Package: a4 Version: 1.20.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo License: GPL-3 MD5sum: e13c630ef36fe6b5cc1c87bc5b236a70 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Umbrella Package Description: Automated Affymetrix Array Analysis Umbrella Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/a4_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4_1.20.0.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 Package: a4Base Version: 1.20.0 Depends: methods, graphics, grid, Biobase, AnnotationDbi, annaffy, mpm, genefilter, limma, multtest, glmnet, a4Preproc, a4Core, gplots Suggests: Cairo, ALL Enhances: gridSVG, JavaGD License: GPL-3 MD5sum: 0497aad31e466da6975fe21eb54c6968 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Base Package Description: Automated Affymetrix Array Analysis biocViews: Microarray Author: Willem Talloen, Tobias Verbeke, Tine Casneuf, An De Bondt, Steven Osselaer and Hinrich Goehlmann, Willem Ligtenberg Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Base_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Base_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Base_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/a4Base_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Base_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Classif Version: 1.20.0 Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet, varSelRF Imports: a4Core Suggests: ALL License: GPL-3 MD5sum: 77ec35b13c5185a238658f3cbbedd6c4 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Classification Package Description: Automated Affymetrix Array Analysis Classification Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Classif_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Classif_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Classif_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/a4Classif_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Classif_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Core Version: 1.20.0 Depends: methods, Biobase, glmnet License: GPL-3 MD5sum: 561ec554e37d90a5c837c24fc018c7ba NeedsCompilation: no Title: Automated Affymetrix Array Analysis Core Package Description: Automated Affymetrix Array Analysis Core Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Core_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Core_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Core_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/a4Core_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Core_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif importsMe: a4Classif Package: a4Preproc Version: 1.20.0 Depends: methods, AnnotationDbi Suggests: ALL, hgu95av2.db License: GPL-3 MD5sum: 8edcb2b02c4d8490a05833d2016fa2c2 NeedsCompilation: no Title: Automated Affymetrix Array Analysis Preprocessing Package Description: Automated Affymetrix Array Analysis Preprocessing Package biocViews: Microarray Author: Willem Talloen, Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Preproc_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Preproc_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Preproc_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/a4Preproc_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Preproc_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif Package: a4Reporting Version: 1.20.0 Depends: methods, annaffy Imports: xtable, utils License: GPL-3 MD5sum: ee081a4841abe64d9c21149563e4b15f NeedsCompilation: no Title: Automated Affymetrix Array Analysis Reporting Package Description: Automated Affymetrix Array Analysis Reporting Package biocViews: Microarray Author: Tobias Verbeke Maintainer: Tobias Verbeke , Willem Ligtenberg source.ver: src/contrib/a4Reporting_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/a4Reporting_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/a4Reporting_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/a4Reporting_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/a4Reporting_1.20.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: ABAEnrichment Version: 1.2.2 Depends: R (>= 3.2) Imports: Rcpp (>= 0.11.5), gplots (>= 2.14.2), ABAData (>= 0.99.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr, testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: c3c68e150c14372524963fa32be328ec 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. biocViews: GeneSetEnrichment, GeneExpression Author: Steffi Grote Maintainer: Steffi Grote VignetteBuilder: knitr source.ver: src/contrib/ABAEnrichment_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ABAEnrichment_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ABAEnrichment_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ABAEnrichment_0.99.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ABAEnrichment_1.2.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.R htmlDocs: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html htmlTitles: ABAEnrichment: gene expression enrichment in human brain regions Package: ABarray Version: 1.40.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: c8383603249daa116bae068c75e01301 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 source.ver: src/contrib/ABarray_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ABarray_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ABarray_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ABarray_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ABarray_1.40.0.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 Package: ABSSeq Version: 1.8.1 Depends: R (>= 2.10), methods Imports: locfit, limma License: GPL (>= 3) MD5sum: 6b78eb54fe7c9d038c3e80d8bae8b8ba 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 source.ver: src/contrib/ABSSeq_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/ABSSeq_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/ABSSeq_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.3/ABSSeq_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ABSSeq_1.8.1.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 Package: acde Version: 1.2.0 Depends: R(>= 3.2), boot(>= 1.3) Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: 1d00b4c57d1e585a68483840bf347f71 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 source.ver: src/contrib/acde_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/acde_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/acde_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/acde_0.99.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/acde_1.2.0.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 Package: aCGH Version: 1.50.0 Depends: R (>= 2.10), cluster, survival, multtest Imports: Biobase, cluster, grDevices, graphics, methods, multtest, stats, survival, splines, utils License: GPL-2 Archs: i386, x64 MD5sum: 1f7f9cd4e3dedbd796fba5438ab3143d 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 source.ver: src/contrib/aCGH_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/aCGH_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/aCGH_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/aCGH_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/aCGH_1.50.0.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 Package: ACME Version: 2.28.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: e3f0d510f251c143b5222c36bb6207a8 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 source.ver: src/contrib/ACME_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ACME_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ACME_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ACME_2.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ACME_2.28.0.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 Package: ADaCGH2 Version: 2.12.0 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: c50bba10d6e9ab14ed7bc0c20cbda20b 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 source.ver: src/contrib/ADaCGH2_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ADaCGH2_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ADaCGH2_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ADaCGH2_2.9.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ADaCGH2_2.12.0.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 Package: adSplit Version: 1.42.0 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: ca388a7eb9c550e4a2b4d78f11b02ca2 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/index.shtml source.ver: src/contrib/adSplit_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/adSplit_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/adSplit_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/adSplit_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/adSplit_1.42.0.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 Package: affxparser Version: 1.44.0 Depends: R (>= 2.6.0) Suggests: R.oo (>= 1.20.0), R.utils (>= 2.2.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: 0b55c596caa3846a080adbb415ba7c91 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 source.ver: src/contrib/affxparser_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affxparser_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affxparser_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affxparser_1.41.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affxparser_1.44.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, pdInfoBuilder, rMAT, Starr importsMe: affyILM, AffyTiling, cn.farms, GeneRegionScan, ITALICS, oligo, rMAT suggestsMe: TIN Package: affy Version: 1.50.0 Depends: R (>= 2.8.0), BiocGenerics (>= 0.1.12), Biobase (>= 2.5.5) Imports: affyio (>= 1.13.3), BiocInstaller, graphics, grDevices, methods, preprocessCore, stats, utils, zlibbioc LinkingTo: preprocessCore Suggests: tkWidgets (>= 1.19.0), affydata, widgetTools License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 78f2eb708878df367a4bd92ad0bd8bc2 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 source.ver: src/contrib/affy_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affy_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affy_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affy_1.47.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affy_1.50.0.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, affypdnn, affyPLM, affyQCReport, AffyRNADegradation, altcdfenvs, arrayMvout, ArrayTools, bgx, Cormotif, DrugVsDisease, dualKS, ExiMiR, farms, frmaTools, gcrma, LMGene, logitT, maskBAD, MLP, panp, plw, prebs, qpcrNorm, ReadqPCR, RefPlus, rHVDM, Risa, RPA, SCAN.UPC, simpleaffy, sscore, Starr, webbioc importsMe: affycoretools, affyILM, affylmGUI, affyQCReport, AffyTiling, arrayQualityMetrics, ArrayTools, CAFE, ChIPXpress, Cormotif, farms, ffpe, frma, gcrma, GEOsubmission, Harshlight, HTqPCR, iCheck, lumi, LVSmiRNA, makecdfenv, MSnbase, PECA, plier, plw, puma, pvac, Rnits, simpleaffy, STATegRa, TCGAbiolinks, tilingArray, TurboNorm, vsn, waveTiling suggestsMe: AnnotationForge, ArrayExpress, beadarray, beadarraySNP, BiocCaseStudies, BiocGenerics, Biostrings, BufferedMatrixMethods, categoryCompare, ecolitk, ExpressionView, factDesign, gCMAPWeb, GeneRegionScan, limma, made4, MLSeq, oneChannelGUI, paxtoolsr, piano, PREDA, qcmetrics, siggenes Package: affycomp Version: 1.48.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: 1576c900a9183e7afb61fe8e95c5192f 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 source.ver: src/contrib/affycomp_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affycomp_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affycomp_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affycomp_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affycomp_1.48.0.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 Package: AffyCompatible Version: 1.32.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: 32c03881ccc77358697149f2053052c1 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 source.ver: src/contrib/AffyCompatible_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AffyCompatible_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AffyCompatible_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AffyCompatible_1.29.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AffyCompatible_1.32.0.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 importsMe: IdMappingRetrieval Package: affyContam Version: 1.30.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata License: Artistic-2.0 MD5sum: 29443525e6f089e0ccd205441ef67cbc 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 source.ver: src/contrib/affyContam_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyContam_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyContam_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affyContam_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyContam_1.30.0.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 Package: affycoretools Version: 1.44.3 Depends: Biobase, methods Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice, S4Vectors, edgeR Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl License: Artistic-2.0 MD5sum: deecd2435a762046063b7ff0826443b0 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 source.ver: src/contrib/affycoretools_1.44.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/affycoretools_1.44.3.zip win64.binary.ver: bin/windows64/contrib/3.3/affycoretools_1.44.3.zip mac.binary.ver: bin/macosx/contrib/3.3/affycoretools_1.41.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affycoretools_1.44.3.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.pdf vignetteTitles: affycoretools,, refactored hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.R Package: AffyExpress Version: 1.38.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: ca15677190e9a3cb34d9c8eb9159b38c 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 source.ver: src/contrib/AffyExpress_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AffyExpress_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AffyExpress_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AffyExpress_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AffyExpress_1.38.0.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 Package: affyILM Version: 1.24.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, methods, Biobase Suggests: AffymetrixDataTestFiles License: GPL version 3 MD5sum: 6dce234bf177198176c1414ccd0174fb 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 source.ver: src/contrib/affyILM_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyILM_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyILM_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affyILM_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyILM_1.24.0.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 Package: affyio Version: 1.42.0 Depends: R (>= 2.6.0) Imports: zlibbioc License: LGPL (>= 2) Archs: i386, x64 MD5sum: 3d7a7f037fe7e1a312b4bb80329d05e4 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: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad URL: https://github.com/bmbolstad/affyio source.ver: src/contrib/affyio_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyio_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyio_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affyio_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyio_1.42.0.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 Package: affylmGUI Version: 1.46.0 Imports: limma, tcltk, affy, BiocInstaller, affyio, tkrplot, affyPLM, R2HTML, xtable, gcrma, AnnotationDbi License: GPL (>=2) MD5sum: d0f49e2d74def15a37c49f82bd6ccadb NeedsCompilation: no Title: GUI for limma package with Affymetrix microarrays Description: A Graphical User Interface 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 [aut], Ken Simpson [aut], Gordon Smyth [aut], Keith Satterley [ctb], Yifang Hu [ctb] Maintainer: Yifang Hu , Gordon Smyth , Keith Satterley URL: http://bioinf.wehi.edu.au/affylmGUI/ source.ver: src/contrib/affylmGUI_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affylmGUI_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affylmGUI_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affylmGUI_1.43.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affylmGUI_1.46.0.tgz vignettes: vignettes/affylmGUI/inst/doc/affylmGUI.pdf, vignettes/affylmGUI/inst/doc/extract.pdf vignetteTitles: affylmGUI Vignette, Extracting affy and limma objects from affylmGUI files hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affylmGUI/inst/doc/affylmGUI.R htmlDocs: vignettes/affylmGUI/inst/doc/about.html, vignettes/affylmGUI/inst/doc/CustMenu.html, vignettes/affylmGUI/inst/doc/index.html, vignettes/affylmGUI/inst/doc/windowsFocus.html htmlTitles: about.html, CustMenu.html, index.html, windowsFocus.html dependsOnMe: oneChannelGUI Package: affyPara Version: 1.32.0 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: 73b0bdbb16dc97468d2894a1ca223521 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 source.ver: src/contrib/affyPara_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyPara_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyPara_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affyPara_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyPara_1.32.0.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 Package: affypdnn Version: 1.46.0 Depends: R (>= 2.13.0), affy (>= 1.5) Suggests: affydata, hgu95av2probe License: LGPL MD5sum: 540423d7dd6ee01a2d388709d4183047 NeedsCompilation: no Title: Probe Dependent Nearest Neighbours (PDNN) for the affy package Description: The package contains functions to perform the PDNN method described by Li Zhang et al. biocViews: OneChannel, Microarray, Preprocessing Author: H. Bjorn Nielsen and Laurent Gautier (Many thanks to Li Zhang early communications about the existence of the PDNN program and related publications). Maintainer: Laurent Gautier source.ver: src/contrib/affypdnn_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affypdnn_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affypdnn_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affypdnn_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affypdnn_1.46.0.tgz vignettes: vignettes/affypdnn/inst/doc/affypdnn.pdf vignetteTitles: affypdnn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/affypdnn/inst/doc/affypdnn.R Package: affyPLM Version: 1.48.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), affy (>= 1.11.0), Biobase (>= 2.17.8), gcrma, stats, preprocessCore (>= 1.5.1) Imports: BiocGenerics, zlibbioc, graphics, grDevices, methods LinkingTo: preprocessCore Suggests: affydata, MASS License: GPL (>= 2) Archs: i386, x64 MD5sum: 1024c5eaee407e0d0a4ae4c12d7edd89 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 source.ver: src/contrib/affyPLM_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyPLM_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyPLM_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affyPLM_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyPLM_1.48.0.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 importsMe: affylmGUI, affyQCReport, arrayQualityMetrics suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, oneChannelGUI, piano Package: affyQCReport Version: 1.50.0 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: 225368c128649777ea766980e5586dc3 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 source.ver: src/contrib/affyQCReport_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/affyQCReport_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/affyQCReport_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/affyQCReport_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/affyQCReport_1.50.0.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 Package: AffyRNADegradation Version: 1.18.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: 878c7a14aeb0141fc56e01d1ca35585c 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 source.ver: src/contrib/AffyRNADegradation_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AffyRNADegradation_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AffyRNADegradation_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AffyRNADegradation_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AffyRNADegradation_1.18.0.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 Package: AffyTiling Version: 1.29.0 Depends: R (>= 2.6) Imports: affxparser, affy (>= 1.16), stats, utils, preprocessCore License: GPL (>= 2) MD5sum: 8c858b4eea13a0b16f217540fcaac6fc NeedsCompilation: yes Title: Easy extraction of individual probes in Affymetrix tiling arrays Description: This package provides easy, fast functions for the extraction and annotation of individual probes from Affymetrix tiling arrays. biocViews: Microarray, Preprocessing Author: Charles G. Danko Maintainer: Charles G. Danko source.ver: src/contrib/AffyTiling_1.29.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/AffyTiling_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AffyTiling_1.29.0.tgz vignettes: vignettes/AffyTiling/inst/doc/AffyTiling.pdf vignetteTitles: AffyTiling hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AffyTiling/inst/doc/AffyTiling.R Package: AGDEX Version: 1.20.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: a0603be82b1cf93baec9bdc23641c519 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 source.ver: src/contrib/AGDEX_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AGDEX_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AGDEX_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AGDEX_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AGDEX_1.20.0.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 Package: agilp Version: 3.4.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: d34cd1cace0425a63006da26d776ef9f 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 source.ver: src/contrib/agilp_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/agilp_3.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/agilp_3.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/agilp_3.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/agilp_3.4.0.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 Package: AgiMicroRna Version: 2.22.0 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: 661d2c82dde2ef461f50f8143a199466 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 source.ver: src/contrib/AgiMicroRna_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AgiMicroRna_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AgiMicroRna_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AgiMicroRna_2.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AgiMicroRna_2.22.0.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 Package: AIMS Version: 1.4.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 53301976a804bc3d6a01de3b4513d8df 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: Classification, RNASeq, Microarray, Software, GeneExpression Author: Eric R. Paquet, Michael T. Hallett Maintainer: Eric R Paquet URL: http://www.bci.mcgill.ca/AIMS source.ver: src/contrib/AIMS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AIMS_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AIMS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AIMS_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AIMS_1.4.0.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 Package: ALDEx2 Version: 1.4.0 Depends: methods, SummarizedExperiment Imports: S4Vectors, IRanges, GenomicRanges Suggests: parallel, BiocParallel License: file LICENSE MD5sum: e60f2e67c20b78879a2541b282a54c6f 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. For example, single-organism and meta-RNA-seq high-throughput sequencing assays, or of selected and unselected values from in-vitro sequence selections. Uses a Dirichlet-multinomial model to infer abundance from counts, that has been optimized for three or more experimental replicates. Infers sampling variation and calculates the expected false discovery rate given the biological and sampling variation using the Wilcox rank test or Welches t-test (aldex.ttest) or the glm and Kruskal Wallis tests (aldex.glm). Reports both P and fdr values calculated by the Benjamini Hochberg correction. biocViews: DifferentialExpression, RNASeq, DNASeq, ChIPSeq, GeneExpression, Bayesian, Sequencing, Software Author: Greg Gloor, Ruth Grace Wong, Andrew Fernandes, Arianne Albert, Matt Links Maintainer: Greg Gloor source.ver: src/contrib/ALDEx2_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ALDEx2_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ALDEx2_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ALDEx2_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ALDEx2_1.4.0.tgz vignettes: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.pdf vignetteTitles: An R Package for determining differential abundance in high throughput sequencing experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ALDEx2/inst/doc/ALDEx2_vignette.R Package: AllelicImbalance Version: 1.10.2 Depends: R (>= 3.2.0), grid, GenomicRanges, SummarizedExperiment (>= 0.2.0), GenomicAlignments Imports: methods, BiocGenerics, AnnotationDbi, BSgenome, VariantAnnotation, Biostrings, S4Vectors (>= 0.9.25), IRanges, Rsamtools, GenomicFeatures, Gviz, lattice, latticeExtra, gridExtra, seqinr, GenomeInfoDb Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP144.GRCh37, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 4cbd7061984042a9985e9d9217ad460e 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 source.ver: src/contrib/AllelicImbalance_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/AllelicImbalance_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/AllelicImbalance_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/AllelicImbalance_1.7.19.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AllelicImbalance_1.10.2.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 Package: alsace Version: 1.8.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice License: GPL (>= 2) MD5sum: d10b8097f8f0bf89800bd110b4e2d5cf 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 source.ver: src/contrib/alsace_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/alsace_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/alsace_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/alsace_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/alsace_1.8.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/alsace/inst/doc/alsace.R Package: altcdfenvs Version: 2.34.0 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: b2b88d6e2e128e933c26213c00725769 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 source.ver: src/contrib/altcdfenvs_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/altcdfenvs_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/altcdfenvs_2.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/altcdfenvs_2.