>= 3.5.0), grid, IRanges(>= 2.2.7), methods, DBI (>= 0.6), RSQLite(>= 1.0.0), rtracklayer(>= 1.28.10), seqLogo(>= 1.34.0), S4Vectors(>= 0.9.25), TFMPvalue(>= 0.0.5), XML(>= 3.98-1.3), XVector(>= 0.8.0), parallel Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0), JASPAR2018(>= 1.0.0) License: GPL-2 Archs: i386, x64 MD5sum: e84a0debde9f458174967613bfd20b2d NeedsCompilation: yes Title: Software Package for Transcription Factor Binding Site (TFBS) Analysis Description: TFBSTools is a package for the analysis and manipulation of transcription factor binding sites. It includes matrices conversion between Position Frequency Matirx (PFM), Position Weight Matirx (PWM) and Information Content Matrix (ICM). It can also scan putative TFBS from sequence/alignment, query JASPAR database and provides a wrapper of de novo motif discovery software. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan [aut, cre] Maintainer: Ge Tan URL: https://github.com/ge11232002/TFBSTools VignetteBuilder: knitr BugReports: https://github.com/ge11232002/TFBSTools/issues git_url: https://git.bioconductor.org/packages/TFBSTools git_branch: RELEASE_3_12 git_last_commit: 15e7cf7 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TFBSTools_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TFBSTools_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TFBSTools_1.28.0.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.html vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R importsMe: chromVAR, enrichTF, esATAC, MatrixRider, motifmatchr, primirTSS suggestsMe: MethReg, pageRank, universalmotif, JASPAR2018, JASPAR2020, CAGEWorkflow, Signac dependencyCount: 112 Package: TFEA.ChIP Version: 1.10.0 Depends: R (>= 3.3) Imports: GenomicRanges, IRanges, biomaRt, GenomicFeatures, grDevices, dplyr, stats, utils, R.utils, methods, org.Hs.eg.db Suggests: knitr, rmarkdown, S4Vectors, plotly, scales, tidyr, ggplot2, GSEABase, DESeq2, BiocGenerics, ggrepel, rcompanion, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: d297905e8a6cbb11daeb75ebb16464d4 NeedsCompilation: no Title: Analyze Transcription Factor Enrichment Description: Package to analize transcription factor enrichment in a gene set using data from ChIP-Seq experiments. biocViews: Transcription, GeneRegulation, GeneSetEnrichment, Transcriptomics, Sequencing, ChIPSeq, RNASeq, ImmunoOncology Author: Laura Puente Santamaría, Luis del Peso Maintainer: Laura Puente Santamaría VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFEA.ChIP git_branch: RELEASE_3_12 git_last_commit: f4e809a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TFEA.ChIP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TFEA.ChIP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TFEA.ChIP_1.10.0.tgz vignettes: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.html vignetteTitles: TFEA.ChIP: a tool kit for transcription factor enrichment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFEA.ChIP/inst/doc/TFEA.ChIP.R dependencyCount: 92 Package: TFHAZ Version: 1.12.0 Depends: R(>= 3.4) Imports: GenomicRanges, S4Vectors, grDevices, graphics, stats, utils, IRanges, methods Suggests: BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 934cc164d4bffc9b3d270f8a31c94af3 NeedsCompilation: no Title: Transcription Factor High Accumulation Zones Description: It finds trascription factor (TF) high accumulation DNA zones, i.e., regions along the genome where there is a high presence of different transcription factors. Starting from a dataset containing the genomic positions of TF binding regions, for each base of the selected chromosome the accumulation of TFs is computed. Three different types of accumulation (TF, region and base accumulation) are available, together with the possibility of considering, in the single base accumulation computing, the TFs present not only in that single base, but also in its neighborhood, within a window of a given width. Two different methods for the search of TF high accumulation DNA zones, called "binding regions" and "overlaps", are available. In addition, some functions are provided in order to analyze, visualize and compare results obtained with different input parameters. biocViews: Software, BiologicalQuestion, Transcription, ChIPSeq, Coverage Author: Alberto Marchesi, Marco Masseroli Maintainer: Alberto Marchesi VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFHAZ git_branch: RELEASE_3_12 git_last_commit: 500fb38 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TFHAZ_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TFHAZ_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TFHAZ_1.12.0.tgz vignettes: vignettes/TFHAZ/inst/doc/TFHAZ.html vignetteTitles: TFHAZ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFHAZ/inst/doc/TFHAZ.R dependencyCount: 18 Package: TFutils Version: 1.10.1 Depends: R (>= 3.5.0) Imports: methods, dplyr, magrittr, miniUI, shiny, Rsamtools, GSEABase, rjson, BiocFileCache, DT, httr, readxl Suggests: knitr, data.table, testthat, AnnotationDbi, AnnotationFilter, Biobase, GenomicFeatures, GenomicRanges, Gviz, IRanges, Rsamtools, S4Vectors, org.Hs.eg.db, EnsDb.Hsapiens.v75, BiocParallel, BiocStyle, GO.db, GenomicFiles, GenomeInfoDb, SummarizedExperiment, UpSetR, ggplot2, png, gwascat, MotifDb, motifStack, RColorBrewer License: Artistic-2.0 MD5sum: bf8ce31ea43efba6c4054e4dca877126 NeedsCompilation: no Title: TFutils Description: Package to work with TF metadata from various sources. biocViews: Transcriptomics Author: Vincent Carey [aut], Shweta Gopaulakrishnan [cre, aut] Maintainer: Shweta Gopaulakrishnan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TFutils git_branch: RELEASE_3_12 git_last_commit: 268035b git_last_commit_date: 2020-11-04 Date/Publication: 2020-11-04 source.ver: src/contrib/TFutils_1.10.1.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/TFutils_1.10.1.tgz vignettes: vignettes/TFutils/inst/doc/fimo16.html, vignettes/TFutils/inst/doc/TFutils.html vignetteTitles: A note on fimo16, TFutils -- representing TFBS and TF target sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFutils/inst/doc/fimo16.R, vignettes/TFutils/inst/doc/TFutils.R suggestsMe: TxRegInfra dependencyCount: 101 Package: tidybulk Version: 1.2.1 Depends: R (>= 4.0.0) Imports: tibble, readr, dplyr, magrittr, tidyr, stringr, rlang, purrr, preprocessCore, stats, parallel, utils, lifecycle, scales, SummarizedExperiment, methods Suggests: BiocStyle, testthat, vctrs, AnnotationDbi, BiocManager, Rsubread, e1071, edgeR, limma, org.Hs.eg.db, org.Mm.eg.db, sva, GGally, knitr, qpdf, covr, Seurat, KernSmooth, Rtsne, S4Vectors, ggplot2, widyr, clusterProfiler, msigdbr, DESeq2, broom, survival, boot, betareg, tidyHeatmap, pasilla, ggrepel, devtools, functional License: GPL-3 MD5sum: 603c286bea273c1fd8763e68ef4367a3 NeedsCompilation: no Title: Brings transcriptomics to the tidyverse Description: This is a collection of utility functions that allow to perform exploration of and calculations to RNA sequencing data, in a modular, pipe-friendly and tidy fashion. biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre], Maria Doyle [ctb] Maintainer: Stefano Mangiola VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tidybulk git_branch: RELEASE_3_12 git_last_commit: 15d7391 git_last_commit_date: 2021-04-06 Date/Publication: 2021-04-06 source.ver: src/contrib/tidybulk_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/tidybulk_1.2.1.zip mac.binary.ver: bin/macosx/contrib/4.0/tidybulk_1.2.1.tgz vignettes: vignettes/tidybulk/inst/doc/comparison_with_base_R.html, vignettes/tidybulk/inst/doc/introduction.html, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.html, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.html vignetteTitles: Comparison with base R, Overview of the tidybulk package, Manuscript code - differential transcript abundance, Manuscript code - transcriptional signature identification hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidybulk/inst/doc/comparison_with_base_R.R, vignettes/tidybulk/inst/doc/introduction.R, vignettes/tidybulk/inst/doc/manuscript_differential_transcript_abundance.R, vignettes/tidybulk/inst/doc/manuscript_transcriptional_signatures.R dependencyCount: 60 Package: tidySingleCellExperiment Version: 1.0.0 Depends: R (>= 4.0.0), SingleCellExperiment Imports: dplyr, tibble, tidyr, ggplot2, plotly, magrittr, rlang, purrr, lifecycle, methods, utils, S4Vectors, tidyselect, ellipsis, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown, SingleCellSignalR, SingleR, scater, scran, tidyHeatmap, igraph, GGally, Matrix, uwot, celldex, dittoSeq License: GPL-3 MD5sum: b65d48e906c19806480fac94bc743bbb NeedsCompilation: no Title: Brings SingleCellExperiment to the Tidyverse Description: tidySingleCellExperiment is an adapter that abstracts the 'SingleCellExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse' biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tidySingleCellExperiment git_branch: RELEASE_3_12 git_last_commit: ef39eab git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tidySingleCellExperiment_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tidySingleCellExperiment_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tidySingleCellExperiment_1.0.0.tgz vignettes: vignettes/tidySingleCellExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySingleCellExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySingleCellExperiment/inst/doc/introduction.R dependencyCount: 83 Package: tidySummarizedExperiment Version: 1.0.0 Depends: R (>= 4.0.0), SummarizedExperiment Imports: tibble (>= 3.0.4), dplyr, magrittr, tidyr, ggplot2, rlang, purrr, lifecycle, methods, plotly, utils, S4Vectors, tidyselect, ellipsis, pillar, stringr, cli, fansi Suggests: BiocStyle, testthat, knitr, markdown License: GPL-3 MD5sum: 93675dd010fbfca824c5225c4b3262cd NeedsCompilation: no Title: Brings SummarizedExperiment to the Tidyverse Description: tidySummarizedExperiment is an adapter that abstracts the 'SingleCellExperiment' container in the form of tibble and allows the data manipulation, plotting and nesting using 'tidyverse' biocViews: AssayDomain, Infrastructure, RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics Author: Stefano Mangiola [aut, cre] Maintainer: Stefano Mangiola VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tidySummarizedExperiment git_branch: RELEASE_3_12 git_last_commit: 7f722f3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tidySummarizedExperiment_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tidySummarizedExperiment_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tidySummarizedExperiment_1.0.0.tgz vignettes: vignettes/tidySummarizedExperiment/inst/doc/introduction.html vignetteTitles: Overview of the tidySummarizedExperiment package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tidySummarizedExperiment/inst/doc/introduction.R dependencyCount: 82 Package: tigre Version: 1.44.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, grDevices, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle, BiocManager License: AGPL-3 Archs: i386, x64 MD5sum: c6279febcd7963c1ed505acb5ee72b92 NeedsCompilation: yes Title: Transcription factor Inference through Gaussian process Reconstruction of Expression Description: The tigre package implements our methodology of Gaussian process differential equation models for analysis of gene expression time series from single input motif networks. The package can be used for inferring unobserved transcription factor (TF) protein concentrations from expression measurements of known target genes, or for ranking candidate targets of a TF. biocViews: Microarray, TimeCourse, GeneExpression, Transcription, GeneRegulation, NetworkInference, Bayesian Author: Antti Honkela, Pei Gao, Jonatan Ropponen, Miika-Petteri Matikainen, Magnus Rattray, Neil D. Lawrence Maintainer: Antti Honkela URL: https://github.com/ahonkela/tigre BugReports: https://github.com/ahonkela/tigre/issues git_url: https://git.bioconductor.org/packages/tigre git_branch: RELEASE_3_12 git_last_commit: 42bffa6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tigre_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tigre_1.44.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tigre_1.44.0.tgz vignettes: vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre.R dependencyCount: 44 Package: TileDBArray Version: 1.0.0 Depends: DelayedArray (>= 0.15.16) Imports: methods, Rcpp, tiledb, S4Vectors LinkingTo: Rcpp Suggests: knitr, Matrix, rmarkdown, BiocStyle, BiocParallel, testthat License: MIT + file LICENSE Archs: i386, x64 MD5sum: 617b4e36d186c6eac146bc44f746353b NeedsCompilation: yes Title: Using TileDB as a DelayedArray Backend Description: Implements a DelayedArray backend for reading and writing dense or sparse arrays in the TileDB format. The resulting TileDBArrays are compatible with all Bioconductor pipelines that can accept DelayedArray instances. biocViews: DataRepresentation, Infrastructure, Software Author: Aaron Lun [aut, cre] Maintainer: Aaron Lun URL: https://github.com/LTLA/TileDBArray VignetteBuilder: knitr BugReports: https://github.com/LTLA/TileDBArray git_url: https://git.bioconductor.org/packages/TileDBArray git_branch: RELEASE_3_12 git_last_commit: 37c4641 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TileDBArray_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TileDBArray_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TileDBArray_1.0.0.tgz vignettes: vignettes/TileDBArray/inst/doc/userguide.html vignetteTitles: User guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TileDBArray/inst/doc/userguide.R dependencyCount: 24 Package: tilingArray Version: 1.68.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: d5694e101b2f1b72ac08d9a6d85cf327 NeedsCompilation: yes Title: Transcript mapping with high-density oligonucleotide tiling arrays Description: The package provides functionality that can be useful for the analysis of high-density tiling microarray data (such as from Affymetrix genechips) for measuring transcript abundance and architecture. The main functionalities of the package are: 1. the class 'segmentation' for representing partitionings of a linear series of data; 2. the function 'segment' for fitting piecewise constant models using a dynamic programming algorithm that is both fast and exact; 3. the function 'confint' for calculating confidence intervals using the strucchange package; 4. the function 'plotAlongChrom' for generating pretty plots; 5. the function 'normalizeByReference' for probe-sequence dependent response adjustment from a (set of) reference hybridizations. biocViews: Microarray, OneChannel, Preprocessing, Visualization Author: Wolfgang Huber, Zhenyu Xu, Joern Toedling with contributions from Matt Ritchie Maintainer: Zhenyu Xu git_url: https://git.bioconductor.org/packages/tilingArray git_branch: RELEASE_3_12 git_last_commit: 93ea2a8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tilingArray_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tilingArray_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tilingArray_1.68.0.tgz vignettes: vignettes/tilingArray/inst/doc/assessNorm.pdf, vignettes/tilingArray/inst/doc/costMatrix.pdf, vignettes/tilingArray/inst/doc/findsegments.pdf, vignettes/tilingArray/inst/doc/plotAlongChrom.pdf, vignettes/tilingArray/inst/doc/segmentation.pdf vignetteTitles: Normalisation with the normalizeByReference function in the tilingArray package, Supplement. Calculation of the cost matrix, Introduction to using the segment function to fit a piecewise constant curve, Introduction to the plotAlongChrom function, Segmentation demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tilingArray/inst/doc/findsegments.R, vignettes/tilingArray/inst/doc/plotAlongChrom.R dependsOnMe: davidTiling importsMe: ADaCGH2, snapCGH dependencyCount: 79 Package: timecourse Version: 1.62.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 40a89b2331ebaa80166ddbe72ac7a2eb NeedsCompilation: no Title: Statistical Analysis for Developmental Microarray Time Course Data Description: Functions for data analysis and graphical displays for developmental microarray time course data. biocViews: Microarray, TimeCourse, DifferentialExpression Author: Yu Chuan Tai Maintainer: Yu Chuan Tai URL: http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/timecourse git_branch: RELEASE_3_12 git_last_commit: 3c0a83b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/timecourse_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/timecourse_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/timecourse_1.62.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timecourse/inst/doc/timecourse.R dependencyCount: 11 Package: timeOmics Version: 1.2.0 Depends: mixOmics, R (>= 4.0) Imports: dplyr, tidyr, tibble, purrr, magrittr, ggplot2, stringr, ggrepel, propr, lmtest Suggests: BiocStyle, knitr, rmarkdown, testthat, snow, tidyverse, igraph, gplots License: GPL-3 MD5sum: 7d300ffccc391f178126230eb26f50b1 NeedsCompilation: no Title: Time-Course Multi-Omics data integration Description: timeOmics is a generic data-driven framework to integrate multi-Omics longitudinal data measured on the same biological samples and select key temporal features with strong associations within the same sample group. The main steps of timeOmics are: 1. Plaform and time-specific normalization and filtering steps; 2. Modelling each biological into one time expression profile; 3. Clustering features with the same expression profile over time; 4. Post-hoc validation step. biocViews: Clustering,FeatureExtraction,TimeCourse,DimensionReduction,Software, Sequencing, Microarray, Metabolomics, Metagenomics, Proteomics, Classification, Regression, ImmunoOncology, GenePrediction, MultipleComparison Author: Antoine Bodein [aut, cre], Olivier Chapleur [aut], Kim-Anh Le Cao [aut], Arnaud Droit [aut] Maintainer: Antoine Bodein VignetteBuilder: knitr BugReports: https://github.com/abodein/timeOmics/issues git_url: https://git.bioconductor.org/packages/timeOmics git_branch: RELEASE_3_12 git_last_commit: 9fff87c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/timeOmics_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/timeOmics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/timeOmics_1.2.0.tgz vignettes: vignettes/timeOmics/inst/doc/vignette.html vignetteTitles: timeOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timeOmics/inst/doc/vignette.R dependencyCount: 72 Package: timescape Version: 1.14.0 Depends: R (>= 3.3) Imports: htmlwidgets (>= 0.5), jsonlite (>= 0.9.19), stringr (>= 1.0.0), dplyr (>= 0.4.3), gtools (>= 3.5.0) Suggests: knitr, rmarkdown License: GPL-3 MD5sum: a4d4d4ebaf375225413153d15285f197 NeedsCompilation: no Title: Patient Clonal Timescapes Description: TimeScape is an automated tool for navigating temporal clonal evolution data. The key attributes of this implementation involve the enumeration of clones, their evolutionary relationships and their shifting dynamics over time. TimeScape requires two inputs: (i) the clonal phylogeny and (ii) the clonal prevalences. Optionally, TimeScape accepts a data table of targeted mutations observed in each clone and their allele prevalences over time. The output is the TimeScape plot showing clonal prevalence vertically, time horizontally, and the plot height optionally encoding tumour volume during tumour-shrinking events. At each sampling time point (denoted by a faint white line), the height of each clone accurately reflects its proportionate prevalence. These prevalences form the anchors for bezier curves that visually represent the dynamic transitions between time points. biocViews: Visualization, BiomedicalInformatics Author: Maia Smith [aut, cre] Maintainer: Maia Smith VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/timescape git_branch: RELEASE_3_12 git_last_commit: 6265a68 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/timescape_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/timescape_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/timescape_1.14.0.tgz vignettes: vignettes/timescape/inst/doc/timescape_vignette.html vignetteTitles: TimeScape vignette hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/timescape/inst/doc/timescape_vignette.R dependencyCount: 32 Package: TimeSeriesExperiment Version: 1.8.0 Depends: R (>= 3.5.0), S4Vectors (>= 0.19.23), SummarizedExperiment (>= 1.11.6) Imports: dynamicTreeCut, dplyr, edgeR, DESeq2, ggplot2 (>= 3.0.0), graphics, Hmisc, limma, methods, magrittr, proxy, stats, tibble, tidyr, vegan, viridis, utils Suggests: Biobase, BiocFileCache (>= 1.5.8), circlize, ComplexHeatmap, GO.db, grDevices, grid, knitr, org.Mm.eg.db, org.Hs.eg.db, MASS, RColorBrewer, rmarkdown, UpSetR, License: LGPL (>= 3) MD5sum: c48af5118f35866d8dfda385aa8bd39f NeedsCompilation: no Title: Analysis for short time-series data Description: Visualization and analysis toolbox for short time course data which includes dimensionality reduction, clustering, two-sample differential expression testing and gene ranking techniques. The package also provides methods for retrieving enriched pathways. biocViews: TimeCourse, Sequencing, RNASeq, Microbiome, GeneExpression, ImmunoOncology, Transcription, Normalization, DifferentialExpression, PrincipalComponent, Clustering, Visualization, Pathways Author: Lan Huong Nguyen Maintainer: Lan Huong Nguyen URL: https://github.com/nlhuong/TimeSeriesExperiment VignetteBuilder: knitr BugReports: https://github.com/nlhuong/TimeSeriesExperiment/issues git_url: https://git.bioconductor.org/packages/TimeSeriesExperiment git_branch: RELEASE_3_12 git_last_commit: 99d7630 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TimeSeriesExperiment_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TimeSeriesExperiment_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TimeSeriesExperiment_1.8.0.tgz vignettes: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.html vignetteTitles: Gene expression time course data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TimeSeriesExperiment/inst/doc/cop1_knockout_timecourse.R dependencyCount: 129 Package: TimiRGeN Version: 1.0.6 Depends: R (>= 4.0), Mfuzz, MultiAssayExperiment Imports: biomaRt, clusterProfiler, dplyr (>= 0.8.4), FreqProf, gtools (>= 3.8.1), gplots, ggdendro, gghighlight, ggplot2, graphics, grDevices, igraph (>= 1.2.4.2), RCy3, readxl, reshape2, rWikiPathways, scales, stats, tidyr (>= 1.0.2), stringr (>= 1.4.0) Suggests: BiocManager, kableExtra, knitr (>= 1.27), org.Hs.eg.db, org.Mm.eg.db, testthat, rmarkdown License: GPL-3 MD5sum: 6f335d4e7c1b51221c35d4b2506480de NeedsCompilation: no Title: Time sensitive microRNA-mRNA integration, analysis and network generation tool Description: TimiRGeN (Time Incorporated miR-mRNA Generation of Networks) is a novel R package which functionally analyses and integrates time course miRNA-mRNA differential expression data. This tool can generate small networks within R or export results into cytoscape or pathvisio for more detailed network construction and hypothesis generation. This tool is created for researchers that wish to dive deep into time series multi-omic datasets. TimiRGeN goes further than many other tools in terms of data reduction. Here, potentially hundreds of thousands of potential miRNA-mRNA interactions can be whittled down into a handful of high confidence miRNA-mRNA interactions effecting a signalling pathway, across a time course. biocViews: Clustering, miRNA, Network, Pathways, Software, TimeCourse, Visualization Author: Krutik Patel [aut, cre] Maintainer: Krutik Patel URL: https://github.com/Krutik6/TimiRGeN/ VignetteBuilder: knitr BugReports: https://github.com/Krutik6/TimiRGeN/issues git_url: https://git.bioconductor.org/packages/TimiRGeN git_branch: RELEASE_3_12 git_last_commit: ef00328 git_last_commit_date: 2021-04-28 Date/Publication: 2021-04-28 source.ver: src/contrib/TimiRGeN_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/4.0/TimiRGeN_1.0.6.zip mac.binary.ver: bin/macosx/contrib/4.0/TimiRGeN_1.0.6.tgz vignettes: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.html vignetteTitles: TimiRGeN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TimiRGeN/inst/doc/TimiRGeN_tutorial.R dependencyCount: 169 Package: TIN Version: 1.22.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 46a0d583e1912fe13de9b18662262331 NeedsCompilation: no Title: Transcriptome instability analysis Description: The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets. biocViews: ExonArray, Microarray, GeneExpression, AlternativeSplicing, Genetics, DifferentialSplicing Author: Bjarne Johannessen, Anita Sveen and Rolf I. Skotheim Maintainer: Bjarne Johannessen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TIN git_branch: RELEASE_3_12 git_last_commit: e3aa524 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TIN_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TIN_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TIN_1.22.0.tgz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TIN/inst/doc/TIN.R dependencyCount: 117 Package: TissueEnrich Version: 1.10.1 Depends: R (>= 3.5), ensurer (>= 1.1.0), ggplot2 (>= 2.2.1), SummarizedExperiment (>= 1.6.5), GSEABase (>= 1.38.2) Imports: dplyr (>= 0.7.3), tidyr (>= 0.8.0), stats Suggests: knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 96c73969d2cf54fbff615fcd992ec512 NeedsCompilation: no Title: Tissue-specific gene enrichment analysis Description: The TissueEnrich package is used to calculate enrichment of tissue-specific genes in a set of input genes. For example, the user can input the most highly expressed genes from RNA-Seq data, or gene co-expression modules to determine which tissue-specific genes are enriched in those datasets. Tissue-specific genes were defined by processing RNA-Seq data from the Human Protein Atlas (HPA) (Uhlén et al. 2015), GTEx (Ardlie et al. 2015), and mouse ENCODE (Shen et al. 2012) using the algorithm from the HPA (Uhlén et al. 2015).The hypergeometric test is being used to determine if the tissue-specific genes are enriched among the input genes. Along with tissue-specific gene enrichment, the TissueEnrich package can also be used to define tissue-specific genes from expression datasets provided by the user, which can then be used to calculate tissue-specific gene enrichments. biocViews: GeneSetEnrichment, GeneExpression, Sequencing Author: Ashish Jain [aut, cre], Geetu Tuteja [aut] Maintainer: Ashish Jain VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TissueEnrich git_branch: RELEASE_3_12 git_last_commit: 52f80f5 git_last_commit_date: 2021-04-04 Date/Publication: 2021-04-04 source.ver: src/contrib/TissueEnrich_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/TissueEnrich_1.10.1.zip mac.binary.ver: bin/macosx/contrib/4.0/TissueEnrich_1.10.1.tgz vignettes: vignettes/TissueEnrich/inst/doc/TissueEnrich.html vignetteTitles: TissueEnrich hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TissueEnrich/inst/doc/TissueEnrich.R dependencyCount: 86 Package: TitanCNA Version: 1.28.0 Depends: R (>= 3.5.1) Imports: BiocGenerics (>= 0.31.6), IRanges (>= 2.6.1), GenomicRanges (>= 1.24.3), VariantAnnotation (>= 1.18.7), foreach (>= 1.4.3), GenomeInfoDb (>= 1.8.7), data.table (>= 1.10.4), dplyr (>= 0.5.0), License: GPL-3 Archs: i386, x64 MD5sum: 6da18640c59f3d807291779c5ab63dc0 NeedsCompilation: yes Title: Subclonal copy number and LOH prediction from whole genome sequencing of tumours Description: Hidden Markov model to segment and predict regions of subclonal copy number alterations (CNA) and loss of heterozygosity (LOH), and estimate cellular prevalence of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation, ImmunoOncology Author: Gavin Ha Maintainer: Gavin Ha URL: https://github.com/gavinha/TitanCNA git_url: https://git.bioconductor.org/packages/TitanCNA git_branch: RELEASE_3_12 git_last_commit: 27bbe2d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TitanCNA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TitanCNA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TitanCNA_1.28.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R dependencyCount: 94 Package: tkWidgets Version: 1.68.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: a174b3b1466447777024b0144c29280d NeedsCompilation: no Title: R based tk widgets Description: Widgets to provide user interfaces. tcltk should have been installed for the widgets to run. biocViews: Infrastructure Author: J. Zhang Maintainer: J. Zhang git_url: https://git.bioconductor.org/packages/tkWidgets git_branch: RELEASE_3_12 git_last_commit: d6d7489 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tkWidgets_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tkWidgets_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tkWidgets_1.68.0.tgz vignettes: vignettes/tkWidgets/inst/doc/importWizard.pdf, vignettes/tkWidgets/inst/doc/tkWidgets.pdf vignetteTitles: tkWidgets importWizard, tkWidgets contents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tkWidgets/inst/doc/importWizard.R, vignettes/tkWidgets/inst/doc/tkWidgets.R importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray dependencyCount: 6 Package: TMixClust Version: 1.12.0 Depends: R (>= 3.4) Imports: gss, mvtnorm, stats, zoo, cluster, utils, BiocParallel, flexclust, grDevices, graphics, Biobase, SPEM Suggests: rmarkdown, knitr, BiocStyle, testthat License: GPL (>=2) MD5sum: 851d6d25eae12151a914ee76473d7030 NeedsCompilation: no Title: Time Series Clustering of Gene Expression with Gaussian Mixed-Effects Models and Smoothing Splines Description: Implementation of a clustering method for time series gene expression data based on mixed-effects models with Gaussian variables and non-parametric cubic splines estimation. The method can robustly account for the high levels of noise present in typical gene expression time series datasets. biocViews: Software, StatisticalMethod, Clustering, TimeCourse, GeneExpression Author: Monica Golumbeanu Maintainer: Monica Golumbeanu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TMixClust git_branch: RELEASE_3_12 git_last_commit: 982b31b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TMixClust_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TMixClust_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TMixClust_1.12.0.tgz vignettes: vignettes/TMixClust/inst/doc/TMixClust.pdf vignetteTitles: Clustering time series gene expression data with TMixClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TMixClust/inst/doc/TMixClust.R dependencyCount: 29 Package: TNBC.CMS Version: 1.6.0 Depends: R (>= 3.6.0), e1071, quadprog, SummarizedExperiment Imports: GSVA (>= 1.26.0), pheatmap, grDevices, RColorBrewer, pracma, GGally, R.utils, forestplot, ggplot2, ggpubr, survival, grid, stats, methods Suggests: knitr License: GPL-3 MD5sum: 037ed90407b5923a23e5769ce6d05fda NeedsCompilation: no Title: TNBC.CMS: Prediction of TNBC Consensus Molecular Subtypes Description: This package implements a machine learning-based classifier for the assignment of consensus molecular subtypes to TNBC samples. It also provides functions to summarize genomic and clinical characteristics. biocViews: Classification, Clustering, GeneExpression, GenePrediction, SupportVectorMachine Author: Doyeong Yu, Jihyun Kim, In Hae Park, Charny Park Maintainer: Doyeong Yu VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TNBC.CMS git_branch: RELEASE_3_12 git_last_commit: ed9230e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TNBC.CMS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TNBC.CMS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TNBC.CMS_1.6.0.tgz vignettes: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.pdf vignetteTitles: TNBC.CMS.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TNBC.CMS/inst/doc/TNBC.CMS.R dependencyCount: 161 Package: TnT Version: 1.12.0 Depends: R (>= 3.4), GenomicRanges Imports: methods, stats, utils, grDevices, htmlwidgets, jsonlite, data.table, Biobase, GenomeInfoDb, IRanges, S4Vectors, knitr Suggests: GenomicFeatures, shiny, BiocManager, rmarkdown, testthat License: AGPL-3 MD5sum: e316957a86ee48bbd92b5af34cfb7b34 NeedsCompilation: no Title: Interactive Visualization for Genomic Features Description: A R interface to the TnT javascript library (https://github.com/ tntvis) to provide interactive and flexible visualization of track-based genomic data. biocViews: Infrastructure, Visualization Author: Jialin Ma [cre, aut], Miguel Pignatelli [aut], Toby Hocking [aut] Maintainer: Jialin Ma URL: https://github.com/Marlin-Na/TnT VignetteBuilder: knitr BugReports: https://github.com/Marlin-Na/TnT/issues git_url: https://git.bioconductor.org/packages/TnT git_branch: RELEASE_3_12 git_last_commit: ee4ea3a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TnT_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TnT_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TnT_1.12.0.tgz vignettes: vignettes/TnT/inst/doc/introduction.html vignetteTitles: Introduction to TnT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TnT/inst/doc/introduction.R dependencyCount: 37 Package: TOAST Version: 1.4.0 Depends: R (>= 3.6), RefFreeEWAS, EpiDISH, limma, nnls Imports: stats, methods, SummarizedExperiment, corpcor Suggests: BiocStyle, knitr, rmarkdown, csSAM, gplots, matrixStats, Matrix License: GPL-2 MD5sum: 43ab3cad870b129179f86746eacde8d3 NeedsCompilation: no Title: Tools for the analysis of heterogeneous tissues Description: This package is devoted to analyzing high-throughput data (e.g. gene expression microarray, DNA methylation microarray, RNA-seq) from complex tissues. Current functionalities include 1. detect cell-type specific or cross-cell type differential signals 2. improve variable selection in reference-free deconvolution 3. partial reference-free deconvolution with prior knowledge. biocViews: DNAMethylation, GeneExpression, DifferentialExpression, DifferentialMethylation, Microarray, GeneTarget, Epigenetics, MethylationArray Author: Ziyi Li and Hao Wu Maintainer: Ziyi Li VignetteBuilder: knitr BugReports: https://github.com/ziyili20/TOAST/issues git_url: https://git.bioconductor.org/packages/TOAST git_branch: RELEASE_3_12 git_last_commit: 2158f75 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TOAST_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TOAST_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TOAST_1.4.0.tgz vignettes: vignettes/TOAST/inst/doc/TOAST.html vignetteTitles: The TOAST User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TOAST/inst/doc/TOAST.R dependencyCount: 42 Package: tofsims Version: 1.18.0 Depends: R (>= 3.3.0), methods, utils, ProtGenerics Imports: Rcpp (>= 0.11.2), ALS, alsace, signal, KernSmooth, graphics, grDevices, stats LinkingTo: Rcpp, RcppArmadillo Suggests: EBImage, knitr, rmarkdown, testthat, tofsimsData, BiocParallel, RColorBrewer Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: 15ac1695c0b960db6e29e4c35fd33acd NeedsCompilation: yes Title: Import, process and analysis of Time-of-Flight Secondary Ion Mass Spectrometry (ToF-SIMS) imaging data Description: This packages offers a pipeline for import, processing and analysis of ToF-SIMS 2D image data. Import of Iontof and Ulvac-Phi raw or preprocessed data is supported. For rawdata, mass calibration, peak picking and peak integration exist. General funcionality includes data binning, scaling, image subsetting and visualization. A range of multivariate tools common in the ToF-SIMS community are implemented (PCA, MCR, MAF, MNF). An interface to the bioconductor image processing package EBImage offers image segmentation functionality. biocViews: ImmunoOncology, Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry, Proteomics, Metabolomics Author: Lorenz Gerber, Viet Mai Hoang Maintainer: Lorenz Gerber URL: https://github.com/lorenzgerber/tofsims VignetteBuilder: knitr BugReports: https://github.com/lorenzgerber/tofsims/issues git_url: https://git.bioconductor.org/packages/tofsims git_branch: RELEASE_3_12 git_last_commit: aa3c69c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tofsims_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tofsims_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tofsims_1.18.0.tgz vignettes: vignettes/tofsims/inst/doc/workflow.html vignetteTitles: Workflow with the `tofsims` package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tofsims/inst/doc/workflow.R dependencyCount: 17 Package: tomoda Version: 1.0.0 Depends: R (>= 4.0.0) Imports: methods, stats, grDevices, reshape2, Rtsne, umap, RColorBrewer, ggplot2, ggrepel, SummarizedExperiment Suggests: knitr, rmarkdown, BiocStyle, testthat License: MIT + file LICENSE MD5sum: fd640bbd9484197b70671ddb82b91cf5 NeedsCompilation: no Title: Tomo-seq data analysis Description: This package provides many easy-to-use methods to analyze and visualize tomo-seq data. The tomo-seq technique is based on cryosectioning of tissue and performing RNA-seq on consecutive sections. (Reference: Kruse F, Junker JP, van Oudenaarden A, Bakkers J. Tomo-seq: A method to obtain genome-wide expression data with spatial resolution. Methods Cell Biol. 2016;135:299-307. doi:10.1016/bs.mcb.2016.01.006) The main purpose of the package is to find zones with similar transcriptional profiles and spatially expressed genes in a tomo-seq sample. Several visulization functions are available to create easy-to-modify plots. biocViews: GeneExpression, Sequencing, RNASeq, Transcriptomics, Clustering, Visualization Author: Wendao Liu [aut, cre] () Maintainer: Wendao Liu URL: https://github.com/liuwd15/tomoda VignetteBuilder: knitr BugReports: https://github.com/liuwd15/tomoda/issues git_url: https://git.bioconductor.org/packages/tomoda git_branch: RELEASE_3_12 git_last_commit: 70780b2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tomoda_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tomoda_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tomoda_1.0.0.tgz vignettes: vignettes/tomoda/inst/doc/tomoda.html vignetteTitles: tomoda hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tomoda/inst/doc/tomoda.R dependencyCount: 73 Package: ToPASeq Version: 1.24.0 Depends: R(>= 3.5.0), graphite Imports: Rcpp, graph, methods, Biobase, RBGL, SummarizedExperiment, gRbase, limma, corpcor LinkingTo: Rcpp Suggests: BiocStyle, airway, knitr, rmarkdown, DESeq2, DESeq, edgeR, plotrix, breastCancerVDX, EnrichmentBrowser License: AGPL-3 Archs: i386, x64 MD5sum: eadf605fa6d6bc1b1cf35cf24eee7a8a NeedsCompilation: yes Title: Topology-based pathway analysis of RNA-seq data Description: Implementation of methods for topology-based pathway analysis of RNA-seq data. This includes Topological Analysis of Pathway Phenotype Association (TAPPA; Gao and Wang, 2007), PathWay Enrichment Analysis (PWEA; Hung et al., 2010), and the Pathway Regulation Score (PRS; Ibrahim et al., 2012). biocViews: ImmunoOncology, GeneExpression, RNASeq, DifferentialExpression, GraphAndNetwork, Pathways, NetworkEnrichment, Visualization Author: Ivana Ihnatova, Eva Budinska, Ludwig Geistlinger Maintainer: Ivana Ihnatova VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToPASeq git_branch: RELEASE_3_12 git_last_commit: c5c4a49 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ToPASeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ToPASeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ToPASeq_1.24.0.tgz vignettes: vignettes/ToPASeq/inst/doc/ToPASeq_allMethods.html, vignettes/ToPASeq/inst/doc/ToPASeq.html vignetteTitles: Eight methods for topology-based pathway analysis of RNA-seq data, Topology-based pathway analysis of RNA-seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ToPASeq/inst/doc/ToPASeq_allMethods.R, vignettes/ToPASeq/inst/doc/ToPASeq.R dependencyCount: 65 Package: topconfects Version: 1.6.0 Depends: R (>= 3.6.0) Imports: methods, utils, stats, assertthat, ggplot2 Suggests: limma, edgeR, statmod, DESeq2, ashr, NBPSeq, dplyr, testthat, reshape2, tidyr, readr, org.At.tair.db, AnnotationDbi, knitr, rmarkdown, BiocStyle License: LGPL-2.1 | file LICENSE MD5sum: 83f787ffe6f248ec6f2a80bdbd5fa1f2 NeedsCompilation: no Title: Top Confident Effect Sizes Description: Rank results by confident effect sizes, while maintaining False Discovery Rate and False Coverage-statement Rate control. Topconfects is an alternative presentation of TREAT results with improved usability, eliminating p-values and instead providing confidence bounds. The main application is differential gene expression analysis, providing genes ranked in order of confident log2 fold change, but it can be applied to any collection of effect sizes with associated standard errors. biocViews: GeneExpression, DifferentialExpression, Transcriptomics, RNASeq, mRNAMicroarray, Regression, MultipleComparison Author: Paul Harrison [aut, cre] () Maintainer: Paul Harrison URL: https://github.com/pfh/topconfects VignetteBuilder: knitr BugReports: https://github.com/pfh/topconfects/issues git_url: https://git.bioconductor.org/packages/topconfects git_branch: RELEASE_3_12 git_last_commit: 98f5ed5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/topconfects_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/topconfects_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/topconfects_1.6.0.tgz vignettes: vignettes/topconfects/inst/doc/an_overview.html, vignettes/topconfects/inst/doc/fold_change.html vignetteTitles: An overview of topconfects, Confident fold change hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/topconfects/inst/doc/an_overview.R, vignettes/topconfects/inst/doc/fold_change.R importsMe: MetaVolcanoR, weitrix dependencyCount: 40 Package: topdownr Version: 1.12.0 Depends: R (>= 3.5), methods, BiocGenerics (>= 0.20.0), ProtGenerics (>= 1.10.0), Biostrings (>= 2.42.1), S4Vectors (>= 0.12.2) Imports: grDevices, stats, tools, utils, Biobase, Matrix (>= 1.2.10), MSnbase (>= 2.3.10), ggplot2 (>= 2.2.1), mzR (>= 2.11.4) Suggests: topdownrdata (>= 0.2), knitr, ranger, testthat, BiocStyle, xml2 License: GPL (>= 3) MD5sum: 9322d227d6cdf0f9bc16c5e9d4543c02 NeedsCompilation: no Title: Investigation of Fragmentation Conditions in Top-Down Proteomics Description: The topdownr package allows automatic and systemic investigation of fragment conditions. It creates Thermo Orbitrap Fusion Lumos method files to test hundreds of fragmentation conditions. Additionally it provides functions to analyse and process the generated MS data and determine the best conditions to maximise overall fragment coverage. biocViews: ImmunoOncology, Infrastructure, Proteomics, MassSpectrometry, Coverage Author: Sebastian Gibb [aut, cre] (), Pavel Shliaha [aut] (), Ole Nørregaard Jensen [aut] () Maintainer: Sebastian Gibb URL: https://github.com/sgibb/topdownr/ VignetteBuilder: knitr BugReports: https://github.com/sgibb/topdownr/issues/ git_url: https://git.bioconductor.org/packages/topdownr git_branch: RELEASE_3_12 git_last_commit: 5f987a2 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/topdownr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/topdownr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/topdownr_1.12.0.tgz vignettes: vignettes/topdownr/inst/doc/analysis.html, vignettes/topdownr/inst/doc/data-generation.html vignetteTitles: Fragmentation Analysis with topdownr, Data Generation for topdownr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topdownr/inst/doc/analysis.R, vignettes/topdownr/inst/doc/data-generation.R dependsOnMe: topdownrdata dependencyCount: 77 Package: topGO Version: 2.42.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.6), graph (>= 1.14.0), Biobase (>= 2.0.0), GO.db (>= 2.3.0), AnnotationDbi (>= 1.7.19), SparseM (>= 0.73) Imports: lattice, matrixStats, DBI Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: a37056b63e3c09549b9641e3aea4355e NeedsCompilation: no Title: Enrichment Analysis for Gene Ontology Description: topGO package provides tools for testing GO terms while accounting for the topology of the GO graph. Different test statistics and different methods for eliminating local similarities and dependencies between GO terms can be implemented and applied. biocViews: Microarray, Visualization Author: Adrian Alexa, Jorg Rahnenfuhrer Maintainer: Adrian Alexa git_url: https://git.bioconductor.org/packages/topGO git_branch: RELEASE_3_12 git_last_commit: 3a33cf5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/topGO_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/topGO_2.42.0.zip mac.binary.ver: bin/macosx/contrib/4.0/topGO_2.42.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/topGO/inst/doc/topGO.R dependsOnMe: BgeeDB, cellTree, compEpiTools, EGSEA, ideal, RNAither, tRanslatome, ccTutorial, maEndToEnd importsMe: BioMM, cellity, FoldGO, GOSim, OmaDB, pcaExplorer, psygenet2r, transcriptogramer, ViSEAGO suggestsMe: FGNet, IntramiRExploreR, miRNAtap, Ringo dependencyCount: 33 Package: ToxicoGx Version: 1.0.1 Depends: R (>= 4.0), CoreGx Imports: SummarizedExperiment, S4Vectors, Biobase, BiocParallel, ggplot2, tibble, dplyr, caTools, downloader, magrittr, methods, reshape2, tidyr, data.table, assertthat, scales, graphics, grDevices, parallel, stats, utils, limma Suggests: rmarkdown, testthat, BiocStyle, knitr, tinytex, devtools, PharmacoGx, xtable License: MIT + file LICENSE MD5sum: 59110ed4d7e69ba0a320711e1f086d66 NeedsCompilation: no Title: Analysis of Large-Scale Toxico-Genomic Data Description: Contains a set of functions to perform large-scale analysis of toxicogenomic data, providing a standardized data structure to hold information relevant to annotation, visualization and statistical analysis of toxicogenomic data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software Author: Sisira Nair [aut], Esther Yoo [aut], Christopher Eeles [aut], Amy Tang [aut], Nehme El-Hachem [aut], Petr Smirnov [aut], Benjamin Haibe-Kains [aut, cre] Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/ToxicoGx git_branch: RELEASE_3_12 git_last_commit: e5d0924 git_last_commit_date: 2020-11-16 Date/Publication: 2020-11-17 source.ver: src/contrib/ToxicoGx_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/ToxicoGx_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/ToxicoGx_1.0.1.tgz vignettes: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.html vignetteTitles: ToxicoGx: An R Platform for Integrated Toxicogenomics Data Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ToxicoGx/inst/doc/toxicoGxCaseStudies.R dependencyCount: 121 Package: TPP Version: 3.18.0 Depends: R (>= 3.4), Biobase, dplyr, magrittr, tidyr Imports: biobroom, broom, data.table, doParallel, foreach, futile.logger, ggplot2, grDevices, gridExtra, grid, knitr, limma, MASS, mefa, nls2, openxlsx (>= 2.4.0), parallel, plyr, purrr, RColorBrewer, RCurl, reshape2, rmarkdown, splines, stats, stringr, tibble, utils, VennDiagram, VGAM Suggests: BiocStyle, testthat License: Artistic-2.0 MD5sum: 53f6e7d3512372a6958abdc5c5f5a04c NeedsCompilation: no Title: Analyze thermal proteome profiling (TPP) experiments Description: Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR). biocViews: ImmunoOncology, Proteomics, MassSpectrometry Author: Dorothee Childs, Nils Kurzawa, Holger Franken, Carola Doce, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TPP git_branch: RELEASE_3_12 git_last_commit: 902f957 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TPP_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TPP_3.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TPP_3.18.0.tgz vignettes: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.pdf, vignettes/TPP/inst/doc/TPP_introduction_1D.pdf, vignettes/TPP/inst/doc/TPP_introduction_2D.pdf vignetteTitles: TPP_introduction_NPARC, TPP_introduction_1D, TPP_introduction_2D hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/NPARC_analysis_of_TPP_TR_data.R, vignettes/TPP/inst/doc/TPP_introduction_1D.R, vignettes/TPP/inst/doc/TPP_introduction_2D.R suggestsMe: Rtpca dependencyCount: 89 Package: TPP2D Version: 1.6.0 Depends: R (>= 3.6.0), stats, utils, dplyr, methods Imports: ggplot2, tidyr, foreach, doParallel, openxlsx, stringr, RCurl, parallel, MASS, BiocParallel, limma Suggests: knitr, testthat License: GPL-3 MD5sum: a9924a71c163f8cf9de0569f6a35131e NeedsCompilation: no Title: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP) Description: Detection of ligand-protein interactions from 2D thermal profiles (DLPTP), Performs an FDR-controlled analysis of 2D-TPP experiments by functional analysis of dose-response curves across temperatures. biocViews: Software, Proteomics, DataImport Author: Nils Kurzawa [aut, cre], Holger Franken [aut], Simon Anders [aut], Wolfgang Huber [aut], Mikhail M. Savitski [aut] Maintainer: Nils Kurzawa URL: http://bioconductor.org/packages/TPP2D VignetteBuilder: knitr BugReports: https://support.bioconductor.org/ git_url: https://git.bioconductor.org/packages/TPP2D git_branch: RELEASE_3_12 git_last_commit: b5b80d6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TPP2D_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TPP2D_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TPP2D_1.6.0.tgz vignettes: vignettes/TPP2D/inst/doc/TPP2D.html vignetteTitles: Introduction to TPP2D for 2D-TPP analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP2D/inst/doc/TPP2D.R dependencyCount: 65 Package: tracktables Version: 1.24.0 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: b0bd333015e67eb688cdd4d789080c95 NeedsCompilation: no Title: Build IGV tracks and HTML reports Description: Methods to create complex IGV genome browser sessions and dynamic IGV reports in HTML pages. biocViews: Sequencing, ReportWriting Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tracktables git_branch: RELEASE_3_12 git_last_commit: 26c4770 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tracktables_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tracktables_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tracktables_1.24.0.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tracktables/inst/doc/tracktables.R dependencyCount: 41 Package: trackViewer Version: 1.26.2 Depends: R (>= 3.5.0), grDevices, methods, GenomicRanges, grid Imports: GenomeInfoDb, GenomicAlignments, GenomicFeatures, Gviz, Rsamtools, S4Vectors, rtracklayer, BiocGenerics, scales, tools, IRanges, AnnotationDbi, grImport, htmlwidgets, plotrix, Rgraphviz, InteractionSet, graph, utils Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocStyle, knitr, VariantAnnotation, httr, htmltools License: GPL (>= 2) MD5sum: 65bcfeb618ba01471bba1cb7ae94f0c3 NeedsCompilation: no Title: A R/Bioconductor package with web interface for drawing elegant interactive tracks or lollipop plot to facilitate integrated analysis of multi-omics data Description: Visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq, SNPs and methylation data. biocViews: Visualization Author: Jianhong Ou [aut, cre] (), Julie Lihua Zhu [aut] Maintainer: Jianhong Ou VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/trackViewer git_branch: RELEASE_3_12 git_last_commit: 369c778 git_last_commit_date: 2021-02-08 Date/Publication: 2021-02-09 source.ver: src/contrib/trackViewer_1.26.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/trackViewer_1.26.2.zip mac.binary.ver: bin/macosx/contrib/4.0/trackViewer_1.26.2.tgz vignettes: vignettes/trackViewer/inst/doc/trackViewer.html vignetteTitles: trackViewer Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/trackViewer.R importsMe: NADfinder suggestsMe: ATACseqQC, ChIPpeakAnno dependencyCount: 143 Package: tradeSeq Version: 1.4.0 Depends: R (>= 3.6) Imports: mgcv, edgeR, SingleCellExperiment, SummarizedExperiment, slingshot, magrittr, RColorBrewer, BiocParallel, Biobase, pbapply, ggplot2, princurve, methods, monocle, igraph, S4Vectors, tibble, Matrix, viridis, matrixStats Suggests: knitr, rmarkdown, testthat, covr, clusterExperiment License: MIT + file LICENSE MD5sum: 3a915215bbd76b504a2df7138becfd76 NeedsCompilation: no Title: trajectory-based differential expression analysis for sequencing data Description: tradeSeq provides a flexible method for fitting regression models that can be used to find genes that are differentially expressed along one or multiple lineages in a trajectory. Based on the fitted models, it uses a variety of tests suited to answer different questions of interest, e.g. the discovery of genes for which expression is associated with pseudotime, or which are differentially expressed (in a specific region) along the trajectory. It fits a negative binomial generalized additive model (GAM) for each gene, and performs inference on the parameters of the GAM. biocViews: Clustering, Regression, TimeCourse, DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software, SingleCell, Transcriptomics, MultipleComparison, Visualization Author: Koen Van den Berge [aut], Hector Roux de Bezieux [aut, cre] (), Kelly Street [ctb], Lieven Clement [ctb], Sandrine Dudoit [ctb] Maintainer: Hector Roux de Bezieux URL: https://statomics.github.io/tradeSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/statOmics/tradeSeq/issues git_url: https://git.bioconductor.org/packages/tradeSeq git_branch: RELEASE_3_12 git_last_commit: 867b90c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tradeSeq_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tradeSeq_1.3.13.zip mac.binary.ver: bin/macosx/contrib/4.0/tradeSeq_1.4.0.tgz vignettes: vignettes/tradeSeq/inst/doc/fitGAM.html, vignettes/tradeSeq/inst/doc/Monocle.html, vignettes/tradeSeq/inst/doc/multipleConditions.html, vignettes/tradeSeq/inst/doc/tradeSeq.html vignetteTitles: More details on working with fitGAM, Monocle + tradeSeq, Differential expression across conditions, The tradeSeq workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tradeSeq/inst/doc/fitGAM.R, vignettes/tradeSeq/inst/doc/Monocle.R, vignettes/tradeSeq/inst/doc/tradeSeq.R dependencyCount: 109 Package: transcriptogramer Version: 1.12.0 Depends: R (>= 3.4), methods Imports: biomaRt, data.table, doSNOW, foreach, ggplot2, graphics, grDevices, igraph, limma, parallel, progress, RedeR, snow, stats, tidyr, topGO Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 062c2080d93603a5e2a5d59d583dfd20 NeedsCompilation: no Title: Transcriptional analysis based on transcriptograms Description: R package for transcriptional analysis based on transcriptograms, a method to analyze transcriptomes that projects expression values on a set of ordered proteins, arranged such that the probability that gene products participate in the same metabolic pathway exponentially decreases with the increase of the distance between two proteins of the ordering. Transcriptograms are, hence, genome wide gene expression profiles that provide a global view for the cellular metabolism, while indicating gene sets whose expression are altered. biocViews: Software, Network, Visualization, SystemsBiology, GeneExpression, GeneSetEnrichment, GraphAndNetwork, Clustering, DifferentialExpression, Microarray, RNASeq, Transcription, ImmunoOncology Author: Diego Morais [aut, cre], Rodrigo Dalmolin [aut] Maintainer: Diego Morais URL: https://github.com/arthurvinx/transcriptogramer SystemRequirements: Java Runtime Environment (>= 6) VignetteBuilder: knitr BugReports: https://github.com/arthurvinx/transcriptogramer/issues git_url: https://git.bioconductor.org/packages/transcriptogramer git_branch: RELEASE_3_12 git_last_commit: a185338 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/transcriptogramer_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/transcriptogramer_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/transcriptogramer_1.12.0.tgz vignettes: vignettes/transcriptogramer/inst/doc/transcriptogramer.html vignetteTitles: The transcriptogramer user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptogramer/inst/doc/transcriptogramer.R dependencyCount: 95 Package: transcriptR Version: 1.18.0 Depends: methods, R (>= 3.3) Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges (>= 2.11.15), pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: 852db2a664395d361fdc39a2db6606f7 NeedsCompilation: no Title: An Integrative Tool for ChIP- And RNA-Seq Based Primary Transcripts Detection and Quantification Description: The differences in the RNA types being sequenced have an impact on the resulting sequencing profiles. mRNA-seq data is enriched with reads derived from exons, while GRO-, nucRNA- and chrRNA-seq demonstrate a substantial broader coverage of both exonic and intronic regions. The presence of intronic reads in GRO-seq type of data makes it possible to use it to computationally identify and quantify all de novo continuous regions of transcription distributed across the genome. This type of data, however, is more challenging to interpret and less common practice compared to mRNA-seq. One of the challenges for primary transcript detection concerns the simultaneous transcription of closely spaced genes, which needs to be properly divided into individually transcribed units. The R package transcriptR combines RNA-seq data with ChIP-seq data of histone modifications that mark active Transcription Start Sites (TSSs), such as, H3K4me3 or H3K9/14Ac to overcome this challenge. The advantage of this approach over the use of, for example, gene annotations is that this approach is data driven and therefore able to deal also with novel and case specific events. Furthermore, the integration of ChIP- and RNA-seq data allows the identification all known and novel active transcription start sites within a given sample. biocViews: ImmunoOncology, Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transcriptR git_branch: RELEASE_3_12 git_last_commit: 96add55 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/transcriptR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/transcriptR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/transcriptR_1.18.0.tgz vignettes: vignettes/transcriptR/inst/doc/transcriptR.html vignetteTitles: transcriptR: an integrative tool for ChIP- and RNA-seq based primary transcripts detection and quantification hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transcriptR/inst/doc/transcriptR.R dependencyCount: 136 Package: transite Version: 1.8.0 Depends: R (>= 3.5) Imports: BiocGenerics (>= 0.26.0), Biostrings (>= 2.48.0), dplyr (>= 0.7.6), GenomicRanges (>= 1.32.6), ggplot2 (>= 3.0.0), ggseqlogo (>= 0.1), grDevices, gridExtra (>= 2.3), methods, parallel, Rcpp (>= 1.0.4.8), scales (>= 1.0.0), stats, TFMPvalue (>= 0.0.8), utils LinkingTo: Rcpp (>= 1.0.4.8) Suggests: knitr (>= 1.20), rmarkdown (>= 1.10), roxygen2 (>= 6.1.0), testthat (>= 2.1.0) License: MIT + file LICENSE Archs: i386, x64 MD5sum: 6e8830d4874924cdb4245eaade39de90 NeedsCompilation: yes Title: RNA-binding protein motif analysis Description: transite is a computational method that allows comprehensive analysis of the regulatory role of RNA-binding proteins in various cellular processes by leveraging preexisting gene expression data and current knowledge of binding preferences of RNA-binding proteins. biocViews: GeneExpression, Transcription, DifferentialExpression, Microarray, mRNAMicroarray, Genetics, GeneSetEnrichment Author: Konstantin Krismer [aut, cre, cph] (), Anna Gattinger [aut] (), Michael Yaffe [ths, cph] (), Ian Cannell [ths] () Maintainer: Konstantin Krismer URL: https://transite.mit.edu SystemRequirements: C++11 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transite git_branch: RELEASE_3_12 git_last_commit: dd0c616 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/transite_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/transite_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/transite_1.8.0.tgz vignettes: vignettes/transite/inst/doc/spma.html vignetteTitles: Spectrum Motif Analysis (SPMA) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/transite/inst/doc/spma.R dependencyCount: 60 Package: tRanslatome Version: 1.28.