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/altcdfenvs_2.34.0.tgz vignettes: vignettes/altcdfenvs/inst/doc/altcdfenvs.pdf, vignettes/altcdfenvs/inst/doc/modify.pdf, vignettes/altcdfenvs/inst/doc/ngenomeschips.pdf vignetteTitles: altcdfenvs, affy primer, affy primer 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 Package: ampliQueso Version: 1.10.0 Depends: R (>= 2.15.0), rnaSeqMap (>= 2.17.1), knitr, rgl, ggplot2, gplots, parallel, doParallel, foreach, VariantAnnotation,genefilter,statmod,xtable Imports: edgeR, DESeq, samr License: GPL-2 MD5sum: ba040bc2324936835f0ea4ec780ae6b6 NeedsCompilation: no Title: Analysis of amplicon enrichment panels Description: The package provides tools and reports for the analysis of amplicon sequencing panels, such as AmpliSeq biocViews: ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, Visualization Author: Alicja Szabelska ; Marek Wiewiorka ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/ampliQueso_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ampliQueso_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ampliQueso_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ampliQueso_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ampliQueso_1.10.0.tgz vignettes: vignettes/ampliQueso/inst/doc/ampliQueso.pdf vignetteTitles: ampliQueso primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ampliQueso/inst/doc/ampliQueso.R Package: AnalysisPageServer Version: 1.6.2 Imports: log4r, tools, rjson, Biobase, graph Suggests: RUnit, XML, SVGAnnotation, knitr Enhances: Rook (>= 1.1), fork, FastRWeb, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: 0c79348b3546a8c50895cae2063db4c0 NeedsCompilation: yes Title: A framework for sharing interactive data and plots from R through the web. Description: AnalysisPageServer is a modular system that enables sharing of customizable R analyses via the web. biocViews: GUI, Visualization, DataRepresentation Author: Brad Friedman , Adrian Nowicki, Hunter Whitney , Matthew Brauer Maintainer: Brad Friedman VignetteBuilder: knitr source.ver: src/contrib/AnalysisPageServer_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnalysisPageServer_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/AnalysisPageServer_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/AnalysisPageServer_1.1.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnalysisPageServer_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.R, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.R, vignettes/AnalysisPageServer/inst/doc/embedding.R, vignettes/AnalysisPageServer/inst/doc/ExampleServers.R, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.R, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.R, vignettes/AnalysisPageServer/inst/doc/Interactivity.R, vignettes/AnalysisPageServer/inst/doc/StaticContent.R, vignettes/AnalysisPageServer/inst/doc/TrappingConditions.R htmlDocs: vignettes/AnalysisPageServer/inst/doc/AnalysisPageServer.html, vignettes/AnalysisPageServer/inst/doc/ApacheDeployment.html, vignettes/AnalysisPageServer/inst/doc/embedding.html, vignettes/AnalysisPageServer/inst/doc/ExampleServers.html, vignettes/AnalysisPageServer/inst/doc/FastRWebDeployment.html, vignettes/AnalysisPageServer/inst/doc/InteractiveApps.html, vignettes/AnalysisPageServer/inst/doc/Interactivity.html, vignettes/AnalysisPageServer/inst/doc/Licenses.html, vignettes/AnalysisPageServer/inst/doc/StaticContent.html, vignettes/AnalysisPageServer/inst/doc/TrappingConditions.html htmlTitles: 0. AnalysisPageServer, 6. Apache Deployment, 2. Embedding APS datasets in other documents, 4. Non-interactive servers and Rook Deployment, 7. FastRWeb Deployment, 5. Interactive Apps AnalysisPageServer, 3. AnalysisPageServer Interactivity, 8. Licenses, 1. Making Static Content Interactive with AnalysisPageServer, 8. Condition Trapping Package: AneuFinder Version: 1.0.3 Depends: R (>= 3.3.0), GenomicRanges, cowplot, AneuFinderData Imports: utils, grDevices, graphics, stats, foreach, doParallel, BiocGenerics, S4Vectors, GenomeInfoDb, IRanges, Rsamtools, Biostrings, GenomicAlignments, preseqR, ggplot2, reshape2, ggdendro, ReorderCluster, mclust Suggests: knitr, testthat, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Mmusculus.UCSC.mm10 License: Artistic-2.0 Archs: i386, x64 MD5sum: aa8b8471ddbe9768945f60cbd8a2a6d4 NeedsCompilation: yes Title: Analysis of Copy Number Variation in Single-Cell-Sequencing Data Description: This package implements functions for CNV calling, plotting, export and analysis from whole-genome single cell sequencing data. biocViews: Software, CopyNumberVariation, GenomicVariation, HiddenMarkovModel, WholeGenome Author: Aaron Taudt, Bjorn Bakker, David Porubsky Maintainer: Aaron Taudt URL: https://github.com/ataudt/aneufinder.git VignetteBuilder: knitr source.ver: src/contrib/AneuFinder_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/AneuFinder_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/AneuFinder_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AneuFinder_1.0.3.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 Package: annaffy Version: 1.44.0 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: ef6a7049382f9ca4dea05690f86930bc 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 source.ver: src/contrib/annaffy_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/annaffy_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/annaffy_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/annaffy_1.41.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annaffy_1.44.0.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: a4Base, a4Reporting, PGSEA, webbioc suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies Package: annmap Version: 1.14.0 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: b7b40edbdd2f96af20786e7e1c43ddaa 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 source.ver: src/contrib/annmap_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/annmap_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annmap_1.14.0.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 Package: annotate Version: 1.50.1 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8), RCurl Suggests: hgu95av2.db, genefilter, Biostrings (>= 2.25.10), 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: dab3842b9bd585f27929e7cf472af227 NeedsCompilation: no Title: Annotation for microarrays Description: Using R enviroments for annotation. biocViews: Annotation, Pathways, GO Author: R. Gentleman Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/annotate_1.50.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/annotate_1.50.1.zip win64.binary.ver: bin/windows64/contrib/3.3/annotate_1.50.1.zip mac.binary.ver: bin/macosx/contrib/3.3/annotate_1.47.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annotate_1.50.1.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, ScISI, SemDist importsMe: CAFE, Category, categoryCompare, codelink, debrowser, DOQTL, DrugVsDisease, facopy, gCMAP, gCMAPWeb, GeneAnswers, genefilter, GlobalAncova, globaltest, GOstats, lumi, methyAnalysis, methylumi, mvGST, phenoTest, qpgraph, ScISI, splicegear, systemPipeR, tigre suggestsMe: BiocCaseStudies, BiocGenerics, biomaRt, GenomicRanges, GlobalAncova, GOstats, GSAR, GSEAlm, maigesPack, metagenomeSeq, MLP, oneChannelGUI, RnBeads, siggenes, SummarizedExperiment Package: AnnotationDbi Version: 1.34.4 Depends: R (>= 2.7.0), methods, utils, stats4, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), IRanges Imports: methods, utils, DBI, RSQLite, stats4, BiocGenerics, Biobase, S4Vectors (>= 0.9.25), IRanges Suggests: DBI (>= 0.2-4), RSQLite (>= 0.6-4), 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: 54359883348d6288189afb85be958990 NeedsCompilation: no Title: Annotation Database Interface Description: Provides user interface and database connection code for annotation data packages using SQLite data storage. biocViews: Annotation, Microarray, Sequencing, GenomeAnnotation Author: Herve Pages, Marc Carlson, Seth Falcon, Nianhua Li Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=8qvGNTVz3Ik source.ver: src/contrib/AnnotationDbi_1.34.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationDbi_1.34.4.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationDbi_1.34.4.zip mac.binary.ver: bin/macosx/contrib/3.3/AnnotationDbi_1.31.17.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationDbi_1.34.4.tgz vignettes: vignettes/AnnotationDbi/inst/doc/AnnotationDbi.pdf, vignettes/AnnotationDbi/inst/doc/IntroToAnnotationPackages.pdf vignetteTitles: How to use bimaps from the ".db" annotation packages, AnnotationDbi: 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: a4Base, a4Preproc, annotate, AnnotationForge, AnnotationFuncs, attract, Category, chimera, ChromHeatMap, customProDB, DEXSeq, EGSEA, eisa, ExpressionView, GenomicFeatures, GOFunction, goProfiles, miRNAtap, MLP, OrganismDbi, PAnnBuilder, pathRender, PGSEA, proBAMr, RpsiXML, safe, SemDist, topGO importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, beadarray, biomaRt, BioNet, biovizBase, bumphunter, CancerMutationAnalysis, categoryCompare, cellity, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, CrispRVariants, csaw, customProDB, debrowser, derfinder, domainsignatures, DOSE, EDASeq, EnrichmentBrowser, ensembldb, erma, ExpressionView, gage, gCMAP, gCMAPWeb, genefilter, geneplotter, GenVisR, GGBase, ggbio, GGtools, GlobalAncova, globaltest, GOFunction, GOSemSim, goseq, GOSim, GOstats, goTools, gQTLstats, graphite, GSEABase, Gviz, gwascat, HTSanalyzeR, InPAS, interactiveDisplay, IVAS, limmaGUI, lumi, mAPKL, mdgsa, MeSHDbi, methyAnalysis, methylumi, MineICA, MiRaGE, mirIntegrator, miRNAmeConverter, missMethyl, mvGST, NanoStringQCPro, PADOG, PAnnBuilder, pathview, pcaExplorer, pcaGoPromoter, PCpheno, PGA, phenoTest, pwOmics, qpgraph, ReactomePA, REDseq, rgsepd, rTRM, ScISI, SGSeq, SLGI, SMITE, SpidermiR, SVM2CRM, tigre, ToPASeq, trackViewer, UniProt.ws, VariantAnnotation, VariantFiltering suggestsMe: BiocCaseStudies, BiocGenerics, FGNet, geecc, GeneAnswers, GeneRegionScan, GenomicRanges, limma, miRLAB, MmPalateMiRNA, neaGUI, oligo, oneChannelGUI, piano, pRoloc, qcmetrics, R3CPET, sigPathway, SummarizedExperiment Package: AnnotationForge Version: 1.14.2 Depends: R (>= 2.7.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.33.14) Imports: DBI, RSQLite, XML, S4Vectors Suggests: RCurl, biomaRt, httr, GenomeInfoDb, affy, hgu95av2.db, human.db0, org.Hs.eg.db, Homo.sapiens, hom.Hs.inp.db, GO.db, RSQLite, XML, BiocStyle, knitr License: Artistic-2.0 MD5sum: 7a4ce57b476f58367b8807d2a7e96c04 NeedsCompilation: no Title: Code for Building Annotation Database 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, Herve Pages Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/AnnotationForge_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationForge_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationForge_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.3/AnnotationForge_1.11.19.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationForge_1.14.2.tgz vignettes: vignettes/AnnotationForge/inst/doc/makeProbePackage.pdf, vignettes/AnnotationForge/inst/doc/MakingNewAnnotationPackages.pdf, vignettes/AnnotationForge/inst/doc/SQLForge.pdf 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. 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 htmlDocs: vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html htmlTitles: Making New Organism Packages importsMe: AnnotationHubData, GOstats suggestsMe: AnnotationDbi, AnnotationHub Package: AnnotationFuncs Version: 1.22.0 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: 68ddb9e8103f6af75b78d099a5316203 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 source.ver: src/contrib/AnnotationFuncs_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationFuncs_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationFuncs_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AnnotationFuncs_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationFuncs_1.22.0.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 Package: AnnotationHub Version: 2.4.2 Depends: BiocGenerics (>= 0.15.10) Imports: utils, methods, grDevices, RSQLite, BiocInstaller, AnnotationDbi (>= 1.31.19), S4Vectors, interactiveDisplayBase, httr Suggests: IRanges, GenomicRanges, GenomeInfoDb, VariantAnnotation, Rsamtools, rtracklayer, BiocStyle, knitr, AnnotationForge, rBiopaxParser, RUnit, GenomicFeatures, MSnbase, mzR, Biostrings, SummarizedExperiment Enhances: AnnotationHubData License: Artistic-2.0 MD5sum: f626af5b32ca7e41c83fce471e22a05d 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: Martin Morgan [cre], Marc Carlson [ctb], Dan Tenenbaum [ctb], Sonali Arora [ctb] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/AnnotationHub_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationHub_2.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationHub_2.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/AnnotationHub_2.1.40.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationHub_2.4.2.tgz 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/AnnotationHubRecipes.R htmlDocs: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/AnnotationHubRecipes.html htmlTitles: AnnotationHub: AnnotationHub HOW TO's, AnnotationHub: Access the AnnotationHub Web Service, How to create new AnnotationHub resources dependsOnMe: AnnotationHubData, ProteomicsAnnotationHubData, RefNet importsMe: ensembldb, gwascat, pwOmics suggestsMe: Chicago, CINdex, clusterProfiler, DNAshapeR, dupRadar, GenomicRanges, OrganismDbi, Pbase, VariantAnnotation Package: AnnotationHubData Version: 1.2.2 Depends: R (>= 3.2.2), methods, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub Imports: GenomicFeatures, Rsamtools, rtracklayer, BiocGenerics, jsonlite, BiocInstaller, httr, AnnotationDbi, Biobase, Biostrings, DBI, GEOquery, GenomeInfoDb, OrganismDbi, RSQLite, rBiopaxParser, AnnotationForge, futile.logger (>= 1.3.0), XML, xml2, curl Suggests: RUnit, knitr,RMySQL, BiocStyle, grasp2db License: Artistic-2.0 MD5sum: 7e1a77b86c3172685863cc702800800d 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], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/AnnotationHubData_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/AnnotationHubData_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/AnnotationHubData_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/AnnotationHubData_0.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AnnotationHubData_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AnnotationHubData/inst/doc/AnnotationHubData.R htmlDocs: vignettes/AnnotationHubData/inst/doc/AnnotationHubData.html htmlTitles: The AnnotationHubData Package Package: annotationTools Version: 1.46.0 Imports: Biobase, stats License: GPL MD5sum: aaeebb62bb3ed32f84df89217b9c4e4f 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 source.ver: src/contrib/annotationTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/annotationTools_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/annotationTools_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/annotationTools_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/annotationTools_1.46.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/annotationTools/inst/doc/annotationTools.R importsMe: DOQTL Package: anota Version: 1.20.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: e7301df043fae5caf3abfd65368c5116 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 source.ver: src/contrib/anota_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/anota_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/anota_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/anota_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/anota_1.20.0.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 Package: antiProfiles Version: 1.12.0 Depends: R (>= 3.0), matrixStats (>= 0.50.0), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: 19de884b95e46124a63537e40ff819f3 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 source.ver: src/contrib/antiProfiles_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/antiProfiles_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/antiProfiles_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/antiProfiles_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/antiProfiles_1.12.0.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 Package: apComplex Version: 2.38.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: fc4dd2490c6b3f40d6b944208504d355 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: NetworkInference, MassSpectrometry, GraphAndNetwork Author: Denise Scholtens Maintainer: Denise Scholtens source.ver: src/contrib/apComplex_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/apComplex_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/apComplex_2.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/apComplex_2.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/apComplex_2.38.0.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 Package: aroma.light Version: 3.2.0 Depends: R (>= 2.15.2) Imports: R.methodsS3 (>= 1.7.0), R.oo (>= 1.19.0), R.utils (>= 2.2.0), matrixStats (>= 0.50.1) Suggests: princurve (>= 1.1-12) License: GPL (>= 2) MD5sum: 100423e650584f1332c1b474e66f753e 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] Maintainer: Henrik Bengtsson URL: https://github.com/HenrikBengtsson/aroma.light, http://www.aroma-project.org BugReports: https://github.com/HenrikBengtsson/aroma.light/issues source.ver: src/contrib/aroma.light_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/aroma.light_3.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/aroma.light_3.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/aroma.light_2.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/aroma.light_3.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq suggestsMe: TIN Package: ArrayExpress Version: 1.32.0 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: e53a114273450ca855f9ab268c922a6b 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: Ugis Sarkans source.ver: src/contrib/ArrayExpress_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ArrayExpress_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ArrayExpress_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ArrayExpress_1.29.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayExpress_1.32.0.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 suggestsMe: gCMAPWeb Package: ArrayExpressHTS Version: 1.22.1 Depends: sampling, Rsamtools (>= 1.19.36), snow Imports: Biobase, BiocGenerics, Biostrings, DESeq, GenomicRanges, Hmisc, IRanges, R2HTML, RColorBrewer, Rsamtools, ShortRead, XML, biomaRt, edgeR, grDevices, graphics, methods, rJava, stats, svMisc, utils, sendmailR, bitops LinkingTo: Rsamtools License: Artistic License 2.0 MD5sum: 4c6d937d9871947cbc95dd9438c3790f NeedsCompilation: yes Title: ArrayExpress High Throughput Sequencing Processing Pipeline Description: RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets biocViews: RNASeq, Sequencing Author: Angela Goncalves, Andrew Tikhonov Maintainer: Angela Goncalves , Andrew Tikhonov source.ver: src/contrib/ArrayExpressHTS_1.22.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/ArrayExpressHTS_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayExpressHTS_1.22.1.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 Package: arrayMvout Version: 1.30.0 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: f23c30684ff31af6cbed5bb4068e8b4c 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 source.ver: src/contrib/arrayMvout_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/arrayMvout_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/arrayMvout_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/arrayMvout_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/arrayMvout_1.30.0.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 Package: arrayQuality Version: 1.50.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 48e056b72d9c064247621a971fa7f892 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/ source.ver: src/contrib/arrayQuality_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/arrayQuality_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/arrayQuality_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/arrayQuality_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/arrayQuality_1.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQualityMetrics Version: 3.28.2 Imports: affy, affyPLM (>= 1.27.3), beadarray, Biobase, Cairo (>= 1.4-6), genefilter, graphics, grDevices, grid, gridSVG (>= 1.4-3), Hmisc, hwriter, lattice, latticeExtra, limma, methods, RColorBrewer, setRNG, stats, SVGAnnotation (>= 0.9-0), utils, vsn (>= 3.23.3), XML Suggests: ALLMLL, CCl4, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 81d717f50bc80f669907894cd29d8c32 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: Audrey Kauffmann VignetteBuilder: knitr source.ver: src/contrib/arrayQualityMetrics_3.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/arrayQualityMetrics_3.28.2.zip win64.binary.ver: bin/windows64/contrib/3.3/arrayQualityMetrics_3.28.2.zip mac.binary.ver: bin/macosx/contrib/3.3/arrayQualityMetrics_3.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/arrayQualityMetrics_3.28.2.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 importsMe: EGAD Package: ArrayTools Version: 1.32.0 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: 090c163f71978e49c00f160aec9d7a96 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 source.ver: src/contrib/ArrayTools_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ArrayTools_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ArrayTools_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ArrayTools_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayTools_1.32.0.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 Package: ArrayTV Version: 1.10.0 Depends: R (>= 2.14) Imports: methods, foreach, S4Vectors (>= 0.9.25), 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) MD5sum: 5ec22fbc221848f4da2a04a9596a4b7a NeedsCompilation: no 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 source.ver: src/contrib/ArrayTV_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ArrayTV_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ArrayTV_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ArrayTV_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ArrayTV_1.10.0.tgz vignettes: vignettes/ArrayTV/inst/doc/ArrayTV.pdf vignetteTitles: ArrayTV Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ArrayTV/inst/doc/ArrayTV.R suggestsMe: VanillaICE Package: ARRmNormalization Version: 1.12.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 64759de0139c82c6157477a5aa6d0492 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 source.ver: src/contrib/ARRmNormalization_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ARRmNormalization_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ARRmNormalization_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ARRmNormalization_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ARRmNormalization_1.12.0.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 Package: ASEB Version: 1.16.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 6d9793b36e181ea5f23a3c289936298c 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 source.ver: src/contrib/ASEB_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASEB_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASEB_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ASEB_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASEB_1.16.0.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 Package: ASGSCA Version: 1.6.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: 804b52ec9ba72d4201c5711b7d8b307d 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 source.ver: src/contrib/ASGSCA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASGSCA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASGSCA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ASGSCA_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASGSCA_1.6.0.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 Package: ASSET Version: 1.10.0 Depends: MASS, msm, rmeta, mvtnorm, tmvnsim Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: ebded477608920e5941d6f0b029aac9c 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, Bioinformatics Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/ASSET_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASSET_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASSET_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ASSET_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASSET_1.10.0.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 Package: ASSIGN Version: 1.8.0 Depends: Rlab, msm, gplots Imports: graphics, grDevices, stats, utils License: MIT MD5sum: 93af82588f4fe12b9ae1614a78da523e 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 literatures 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, and W. Evan Johnson Maintainer: Ying Shen source.ver: src/contrib/ASSIGN_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ASSIGN_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ASSIGN_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ASSIGN_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ASSIGN_1.8.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.R Package: AtlasRDF Version: 1.8.0 Depends: R (>= 2.10), hash, SPARQL, methods License: Apache License 2.0 MD5sum: 3bab139d3b7afed42fd54b505cd967ec NeedsCompilation: no Title: Gene Expression Atlas query and gene set enrichment package. Description: Query the Gene Expression Atlas RDF data at the European Bioinformatics Institute using genes, experimental factors (such as disease, cell type, compound treatments), pathways and proteins. Also contains a function to perform an enrichment of your gene list across Experimental Factor Ontology (EFO) using the Atlas background set. biocViews: Microarray, DataImport, GeneSetEnrichment, GeneExpression, DifferentialExpression, DataRepresentation Author: James Malone, Simon Jupp, Maryam Soleimani Maintainer: James Malone source.ver: src/contrib/AtlasRDF_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/AtlasRDF_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/AtlasRDF_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/AtlasRDF_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/AtlasRDF_1.8.0.tgz vignettes: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.pdf vignetteTitles: An introduction to the AtlasRDF-R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/AtlasRDF/inst/doc/AtlasRDF_vignette.R Package: attract Version: 1.24.0 Depends: R (>= 3.2.4), methods, AnnotationDbi Imports: Biobase, limma, cluster, GOstats, graphics, stats, reactome.db, KEGGREST, org.Hs.eg.db Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: b06d8ffd484abd0125b6bd34fdc219fc 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: KEGG, Reactome, GeneExpression, Pathways, GeneSetEnrichment, Microarray, RNASeq Author: Jessica Mar Maintainer: Samuel Zimmerman source.ver: src/contrib/attract_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/attract_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/attract_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/attract_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/attract_1.24.0.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 Package: BAC Version: 1.32.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: db8e6f4204b92db4fceb493a71bc33e6 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 source.ver: src/contrib/BAC_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BAC_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BAC_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BAC_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BAC_1.32.0.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 Package: bacon Version: 1.0.5 Depends: R (>= 3.3), methods, stats, ggplot2, graphics, BiocParallel, ellipse Suggests: BiocStyle, knitr, rmarkdown, testthat, roxygen2 License: GPL (>= 2) Archs: i386, x64 MD5sum: 7acdeddeec422c5c6017139325bbeffb 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: StatisticalMethod, Bayesian, Regression, GenomeWideAssociation, Transcriptomics, RNASeq, MethylationArray, BatchEffect, MultipleComparison Author: Maarten van Iterson [aut, cre], Erik van Zwet [ctb] Maintainer: Maarten van Iterson URL: https://github.com/mvaniterson/bacon VignetteBuilder: knitr BugReports: https://github.com/mvaniterson/bacon/issues source.ver: src/contrib/bacon_1.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/bacon_1.0.5.zip win64.binary.ver: bin/windows64/contrib/3.3/bacon_1.0.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bacon_1.0.