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: 84211cd3b5b3a15191751f9febb23a77 NeedsCompilation: no Title: Comparison between multiple levels of gene expression Description: Detection of differentially expressed genes (DEGs) from the comparison of two biological conditions (treated vs. untreated, diseased vs. normal, mutant vs. wild-type) among different levels of gene expression (transcriptome ,translatome, proteome), using several statistical methods: Rank Product, Translational Efficiency, t-test, Limma, ANOTA, DESeq, edgeR. Possibility to plot the results with scatterplots, histograms, MA plots, standard deviation (SD) plots, coefficient of variation (CV) plots. Detection of significantly enriched post-transcriptional regulatory factors (RBPs, miRNAs, etc) and Gene Ontology terms in the lists of DEGs previously identified for the two expression levels. Comparison of GO terms enriched only in one of the levels or in both. Calculation of the semantic similarity score between the lists of enriched GO terms coming from the two expression levels. Visual examination and comparison of the enriched terms with heatmaps, radar plots and barplots. biocViews: CellBiology, GeneRegulation, Regulation, GeneExpression, DifferentialExpression, Microarray, HighThroughputSequencing, QualityControl, GO, MultipleComparisons, Bioinformatics Author: Toma Tebaldi, Erik Dassi, Galena Kostoska Maintainer: Toma Tebaldi , Erik Dassi git_url: https://git.bioconductor.org/packages/tRanslatome git_branch: RELEASE_3_12 git_last_commit: 5f9ed1a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-30 source.ver: src/contrib/tRanslatome_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tRanslatome_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tRanslatome_1.28.0.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tRanslatome/inst/doc/tRanslatome_package.R dependencyCount: 86 Package: transomics2cytoscape Version: 1.0.0 Imports: RCy3, KEGGREST, dplyr Suggests: testthat, roxygen2, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 683d8bffb3b58849fdc3ade7280d371c NeedsCompilation: no Title: A tool set for 3D Trans-Omic network visualization with Cytoscape Description: transomics2cytoscape generates a file for 3D transomics visualization by providing input that specifies the IDs of multiple KEGG pathway layers, their corresponding Z-axis heights, and an input that represents the edges between the pathway layers. The edges are used, for example, to describe the relationships between kinase on a pathway and enzyme on another pathway. This package automates creation of a transomics network as shown in the figure in Yugi.2014 (https://doi.org/10.1016/j.celrep.2014.07.021) using Cytoscape automation (https://doi.org/10.1186/s13059-019-1758-4). biocViews: Network, Software, Pathways, DataImport, KEGG Author: Kozo Nishida [aut, cre] (), Katsuyuki Yugi [aut] () Maintainer: Kozo Nishida SystemRequirements: Java 11, Cytoscape 3.8.*, Cy3D >= 1.1.2 VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/transomics2cytoscape git_branch: RELEASE_3_12 git_last_commit: 3891d0e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/transomics2cytoscape_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/transomics2cytoscape_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/transomics2cytoscape_1.0.0.tgz vignettes: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.html vignetteTitles: transomics2cytoscape hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/transomics2cytoscape/inst/doc/transomics2cytoscape.R dependencyCount: 52 Package: TransView Version: 1.34.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, zlibbioc, gplots LinkingTo: Rhtslib (>= 1.15.3) Suggests: RUnit, pasillaBamSubset, BiocManager License: GPL-3 Archs: i386, x64 MD5sum: 852c0eb54ea5c575eebb22167486cad9 NeedsCompilation: yes Title: Read density map construction and accession. Visualization of ChIPSeq and RNASeq data sets Description: This package provides efficient tools to generate, access and display read densities of sequencing based data sets such as from RNA-Seq and ChIP-Seq. biocViews: ImmunoOncology, DNAMethylation, GeneExpression, Transcription, Microarray, Sequencing, Sequencing, ChIPSeq, RNASeq, MethylSeq, DataImport, Visualization, Clustering, MultipleComparison Author: Julius Muller Maintainer: Julius Muller URL: http://bioconductor.org/packages/release/bioc/html/TransView.html SystemRequirements: GNU make git_url: https://git.bioconductor.org/packages/TransView git_branch: RELEASE_3_12 git_last_commit: 8a97404 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TransView_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TransView_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TransView_1.34.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TransView/inst/doc/TransView.R dependencyCount: 22 Package: traseR Version: 1.20.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: 3b49d95c30e331a0b0ba7323071d43d2 NeedsCompilation: no Title: GWAS trait-associated SNP enrichment analyses in genomic intervals Description: traseR performs GWAS trait-associated SNP enrichment analyses in genomic intervals using different hypothesis testing approaches, also provides various functionalities to explore and visualize the results. biocViews: Genetics,Sequencing, Coverage, Alignment, QualityControl, DataImport Author: Li Chen, Zhaohui S.Qin Maintainer: li chen git_url: https://git.bioconductor.org/packages/traseR git_branch: RELEASE_3_12 git_last_commit: 2562769 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/traseR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/traseR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/traseR_1.20.0.tgz vignettes: vignettes/traseR/inst/doc/traseR.pdf vignetteTitles: Perform GWAS trait-associated SNP enrichment analyses in genomic intervals hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/traseR/inst/doc/traseR.R dependencyCount: 42 Package: TreeAndLeaf Version: 1.2.0 Depends: R (>= 4.0) Imports: RedeR, igraph, ape, methods Suggests: knitr, rmarkdown, BiocStyle, RUnit, BiocGenerics, stringr, RColorBrewer, geneplast License: Artistic-2.0 MD5sum: 512742f974d9351dbadd7b70ce42577a NeedsCompilation: no Title: An alternative to dendrogram visualization and insertion of multiple layers of information Description: TreeAndLeaf package comes as an alternative to solve problems regarding dendrogram plotting, such as the lack of space when the dendrogram is too large and the need for adding more layers of information. It treats a whole dendrogram as a tree, in which the observations are represented by the leaves. biocViews: Infrastructure, GraphAndNetwork, Software, Network, Visualization, DataRepresentation Author: Leonardo W. Kume, Luis E. A. Rizzardi, Milena A. Cardoso, Mauro A. A. Castro Maintainer: Milena A. Cardoso VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeAndLeaf git_branch: RELEASE_3_12 git_last_commit: 798c311 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TreeAndLeaf_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TreeAndLeaf_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TreeAndLeaf_1.2.0.tgz vignettes: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.html vignetteTitles: TreeAndLeaf: an alternative to dendrogram visualization. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeAndLeaf/inst/doc/TreeAndLeaf.R dependencyCount: 17 Package: treeio Version: 1.14.4 Depends: R (>= 3.6.0) Imports: ape, dplyr, jsonlite, magrittr, methods, rlang, tibble, tidytree (>= 0.3.0), utils Suggests: Biostrings, ggplot2, ggtree, igraph, knitr, rmarkdown, phangorn, prettydoc, testthat, tidyr, vroom, xml2, yaml License: Artistic-2.0 MD5sum: c415089e000b0624baef19b6858a136d NeedsCompilation: no Title: Base Classes and Functions for Phylogenetic Tree Input and Output Description: 'treeio' is an R package to make it easier to import and store phylogenetic tree with associated data; and to link external data from different sources to phylogeny. It also supports exporting phylogenetic tree with heterogeneous associated data to a single tree file and can be served as a platform for merging tree with associated data and converting file formats. biocViews: Software, Annotation, Clustering, DataImport, DataRepresentation, Alignment, MultipleSequenceAlignment, Phylogenetics Author: Guangchuang Yu [aut, cre] (), Tommy Tsan-Yuk Lam [ctb, ths], Casey Dunn [ctb], Bradley Jones [ctb], Tyler Bradley [ctb], Shuangbin Xu [ctb] (), Konstantinos Geles [ctb] Maintainer: Guangchuang Yu URL: https://github.com/YuLab-SMU/treeio (devel), https://docs.ropensci.org/treeio/ (docs), https://yulab-smu.top/treedata-book/ (book) VignetteBuilder: knitr BugReports: https://github.com/YuLab-SMU/treeio/issues git_url: https://git.bioconductor.org/packages/treeio git_branch: RELEASE_3_12 git_last_commit: ac2277e git_last_commit_date: 2021-04-26 Date/Publication: 2021-04-26 source.ver: src/contrib/treeio_1.14.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/treeio_1.14.4.zip mac.binary.ver: bin/macosx/contrib/4.0/treeio_1.14.4.tgz vignettes: vignettes/treeio/inst/doc/treeio.html vignetteTitles: treeio hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/treeio/inst/doc/treeio.R importsMe: ggtree suggestsMe: ggtreeExtra, MicrobiotaProcess, rfaRm, idiogramFISH, nosoi, tidytree dependencyCount: 33 Package: TreeSummarizedExperiment Version: 1.6.2 Depends: R(>= 3.6.0), SingleCellExperiment, S4Vectors (>= 0.23.18) Imports: methods, BiocGenerics, utils, ape, rlang, dplyr, SummarizedExperiment Suggests: ggtree, ggplot2, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: 57040db95aa78d2ba77b3fe1bfca77f9 NeedsCompilation: no Title: TreeSummarizedExperiment: a S4 Class for Data with Tree Structures Description: TreeSummarizedExperiment has extended SingleCellExperiment to include hierarchical information on the rows or columns of the rectangular data. biocViews: DataRepresentation, Infrastructure Author: Ruizhu Huang [aut, cre] (), Felix G.M. Ernst [ctb] () Maintainer: Ruizhu Huang VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TreeSummarizedExperiment git_branch: RELEASE_3_12 git_last_commit: 30ebcf8 git_last_commit_date: 2020-12-06 Date/Publication: 2020-12-06 source.ver: src/contrib/TreeSummarizedExperiment_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/TreeSummarizedExperiment_1.6.2.zip mac.binary.ver: bin/macosx/contrib/4.0/TreeSummarizedExperiment_1.6.2.tgz vignettes: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.html vignetteTitles: Introduction to TreeSE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TreeSummarizedExperiment/inst/doc/Introduction_to_treeSummarizedExperiment.R dependencyCount: 48 Package: trena Version: 1.12.1 Depends: R (>= 3.5.0), utils, glmnet (>= 2.0.3), MotifDb (>= 1.19.17) Imports: RSQLite, RMySQL, lassopv, randomForest, vbsr, xgboost, BiocParallel, RPostgreSQL, methods, DBI, BSgenome, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10, SNPlocs.Hsapiens.dbSNP150.GRCh38, org.Hs.eg.db, Biostrings, GenomicRanges, biomaRt, AnnotationDbi Suggests: RUnit, plyr, knitr, BiocGenerics, rmarkdown, BSgenome.Scerevisiae.UCSC.sacCer3, BSgenome.Athaliana.TAIR.TAIR9 License: GPL-3 MD5sum: 3a50d78db9f9e2b46b0543b1826cabbc NeedsCompilation: no Title: Fit transcriptional regulatory networks using gene expression, priors, machine learning Description: Methods for reconstructing transcriptional regulatory networks, especially in species for which genome-wide TF binding site information is available. biocViews: Transcription, GeneRegulation, NetworkInference, FeatureExtraction, Regression, SystemsBiology, GeneExpression Author: Seth Ament , Paul Shannon , Matthew Richards Maintainer: Paul Shannon URL: https://pricelab.github.io/trena/ VignetteBuilder: knitr BugReports: https://github.com/PriceLab/trena/issues git_url: https://git.bioconductor.org/packages/trena git_branch: RELEASE_3_12 git_last_commit: 8e38d79 git_last_commit_date: 2020-11-13 Date/Publication: 2020-11-13 source.ver: src/contrib/trena_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/trena_1.12.1.zip mac.binary.ver: bin/macosx/contrib/4.0/trena_1.12.1.tgz vignettes: vignettes/trena/inst/doc/caseStudyFour.html, vignettes/trena/inst/doc/caseStudyOne.html, vignettes/trena/inst/doc/caseStudyThree.html, vignettes/trena/inst/doc/caseStudyTwo.html, vignettes/trena/inst/doc/overview.html, vignettes/trena/inst/doc/simple.html, vignettes/trena/inst/doc/tiny.html, vignettes/trena/inst/doc/TReNA_Vignette.html vignetteTitles: "Case Study Four: a novel regulator of GATA2 in erythropoieis?", "Case Study One: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Three: reproduce known regulation of NFE2 by GATA1 in bulk RNA-seq", "Case Study Two reproduces known regulation of NFE2 by GATA1 in erytrhop RNA-seq", "TRENA: computational prediction of gene regulation", "Explore output controls", "Tiny Vignette Example", A Brief Introduction to TReNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trena/inst/doc/overview.R, vignettes/trena/inst/doc/simple.R, vignettes/trena/inst/doc/tiny.R, vignettes/trena/inst/doc/TReNA_Vignette.R dependencyCount: 110 Package: Trendy Version: 1.12.0 Depends: R (>= 3.4) Imports: stats, utils, graphics, grDevices, segmented, gplots, parallel, magrittr, BiocParallel, DT, S4Vectors, SummarizedExperiment, methods, shiny, shinyFiles Suggests: BiocStyle, knitr, rmarkdown, devtools License: GPL-3 MD5sum: a24a29243c881f102c774731415ac445 NeedsCompilation: no Title: Breakpoint analysis of time-course expression data Description: Trendy implements segmented (or breakpoint) regression models to estimate breakpoints which represent changes in expression for each feature/gene in high throughput data with ordered conditions. biocViews: TimeCourse, RNASeq, Regression, ImmunoOncology Author: Rhonda Bacher and Ning Leng Maintainer: Rhonda Bacher URL: https://github.com/rhondabacher/Trendy VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Trendy git_branch: RELEASE_3_12 git_last_commit: 4d48574 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Trendy_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Trendy_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Trendy_1.12.0.tgz vignettes: vignettes/Trendy/inst/doc/Trendy_vignette.pdf vignetteTitles: Trendy Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Trendy/inst/doc/Trendy_vignette.R dependencyCount: 79 Package: trigger Version: 1.36.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 Archs: i386, x64 MD5sum: 92412468b383e9747924a40f90e5f2cd NeedsCompilation: yes Title: Transcriptional Regulatory Inference from Genetics of Gene ExpRession Description: This R package provides tools for the statistical analysis of integrative genomic data that involve some combination of: genotypes, high-dimensional intermediate traits (e.g., gene expression, protein abundance), and higher-order traits (phenotypes). The package includes functions to: (1) construct global linkage maps between genetic markers and gene expression; (2) analyze multiple-locus linkage (epistasis) for gene expression; (3) quantify the proportion of genome-wide variation explained by each locus and identify eQTL hotspots; (4) estimate pair-wise causal gene regulatory probabilities and construct gene regulatory networks; and (5) identify causal genes for a quantitative trait of interest. biocViews: GeneExpression, SNP, GeneticVariability, Microarray, Genetics Author: Lin S. Chen , Dipen P. Sangurdekar and John D. Storey Maintainer: John D. Storey git_url: https://git.bioconductor.org/packages/trigger git_branch: RELEASE_3_12 git_last_commit: aadf999 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/trigger_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/trigger_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/trigger_1.36.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trigger/inst/doc/trigger.R dependencyCount: 87 Package: trio Version: 3.28.0 Depends: R (>= 3.0.1) Imports: grDevices, graphics, methods, stats, survival, utils, siggenes, LogicReg (>= 1.6.1) Suggests: haplo.stats, mcbiopi, splines, logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: 21a6d7334af7b704dea272a32d898e4a NeedsCompilation: no Title: Testing of SNPs and SNP Interactions in Case-Parent Trio Studies Description: Testing SNPs and SNP interactions with a genotypic TDT. This package furthermore contains functions for computing pairwise values of LD measures and for identifying LD blocks, as well as functions for setting up matched case pseudo-control genotype data for case-parent trios in order to run trio logic regression, for imputing missing genotypes in trios, for simulating case-parent trios with disease risk dependent on SNP interaction, and for power and sample size calculation in trio data. biocViews: SNP, GeneticVariability, Microarray, Genetics Author: Holger Schwender, Qing Li, Philipp Berger, Christoph Neumann, Margaret Taub, Ingo Ruczinski Maintainer: Holger Schwender git_url: https://git.bioconductor.org/packages/trio git_branch: RELEASE_3_12 git_last_commit: cf7985a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/trio_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/trio_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/trio_3.28.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trio/inst/doc/trio.R dependencyCount: 19 Package: triplex Version: 1.30.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 2.5.27), XVector (>= 0.11.6), Biostrings (>= 2.39.10) Imports: methods, grid, GenomicRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: rgl (>= 0.93.932), BSgenome.Celegans.UCSC.ce10, rtracklayer License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: f9c9d862a6f8f5e7c2e26c0720cd1313 NeedsCompilation: yes Title: Search and visualize intramolecular triplex-forming sequences in DNA Description: This package provides functions for identification and visualization of potential intramolecular triplex patterns in DNA sequence. The main functionality is to detect the positions of subsequences capable of folding into an intramolecular triplex (H-DNA) in a much larger sequence. The potential H-DNA (triplexes) should be made of as many cannonical nucleotide triplets as possible. The package includes visualization showing the exact base-pairing in 1D, 2D or 3D. biocViews: SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek Maintainer: Jiri Hon URL: http://www.fi.muni.cz/~lexa/triplex/ git_url: https://git.bioconductor.org/packages/triplex git_branch: RELEASE_3_12 git_last_commit: d2a1495 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/triplex_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/triplex_1.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/triplex_1.30.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/triplex/inst/doc/triplex.R dependencyCount: 21 Package: tRNA Version: 1.8.0 Depends: R (>= 3.5), GenomicRanges, Structstrings Imports: stringr, S4Vectors, methods, BiocGenerics, IRanges, XVector, Biostrings, Modstrings, ggplot2, scales Suggests: knitr, rmarkdown, testthat, BiocStyle, tRNAscanImport License: GPL-3 + file LICENSE MD5sum: 684750a4d3f7b2a5a89469d672c6c4fe NeedsCompilation: no Title: Analyzing tRNA sequences and structures Description: The tRNA package allows tRNA sequences and structures to be accessed and used for subsetting. In addition, it provides visualization tools to compare feature parameters of multiple tRNA sets and correlate them to additional data. The tRNA package uses GRanges objects as inputs requiring only few additional column data sets. biocViews: Software, Visualization Author: Felix GM Ernst [aut, cre] () Maintainer: Felix GM Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNA/issues git_url: https://git.bioconductor.org/packages/tRNA git_branch: RELEASE_3_12 git_last_commit: 89dc3cf git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tRNA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tRNA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tRNA_1.8.0.tgz vignettes: vignettes/tRNA/inst/doc/tRNA.html vignetteTitles: tRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNA/inst/doc/tRNA.R dependsOnMe: tRNAdbImport, tRNAscanImport dependencyCount: 56 Package: tRNAdbImport Version: 1.8.0 Depends: R (>= 3.5), GenomicRanges, Modstrings, Structstrings, tRNA Imports: Biostrings, BiocGenerics, stringr, xml2, S4Vectors, methods, httr, IRanges, utils Suggests: knitr, rmarkdown, testthat, httptest, BiocStyle, rtracklayer License: GPL-3 + file LICENSE MD5sum: d75d1b527b5599de64cf66995228b697 NeedsCompilation: no Title: Importing from tRNAdb and mitotRNAdb as GRanges objects Description: tRNAdbImport imports the entries of the tRNAdb and mtRNAdb (http://trna.bioinf.uni-leipzig.de) as GRanges object. biocViews: Software, Visualization, DataImport Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAdbImport/issues git_url: https://git.bioconductor.org/packages/tRNAdbImport git_branch: RELEASE_3_12 git_last_commit: 9cb0f7f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tRNAdbImport_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tRNAdbImport_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tRNAdbImport_1.8.0.tgz vignettes: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.html vignetteTitles: tRNAdbImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAdbImport/inst/doc/tRNAdbImport.R importsMe: EpiTxDb dependencyCount: 65 Package: tRNAscanImport Version: 1.10.0 Depends: R (>= 3.5), GenomicRanges, tRNA Imports: methods, stringr, BiocGenerics, Biostrings, Structstrings, S4Vectors, IRanges, XVector, GenomeInfoDb, rtracklayer, BSgenome, Rsamtools Suggests: BiocStyle, knitr, rmarkdown, testthat, ggplot2, BSgenome.Scerevisiae.UCSC.sacCer3 License: GPL-3 + file LICENSE MD5sum: 1349b6d20318d1496ab69884494fe0e3 NeedsCompilation: no Title: Importing a tRNAscan-SE result file as GRanges object Description: The package imports the result of tRNAscan-SE as a GRanges object. biocViews: Software, DataImport, WorkflowStep, Preprocessing, Visualization Author: Felix G.M. Ernst [aut, cre] () Maintainer: Felix G.M. Ernst URL: https://github.com/FelixErnst/tRNAscanImport VignetteBuilder: knitr BugReports: https://github.com/FelixErnst/tRNAscanImport/issues git_url: https://git.bioconductor.org/packages/tRNAscanImport git_branch: RELEASE_3_12 git_last_commit: e150ec0 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tRNAscanImport_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tRNAscanImport_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tRNAscanImport_1.10.0.tgz vignettes: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.html vignetteTitles: tRNAscanImport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/tRNAscanImport/inst/doc/tRNAscanImport.R suggestsMe: Structstrings, tRNA dependencyCount: 75 Package: TRONCO Version: 2.22.0 Depends: R (>= 4.0.0), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, grDevices, graphics, stats, utils, methods Suggests: BiocGenerics, BiocStyle, testthat, knitr, rWikiPathways License: GPL-3 MD5sum: e735fe41b8e11dc7b4c29379ff2954f1 NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: The TRONCO (TRanslational ONCOlogy) R package collects algorithms to infer progression models via the approach of Suppes-Bayes Causal Network, both from an ensemble of tumors (cross-sectional samples) and within an individual patient (multi-region or single-cell samples). The package provides parallel implementation of algorithms that process binary matrices where each row represents a tumor sample and each column a single-nucleotide or a structural variant driving the progression; a 0/1 value models the absence/presence of that alteration in the sample. The tool can import data from plain, MAF or GISTIC format files, and can fetch it from the cBioPortal for cancer genomics. Functions for data manipulation and visualization are provided, as well as functions to import/export such data to other bioinformatics tools for, e.g, clustering or detection of mutually exclusive alterations. Inferred models can be visualized and tested for their confidence via bootstrap and cross-validation. TRONCO is used for the implementation of the Pipeline for Cancer Inference (PICNIC). biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport, SingleCell, ImmunoOncology Author: Marco Antoniotti [ctb], Giulio Caravagna [aut, cre], Luca De Sano [aut], Alex Graudenzi [aut], Giancarlo Mauri [ctb], Bud Mishra [ctb], Daniele Ramazzotti [aut] Maintainer: Luca De Sano URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO git_url: https://git.bioconductor.org/packages/TRONCO git_branch: RELEASE_3_12 git_last_commit: cc6b9ea git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TRONCO_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TRONCO_2.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TRONCO_2.22.0.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TRONCO/inst/doc/vignette.R dependencyCount: 52 Package: TSCAN Version: 1.28.0 Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots, methods, stats, Matrix, SummarizedExperiment, SingleCellExperiment, DelayedArray, S4Vectors Suggests: knitr, testthat, beachmat, scuttle, scran, BiocParallel, BiocNeighbors, batchelor License: GPL(>=2) MD5sum: 5efb925043001df420e93318a24d0bcc NeedsCompilation: no Title: Tools for Single-Cell Analysis Description: Provides methods to perform trajectory analysis based on a minimum spanning tree constructed from cluster centroids. Computes pseudotemporal cell orderings by mapping cells in each cluster (or new cells) to the closest edge in the tree. Uses linear modelling to identify differentially expressed genes along each path through the tree. Several plotting and interactive visualization functions are also implemented. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji [aut, cre], Hongkai Ji [aut], Aaron Lun [ctb] Maintainer: Zhicheng Ji VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/TSCAN git_branch: RELEASE_3_12 git_last_commit: 7c1c17e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TSCAN_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TSCAN_1.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TSCAN_1.28.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R importsMe: ctgGEM, DIscBIO dependencyCount: 85 Package: tscR Version: 1.2.0 Depends: R (>= 4.0), dplyr Imports: gridExtra, methods, dtw, class, kmlShape, graphics, cluster, RColorBrewer, grDevices, knitr, rmarkdown, prettydoc, grid, ggplot2, latex2exp, stats, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors Suggests: testthat License: GPL (>=2) Archs: i386, x64 MD5sum: ec418fc28b2c5cb98b666fe37cb64d88 NeedsCompilation: yes Title: A time series clustering package combining slope and Frechet distances Description: Clustering for time series data using slope distance and/or shape distance. biocViews: GeneExpression, Clustering, DNAMethylation, Microarray Author: Miriam Riquelme-Pérez and Fernando Pérez-Sanz Maintainer: Pérez-Sanz, Fernando VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tscR git_branch: RELEASE_3_12 git_last_commit: c6f7271 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tscR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tscR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tscR_1.2.0.tgz vignettes: vignettes/tscR/inst/doc/tscR.html vignetteTitles: tscR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tscR/inst/doc/tscR.R dependencyCount: 113 Package: tspair Version: 1.48.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Archs: i386, x64 MD5sum: c055a11609b39ebf15f0649cf61b791b NeedsCompilation: yes Title: Top Scoring Pairs for Microarray Classification Description: These functions calculate the pair of genes that show the maximum difference in ranking between two user specified groups. This "top scoring pair" maximizes the average of sensitivity and specificity over all rank based classifiers using a pair of genes in the data set. The advantage of classifying samples based on only the relative rank of a pair of genes is (a) the classifiers are much simpler and often more interpretable than more complicated classification schemes and (b) if arrays can be classified using only a pair of genes, PCR based tests could be used for classification of samples. See the references for the tspcalc() function for references regarding TSP classifiers. biocViews: Microarray Author: Jeffrey T. Leek Maintainer: Jeffrey T. Leek git_url: https://git.bioconductor.org/packages/tspair git_branch: RELEASE_3_12 git_last_commit: 88a62f4 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tspair_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tspair_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tspair_1.48.