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bacon/inst/doc/bacon.R htmlDocs: vignettes/bacon/inst/doc/bacon.html htmlTitles: Controlling bias and inflation in association studies using the empirical null distribution Package: BADER Version: 1.10.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: f5f03d963b8ea48bcb00038cd418126c 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: Sequencing, RNASeq, DifferentialExpression, Software, SAGE Author: Andreas Neudecker, Matthias Katzfuss Maintainer: Andreas Neudecker source.ver: src/contrib/BADER_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BADER_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BADER_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BADER_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BADER_1.10.0.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 Package: BadRegionFinder Version: 1.0.0 Imports: VariantAnnotation, Rsamtools, biomaRt, GenomicRanges, S4Vectors, utils, stats, grDevices, graphics Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 850e343e560f2d66b405e355dcdaf42b 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 source.ver: src/contrib/BadRegionFinder_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BadRegionFinder_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BadRegionFinder_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BadRegionFinder_1.0.0.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 Package: BAGS Version: 2.12.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 914cf71aaa7ec5f854216164706cf180 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 source.ver: src/contrib/BAGS_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BAGS_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BAGS_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BAGS_2.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BAGS_2.12.0.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 Package: ballgown Version: 2.4.3 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: 4be7753aaf5e3218010aa85ab014ad3f 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: 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 source.ver: src/contrib/ballgown_2.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ballgown_2.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ballgown_2.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/ballgown_2.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ballgown_2.4.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ballgown/inst/doc/ballgown.R htmlDocs: vignettes/ballgown/inst/doc/ballgown.html htmlTitles: Flexible isoform-level differential expression analysis with Ballgown suggestsMe: polyester, variancePartition Package: bamsignals Version: 1.4.3 Depends: R (>= 3.2.0) Imports: methods, BiocGenerics, Rcpp (>= 0.10.6), IRanges, GenomicRanges, zlibbioc LinkingTo: Rcpp, Rhtslib, zlibbioc Suggests: testthat (>= 0.9), Rsamtools, BiocStyle, knitr, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 617f749150eded42a7e87883de00cac4 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: Alessandro Mammana VignetteBuilder: knitr source.ver: src/contrib/bamsignals_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/bamsignals_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/bamsignals_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/bamsignals_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bamsignals_1.4.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bamsignals/inst/doc/bamsignals.R htmlDocs: vignettes/bamsignals/inst/doc/bamsignals.html htmlTitles: Introduction to the bamsignals package Package: BaseSpaceR Version: 1.16.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: d0af414ff4dad55ca9aa2b7e9f95b5b8 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 source.ver: src/contrib/BaseSpaceR_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BaseSpaceR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BaseSpaceR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BaseSpaceR_1.13.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BaseSpaceR_1.16.0.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 Package: Basic4Cseq Version: 1.8.0 Depends: R (>= 3.0.0), Biostrings, GenomicAlignments, caTools, GenomicRanges Imports: methods, RCircos, BSgenome.Ecoli.NCBI.20080805 Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 865e526563aaaf1d9e412ecef26e8ab5 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: Visualization, QualityControl Author: Carolin Walter Maintainer: Carolin Walter source.ver: src/contrib/Basic4Cseq_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Basic4Cseq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Basic4Cseq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Basic4Cseq_1.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Basic4Cseq_1.8.0.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 Package: BasicSTARRseq Version: 1.0.2 Depends: GenomicRanges,GenomicAlignments Imports: S4Vectors,methods,IRanges,GenomeInfoDb,stats Suggests: knitr License: LGPL-3 MD5sum: 3c9e15873fd71ef6abd289db4f7c824f 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 source.ver: src/contrib/BasicSTARRseq_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/BasicSTARRseq_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/BasicSTARRseq_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BasicSTARRseq_1.0.2.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 Package: BatchQC Version: 1.0.22 Depends: R (>= 3.3.0) Imports: utils, rmarkdown, knitr, pander, gplots, MCMCpack, shiny, sva, corpcor, moments, matrixStats, ggvis, d3heatmap, reshape2, limma, grDevices, graphics, stats, methods Suggests: testthat License: GPL (>= 2) MD5sum: e4d4283cf3cd950ea18062218c2b1877 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 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 source.ver: src/contrib/BatchQC_1.0.22.tar.gz win.binary.ver: bin/windows/contrib/3.3/BatchQC_1.0.22.zip win64.binary.ver: bin/windows64/contrib/3.3/BatchQC_1.0.22.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BatchQC_1.0.22.tgz vignettes: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.pdf vignetteTitles: BatchQC_usage_advanced hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BatchQC/inst/doc/BatchQC_usage_advanced.R htmlDocs: vignettes/BatchQC/inst/doc/BatchQC_examples.html, vignettes/BatchQC/inst/doc/BatchQCIntro.html htmlTitles: BatchQC_examples, BatchQCIntro Package: BayesPeak Version: 1.24.0 Depends: R (>= 2.14), IRanges Imports: IRanges, graphics Suggests: BiocStyle, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 50659624120dd9893687f06c6c57c221 NeedsCompilation: yes Title: Bayesian Analysis of ChIP-seq Data Description: This package is an implementation of the BayesPeak algorithm for peak-calling in ChIP-seq data. biocViews: ChIPSeq Author: Christiana Spyrou, Jonathan Cairns, Rory Stark, Andy Lynch, Simon Tavar\\'{e}, Maintainer: Jonathan Cairns source.ver: src/contrib/BayesPeak_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BayesPeak_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BayesPeak_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BayesPeak_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BayesPeak_1.24.0.tgz vignettes: vignettes/BayesPeak/inst/doc/BayesPeak.pdf vignetteTitles: BayesPeak Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BayesPeak/inst/doc/BayesPeak.R Package: baySeq Version: 2.6.0 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, perm Suggests: edgeR, BiocStyle, BiocGenerics License: GPL-3 MD5sum: 2f1e50fe0c163638dc4175f5fd7bab59 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 source.ver: src/contrib/baySeq_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/baySeq_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/baySeq_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/baySeq_2.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/baySeq_2.6.0.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: Rcade, segmentSeq, TCC importsMe: EDDA, metaseqR suggestsMe: compcodeR, oneChannelGUI, riboSeqR Package: BBCAnalyzer Version: 1.2.0 Imports: SummarizedExperiment, VariantAnnotation, Rsamtools, grDevices, GenomicRanges, IRanges, Biostrings Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 098d08956e5b527f0696d5928b3cb376 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 source.ver: src/contrib/BBCAnalyzer_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BBCAnalyzer_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BBCAnalyzer_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BBCAnalyzer_0.99.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BBCAnalyzer_1.2.0.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 Package: BCRANK Version: 1.34.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: 4b5b105bd8076d25942cc0d00b6b4e91 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 source.ver: src/contrib/BCRANK_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BCRANK_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BCRANK_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BCRANK_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BCRANK_1.34.0.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 Package: beadarray Version: 2.22.2 Depends: R (>= 2.13.0), BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, ggplot2 Imports: BeadDataPackR, limma, AnnotationDbi, stats4, reshape2, GenomicRanges, IRanges, illuminaio Suggests: lumi, vsn, affy, hwriter, beadarrayExampleData, illuminaHumanv3.db, gridExtra, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, ggbio, Nozzle.R1, knitr License: GPL-2 Archs: i386, x64 MD5sum: 910277f7d577b053a49ff1870be06978 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 source.ver: src/contrib/beadarray_2.22.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/beadarray_2.22.2.zip win64.binary.ver: bin/windows64/contrib/3.3/beadarray_2.22.2.zip mac.binary.ver: bin/macosx/contrib/3.3/beadarray_2.19.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/beadarray_2.22.2.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: FALSE 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 importsMe: arrayQualityMetrics, blima, epigenomix suggestsMe: beadarraySNP, lumi Package: beadarraySNP Version: 1.38.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: e3ccf7e00a82e74d641f2bde97cb8616 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 source.ver: src/contrib/beadarraySNP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/beadarraySNP_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/beadarraySNP_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/beadarraySNP_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/beadarraySNP_1.38.0.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 Package: BeadDataPackR Version: 1.24.2 Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: f1db07c14501eaec4a65438c66d16d9d 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 source.ver: src/contrib/BeadDataPackR_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/BeadDataPackR_1.24.2.zip win64.binary.ver: bin/windows64/contrib/3.3/BeadDataPackR_1.24.2.zip mac.binary.ver: bin/macosx/contrib/3.3/BeadDataPackR_1.21.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BeadDataPackR_1.24.2.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 Package: BEAT Version: 1.10.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: 306a70dc1cfba39f5927b033031c99a8 NeedsCompilation: no Title: BEAT - BS-Seq Epimutation Analysis Toolkit Description: Model-based analysis of single-cell methylation data biocViews: Genetics, MethylSeq, Software, DNAMethylation, Epigenetics Author: Kemal Akman Maintainer: Kemal Akman source.ver: src/contrib/BEAT_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BEAT_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BEAT_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BEAT_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BEAT_1.10.0.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 Package: BEclear Version: 1.4.0 Depends: snowfall, Matrix License: GPL-2 MD5sum: ca1c8427576c217eb03cc3c2a5c139e6 NeedsCompilation: no Title: Correct for batch effects in DNA methylation data Description: Provides some functions to detect and correct for batch effects in DNA methylation data. The core function "BEclear" 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 Author: Markus Merl, Ruslan Akulenko Maintainer: Markus Merl source.ver: src/contrib/BEclear_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BEclear_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BEclear_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BEclear_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BEclear_1.4.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.pdf vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BEclear/inst/doc/BEclear.R Package: betr Version: 1.28.0 Depends: R(>= 2.6.0) Imports: Biobase (>= 2.5.5), limma, mvtnorm, methods, stats Suggests: Biobase License: LGPL MD5sum: a47c6946d9cd1d112048ab042a3d3f82 NeedsCompilation: no Title: Identify differentially expressed genes in microarray time-course data Description: The betr package implements the BETR (Bayesian Estimation of Temporal Regulation) algorithm to identify differentially expressed genes in microarray time-course data. biocViews: Microarray, DifferentialExpression, TimeCourse Author: Martin Aryee Maintainer: Martin Aryee source.ver: src/contrib/betr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/betr_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/betr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/betr_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/betr_1.28.0.tgz vignettes: vignettes/betr/inst/doc/betr.pdf vignetteTitles: BETR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/betr/inst/doc/betr.R Package: bgafun Version: 1.34.0 Depends: made4, seqinr,ade4 License: Artistic-2.0 MD5sum: ad6184d6f96d004a554c0f39f4ed2d21 NeedsCompilation: no Title: BGAfun A method to identify specifity determining residues in protein families Description: A method to identify specifity determining residues in protein families using Between Group Analysis biocViews: Classification Author: Iain Wallace Maintainer: Iain Wallace source.ver: src/contrib/bgafun_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bgafun_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bgafun_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/bgafun_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bgafun_1.34.0.tgz vignettes: vignettes/bgafun/inst/doc/bgafun.pdf vignetteTitles: bgafun.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bgafun/inst/doc/bgafun.R Package: BgeeDB Version: 1.0.3 Depends: R (>= 3.3), topGO, tidyr Imports: data.table, RCurl, methods, stats, utils, dplyr, graph, Biobase Suggests: knitr, BiocStyle, testthat, rmarkdown License: GPL-2 MD5sum: 6c1a4790f5bbfcba045c333135975e97 NeedsCompilation: no Title: Annotation and gene expression data from Bgee database 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 Author: Andrea Komljenovic [aut, cre], Julien Roux [aut, cre] Maintainer: Andrea Komljenovic , Frederic Bastian VignetteBuilder: knitr source.ver: src/contrib/BgeeDB_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/BgeeDB_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/BgeeDB_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BgeeDB_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.R htmlDocs: vignettes/BgeeDB/inst/doc/BgeeDB_Manual.html htmlTitles: Vignette Title importsMe: psygenet2r Package: BGmix Version: 1.32.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: f337fda27cc1abc54c69507ccc1c71af 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 source.ver: src/contrib/BGmix_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/BGmix_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BGmix_1.32.0.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 Package: bgx Version: 1.38.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: a83f3db6f4bf1a07ade5ec4f89759eed 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 source.ver: src/contrib/bgx_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bgx_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bgx_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/bgx_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bgx_1.38.0.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 Package: BHC Version: 1.24.0 License: GPL-3 Archs: i386, x64 MD5sum: c0d4e70d58cdc113084f9c7fd8968991 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 source.ver: src/contrib/BHC_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BHC_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BHC_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BHC_1.21.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BHC_1.24.0.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 Package: BicARE Version: 1.30.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: 563548f56b618552ff2ad9b07e6dcadd 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 source.ver: src/contrib/BicARE_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BicARE_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BicARE_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BicARE_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BicARE_1.30.0.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 Package: BiGGR Version: 1.8.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph License: file LICENSE MD5sum: 98f0622a91270631826b3162ac55dbf4 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/ source.ver: src/contrib/BiGGR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiGGR_1.7.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiGGR_1.7.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BiGGR_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiGGR_1.8.0.tgz vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/BiGGR/inst/doc/BiGGR.R Package: bigmemoryExtras Version: 1.18.1 Depends: R (>= 2.12), bigmemory (>= 4.3) Imports: methods Suggests: RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 OS_type: unix MD5sum: 03f23356635de880276884912ca55faf 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 source.ver: src/contrib/bigmemoryExtras_1.18.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/bigmemoryExtras_1.13.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bigmemoryExtras_1.18.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: bioassayR Version: 1.10.15 Depends: R (>= 3.1.0), 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, RefManageR, testthat, ggplot2 License: Artistic-2.0 MD5sum: ea8e15c3c2ae7ded7e32a7ca2b5650dd 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: MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics, Metabolomics Author: Tyler Backman, Ronly Schlenk, Thomas Girke Maintainer: Tyler Backman URL: https://github.com/TylerBackman/bioassayR VignetteBuilder: knitr BugReports: https://github.com/TylerBackman/bioassayR/issues source.ver: src/contrib/bioassayR_1.10.15.tar.gz win.binary.ver: bin/windows/contrib/3.3/bioassayR_1.10.15.zip win64.binary.ver: bin/windows64/contrib/3.3/bioassayR_1.10.15.zip mac.binary.ver: bin/macosx/contrib/3.3/bioassayR_1.7.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bioassayR_1.10.15.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/bioassayR/inst/doc/bioassayR.R htmlDocs: vignettes/bioassayR/inst/doc/bioassayR.html htmlTitles: Introduction and Examples Package: Biobase Version: 2.32.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), utils Imports: methods Suggests: tools, tkWidgets, ALL, RUnit, golubEsets License: Artistic-2.0 Archs: i386, x64 MD5sum: 8f3fa0b65c0fe3db21891ba6a13a2c91 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 source.ver: src/contrib/Biobase_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Biobase_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Biobase_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Biobase_2.29.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Biobase_2.32.0.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: a4Base, a4Core, ACME, affy, affycomp, affyContam, affycoretools, affyPLM, affyQCReport, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, 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simpleaffy, SLGI, SNPchip, SomaticSignatures, spade, spkTools, splicegear, STATegRa, subSeq, synapter, TCGAbiolinks, TEQC, TFBSTools, timecourse, ToPASeq, TSSi, twilight, VanillaICE, VariantAnnotation, VariantFiltering, VariantTools, wateRmelon, XBSeq, XDE suggestsMe: betr, BiocCaseStudies, BiocCheck, BiocGenerics, BSgenome, cellTree, clustComp, CountClust, DAPAR, DART, epivizr, epivizrStandalone, farms, genefu, GenomicRanges, GlobalAncova, GSAR, Heatplus, interactiveDisplay, kebabs, les, limma, messina, msa, multiClust, nem, OSAT, pkgDepTools, ROC, RTCGA, SeqArray, survcomp, TargetScore, tkWidgets, TypeInfo, vbmp, widgetTools Package: biobroom Version: 1.4.2 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: 6f2791ad4103535203397db1d47ca9ff 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 source.ver: src/contrib/biobroom_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/biobroom_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/biobroom_1.4.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biobroom_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biobroom/inst/doc/biobroom_vignette.R htmlDocs: vignettes/biobroom/inst/doc/biobroom_vignette.html htmlTitles: Vignette Title Package: BiocCaseStudies Version: 1.34.0 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: 3d08261681e821a85ecafadd46ec1212 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 source.ver: src/contrib/BiocCaseStudies_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiocCaseStudies_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiocCaseStudies_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BiocCaseStudies_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiocCaseStudies_1.34.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiocCheck Version: 1.8.2 Depends: R (>= 3.2.0) Imports: biocViews (>= 1.33.7), BiocInstaller, graph, devtools (>= 1.4.1), httr, knitr, tools, optparse, codetools, methods Suggests: RUnit, BiocGenerics, Biobase, RJSONIO, rmarkdown Enhances: codetoolsBioC License: Artistic-2.0 MD5sum: 31bd21408a7f413d7f937d4087fb49f1 NeedsCompilation: no Title: Bioconductor-specific package checks Description: Executes Bioconductor-specific package checks. biocViews: Infrastructure Author: Bioconductor Package Maintainer [aut, cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocCheck/issues VignetteBuilder: knitr source.ver: src/contrib/BiocCheck_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiocCheck_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/BiocCheck_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/BiocCheck_1.5.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiocCheck_1.8.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocCheck/inst/doc/BiocCheck.R htmlDocs: vignettes/BiocCheck/inst/doc/BiocCheck.html htmlTitles: BiocCheck suggestsMe: CNPBayes Package: BiocGenerics Version: 0.18.0 Depends: methods, utils, graphics, stats, parallel Imports: methods, utils, graphics, stats, parallel Suggests: Biobase, S4Vectors, IRanges, GenomicRanges, AnnotationDbi, oligoClasses, oligo, affyPLM, flowClust, affy, DESeq2, MSnbase, annotate, RUnit License: Artistic-2.0 MD5sum: baaff00eb2c2b15396fed2f7f43e634c NeedsCompilation: no Title: S4 generic functions for Bioconductor Description: S4 generic functions needed by many Bioconductor packages. biocViews: Infrastructure Author: The Bioconductor Dev Team Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocGenerics_0.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiocGenerics_0.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiocGenerics_0.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BiocGenerics_0.15.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiocGenerics_0.18.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ACME, affy, affyPLM, altcdfenvs, AnnotationDbi, AnnotationForge, AnnotationHub, beadarray, bioassayR, Biobase, Biostrings, BSgenome, bsseq, Cardinal, Category, categoryCompare, chipseq, ChIPseqR, ChromHeatMap, cleanUpdTSeq, codelink, consensusSeekeR, copynumber, CopyNumber450k, CRISPRseek, cummeRbund, DESeq, dexus, ensembldb, ensemblVEP, flowQ, 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Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 92def98f3f940fd3c7c7df9923ae0cd1 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 source.ver: src/contrib/biocGraph_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biocGraph_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biocGraph_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/biocGraph_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biocGraph_1.34.0.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 importsMe: EnrichmentBrowser suggestsMe: BiocCaseStudies Package: BiocInstaller Version: 1.22.3 Depends: R (>= 3.3.0) Suggests: devtools, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: b4547d5fd23d431f2e9ce78a1f619bac NeedsCompilation: no Title: Install/Update Bioconductor, CRAN, and github Packages Description: This package is used to install and update Bioconductor, CRAN, and (some) github packages. biocViews: Infrastructure Author: Dan Tenenbaum and Biocore Team Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/BiocInstaller_1.22.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiocInstaller_1.22.3.zip win64.binary.ver: bin/windows64/contrib/3.3/BiocInstaller_1.22.3.zip mac.binary.ver: bin/macosx/contrib/3.3/BiocInstaller_1.19.14.tgz mac.binary.mavericks.ver: 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TRONCO, TurboNorm, variancePartition, VariantAnnotation, VariantFiltering, wavClusteR, XBSeq Package: biocViews Version: 1.40.1 Depends: R (>= 2.4.0) Imports: Biobase, graph (>= 1.9.26), methods, RBGL (>= 1.13.5), tools, utils, XML, RCurl, knitr, RUnit Suggests: BiocGenerics License: Artistic-2.0 MD5sum: 5f59368ea17fb1f2c5187419c7c28023 NeedsCompilation: no Title: Categorized views of R package repositories Description: structures for vocabularies and narratives of views biocViews: Infrastructure Author: VJ Carey , BJ Harshfield , S Falcon , Sonali Arora Maintainer: Bioconductor Package Maintainer URL: http://www.bioconductor.org/packages/release/BiocViews.html source.ver: src/contrib/biocViews_1.40.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/biocViews_1.40.1.zip win64.binary.ver: bin/windows64/contrib/3.3/biocViews_1.40.1.zip mac.binary.ver: bin/macosx/contrib/3.3/biocViews_1.37.13.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biocViews_1.40.1.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: BiocCheck Package: bioDist Version: 1.44.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: 5ad15b053cf2642be05e9bda8342e525 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 source.ver: src/contrib/bioDist_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bioDist_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bioDist_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/bioDist_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bioDist_1.44.0.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 dependsOnMe: flowQ suggestsMe: BiocCaseStudies Package: biomaRt Version: 2.28.0 Depends: methods Imports: utils, XML, RCurl, AnnotationDbi Suggests: annotate License: Artistic-2.0 MD5sum: 043ed61b4e19c085968db0f9524c57b7 NeedsCompilation: no Title: Interface to BioMart databases (e.g. Ensembl, COSMIC ,Wormbase and Gramene) 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 (http://www.biomart.org). 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. Examples of BioMart databases are Ensembl, COSMIC, Uniprot, HGNC, Gramene, Wormbase and dbSNP mapped to Ensembl. These major databases give biomaRt users direct access to a diverse set of data and enable a wide range of powerful online queries from gene annotation to database mining. biocViews: Annotation Author: Steffen Durinck , Wolfgang Huber Maintainer: Steffen Durinck source.ver: src/contrib/biomaRt_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biomaRt_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biomaRt_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/biomaRt_2.25.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biomaRt_2.28.0.tgz vignettes: vignettes/biomaRt/inst/doc/biomaRt.pdf vignetteTitles: The biomaRt users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomaRt/inst/doc/biomaRt.R dependsOnMe: chromPlot, coMET, customProDB, dagLogo, domainsignatures, DrugVsDisease, GenomeGraphs, MineICA, PSICQUIC, Roleswitch, VegaMC importsMe: ArrayExpressHTS, BadRegionFinder, ChIPpeakAnno, CHRONOS, cobindR, customProDB, DEXSeq, diffloop, DOQTL, easyRNASeq, EDASeq, EWCE, GenomicFeatures, GenVisR, gespeR, GOexpress, Gviz, HTSanalyzeR, IdMappingRetrieval, MEDIPS, metaseqR, methyAnalysis, OncoScore, oposSOM, Pbase, PGA, phenoTest, pRoloc, psygenet2r, pwOmics, R453Plus1Toolbox, recoup, rgsepd, RNAither, scater, seq2pathway, SeqGSEA, TCGAbiolinks suggestsMe: AnnotationForge, bioassayR, BiocCaseStudies, cellTree, DEGreport, GeneAnswers, Genominator, h5vc, isobar, massiR, MineICA, MiRaGE, oligo, oneChannelGUI, OrganismDbi, paxtoolsr, piano, pqsfinder, R3CPET, Rcade, RIPSeeker, RnBeads, rTANDEM, rTRM, ShortRead, SIM, sincell, systemPipeR, trackViewer Package: biomformat Version: 1.0.2 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: 771fa8ee90c441939d4abbc40011b8b1 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: 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 source.ver: src/contrib/biomformat_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/biomformat_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/biomformat_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biomformat_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biomformat/inst/doc/biomformat.R htmlDocs: vignettes/biomformat/inst/doc/biomformat.html htmlTitles: The biomformat package Vignette importsMe: phyloseq suggestsMe: metagenomeSeq Package: BioMVCClass Version: 1.40.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: bd57136b6d7d662f1004ceced09c4de8 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 source.ver: src/contrib/BioMVCClass_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioMVCClass_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioMVCClass_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BioMVCClass_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioMVCClass_1.40.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biomvRCNS Version: 1.12.0 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: 27c8b3ea670743dbbcbda876b686406f 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 source.ver: src/contrib/biomvRCNS_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biomvRCNS_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biomvRCNS_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/biomvRCNS_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biomvRCNS_1.12.0.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 Package: BioNet Version: 1.32.0 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: a225d4f9b9370248669504f6c0b75ec9 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/ source.ver: src/contrib/BioNet_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioNet_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioNet_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BioNet_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioNet_1.32.0.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: HTSanalyzeR, SMITE suggestsMe: SANTA Package: BioQC Version: 1.0.0 Depends: Rcpp, Biobase Suggests: testthat License: LGPL (>=2) Archs: i386, x64 MD5sum: ec72f0de3da8adf2e0a3d662554c52e6 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 biocViews: GeneExpression,QualityControl,StatisticalMethod Author: Jitao David Zhang , with inputs from Laura Badi Maintainer: Jitao David Zhang source.ver: src/contrib/BioQC_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioQC_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioQC_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioQC_1.0.0.tgz vignettes: vignettes/BioQC/inst/doc/bioqc.pdf vignetteTitles: BioQC: The kidney expression example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioQC/inst/doc/bioqc.R Package: BioSeqClass Version: 1.30.0 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) MD5sum: 82511de69e890c007f4b293d36a1ee97 NeedsCompilation: no 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 source.ver: src/contrib/BioSeqClass_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BioSeqClass_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BioSeqClass_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BioSeqClass_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BioSeqClass_1.30.0.tgz vignettes: vignettes/BioSeqClass/inst/doc/BioSeqClass.pdf vignetteTitles: Using the BioSeqClass Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BioSeqClass/inst/doc/BioSeqClass.R Package: biosigner Version: 1.0.8 Depends: methods, e1071, randomForest, ropls Imports: grDevices, graphics, stats, utils Suggests: RUnit, BiocGenerics, BiocStyle, golubEsets, hu6800.db, BioMark License: CeCILL MD5sum: 9d4a67d856f4c2f54442ed070311b12a 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 source.ver: src/contrib/biosigner_1.0.8.tar.gz win.binary.ver: bin/windows/contrib/3.3/biosigner_1.0.8.zip win64.binary.ver: bin/windows64/contrib/3.3/biosigner_1.0.8.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biosigner_1.0.8.tgz vignettes: vignettes/biosigner/inst/doc/biosigner.pdf vignetteTitles: \emph{biosigner} package for molecular signature discovery hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosigner/inst/doc/biosigner.R Package: Biostrings Version: 2.40.2 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.6), S4Vectors (>= 0.10.1), IRanges (>= 2.5.27), XVector (>= 0.11.6) Imports: graphics, methods, stats, utils, BiocGenerics, IRanges, XVector 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: a2bfabeda6578c2dcd2c8129b293ddf9 NeedsCompilation: yes Title: String objects representing biological sequences, and matching algorithms 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 source.ver: src/contrib/Biostrings_2.40.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Biostrings_2.40.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Biostrings_2.40.2.zip mac.binary.ver: bin/macosx/contrib/3.3/Biostrings_2.37.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Biostrings_2.40.2.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, Basic4Cseq, BRAIN, BSgenome, ChIPpeakAnno, ChIPsim, cleaver, CODEX, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, GenomicAlignments, genphen, GOTHiC, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, methVisual, minfi, MotifDb, motifRG, motifStack, msa, muscle, oligo, oneChannelGUI, pcaGoPromoter, PGA, pqsfinder, qrqc, R453Plus1Toolbox, R4RNA, REDseq, rGADEM, RiboProfiling, Roleswitch, rRDP, Rsamtools, RSVSim, sangerseqR, SCAN.UPC, scsR, SELEX, seqbias, ShortRead, SICtools, systemPipeR, triplex, waveTiling importsMe: AffyCompatible, AllelicImbalance, AneuFinder, AnnotationHubData, ArrayExpressHTS, BBCAnalyzer, BCRANK, BEAT, BioSeqClass, biovizBase, BSgenome, charm, ChIPseqR, ChIPsim, CNEr, cobindR, compEpiTools, CrispRVariants, customProDB, dada2, dagLogo, diffHic, DNAshapeR, easyRNASeq, EDASeq, ensemblVEP, eudysbiome, FindMyFriends, FourCSeq, gcrma, genbankr, GeneRegionScan, genomation, GenomicAlignments, GenomicFeatures, GenVisR, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GUIDEseq, Gviz, gwascat, h5vc, HiTC, HTSeqGenie, IONiseR, KEGGREST, LowMACA, MatrixRider, MEDIPS, MEDME, metagenomeFeatures, methVisual, methylPipe, microRNA, MMDiff2, motifbreakR, MotIV, oligoClasses, OTUbase, Pbase, pdInfoBuilder, phyloseq, podkat, polyester, proBAMr, procoil, ProteomicsAnnotationHubData, Pviz, qrqc, QuasR, r3Cseq, Rcpi, REDseq, Repitools, rGADEM, RNAprobR, Rolexa, Rqc, rSFFreader, rtracklayer, SeqArray, seqPattern, seqplots, SGSeq, SNPhood, soGGi, SomaticSignatures, sscu, synapter, TarSeqQC, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: annotate, AnnotationHub, CSAR, exomeCopy, GenomicFiles, GenomicRanges, genoset, ggtree, methylumi, microRNA, MiRaGE, rpx, rTRM, XVector Package: biosvd Version: 2.8.0 Depends: R (>= 3.1.0) Imports: BiocGenerics, Biobase, methods, grid, graphics, NMF License: Artistic-2.0 MD5sum: db33db47bf34b9644d7d272b7676d6e2 NeedsCompilation: no Title: Package for high-throughput data processing, outlier detection, noise removal and dynamic modeling Description: The biosvd package contains functions to reduce the input data set from the feature x assay space to the reduced diagonalized eigenfeature x eigenassay space, with the eigenfeatures and eigenassays unique orthonormal superpositions of the features and assays, respectively. Results of SVD applied to the data can subsequently be inspected based on generated graphs, such as a heatmap of the eigenfeature x assay matrix and a bar plot with the eigenexpression fractions of all eigenfeatures. These graphs aid in deciding which eigenfeatures and eigenassays to filter out (i.e., eigenfeatures representing steady state, noise, or experimental artifacts; or when applied to the variance in the data, eigenfeatures representing steady-scale variance). After possible removal of steady state expression, steady-scale variance, noise and experimental artifacts, and after re-applying SVD to the normalized data, a summary html report of the eigensystem is generated, containing among others polar plots of the assays and features, a table with the list of features sortable according to their coordinates, radius and phase in the polar plot, and a visualization of the data sorted according to the two selected eigenfeatures and eigenassays with colored feature/assay annotation information when provided. This gives a global picture of the dynamics of expression/intensity levels, in which individual features and assays are classified in groups of similar regulation and function or similar cellular state and biological phenotype. biocViews: TimeCourse, Visualization Author: Anneleen Daemen , Matthew Brauer Maintainer: Anneleen Daemen , Matthew Brauer source.ver: src/contrib/biosvd_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biosvd_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biosvd_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/biosvd_2.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biosvd_2.8.0.tgz vignettes: vignettes/biosvd/inst/doc/biosvd.pdf vignetteTitles: biosvd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/biosvd/inst/doc/biosvd.R Package: biovizBase Version: 1.20.0 Depends: R (>= 2.10), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.9.25), 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 (>= 1.3.8) Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer, EnsDb.Hsapiens.v75, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 9b7892163aa01ea927ab3eb287b304ba 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 source.ver: src/contrib/biovizBase_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/biovizBase_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/biovizBase_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/biovizBase_1.17.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/biovizBase_1.20.0.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, ggbio, Gviz, Pviz, qrqc, Rqc suggestsMe: CINdex, derfinder, derfinderPlot, R3CPET, regionReport Package: BiRewire Version: 3.2.1 Depends: igraph, slam, tsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 7297692268265a7ba034d96d652424b0 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. 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 source.ver: src/contrib/BiRewire_3.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiRewire_3.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/BiRewire_3.2.1.zip mac.binary.ver: bin/macosx/contrib/3.3/BiRewire_2.3.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiRewire_3.2.1.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 Package: birta Version: 1.16.0 Depends: limma, MASS, R(>= 2.10), Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: dfb8410593c17c052a8db307a570339e NeedsCompilation: yes Title: Bayesian Inference of Regulation of Transcriptional Activity Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. birta (Bayesian Inference of Regulation of Transcriptional Activity) uses the regulatory networks of TFs and miRNAs together with mRNA and miRNA expression data to predict switches in regulatory activity between two conditions. A Bayesian network is used to model the regulatory structure and Markov-Chain-Monte-Carlo is applied to sample the activity states. biocViews: Microarray, Sequencing, GeneExpression, Transcription, GraphAndNetwork Author: Benedikt Zacher, Khalid Abnaof, Stephan Gade, Erfan Younesi, Achim Tresch, Holger Froehlich Maintainer: Benedikt Zacher , Holger Froehlich source.ver: src/contrib/birta_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/birta_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/birta_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/birta_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/birta_1.16.0.tgz vignettes: vignettes/birta/inst/doc/birta.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birta/inst/doc/birta.R Package: birte Version: 1.8.1 Depends: R(>= 3.0.0), RcppArmadillo (>= 0.3.6.1), Rcpp Imports: MASS, limma(>= 3.22.0), glmnet, Biobase, nem LinkingTo: RcppArmadillo, Rcpp Suggests: knitr Enhances: Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: 1c3846c044e07147c502a8ce73f4101f NeedsCompilation: yes Title: Bayesian Inference of Regulatory Influence on Expression (biRte) Description: Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. biRte uses regulatory networks of TFs, miRNAs and possibly other factors, together with mRNA, miRNA and other available expression data to predict the relative influence of a regulator on the expression of its target genes. Inference is done in a Bayesian modeling framework using Markov-Chain-Monte-Carlo. A special feature is the possibility for follow-up network reverse engineering between active regulators. biocViews: Microarray, Sequencing, GeneExpression, Transcription, Network, Bayesian, Regression, NetworkInference Author: Holger Froehlich, contributions by Benedikt Zacher Maintainer: Holger Froehlich SystemRequirements: BLAS, LAPACK VignetteBuilder: knitr source.ver: src/contrib/birte_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/birte_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/birte_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.3/birte_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/birte_1.8.1.tgz vignettes: vignettes/birte/inst/doc/birte.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/birte/inst/doc/birte.R Package: BiSeq Version: 1.12.0 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: 159a4e99230bf4292909aff59e1eb78b 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 source.ver: src/contrib/BiSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BiSeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BiSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BiSeq_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BiSeq_1.12.0.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 importsMe: M3D Package: BitSeq Version: 1.16.0 Depends: Rsamtools, zlibbioc Imports: S4Vectors, IRanges LinkingTo: Rsamtools (>= 1.19.38), zlibbioc Suggests: edgeR, DESeq, BiocStyle License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 3f1f095e2677a1b9a87909cd2472f5a5 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: GeneExpression, DifferentialExpression, Sequencing, RNASeq, Bayesian, AlternativeSplicing, DifferentialSplicing, Transcription Author: Peter Glaus, Antti Honkela and Magnus Rattray Maintainer: Antti Honkela , Panagiotis Papastamoulis source.ver: src/contrib/BitSeq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BitSeq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BitSeq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BitSeq_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BitSeq_1.16.0.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 Package: blima Version: 1.6.0 Depends: R(>= 3.0.0) Imports: beadarray(>= 2.0.0), Biobase(>= 2.0.0), BiocGenerics, grDevices, stats, graphics Suggests: xtable, blimaTestingData, BiocStyle, illuminaHumanv4.db, lumi License: GPL-3 MD5sum: 664059e082b4f4088b2813df737e75b9 NeedsCompilation: no Title: Package 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 Author: Vojtech Kulvait Maintainer: Vojtech Kulvait URL: https://bitbucket.org/kulvait/blima source.ver: src/contrib/blima_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/blima_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/blima_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/blima_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/blima_1.6.0.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 Package: BRAIN Version: 1.18.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: ac93ec72b6e8b81df8e30b030f654bf2 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: MassSpectrometry, Proteomics Author: Piotr Dittwald, with contributions of Dirk Valkenborg and Jurgen Claesen Maintainer: Piotr Dittwald source.ver: src/contrib/BRAIN_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BRAIN_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BRAIN_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BRAIN_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BRAIN_1.18.0.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 Package: BrainStars Version: 1.16.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: 6da3b2b2fc968a3589fc1dc4d506ba7d 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 source.ver: src/contrib/BrainStars_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BrainStars_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BrainStars_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BrainStars_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BrainStars_1.16.0.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 Package: bridge Version: 1.36.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: b3fd5446772236ebd4b8f45781916197 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 source.ver: src/contrib/bridge_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bridge_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bridge_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/bridge_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bridge_1.36.0.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 Package: BridgeDbR Version: 1.6.0 Depends: R (>= 2.0.0), rJava Imports: RCurl License: AGPL-3 MD5sum: a335c891da36b1c23a8c837f596dbf2e NeedsCompilation: no Title: Code for using BridgeDb identifier mapping framework from within R Description: Use BridgeDb functions and load identifier mapping databases in R biocViews: Software, Annotation Author: Christ Leemans , Egon Willighagen , Anwesha Bohler Maintainer: Anwesha Bohler URL: https://github.com/bridgedb/BridgeDb, https://github.com/BiGCAT-UM/bridgedb-r BugReports: https://github.com/BiGCAT-UM/bridgedb-r/issues source.ver: src/contrib/BridgeDbR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BridgeDbR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BridgeDbR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BridgeDbR_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BridgeDbR_1.6.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.pdf vignetteTitles: tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BridgeDbR/inst/doc/tutorial.R Package: BrowserViz Version: 1.4.0 Depends: R (>= 3.2.1), jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: 071bc7f48b5cc546e50f4f2e833fd19b 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 source.ver: src/contrib/BrowserViz_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/BrowserViz_1.1.2.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.pdf vignetteTitles: BrowserViz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserViz/inst/doc/BrowserViz.R dependsOnMe: BrowserVizDemo, RCyjs Package: BrowserVizDemo Version: 1.4.0 Depends: R (>= 3.2.3), BrowserViz, Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: ea9e4839da1aa07b5f1bbcb51c8ecd5d NeedsCompilation: no Title: BrowserVizDemo: How to subclass BrowserViz Description: A BrowserViz subclassing example, xy plotting in the browser using d3. biocViews: Visualization, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/BrowserVizDemo_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/BrowserVizDemo_1.1.0.tgz vignettes: vignettes/BrowserVizDemo/inst/doc/BrowserVizDemo.pdf vignetteTitles: BrowserVizDemo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BrowserVizDemo/inst/doc/BrowserVizDemo.R Package: BSgenome Version: 1.40.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.9.36), IRanges (>= 2.1.33), GenomeInfoDb (>= 1.3.19), GenomicRanges (>= 1.23.15), Biostrings (>= 2.35.3), rtracklayer (>= 1.25.8) Imports: methods, utils, stats, BiocGenerics, S4Vectors, IRanges, XVector, GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, rtracklayer Suggests: BiocInstaller, Biobase, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.UCSC.hg38, BSgenome.Hsapiens.UCSC.hg38.masked, BSgenome.Mmusculus.UCSC.mm10, BSgenome.Rnorvegicus.UCSC.rn5, TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, SNPlocs.Hsapiens.dbSNP141.GRCh38, XtraSNPlocs.Hsapiens.dbSNP141.GRCh38, hgu95av2probe, RUnit License: Artistic-2.0 MD5sum: 52fcebf91ba05468fa6c8320f45a0876 NeedsCompilation: no Title: Infrastructure for Biostrings-based genome data packages and support for efficient SNP representation Description: Infrastructure shared by all the Biostrings-based genome data packages biocViews: Genetics, Infrastructure, DataRepresentation, SequenceMatching, Annotation, SNP Author: Hervé Pagès Maintainer: H. Pagès source.ver: src/contrib/BSgenome_1.40.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/BSgenome_1.40.1.zip win64.binary.ver: bin/windows64/contrib/3.3/BSgenome_1.40.1.zip mac.binary.ver: bin/macosx/contrib/3.3/BSgenome_1.37.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BSgenome_1.40.1.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: CAGEr, cleanUpdTSeq, GOTHiC, htSeqTools, MEDIPS, motifRG, REDseq, regioneR, rGADEM importsMe: AllelicImbalance, BEAT, charm, ChIPpeakAnno, cobindR, CRISPRseek, diffHic, genomation, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, MethylSeekR, MMDiff2, motifbreakR, PING, podkat, QuasR, R453Plus1Toolbox, regioneR, Repitools, seqplots, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools suggestsMe: Biostrings, biovizBase, chipseq, easyRNASeq, GeneRegionScan, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, genoset, metaseqR, MiRaGE, oneChannelGUI, QDNAseq, recoup, rtracklayer, spliceR, waveTiling Package: bsseq Version: 1.8.2 Depends: R (>= 3.3), methods, BiocGenerics, GenomicRanges (>= 1.23.7), SummarizedExperiment (>= 0.1.1), parallel, limma Imports: IRanges (>= 2.5.17), GenomeInfoDb, scales, stats, graphics, Biobase, locfit, gtools, data.table, S4Vectors, R.utils (>= 2.0.0), matrixStats (>= 0.50.0), permute Suggests: RUnit, bsseqData, BiocStyle, knitr License: Artistic-2.0 MD5sum: 7831fd66d779b127ffe0a6370ff02e53 NeedsCompilation: no 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 [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/bsseq VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/bsseq/issues source.ver: src/contrib/bsseq_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/bsseq_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/bsseq_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/bsseq_1.5.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bsseq_1.8.2.tgz vignettes: vignettes/bsseq/inst/doc/bsseq_analysis.pdf, vignettes/bsseq/inst/doc/bsseq.pdf vignetteTitles: Analyzing WGBS with bsseq, The 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: DSS Package: BubbleTree Version: 2.2.2 Depends: R (>= 3.2.1), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.7.5), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, grid, gtable, gridExtra, biovizBase, rainbow, e1071 Suggests: methods, knitr, rmarkdown License: LGPL (>= 3) MD5sum: e1a833dd2957580972e594d2ac950b40 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 (Publication Pending). biocViews: CopyNumberVariation, Software, Sequencing, Coverage Author: Wei Zhu , Michael Kuziora , Todd Creasy , Brandon Higgs Maintainer: Todd Creasy , Wei Zhu VignetteBuilder: knitr source.ver: src/contrib/BubbleTree_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/BubbleTree_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/BubbleTree_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/BubbleTree_1.99.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BubbleTree_2.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.R htmlDocs: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html htmlTitles: BubbleTree Tutorial Package: BufferedMatrix Version: 1.36.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 8042a89d030ead1ca93f00943e30f0fe 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: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad URL: https://github.com/bmbolstad/BufferedMatrix source.ver: src/contrib/BufferedMatrix_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BufferedMatrix_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BufferedMatrix_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BufferedMatrix_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BufferedMatrix_1.36.0.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 Package: BufferedMatrixMethods Version: 1.36.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: f0f4601ef19552e1c6b687a69d4df993 NeedsCompilation: yes Title: Microarray Data related methods that utlize BufferedMatrix objects Description: Microarray analysis methods that use BufferedMatrix objects biocViews: Infrastructure Author: B. M. Bolstad Maintainer: B. M. Bolstad URL: https://github.bom/bmbolstad/BufferedMatrixMethods source.ver: src/contrib/BufferedMatrixMethods_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BufferedMatrixMethods_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BufferedMatrixMethods_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BufferedMatrixMethods_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BufferedMatrixMethods_1.36.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bumphunter Version: 1.12.0 Depends: R (>= 2.10), S4Vectors (>= 0.9.25), IRanges (>= 2.3.23), GenomeInfoDb, GenomicRanges, foreach, iterators, methods, parallel, locfit Imports: matrixStats, limma, doRNG, BiocGenerics, utils, GenomicFeatures, AnnotationDbi Suggests: testthat, RUnit, doParallel, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: b303ab3afeef4bc2015807d181312c7a NeedsCompilation: no Title: Bump Hunter Description: Tools for finding bumps in genomic data biocViews: DNAMethylation, Epigenetics, Infrastructure, MultipleComparison 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 source.ver: src/contrib/bumphunter_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/bumphunter_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/bumphunter_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/bumphunter_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/bumphunter_1.12.0.