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tspair/inst/doc/tsp.R dependencyCount: 7 Package: TSRchitect Version: 1.16.0 Depends: R (>= 3.5) Imports: AnnotationHub, BiocGenerics, BiocParallel, dplyr, GenomicAlignments, GenomeInfoDb, GenomicRanges, gtools, IRanges, methods, readxl, Rsamtools (>= 1.14.3), rtracklayer, S4Vectors, SummarizedExperiment, tools, utils Suggests: ENCODExplorer, ggplot2, knitr, rmarkdown License: GPL-3 MD5sum: 1bf521dd8116442f34539a01eba72a96 NeedsCompilation: no Title: Promoter identification from large-scale TSS profiling data Description: In recent years, large-scale transcriptional sequence data has yielded considerable insights into the nature of gene expression and regulation in eukaryotes. Techniques that identify the 5' end of mRNAs, most notably CAGE, have mapped the promoter landscape across a number of model organisms. Due to the variability of TSS distributions and the transcriptional noise present in datasets, precisely identifying the active promoter(s) for genes from these datasets is not straightforward. TSRchitect allows the user to efficiently identify the putative promoter (the transcription start region, or TSR) from a variety of TSS profiling data types, including both single-end (e.g. CAGE) as well as paired-end (RAMPAGE, PEAT, STRIPE-seq). In addition, (new with version 1.3.0) TSRchitect provides the ability to import aligned EST and cDNA data. Along with the coordiantes of identified TSRs, TSRchitect also calculates the width, abundance and two forms of the Shape Index, and handles biological replicates for expression profiling. Finally, TSRchitect imports annotation files, allowing the user to associate identified promoters with genes and other genomic features. Three detailed examples of TSRchitect's utility are provided in the User's Guide, included with this package. biocViews: Clustering, FunctionalGenomics, GeneExpression, GeneRegulation, GenomeAnnotation, Sequencing, Transcription Author: R. Taylor Raborn [aut, cre, cph] Volker P. Brendel [aut, cph] Krishnakumar Sridharan [ctb] Maintainer: R. Taylor Raborn URL: https://github.com/brendelgroup/tsrchitect VignetteBuilder: knitr BugReports: https://github.com/brendelgroup/tsrchitect/issues git_url: https://git.bioconductor.org/packages/TSRchitect git_branch: RELEASE_3_12 git_last_commit: 75ac941 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TSRchitect_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TSRchitect_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TSRchitect_1.16.0.tgz vignettes: vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.pdf, vignettes/TSRchitect/inst/doc/TSRchitect.html, vignettes/TSRchitect/inst/doc/TSRchitectUsersGuide.html vignetteTitles: TSRchitect User's Guide, TSRchitect vignette, TSRchitect User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TSRchitect/inst/doc/TSRchitect.R dependencyCount: 110 Package: TTMap Version: 1.12.0 Depends: rgl, colorRamps Imports: grDevices,graphics,stats,utils, methods, SummarizedExperiment, Biobase Suggests: BiocStyle, airway License: GPL-2 MD5sum: ddf35245b0b709d09811df57444df4cf NeedsCompilation: no Title: Two-Tier Mapper: a clustering tool based on topological data analysis Description: TTMap is a clustering method that groups together samples with the same deviation in comparison to a control group. It is specially useful when the data is small. It is parameter free. biocViews: Software, Microarray, DifferentialExpression, MultipleComparison, Clustering, Classification Author: Rachel Jeitziner Maintainer: Rachel Jeitziner git_url: https://git.bioconductor.org/packages/TTMap git_branch: RELEASE_3_12 git_last_commit: 270469b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TTMap_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TTMap_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TTMap_1.12.0.tgz vignettes: vignettes/TTMap/inst/doc/TTMap.pdf vignetteTitles: Manual for the TTMap library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TTMap/inst/doc/TTMap.R dependencyCount: 74 Package: TurboNorm Version: 1.38.0 Depends: R (>= 2.12.0), convert, limma (>= 1.7.0), marray Imports: stats, grDevices, affy, lattice Suggests: BiocStyle, affydata License: LGPL Archs: i386, x64 MD5sum: eff082cf4a28bb71712ff682e469d1a9 NeedsCompilation: yes Title: A fast scatterplot smoother suitable for microarray normalization Description: A fast scatterplot smoother based on B-splines with second-order difference penalty. Functions for microarray normalization of single-colour data i.e. Affymetrix/Illumina and two-colour data supplied as marray MarrayRaw-objects or limma RGList-objects are available. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing, DNAMethylation, CpGIsland, MethylationArray, Normalization Author: Maarten van Iterson and Chantal van Leeuwen Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html git_url: https://git.bioconductor.org/packages/TurboNorm git_branch: RELEASE_3_12 git_last_commit: f58d015 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TurboNorm_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TurboNorm_1.38.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TurboNorm_1.38.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TurboNorm/inst/doc/turbonorm.R dependencyCount: 18 Package: TVTB Version: 1.16.0 Depends: R (>= 3.4), methods, utils, stats Imports: AnnotationFilter, BiocGenerics (>= 0.25.1), BiocParallel, Biostrings, ensembldb, ensemblVEP, GenomeInfoDb, GenomicRanges, GGally, ggplot2, Gviz, limma, IRanges (>= 2.21.6), reshape2, Rsamtools, S4Vectors (>= 0.25.14), SummarizedExperiment, VariantAnnotation (>= 1.19.9) Suggests: EnsDb.Hsapiens.v75 (>= 0.99.7), shiny (>= 0.13.2.9005), DT (>= 0.1.67), rtracklayer, BiocStyle (>= 2.5.19), knitr (>= 1.12), rmarkdown, testthat, covr, pander License: Artistic-2.0 MD5sum: 831e543150ba9b0cdbc191dbb0bb208f NeedsCompilation: no Title: TVTB: The VCF Tool Box Description: The package provides S4 classes and methods to filter, summarise and visualise genetic variation data stored in VCF files. In particular, the package extends the FilterRules class (S4Vectors package) to define news classes of filter rules applicable to the various slots of VCF objects. Functionalities are integrated and demonstrated in a Shiny web-application, the Shiny Variant Explorer (tSVE). biocViews: Software, Genetics, GeneticVariability, GenomicVariation, DataRepresentation, GUI, Genetics, DNASeq, WholeGenome, Visualization, MultipleComparison, DataImport, VariantAnnotation, Sequencing, Coverage, Alignment, SequenceMatching Author: Kevin Rue-Albrecht [aut, cre] Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/TVTB VignetteBuilder: knitr BugReports: https://github.com/kevinrue/TVTB/issues git_url: https://git.bioconductor.org/packages/TVTB git_branch: RELEASE_3_12 git_last_commit: e73ba32 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TVTB_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/4.0/TVTB_1.16.0.tgz vignettes: vignettes/TVTB/inst/doc/Introduction.html, vignettes/TVTB/inst/doc/tSVE.html, vignettes/TVTB/inst/doc/VcfFilterRules.html vignetteTitles: Introduction to TVTB, The Shiny Variant Explorer, VCF filter rules hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TVTB/inst/doc/Introduction.R, vignettes/TVTB/inst/doc/tSVE.R, vignettes/TVTB/inst/doc/VcfFilterRules.R dependencyCount: 147 Package: tweeDEseq Version: 1.36.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 50abe84258695b1c1b441f9eaefeae14 NeedsCompilation: yes Title: RNA-seq data analysis using the Poisson-Tweedie family of distributions Description: Differential expression analysis of RNA-seq using the Poisson-Tweedie family of distributions. biocViews: ImmunoOncology, StatisticalMethod, DifferentialExpression, Sequencing, RNASeq Author: Juan R Gonzalez and Mikel Esnaola (with contributions from Robert Castelo ) Maintainer: Juan R Gonzalez URL: http://www.creal.cat/jrgonzalez/software.htm git_url: https://git.bioconductor.org/packages/tweeDEseq git_branch: RELEASE_3_12 git_last_commit: 0c9d10f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tweeDEseq_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tweeDEseq_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tweeDEseq_1.36.0.tgz vignettes: vignettes/tweeDEseq/inst/doc/tweeDEseq.pdf vignetteTitles: tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tweeDEseq/inst/doc/tweeDEseq.R importsMe: ptmixed dependencyCount: 25 Package: twilight Version: 1.66.0 Depends: R (>= 2.10), splines (>= 2.2.0), stats (>= 2.2.0), Biobase(>= 1.12.0) Imports: Biobase, graphics, grDevices, stats Suggests: golubEsets (>= 1.4.2), vsn (>= 1.7.2) License: GPL (>= 2) Archs: i386, x64 MD5sum: edf775cf968214ccd3a29c3b4cacbe58 NeedsCompilation: yes Title: Estimation of local false discovery rate Description: In a typical microarray setting with gene expression data observed under two conditions, the local false discovery rate describes the probability that a gene is not differentially expressed between the two conditions given its corrresponding observed score or p-value level. The resulting curve of p-values versus local false discovery rate offers an insight into the twilight zone between clear differential and clear non-differential gene expression. Package 'twilight' contains two main functions: Function twilight.pval performs a two-condition test on differences in means for a given input matrix or expression set and computes permutation based p-values. Function twilight performs a stochastic downhill search to estimate local false discovery rates and effect size distributions. The package further provides means to filter for permutations that describe the null distribution correctly. Using filtered permutations, the influence of hidden confounders could be diminished. biocViews: Microarray, DifferentialExpression, MultipleComparison Author: Stefanie Scheid Maintainer: Stefanie Scheid URL: http://compdiag.molgen.mpg.de/software/twilight.shtml git_url: https://git.bioconductor.org/packages/twilight git_branch: RELEASE_3_12 git_last_commit: a91a908 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/twilight_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/twilight_1.66.0.zip mac.binary.ver: bin/macosx/contrib/4.0/twilight_1.66.0.tgz vignettes: vignettes/twilight/inst/doc/tr_2004_01.pdf vignetteTitles: Estimation of Local False Discovery Rates hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twilight/inst/doc/tr_2004_01.R dependsOnMe: OrderedList dependencyCount: 9 Package: twoddpcr Version: 1.14.0 Depends: R (>= 3.4) Imports: class, ggplot2, hexbin, methods, scales, shiny, stats, utils, RColorBrewer, S4Vectors Suggests: devtools, knitr, reshape2, rmarkdown, testthat, BiocStyle License: GPL-3 MD5sum: bce3f9f71351dd5fc54e17432a865650 NeedsCompilation: no Title: Classify 2-d Droplet Digital PCR (ddPCR) data and quantify the number of starting molecules Description: The twoddpcr package takes Droplet Digital PCR (ddPCR) droplet amplitude data from Bio-Rad's QuantaSoft and can classify the droplets. A summary of the positive/negative droplet counts can be generated, which can then be used to estimate the number of molecules using the Poisson distribution. This is the first open source package that facilitates the automatic classification of general two channel ddPCR data. Previous work includes 'definetherain' (Jones et al., 2014) and 'ddpcRquant' (Trypsteen et al., 2015) which both handle one channel ddPCR experiments only. The 'ddpcr' package available on CRAN (Attali et al., 2016) supports automatic gating of a specific class of two channel ddPCR experiments only. biocViews: ddPCR, Software, Classification Author: Anthony Chiu [aut, cre] Maintainer: Anthony Chiu URL: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/ VignetteBuilder: knitr BugReports: http://github.com/CRUKMI-ComputationalBiology/twoddpcr/issues/ git_url: https://git.bioconductor.org/packages/twoddpcr git_branch: RELEASE_3_12 git_last_commit: c08b10c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/twoddpcr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/twoddpcr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/twoddpcr_1.14.0.tgz vignettes: vignettes/twoddpcr/inst/doc/twoddpcr.html vignetteTitles: twoddpcr: A package for Droplet Digital PCR analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/twoddpcr/inst/doc/twoddpcr.R dependencyCount: 64 Package: tximeta Version: 1.8.5 Imports: SummarizedExperiment, tximport, jsonlite, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, GenomicFeatures, ensembldb, BiocFileCache, AnnotationHub, Biostrings, tibble, GenomeInfoDb, rappdirs, utils, methods, Matrix Suggests: knitr, rmarkdown, testthat, tximportData, org.Dm.eg.db, DESeq2, edgeR, limma, devtools License: GPL-2 MD5sum: f592d0b2b20b5dbe958001cc870db778 NeedsCompilation: no Title: Transcript Quantification Import with Automatic Metadata Description: Transcript quantification import from Salmon and alevin with automatic attachment of transcript ranges and release information, and other associated metadata. De novo transcriptomes can be linked to the appropriate sources with linkedTxomes and shared for computational reproducibility. biocViews: Annotation, GenomeAnnotation, DataImport, Preprocessing, RNASeq, SingleCell, Transcriptomics, Transcription, GeneExpression, FunctionalGenomics, ReproducibleResearch, ReportWriting, ImmunoOncology Author: Michael Love [aut, cre], Charlotte Soneson [aut, ctb], Peter Hickey [aut, ctb], Rob Patro [aut, ctb] Maintainer: Michael Love URL: https://github.com/mikelove/tximeta VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximeta git_branch: RELEASE_3_12 git_last_commit: df04af8 git_last_commit_date: 2021-04-12 Date/Publication: 2021-04-12 source.ver: src/contrib/tximeta_1.8.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/tximeta_1.8.5.zip mac.binary.ver: bin/macosx/contrib/4.0/tximeta_1.8.5.tgz vignettes: vignettes/tximeta/inst/doc/tximeta.html vignetteTitles: Transcript quantification import with automatic metadata hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximeta/inst/doc/tximeta.R dependsOnMe: rnaseqGene importsMe: IsoformSwitchAnalyzeR suggestsMe: DESeq2, fishpond, fluentGenomics dependencyCount: 115 Package: tximport Version: 1.18.0 Imports: utils, stats, methods Suggests: knitr, rmarkdown, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, csaw, DESeq2 (>= 1.11.6), rhdf5, jsonlite, matrixStats, Matrix, fishpond License: GPL (>=2) MD5sum: 2d19640a8769386ff6116b7e50415be0 NeedsCompilation: no Title: Import and summarize transcript-level estimates for transcript- and gene-level analysis Description: Imports transcript-level abundance, estimated counts and transcript lengths, and summarizes into matrices for use with downstream gene-level analysis packages. Average transcript length, weighted by sample-specific transcript abundance estimates, is provided as a matrix which can be used as an offset for different expression of gene-level counts. biocViews: DataImport, Preprocessing, RNASeq, Transcriptomics, Transcription, GeneExpression, ImmunoOncology Author: Michael Love [cre,aut], Charlotte Soneson [aut], Mark Robinson [aut], Rob Patro [ctb], Andrew Parker Morgan [ctb], Ryan C. Thompson [ctb], Matt Shirley [ctb], Avi Srivastava [ctb] Maintainer: Michael Love URL: https://github.com/mikelove/tximport VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/tximport git_branch: RELEASE_3_12 git_last_commit: 58b20cb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/tximport_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/tximport_1.18.0.zip mac.binary.ver: bin/macosx/contrib/4.0/tximport_1.18.0.tgz vignettes: vignettes/tximport/inst/doc/tximport.html vignetteTitles: Importing transcript abundance datasets with tximport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R importsMe: alevinQC, BgeeCall, IsoformSwitchAnalyzeR, tximeta suggestsMe: BANDITS, DESeq2, HumanTranscriptomeCompendium, SummarizedBenchmark, variancePartition dependencyCount: 3 Package: TxRegInfra Version: 1.10.0 Depends: R (>= 3.5), RaggedExperiment (>= 1.3.11), mongolite Imports: methods, rjson, GenomicRanges, IRanges, BiocParallel, GenomeInfoDb, S4Vectors, SummarizedExperiment, utils Suggests: knitr, GenomicFiles, EnsDb.Hsapiens.v75, testthat, shiny, biovizBase (>= 1.27.2), Gviz, AnnotationFilter, ensembldb, ontoProc, rjson, graph, TFutils (>= 1.5.4) License: Artistic-2.0 MD5sum: 7610d7effab88ee8340c34f8deebccbd NeedsCompilation: no Title: Metadata management for multiomic specification of transcriptional regulatory networks Description: This package provides interfaces to genomic metadata employed in regulatory network creation, with a focus on noSQL solutions. Currently quantitative representations of eQTLs, DnaseI hypersensitivity sites and digital genomic footprints are assembled using an out-of-memory extension of the RaggedExperiment API. biocViews: Network Author: Vince Carey Maintainer: VJ Carey VignetteBuilder: knitr PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/TxRegInfra git_branch: RELEASE_3_12 git_last_commit: 386b161 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TxRegInfra_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TxRegInfra_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TxRegInfra_1.10.0.tgz vignettes: vignettes/TxRegInfra/inst/doc/shims.html, vignettes/TxRegInfra/inst/doc/TxRegInfra.html vignetteTitles: shims in TxRegInfra, TxRegInfra -- classes and methods for TxRegQuery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TxRegInfra/inst/doc/shims.R, vignettes/TxRegInfra/inst/doc/TxRegInfra.R dependencyCount: 41 Package: TypeInfo Version: 1.56.0 Depends: methods Suggests: Biobase License: BSD MD5sum: 5fe08d628f8c1f46a12ee7f663a520ff NeedsCompilation: no Title: Optional Type Specification Prototype Description: A prototype for a mechanism for specifying the types of parameters and the return value for an R function. This is meta-information that can be used to generate stubs for servers and various interfaces to these functions. Additionally, the arguments in a call to a typed function can be validated using the type specifications. We allow types to be specified as either i) by class name using either inheritance - is(x, className), or strict instance of - class(x) %in% className, or ii) a dynamic test given as an R expression which is evaluated at run-time. More precise information and interesting tests can be done via ii), but it is harder to use this information as meta-data as it requires more effort to interpret it and it is of course run-time information. It is typically more meaningful. biocViews: Infrastructure Author: Duncan Temple Lang Robert Gentleman () Maintainer: Duncan Temple Lang git_url: https://git.bioconductor.org/packages/TypeInfo git_branch: RELEASE_3_12 git_last_commit: 5a1967a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/TypeInfo_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/TypeInfo_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/TypeInfo_1.56.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TypeInfo/inst/doc/TypeInfoNews.R dependencyCount: 1 Package: Ularcirc Version: 1.8.0 Depends: R (>= 3.4.0) Imports: AnnotationHub, AnnotationDbi, BiocGenerics, Biostrings, BSgenome, data.table (>= 1.9.4), DT, GenomicFeatures, GenomeInfoDb, GenomeInfoDbData, GenomicAlignments, GenomicRanges, ggplot2, ggrepel, gsubfn, mirbase.db, moments, Organism.dplyr, S4Vectors, shiny, shinydashboard, shinyFiles, shinyjs, Sushi, yaml Suggests: BSgenome.Hsapiens.UCSC.hg38, BiocStyle, httpuv, knitr, org.Hs.eg.db, rmarkdown, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: df6fe8536f173152aef0d0006decba73 NeedsCompilation: no Title: Shiny app for canonical and back splicing analysis (i.e. circular and mRNA analysis) Description: Ularcirc reads in STAR aligned splice junction files and provides visualisation and analysis tools for splicing analysis. Users can assess backsplice junctions and forward canonical junctions. biocViews: DataRepresentation,Visualization, Genetics, Sequencing, Annotation, Coverage, AlternativeSplicing, DifferentialSplicing Author: David Humphreys [aut, cre] Maintainer: David Humphreys VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Ularcirc git_branch: RELEASE_3_12 git_last_commit: c22748f git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Ularcirc_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Ularcirc_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Ularcirc_1.8.0.tgz vignettes: vignettes/Ularcirc/inst/doc/Ularcirc.html vignetteTitles: Ularcirc hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Ularcirc/inst/doc/Ularcirc.R dependencyCount: 139 Package: UMI4Cats Version: 1.0.1 Depends: R (>= 4.0.0), SummarizedExperiment Imports: magick, cowplot, scales, GenomicRanges, ShortRead, zoo, ggplot2, reshape2, regioneR, IRanges, S4Vectors, magrittr, dplyr, BSgenome, Biostrings, DESeq2, R.utils, Rsamtools, stringr, Rbowtie2, methods, GenomeInfoDb, GenomicAlignments, RColorBrewer, utils, grDevices, stats, org.Hs.eg.db, annotate, TxDb.Hsapiens.UCSC.hg19.knownGene, rlang, GenomicFeatures, BiocFileCache, rappdirs, fda Suggests: knitr, rmarkdown, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, tidyr, testthat License: Artistic-2.0 MD5sum: c645163c70ce554335d8b618a88639c7 NeedsCompilation: no Title: UMI4Cats: Processing, analysis and visualization of UMI-4C chromatin contact data Description: UMI-4C is a technique that allows characterization of 3D chromatin interactions with a bait of interest, taking advantage of a sonication step to produce unique molecular identifiers (UMIs) that help remove duplication bias, thus allowing a better differential comparsion of chromatin interactions between conditions. This package allows processing of UMI-4C data, starting from FastQ files provided by the sequencing facility. It provides two statistical methods for detecting differential contacts and includes a visualization function to plot integrated information from a UMI-4C assay. biocViews: QualityControl, Preprocessing, Alignment, Normalization, Visualization, Sequencing, Coverage Author: Mireia Ramos-Rodriguez [aut, cre] (), Marc Subirana-Granes [aut] Maintainer: Mireia Ramos-Rodriguez URL: https://github.com/Pasquali-lab/UMI4Cats VignetteBuilder: knitr BugReports: https://github.com/Pasquali-lab/UMI4Cats/issues git_url: https://git.bioconductor.org/packages/UMI4Cats git_branch: RELEASE_3_12 git_last_commit: 1f6f3d8 git_last_commit_date: 2020-12-29 Date/Publication: 2020-12-29 source.ver: src/contrib/UMI4Cats_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/4.0/UMI4Cats_1.0.1.zip mac.binary.ver: bin/macosx/contrib/4.0/UMI4Cats_1.0.1.tgz vignettes: vignettes/UMI4Cats/inst/doc/UMI4Cats.html vignetteTitles: Analyzing UMI-4C data with UMI4Cats hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UMI4Cats/inst/doc/UMI4Cats.R dependencyCount: 146 Package: uncoverappLib Version: 1.0.0 Imports: markdown, shiny, shinyjs, shinyBS, shinyWidgets,shinycssloaders, DT, Gviz, Homo.sapiens, openxlsx, condformat, stringr, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, BiocFileCache,rappdirs, TxDb.Hsapiens.UCSC.hg19.knownGene, rlist, utils, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v86, OrganismDbi, BSgenome.Hsapiens.UCSC.hg19, processx, Rsamtools, GenomicRanges Suggests: BiocStyle, knitr, testthat, rmarkdown, dplyr License: MIT + file LICENSE MD5sum: 8b669167014897a092ef31ee19ad73f3 NeedsCompilation: no Title: Interactive graphical application for clinical assessment of sequence coverage at the base-pair level Description: a Shiny application containing a suite of graphical and statistical tools to support clinical assessment of low coverage regions.It displays three web pages each providing a different analysis module: Coverage analysis, calculate AF by allele frequency app and binomial distribution. biocViews: Software, Visualization, Annotation, Coverage Author: Emanuela Iovino [cre, aut], Tommaso Pippucci [aut] Maintainer: Emanuela Iovino URL: https://github.com/Manuelaio/uncoverappLib VignetteBuilder: knitr BugReports: https://github.com/Manuelaio/uncoverappLib/issues git_url: https://git.bioconductor.org/packages/uncoverappLib git_branch: RELEASE_3_12 git_last_commit: c39ff3a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/uncoverappLib_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/uncoverappLib_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/uncoverappLib_1.0.0.tgz vignettes: vignettes/uncoverappLib/inst/doc/uncoverappLib.html vignetteTitles: uncoverappLib hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/uncoverappLib/inst/doc/uncoverappLib.R dependencyCount: 175 Package: UNDO Version: 1.32.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 76e07fb8c93c1cb95665826dca97652c NeedsCompilation: no Title: Unsupervised Deconvolution of Tumor-Stromal Mixed Expressions Description: UNDO is an R package for unsupervised deconvolution of tumor and stromal mixed expression data. It detects marker genes and deconvolutes the mixing expression data without any prior knowledge. biocViews: Software Author: Niya Wang Maintainer: Niya Wang git_url: https://git.bioconductor.org/packages/UNDO git_branch: RELEASE_3_12 git_last_commit: 64c1127 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/UNDO_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/UNDO_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/UNDO_1.32.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UNDO/inst/doc/UNDO-vignette.R dependencyCount: 11 Package: unifiedWMWqPCR Version: 1.26.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: 683d520dcfaeb1fb196808498364450b NeedsCompilation: no Title: Unified Wilcoxon-Mann Whitney Test for testing differential expression in qPCR data Description: This packages implements the unified Wilcoxon-Mann-Whitney Test for qPCR data. This modified test allows for testing differential expression in qPCR data. biocViews: DifferentialExpression, GeneExpression, MicrotitrePlateAssay, MultipleComparison, QualityControl, Software, Visualization, qPCR Author: Jan R. De Neve & Joris Meys Maintainer: Joris Meys git_url: https://git.bioconductor.org/packages/unifiedWMWqPCR git_branch: RELEASE_3_12 git_last_commit: 3463add git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/unifiedWMWqPCR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/unifiedWMWqPCR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/unifiedWMWqPCR_1.26.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: Using unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.R dependencyCount: 22 Package: UniProt.ws Version: 2.30.0 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi, BiocFileCache, rappdirs Suggests: RUnit, BiocStyle, knitr License: Artistic License 2.0 MD5sum: 5eb0af3e2ee8fb308d73aed1c7fe4aa4 NeedsCompilation: no Title: R Interface to UniProt Web Services Description: A collection of functions for retrieving, processing and repackaging the UniProt web services. biocViews: Annotation, Infrastructure, GO, KEGG, BioCarta Author: Marc Carlson [aut], Csaba Ortutay [ctb], Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/UniProt.ws git_branch: RELEASE_3_12 git_last_commit: dde7635 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-31 source.ver: src/contrib/UniProt.ws_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/UniProt.ws_2.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/UniProt.ws_2.30.0.tgz vignettes: vignettes/UniProt.ws/inst/doc/UniProt.ws.pdf vignetteTitles: UniProt.ws: A package for retrieving data from the UniProt web service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/UniProt.ws/inst/doc/UniProt.ws.R importsMe: CARNIVAL, dagLogo suggestsMe: cleaver, qPLEXanalyzer dependencyCount: 55 Package: Uniquorn Version: 2.10.0 Depends: R (>= 3.5) Imports: stringr, R.utils, WriteXLS, stats, doParallel, foreach, GenomicRanges, IRanges, VariantAnnotation Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 0ca90493cbb8293089c09f38be969d37 NeedsCompilation: no Title: Identification of cancer cell lines based on their weighted mutational/ variational fingerprint Description: This packages enables users to identify cancer cell lines. Cancer cell line misidentification and cross-contamination reprents a significant challenge for cancer researchers. The identification is vital and in the frame of this package based on the locations/ loci of somatic and germline mutations/ variations. The input format is vcf/ vcf.gz and the files have to contain a single cancer cell line sample (i.e. a single member/genotype/gt column in the vcf file). The implemented method is optimized for the Next-generation whole exome and whole genome DNA-sequencing technology. RNA-seq data is very likely to work as well but hasn't been rigiously tested yet. Panel-seq will require manual adjustment of thresholds biocViews: ImmunoOncology, StatisticalMethod, WholeGenome, ExomeSeq Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/Uniquorn git_branch: RELEASE_3_12 git_last_commit: 762a006 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Uniquorn_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Uniquorn_2.