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: derfinder suggestsMe: derfinderPlot, epivizrData, regionReport Package: BUS Version: 1.28.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: dce613f0aa9aaa9f23339c1151634707 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 source.ver: src/contrib/BUS_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/BUS_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/BUS_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/BUS_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/BUS_1.28.0.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 Package: CAFE Version: 1.8.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: af49336b50affdabc1476e57ba8f542b 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 source.ver: src/contrib/CAFE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAFE_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAFE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CAFE_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAFE_1.8.0.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 Package: CAGEr Version: 1.14.0 Depends: methods, R (>= 2.15.0), BSgenome Imports: utils, Rsamtools, GenomicRanges (>= 1.23.16), IRanges (>= 2.5.27), data.table, beanplot, rtracklayer, som, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, FANTOM3and4CAGE Enhances: parallel License: GPL-3 MD5sum: d880b0afd8a5a1db5d62a75a82a88aae 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, Department of Biology, University of Bergen, Norway Maintainer: Vanja Haberle source.ver: src/contrib/CAGEr_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAGEr_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAGEr_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CAGEr_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAGEr_1.14.0.tgz vignettes: vignettes/CAGEr/inst/doc/CAGEr.pdf vignetteTitles: CAGEr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAGEr/inst/doc/CAGEr.R suggestsMe: seqPattern Package: CALIB Version: 1.38.0 Depends: R (>= 2.10), limma, methods Imports: limma, methods, graphics, stats, utils License: LGPL Archs: i386, x64 MD5sum: 7356996dc0408f453baa7f2992b9bcf6 NeedsCompilation: yes Title: Calibration model for estimating absolute expression levels from microarray data Description: This package contains functions for normalizing spotted microarray data, based on a physically motivated calibration model. The model parameters and error distributions are estimated from external control spikes. biocViews: Microarray,TwoChannel,Preprocessing Author: Hui Zhao, Kristof Engelen, Bart De Moor and Kathleen Marchal Maintainer: Hui Zhao source.ver: src/contrib/CALIB_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CALIB_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CALIB_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CALIB_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CALIB_1.38.0.tgz vignettes: vignettes/CALIB/inst/doc/quickstart.pdf vignetteTitles: CALIB Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CALIB/inst/doc/quickstart.R Package: CAMERA Version: 1.28.0 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: 9f42d5dbf167c3a03d3bda4cf52f27cd 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, EIC correlation based tagging of unknown adducts and fragments biocViews: MassSpectrometry, Metabolomics Author: Carsten Kuhl, Ralf Tautenhahn, Steffen Neumann {ckuhl|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 source.ver: src/contrib/CAMERA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAMERA_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAMERA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CAMERA_1.25.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAMERA_1.28.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf vignetteTitles: Molecule Identification with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CAMERA/inst/doc/CAMERA.R dependsOnMe: flagme, MAIT, metaMS importsMe: metaX suggestsMe: RMassBank, ropls Package: canceR Version: 1.4.0 Depends: R (>= 3.3), tcltk, tcltk2, cgdsr Imports: GSEABase, GSEAlm, tkrplot, geNetClassifier, RUnit, Formula, rpart, survival, Biobase, phenoTest, circlize, plyr, graphics, stats, utils Suggests: testthat (>= 0.10.0), R.rsp License: GPL-2 MD5sum: 57332260edfc45ab354d62eb4e994bb6 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 source.ver: src/contrib/canceR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/canceR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/canceR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/canceR_1.1.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/canceR_1.4.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.pdf vignetteTitles: canceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: cancerclass Version: 1.16.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: 686b24c51df5969e56970e6b1a88d103 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 source.ver: src/contrib/cancerclass_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cancerclass_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cancerclass_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cancerclass_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cancerclass_1.16.0.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 Package: CancerMutationAnalysis Version: 1.14.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 6648fca18a3131fac2e7f23d8e51205f 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 source.ver: src/contrib/CancerMutationAnalysis_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CancerMutationAnalysis_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CancerMutationAnalysis_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CancerMutationAnalysis_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CancerMutationAnalysis_1.14.0.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 Package: CAnD Version: 1.4.0 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: d498bb1c4562ffbe02961bb67f5fda08 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 Maintainer: Caitlin McHugh source.ver: src/contrib/CAnD_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CAnD_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CAnD_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CAnD_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CAnD_1.4.0.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 Package: caOmicsV Version: 1.2.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: 20ada012fed55f17b3be97f6a336acf5 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: Visualization, Network, RNASeq Author: Henry Zhang Maintainer: Henry Zhang source.ver: src/contrib/caOmicsV_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/caOmicsV_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/caOmicsV_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/caOmicsV_0.99.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/caOmicsV_1.2.0.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 Package: Cardinal Version: 1.4.0 Depends: BiocGenerics, Biobase, graphics, methods, stats, ProtGenerics Imports: grid, irlba, lattice, signal, sp, stats4, utils Suggests: BiocStyle, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: cd587d0b37affdfdbe536130d34e7a71 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, Normalization, MassSpectrometry, ImagingMassSpectrometry, Clustering, Classification Author: Kyle D. Bemis Maintainer: Kyle D. Bemis URL: http://www.cardinalmsi.org source.ver: src/contrib/Cardinal_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Cardinal_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Cardinal_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Cardinal_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Cardinal_1.4.0.tgz vignettes: vignettes/Cardinal/inst/doc/Cardinal-development.pdf, vignettes/Cardinal/inst/doc/Cardinal-walkthrough.pdf vignetteTitles: Cardinal design and development, Cardinal: Analytic tools for mass spectrometry imaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Cardinal/inst/doc/Cardinal-development.R, vignettes/Cardinal/inst/doc/Cardinal-walkthrough.R Package: casper Version: 2.6.0 Depends: R (>= 2.14.1), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics, 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: cd1293c042e2fc8ca50c6f2761c13d91 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: GeneExpression, DifferentialExpression, Transcription, RNASeq, Sequencing Author: David Rossell, Camille Stephan-Otto, Manuel Kroiss, Miranda Stobbe, Victor Pena Maintainer: David Rossell source.ver: src/contrib/casper_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/casper_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/casper_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/casper_2.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/casper_2.6.0.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 Package: Category Version: 2.38.0 Depends: methods, stats4, BiocGenerics, AnnotationDbi, Biobase, Matrix Imports: utils, stats, graph, RBGL, GSEABase, genefilter, annotate, RSQLite Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGG.db, SNPchip, geneplotter, limma, lattice, RUnit, org.Sc.sgd.db, GOstats, GO.db License: Artistic-2.0 MD5sum: 4562ae7a6d4d5d52ee883c71ab2a81f8 NeedsCompilation: no Title: Category Analysis Description: A collection of tools for performing category analysis. biocViews: Annotation, GO, Pathways, GeneSetEnrichment Author: R. Gentleman with contributions from S. Falcon and D.Sarkar Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/Category_2.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Category_2.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Category_2.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Category_2.35.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Category_2.38.0.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, meshr, PCpheno importsMe: categoryCompare, cellHTS2, eisa, gCMAP, GOstats, interactiveDisplay, PCpheno, phenoTest, ppiStats, RDAVIDWebService suggestsMe: BiocCaseStudies, cellHTS, miRLAB, MmPalateMiRNA, qpgraph, RnBeads Package: categoryCompare Version: 1.16.2 Depends: R (>= 2.10), Biobase, BiocGenerics (>= 0.13.8), Imports: AnnotationDbi, hwriter, GSEABase, Category (>= 2.33.1), GOstats, annotate, colorspace, graph, RCytoscape (>= 1.5.11) Suggests: knitr, methods, GO.db, KEGG.db, estrogen, org.Hs.eg.db, hgu95av2.db, limma, affy, genefilter License: GPL-2 MD5sum: ab3d2bcafc4f3411b09d270bcf304e77 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 (>= 2.8.0) (if used for visualization of results, heavily suggested), CytoscapeRPC plugin (>= 1.8) VignetteBuilder: knitr BugReports: https://github.com/rmflight/categoryCompare/issues source.ver: src/contrib/categoryCompare_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/categoryCompare_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.3/categoryCompare_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.3/categoryCompare_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/categoryCompare_1.16.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.R htmlDocs: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html htmlTitles: categoryCompare: High-throughput data meta-analysis using gene annotations Package: CausalR Version: 1.4.3 Depends: R (>= 3.2) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: a16f8b38794d11c3e10b97b97fc3cd9a NeedsCompilation: no Title: Causal Reasoning Methods Description: Causal reasoning methods for biological networks, to enable regulator prediction and reconstruction of regulatory networks from high dimensional data. biocViews: GraphAndNetwork, Network Author: Glyn Bradley, Steven Barrett, David Wiley, Bhushan Bonde, Peter Woollard, Chirag Mistry, David Riley, Mark Pipe Maintainer: Glyn Bradley , Steven Barrett , Bhushan Bonde VignetteBuilder: knitr source.ver: src/contrib/CausalR_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/CausalR_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/CausalR_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/CausalR_0.99.12.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CausalR_1.4.3.tgz vignettes: vignettes/CausalR/inst/doc/CausalR.pdf vignetteTitles: CausalR : an R Package for causal reasoning on networks hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CausalR/inst/doc/CausalR.R Package: ccrepe Version: 1.8.2 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: e4359ae5b1d41cdaaec6c2cccd5ad7db 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: Statistics, Metagenomics, Bioinformatics, Software Author: Emma Schwager ,Craig Bielski, George Weingart Maintainer: Emma Schwager ,Craig Bielski, George Weingart VignetteBuilder: knitr source.ver: src/contrib/ccrepe_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ccrepe_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ccrepe_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ccrepe_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ccrepe_1.8.2.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 Package: cellGrowth Version: 1.16.0 Depends: R (>= 2.12.0), locfit (>= 1.5-4) Imports: lattice License: Artistic-2.0 MD5sum: 8da23b0d9ec128858cb5f5ffdfb89863 NeedsCompilation: no Title: Fitting cell population growth models Description: This package provides functionalities for the fitting of cell population growth models on experimental OD curves. biocViews: CellBasedAssays, MicrotitrePlateAssay, DataImport, Visualization, TimeCourse Author: Julien Gagneur , Andreas Neudecker Maintainer: Julien Gagneur source.ver: src/contrib/cellGrowth_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellGrowth_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cellGrowth_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cellGrowth_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellGrowth_1.16.0.tgz vignettes: vignettes/cellGrowth/inst/doc/cellGrowth.pdf vignetteTitles: Overview of the cellGrowth package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellGrowth/inst/doc/cellGrowth.R Package: cellHTS Version: 1.42.0 Depends: R (>= 2.10), grid Imports: Biobase, prada, RColorBrewer, genefilter Suggests: Category, ggplot2, GO.db, gridBase, vsn (>= 2.0.35) License: Artistic-2.0 MD5sum: 4160a7dadaaf393644f2ced59e8de9e9 NeedsCompilation: no Title: Analysis of cell-based screens Description: Analysis of cell-based RNA interference screens. biocViews: CellBasedAssays, Visualization Author: Wolfgang Huber , Ligia Bras , Michael Boutros Maintainer: Andrzej Oleś URL: http://www.dkfz.de/signaling, http://www.ebi.ac.uk/huber PackageStatus: Deprecated source.ver: src/contrib/cellHTS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellHTS_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cellHTS_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cellHTS_1.39.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellHTS_1.42.0.tgz vignettes: vignettes/cellHTS/inst/doc/cellhts.pdf, vignettes/cellHTS/inst/doc/twoChannels.pdf, vignettes/cellHTS/inst/doc/twoWay.pdf vignetteTitles: Main vignette: End-to-end analysis of cell-based screens, Supplement: multi-channel assays, Supplement: two-way assays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellHTS/inst/doc/cellhts.R, vignettes/cellHTS/inst/doc/twoChannels.R, vignettes/cellHTS/inst/doc/twoWay.R suggestsMe: prada Package: cellHTS2 Version: 2.36.0 Depends: R (>= 2.10), RColorBrewer, Biobase, methods, genefilter, splots, vsn, hwriter, locfit, grid Imports: prada, GSEABase, Category, stats4 Suggests: ggplot2 License: Artistic-2.0 MD5sum: 410d124bb294d9ba1b28c8712a8124c5 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: 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 source.ver: src/contrib/cellHTS2_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellHTS2_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cellHTS2_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cellHTS2_2.33.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellHTS2_2.36.0.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: coRNAi, imageHTS, staRank importsMe: gespeR, HTSanalyzeR, RNAinteract suggestsMe: bioassayR Package: cellity Version: 1.0.2 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: d2894d6114c2cb19f33838f01a044460 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: RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, SupportVectorMachine Author: Tomislav Illicic, Davis McCarthy Maintainer: Tomislav Ilicic VignetteBuilder: knitr source.ver: src/contrib/cellity_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellity_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/cellity_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellity_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cellity/inst/doc/cellity_vignette.R htmlDocs: vignettes/cellity/inst/doc/cellity_vignette.html htmlTitles: An introduction to the cellity package Package: CellNOptR Version: 1.18.0 Depends: R (>= 2.15.0), RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML Suggests: RUnit, BiocGenerics, igraph License: GPL-3 Archs: i386, x64 MD5sum: e0be2f1c35cc218a5405b82c0762e41c 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, TimeCourse Author: T.Cokelaer, F.Eduati, A.MacNamara, S.Schrier, C.Terfve Maintainer: T.Cokelaer SystemRequirements: Graphviz version >= 2.2 source.ver: src/contrib/CellNOptR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CellNOptR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CellNOptR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CellNOptR_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CellNOptR_1.18.0.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 Package: cellTree Version: 1.2.2 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: 02368d99e0561c21ff018033f275da06 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: 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 source.ver: src/contrib/cellTree_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/cellTree_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/cellTree_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cellTree_1.2.2.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 Package: CexoR Version: 1.10.0 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: 42c27c36fe737f42f96271454d9da043 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 for overlapping peak-pairs across biological replicates is estimated using the package 'idr'. biocViews: Transcription, Genetics, Sequencing Author: Pedro Madrigal Maintainer: Pedro Madrigal source.ver: src/contrib/CexoR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CexoR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CexoR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CexoR_1.7.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CexoR_1.10.0.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 Package: CFAssay Version: 1.6.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: c48e408d97fd0cec9de284cdb1e374a9 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, Regression, Survival Author: Herbert Braselmann Maintainer: Herbert Braselmann source.ver: src/contrib/CFAssay_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CFAssay_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CFAssay_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CFAssay_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CFAssay_1.6.0.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 Package: CGEN Version: 3.8.0 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: 839a253a0d04ea2f358f65419fcb5892 NeedsCompilation: yes 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 and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/CGEN_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGEN_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGEN_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CGEN_3.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGEN_3.8.0.tgz vignettes: vignettes/CGEN/inst/doc/vignette_GxE.pdf, vignettes/CGEN/inst/doc/vignette.pdf vignetteTitles: CGEN Vignette, CGEN Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CGEN/inst/doc/vignette_GxE.R, vignettes/CGEN/inst/doc/vignette.R Package: CGHbase Version: 1.32.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: 7841236eec76bb5af0c0045e13a636d6 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 source.ver: src/contrib/CGHbase_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHbase_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHbase_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CGHbase_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHbase_1.32.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak, sigaR importsMe: CGHnormaliter, plrs, QDNAseq Package: CGHcall Version: 2.34.1 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: ac2423fcb77f41e725a1647c611c987f 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 source.ver: src/contrib/CGHcall_2.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHcall_2.34.1.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHcall_2.34.1.zip mac.binary.ver: bin/macosx/contrib/3.3/CGHcall_2.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHcall_2.34.1.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, focalCall, GeneBreak importsMe: CGHnormaliter, QDNAseq Package: cghMCR Version: 1.30.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 2c414688ba8e6f4267e67fc972da45ac 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 source.ver: src/contrib/cghMCR_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cghMCR_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cghMCR_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cghMCR_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cghMCR_1.30.0.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 Package: CGHnormaliter Version: 1.26.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: 456bdea2034f7fa6e25673fb0e9bbfdb 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 source.ver: src/contrib/CGHnormaliter_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHnormaliter_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHnormaliter_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CGHnormaliter_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHnormaliter_1.26.0.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 Package: CGHregions Version: 1.30.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: f956828cf1129214cbc94820687c2ace 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 source.ver: src/contrib/CGHregions_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CGHregions_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CGHregions_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CGHregions_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CGHregions_1.30.0.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 Package: ChAMP Version: 1.10.0 Depends: R (>= 3.2), minfi, ChAMPdata, Illumina450ProbeVariants.db Imports: sva, IlluminaHumanMethylation450kmanifest, limma,RPMM, DNAcopy, preprocessCore, impute, marray, wateRmelon, plyr, GenomicRanges,RefFreeEWAS,qvalue,isva,doParallel,bumphunter,quadprog License: GPL-3 MD5sum: 365f49cddfa844d1471ef8e46604d0c8 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. In addition there is a method to help calculate hmC using BS and oxBS samples. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber, DNAMethylation Author: Tiffany Morris [cre, aut], Yuan Tian [aut], Lee Stirling [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Yuan Tian, Tiffany Morris source.ver: src/contrib/ChAMP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChAMP_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChAMP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ChAMP_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChAMP_1.10.0.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.pdf vignetteTitles: The \Rpackage{ChAMP} Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChAMP/inst/doc/ChAMP.R Package: charm Version: 2.18.0 Depends: R (>= 2.14.0), Biobase, SQN, fields, RColorBrewer, genefilter Imports: BSgenome, Biobase, oligo (>= 1.11.31), oligoClasses(>= 1.17.39), ff, preprocessCore, methods, stats, Biostrings, IRanges, siggenes, nor1mix, gtools, grDevices, graphics, utils, limma, parallel, sva(>= 3.1.2) Suggests: charmData, BSgenome.Hsapiens.UCSC.hg18, corpcor License: LGPL (>= 2) MD5sum: 3afbfc4ed94c20463ba53418b8b320c8 NeedsCompilation: no Title: Analysis of DNA methylation data from CHARM microarrays Description: This package implements analysis tools for DNA methylation data generated using Nimblegen microarrays and the McrBC protocol. It finds differentially methylated regions between samples, calculates percentage methylation estimates and includes array quality assessment tools. biocViews: Microarray, DNAMethylation Author: Martin Aryee, Peter Murakami, Harris Jaffee, Rafael Irizarry Maintainer: Peter Murakami source.ver: src/contrib/charm_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/charm_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/charm_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/charm_2.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/charm_2.18.0.tgz vignettes: vignettes/charm/inst/doc/charm.pdf vignetteTitles: charm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/charm/inst/doc/charm.R Package: ChemmineOB Version: 1.10.2 Depends: R (>= 2.15.1), methods Imports: BiocGenerics, zlibbioc, Rcpp (>= 0.11.0) LinkingTo: BH, Rcpp Suggests: ChemmineR, BiocStyle, knitr, knitcitations, knitrBootstrap Enhances: ChemmineR (>= 2.13.0) License: file LICENSE Archs: i386, x64 MD5sum: 02f39b80ca8b767d82c50497992df6ca 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 Author: Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/ChemmineOB SystemRequirements: OpenBabel (>= 2.3.1) with headers (http://openbabel.org). VignetteBuilder: knitr source.ver: src/contrib/ChemmineOB_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChemmineOB_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ChemmineOB_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ChemmineOB_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChemmineOB_1.10.2.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ChemmineOB/inst/doc/ChemmineOB.R htmlDocs: vignettes/ChemmineOB/inst/doc/ChemmineOB.html htmlTitles: ChemmineOB suggestsMe: ChemmineR Package: ChemmineR Version: 2.24.2 Depends: R (>= 2.10.0), methods Imports: rjson, graphics, stats, RCurl, DBI, digest, BiocGenerics, Rcpp (>= 0.11.0), ggplot2 LinkingTo: Rcpp, BH Suggests: RSQLite, scatterplot3d, gplots, fmcsR, snow, RPostgreSQL, BiocStyle, knitr, knitcitations, knitrBootstrap, ChemmineOB (>= 1.3.8), ChemmineDrugs, grid, gridExtra, png Enhances: ChemmineOB License: Artistic-2.0 Archs: i386, x64 MD5sum: 6a8f2c1725baa2eb2b8b8501b32f1be7 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 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 source.ver: src/contrib/ChemmineR_2.24.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChemmineR_2.24.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ChemmineR_2.24.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ChemmineR_2.21.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChemmineR_2.24.2.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChemmineR/inst/doc/ChemmineR.R htmlDocs: vignettes/ChemmineR/inst/doc/ChemmineR.html htmlTitles: ChemmineR dependsOnMe: eiR, fmcsR importsMe: bioassayR, eiR, fmcsR, Rchemcpp, Rcpi suggestsMe: ChemmineOB Package: Chicago Version: 1.0.4 Depends: R (>= 3.2), data.table Imports: matrixStats, MASS, Hmisc, Delaporte, methods Suggests: argparser, BiocStyle, knitr, rmarkdown, PCHiCdata, testthat, Rsamtools, GenomicInteractions, GenomicRanges, IRanges, AnnotationHub License: Artistic-2.0 MD5sum: 6cb2c93a12a5b9321b6c91ec2a68c5cc 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 source.ver: src/contrib/Chicago_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/Chicago_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/Chicago_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Chicago_1.