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Uniquorn_2.10.0.tgz vignettes: vignettes/Uniquorn/inst/doc/Uniquorn.html vignetteTitles: Uniquorn vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R dependencyCount: 98 Package: universalmotif Version: 1.8.5 Depends: R (>= 3.5.0) Imports: methods, stats, utils, MASS, ggplot2, ggseqlogo, yaml, IRanges, Rcpp, Rdpack (>= 0.7), Biostrings, BiocGenerics, S4Vectors, rlang, grid LinkingTo: Rcpp, RcppThread Suggests: spelling, knitr, bookdown, TFBSTools, rmarkdown, MotifDb, testthat, Logolas, BiocParallel, seqLogo, motifStack, dplyr, ape, ggtree, processx Enhances: MotIV, PWMEnrich, rGADEM, motifRG License: GPL-3 Archs: i386, x64 MD5sum: 5bb0460f7fd19e7cca6c90d5ea06580a NeedsCompilation: yes Title: Import, Modify, and Export Motifs with R Description: Allows for importing most common motif types into R for use by functions provided by other Bioconductor motif-related packages. Motifs can be exported into most major motif formats from various classes as defined by other Bioconductor packages. A suite of motif and sequence manipulation and analysis functions are included, including enrichment, comparison, P-value calculation, shuffling, trimming, higher-order motifs, and others. biocViews: MotifAnnotation, MotifDiscovery, DataImport, GeneRegulation Author: Benjamin Jean-Marie Tremblay [aut, cre] (), Spencer Nystrom [ctb] Maintainer: Benjamin Jean-Marie Tremblay URL: https://bioconductor.org/packages/universalmotif/ VignetteBuilder: knitr BugReports: https://github.com/bjmt/universalmotif/issues git_url: https://git.bioconductor.org/packages/universalmotif git_branch: RELEASE_3_12 git_last_commit: 9ddd378 git_last_commit_date: 2021-04-06 Date/Publication: 2021-04-07 source.ver: src/contrib/universalmotif_1.8.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/universalmotif_1.8.5.zip mac.binary.ver: bin/macosx/contrib/4.0/universalmotif_1.8.5.tgz vignettes: vignettes/universalmotif/inst/doc/Introduction.pdf, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.pdf, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.pdf, vignettes/universalmotif/inst/doc/MotifManipulation.pdf, vignettes/universalmotif/inst/doc/SequenceSearches.pdf vignetteTitles: Introduction to "universalmotif", Introduction to sequence motifs, Motif comparisons and P-values, Motif import,, export,, and manipulation, Sequence manipulation and scanning hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/universalmotif/inst/doc/Introduction.R, vignettes/universalmotif/inst/doc/IntroductionToSequenceMotifs.R, vignettes/universalmotif/inst/doc/MotifComparisonAndPvalues.R, vignettes/universalmotif/inst/doc/MotifManipulation.R, vignettes/universalmotif/inst/doc/SequenceSearches.R importsMe: circRNAprofiler dependencyCount: 53 Package: uSORT Version: 1.16.0 Depends: R (>= 3.3.0), tcltk Imports: igraph, Matrix, RANN, RSpectra, VGAM, gplots, parallel, plyr, methods, cluster, Biobase, fpc, BiocGenerics, monocle, grDevices, graphics, stats, utils Suggests: knitr, RUnit, testthat, ggplot2 License: Artistic-2.0 MD5sum: c2a9b9400c93aa952578b174ec27b8ca NeedsCompilation: no Title: uSORT: A self-refining ordering pipeline for gene selection Description: This package is designed to uncover the intrinsic cell progression path from single-cell RNA-seq data. It incorporates data pre-processing, preliminary PCA gene selection, preliminary cell ordering, feature selection, refined cell ordering, and post-analysis interpretation and visualization. biocViews: ImmunoOncology, RNASeq, GUI, CellBiology, DNASeq Author: Mai Chan Lau, Hao Chen, Jinmiao Chen Maintainer: Hao Chen VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/uSORT git_branch: RELEASE_3_12 git_last_commit: 48962a5 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/uSORT_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/uSORT_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/uSORT_1.16.0.tgz vignettes: vignettes/uSORT/inst/doc/uSORT_quick_start.html vignetteTitles: Quick Start hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/uSORT/inst/doc/uSORT_quick_start.R dependencyCount: 103 Package: VanillaICE Version: 1.52.0 Depends: R (>= 3.5.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.27.6), SummarizedExperiment (>= 1.5.3) Imports: MatrixGenerics, Biobase, S4Vectors (>= 0.23.18), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb (>= 1.11.4), crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, human610quadv1bCrlmm, ArrayTV Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 Archs: i386, x64 MD5sum: f6a982ebd8b134f269413f39364d7fa6 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alteration in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf [aut, cre] Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/VanillaICE git_branch: RELEASE_3_12 git_last_commit: ae97bd8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VanillaICE_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VanillaICE_1.52.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VanillaICE_1.52.0.tgz vignettes: vignettes/VanillaICE/inst/doc/crlmmDownstream.pdf, vignettes/VanillaICE/inst/doc/VanillaICE.pdf vignetteTitles: crlmmDownstream, VanillaICE Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VanillaICE/inst/doc/crlmmDownstream.R, vignettes/VanillaICE/inst/doc/VanillaICE.R dependsOnMe: MinimumDistance suggestsMe: oligoClasses dependencyCount: 79 Package: variancePartition Version: 1.20.0 Depends: R (>= 3.6.0), ggplot2, limma, BiocParallel, scales, Biobase, methods Imports: MASS, pbkrtest (>= 0.4-4), lmerTest, iterators, splines, foreach, doParallel, colorRamps, gplots, progress, reshape2, lme4 (>= 1.1-10), grDevices, graphics, utils, stats Suggests: BiocStyle, knitr, pander, rmarkdown, edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, RUnit, BiocGenerics, r2glmm, readr License: GPL (>= 2) MD5sum: 1d1fa2cab04eed88dd36836f6778d543 NeedsCompilation: no Title: Quantify and interpret divers of variation in multilevel gene expression experiments Description: Quantify and interpret multiple sources of biological and technical variation in gene expression experiments. Uses a linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. Includes dream differential expression analysis for repeated measures. biocViews: RNASeq, GeneExpression, GeneSetEnrichment, DifferentialExpression, BatchEffect, QualityControl, Regression, Epigenetics, FunctionalGenomics, Transcriptomics, Normalization, Preprocessing, Microarray, ImmunoOncology, Software Author: Gabriel E. Hoffman Maintainer: Gabriel E. Hoffman VignetteBuilder: knitr BugReports: https://github.com/GabrielHoffman/variancePartition/issues git_url: https://git.bioconductor.org/packages/variancePartition git_branch: RELEASE_3_12 git_last_commit: bd06b39 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/variancePartition_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/variancePartition_1.20.0.zip mac.binary.ver: bin/macosx/contrib/4.0/variancePartition_1.20.0.tgz vignettes: vignettes/variancePartition/inst/doc/theory_practice_random_effects.pdf, vignettes/variancePartition/inst/doc/variancePartition.pdf, vignettes/variancePartition/inst/doc/additional_visualization.html, vignettes/variancePartition/inst/doc/dream.html, vignettes/variancePartition/inst/doc/FAQ.html vignetteTitles: 3) Theory and practice of random effects and REML, 1) Tutorial on using variancePartition, 2) Additional visualizations, 4) dream: differential expression testing with repeated measures designs, 5) Frequently asked questions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/dream.R, vignettes/variancePartition/inst/doc/FAQ.R, vignettes/variancePartition/inst/doc/theory_practice_random_effects.R, vignettes/variancePartition/inst/doc/variancePartition.R importsMe: muscat dependencyCount: 92 Package: VariantAnnotation Version: 1.36.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.3), MatrixGenerics, GenomeInfoDb (>= 1.15.2), GenomicRanges (>= 1.41.5), SummarizedExperiment (>= 1.19.5), Rsamtools (>= 1.99.0) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.27.12), IRanges (>= 2.23.9), XVector (>= 0.29.2), Biostrings (>= 2.57.2), AnnotationDbi (>= 1.27.9), rtracklayer (>= 1.39.7), BSgenome (>= 1.47.3), GenomicFeatures (>= 1.31.3) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rhtslib Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 812e968772fa54c7cf2763a06032ee8e NeedsCompilation: yes Title: Annotation of Genetic Variants Description: Annotate variants, compute amino acid coding changes, predict coding outcomes. biocViews: DataImport, Sequencing, SNP, Annotation, Genetics, VariantAnnotation Author: Bioconductor Package Maintainer [aut, cre], Valerie Oberchain [aut], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Bioconductor Package Maintainer SystemRequirements: GNU make Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q git_url: https://git.bioconductor.org/packages/VariantAnnotation git_branch: RELEASE_3_12 git_last_commit: 9918bd1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VariantAnnotation_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VariantAnnotation_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VariantAnnotation_1.36.0.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: 2. Using filterVcf to Select Variants from VCF Files, 1. Introduction to VariantAnnotation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantAnnotation/inst/doc/filterVcf.R, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.R dependsOnMe: CNVrd2, deepSNV, ensemblVEP, genotypeeval, HelloRanges, HTSeqGenie, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, Rariant, seqCAT, signeR, SomaticSignatures, StructuralVariantAnnotation, VariantFiltering, VariantTools, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, cgdv17, VariantToolsData, annotation, sequencing, variants, PlasmaMutationDetector importsMe: AllelicImbalance, APAlyzer, appreci8R, BadRegionFinder, BBCAnalyzer, biovizBase, biscuiteer, CNVfilteR, CopyNumberPlots, customProDB, DAMEfinder, decompTumor2Sig, DominoEffect, fcScan, FunciSNP, GA4GHclient, genbankr, GenomicFiles, GenVisR, ggbio, GGtools, gmapR, gQTLstats, gwascat, gwasurvivr, icetea, igvR, karyoploteR, ldblock, MADSEQ, methyAnalysis, MMAPPR2, motifbreakR, musicatk, MutationalPatterns, PGA, scoreInvHap, SigsPack, SNPhood, systemPipeR, TitanCNA, TVTB, Uniquorn, VCFArray, XCIR, YAPSA, COSMIC.67, yriMulti suggestsMe: AnnotationHub, BiocParallel, cellbaseR, CrispRVariants, GenomicRanges, GenomicScores, GWASTools, omicsPrint, podkat, RVS, SeqArray, splatter, trackViewer, trio, vtpnet, AshkenazimSonChr21, GeuvadisTranscriptExpr, deconstructSigs, ldsep, polyRAD dependencyCount: 89 Package: VariantExperiment Version: 1.4.2 Depends: R (>= 3.6.0), S4Vectors (>= 0.21.24), SummarizedExperiment (>= 1.13.0), GenomicRanges, GDSArray (>= 1.3.0), DelayedDataFrame (>= 1.0.0) Imports: tools, utils, stats, methods, gdsfmt, SNPRelate, SeqArray, SeqVarTools, DelayedArray, Biostrings, IRanges Suggests: testthat, knitr License: GPL-3 MD5sum: c8f3d7304c0f6a2a84b63bd1c27b6be3 NeedsCompilation: no Title: A RangedSummarizedExperiment Container for VCF/GDS Data with GDS Backend Description: VariantExperiment is a Bioconductor package for saving data in VCF/GDS format into RangedSummarizedExperiment object. The high-throughput genetic/genomic data are saved in GDSArray objects. The annotation data for features/samples are saved in DelayedDataFrame format with mono-dimensional GDSArray in each column. The on-disk representation of both assay data and annotation data achieves on-disk reading and processing and saves memory space significantly. The interface of RangedSummarizedExperiment data format enables easy and common manipulations for high-throughput genetic/genomic data with common SummarizedExperiment metaphor in R and Bioconductor. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, GenomeAnnotation, GenotypingArray Author: Qian Liu [aut, cre], Hervé Pagès [aut], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Bioconductor/VariantExperiment VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/VariantExperiment/issues git_url: https://git.bioconductor.org/packages/VariantExperiment git_branch: RELEASE_3_12 git_last_commit: 2e0e24c git_last_commit_date: 2021-04-09 Date/Publication: 2021-04-09 source.ver: src/contrib/VariantExperiment_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/4.0/VariantExperiment_1.4.2.zip mac.binary.ver: bin/macosx/contrib/4.0/VariantExperiment_1.4.2.tgz vignettes: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.html, vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.html vignetteTitles: VariantExperiment-class, VariantExperiment-methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantExperiment/inst/doc/VariantExperiment-class.R, vignettes/VariantExperiment/inst/doc/VariantExperiment-methods.R dependencyCount: 67 Package: VariantFiltering Version: 1.26.0 Depends: R (>= 3.5.0), methods, BiocGenerics (>= 0.25.1), VariantAnnotation (>= 1.13.29) Imports: utils, stats, Biobase, S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), SummarizedExperiment, GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, GenomicScores (>= 1.0.0), Gviz, shiny, shinythemes, shinyjs, DT, shinyTree LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: RUnit, BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.1000genomes.hs37d5, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, MafDb.1Kgenomes.phase1.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: 92c8de368123ce4674d6d9f5d3115de3 NeedsCompilation: yes Title: Filtering of coding and non-coding genetic variants Description: Filter genetic variants using different criteria such as inheritance model, amino acid change consequence, minor allele frequencies across human populations, splice site strength, conservation, etc. biocViews: Genetics, Homo_sapiens, Annotation, SNP, Sequencing, HighThroughputSequencing Author: Robert Castelo [aut, cre], Dei Martinez Elurbe [ctb], Pau Puigdevall [ctb], Joan Fernandez [ctb] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues git_url: https://git.bioconductor.org/packages/VariantFiltering git_branch: RELEASE_3_12 git_last_commit: 3acde0e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VariantFiltering_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VariantFiltering_1.26.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VariantFiltering_1.26.0.tgz vignettes: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.pdf vignetteTitles: VariantFiltering: filter coding and non-coding genetic variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantFiltering/inst/doc/usingVariantFiltering.R dependencyCount: 165 Package: VariantTools Version: 1.32.0 Depends: S4Vectors (>= 0.17.33), IRanges (>= 2.13.12), GenomicRanges (>= 1.31.8), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.31.2), BiocGenerics, Biostrings, parallel, GenomicFeatures (>= 1.31.3), Matrix, rtracklayer (>= 1.39.7), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph, gmapR (>= 1.21.3) License: Artistic-2.0 MD5sum: ea1074ffe7e42b5309aa1231b1c312fd NeedsCompilation: no Title: Tools for Exploratory Analysis of Variant Calls Description: Explore, diagnose, and compare variant calls using filters. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence git_url: https://git.bioconductor.org/packages/VariantTools git_branch: RELEASE_3_12 git_last_commit: ed6c8be git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VariantTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VariantTools_1.32.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VariantTools_1.32.0.tgz vignettes: vignettes/VariantTools/inst/doc/tutorial.pdf, vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: tutorial.pdf, Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie, MMAPPR2 suggestsMe: VariantToolsData dependencyCount: 90 Package: vasp Version: 1.2.4 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, Sushi, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) MD5sum: e099edfa0e1035a2990a175418263d74 NeedsCompilation: no Title: Quantification and Visualization of Variations of Splicing in Population Description: Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures. biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Visualization, Preprocessing, Clustering, DifferentialExpression, KEGG, ImmunoOncology Author: Huihui Yu, Qian Du, Chi Zhang Maintainer: Huihui Yu URL: https://github.com/yuhuihui2011/vasp VignetteBuilder: knitr BugReports: https://github.com/yuhuihui2011/vasp/issues git_url: https://git.bioconductor.org/packages/vasp git_branch: RELEASE_3_12 git_last_commit: 22d830e git_last_commit_date: 2021-01-22 Date/Publication: 2021-01-23 source.ver: src/contrib/vasp_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/4.0/vasp_1.2.4.zip mac.binary.ver: bin/macosx/contrib/4.0/vasp_1.2.4.tgz vignettes: vignettes/vasp/inst/doc/vasp.html vignetteTitles: Variations of Splicing in Population hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vasp/inst/doc/vasp.R dependencyCount: 103 Package: VaSP Version: 1.2.5 Depends: R (>= 4.0), ballgown Imports: IRanges, GenomicRanges, S4Vectors, Sushi, parallel, matrixStats, GenomicAlignments, GenomeInfoDb, Rsamtools, cluster, stats, graphics, methods Suggests: knitr, rmarkdown License: GPL (>= 2.0) MD5sum: dfd084f084910b6ebcb9f08c19dc3e3f NeedsCompilation: no Title: Quantification and Visualization of Variations of Splicing in Population Description: Discovery of genome-wide variable alternative splicing events from short-read RNA-seq data and visualizations of gene splicing information for publication-quality multi-panel figures in a population. biocViews: RNASeq, AlternativeSplicing, DifferentialSplicing, StatisticalMethod, Visualization, Preprocessing, Clustering, DifferentialExpression, KEGG, ImmunoOncology Author: Huihui Yu [aut, cre] (), Qian Du [aut] (), Chi Zhang [aut] () Maintainer: Huihui Yu URL: https://github.com/yuhuihui2011/VaSP VignetteBuilder: knitr BugReports: https://github.com/yuhuihui2011/VaSP/issues git_url: https://git.bioconductor.org/packages/VaSP git_branch: RELEASE_3_12 git_last_commit: f6f0188 git_last_commit_date: 2021-02-14 Date/Publication: 2021-02-26 source.ver: src/contrib/VaSP_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/4.0/VaSP_1.2.5.zip mac.binary.ver: bin/macosx/contrib/4.0/VaSP_1.2.5.tgz vignettes: vignettes/VaSP/inst/doc/VaSP.html vignetteTitles: user guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VaSP/inst/doc/VaSP.R dependencyCount: 103 Package: vbmp Version: 1.58.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: 2cf36fe26e579da4446e1565f1bea82e NeedsCompilation: no Title: Variational Bayesian Multinomial Probit Regression Description: Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors. It estimates class membership posterior probability employing variational and sparse approximation to the full posterior. This software also incorporates feature weighting by means of Automatic Relevance Determination. biocViews: Classification Author: Nicola Lama , Mark Girolami Maintainer: Nicola Lama URL: http://bioinformatics.oxfordjournals.org/cgi/content/short/btm535v1 git_url: https://git.bioconductor.org/packages/vbmp git_branch: RELEASE_3_12 git_last_commit: 0154e2c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/vbmp_1.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/vbmp_1.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/vbmp_1.58.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vbmp/inst/doc/vbmp.R dependencyCount: 0 Package: VCFArray Version: 1.6.0 Depends: R (>= 3.6), methods, BiocGenerics, DelayedArray (>= 0.7.28) Imports: tools, GenomicRanges, VariantAnnotation (>= 1.29.3), GenomicFiles (>= 1.17.3), S4Vectors (>= 0.19.19), Rsamtools Suggests: SeqArray, BiocStyle, BiocManager, testthat, knitr, rmarkdown License: GPL-3 MD5sum: 2f58cbb028afc238a8317b7033ca295f NeedsCompilation: no Title: Representing on-disk / remote VCF files as array-like objects Description: VCFArray extends the DelayedArray to represent VCF data entries as array-like objects with on-disk / remote VCF file as backend. Data entries from VCF files, including info fields, FORMAT fields, and the fixed columns (REF, ALT, QUAL, FILTER) could be converted into VCFArray instances with different dimensions. biocViews: Infrastructure, DataRepresentation, Sequencing, VariantAnnotation Author: Qian Liu [aut, cre], Martin Morgan [aut] Maintainer: Qian Liu URL: https://github.com/Liubuntu/VCFArray VignetteBuilder: knitr BugReports: https://github.com/Liubuntu/VCFArray/issues git_url: https://git.bioconductor.org/packages/VCFArray git_branch: RELEASE_3_12 git_last_commit: 07c9d67 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VCFArray_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VCFArray_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VCFArray_1.6.0.tgz vignettes: vignettes/VCFArray/inst/doc/VCFArray.html vignetteTitles: VCFArray: DelayedArray objects with on-disk/remote VCF backend hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VCFArray/inst/doc/VCFArray.R dependencyCount: 91 Package: VegaMC Version: 3.28.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods, genoset License: GPL-2 Archs: i386, x64 MD5sum: bab1084eba8a314030a9548b0429125b NeedsCompilation: yes Title: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer Description: This package enables the detection of driver chromosomal imbalances including loss of heterozygosity (LOH) from array comparative genomic hybridization (aCGH) data. VegaMC performs a joint segmentation of a dataset and uses a statistical framework to distinguish between driver and passenger mutation. VegaMC has been implemented so that it can be immediately integrated with the output produced by PennCNV tool. In addition, VegaMC produces in output two web pages that allows a rapid navigation between both the detected regions and the altered genes. In the web page that summarizes the altered genes, the link to the respective Ensembl gene web page is reported. biocViews: aCGH, CopyNumberVariation Author: S. Morganella and M. Ceccarelli Maintainer: Sandro Morganella git_url: https://git.bioconductor.org/packages/VegaMC git_branch: RELEASE_3_12 git_last_commit: f236ddb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VegaMC_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VegaMC_3.28.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VegaMC_3.28.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VegaMC/inst/doc/VegaMC.R dependencyCount: 76 Package: velociraptor Version: 1.0.0 Depends: SummarizedExperiment Imports: methods, stats, Matrix, BiocGenerics, reticulate, S4Vectors, DelayedArray, basilisk, zellkonverter, scuttle, SingleCellExperiment, BiocParallel, BiocSingular Suggests: BiocStyle, testthat, knitr, rmarkdown, scran, scater, scRNAseq, ggplot2, Rtsne License: MIT + file LICENSE MD5sum: bb6dafc1d3a95b54b305ec70901f0126 NeedsCompilation: no Title: Toolkit for Single-Cell Velocity Description: This package provides Bioconductor-friendly wrappers for RNA velocity calculations in single-cell RNA-seq data. We use the basilisk package to manage Conda environments, and the zellkonverter package to convert data structures between SingleCellExperiment (R) and AnnData (Python). The information produced by the velocity methods is stored in the various components of the SingleCellExperiment class. biocViews: SingleCell, GeneExpression, Sequencing, Coverage Author: Kevin Rue-Albrecht [aut, cre] (), Aaron Lun [aut] (), Charlotte Soneson [aut] () Maintainer: Kevin Rue-Albrecht URL: https://github.com/kevinrue/velociraptor VignetteBuilder: knitr BugReports: https://github.com/kevinrue/velociraptor/issues git_url: https://git.bioconductor.org/packages/velociraptor git_branch: RELEASE_3_12 git_last_commit: 7cd4624 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/velociraptor_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/velociraptor_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/velociraptor_1.0.0.tgz vignettes: vignettes/velociraptor/inst/doc/userguide.html vignetteTitles: User's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/velociraptor/inst/doc/userguide.R dependencyCount: 55 Package: VennDetail Version: 1.6.0 Imports: utils, grDevices, stats, methods, dplyr, purrr, tibble, magrittr, ggplot2, UpSetR, VennDiagram, grid, futile.logger Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 1dcca4da645689457697c50e1293861b NeedsCompilation: no Title: A package for visualization and extract details Description: A set of functions to generate high-resolution Venn,Vennpie plot,extract and combine details of these subsets with user datasets in data frame is available. biocViews: DataRepresentation,GraphAndNetwork Author: Kai Guo, Brett McGregor Maintainer: Kai Guo URL: https://github.com/guokai8/VennDetail VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/VennDetail git_branch: RELEASE_3_12 git_last_commit: 4f21af6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VennDetail_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VennDetail_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VennDetail_1.6.0.tgz vignettes: vignettes/VennDetail/inst/doc/VennDetail.html vignetteTitles: VennDetail hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VennDetail/inst/doc/VennDetail.R dependencyCount: 51 Package: VERSO Version: 1.0.0 Depends: R (>= 4.0.0) Imports: ape, parallel, Rfast, stats Suggests: BiocGenerics, BiocStyle, testthat, knitr License: file LICENSE MD5sum: c3e71fa139cc16b491ad31a5b79f7898 NeedsCompilation: no Title: Viral Evolution ReconStructiOn (VERSO) Description: Mutations that rapidly accumulate in viral genomes during a pandemic can be used to track the evolution of the virus and, accordingly, unravel the viral infection network. To this extent, sequencing samples of the virus can be employed to estimate models from genomic epidemiology and may serve, for instance, to estimate the proportion of undetected infected people by uncovering cryptic transmissions, as well as to predict likely trends in the number of infected, hospitalized, dead and recovered people. VERSO is an algorithmic framework that processes variants profiles from viral samples to produce phylogenetic models of viral evolution. The approach solves a Boolean Matrix Factorization problem with phylogenetic constraints, by maximizing a log-likelihood function. VERSO includes two separate and subsequent steps; in this package we provide an R implementation of VERSO STEP 1. biocViews: BiomedicalInformatics, Sequencing, SomaticMutation Author: Daniele Ramazzotti [aut] (), Fabrizio Angaroni [aut], Davide Maspero [cre, aut], Alex Graudenzi [aut], Luca De Sano [ctb] Maintainer: Davide Maspero URL: https://github.com/BIMIB-DISCo/VERSO VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/VERSO git_url: https://git.bioconductor.org/packages/VERSO git_branch: RELEASE_3_12 git_last_commit: c23a153 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VERSO_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VERSO_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VERSO_1.0.0.tgz vignettes: vignettes/VERSO/inst/doc/vignette.pdf vignetteTitles: VERSO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/VERSO/inst/doc/vignette.R dependencyCount: 16 Package: vidger Version: 1.10.0 Depends: R (>= 3.5) Imports: Biobase, DESeq2, edgeR, GGally, ggplot2, ggrepel, knitr, RColorBrewer, rmarkdown, scales, stats, SummarizedExperiment, tidyr, utils Suggests: BiocStyle, testthat License: GPL-3 | file LICENSE MD5sum: 58789b2bb6b2d6d8b5d352924b3eeb7a NeedsCompilation: no Title: Create rapid visualizations of RNAseq data in R Description: The aim of vidger is to rapidly generate information-rich visualizations for the interpretation of differential gene expression results from three widely-used tools: Cuffdiff, DESeq2, and edgeR. biocViews: ImmunoOncology, Visualization, RNASeq, DifferentialExpression, GeneExpression, ImmunoOncology Author: Brandon Monier [aut, cre], Adam McDermaid [aut], Jing Zhao [aut], Qin Ma [aut, fnd] Maintainer: Brandon Monier URL: https://github.com/btmonier/vidger, https://bioconductor.org/packages/release/bioc/html/vidger.html VignetteBuilder: knitr BugReports: https://github.com/btmonier/vidger/issues git_url: https://git.bioconductor.org/packages/vidger git_branch: RELEASE_3_12 git_last_commit: 3d28424 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/vidger_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/vidger_1.10.0.zip mac.binary.ver: bin/macosx/contrib/4.0/vidger_1.10.0.tgz vignettes: vignettes/vidger/inst/doc/vidger.html vignetteTitles: Visualizing RNA-seq data with ViDGER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/vidger/inst/doc/vidger.R dependencyCount: 118 Package: viper Version: 1.24.0 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: ebcaa11d62ebabf0ce20a9f2abb06df7 NeedsCompilation: no Title: Virtual Inference of Protein-activity by Enriched Regulon analysis Description: Inference of protein activity from gene expression data, including the VIPER and msVIPER algorithms biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, FunctionalPrediction, GeneRegulation Author: Mariano J Alvarez Maintainer: Mariano J Alvarez git_url: https://git.bioconductor.org/packages/viper git_branch: RELEASE_3_12 git_last_commit: 579a1b6 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/viper_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/viper_1.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/viper_1.24.0.