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Chicago/inst/doc/Chicago.R htmlDocs: vignettes/Chicago/inst/doc/Chicago.html htmlTitles: CHiCAGO Vignette Package: chimera Version: 1.14.0 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 Archs: i386, x64 MD5sum: cd773d185f2a7bb5b8207b3d20feda4a 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 source.ver: src/contrib/chimera_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chimera_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chimera_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/chimera_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chimera_1.14.0.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 dependsOnMe: oneChannelGUI Package: ChIPComp Version: 1.2.0 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: 6c2453550e5f8ed0d3a304983bfb4bef 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 source.ver: src/contrib/ChIPComp_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPComp_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPComp_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ChIPComp_0.99.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPComp_1.2.0.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 Package: chipenrich Version: 1.10.0 Depends: R (>= 2.15.1) Imports: chipenrich.data, methods, GenomicRanges (>= 1.10.0), IRanges (>= 1.16.0), mgcv, plyr (>= 1.7.0), lattice, latticeExtra, grid, stringr (>= 0.6), rms Suggests: testthat Enhances: parallel License: GPL-3 MD5sum: d328dc0cd7ed6954a799e28a3fff3930 NeedsCompilation: no Title: Gene Set Enrichment For ChIP-seq Peak Data Description: ChIP-Enrich performs 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: Software, Bioinformatics, Enrichment, GeneSetEnrichment Author: Ryan P. Welch [aut, cre, cph], Chee Lee [aut, cre], Raymond G. Cavalcante [aut, cre], Laura J. Scott [ths], Maureen A. Sartor [ths] Maintainer: Raymond G. Cavalcante , Chee Lee source.ver: src/contrib/chipenrich_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chipenrich_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chipenrich_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/chipenrich_1.7.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chipenrich_1.10.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich.pdf vignetteTitles: ChIP-Enrich Vignette/Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chipenrich/inst/doc/chipenrich.R Package: ChIPpeakAnno Version: 3.6.5 Depends: R (>= 3.2), methods, grid, IRanges (>= 2.5.27), Biostrings, GenomicRanges (>= 1.23.16), S4Vectors (>= 0.9.25), VennDiagram Imports: BiocGenerics (>= 0.1.0), GO.db, biomaRt, BSgenome, GenomicFeatures, GenomeInfoDb, matrixStats, AnnotationDbi, limma, multtest, RBGL, graph, BiocInstaller, stats, regioneR, DBI, ensembldb, Biobase, seqinr, idr, GenomicAlignments, SummarizedExperiment Suggests: reactome.db, BSgenome.Ecoli.NCBI.20080805, BSgenome.Hsapiens.UCSC.hg19, org.Ce.eg.db, org.Hs.eg.db, BSgenome.Celegans.UCSC.ce10, BSgenome.Drerio.UCSC.danRer7, EnsDb.Hsapiens.v75, EnsDb.Hsapiens.v79, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg38.knownGene, gplots, BiocStyle, rtracklayer, knitr, testthat, motifStack, MMDiffBamSubset License: GPL (>= 2) MD5sum: 35df9cacd4be7a2f2c8e9465c2cd72dd 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, Herve Pages, Claude Gazin, Nathan Lawson, Ryan Thompson, Simon Lin, David Lapointe and Michael Green Maintainer: Lihua Julie Zhu , Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/ChIPpeakAnno_3.6.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPpeakAnno_3.6.5.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPpeakAnno_3.6.5.zip mac.binary.ver: bin/macosx/contrib/3.3/ChIPpeakAnno_3.3.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPpeakAnno_3.6.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.R, vignettes/ChIPpeakAnno/inst/doc/pipeline.R, vignettes/ChIPpeakAnno/inst/doc/quickStart.R htmlDocs: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html, vignettes/ChIPpeakAnno/inst/doc/pipeline.html, vignettes/ChIPpeakAnno/inst/doc/quickStart.html htmlTitles: ChIPpeakAnno Vignette, ChIPpeakAnno Annotation Pipeline, ChIPpeakAnno Quick Start dependsOnMe: REDseq importsMe: DChIPRep, FunciSNP, GUIDEseq, REDseq suggestsMe: oneChannelGUI, R3CPET, RIPSeeker Package: ChIPQC Version: 1.8.9 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.hg38.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: bc5e681332e200f1855cc03d7e3136f6 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 source.ver: src/contrib/ChIPQC_1.8.9.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPQC_1.8.9.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPQC_1.8.9.zip mac.binary.ver: bin/macosx/contrib/3.3/ChIPQC_1.5.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPQC_1.8.9.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 Package: ChIPseeker Version: 1.8.9 Depends: R (>= 3.2.0) Imports: BiocGenerics, boot, AnnotationDbi, DOSE, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, grid, gridBase, gtools, methods, plotrix, dplyr, parallel, magrittr, RColorBrewer, rtracklayer, S4Vectors (>= 0.9.25), stats, TxDb.Hsapiens.UCSC.hg19.knownGene, UpSetR, utils Suggests: clusterProfiler, ReactomePA, org.Hs.eg.db, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: dfe671f4998f656f50393ec4c0213791 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 with contributions from Yun Yan, Herve Pages, Michael Kluge and Thomas Schwarzl. Maintainer: Guangchuang Yu URL: http://guangchuangyu.github.io/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ChIPseeker/issues source.ver: src/contrib/ChIPseeker_1.8.9.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPseeker_1.8.9.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPseeker_1.8.9.zip mac.binary.ver: bin/macosx/contrib/3.3/ChIPseeker_1.5.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPseeker_1.8.9.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ChIPseeker/inst/doc/ChIPseeker.R htmlDocs: vignettes/ChIPseeker/inst/doc/ChIPseeker.html htmlTitles: ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization Package: chipseq Version: 1.22.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.25), IRanges (>= 1.99.1), GenomicRanges (>= 1.17.7), 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: 5af54169b26136913b9ab31afef48734 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 source.ver: src/contrib/chipseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chipseq_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chipseq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/chipseq_1.19.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chipseq_1.22.0.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 dependsOnMe: PING importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi, transcriptR suggestsMe: GenoGAM, ggbio, oneChannelGUI Package: ChIPseqR Version: 1.26.0 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: 71135bdafd04720e7208e4b6e1b6d9bb 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 source.ver: src/contrib/ChIPseqR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPseqR_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPseqR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ChIPseqR_1.23.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPseqR_1.26.0.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 Package: ChIPsim Version: 1.26.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: 7d8e13767490f10647023ab9b169dd94 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 source.ver: src/contrib/ChIPsim_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChIPsim_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChIPsim_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ChIPsim_1.23.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPsim_1.26.0.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 Package: ChIPXpress Version: 1.14.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: c42a0dda82cbef32ee83a30d44806dc3 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 source.ver: src/contrib/ChIPXpress_1.14.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/ChIPXpress_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChIPXpress_1.14.0.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 Package: chopsticks Version: 1.36.0 Depends: R(>= 2.10.0), survival, methods Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: e4dbf98a929d13dd0cda8f14b613f82b 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/ source.ver: src/contrib/chopsticks_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chopsticks_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chopsticks_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/chopsticks_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chopsticks_1.36.0.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 Package: chroGPS Version: 1.16.0 Depends: R (>= 2.13.0), IRanges, methods, Biobase, MASS, graphics, stats, changepoint Imports: cluster, DPpackage, ICSNP Enhances: parallel, XML, rgl License: GPL (>=2.14) MD5sum: 35d7d4a7874de66c3e42930fb9e78112 NeedsCompilation: no Title: chroGPS: visualizing the epigenome Description: We provide intuitive maps to visualize the association between genetic elements, with emphasis on epigenetics. The approach is based on Multi-Dimensional Scaling. We provide several sensible distance metrics, and adjustment procedures to remove systematic biases typically observed when merging data obtained under different technologies or genetic backgrounds. biocViews: Visualization, Clustering Author: Oscar Reina, David Rossell Maintainer: Oscar Reina source.ver: src/contrib/chroGPS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chroGPS_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chroGPS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/chroGPS_1.13.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chroGPS_1.16.0.tgz vignettes: vignettes/chroGPS/inst/doc/chroGPS.pdf vignetteTitles: Manual for the chroGPS library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/chroGPS/inst/doc/chroGPS.R Package: chromDraw Version: 2.2.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 939baf2916d3e548f09d5b47b6eecb6b 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) source.ver: src/contrib/chromDraw_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chromDraw_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chromDraw_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/chromDraw_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chromDraw_2.2.0.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 Package: ChromHeatMap Version: 1.26.0 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 Suggests: ALL, hgu95av2.db License: Artistic-2.0 MD5sum: fce8de882a6ea0775b976dfb10946cd2 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 source.ver: src/contrib/ChromHeatMap_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ChromHeatMap_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ChromHeatMap_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ChromHeatMap_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ChromHeatMap_1.26.0.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 Package: chromPlot Version: 1.0.0 Depends: stats, utils, graphics, grDevices, datasets, base, biomaRt, GenomicRanges, R (>= 3.3.0) Suggests: qtl, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL (>= 2) MD5sum: b03ed9afc484ef53bec54fa322419200 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 source.ver: src/contrib/chromPlot_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/chromPlot_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/chromPlot_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/chromPlot_1.0.0.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 Package: CHRONOS Version: 1.0.3 Depends: R (>= 3.3) Imports: XML, RCurl, RBGL, parallel, foreach, doParallel, openxlsx, circlize, graph, stats, utils, grDevices, graphics, biomaRt Suggests: RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 287b84f73d956eb7973ccaa0312a7d05 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 source.ver: src/contrib/CHRONOS_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/CHRONOS_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/CHRONOS_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CHRONOS_1.0.3.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 Package: CINdex Version: 1.0.2 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: 953d3d9a9a1ef778ed8aae107e767984 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 source.ver: src/contrib/CINdex_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CINdex_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CINdex_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CINdex_1.0.2.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 Package: cisPath Version: 1.12.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: b7c464abcde0c4fb826f0ddfaf5be2a1 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 source.ver: src/contrib/cisPath_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cisPath_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cisPath_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cisPath_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cisPath_1.12.0.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 Package: ClassifyR Version: 1.6.2 Depends: R (>= 3.0.3), methods, Biobase, BiocParallel Imports: locfit, ROCR, grid Suggests: limma, edgeR, car, Rmixmod, ggplot2 (>= 2.0.0), gridExtra (>= 2.0.0), BiocStyle, pamr, sparsediscrim, PoiClaClu, curatedOvarianData, parathyroidSE, knitr, klaR, gtable, scales, e1071, rmarkdown, IRanges License: GPL-3 MD5sum: 9b69feeac88d1afbb6d3baf89ce2ac36 NeedsCompilation: no Title: A framework for two-class 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 source.ver: src/contrib/ClassifyR_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ClassifyR_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ClassifyR_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ClassifyR_1.3.15.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ClassifyR_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClassifyR/inst/doc/ClassifyR.R htmlDocs: vignettes/ClassifyR/inst/doc/ClassifyR.html htmlTitles: An Introduction to the ClassifyR Package Package: cleanUpdTSeq Version: 1.10.2 Depends: R (>= 2.15), BiocGenerics (>= 0.1.0), methods, BSgenome, BSgenome.Drerio.UCSC.danRer7, GenomicRanges, seqinr, e1071 Suggests: BiocStyle, knitr, RUnit License: GPL-2 MD5sum: f5e610227a6beffc4ae95c19c943baf3 NeedsCompilation: no Title: This package classifies putative polyadenylation sites as true or false/internally oligodT primed Description: This package uses the Naive Bayes classifier (from e1071) to assign probability values to putative polyadenylation sites (pA sites) based on training data from zebrafish. This will allow the user to separate true, biologically relevant pA sites from false, oligodT primed pA sites. biocViews: Sequencing, SequenceMatching, Genetics, GeneRegulation Author: Sarah Sheppard, Jianhong Ou, Nathan Lawson, Lihua Julie Zhu Maintainer: Sarah Sheppard ; Jianhong Ou ; Lihua Julie Zhu VignetteBuilder: knitr source.ver: src/contrib/cleanUpdTSeq_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/cleanUpdTSeq_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/cleanUpdTSeq_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/cleanUpdTSeq_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cleanUpdTSeq_1.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.R htmlDocs: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html htmlTitles: cleanUpdTSeq Vignette importsMe: InPAS Package: cleaver Version: 1.10.2 Depends: R (>= 3.0.0), methods, Biostrings (>= 1.29.8) Imports: S4Vectors, IRanges Suggests: testthat (>= 0.8), knitr, BiocStyle (>= 0.0.14), BRAIN, UniProt.ws (>= 2.1.4) License: GPL (>= 3) MD5sum: c46f54440fdb06ae29ff291a12e1eee2 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/ source.ver: src/contrib/cleaver_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/cleaver_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/cleaver_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/cleaver_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cleaver_1.10.2.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.pdf vignetteTitles: in-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cleaver/inst/doc/cleaver.R importsMe: Pbase, synapter Package: clippda Version: 1.22.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: af8597133ae1890bcbd7828700779181 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 source.ver: src/contrib/clippda_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clippda_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clippda_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/clippda_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clippda_1.22.0.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 Package: clipper Version: 1.12.0 Depends: R (>= 2.15.0), Matrix, graph Imports: methods, Biobase, Rcpp, igraph, gRbase (>= 1.6.6), qpgraph, KEGGgraph, corpcor, RBGL Suggests: RUnit, BiocGenerics, RCytoscape (>= 1.6.3), graphite, ALL, hgu95av2.db, MASS, BiocStyle License: AGPL-3 MD5sum: bdb5a710f6a85213b45b3de1c8959ff3 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 source.ver: src/contrib/clipper_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clipper_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clipper_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/clipper_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clipper_1.12.0.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 importsMe: ToPASeq suggestsMe: graphite Package: Clomial Version: 1.8.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: c8f111a54341912be3f2a5920a396d7e 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 Author: Habil Zare and Alex Hu Maintainer: Habil Zare source.ver: src/contrib/Clomial_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Clomial_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Clomial_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Clomial_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Clomial_1.8.0.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 Package: Clonality Version: 1.20.0 Depends: R (>= 2.12.2), DNAcopy Imports: DNAcopy, grDevices, graphics, stats, utils Suggests: gdata, DNAcopy License: GPL-3 MD5sum: 6870fbda8e743628911b6ba64701d390 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: Microarray, CopyNumberVariation, Classification, aCGH Author: Irina Ostrovnaya Maintainer: Irina Ostrovnaya source.ver: src/contrib/Clonality_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Clonality_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Clonality_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Clonality_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Clonality_1.20.0.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 Package: clonotypeR Version: 1.10.2 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: 775db4d4bd04bb99ae1e5ccad609d6ab 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/ source.ver: src/contrib/clonotypeR_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/clonotypeR_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/clonotypeR_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/clonotypeR_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clonotypeR_1.10.2.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/clonotypeR/inst/doc/clonotypeR.R htmlDocs: vignettes/clonotypeR/inst/doc/clonotypeR.html htmlTitles: clonotypeR User's Guide Package: clst Version: 1.20.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: 0000854e64bd5f51c43e37a802e6d601 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 source.ver: src/contrib/clst_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clst_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clst_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/clst_1.17.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clst_1.20.0.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 Package: clstutils Version: 1.20.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: 740d8656d12cd894071d4473bd2a5174 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 source.ver: src/contrib/clstutils_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clstutils_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clstutils_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/clstutils_1.17.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clstutils_1.20.0.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 Package: clustComp Version: 1.0.0 Depends: R (>= 3.3) Imports: sm, stats, graphics Suggests: Biobase, colonCA, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: bec6e44c58c23e13259c377b127fd581 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 source.ver: src/contrib/clustComp_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clustComp_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clustComp_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clustComp_1.0.0.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 Package: clusterProfiler Version: 3.0.5 Depends: R (>= 3.2.0), DOSE (>= 2.9.0) Imports: AnnotationDbi, ggplot2, GO.db, GOSemSim, GSEABase, IRanges, magrittr, methods, plyr, qvalue, stats, stats4, S4Vectors, tidyr, topGO, utils Suggests: AnnotationHub, BiocStyle, KEGG.db, knitr, org.Hs.eg.db, pathview, ReactomePA License: Artistic-2.0 MD5sum: 501b5ff766c9458faffcd3e483d527c6 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 with contributions from Li-Gen Wang and Giovanni Dall'Olio. Maintainer: Guangchuang Yu URL: http://guangchuangyu.github.io/clusterProfiler VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues source.ver: src/contrib/clusterProfiler_3.0.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/clusterProfiler_3.0.5.zip win64.binary.ver: bin/windows64/contrib/3.3/clusterProfiler_3.0.5.zip mac.binary.ver: bin/macosx/contrib/3.3/clusterProfiler_2.3.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clusterProfiler_3.0.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/clusterProfiler/inst/doc/clusterProfiler.R htmlDocs: vignettes/clusterProfiler/inst/doc/clusterProfiler.html htmlTitles: Statistical analysis and visualization of functional profiles for genes and gene clusters importsMe: debrowser suggestsMe: ChIPseeker, DOSE, GOSemSim, ReactomePA Package: ClusterSignificance Version: 1.0.3 Depends: R (>= 3.3.0) Imports: methods, pracma, princurve, scatterplot3d, RColorBrewer, grDevices, graphics, utils Suggests: knitr, rmarkdown, testthat, BiocStyle, ggplot2, plsgenomics License: GPL-3 MD5sum: a5ed95c30eb0f58a3a97719e86a6881e NeedsCompilation: no Title: Investigates Significance of Clusters by Reducing the Data to One Dimension to be Able to Easy Set a Score for the Separation, and a p-Value is then Calculated from Permutations of the Original Data Description: The ClusterSignificance package provides tools to assess if clusters have a separation different from random or permuted data. ClusterSignificance investigates clusters of two or more groups by first, 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 and Jesper R. Gadin Maintainer: Jason T Serviss VignetteBuilder: knitr source.ver: src/contrib/ClusterSignificance_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ClusterSignificance_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ClusterSignificance_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ClusterSignificance_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.R htmlDocs: vignettes/ClusterSignificance/inst/doc/ClusterSignificance-vignette.html htmlTitles: ClusterSignificance Vignette Package: clusterStab Version: 1.44.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 653f221a0fd43a963db47df27c2aeba4 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 source.ver: src/contrib/clusterStab_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/clusterStab_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/clusterStab_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/clusterStab_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/clusterStab_1.44.0.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 Package: CMA Version: 1.30.0 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: f4e69be5f47a1b3a038b07713fed305c 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: Christoph Bernau source.ver: src/contrib/CMA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CMA_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CMA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CMA_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CMA_1.30.0.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 Package: cn.farms Version: 1.20.0 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: 998a727be2a8116382e4268ca22a41df 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 source.ver: src/contrib/cn.farms_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cn.farms_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cn.farms_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cn.farms_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cn.farms_1.20.0.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 Package: cn.mops Version: 1.18.0 Depends: R (>= 2.12), methods, utils, stats, graphics, parallel, GenomicRanges Imports: BiocGenerics, Biobase, IRanges, Rsamtools, GenomeInfoDb, S4Vectors Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 3b763aedfb1ea1004141194f917432ef 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: Guenter Klambauer URL: http://www.bioinf.jku.at/software/cnmops/cnmops.html source.ver: src/contrib/cn.mops_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cn.mops_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cn.mops_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cn.mops_1.15.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cn.mops_1.18.0.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 Package: CNAnorm Version: 1.18.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: cee53305ede919751dda10d927ec55eb 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, source.ver: src/contrib/CNAnorm_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNAnorm_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNAnorm_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CNAnorm_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNAnorm_1.18.0.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 Package: CNEr Version: 1.8.3 Depends: R (>= 3.2.2) Imports: Biostrings(>= 2.33.4), RSQLite(>= 0.11.4), GenomeInfoDb(>= 1.1.3), GenomicRanges(>= 1.23.16), rtracklayer(>= 1.25.5), XVector(>= 0.5.4), DBI(>= 0.2-7), GenomicAlignments(>= 1.1.9), methods, S4Vectors(>= 0.9.25), IRanges(>= 2.5.27), readr(>= 0.2.2), BiocGenerics, tools, parallel LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz(>= 1.7.4), BiocStyle, knitr, rmarkdown, testthat License: GPL-2 | file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 1b2678a06b29d81c345ee11dc5ad0fed 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 source.ver: src/contrib/CNEr_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNEr_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/CNEr_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.3/CNEr_1.5.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNEr_1.8.