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/viper/inst/doc/viper.R dependsOnMe: vulcan, aracne.networks importsMe: CARNIVAL, diggit, RTN, diggitdata, dorothea suggestsMe: MethReg, MOMA dependencyCount: 21 Package: ViSEAGO Version: 1.4.0 Depends: R (>= 3.6) Imports: data.table, AnnotationDbi, AnnotationForge, biomaRt, dendextend, DiagrammeR, DT, dynamicTreeCut, fgsea, GOSemSim, ggplot2, GO.db, grDevices, heatmaply, htmlwidgets, igraph, methods, plotly, processx, topGO, RColorBrewer, R.utils, scales, stats, UpSetR, utils Suggests: htmltools, org.Mm.eg.db, limma, Rgraphviz, BiocStyle, knitr, rmarkdown, corrplot, remotes, BiocManager License: GPL-3 MD5sum: ff7ea736b79be7cac5366b16b80da960 NeedsCompilation: no Title: ViSEAGO: a Bioconductor package for clustering biological functions using Gene Ontology and semantic similarity Description: The main objective of ViSEAGO package is to carry out a data mining of biological functions and establish links between genes involved in the study. We developed ViSEAGO in R to facilitate functional Gene Ontology (GO) analysis of complex experimental design with multiple comparisons of interest. It allows to study large-scale datasets together and visualize GO profiles to capture biological knowledge. The acronym stands for three major concepts of the analysis: Visualization, Semantic similarity and Enrichment Analysis of Gene Ontology. It provides access to the last current GO annotations, which are retrieved from one of NCBI EntrezGene, Ensembl or Uniprot databases for several species. Using available R packages and novel developments, ViSEAGO extends classical functional GO analysis to focus on functional coherence by aggregating closely related biological themes while studying multiple datasets at once. It provides both a synthetic and detailed view using interactive functionalities respecting the GO graph structure and ensuring functional coherence supplied by semantic similarity. ViSEAGO has been successfully applied on several datasets from different species with a variety of biological questions. Results can be easily shared between bioinformaticians and biologists, enhancing reporting capabilities while maintaining reproducibility. biocViews: Software, Annotation, GO, GeneSetEnrichment, MultipleComparison, Clustering, Visualization Author: Aurelien Brionne [aut, cre], Amelie Juanchich [aut], Christelle hennequet-antier [aut] Maintainer: Aurelien Brionne URL: https://www.bioconductor.org/packages/release/bioc/html/ViSEAGO.html, https://forgemia.inra.fr/UMR-BOA/ViSEAGO VignetteBuilder: knitr BugReports: https://forgemia.inra.fr/UMR-BOA/ViSEAGO/issues git_url: https://git.bioconductor.org/packages/ViSEAGO git_branch: RELEASE_3_12 git_last_commit: dd2376d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/ViSEAGO_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/ViSEAGO_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/ViSEAGO_1.4.0.tgz vignettes: vignettes/ViSEAGO/inst/doc/fgsea_alternative.html, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.html, vignettes/ViSEAGO/inst/doc/SS_choice.html, vignettes/ViSEAGO/inst/doc/ViSEAGO.html vignetteTitles: 3: fgsea_alternative, 2: mouse_bionconductor, 4: SS_choice, 1: ViSEAGO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ViSEAGO/inst/doc/fgsea_alternative.R, vignettes/ViSEAGO/inst/doc/mouse_bioconductor.R, vignettes/ViSEAGO/inst/doc/SS_choice.R, vignettes/ViSEAGO/inst/doc/ViSEAGO.R dependencyCount: 148 Package: VplotR Version: 1.0.0 Depends: R (>= 4.0), GenomicRanges, IRanges, ggplot2 Imports: cowplot, magrittr, GenomeInfoDb, GenomicAlignments, RColorBrewer, zoo, Rsamtools, S4Vectors, parallel, reshape2, methods, graphics, stats Suggests: GenomicFeatures, TxDb.Scerevisiae.UCSC.sacCer3.sgdGene, testthat, covr, knitr, rmarkdown, pkgdown License: GPL-3 MD5sum: 9d67f2bc3faf5e69b5a108e391e7f979 NeedsCompilation: no Title: Set of tools to make V-plots and compute footprint profiles Description: The pattern of digestion and protection from DNA nucleases such as DNAse I, micrococcal nuclease, and Tn5 transposase can be used to infer the location of associated proteins. This package contains useful functions to analyze patterns of paired-end sequencing fragment density. VplotR facilitates the generation of V-plots and footprint profiles over single or aggregated genomic loci of interest. biocViews: NucleosomePositioning, Coverage, Sequencing, BiologicalQuestion, ATACSeq, Alignment Author: Jacques Serizay [aut, cre] () Maintainer: Jacques Serizay URL: https://github.com/js2264/VplotR VignetteBuilder: knitr BugReports: https://github.com/js2264/VplotR/issues git_url: https://git.bioconductor.org/packages/VplotR git_branch: RELEASE_3_12 git_last_commit: 6e7c2ee git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/VplotR_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/VplotR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/VplotR_1.0.0.tgz vignettes: vignettes/VplotR/inst/doc/VplotR.html vignetteTitles: VplotR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VplotR/inst/doc/VplotR.R dependencyCount: 74 Package: vsn Version: 3.58.0 Depends: R (>= 3.4.0), Biobase Imports: methods, affy, limma, lattice, ggplot2 Suggests: affydata, hgu95av2cdf, BiocStyle, knitr, dplyr, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: e943f1c5a77d6d1b4427160e00c167f2 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities, and works for single- and multiple-color arrays. It can also be used for data from other technologies, as long as they have similar format. The method uses a robust variant of the maximum-likelihood estimator for an additive-multiplicative error model and affine calibration. The model incorporates data calibration step (a.k.a. normalization), a model for the dependence of the variance on the mean intensity and a variance stabilizing data transformation. Differences between transformed intensities are analogous to "normalized log-ratios". However, in contrast to the latter, their variance is independent of the mean, and they are usually more sensitive and specific in detecting differential transcription. biocViews: Microarray, OneChannel, TwoChannel, Preprocessing Author: Wolfgang Huber, with contributions from Anja von Heydebreck. Many comments and suggestions by users are acknowledged, among them Dennis Kostka, David Kreil, Hans-Ulrich Klein, Robert Gentleman, Deepayan Sarkar and Gordon Smyth Maintainer: Wolfgang Huber URL: http://www.r-project.org, http://www.ebi.ac.uk/huber VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/vsn git_branch: RELEASE_3_12 git_last_commit: a451e6a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/vsn_3.58.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/vsn_3.58.0.zip mac.binary.ver: bin/macosx/contrib/4.0/vsn_3.58.0.tgz vignettes: vignettes/vsn/inst/doc/C-likelihoodcomputations.pdf, vignettes/vsn/inst/doc/D-convergence.pdf, vignettes/vsn/inst/doc/A-vsn.html vignetteTitles: Likelihood Calculations for vsn, Verifying and assessing the performance with simulated data, Introduction to vsn (HTML version) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/A-vsn.R, vignettes/vsn/inst/doc/C-likelihoodcomputations.R dependsOnMe: affyPara, cellHTS2, webbioc, rnaseqGene, NanoStringNorm importsMe: arrayQualityMetrics, coexnet, DEP, Doscheda, imageHTS, metaseqR, metaseqR2, MSnbase, NormalyzerDE, pvca, Ringo, tilingArray, ExpressionNormalizationWorkflow suggestsMe: adSplit, beadarray, BiocCaseStudies, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, MsCoreUtils, PAA, QFeatures, twilight, estrogen, wrMisc dependencyCount: 47 Package: vtpnet Version: 0.30.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 140101f938d1ac0b92058afd917db6b0 NeedsCompilation: no Title: variant-transcription factor-phenotype networks Description: variant-transcription factor-phenotype networks, inspired by Maurano et al., Science (2012), PMID 22955828 biocViews: Network Author: VJ Carey Maintainer: VJ Carey git_url: https://git.bioconductor.org/packages/vtpnet git_branch: RELEASE_3_12 git_last_commit: 7ab061a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/vtpnet_0.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/vtpnet_0.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/vtpnet_0.30.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vtpnet/inst/doc/vtpnet.R dependencyCount: 102 Package: vulcan Version: 1.12.0 Depends: R (>= 3.4), ChIPpeakAnno,TxDb.Hsapiens.UCSC.hg19.knownGene, zoo, GenomicRanges, S4Vectors, viper, DiffBind, locfit Imports: wordcloud, csaw, gplots, stats, utils, caTools, graphics, DESeq, Biobase Suggests: vulcandata License: LGPL-3 MD5sum: c0d1b4d4ffed281db2d9d04fa701c5d8 NeedsCompilation: no Title: VirtUaL ChIP-Seq data Analysis using Networks Description: Vulcan (VirtUaL ChIP-Seq Analysis through Networks) is a package that interrogates gene regulatory networks to infer cofactors significantly enriched in a differential binding signature coming from ChIP-Seq data. In order to do so, our package combines strategies from different BioConductor packages: DESeq for data normalization, ChIPpeakAnno and DiffBind for annotation and definition of ChIP-Seq genomic peaks, csaw to define optimal peak width and viper for applying a regulatory network over a differential binding signature. biocViews: SystemsBiology, NetworkEnrichment, GeneExpression, ChIPSeq Author: Federico M. Giorgi, Andrew N. Holding, Florian Markowetz Maintainer: Federico M. Giorgi git_url: https://git.bioconductor.org/packages/vulcan git_branch: RELEASE_3_12 git_last_commit: 43d3508 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/vulcan_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/vulcan_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/vulcan_1.12.0.tgz vignettes: vignettes/vulcan/inst/doc/vulcan.pdf vignetteTitles: Vulcan: VirtUaL ChIP-Seq Analysis through Networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vulcan/inst/doc/vulcan.R dependencyCount: 188 Package: waddR Version: 1.4.0 Depends: R (>= 3.6.0) Imports: Rcpp (>= 1.0.1), arm (>= 1.10-1), BiocFileCache, BiocParallel, SingleCellExperiment, parallel, methods, stats LinkingTo: Rcpp, RcppArmadillo, Suggests: knitr, devtools, testthat, roxygen2, rprojroot, rmarkdown, scater License: MIT + file LICENSE Archs: i386, x64 MD5sum: 34f74a327560510e758a294e3dfe3449 NeedsCompilation: yes Title: Statistical tests for detecting differential distributions based on the 2-Wasserstein distance Description: The package offers statistical tests based on the 2-Wasserstein distance for detecting and characterizing differences between two distributions given in the form of samples. Functions for calculating the 2-Wasserstein distance and testing for differential distributions are provided, as welll as specifically tailored test for differential expression in single-cell RNA sequencing data. biocViews: Software, StatisticalMethod, SingleCell, DifferentialExpression Author: Roman Schefzik [aut], Julian Flesch [cre] Maintainer: Julian Flesch URL: https://github.com/goncalves-lab/waddR.git VignetteBuilder: knitr BugReports: https://github.com/goncalves-lab/waddR/issues git_url: https://git.bioconductor.org/packages/waddR git_branch: RELEASE_3_12 git_last_commit: 8e4b97d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/waddR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/waddR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/waddR_1.4.0.tgz vignettes: vignettes/waddR/inst/doc/waddR.html, vignettes/waddR/inst/doc/wasserstein_metric.html, vignettes/waddR/inst/doc/wasserstein_singlecell.html, vignettes/waddR/inst/doc/wasserstein_test.html vignetteTitles: waddR, wasserstein_metric, wasserstein_singlecell, wasserstein_test hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/waddR/inst/doc/waddR.R, vignettes/waddR/inst/doc/wasserstein_metric.R, vignettes/waddR/inst/doc/wasserstein_singlecell.R, vignettes/waddR/inst/doc/wasserstein_test.R dependencyCount: 127 Package: wateRmelon Version: 1.34.0 Depends: R (>= 2.10), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM, IlluminaHumanMethylationEPICanno.ilm10b2.hg19, IlluminaHumanMethylationEPICmanifest, irlba Enhances: minfi License: GPL-3 MD5sum: 5dac86557546564d676cd4fc2c42b1cf NeedsCompilation: no Title: Illumina 450 methylation array normalization and metrics Description: 15 flavours of betas and three performance metrics, with methods for objects produced by methylumi and minfi packages. biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Leonard C Schalkwyk, Ruth Pidsley, Chloe CY Wong, with functions contributed by Nizar Touleimat, Matthieu Defrance, Andrew Teschendorff, Jovana Maksimovic, Tyler Gorrie-Stone, Louis El Khoury Maintainer: Leo git_url: https://git.bioconductor.org/packages/wateRmelon git_branch: RELEASE_3_12 git_last_commit: 3fa2745 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/wateRmelon_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/wateRmelon_1.34.0.zip mac.binary.ver: bin/macosx/contrib/4.0/wateRmelon_1.34.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf vignetteTitles: The \Rpackage{wateRmelon} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wateRmelon/inst/doc/wateRmelon.R dependsOnMe: bigmelon, skewr importsMe: ChAMP, MEAT suggestsMe: RnBeads dependencyCount: 159 Package: wavClusteR Version: 2.24.0 Depends: R (>= 3.2), GenomicRanges (>= 1.31.8), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.17.25), IRanges (>= 2.13.12), Biostrings (>= 2.47.6), foreach, GenomicFeatures (>= 1.31.3), ggplot2, Hmisc, mclust, rtracklayer (>= 1.39.7), seqinr, stringr Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: c8402a945ad486abce6c5ce55935f544 NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: The package provides an integrated pipeline for the analysis of PAR-CLIP data. PAR-CLIP-induced transitions are first discriminated from sequencing errors, SNPs and additional non-experimental sources by a non- parametric mixture model. The protein binding sites (clusters) are then resolved at high resolution and cluster statistics are estimated using a rigorous Bayesian framework. Post-processing of the results, data export for UCSC genome browser visualization and motif search analysis are provided. In addition, the package allows to integrate RNA-Seq data to estimate the False Discovery Rate of cluster detection. Key functions support parallel multicore computing. Note: while wavClusteR was designed for PAR-CLIP data analysis, it can be applied to the analysis of other NGS data obtained from experimental procedures that induce nucleotide substitutions (e.g. BisSeq). biocViews: ImmunoOncology, Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wavClusteR git_branch: RELEASE_3_12 git_last_commit: f2ebb9b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/wavClusteR_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/wavClusteR_2.24.0.zip mac.binary.ver: bin/macosx/contrib/4.0/wavClusteR_2.24.0.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html vignetteTitles: wavClusteR: a workflow for PAR-CLIP data analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R dependencyCount: 138 Package: weaver Version: 1.56.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 51ad0bd6f2cf69e96b0b52a56de63111 NeedsCompilation: no Title: Tools and extensions for processing Sweave documents Description: This package provides enhancements on the Sweave() function in the base package. In particular a facility for caching code chunk results is included. biocViews: Infrastructure Author: Seth Falcon Maintainer: Seth Falcon git_url: https://git.bioconductor.org/packages/weaver git_branch: RELEASE_3_12 git_last_commit: 94a62c8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/weaver_1.56.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/weaver_1.56.0.zip mac.binary.ver: bin/macosx/contrib/4.0/weaver_1.56.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/weaver/inst/doc/weaver_howTo.R suggestsMe: BiocCaseStudies dependencyCount: 4 Package: webbioc Version: 1.62.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocManager License: GPL (>= 2) MD5sum: de741d4132212b56f7e7e379109a2448 NeedsCompilation: no Title: Bioconductor Web Interface Description: An integrated web interface for doing microarray analysis using several of the Bioconductor packages. It is intended to be deployed as a centralized bioinformatics resource for use by many users. (Currently only Affymetrix oligonucleotide analysis is supported.) biocViews: Infrastructure, Microarray, OneChannel, DifferentialExpression Author: Colin A. Smith Maintainer: Colin A. Smith URL: http://www.bioconductor.org/ SystemRequirements: Unix, Perl (>= 5.6.0), Netpbm git_url: https://git.bioconductor.org/packages/webbioc git_branch: RELEASE_3_12 git_last_commit: d2a6ff9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/webbioc_1.62.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/webbioc_1.62.0.zip mac.binary.ver: bin/macosx/contrib/4.0/webbioc_1.62.0.tgz vignettes: vignettes/webbioc/inst/doc/demoscript.pdf, vignettes/webbioc/inst/doc/webbioc.pdf vignetteTitles: webbioc Demo Script, webbioc Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependencyCount: 76 Package: weitrix Version: 1.2.0 Depends: R (>= 3.6), SummarizedExperiment Imports: methods, utils, stats, grDevices, assertthat, S4Vectors, DelayedArray, DelayedMatrixStats, BiocParallel, BiocGenerics, limma, topconfects, dplyr, purrr, ggplot2, rlang, scales, reshape2, splines, Ckmeans.1d.dp, glm2, RhpcBLASctl Suggests: knitr, rmarkdown, BiocStyle, tidyverse, airway, edgeR, EnsDb.Hsapiens.v86, org.Sc.sgd.db, AnnotationDbi, ComplexHeatmap, patchwork, testthat (>= 2.1.0) License: LGPL-2.1 | file LICENSE MD5sum: 5266a4472bc29f15c4c1c0648c2c0a7f NeedsCompilation: no Title: Tools for matrices with precision weights, test and explore weighted or sparse data Description: Data type and tools for working with matrices having precision weights and missing data. This package provides a common representation and tools that can be used with many types of high-throughput data. The meaning of the weights is compatible with usage in the base R function "lm" and the package "limma". Calibrate weights to account for known predictors of precision. Find rows with excess variability. Perform differential testing and find rows with the largest confident differences. Find PCA-like components of variation even with many missing values, rotated so that individual components may be meaningfully interpreted. DelayedArray matrices and BiocParallel are supported. biocViews: Software, DataRepresentation, DimensionReduction, GeneExpression, Transcriptomics, RNASeq, SingleCell, Regression Author: Paul Harrison [aut, cre] () Maintainer: Paul Harrison VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/weitrix git_branch: RELEASE_3_12 git_last_commit: 72b5965 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/weitrix_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/weitrix_1.2.0.zip mac.binary.ver: bin/macosx/contrib/4.0/weitrix_1.2.0.tgz vignettes: vignettes/weitrix/inst/doc/V1_overview.html, vignettes/weitrix/inst/doc/V2_tail_length.html, vignettes/weitrix/inst/doc/V3_shift.html, vignettes/weitrix/inst/doc/V4_airway.html, vignettes/weitrix/inst/doc/V5_slam_seq.html vignetteTitles: 1. Concepts and practical details, 2. poly(A) tail length example, 3. Alternative polyadenylation, 4. RNA-Seq expression example, 5. Proportions data example with SLAM-Seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/weitrix/inst/doc/V2_tail_length.R, vignettes/weitrix/inst/doc/V3_shift.R, vignettes/weitrix/inst/doc/V4_airway.R, vignettes/weitrix/inst/doc/V5_slam_seq.R dependencyCount: 86 Package: widgetTools Version: 1.68.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 9de72ea3c8dca526997e1d8ff349aabe NeedsCompilation: no Title: Creates an interactive tcltk widget Description: This packages contains tools to support the construction of tcltk widgets biocViews: Infrastructure Author: Jianhua Zhang Maintainer: Jianhua Zhang git_url: https://git.bioconductor.org/packages/widgetTools git_branch: RELEASE_3_12 git_last_commit: e694832 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/widgetTools_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/widgetTools_1.68.0.zip mac.binary.ver: bin/macosx/contrib/4.0/widgetTools_1.68.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/widgetTools/inst/doc/widgetTools.R dependsOnMe: tkWidgets importsMe: OLINgui, SeqFeatR suggestsMe: affy dependencyCount: 3 Package: wiggleplotr Version: 1.14.0 Depends: R (>= 3.6) Imports: dplyr, ggplot2 (>= 2.2.0), GenomicRanges, rtracklayer, cowplot, assertthat, purrr, S4Vectors, IRanges, GenomeInfoDb Suggests: knitr, rmarkdown, biomaRt, GenomicFeatures, testthat, ensembldb, EnsDb.Hsapiens.v86, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationDbi, AnnotationFilter License: Apache License 2.0 MD5sum: 7f2e8e45bbe3220b103bfa1b47b69e3f NeedsCompilation: no Title: Make read coverage plots from BigWig files Description: Tools to visualise read coverage from sequencing experiments together with genomic annotations (genes, transcripts, peaks). Introns of long transcripts can be rescaled to a fixed length for better visualisation of exonic read coverage. biocViews: ImmunoOncology, Coverage, RNASeq, ChIPSeq, Sequencing, Visualization, GeneExpression, Transcription, AlternativeSplicing Author: Kaur Alasoo [aut, cre] Maintainer: Kaur Alasoo VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wiggleplotr git_branch: RELEASE_3_12 git_last_commit: 9d06b5d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/wiggleplotr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/wiggleplotr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/4.0/wiggleplotr_1.14.0.tgz vignettes: vignettes/wiggleplotr/inst/doc/wiggleplotr.html vignetteTitles: Introduction to wiggleplotr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wiggleplotr/inst/doc/wiggleplotr.R dependencyCount: 75 Package: wpm Version: 1.0.0 Depends: R (>= 4.0.0) Imports: utils, methods, cli, Biobase, SummarizedExperiment, config, golem, shiny, DT, ggplot2, dplyr, rlang, stringr, shinydashboard, shinyWidgets, shinycustomloader, RColorBrewer, logging Suggests: MSnbase, testthat, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: c7c9b79bea4a1d58d60ce5b5b5970aa4 NeedsCompilation: no Title: Well Plate Maker Description: This is a shiny application for creating well-plate plans. It uses a backtracking-inspired algorithm to place samples on plates based on specific neighborhood constraints. biocViews: GUI, Proteomics, MassSpectrometry, BatchEffect, ExperimentalDesign Author: Helene Borges [aut, cre], Thomas Burger [aut] Maintainer: Helene Borges VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/wpm git_branch: RELEASE_3_12 git_last_commit: aef0431 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/wpm_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/wpm_1.0.0.zip mac.binary.ver: bin/macosx/contrib/4.0/wpm_1.0.0.tgz vignettes: vignettes/wpm/inst/doc/wpm_vignette.html vignetteTitles: How to use Well Plate Maker hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wpm/inst/doc/wpm_vignette.R dependencyCount: 131 Package: Wrench Version: 1.8.0 Depends: R (>= 3.5.0) Imports: limma, matrixStats, locfit, stats, graphics Suggests: knitr, rmarkdown, metagenomeSeq, DESeq2, edgeR License: Artistic-2.0 MD5sum: d6f433642c81d9dbfd92538f0126f18a NeedsCompilation: no Title: Wrench normalization for sparse count data Description: Wrench is a package for normalization sparse genomic count data, like that arising from 16s metagenomic surveys. biocViews: Normalization, Sequencing, Software Author: Senthil Kumar Muthiah [aut], Hector Corrada Bravo [aut, cre] Maintainer: Hector Corrada Bravo URL: https://github.com/HCBravoLab/Wrench VignetteBuilder: knitr BugReports: https://github.com/HCBravoLab/Wrench/issues git_url: https://git.bioconductor.org/packages/Wrench git_branch: RELEASE_3_12 git_last_commit: cb5e71d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Wrench_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Wrench_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Wrench_1.8.0.tgz vignettes: vignettes/Wrench/inst/doc/vignette.html vignetteTitles: Wrench hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Wrench/inst/doc/vignette.R importsMe: metagenomeSeq suggestsMe: PLNmodels dependencyCount: 10 Package: XBSeq Version: 1.22.0 Depends: DESeq2, R (>= 3.3) Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr, magrittr, roar Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat License: GPL (>=3) MD5sum: bc5af3cbfda7bb639967ee85965e313c NeedsCompilation: no Title: Test for differential expression for RNA-seq data Description: We developed a novel algorithm, XBSeq, where a statistical model was established based on the assumption that observed signals are the convolution of true expression signals and sequencing noises. The mapped reads in non-exonic regions are considered as sequencing noises, which follows a Poisson distribution. Given measureable observed and noise signals from RNA-seq data, true expression signals, assuming governed by the negative binomial distribution, can be delineated and thus the accurate detection of differential expressed genes. biocViews: ImmunoOncology, RNASeq, DifferentialExpression, Sequencing, Software, ExperimentalDesign Author: Yuanhang Liu Maintainer: Yuanhang Liu URL: https://github.com/Liuy12/XBSeq VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XBSeq git_branch: RELEASE_3_12 git_last_commit: 340f654 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/XBSeq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/XBSeq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/4.0/XBSeq_1.22.0.tgz vignettes: vignettes/XBSeq/inst/doc/XBSeq.html vignetteTitles: Differential expression and apa usage analysis of count data using XBSeq package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XBSeq/inst/doc/XBSeq.R dependencyCount: 101 Package: XCIR Version: 1.4.0 Depends: methods Imports: stats, utils, data.table, IRanges, VariantAnnotation, seqminer, ggplot2, biomaRt, readxl, S4Vectors Suggests: knitr, rmarkdown License: GPL-2 MD5sum: 656497ce9b2bf9076b502fd5d18b7dca NeedsCompilation: no Title: XCI-inference Description: Models and tools for subject level analysis of X chromosome inactivation (XCI) and XCI-escape inference. biocViews: StatisticalMethod, RNASeq, Sequencing, Coverage Author: Renan Sauteraud, Dajiang Liu Maintainer: Renan Sauteraud URL: https://github.com/SRenan/XCIR VignetteBuilder: knitr BugReports: https://github.com/SRenan/XCIR/issues git_url: https://git.bioconductor.org/packages/XCIR git_branch: RELEASE_3_12 git_last_commit: 38d9b27 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/XCIR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/XCIR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/4.0/XCIR_1.4.0.tgz vignettes: vignettes/XCIR/inst/doc/xcir_intro.html vignetteTitles: Introduction to XCIR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XCIR/inst/doc/xcir_intro.R dependencyCount: 110 Package: xcms Version: 3.12.0 Depends: R (>= 3.5.0), methods, Biobase, BiocParallel (>= 1.8.0), MSnbase (>= 2.15.3) Imports: mzR (>= 2.19.5), BiocGenerics, ProtGenerics (>= 1.17.2), lattice, RColorBrewer, plyr, RANN, MassSpecWavelet (>= 1.5.2), S4Vectors, robustbase, IRanges, SummarizedExperiment, MsCoreUtils Suggests: BiocStyle, caTools, knitr (>= 1.1.0), faahKO, msdata (>= 0.25.1), ncdf4, rgl, microbenchmark, testthat, pander, magrittr, multtest, MALDIquant, pheatmap Enhances: Rgraphviz, XML License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 16966db7619a9c285c940746fdc6d180 NeedsCompilation: yes Title: LC-MS and GC-MS Data Analysis Description: Framework for processing and visualization of chromatographically separated and single-spectra mass spectral data. Imports from AIA/ANDI NetCDF, mzXML, mzData and mzML files. Preprocesses data for high-throughput, untargeted analyte profiling. biocViews: ImmunoOncology, MassSpectrometry, Metabolomics Author: Colin A. Smith , Ralf Tautenhahn , Steffen Neumann , Paul Benton , Christopher Conley , Johannes Rainer , Michael Witting Maintainer: Steffen Neumann URL: https://github.com/sneumann/xcms VignetteBuilder: knitr BugReports: https://github.com/sneumann/xcms/issues/new git_url: https://git.bioconductor.org/packages/xcms git_branch: RELEASE_3_12 git_last_commit: 1518ef3 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/xcms_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/xcms_3.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/xcms_3.12.0.tgz vignettes: vignettes/xcms/inst/doc/xcms-direct-injection.html, vignettes/xcms/inst/doc/xcms-lcms-ms.html, vignettes/xcms/inst/doc/xcms.html vignetteTitles: Grouping FTICR-MS data with xcms, LC-MS/MS data analysis with xcms, LCMS data preprocessing and analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/xcms-direct-injection.R, vignettes/xcms/inst/doc/xcms-lcms-ms.R, vignettes/xcms/inst/doc/xcms.R dependsOnMe: CAMERA, flagme, IPO, LOBSTAHS, Metab, metaMS, ncGTW, proFIA, faahKO, PtH2O2lipids, MetaClean importsMe: CAMERA, cliqueMS, cosmiq, Risa, specmine.datasets suggestsMe: CluMSID, MassSpecWavelet, msPurity, RMassBank, msdata, mtbls2, RforProteomics, CorrectOverloadedPeaks, enviGCMS, RAMClustR, specmine dependencyCount: 91 Package: XDE Version: 2.36.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5) Imports: BiocGenerics, genefilter, graphics, grDevices, gtools, methods, stats, utils, mvtnorm, RColorBrewer, GeneMeta, siggenes Suggests: MASS, RUnit Enhances: coda License: LGPL-2 Archs: i386, x64 MD5sum: f0571bb10354cdfa8ca514170237d66e NeedsCompilation: yes Title: XDE: a Bayesian hierarchical model for cross-study analysis of differential gene expression Description: Multi-level model for cross-study detection of differential gene expression. biocViews: Microarray, DifferentialExpression Author: R.B. Scharpf, G. Parmigiani, A.B. Nobel, and H. Tjelmeland Maintainer: Robert Scharpf git_url: https://git.bioconductor.org/packages/XDE git_branch: RELEASE_3_12 git_last_commit: 0277f9d git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/XDE_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/XDE_2.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/XDE_2.36.0.tgz vignettes: vignettes/XDE/inst/doc/XDE.pdf, vignettes/XDE/inst/doc/XdeParameterClass.pdf vignetteTitles: XDE Vignette, XdeParameterClass Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XDE/inst/doc/XDE.R, vignettes/XDE/inst/doc/XdeParameterClass.R dependencyCount: 53 Package: Xeva Version: 1.6.0 Depends: R (>= 3.6) Imports: methods, stats, utils, BBmisc, Biobase, grDevices, ggplot2, scales, ComplexHeatmap, parallel, doParallel, Rmisc, grid, nlme, PharmacoGx, downloader Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 9e88037af1a4f725d3db0c04c2370742 NeedsCompilation: no Title: Analysis of patient-derived xenograft (PDX) data Description: Contains set of functions to perform analysis of patient-derived xenograft (PDX) data. biocViews: GeneExpression, Pharmacogenetics, Pharmacogenomics, Software, Classification Author: Arvind Mer, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains VignetteBuilder: knitr BugReports: https://github.com/bhklab/Xeva/issues git_url: https://git.bioconductor.org/packages/Xeva git_branch: RELEASE_3_12 git_last_commit: 87d94c1 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/Xeva_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/Xeva_1.6.0.zip mac.binary.ver: bin/macosx/contrib/4.0/Xeva_1.6.0.tgz vignettes: vignettes/Xeva/inst/doc/Xeva.pdf vignetteTitles: The Xeva User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Xeva/inst/doc/Xeva.R dependencyCount: 141 Package: XINA Version: 1.8.0 Depends: R (>= 3.5) Imports: mclust, plyr, alluvial, ggplot2, igraph, gridExtra, tools, grDevices, graphics, utils, STRINGdb Suggests: knitr, rmarkdown License: GPL-3 MD5sum: be62b72ae9b1765f53ea04216ae05bce NeedsCompilation: no Title: Multiplexes Isobaric Mass Tagged-based Kinetics Data for Network Analysis Description: The aim of XINA is to determine which proteins exhibit similar patterns within and across experimental conditions, since proteins with co-abundance patterns may have common molecular functions. XINA imports multiple datasets, tags dataset in silico, and combines the data for subsequent subgrouping into multiple clusters. The result is a single output depicting the variation across all conditions. XINA, not only extracts coabundance profiles within and across experiments, but also incorporates protein-protein interaction databases and integrative resources such as KEGG to infer interactors and molecular functions, respectively, and produces intuitive graphical outputs. biocViews: SystemsBiology, Proteomics, RNASeq, Network Author: Lang Ho Lee and Sasha A. Singh Maintainer: Lang Ho Lee and Sasha A. Singh VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/XINA git_branch: RELEASE_3_12 git_last_commit: 211b8b8 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/XINA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/XINA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/4.0/XINA_1.8.0.tgz vignettes: vignettes/XINA/inst/doc/xina_user_code.html vignetteTitles: xina_user_code hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XINA/inst/doc/xina_user_code.R dependencyCount: 68 Package: xmapbridge Version: 1.48.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 00bd3d376c29b1bc3e56930cb715d96e NeedsCompilation: no Title: Export plotting files to the xmapBridge for visualisation in X:Map Description: xmapBridge can plot graphs in the X:Map genome browser. This package exports plotting files in a suitable format. biocViews: Annotation, ReportWriting, Visualization Author: Tim Yates and Crispin J Miller Maintainer: Chris Wirth URL: http://xmap.picr.man.ac.uk, http://www.bioconductor.org git_url: https://git.bioconductor.org/packages/xmapbridge git_branch: RELEASE_3_12 git_last_commit: 1cefe6b git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/xmapbridge_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/xmapbridge_1.48.0.zip mac.binary.ver: bin/macosx/contrib/4.0/xmapbridge_1.48.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/xmapbridge/inst/doc/xmapbridge.R dependencyCount: 1 Package: xps Version: 1.50.0 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) MD5sum: 4fab64fa6be66c9c0818d6b812955e8f NeedsCompilation: yes Title: Processing and Analysis of Affymetrix Oligonucleotide Arrays including Exon Arrays, Whole Genome Arrays and Plate Arrays Description: The package handles pre-processing, normalization, filtering and analysis of Affymetrix GeneChip expression arrays, including exon arrays (Exon 1.0 ST: core, extended, full probesets), gene arrays (Gene 1.0 ST) and plate arrays on computers with 1 GB RAM only. It imports Affymetrix .CDF, .CLF, .PGF and .CEL as well as annotation files, and computes e.g. RMA, MAS5, FARMS, DFW, FIRMA, tRMA, MAS5-calls, DABG-calls, I/NI-calls. It is an R wrapper to XPS (eXpression Profiling System), which is based on ROOT, an object-oriented framework developed at CERN. Thus, the prior installation of ROOT is a prerequisite for the usage of this package, however, no knowledge of ROOT is required. ROOT is licensed under LGPL and can be downloaded from http://root.cern.ch. biocViews: ExonArray, GeneExpression, Microarray, OneChannel, DataImport, Preprocessing, Transcription, DifferentialExpression Author: Christian Stratowa, Vienna, Austria Maintainer: Christian Stratowa SystemRequirements: GNU make, root_v5.34.36 - See README file for installation instructions. PackageStatus: Deprecated git_url: https://git.bioconductor.org/packages/xps git_branch: RELEASE_3_12 git_last_commit: 8aa67ec git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/xps_1.50.0.tar.gz vignettes: vignettes/xps/inst/doc/APTvsXPS.pdf, vignettes/xps/inst/doc/xps.pdf, vignettes/xps/inst/doc/xpsClasses.pdf, vignettes/xps/inst/doc/xpsPreprocess.pdf vignetteTitles: 3. XPS Vignette: Comparison APT vs XPS, 1. XPS Vignette: Overview, 2. XPS Vignette: Classes, 4. XPS Vignette: Function express() hasREADME: TRUE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/xps/inst/doc/APTvsXPS.R, vignettes/xps/inst/doc/xps.R, vignettes/xps/inst/doc/xpsClasses.R, vignettes/xps/inst/doc/xpsPreprocess.R dependencyCount: 2 Package: XVector Version: 0.30.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.19.2), S4Vectors (>= 0.27.12), IRanges (>= 2.23.9) Imports: methods, utils, tools, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: cd3d6f5db37376aa19cfcea51fb076fa NeedsCompilation: yes Title: Foundation of external vector representation and manipulation in Bioconductor Description: Provides memory efficient S4 classes for storing sequences "externally" (e.g. behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès URL: https://bioconductor.org/packages/XVector BugReports: https://github.com/Bioconductor/XVector/issues git_url: https://git.bioconductor.org/packages/XVector git_branch: RELEASE_3_12 git_last_commit: 985e963 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/XVector_0.30.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/XVector_0.30.0.zip mac.binary.ver: bin/macosx/contrib/4.0/XVector_0.30.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, triplex importsMe: BSgenome, ChIPsim, CNEr, compEpiTools, dada2, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, HiLDA, IONiseR, IsoformSwitchAnalyzeR, kebabs, MatrixRider, Modstrings, R453Plus1Toolbox, ribosomeProfilingQC, Rsamtools, rtracklayer, Structstrings, TFBSTools, tracktables, tRNA, tRNAscanImport, VariantAnnotation, simMP suggestsMe: IRanges, musicatk linksToMe: Biostrings, CNEr, DECIPHER, kebabs, MatrixRider, Rsamtools, rtracklayer, ShortRead, triplex, VariantAnnotation, VariantFiltering dependencyCount: 11 Package: yamss Version: 1.16.0 Depends: R (>= 3.3.0), methods, BiocGenerics (>= 0.15.3), SummarizedExperiment Imports: IRanges, stats, S4Vectors, EBImage, Matrix, mzR, data.table, grDevices, limma Suggests: BiocStyle, knitr, rmarkdown, digest, mtbls2, testthat License: Artistic-2.0 MD5sum: 42cdc2f839b5803f9d50c9401c682c27 NeedsCompilation: no Title: Tools for high-throughput metabolomics Description: Tools to analyze and visualize high-throughput metabolomics data aquired using chromatography-mass spectrometry. These tools preprocess data in a way that enables reliable and powerful differential analysis. biocViews: MassSpectrometry, Metabolomics, ImmunoOncology, Software Author: Leslie Myint [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Leslie Myint URL: https://github.com/hansenlab/yamss VignetteBuilder: knitr BugReports: https://github.com/hansenlab/yamss/issues git_url: https://git.bioconductor.org/packages/yamss git_branch: RELEASE_3_12 git_last_commit: 4d52fba git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/yamss_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/yamss_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/yamss_1.16.0.tgz vignettes: vignettes/yamss/inst/doc/yamss.html vignetteTitles: yamss User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yamss/inst/doc/yamss.R dependencyCount: 47 Package: YAPSA Version: 1.16.0 Depends: R (>= 3.6.0), GenomicRanges, ggplot2, grid Imports: limSolve, SomaticSignatures, VariantAnnotation, GenomeInfoDb, reshape2, gridExtra, corrplot, dendextend, GetoptLong, circlize, gtrellis, doParallel, PMCMR, ggbeeswarm, ComplexHeatmap, KEGGREST, grDevices, Biostrings, BSgenome.Hsapiens.UCSC.hg19, magrittr, pracma, dplyr, utils Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: fb87e10e1ae686298d5f569a2a692dfd NeedsCompilation: no Title: Yet Another Package for Signature Analysis Description: This package provides functions and routines for supervised analyses of mutational signatures (i.e., the signatures have to be known, cf. L. Alexandrov et al., Nature 2013 and L. Alexandrov et al., Bioaxiv 2018). In particular, the family of functions LCD (LCD = linear combination decomposition) can use optimal signature-specific cutoffs which takes care of different detectability of the different signatures. Moreover, the package provides different sets of mutational signatures, including the COSMIC and PCAWG SNV signatures and the PCAWG Indel signatures; the latter infering that with YAPSA, the concept of supervised analysis of mutational signatures is extended to Indel signatures. YAPSA also provides confidence intervals as computed by profile likelihoods and can perform signature analysis on a stratified mutational catalogue (SMC = stratify mutational catalogue) in order to analyze enrichment and depletion patterns for the signatures in different strata. biocViews: Sequencing, DNASeq, SomaticMutation, Visualization, Clustering, GenomicVariation, StatisticalMethod, BiologicalQuestion Author: Daniel Huebschmann, Lea Jopp-Saile, Carolin Andresen, Zuguang Gu and Matthias Schlesner Maintainer: Daniel Huebschmann VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/YAPSA git_branch: RELEASE_3_12 git_last_commit: f344cdb git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/YAPSA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/YAPSA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/YAPSA_1.16.0.tgz vignettes: vignettes/YAPSA/inst/doc/index.html, vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.html, vignettes/YAPSA/inst/doc/vignette_exomes.html, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.html, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.html, vignettes/YAPSA/inst/doc/vignettes_Indel.html, vignettes/YAPSA/inst/doc/YAPSA.html vignetteTitles: index.html, 3. Confidence Intervals, 6. Usage of YAPSA for WES data, 2. Signature-specific cutoffs, 4. Stratified Analysis of Mutational Signatures, 5. Indel signature analysis, 1. Usage of YAPSA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/YAPSA/inst/doc/vignette_confidenceIntervals.R, vignettes/YAPSA/inst/doc/vignette_exomes.R, vignettes/YAPSA/inst/doc/vignette_signature_specific_cutoffs.R, vignettes/YAPSA/inst/doc/vignette_stratifiedAnalysis.R, vignettes/YAPSA/inst/doc/vignettes_Indel.R, vignettes/YAPSA/inst/doc/YAPSA.R dependencyCount: 185 Package: yaqcaffy Version: 1.50.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: 9f1e1dba51d06dd1a089865b504d7012 NeedsCompilation: no Title: Affymetrix expression data quality control and reproducibility analysis Description: Quality control of Affymetrix GeneChip expression data and reproducibility analysis of human whole genome chips with the MAQC reference datasets. biocViews: Microarray,OneChannel,QualityControl,ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto git_url: https://git.bioconductor.org/packages/yaqcaffy git_branch: RELEASE_3_12 git_last_commit: b32e6b9 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/yaqcaffy_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/yaqcaffy_1.50.0.zip mac.binary.ver: bin/macosx/contrib/4.0/yaqcaffy_1.50.0.tgz vignettes: vignettes/yaqcaffy/inst/doc/yaqcaffy.pdf vignetteTitles: yaqcaffy: Affymetrix quality control and MAQC reproducibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yaqcaffy/inst/doc/yaqcaffy.R suggestsMe: qcmetrics dependencyCount: 55 Package: yarn Version: 1.16.0 Depends: Biobase Imports: biomaRt, downloader, edgeR, gplots, graphics, limma, matrixStats, preprocessCore, readr, RColorBrewer, stats, quantro Suggests: knitr, rmarkdown, testthat (>= 0.8) License: Artistic-2.0 MD5sum: d81a7fa31f7ac286322c2eddb8690e42 NeedsCompilation: no Title: YARN: Robust Multi-Condition RNA-Seq Preprocessing and Normalization Description: Expedite large RNA-Seq analyses using a combination of previously developed tools. YARN is meant to make it easier for the user in performing basic mis-annotation quality control, filtering, and condition-aware normalization. YARN leverages many Bioconductor tools and statistical techniques to account for the large heterogeneity and sparsity found in very large RNA-seq experiments. biocViews: Software, QualityControl, GeneExpression, Sequencing, Preprocessing, Normalization, Annotation, Visualization, Clustering Author: Joseph N Paulson [aut, cre], Cho-Yi Chen [aut], Camila Lopes-Ramos [aut], Marieke Kuijjer [aut], John Platig [aut], Abhijeet Sonawane [aut], Maud Fagny [aut], Kimberly Glass [aut], John Quackenbush [aut] Maintainer: Joseph N Paulson VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/yarn git_branch: RELEASE_3_12 git_last_commit: ff5a18c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/yarn_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/yarn_1.16.0.zip mac.binary.ver: bin/macosx/contrib/4.0/yarn_1.16.0.tgz vignettes: vignettes/yarn/inst/doc/yarn.pdf vignetteTitles: YARN: Robust Multi-Tissue RNA-Seq Preprocessing and Normalization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/yarn/inst/doc/yarn.R dependencyCount: 148 Package: zellkonverter Version: 1.0.3 Imports: Matrix, basilisk, reticulate, SingleCellExperiment (>= 1.11.6), SummarizedExperiment, DelayedArray, methods, S4Vectors, utils Suggests: covr, spelling, testthat, knitr, rmarkdown, BiocStyle, scRNAseq, HDF5Array, rhdf5 License: MIT + file LICENSE MD5sum: fca311c63c49bfdc0fef7543d6626edf NeedsCompilation: no Title: Conversion Between scRNA-seq Objects Description: Provides methods to convert between Python AnnData objects and SingleCellExperiment objects. These are primarily intended for use by downstream Bioconductor packages that wrap Python methods for single-cell data analysis. It also includes functions to read and write H5AD files used for saving AnnData objects to disk. biocViews: SingleCell, DataImport, DataRepresentation Author: Luke Zappia [aut, cre] (), Aaron Lun [aut] () Maintainer: Luke Zappia URL: https://github.com/theislab/zellkonverter VignetteBuilder: knitr BugReports: https://github.com/theislab/zellkonverter/issues git_url: https://git.bioconductor.org/packages/zellkonverter git_branch: RELEASE_3_12 git_last_commit: e504d10 git_last_commit_date: 2021-03-08 Date/Publication: 2021-03-08 source.ver: src/contrib/zellkonverter_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/4.0/zellkonverter_1.0.3.zip mac.binary.ver: bin/macosx/contrib/4.0/zellkonverter_1.0.3.tgz vignettes: vignettes/zellkonverter/inst/doc/zellkonverter.html vignetteTitles: Converting to/from AnnData to SingleCellExperiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zellkonverter/inst/doc/zellkonverter.R importsMe: velociraptor dependencyCount: 36 Package: zFPKM Version: 1.12.0 Depends: R (>= 3.4.0) Imports: checkmate, dplyr, ggplot2, tidyr, SummarizedExperiment Suggests: knitr, limma, edgeR, GEOquery, stringr, printr License: GPL-3 | file LICENSE MD5sum: 196c811bb0fcdfa07cf74292e7af00bc NeedsCompilation: no Title: A suite of functions to facilitate zFPKM transformations Description: Perform the zFPKM transform on RNA-seq FPKM data. This algorithm is based on the publication by Hart et al., 2013 (Pubmed ID 24215113). Reference recommends using zFPKM > -3 to select expressed genes. Validated with encode open/closed chromosome data. Works well for gene level data using FPKM or TPM. Does not appear to calibrate well for transcript level data. biocViews: ImmunoOncology, RNASeq, FeatureExtraction, Software, GeneExpression Author: Ron Ammar [aut, cre], John Thompson [aut] Maintainer: Ron Ammar URL: https://github.com/ronammar/zFPKM/ VignetteBuilder: knitr BugReports: https://github.com/ronammar/zFPKM/issues git_url: https://git.bioconductor.org/packages/zFPKM git_branch: RELEASE_3_12 git_last_commit: 1a43a16 git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/zFPKM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/zFPKM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/zFPKM_1.12.0.tgz vignettes: vignettes/zFPKM/inst/doc/zFPKM.html vignetteTitles: Introduction to zFPKM Transformation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/zFPKM/inst/doc/zFPKM.R importsMe: DGEobj.utils dependencyCount: 64 Package: zinbwave Version: 1.12.0 Depends: R (>= 3.4), methods, SummarizedExperiment, SingleCellExperiment Imports: BiocParallel, softImpute, stats, genefilter, edgeR, Matrix Suggests: knitr, rmarkdown, testthat, matrixStats, magrittr, scRNAseq, ggplot2, biomaRt, BiocStyle, Rtsne, DESeq2, Seurat License: Artistic-2.0 MD5sum: f878e1c86e4e0b0cc562f05a152d1c71 NeedsCompilation: no Title: Zero-Inflated Negative Binomial Model for RNA-Seq Data Description: Implements a general and flexible zero-inflated negative binomial model that can be used to provide a low-dimensional representations of single-cell RNA-seq data. The model accounts for zero inflation (dropouts), over-dispersion, and the count nature of the data. The model also accounts for the difference in library sizes and optionally for batch effects and/or other covariates, avoiding the need for pre-normalize the data. biocViews: ImmunoOncology, DimensionReduction, GeneExpression, RNASeq, Software, Transcriptomics, Sequencing, SingleCell Author: Davide Risso [aut, cre, cph], Svetlana Gribkova [aut], Fanny Perraudeau [aut], Jean-Philippe Vert [aut], Clara Bagatin [aut] Maintainer: Davide Risso VignetteBuilder: knitr BugReports: https://github.com/drisso/zinbwave/issues git_url: https://git.bioconductor.org/packages/zinbwave git_branch: RELEASE_3_12 git_last_commit: b936f8e git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/zinbwave_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/zinbwave_1.12.0.zip mac.binary.ver: bin/macosx/contrib/4.0/zinbwave_1.12.0.tgz vignettes: vignettes/zinbwave/inst/doc/intro.html vignetteTitles: zinbwave Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/zinbwave/inst/doc/intro.R importsMe: clusterExperiment, scBFA, singleCellTK suggestsMe: MAST, splatter dependencyCount: 68 Package: zlibbioc Version: 1.36.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: adba470555858709370db859d7ce5382 NeedsCompilation: yes Title: An R packaged zlib-1.2.5 Description: This package uses the source code of zlib-1.2.5 to create libraries for systems that do not have these available via other means (most Linux and Mac users should have system-level access to zlib, and no direct need for this package). See the vignette for instructions on use. biocViews: Infrastructure Author: Martin Morgan Maintainer: Bioconductor Package Maintainer URL: https://bioconductor.org/packages/zlibbioc BugReports: https://github.com/Bioconductor/zlibbioc/issues git_url: https://git.bioconductor.org/packages/zlibbioc git_branch: RELEASE_3_12 git_last_commit: 62e888c git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 source.ver: src/contrib/zlibbioc_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/4.0/zlibbioc_1.36.0.zip mac.binary.ver: bin/macosx/contrib/4.0/zlibbioc_1.36.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: SimRAD importsMe: affy, affyio, affyPLM, bamsignals, ChemmineOB, MADSEQ, makecdfenv, NanoMethViz, oligo, polyester, qckitfastq, Rhtslib, Rsamtools, rtracklayer, ShortRead, snpStats, TransView, VariantAnnotation, XVector, jackalope suggestsMe: metacoder linksToMe: bamsignals, csaw, diffHic, methylKit, mzR, Rfastp, Rhtslib, scPipe, seqTools, ShortRead, jackalope dependencyCount: 0 Package: easyRNASeq Version: 2.26.0 Imports: Biobase (>= 2.44.0), BiocFileCache (>= 1.7.10), BiocGenerics (>= 0.30.0), BiocParallel (>= 1.18.1), biomaRt (>= 2.40.5), Biostrings (>= 2.52.0), DESeq (>= 1.36.0), edgeR (>= 3.26.8), GenomeInfoDb (>= 1.20.0), genomeIntervals (>= 1.40.0), GenomicAlignments (>= 1.20.1), GenomicRanges (>= 1.36.1), SummarizedExperiment (>= 1.14.1), graphics, IRanges (>= 2.18.3), LSD (>= 4.0), locfit, methods, parallel, rappdirs (>= 0.3.1), Rsamtools (>= 2.0.3), S4Vectors (>= 0.22.1), ShortRead (>= 1.42.0), utils Suggests: BiocStyle (>= 2.12.0), BSgenome (>= 1.52.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, knitr, rmarkdown, RUnit (>= 0.4.32) License: Artistic-2.0 MD5sum: edaf32e1fc8acd0d06300ae2b5734021 NeedsCompilation: no Title: Count summarization and normalization for RNA-Seq data Description: Calculates the coverage of high-throughput short-reads against a genome of reference and summarizes it per feature of interest (e.g. exon, gene, transcript). The data can be normalized as 'RPKM' or by the 'DESeq' or 'edgeR' package. biocViews: GeneExpression, RNASeq, Genetics, Preprocessing, ImmunoOncology Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr git_url: https://git.bioconductor.org/packages/easyRNASeq git_branch: RELEASE_3_12 git_last_commit: e3ce60a git_last_commit_date: 2020-10-27 Date/Publication: 2020-10-27 mac.binary.ver: bin/macosx/contrib/4.0/easyRNASeq_2.26.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: prada Version: 1.66.0 Depends: R (>= 2.10), Biobase, RColorBrewer, grid, methods, rrcov Imports: Biobase, BiocGenerics, graphics, grDevices, grid, MASS, methods, RColorBrewer, rrcov, stats4, utils Suggests: cellHTS2, tcltk License: LGPL Title: Data analysis for cell-based functional assays Description: Tools for analysing and navigating data from high-throughput phenotyping experiments based on cellular assays and fluorescent detection (flow cytometry (FACS), high-content screening microscopy). biocViews: ImmunoOncology, CellBasedAssays, Visualization Author: Florian Hahne , Wolfgang Huber , Markus Ruschhaupt, Joern Toedling , Joseph Barry Maintainer: Florian Hahne PackageStatus: Deprecated Package: spotSegmentation Version: 1.64.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) Title: Microarray Spot Segmentation and Gridding for Blocks of Microarray Spots Description: Spot segmentation via model-based clustering and gridding for blocks within microarray slides, as described in Li et al, Robust Model-Based Segmentation of Microarray Images, Technical Report no. 473, Department of Statistics, University of Washington. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing Author: Qunhua Li, Chris Fraley, Adrian Raftery Department of Statistics, University of Washington Maintainer: Chris Fraley URL: http://www.stat.washington.edu/fraley PackageStatus: Deprecated Package: reb Version: 1.68.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 Title: Regional Expression Biases Description: A set of functions to dentify regional expression biases biocViews: Microarray, CopyNumberVariation, Visualization Author: Kyle A. Furge and Karl Dykema Maintainer: Karl J. Dykema PackageStatus: Deprecated Package: metaArray Version: 1.68.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 Title: Integration of Microarray Data for Meta-analysis Description: 1) Data transformation for meta-analysis of microarray Data: Transformation of gene expression data to signed probability scale (MCMC/EM methods) 2) Combined differential expression on raw scale: Weighted Z-score after stabilizing mean-variance relation within platform biocViews: Microarray, DifferentialExpression Author: Debashis Ghosh Hyungwon Choi Maintainer: Hyungwon Choi PackageStatus: Deprecated Package: GeneticsDesign Version: 1.58.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 Title: Functions for designing genetics studies Description: This package contains functions useful for designing genetics studies, including power and sample-size calculations. biocViews: Genetics Author: Gregory Warnes David Duffy , Michael Man Weiliang Qiu Ross Lazarus Maintainer: The R Genetics Project PackageStatus: Deprecated Package: PGSEA Version: 1.64.0 Depends: R (>= 2.10), GO.db, KEGG.db, AnnotationDbi, annaffy, methods, Biobase (>= 2.5.5) Suggests: GSEABase, GEOquery, org.Hs.eg.db, hgu95av2.db, limma License: GPL-2 Title: Parametric Gene Set Enrichment Analysis Description: Parametric Analysis of Gene Set Enrichment biocViews: Microarray Author: Kyle Furge and Karl Dykema Maintainer: Karl Dykema PackageStatus: Deprecated Package: Starr Version: 1.46.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 Title: Simple tiling array analysis of Affymetrix ChIP-chip data Description: Starr facilitates the analysis of ChIP-chip data, in particular that of Affymetrix tiling arrays. The package provides functions for data import, quality assessment, data visualization and exploration. Furthermore, it includes high-level analysis features like association of ChIP signals with annotated features, correlation analysis of ChIP signals and other genomic data (e.g. gene expression), peak-finding with the CMARRT algorithm and comparative display of multiple clusters of ChIP-profiles. It uses the basic Bioconductor classes ExpressionSet and probeAnno for maximum compatibility with other software on Bioconductor. All functions from Starr can be used to investigate preprocessed data from the Ringo package, and vice versa. An important novel tool is the the automated generation of correct, up-to-date microarray probe annotation (bpmap) files, which relies on an efficient mapping of short sequences (e.g. the probe sequences on a microarray) to an arbitrary genome. biocViews: Microarray,OneChannel,DataImport,QualityControl,Preprocessing,ChIPchip Author: Benedikt Zacher, Johannes Soeding, Pei Fen Kuan, Matthias Siebert, Achim Tresch Maintainer: Benedikt Zacher PackageStatus: Deprecated Package: CNVtools Version: 1.84.0 Depends: R (>= 2.10), survival License: GPL-3 Title: A package to test genetic association with CNV data Description: This package is meant to facilitate the testing of Copy Number Variant data for genetic association, typically in case-control studies. biocViews: GeneticVariability Author: Chris Barnes and Vincent Plagnol Maintainer: Chris Barnes PackageStatus: Deprecated Package: BioSeqClass Version: 1.48.0 Depends: R (>= 2.10), scatterplot3d Imports: Biostrings, ipred, e1071, klaR, randomForest, class, tree, nnet, rpart, party, foreign, Biobase, utils, stats, grDevices Suggests: scatterplot3d License: LGPL (>= 2.0) Title: Classification for Biological Sequences Description: Extracting Features from Biological Sequences and Building Classification Model biocViews: Classification Author: Li Hong sysptm@gmail.com Maintainer: Li Hong PackageStatus: Deprecated Package: DESeq Version: 1.42.0 Depends: BiocGenerics (>= 0.7.5), Biobase (>= 2.21.7), locfit, lattice Imports: genefilter, geneplotter, methods, MASS, RColorBrewer Suggests: pasilla (>= 0.2.10), vsn, gplots License: GPL (>= 3) Title: Differential gene expression analysis based on the negative binomial distribution Description: Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution biocViews: ImmunoOncology, Sequencing, ChIPSeq, RNASeq, SAGE, DifferentialExpression Author: Simon Anders, EMBL Heidelberg Maintainer: Simon Anders URL: http://www-huber.embl.de/users/anders/DESeq PackageStatus: Deprecated Package: methVisual Version: 1.42.0 Depends: R (>= 2.11.0), Biostrings(>= 2.4.8), plotrix,gsubfn, grid,sqldf Imports: Biostrings, ca, graphics, grDevices, grid, gridBase, IRanges, stats, utils License: GPL (>= 2) Title: Methods for visualization and statistics on DNA methylation data Description: The package 'methVisual' allows the visualization of DNA methylation data after bisulfite sequencing. biocViews: DNAMethylation, Clustering, Classification Author: A. Zackay, C. Steinhoff Maintainer: Arie Zackay PackageStatus: Deprecated Package: MotIV Version: 1.46.0 Depends: R (>= 2.10), graphics, BiocGenerics, GenomicRanges Imports: methods, stats, grid, S4Vectors, IRanges (>= 1.13.5), Biostrings, lattice, rGADEM, utils Suggests: rtracklayer License: GPL-2 Title: Motif Identification and Validation Description: This package makes use of STAMP for comparing a set of motifs to a given database (e.g. JASPAR). It can also be used to visualize motifs, motif distributions, modules and filter motifs. biocViews: Microarray, ChIPchip, ChIPSeq, GenomicSequence, MotifAnnotation Author: Eloi Mercier, Raphael Gottardo Maintainer: Eloi Mercier , Raphael Gottardo SystemRequirements: GNU Scientific Library >= 1.6 (http://www.gnu.org/software/gsl/) PackageStatus: Deprecated Package: CGEN Version: 3.26.1 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE Title: An R package for analysis of case-control studies in genetic epidemiology Description: An R package for analysis of case-control studies in genetic epidemiology. biocViews: SNP, MultipleComparisons, Clustering Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee, Summer Han, Minsun Song and William Wheeler Maintainer: William Wheeler Package: joda Version: 1.38.