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/CNEr/inst/doc/CNEr.R, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.R htmlDocs: vignettes/CNEr/inst/doc/CNEr.html, vignettes/CNEr/inst/doc/PairwiseWholeGenomeAlignment.html htmlTitles: CNE identification and visualisation, Pairwise whole genome alignment importsMe: TFBSTools Package: CNORdt Version: 1.14.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 430da838c3341aa4a2f09e66cbc99a9f 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: CellBasedAssays, CellBiology, Proteomics, TimeCourse Author: A. MacNamara Maintainer: A. MacNamara source.ver: src/contrib/CNORdt_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORdt_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORdt_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CNORdt_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORdt_1.14.0.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 Package: CNORfeeder Version: 1.12.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph License: GPL-3 MD5sum: 85114ef68c49ac7b7673906f764a3092 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, Bioinformatics, NetworkInference Author: F.Eduati Maintainer: F.Eduati source.ver: src/contrib/CNORfeeder_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORfeeder_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORfeeder_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CNORfeeder_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORfeeder_1.12.0.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 Package: CNORfuzzy Version: 1.14.0 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: ef66b21393d564f03635cb84b2042fd2 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 source.ver: src/contrib/CNORfuzzy_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORfuzzy_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORfuzzy_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CNORfuzzy_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORfuzzy_1.14.0.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 Package: CNORode Version: 1.14.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: 65f4d0cd7e0b86ebc66bcf75a8f85fa0 NeedsCompilation: yes Title: ODE add-on to CellNOptR Description: ODE add-on to CellNOptR biocViews: CellBasedAssays, CellBiology, Proteomics, Bioinformatics, TimeCourse Author: David Henriques, Thomas Cokelaer Maintainer: David Henriques source.ver: src/contrib/CNORode_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNORode_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNORode_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CNORode_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNORode_1.14.0.tgz vignettes: vignettes/CNORode/inst/doc/CNORode-vignette.pdf vignetteTitles: Main vignette:Playing with networks using CNORode hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNORode/inst/doc/CNORode-vignette.R dependsOnMe: MEIGOR Package: CNPBayes Version: 1.2.2 Depends: GenomicRanges Imports: Rcpp (>= 0.12.1), S4Vectors (>= 0.9.25), matrixStats, RColorBrewer, gtools, combinat, IRanges, GenomeInfoDb, GenomicRanges, methods, BiocGenerics, graphics, stats, coda, SummarizedExperiment LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, VanillaICE (>= 1.31.3), BiocCheck, MASS, oligoClasses, dplyr, tidyr, ggplot2 License: Artistic-2.0 Archs: i386, x64 MD5sum: be374c696205529c6ca086b9c0e0b15a NeedsCompilation: yes Title: Bayesian mixture models for copy number polymorphisms Description: Bayesian hierarchical mixture models for batch effects and copy number. biocViews: CopyNumberVariation, Bayesian Author: Stephen Cristiano, Robert Scharpf, and Jacob Carey Maintainer: Jacob Carey URL: https://github.com/scristia/CNPBayes VignetteBuilder: knitr BugReports: https://github.com/scristia/CNPBayes/issues source.ver: src/contrib/CNPBayes_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNPBayes_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CNPBayes_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNPBayes_1.2.2.tgz vignettes: vignettes/CNPBayes/inst/doc/Convergence.pdf, vignettes/CNPBayes/inst/doc/FindCNPs.pdf, vignettes/CNPBayes/inst/doc/Implementation.pdf, vignettes/CNPBayes/inst/doc/Overview.pdf vignetteTitles: Overview of CNPBayes package, Identifying Copy Number Polymorphisms, Implementation of Bayesian mixture models for copy number estimation, Overview of CNPBayes package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNPBayes/inst/doc/Convergence.R, vignettes/CNPBayes/inst/doc/FindCNPs.R, vignettes/CNPBayes/inst/doc/Implementation.R, vignettes/CNPBayes/inst/doc/Overview.R Package: CNTools Version: 1.28.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: 834b815fca25d488e680939508bcb7a9 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 source.ver: src/contrib/CNTools_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNTools_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNTools_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CNTools_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNTools_1.28.0.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 Package: cnvGSA Version: 1.16.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: bc2e1c5f5647e6f2da97e3f712c506ec 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 source.ver: src/contrib/cnvGSA_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cnvGSA_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cnvGSA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cnvGSA_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cnvGSA_1.16.0.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 Package: CNVPanelizer Version: 1.2.2 Depends: R (>= 3.2.0), GenomicRanges Imports: S4Vectors, grDevices, stats, utils, NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, openxlsx Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 361b9531c91fc289b8d66c5cb0cdc312 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 source.ver: src/contrib/CNVPanelizer_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNVPanelizer_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CNVPanelizer_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/CNVPanelizer_0.99.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNVPanelizer_1.2.2.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 Package: CNVrd2 Version: 1.10.2 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 4c05915d7ba641520b901b8770c5c545 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 source.ver: src/contrib/CNVrd2_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNVrd2_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CNVrd2_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/CNVrd2_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNVrd2_1.10.2.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 Package: CNVtools Version: 1.66.0 Depends: R (>= 2.10), survival License: GPL-3 Archs: i386, x64 MD5sum: f124edbe9e523ceb731e4b0c7ccb3fa8 NeedsCompilation: yes 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 source.ver: src/contrib/CNVtools_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CNVtools_1.66.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CNVtools_1.66.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CNVtools_1.63.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CNVtools_1.66.0.tgz vignettes: vignettes/CNVtools/inst/doc/CNVtools-vignette.pdf vignetteTitles: Copy Number Variation Tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CNVtools/inst/doc/CNVtools-vignette.R Package: cobindR Version: 1.10.0 Imports: methods, seqinr, yaml, rtfbs, gplots, mclust, gmp, BiocGenerics (>= 0.13.8), IRanges, Biostrings, BSgenome, biomaRt Suggests: RUnit Enhances: rGADEM, seqLogo, genoPlotR, parallel, VennDiagram, RColorBrewer, vcd, MotifDb, snowfall License: Artistic-2.0 MD5sum: 3c6f3d9cc992baea600907d111fc1790 NeedsCompilation: no Title: Finding Co-occuring motifs of transcription factor binding sites Description: Finding and analysing co-occuring motifs of transcription factor binding sites in groups of genes biocViews: ChIPSeq, CellBiology, MultipleComparison, SequenceMatching Author: Manuela Benary, Stefan Kroeger, Yuehien Lee, Robert Lehmann Maintainer: Manuela Benary source.ver: src/contrib/cobindR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cobindR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cobindR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cobindR_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cobindR_1.10.0.tgz vignettes: vignettes/cobindR/inst/doc/cobindR.pdf vignetteTitles: Using cobindR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cobindR/inst/doc/cobindR.R Package: CoCiteStats Version: 1.44.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 4e745aee2bf7b0c755626e9c7d4b1c4b 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 source.ver: src/contrib/CoCiteStats_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CoCiteStats_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CoCiteStats_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CoCiteStats_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoCiteStats_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: codelink Version: 1.40.2 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 0263f70ff86ee01600be05b9ee720ea6 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 source.ver: src/contrib/codelink_1.40.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/codelink_1.40.2.zip win64.binary.ver: bin/windows64/contrib/3.3/codelink_1.40.2.zip mac.binary.ver: bin/macosx/contrib/3.3/codelink_1.37.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/codelink_1.40.2.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 Package: CODEX Version: 1.4.0 Depends: R (>= 3.2.3), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19, IRanges, Biostrings Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: 6414a20c2d68d421f1fa330918484194 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: ExomeSeq, Normalization, QualityControl, CopyNumberVariation Author: Yuchao Jiang, Nancy R. Zhang Maintainer: Yuchao Jiang source.ver: src/contrib/CODEX_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CODEX_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CODEX_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CODEX_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CODEX_1.4.0.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 Package: CoGAPS Version: 2.6.0 Depends: R (>= 3.0.1), Rcpp (>= 0.11.2), RColorBrewer (>= 1.0.5), gplots (>= 2.8.0) Imports: graphics, grDevices, methods, stats, utils LinkingTo: Rcpp, BH License: GPL (==2) Archs: i386, x64 MD5sum: 9ee17756bb28d57e223e5fe63fc87576 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 Author: Elana J. Fertig, Michael F. Ochs Maintainer: Elana J. Fertig source.ver: src/contrib/CoGAPS_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CoGAPS_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CoGAPS_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CoGAPS_2.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoGAPS_2.6.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.pdf vignetteTitles: GAPS/CoGAPS Users Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.R Package: cogena Version: 1.6.2 Depends: R (>= 3.2), cluster, ggplot2, kohonen Imports: methods, class, gplots, mclust, amap, apcluster, foreach, parallel, doParallel, fastcluster, corrplot, biwt, Biobase, reshape2, dplyr, devtools Suggests: knitr, rmarkdown License: LGPL-3 MD5sum: fe4e61131e6976bb97710dd93fa7d6ad 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 source.ver: src/contrib/cogena_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/cogena_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/cogena_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/cogena_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cogena_1.6.2.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette_pdf.pdf vignetteTitles: Vignette Title 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 htmlDocs: vignettes/cogena/inst/doc/cogena-vignette_html.html htmlTitles: Vignette Title Package: coGPS Version: 1.16.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: af372c838afdb3e6a608dfab0796cddf 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 source.ver: src/contrib/coGPS_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/coGPS_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/coGPS_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/coGPS_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/coGPS_1.16.0.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 Package: COHCAP Version: 1.10.0 Depends: WriteXLS, COHCAPanno License: GPL-3 MD5sum: 314b3f2b864710fbbb548294899bcc24 NeedsCompilation: no Title: CpG Island Analysis Pipeline for Illumina Methylation Array and Targeted BS-Seq Data Description: This package provides a pipeline to analyze single-nucleotide resolution methylation data (Illumina 450k methylation array, targeted BS-Seq, etc.). It provides QC metrics, differential methylation for CpG Sites, differential methylation for CpG Islands, integration with gene expression data, and visualization of methylation values. biocViews: DNAMethylation, Microarray, MethylSeq, Epigenetics, DifferentialMethylation Author: Charles Warden Maintainer: Charles Warden source.ver: src/contrib/COHCAP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/COHCAP_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/COHCAP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/COHCAP_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/COHCAP_1.10.0.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 Package: coMET Version: 1.4.4 Depends: R (>= 3.3.0), grid, utils, biomaRt, Gviz, psych Imports: colortools, hash, grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, ggbio, ggplot2, trackViewer, stats, corrplot Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 4151cc078d336dea087cfb7c20692810 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 Author: Tiphaine C. Martin, Thomas Hardiman, Idil Yet, Pei-Chien Tsai, Jordana T. Bell Maintainer: Tiphaine Martin URL: http://epigen.kcl.ac.uk/comet VignetteBuilder: knitr source.ver: src/contrib/coMET_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/coMET_1.4.4.zip win64.binary.ver: bin/windows64/contrib/3.3/coMET_1.4.4.zip mac.binary.ver: bin/macosx/contrib/3.3/coMET_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/coMET_1.4.4.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 Package: COMPASS Version: 1.10.2 Depends: R (>= 3.0.2) Imports: Rcpp, data.table, RColorBrewer, scales, grid, plyr, knitr, abind, clue, grDevices, utils LinkingTo: Rcpp (>= 0.11.0) Suggests: flowWorkspace (>= 3.9.66), flowCore, ncdfFlow, shiny, testthat, devtools, Kmisc License: Artistic-2.0 Archs: i386, x64 MD5sum: 673e5a6529b54ac4b480d433cde35748 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: FlowCytometry Author: Lynn Lin, Kevin Ushey, Greg Finak, Ravio Kolde (pheatmap) Maintainer: Greg Finak VignetteBuilder: knitr BugReports: https://github.com/RGLab/COMPASS/issues source.ver: src/contrib/COMPASS_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/COMPASS_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/COMPASS_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/COMPASS_1.7.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/COMPASS_1.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/COMPASS/inst/doc/COMPASS.R htmlDocs: vignettes/COMPASS/inst/doc/COMPASS.html htmlTitles: COMPASS Package: compcodeR Version: 1.8.2 Depends: R (>= 3.0.2), 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 Suggests: BiocStyle, EBSeq, DESeq, DESeq2 (>= 1.1.31), baySeq (>= 2.2.0), genefilter, NOISeq, TCC, samr, NBPSeq (>= 0.3.0) Enhances: rpanel, DSS License: GPL (>= 2) MD5sum: 73de742aab69720873690400e4fcfabc 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 and interfaces to several packages for performing the differential expression analysis. biocViews: RNASeq, DifferentialExpression Author: Charlotte Soneson Maintainer: Charlotte Soneson VignetteBuilder: knitr source.ver: src/contrib/compcodeR_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/compcodeR_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/compcodeR_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/compcodeR_1.5.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/compcodeR_1.8.2.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.pdf vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/compcodeR/inst/doc/compcodeR.R Package: compEpiTools Version: 1.6.4 Depends: R (>= 3.1.1), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb, ggplot2 Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 458dc904d37185b188041ba74aee8f0d 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, Kamal Kishore Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/compEpiTools_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/compEpiTools_1.6.4.zip win64.binary.ver: bin/windows64/contrib/3.3/compEpiTools_1.6.4.zip mac.binary.ver: bin/macosx/contrib/3.3/compEpiTools_1.3.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/compEpiTools_1.6.4.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 Package: CompGO Version: 1.8.1 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: 133dde4eab775ccbf2719b7ce65173ee NeedsCompilation: no Title: An R pipeline for .bed file annotation, statistical comparison of 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), statistically compare gene ontology enrichment between multiple experiments and finally visualise and compare these enrichments using directed acyclic graphs or scatterplots. CompGO also enables clustering of experiments based on their GO profiles and provides functions for performing and visualising principle component analysis. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg source.ver: src/contrib/CompGO_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/CompGO_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/CompGO_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.3/CompGO_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CompGO_1.8.1.tgz vignettes: vignettes/CompGO/inst/doc/CompGO-Intro.pdf, vignettes/CompGO/inst/doc/CompGO-vignette.pdf vignetteTitles: Introduction, CompGO-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CompGO/inst/doc/CompGO-Intro.R Package: ComplexHeatmap Version: 1.10.2 Depends: R (>= 3.1.2), grid, graphics, stats, grDevices Imports: methods, circlize (>= 0.3.4), GetoptLong, colorspace, RColorBrewer, dendextend (>= 1.0.1), GlobalOptions (>= 0.0.10) Suggests: testthat (>= 0.3), knitr, markdown, cluster, MASS, pvclust, dendsort, HilbertCurve, Cairo, png, jpeg, tiff License: GPL (>= 2) MD5sum: 0750603c13899fd21dca6dbc3909cf6c NeedsCompilation: no Title: Making Complex Heatmaps Description: Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential structures. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports self-defined annotation graphics. biocViews: Software, Visualization, Sequencing Author: Zuguang Gu Maintainer: Zuguang Gu URL: https://github.com/jokergoo/ComplexHeatmap VignetteBuilder: knitr source.ver: src/contrib/ComplexHeatmap_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ComplexHeatmap_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ComplexHeatmap_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ComplexHeatmap_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ComplexHeatmap_1.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ComplexHeatmap/inst/doc/s1.introduction.R, vignettes/ComplexHeatmap/inst/doc/s2.single_heatmap.R, vignettes/ComplexHeatmap/inst/doc/s3.a_list_of_heatmaps.R, vignettes/ComplexHeatmap/inst/doc/s4.heatmap_annotation.R, vignettes/ComplexHeatmap/inst/doc/s5.legend.R, vignettes/ComplexHeatmap/inst/doc/s6.heatmap_decoration.R, vignettes/ComplexHeatmap/inst/doc/s7.interactive.R, vignettes/ComplexHeatmap/inst/doc/s8.oncoprint.R, vignettes/ComplexHeatmap/inst/doc/s9.examples.R htmlDocs: vignettes/ComplexHeatmap/inst/doc/s1.introduction.html, vignettes/ComplexHeatmap/inst/doc/s2.single_heatmap.html, vignettes/ComplexHeatmap/inst/doc/s3.a_list_of_heatmaps.html, vignettes/ComplexHeatmap/inst/doc/s4.heatmap_annotation.html, vignettes/ComplexHeatmap/inst/doc/s5.legend.html, vignettes/ComplexHeatmap/inst/doc/s6.heatmap_decoration.html, vignettes/ComplexHeatmap/inst/doc/s7.interactive.html, vignettes/ComplexHeatmap/inst/doc/s8.oncoprint.html, vignettes/ComplexHeatmap/inst/doc/s9.examples.html htmlTitles: 1. Introduction to ComplexHeatmap package, 2. Making a single heatmap, 3. Making a list of Heatmaps, 4. Heatmap Annotations, 5. Heatmap and Annotation Legends, 6. Heatmap Decoration, 7. Interactive with Heatmaps, 8. OncoPrint, 9. More Examples dependsOnMe: EnrichedHeatmap, recoup importsMe: EnrichmentBrowser, TCGAbiolinks suggestsMe: gtrellis, HilbertCurve Package: CONFESS Version: 1.0.2 Depends: R (>= 3.3),grDevices,utils,stats,graphics Imports: methods,changepoint,cluster,contrast,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: cc2f222cfe019dcad032b1f2be220d75 NeedsCompilation: no Title: Cell OrderiNg by FluorEScence Signal Description: Single Cell Fluidigm Spot Detector. biocViews: GeneExpression,DataImport,CellBiology,Clustering,RNASeq,QualityControl,Visualization,TimeCourse,Regression,Classification Author: Diana LOW and Efthimios MOTAKIS Maintainer: Diana LOW VignetteBuilder: knitr source.ver: src/contrib/CONFESS_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CONFESS_0.99.14.zip win64.binary.ver: bin/windows64/contrib/3.3/CONFESS_0.99.14.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CONFESS_1.0.2.tgz vignettes: vignettes/CONFESS/inst/doc/vignette_tex.pdf vignetteTitles: CONFESS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CONFESS/inst/doc/vignette_tex.R, vignettes/CONFESS/inst/doc/vignette.R htmlDocs: vignettes/CONFESS/inst/doc/vignette.html htmlTitles: CONFESS Package: ConsensusClusterPlus Version: 1.36.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: de88e21f2f182b4c24eb3588c593c458 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 source.ver: src/contrib/ConsensusClusterPlus_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ConsensusClusterPlus_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ConsensusClusterPlus_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ConsensusClusterPlus_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ConsensusClusterPlus_1.36.0.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: FlowSOM, TCGAbiolinks Package: consensusSeekeR Version: 1.0.2 Depends: R (>= 2.10), BiocGenerics, IRanges, GenomicRanges, BiocParallel Imports: GenomeInfoDb, rtracklayer, stringr, S4Vectors Suggests: BiocStyle, ggplot2, knitr, RUnit License: Artistic-2.0 MD5sum: 26dcfa7aca1524802a2ff49ddabc3fd2 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 Louise Deschenes URL: https://github.com/ArnaudDroitLab/consensusSeekeR VignetteBuilder: knitr BugReports: https://github.com/ArnaudDroitLab/consensusSeekeR/issues source.ver: src/contrib/consensusSeekeR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/consensusSeekeR_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/consensusSeekeR_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/consensusSeekeR_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.R htmlDocs: vignettes/consensusSeekeR/inst/doc/consensusSeekeR.html htmlTitles: Detection of consensus regions inside a group of experiences using genomic positions and genomic ranges Package: contiBAIT Version: 1.0.0 Depends: BH (>= 1.51.0-3), Rsamtools (>= 1.21) Imports: grDevices, clue, cluster, gplots, IRanges, GenomicRanges, S4Vectors, Rcpp, TSP, GenomicFiles, gtools, rtracklayer, BiocParallel, DNAcopy, colorspace, reshape2, ggplot2, methods, exomeCopy, diagram LinkingTo: Rcpp, BH Suggests: BiocStyle License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: 62001407151e3c9926e4b680e6af02ee 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: CellBasedAssays, QualityControl, WholeGenome, Genetics, GenomeAssembly Author: Kieran O'Neill, Mark Hills, Mike Gottlieb Maintainer: Kieran O'Neill source.ver: src/contrib/contiBAIT_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/contiBAIT_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/contiBAIT_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/contiBAIT_1.0.0.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 Package: conumee Version: 1.4.2 Depends: R (>= 3.0), minfi, IlluminaHumanMethylation450kmanifest, IlluminaHumanMethylation450kanno.ilmn12.hg19 Imports: methods, stats, DNAcopy, rtracklayer, GenomicRanges, IRanges, GenomeInfoDb Suggests: BiocStyle, knitr, rmarkdown, minfiData, CopyNumber450kData, RCurl License: GPL (>= 2) MD5sum: d887eec91e83fe19eaef8f2544d641d4 NeedsCompilation: no Title: Enhanced copy-number variation analysis using Illumina 450k methylation arrays Description: This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k methylation arrays. biocViews: CopyNumberVariation, DNAMethylation, MethylationArray, Microarray, Normalization, Preprocessing, QualityControl, Software Author: Volker Hovestadt, Marc Zapatka Maintainer: Volker Hovestadt VignetteBuilder: knitr source.ver: src/contrib/conumee_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/conumee_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/conumee_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/conumee_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/conumee_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/conumee/inst/doc/conumee.R htmlDocs: vignettes/conumee/inst/doc/conumee.html htmlTitles: conumee Package: convert Version: 1.48.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: da4969e4877d9d31fa1e0387b970b3e0 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 Martin Morgan Maintainer: Yee Hwa (Jean) Yang URL: http://bioinf.wehi.edu.au/limma/convert.html source.ver: src/contrib/convert_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/convert_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/convert_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/convert_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/convert_1.48.0.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 Package: copa Version: 1.40.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: 3111c655b041d33a161ec9decaacd65e 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 source.ver: src/contrib/copa_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/copa_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/copa_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/copa_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/copa_1.40.0.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 Package: copynumber Version: 1.12.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: ada2efa4f3555c80922503417ad599b2 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 source.ver: src/contrib/copynumber_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/copynumber_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/copynumber_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/copynumber_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/copynumber_1.12.0.