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) Title: JODA algorithm for quantifying gene deregulation using knowledge Description: Package 'joda' implements three steps of an algorithm called JODA. The algorithm computes gene deregulation scores. For each gene, its deregulation score reflects how strongly an effect of a certain regulator's perturbation on this gene differs between two different cell populations. The algorithm utilizes regulator knockdown expression data as well as knowledge about signaling pathways in which the regulators are involved (formalized in a simple matrix model). biocViews: Microarray, Pathways, GraphAndNetwork, StatisticalMethod, NetworkInference Author: Ewa Szczurek Maintainer: Ewa Szczurek URL: http://www.bioconductor.org PackageStatus: Deprecated Package: flowType Version: 2.28.0 Depends: R (>= 2.10), Rcpp (>= 0.10.4), BH (>= 1.51.0-3) Imports: Biobase, graphics, grDevices, methods, flowCore, flowMeans, sfsmisc, rrcov, flowClust, flowMerge, stats LinkingTo: Rcpp, BH Suggests: xtable License: Artistic-2.0 Title: Phenotyping Flow Cytometry Assays Description: Phenotyping Flow Cytometry Assays using multidimentional expansion of single dimentional partitions. biocViews: ImmunoOncology, FlowCytometry Author: Nima Aghaeepour, Kieran O'Neill, Adrin Jalali Maintainer: Nima Aghaeepour PackageStatus: Deprecated Package: GOFunction Version: 1.38.0 Depends: R (>= 2.11.0), methods, Biobase (>= 2.8.0), graph (>= 1.26.0), Rgraphviz (>= 1.26.0), GO.db (>= 2.4.1), AnnotationDbi (>= 1.10.2), SparseM (>= 0.85) Imports: methods, Biobase, graph, Rgraphviz, GO.db, AnnotationDbi, DBI, SparseM License: GPL (>= 2) Title: GO-function: deriving biologcially relevant functions from statistically significant functions Description: The GO-function package provides a tool to address the redundancy that result from the GO structure or multiple annotation genes and derive biologically relevant functions from the statistically significant functions based on some intuitive assumption and statistical testing. biocViews: GO, Pathways, Microarray, GeneSetEnrichment Author: Jing Wang Maintainer: Jing Wang PackageStatus: Deprecated Package: MmPalateMiRNA Version: 1.40.0 Depends: R (>= 2.13.0), methods, Biobase, xtable, limma, statmod, lattice, vsn Imports: limma, lattice, Biobase Suggests: GOstats, graph, Category, org.Mm.eg.db, microRNA, targetscan.Mm.eg.db, RSQLite, DBI, AnnotationDbi, clValid, class, cluster, multtest, RColorBrewer, latticeExtra License: GPL-3 Title: Murine Palate miRNA Expression Analysis Description: R package compendium for the analysis of murine palate miRNA two-color expression data. biocViews: Microarray, TwoChannel, QualityControl, Preprocessing, DifferentialExpression, MultipleComparison, Clustering, GO, Pathways, ReportWriting, SequenceMatching Author: Guy Brock , Partha Mukhopadhyay , Vasyl Pihur , Robert M. Greene , and M. Michele Pisano Maintainer: Guy Brock PackageStatus: Deprecated Package: sigaR Version: 1.38.0 Depends: Biobase, CGHbase, methods, mvtnorm, Imports: corpcor (>= 1.6.2), graphics, igraph, limma, marray, MASS, penalized, quadprog, snowfall, stats Suggests: CGHcall License: GPL (>= 2) NeedsCompilation: no Title: Statistics for Integrative Genomics Analyses in R Description: Facilitates the joint analysis of high-throughput data from multiple molecular levels. Contains functions for manipulation of objects, various analysis types, and some visualization. biocViews: Microarray, DifferentialExpression, aCGH, GeneExpression, Pathways Author: Wessel N. van Wieringen Maintainer: Wessel N. van Wieringen URL: http://www.few.vu.nl/~wvanwie PackageStatus: Deprecated Package: NarrowPeaks Version: 1.34.0 Depends: R (>= 2.10.0), splines Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, fda, CSAR, ICSNP Suggests: rtracklayer, BiocStyle, GenomicRanges, CSAR License: Artistic-2.0 Title: Shape-based Analysis of Variation in ChIP-seq using Functional PCA Description: The package applies a functional version of principal component analysis (FPCA) to: (1) Postprocess data in wiggle track format, commonly produced by generic ChIP-seq peak callers, by applying FPCA over a set of read-enriched regions (ChIP-seq peaks). This is done to study variability of the the peaks, or to shorten their genomic locations accounting for a given proportion of variation among the enrichment-score profiles. (2) Analyse differential variation between multiple ChIP-seq samples with replicates. The function 'narrowpeaksDiff' quantifies differences between the shapes, and uses Hotelling's T2 tests on the functional principal component scores to identify significant differences across conditions. An application of the package for Arabidopsis datasets is described in Mateos, Madrigal, et al. (2015) Genome Biology: 16:31. biocViews: Visualization, ChIPSeq, Transcription, Genetics, Sequencing, Sequencing Author: Pedro Madrigal , Pawel Krajewski Maintainer: Pedro Madrigal PackageStatus: Deprecated Package: CorMut Version: 1.32.0 Depends: methods,seqinr,igraph License: GPL-2 Title: Detect the correlated mutations based on selection pressure Description: CorMut provides functions for computing kaks for individual sites or specific amino acids and detecting correlated mutations among them. Three methods are provided for detecting correlated mutations ,including conditional selection pressure, mutual information and Jaccard index. The computation consists of two steps: First, the positive selection sites are detected; Second, the mutation correlations are computed among the positive selection sites. Note that the first step is optional. Meanwhile, CorMut facilitates the comparison of the correlated mutations between two conditions by the means of correlated mutation network. The reference sequence should be the first sequence of the sequence file, and does not allow the presence of gap. biocViews: Sequencing Author: Zhenpeng Li Maintainer: Zhenpeng Li PackageStatus: Deprecated Package: plrs Version: 1.30.0 Depends: R (>= 2.10), Biobase Imports: BiocGenerics, CGHbase, graphics, grDevices, ic.infer, marray, methods, quadprog, Rcsdp, stats, stats4, utils Suggests: mvtnorm, methods License: GPL (>=2.0) Title: Piecewise Linear Regression Splines (PLRS) for the association between DNA copy number and gene expression Description: The present package implements a flexible framework for modeling the relationship between DNA copy number and gene expression data using Piecewise Linear Regression Splines (PLRS). biocViews: Regression Author: Gwenael G.R. Leday Maintainer: Gwenael G.R. Leday to PackageStatus: Deprecated Package: rTANDEM Version: 1.30.0 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE Title: Interfaces the tandem protein identification algorithm in R Description: This package interfaces the tandem protein identification algorithm in R. Identification can be launched in the X!Tandem style, by using as sole parameter the path to a parameter file. But rTANDEM aslo provides extended syntax and functions to streamline launching analyses, as well as function to convert results, parameters and taxonomy to/from R. A related package, shinyTANDEM, provides visualization interface for result objects. biocViews: ImmunoOncology, MassSpectrometry, Proteomics Author: Frederic Fournier , Charles Joly Beauparlant , Rene Paradis , Arnaud Droit Maintainer: Frederic Fournier SystemRequirements: rTANDEM uses expat and pthread libraries. See the README file for details. PackageStatus: Deprecated Package: flowFit Version: 1.28.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, kza, methods, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 Title: Estimate proliferation in cell-tracking dye studies Description: This package estimate the proliferation of a cell population in cell-tracking dye studies. The package uses an R implementation of the Levenberg-Marquardt algorithm (minpack.lm) to fit a set of peaks (corresponding to different generations of cells) over the proliferation-tracking dye distribution in a FACS experiment. biocViews: ImmunoOncology, FlowCytometry, CellBasedAssays Author: Davide Rambaldi Maintainer: Davide Rambaldi BugReports: Davide Rambaldi PackageStatus: Deprecated Package: Roleswitch Version: 1.28.0 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 Title: Infer miRNA-mRNA interactions using paired expression data from a single sample Description: Infer Probabilities of MiRNA-mRNA Interaction Signature (ProMISe) using paired expression data from a single sample. Roleswitch operates in two phases by inferring the probability of mRNA (miRNA) being the targets ("targets") of miRNA (mRNA), taking into account the expression of all of the mRNAs (miRNAs) due to their potential competition for the same miRNA (mRNA). Due to dynamic miRNA repression in the cell, Roleswitch assumes that the total transcribed mRNA levels are higher than the observed (equilibrium) mRNA levels and iteratively updates the total transcription of each mRNA targets based on the above inference. NB: in the paper, we used ProMISe as both the model name and inferred score name. biocViews: miRNA Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/roleswitch.html PackageStatus: Deprecated Package: ArrayTV Version: 1.28.0 Depends: R (>= 2.14) Imports: methods, foreach, S4Vectors (>= 0.9.25), IRanges (>= 2.13.24), DNAcopy, oligoClasses (>= 1.21.3) Suggests: RColorBrewer, crlmm, ff, BSgenome.Hsapiens.UCSC.hg18,BSgenome.Hsapiens.UCSC.hg19, lattice, latticeExtra, RUnit, BiocGenerics Enhances: doMC, doSNOW, doParallel License: GPL (>= 2) Title: Implementation of wave correction for arrays Description: Wave correction for genotyping and copy number arrays biocViews: CopyNumberVariation Author: Eitan Halper-Stromberg Maintainer: Eitan Halper-Stromberg PackageStatus: Deprecated Package: Mirsynergy Version: 1.26.0 Depends: R (>= 3.0.2), igraph, ggplot2 Imports: graphics, grDevices, gridExtra, Matrix, parallel, RColorBrewer, reshape, scales, utils Suggests: glmnet, RUnit, BiocGenerics, knitr License: GPL-2 Title: Mirsynergy Description: Detect synergistic miRNA regulatory modules by overlapping neighbourhood expansion. biocViews: Clustering Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/Mirsynergy.html VignetteBuilder: knitr PackageStatus: Deprecated Package: scsR Version: 1.26.0 Depends: R (>= 2.14.0), STRINGdb, methods, BiocGenerics, Biostrings, IRanges, plyr, tcltk Imports: sqldf, hash, ggplot2, graphics,grDevices, RColorBrewer Suggests: RUnit License: GPL-2 Title: SiRNA correction for seed mediated off-target effect Description: Corrects genome-wide siRNA screens for seed mediated off-target effect. Suitable functions to identify the effective seeds/miRNAs and to visualize their effect are also provided in the package. biocViews: Preprocessing Author: Andrea Franceschini Maintainer: Andrea Franceschini , Roger Meier , Christian von Mering PackageStatus: Deprecated Package: focalCall Version: 1.24.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 Title: Detection of focal aberrations in DNA copy number data Description: Detection of genomic focal aberrations in high-resolution DNA copy number data biocViews: Microarray,Preprocessing,Visualization,Sequencing Author: Oscar Krijgsman Maintainer: Oscar Krijgsman URL: https://github.com/OscarKrijgsman/focalCall PackageStatus: Deprecated Package: netbenchmark Version: 1.22.0 Depends: grndata (>= 0.99.3) Imports: Rcpp (>= 0.11.0), minet, GENIE3, c3net, PCIT, GeneNet, tools, pracma, Matrix, corpcor, fdrtool LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr, graph License: CC BY-NC-SA 4.0 Title: Benchmarking of several gene network inference methods Description: This package implements a benchmarking of several gene network inference algorithms from gene expression data. biocViews: Microarray, GraphAndNetwork, Network, NetworkInference, GeneExpression Author: Pau Bellot, Catharina Olsen, Patrick Meyer Maintainer: Pau Bellot URL: https://imatge.upc.edu/netbenchmark/ VignetteBuilder: knitr PackageStatus: Deprecated Package: OmicsMarkeR Version: 1.22.0 Depends: R (>= 3.2.0) Imports: graphics, stats, utils, plyr (>= 1.8), data.table (>= 1.9.4), caret (>= 6.0-37), DiscriMiner (>= 0.1-29), e1071 (>= 1.6-1), randomForest (>= 4.6-10), gbm (>= 2.1), pamr (>= 1.54.1), glmnet (>= 1.9-5), caTools (>= 1.14), foreach (>= 1.4.1), permute (>= 0.7-0), assertive (>= 0.3-0), assertive.base (>= 0.0-1) Suggests: testthat, BiocStyle, knitr License: GPL-3 Title: Classification and Feature Selection for 'Omics' Datasets Description: Tools for classification and feature selection for 'omics' level datasets. It is a tool to provide multiple multivariate classification and feature selection techniques complete with multiple stability metrics and aggregation techniques. It is primarily designed for analysis of metabolomics datasets but potentially extendable to proteomics and transcriptomics applications. biocViews: Metabolomics, Classification, FeatureExtraction Author: Charles E. Determan Jr. Maintainer: Charles E. Determan Jr. URL: http://github.com/cdeterman/OmicsMarkeR VignetteBuilder: knitr BugReports: http://github.com/cdeterman/OmicsMarkeR/issues/new PackageStatus: Deprecated Package: Prize Version: 1.20.0 Imports: diagram, stringr, ggplot2, reshape2, grDevices, matrixcalc, stats, gplots, methods, utils, graphics Suggests: RUnit, BiocGenerics License: Artistic-2.0 Title: Prize: an R package for prioritization estimation based on analytic hierarchy process Description: The high throughput studies often produce large amounts of numerous genes and proteins of interest. While it is difficult to study and validate all of them. Analytic Hierarchy Process (AHP) offers a novel approach to narrowing down long lists of candidates by prioritizing them based on how well they meet the research goal. AHP is a mathematical technique for organizing and analyzing complex decisions where multiple criteria are involved. The technique structures problems into a hierarchy of elements, and helps to specify numerical weights representing the relative importance of each element. Numerical weight or priority derived from each element allows users to find alternatives that best suit their goal and their understanding of the problem. biocViews: ImmunoOncology, Software, MultipleComparison, GeneExpression, CellBiology, RNASeq Author: Daryanaz Dargahi Maintainer: Daryanaz Dargahi PackageStatus: Deprecated Package: OGSA Version: 1.20.0 Depends: R (>= 3.2.0) Imports: gplots(>= 2.8.0), limma(>= 3.18.13), Biobase License: GPL (== 2) NeedsCompilation: no Title: Outlier Gene Set Analysis Description: OGSA provides a global estimate of pathway deregulation in cancer subtypes by integrating the estimates of significance for individual pathway members that have been identified by outlier analysis. biocViews: GeneExpression, Microarray, CopyNumberVariation Author: Michael F. Ochs Maintainer: Michael F. Ochs PackageStatus: Deprecated Package: JunctionSeq Version: 1.20.0 Depends: R (>= 3.2.2), methods, SummarizedExperiment (>= 0.2.0), Rcpp (>= 0.11.0), RcppArmadillo (>= 0.3.4.4) Imports: DESeq2 (>= 1.10.0), statmod, Hmisc, plotrix, stringr, Biobase (>= 2.30.0), locfit, BiocGenerics (>= 0.7.5), BiocParallel, genefilter, geneplotter, S4Vectors, IRanges, GenomicRanges, LinkingTo: Rcpp, RcppArmadillo Suggests: MASS, knitr, JctSeqData, BiocStyle Enhances: Cairo, pryr License: file LICENSE NeedsCompilation: yes Title: JunctionSeq: A Utility for Detection of Differential Exon and Splice-Junction Usage in RNA-Seq data Description: A Utility for Detection and Visualization of Differential Exon or Splice-Junction Usage in RNA-Seq data. biocViews: ImmunoOncology, Sequencing, RNASeq, DifferentialExpression Author: Stephen Hartley [aut, cre] (PhD), Simon Anders [cph], Alejandro Reyes [cph] Maintainer: Stephen Hartley URL: http://hartleys.github.io/JunctionSeq/index.html VignetteBuilder: knitr BugReports: https://github.com/hartleys/JunctionSeq/issues PackageStatus: Deprecated Package: GenRank Version: 1.18.0 Depends: R (>= 3.2.3) Imports: matrixStats, reshape2, survcomp Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 NeedsCompilation: no Title: Candidate gene prioritization based on convergent evidence Description: Methods for ranking genes based on convergent evidence obtained from multiple independent evidence layers. This package adapts three methods that are popular for meta-analysis. biocViews: GeneExpression, SNP, CopyNumberVariation, Microarray, Sequencing, Software, Genetics Author: Chakravarthi Kanduri Maintainer: Chakravarthi Kanduri URL: https://github.com/chakri9/GenRank VignetteBuilder: knitr BugReports: https://github.com/chakri9/GenRank/issues PackageStatus: Deprecated Package: ImpulseDE Version: 1.16.0 Depends: graphics, grDevices, stats, utils, parallel, compiler, R (>= 3.2.3) Imports: amap, boot Suggests: longitudinal, knitr License: GPL-3 NeedsCompilation: no Title: Detection of DE genes in time series data using impulse models Description: ImpulseDE is suited to capture single impulse-like patterns in high throughput time series datasets. By fitting a representative impulse model to each gene, it reports differentially expressed genes whether across time points in a single experiment or between two time courses from two experiments. To optimize the running time, the code makes use of clustering steps and multi-threading. biocViews: Software, StatisticalMethod, TimeCourse Author: Jil Sander [aut, cre], Nir Yosef [aut] Maintainer: Jil Sander , Nir Yosef URL: https://github.com/YosefLab/ImpulseDE VignetteBuilder: knitr BugReports: https://github.com/YosefLab/ImpulseDE/issues PackageStatus: Deprecated Package: SVAPLSseq Version: 1.16.0 Depends: R (>= 3.4) Imports: methods, stats, SummarizedExperiment, edgeR, ggplot2, limma, lmtest, parallel, pls Suggests: BiocStyle License: GPL-3 NeedsCompilation: no Title: SVAPLSseq-An R package to estimate the hidden factors of unwanted variability and adjust for them to enable a more powerful and accurate differential expression analysis based on RNAseq data Description: The package contains functions that are intended for extracting the signatures of latent variation in RNAseq data and using them to perform an improved differential expression analysis for a set of features (genes, transcripts) between two specified biological groups. biocViews: ImmunoOncology, GeneExpression, RNASeq, Normalization, BatchEffect Author: Sutirtha Chakraborty Maintainer: Sutirtha Chakraborty PackageStatus: Deprecated Package: LINC Version: 1.18.0 Depends: R (>= 3.3.1), methods, stats Imports: Rcpp (>= 0.11.0), DOSE, ggtree, gridExtra, ape, grid, png, Biobase, sva, reshape2, utils, grDevices, org.Hs.eg.db, clusterProfiler, ggplot2, ReactomePA LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr, biomaRt License: Artistic-2.0 Title: co-expression of lincRNAs and protein-coding genes Description: This package provides methods to compute co-expression networks of lincRNAs and protein-coding genes. Biological terms associated with the sets of protein-coding genes predict the biological contexts of lincRNAs according to the 'Guilty by Association' approach. biocViews: Software, BiologicalQuestion, GeneRegulation, GeneExpression Author: Manuel Goepferich, Carl Herrmann Maintainer: Manuel Goepferich VignetteBuilder: knitr PackageStatus: Deprecated Package: Logolas Version: 1.14.0 Depends: R (>= 3.4) Imports: grid, SQUAREM, LaplacesDemon, stats, graphics, utils, ggplot2, gridBase, Biostrings Suggests: knitr, rmarkdown, BiocStyle, Biobase, devtools, xtable, gridExtra, RColorBrewer, seqLogo, ggseqlogo License: GPL (>= 2) NeedsCompilation: no Title: EDLogo Plots Featuring String Logos and Adaptive Scaling of Position-Weight Matrices Description: Produces logo plots highlighting both enrichment and depletion of characters, allows for plotting of string symbols, and performs scaling of position-weights adaptively, along with several fun stylizations. biocViews: SequenceMatching, Alignment, Software, Visualization, Bayesian Author: Kushal Dey , Dongyue Xie , Peter Carbonetto , Matthew Stephens Maintainer: Kushal Dey URL: https://github.com/kkdey/Logolas VignetteBuilder: knitr BugReports: http://github.com/kkdey/Logolas/issues PackageStatus: Deprecated Package: hicrep Version: 1.14.0 Depends: R (>= 3.3) Imports: stats, rhdf5, Rcpp, rmarkdown, testthat, strawr LinkingTo: Rcpp Suggests: knitr License: GPL (>= 2.0) NeedsCompilation: yes Title: Measuring the reproducibility of Hi-C data Description: Hi-C is a powerful technology for studying genome-wide chromatin interactions. However, current methods for assessing Hi-C data reproducibility can produce misleading results because they ignore spatial features in Hi-C data, such as domain structure and distance-dependence. We present a novel reproducibility measure that systematically takes these features into consideration. This measure can assess pairwiseß differences between Hi-C matrices under a wide range of settings, and can be used to determine optimal sequencing depth. Compared to existing approaches, it consistently shows higher accuracy in distinguishing subtle differences in reproducibility and depicting interrelationships of cell lineages than existing approaches. This R package `hicrep` implements our approach. biocViews: Sequencing, HiC, QualityControl Author: Tao Yang [aut, cre], Fan Song [ctb] Maintainer: Tao Yang VignetteBuilder: knitr PackageStatus: Deprecated Package: pathprint Version: 1.20.0 Depends: R (>= 3.4) Imports: stats, utils Suggests: ALL, GEOquery, pathprintGEOData, SummarizedExperiment License: GPL Title: Pathway fingerprinting for analysis of gene expression arrays Description: Algorithms to convert a gene expression array provided as an expression table or a GEO reference to a 'pathway fingerprint', a vector of discrete ternary scores representing high (1), low(-1) or insignificant (0) expression in a suite of pathways. biocViews: Transcription, GeneExpression, KEGG, Reactome Author: Gabriel Altschuler, Sokratis Kariotis Maintainer: Sokratis Kariotis PackageStatus: Deprecated Package: ImpulseDE2 Version: 1.14.0 Imports: Biobase, BiocParallel, ComplexHeatmap, circlize, compiler, cowplot, DESeq2, ggplot2, grDevices, knitr, Matrix, methods, S4Vectors, stats, SummarizedExperiment, utils License: Artistic-2.0 Title: Differential expression analysis of longitudinal count data sets Description: ImpulseDE2 is a differential expression algorithm for longitudinal count data sets which arise in sequencing experiments such as RNA-seq, ChIP-seq, ATAC-seq and DNaseI-seq. ImpulseDE2 is based on a negative binomial noise model with dispersion trend smoothing by DESeq2 and uses the impulse model to constrain the mean expression trajectory of each gene. The impulse model was empirically found to fit global expression changes in cells after environmental and developmental stimuli and is therefore appropriate in most cell biological scenarios. The constraint on the mean expression trajectory prevents overfitting to small expression fluctuations. Secondly, ImpulseDE2 has higher statistical testing power than generalized linear model-based differential expression algorithms which fit time as a categorial variable if more than six time points are sampled because of the fixed number of parameters. biocViews: ImmunoOncology, Software, StatisticalMethod, TimeCourse, Sequencing, DifferentialExpression, GeneExpression, CellBiology, CellBasedAssays Author: David S Fischer [aut, cre], Fabian J Theis [ctb], Nir Yosef [ctb] Maintainer: David S Fischer VignetteBuilder: knitr BugReports: https://github.com/YosefLab/ImpulseDE2/issues PackageStatus: Deprecated Package: PathwaySplice Version: 1.14.0 Depends: R (>= 3.5.0) Imports: goseq, Biobase, DOSE, reshape2, igraph, org.Hs.eg.db, org.Mm.eg.db, BiocGenerics, AnnotationDbi, JunctionSeq, BiasedUrn, GO.db,gdata, geneLenDataBase, grDevices, graphics, stats, utils, VennDiagram, RColorBrewer, ensembldb, AnnotationHub, S4Vectors, dplyr, plotly, webshot, htmlwidgets , mgcv ,gridExtra, grid ,gplots, tibble , EnrichmentBrowser, annotate , KEGGREST Suggests: testthat, knitr, rmarkdown License: LGPL(>=2) Title: An R Package for Unbiased Splicing Pathway Analysis Description: Pathway analysis of alternative splicing would be biased without accounting for the different number of exons associated with each gene, because genes with higher number of exons are more likely to be included in the 'significant' gene list in alternative splicing. PathwaySplice is an R package that: (1) performs pathway analysis that explicitly adjusts for the number of exons associated with each gene (2) visualizes selection bias due to different number of exons for each gene (3) formally tests for presence of bias using logistic regression (4) supports gene sets based on the Gene Ontology terms, as well as more broadly defined gene sets (e.g. MSigDB) or user defined gene sets (5) identifies the significant genes driving pathway significance (6) organizes significant pathways with an enrichment map, where pathways with large number of overlapping genes are grouped together in a network graph biocViews: ImmunoOncology, AlternativeSplicing, DifferentialSplicing, GeneSetEnrichment, GO, RNASeq, Sequencing, Software, Visualization, NetworkEnrichment, Network, Pathways, GraphAndNetwork, Regression Author: Aimin Yan, Xi Chen, Lily Wang Maintainer: Aimin Yan VignetteBuilder: knitr PackageStatus: Deprecated Package: signet Version: 1.10.0 Depends: R (>= 3.4.0) Imports: graph, igraph, RBGL, graphics, utils, stats, methods Suggests: graphite, BiocStyle, knitr, rmarkdown License: GPL-2 Title: signet: Selection Inference in Gene NETworks Description: An R package to detect selection in biological pathways. Using gene selection scores and biological pathways data, one can search for high-scoring subnetworks of genes within pathways and test their significance. biocViews: Software, Pathways, DifferentialExpression, GeneExpression, NetworkEnrichment, GraphAndNetwork, KEGG Author: Alexandre Gouy Maintainer: <> VignetteBuilder: knitr PackageStatus: Deprecated Package: CHARGE Version: 1.10.0 Depends: R (>= 3.5), GenomicRanges, methods Imports: SummarizedExperiment, FactoMineR, factoextra, IRanges, graphics, modes, parallel, plyr, cluster, diptest, stats, matrixStats Suggests: roxygen2, EnsDb.Hsapiens.v86 License: GPL-2 Title: CHARGE: CHromosome Assessment in R from Gene Expression data Description: Identifies genomic duplications or deletions from gene expression data. biocViews: GeneExpression, Clustering Author: Benjamin Mayne Maintainer: Benjamin Mayne PackageStatus: Deprecated Package: adaptest Version: 1.10.0 Depends: R (>= 3.6.0) Imports: methods, graphics, stats, utils, calibrate, origami (>= 1.0.0), SummarizedExperiment, S4Vectors, tmle Suggests: Matrix, testthat, rmarkdown, knitr, BiocStyle, SuperLearner, earth, gam, nnls, airway License: GPL-2 Title: Data-Adaptive Statistics for High-Dimensional Multiple Testing Description: Data-adaptive test statistics represent a general methodology for performing multiple hypothesis testing on effects sizes while maintaining honest statistical inference when operating in high-dimensional settings (). The utilities provided here extend the use of this general methodology to many common data analytic challenges that arise in modern computational and genomic biology. biocViews: Genetics, GeneExpression, DifferentialExpression, Sequencing, Microarray, Regression, DimensionReduction, MultipleComparison Author: Weixin Cai , Nima Hejazi , Alan Hubbard Maintainer: Weixin Cai URL: https://github.com/wilsoncai1992/adaptest VignetteBuilder: knitr BugReports: https://github.com/wilsoncai1992/adaptest/issues PackageStatus: Deprecated Package: CrossICC Version: 1.4.0 Depends: R (>= 3.5), MASS Imports: data.table, methods, MergeMaid, ConsensusClusterPlus, limma, cluster, dplyr, Biobase, grDevices, stats, graphics, utils Suggests: rmarkdown, testthat, knitr, shiny, shinydashboard, shinyWidgets, shinycssloaders, DT, ggthemes, ggplot2, pheatmap, RColorBrewer, tibble, ggalluvial License: GPL-3 | file LICENSE Title: An Interactive Consensus Clustering Framework for Multi-platform Data Analysis Description: CrossICC utilizes an iterative strategy to derive the optimal gene set and cluster number from consensus similarity matrix generated by consensus clustering and it is able to deal with multiple cross platform datasets so that requires no between-dataset normalizations. This package also provides abundant functions for visualization and identifying subtypes of cancer. Specially, many cancer-related analysis methods are embedded to facilitate the clinical translation of the identified cancer subtypes. biocViews: Software, GeneExpression, DifferentialExpression, GUI, GeneSetEnrichment, Classification, Clustering, FeatureExtraction, Survival, Microarray, RNASeq, BatchEffect, Normalization, Preprocessing, Visualization Author: Yu Sun , Qi Zhao Maintainer: Yu Sun VignetteBuilder: knitr