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 Package: CopyNumber450k Version: 1.8.0 Depends: Biobase, minfi, DNAcopy, preprocessCore, BiocGenerics Imports: methods Suggests: CopyNumber450kData, minfiData License: Artistic-2.0 MD5sum: fa38cd350285848a9435a412652c99de NeedsCompilation: no Title: R package for calling CNV from Illumina 450k methylation microarrays Description: This package contains a set of functions that allow CNV calling from Illumina 450k methylation microarrays. biocViews: DNAMethylation, Microarray, Preprocessing, QualityControl, CopyNumberVariation Author: Simon Papillon-Cavanagh, Jean-Philippe Fortin, Nicolas De Jay Maintainer: Simon Papillon-Cavanagh source.ver: src/contrib/CopyNumber450k_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CopyNumber450k_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CopyNumber450k_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CopyNumber450k_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CopyNumber450k_1.8.0.tgz vignettes: vignettes/CopyNumber450k/inst/doc/CopyNumber450k.pdf vignetteTitles: CopyNumber450k User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CopyNumber450k/inst/doc/CopyNumber450k.R Package: CopywriteR Version: 2.4.0 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: 4a8b05da4b7c4ddb6b30dda87f12d8ed 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: TargetedResequencing, ExomeSeq, CopyNumberVariation, Preprocessing, Visualization, Coverage Author: Thomas Kuilman Maintainer: Thomas Kuilman URL: https://github.com/PeeperLab/CopywriteR source.ver: src/contrib/CopywriteR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CopywriteR_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CopywriteR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CopywriteR_2.1.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CopywriteR_2.4.0.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 Package: CoRegNet Version: 1.8.2 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: d3e0bb743678826a58ff3da47fc83334 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 source.ver: src/contrib/CoRegNet_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CoRegNet_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CoRegNet_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/CoRegNet_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoRegNet_1.8.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CoRegNet/inst/doc/CoRegNet.R htmlDocs: vignettes/CoRegNet/inst/doc/CoRegNet.html htmlTitles: Custom Print Methods Package: Cormotif Version: 1.18.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: dae2e304476e5574d398d1c97b1e0079 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 source.ver: src/contrib/Cormotif_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Cormotif_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Cormotif_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Cormotif_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Cormotif_1.18.0.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 Package: CorMut Version: 1.14.0 Depends: seqinr,igraph License: GPL-2 MD5sum: 088321e83732fff7c85e9975a97dd077 NeedsCompilation: no 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. biocViews: Sequencing Author: Zhenpeng Li, Yang Huang, Yabo Ouyang, Yiming Shao, Liying Ma Maintainer: Zhenpeng Li source.ver: src/contrib/CorMut_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CorMut_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CorMut_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CorMut_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CorMut_1.14.0.tgz vignettes: vignettes/CorMut/inst/doc/CorMut.pdf vignetteTitles: CorMut hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CorMut/inst/doc/CorMut.R Package: coRNAi Version: 1.22.0 Depends: R (>= 2.10), cellHTS2, limma, locfit Imports: MASS, gplots, lattice, grDevices, graphics, stats License: Artistic-2.0 MD5sum: 246680a8226ca8c6ab722d8839ed1b38 NeedsCompilation: no Title: Analysis of co-knock-down RNAi data Description: Analysis of combinatorial cell-based RNAi screens biocViews: CellBasedAssays Author: Elin Axelsson Maintainer: Elin Axelsson SystemRequirements: Graphviz source.ver: src/contrib/coRNAi_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/coRNAi_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/coRNAi_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/coRNAi_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/coRNAi_1.22.0.tgz vignettes: vignettes/coRNAi/inst/doc/coRNAi.pdf vignetteTitles: coRNAi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/coRNAi/inst/doc/coRNAi.R Package: CORREP Version: 1.38.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: f05d867c19d6d900026b38f1cd1de8de 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 source.ver: src/contrib/CORREP_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CORREP_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CORREP_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CORREP_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CORREP_1.38.0.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 Package: cosmiq Version: 1.6.0 Depends: R (>= 3.0.2), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 26c6950b24f0259ba44d88f13b050a6e 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: MassSpectrometry, Metabolomics Author: David Fischer , Christian Panse , Endre Laczko Maintainer: David Fischer , Christian Panse URL: http://www.bioconductor.org/packages/devel/bioc/html/cosmiq.html source.ver: src/contrib/cosmiq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cosmiq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cosmiq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cosmiq_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cosmiq_1.6.0.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 Package: COSNet Version: 1.6.0 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: ccda385eb8c2ad4f63f220357f37d93e 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 source.ver: src/contrib/COSNet_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/COSNet_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/COSNet_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/COSNet_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/COSNet_1.6.0.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 Package: CountClust Version: 1.0.2 Depends: R (>= 3.3.0), ggplot2 (>= 2.1.0) Imports: maptpx, slam, plyr(>= 1.7.1), cowplot, gtools, flexmix, picante, limma, parallel, reshape2, stats, utils, graphics, grDevices Suggests: knitr, BiocStyle, Biobase, roxygen2, RColorBrewer, devtools, xtable License: GPL (>= 2) MD5sum: 5a13de778c5e327e8438412a2f449ae7 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: 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 source.ver: src/contrib/CountClust_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CountClust_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CountClust_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CountClust_1.0.2.tgz vignettes: vignettes/CountClust/inst/doc/count-clust.pdf vignetteTitles: Grade of Membership Clustering and Visualization using CountClust hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CountClust/inst/doc/count-clust.R Package: CoverageView Version: 1.8.0 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: 14dd8ab6399e372c4acb4638ad5a77e0 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: Visualization,RNASeq,ChIPSeq,Sequencing,Technology,Software Author: Ernesto Lowy Maintainer: Ernesto Lowy source.ver: src/contrib/CoverageView_1.8.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/CoverageView_1.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CoverageView_1.8.0.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 Package: cpvSNP Version: 1.4.0 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: 0a143b1aa06e7efee01ff8e6e7c4c5ed 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 source.ver: src/contrib/cpvSNP_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cpvSNP_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cpvSNP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cpvSNP_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cpvSNP_1.4.0.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 Package: cqn Version: 1.18.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: bab2e439c72230375126fd2af1c35c56 NeedsCompilation: no Title: Conditional quantile normalization Description: A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method. biocViews: RNASeq, Preprocessing, DifferentialExpression Author: Jean (Zhijin) Wu, Kasper Daniel Hansen Maintainer: Kasper Daniel Hansen source.ver: src/contrib/cqn_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cqn_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cqn_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cqn_1.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cqn_1.18.0.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 importsMe: tweeDEseq Package: CRImage Version: 1.20.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 4a73cbce909070e6ae318fbe0c5d65c4 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 source.ver: src/contrib/CRImage_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CRImage_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CRImage_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CRImage_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CRImage_1.20.0.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 Package: CRISPRseek Version: 1.12.0 Depends: R (>= 3.0.1), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors (>= 0.9.25), IRanges, BSgenome, BiocParallel, hash Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: 5d276f055ea3da3f14b9500f1378f692 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 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. If GeneRfold and GeneR are installed (http://bioconductor.case.edu/bioconductor/2.8/bioc/html/GeneRfold.html, http://bioc.ism.ac.jp/packages/2.8/bioc/html/GeneR.html), then the minimum free energy and bracket notation of secondary structure of gRNA and gRNA backbone constant region will be included in the summary file. This package leverages Biostrings and BSgenome packages. biocViews: GeneRegulation, SequenceMatching Author: Lihua Julie Zhu, Benjamin R. Holmes, Herve Pages, Michael Lawrence, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu source.ver: src/contrib/CRISPRseek_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CRISPRseek_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CRISPRseek_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CRISPRseek_1.9.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CRISPRseek_1.12.0.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 importsMe: GUIDEseq Package: CrispRVariants Version: 1.0.2 Depends: R (>= 3.3), ggplot2 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: ccade09ea3ef27d5e8c013872b165639 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: GenomicVariation, VariantDetection, GeneticVariability, DataRepresentation, Visualization Author: Helen Lindsay [aut, cre] Maintainer: Helen Lindsay VignetteBuilder: knitr source.ver: src/contrib/CrispRVariants_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/CrispRVariants_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/CrispRVariants_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CrispRVariants_1.0.2.tgz vignettes: vignettes/CrispRVariants/inst/doc/user_guide.pdf vignetteTitles: CrispRVariants hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/CrispRVariants/inst/doc/user_guide.R Package: crlmm Version: 1.30.0 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, SNPchip, utils, lattice, ff, foreach, RcppEigen (>= 0.3.1.2.1), matrixStats, VGAM, parallel LinkingTo: preprocessCore (>= 1.17.7) Suggests: hapmapsnp6, genomewidesnp6Crlmm (>= 1.0.7), GGdata, snpStats, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 04364b3a0f806d807ba387fc575d9b51 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 source.ver: src/contrib/crlmm_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/crlmm_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/crlmm_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/crlmm_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/crlmm_1.30.0.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: ArrayTV, oligoClasses, SNPchip Package: CSAR Version: 1.24.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: b31ca3133e455355783aa0416e288d4a 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 source.ver: src/contrib/CSAR_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CSAR_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CSAR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CSAR_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CSAR_1.24.0.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 importsMe: NarrowPeaks suggestsMe: NarrowPeaks Package: csaw Version: 1.6.1 Depends: R (>= 3.3.0), GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, S4Vectors, IRanges, GenomeInfoDb, BiocGenerics, Rhtslib, stats LinkingTo: Rhtslib, zlibbioc Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 4a700b2bb3929766ef98be2824f0a431 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 , Gordon Smyth Maintainer: Aaron Lun source.ver: src/contrib/csaw_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/csaw_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/csaw_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.3/csaw_1.3.12.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/csaw_1.6.1.tgz vignettes: vignettes/csaw/inst/doc/csaw.pdf, vignettes/csaw/inst/doc/csawUserGuide.pdf vignetteTitles: csaw Vignette, csawUserGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: diffHic Package: CSSP Version: 1.10.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: 9cdf73129c5d7e61acb740624e123155 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 source.ver: src/contrib/CSSP_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/CSSP_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/CSSP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/CSSP_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/CSSP_1.10.0.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 Package: ctc Version: 1.46.0 Depends: amap License: GPL-2 MD5sum: cbcfa4e425aa9cbaeeaba158605ecd99 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 source.ver: src/contrib/ctc_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ctc_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ctc_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ctc_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ctc_1.46.0.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: multiClust Package: cummeRbund Version: 2.14.0 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: 6cdb5be846eecd0022c4885152bad617 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 source.ver: src/contrib/cummeRbund_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cummeRbund_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cummeRbund_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cummeRbund_2.11.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cummeRbund_2.14.0.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 dependsOnMe: meshr, spliceR suggestsMe: oneChannelGUI Package: customProDB Version: 1.12.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt Imports: S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, Rsamtools (>= 1.10.2), GenomicAlignments, Biostrings (>= 2.26.3), GenomicFeatures (>= 1.17.13), biomaRt (>= 2.17.1), stringr, RCurl, plyr, VariantAnnotation (>= 1.13.44), rtracklayer, RSQLite, AnnotationDbi Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 145e75dd6a3e9317ab1a81750ef834b9 NeedsCompilation: no Title: Generate customized protein database from NGS data, with a focus on RNA-Seq data, for proteomics search. Description: Generate customized protein sequence database from RNA-Seq data for proteomics search biocViews: MassSpectrometry, Proteomics, SNP, RNASeq, Software, Transcription, AlternativeSplicing Author: xiaojing wang Maintainer: xiaojing wang source.ver: src/contrib/customProDB_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/customProDB_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/customProDB_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/customProDB_1.9.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/customProDB_1.12.0.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 Package: cycle Version: 1.26.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: a0b67bc4586ca792695728e3d3173026 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 source.ver: src/contrib/cycle_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/cycle_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/cycle_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/cycle_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cycle_1.26.0.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 Package: cytofkit Version: 1.4.10 Depends: R (>= 2.10.0), ggplot2, plyr Imports: tcltk, stats, Rtsne, e1071, flowCore, gplots, VGAM, reshape2, ggrepel, shiny, vegan, Biobase, doParallel, parallel, pdist, methods, destiny, FlowSOM(>= 1.4.0), igraph(>= 1.0.1), RANN(>= 2.5), Rcpp (>= 0.12.0) LinkingTo: Rcpp Suggests: knitr, RUnit, testthat, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: bb1f65c60a55ee9ff7d31892d93d8d0e NeedsCompilation: yes Title: cytofkit: an integrated mass cytometry data analysis pipeline Description: An integrated mass cytometry data analysis pipeline that enables simultaneous illustration of cellular diversity and progression. biocViews: FlowCytometry, GUI, CellBiology, Clustering, DimensionReduction, BiomedicalInformatics Author: Jinmiao Chen, Hao Chen Maintainer: Jinmiao Chen , Hao Chen VignetteBuilder: knitr source.ver: src/contrib/cytofkit_1.4.10.tar.gz win.binary.ver: bin/windows/contrib/3.3/cytofkit_1.4.10.zip win64.binary.ver: bin/windows64/contrib/3.3/cytofkit_1.4.10.zip mac.binary.ver: bin/macosx/contrib/3.3/cytofkit_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/cytofkit_1.4.10.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/cytofkit/inst/doc/cytofkit_example.R, vignettes/cytofkit/inst/doc/cytofkit_shinyAPP.R, vignettes/cytofkit/inst/doc/cytofkit_workflow.R htmlDocs: vignettes/cytofkit/inst/doc/cytofkit_example.html, vignettes/cytofkit/inst/doc/cytofkit_shinyAPP.html, vignettes/cytofkit/inst/doc/cytofkit_workflow.html htmlTitles: Quick Start, ShinyAPP tutorial, Analysis Pipeline Package: dada2 Version: 1.0.3 Depends: R (>= 3.2.0), Rcpp (>= 0.11.2) Imports: Biostrings (>= 2.32.1), ggplot2 (>= 1.0), data.table (>= 1.9.4), reshape2 (>= 1.4.1), ShortRead (>= 1.24.0) LinkingTo: Rcpp Suggests: testthat (>= 0.9.1), microbenchmark (>= 1.4.2), BiocStyle, knitr, rmarkdown License: LGPL-3 Archs: i386, x64 MD5sum: e77ec20798d7610c24ef86832d62cdbe NeedsCompilation: yes Title: Accurate, high-resolution sample inference from amplicon sequencing data Description: The dada2 package provides "OTU picking" functionality, but instead of picking OTUs the DADA2 algorithm exactly infers samples sequences. The dada2 pipeline starts from demultiplexed fastq files, and outputs inferred sample sequences and associated abundances after removing substitution and chimeric errors. Taxonomic classification is also available via a native implementation of the RDP classifier method. biocViews: Microbiome, Sequencing, Classification, Metagenomics Author: Benjamin Callahan , Paul McMurdie, Susan Holmes Maintainer: Benjamin Callahan URL: http://benjjneb.github.io/dada2/ VignetteBuilder: knitr BugReports: https://github.com/benjjneb/dada2/issues source.ver: src/contrib/dada2_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/dada2_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/dada2_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dada2_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dada2/inst/doc/dada2-intro.R htmlDocs: vignettes/dada2/inst/doc/dada2-intro.html htmlTitles: Introduction to dada2 Package: dagLogo Version: 1.10.2 Depends: R (>= 3.0.1), methods, biomaRt, grImport, grid, motifStack Imports: pheatmap, Biostrings Suggests: XML, UniProt.ws, BiocStyle, knitr, rmarkdown, testthat License: GPL (>=2) MD5sum: e6c3824fde2c2560b0b650e42c210623 NeedsCompilation: no Title: dagLogo Description: Visualize significant conserved amino acid sequence pattern in groups based on probability theory. biocViews: SequenceMatching, Visualization Author: Jianhong Ou, Alexey Stukalov, Niraj Nirala, Usha Acharya, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/dagLogo_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/dagLogo_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/dagLogo_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/dagLogo_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dagLogo_1.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dagLogo/inst/doc/dagLogo.R htmlDocs: vignettes/dagLogo/inst/doc/dagLogo.html htmlTitles: dagLogo Vignette Package: daMA Version: 1.44.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: e932c0995bc3f43d1226a97d64db97dd 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 source.ver: src/contrib/daMA_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/daMA_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/daMA_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/daMA_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/daMA_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DAPAR Version: 1.4.8 Depends: R (>= 3.3) Imports: MSnbase, RColorBrewer,stats,preprocessCore,Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2, limma,knitr,tmvtnorm,norm,impute, imputeLCMD, doParallel, parallel, foreach,grDevices, graphics, XLConnect, utils, cp4p (>= 0.3.5), scales, Matrix Suggests: BiocGenerics, Biobase, testthat, BiocStyle, Prostar License: Artistic-2.0 MD5sum: e7e29bff670ebab52556bb0f9bd90b55 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, DataImport Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut], Cosmin Lazar [ctb], Alexia Dorffer [ctb] Maintainer: Samuel Wieczorek VignetteBuilder: knitr source.ver: src/contrib/DAPAR_1.4.8.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DAPAR_1.4.8.tgz vignettes: vignettes/DAPAR/inst/doc/intro.pdf, vignettes/DAPAR/inst/doc/Prostar_UserManual.pdf, vignettes/DAPAR/inst/doc/UPSprotx2.pdf vignetteTitles: DAPAR One Page Introduction, Prostar user manual, UPSprotx2 dataset description hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DAPAR/inst/doc/Prostar_UserManual.R importsMe: Prostar Package: DART Version: 1.20.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 7f216e4213e124be2b33f4c07cd653e9 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 source.ver: src/contrib/DART_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DART_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DART_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DART_1.17.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DART_1.20.0.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 Package: DASiR Version: 1.11.0 Depends: Biostrings,IRanges,GenomicRanges Imports: XML License: LGPL (>= 3) MD5sum: 5d4ab9d6f8983d1159cd677566e251d7 NeedsCompilation: no Title: Distributed Annotation System in R Description: R package for programmatic retrieval of information from DAS servers. biocViews: Annotation Author: Oscar Flores, Anna Mantsoki Maintainer: Ricard Illa source.ver: src/contrib/DASiR_1.11.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/DASiR_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DASiR_1.11.0.tgz vignettes: vignettes/DASiR/inst/doc/DASiR.pdf vignetteTitles: Programmatic retrieval of information from DAS servers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DASiR/inst/doc/DASiR.R Package: DBChIP Version: 1.16.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: 648dc3515ba4872d9a58dd487f3ba3d0 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 source.ver: src/contrib/DBChIP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DBChIP_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DBChIP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DBChIP_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DBChIP_1.16.0.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 Package: dcGSA Version: 1.0.1 Depends: R (>= 3.3), Matrix Imports: BiocParallel Suggests: knitr License: GPL-2 MD5sum: 005189df0e124de136984d3df8a79f0f 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: 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 source.ver: src/contrib/dcGSA_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/dcGSA_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/dcGSA_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dcGSA_1.0.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DChIPRep Version: 1.2.3 Depends: R (>= 3.3), DESeq2 Imports: methods, stats, utils, ggplot2, fdrtool, reshape2, GenomicRanges, SummarizedExperiment, smoothmest, plyr, tidyr, assertthat, S4Vectors, purrr, soGGi, ChIPpeakAnno Suggests: mgcv, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENCE MD5sum: af658772698f99d77134609220a1e027 NeedsCompilation: no Title: DChIPRep - Analysis of chromatin modification ChIP-Seq data with replication Description: The DChIPRep package implements a methodology to assess differences between chromatin modification profiles in replicated ChIP-Seq studies as described in Chabbert et. al - http://www.dx.doi.org/10.15252/msb.20145776. A detailed description of the method is given in the software paper at https://doi.org/10.7717/peerj.1981 biocViews: Sequencing, ChIPSeq Author: Bernd Klaus [aut, cre], Christophe Chabbert [aut], Sebastian Gibb [ctb] Maintainer: Bernd Klaus SystemRequirements: Python 2.7, HTSeq (>= 0.6.1), numpy, argparse, sys VignetteBuilder: knitr source.ver: src/contrib/DChIPRep_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/DChIPRep_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/DChIPRep_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DChIPRep_1.2.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DChIPRep/inst/doc/DChIPRepVignette.R htmlDocs: vignettes/DChIPRep/inst/doc/DChIPRepVignette.html htmlTitles: DChIPRepVignette Package: ddCt Version: 1.28.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: RUnit License: LGPL-3 MD5sum: 279ffa04e5080c4c3033826ad1fc19bb 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 source.ver: src/contrib/ddCt_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ddCt_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ddCt_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ddCt_1.24.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ddCt_1.28.0.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: TRUE 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 Package: ddgraph Version: 1.16.0 Depends: graph, methods, Rcpp Imports: bnlearn (>= 2.8), gtools, pcalg, RColorBrewer, plotrix, MASS LinkingTo: Rcpp Suggests: Rgraphviz, e1071, ROCR, testthat License: GPL-3 Archs: i386, x64 MD5sum: 90ade74205c33ea08ac250017149bfc0 NeedsCompilation: yes Title: Distinguish direct and indirect interactions with Graphical Modelling Description: Distinguish direct from indirect interactions in gene regulation and infer combinatorial code from highly correlated variables such as transcription factor binding profiles. The package implements the Neighbourhood Consistent PC algorithm (NCPC) and draws Direct Dependence Graphs to represent dependence structure around a target variable. The package also provides a unified interface to other Graphical Modelling (Bayesian Network) packages for distinguishing direct and indirect interactions. biocViews: GraphAndNetwork Author: Robert Stojnic Maintainer: Robert Stojnic source.ver: src/contrib/ddgraph_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ddgraph_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ddgraph_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ddgraph_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ddgraph_1.16.0.tgz vignettes: vignettes/ddgraph/inst/doc/ddgraph.pdf vignetteTitles: Overview of the 'ddgraph' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ddgraph/inst/doc/ddgraph.R Package: debrowser Version: 1.0.10 Depends: R (>= 3.3.0), shiny, ggvis, jsonlite, edgeR, shinyjs Imports: clusterProfiler, DT, ReactomePA, ggplot2, RColorBrewer, annotate, gplots, AnnotationDbi, DESeq2, DOSE, igraph, grDevices, graphics, stats, utils, GenomicRanges, IRanges, S4Vectors, SummarizedExperiment, stringi, reshape2, org.Hs.eg.db, org.Mm.eg.db Suggests: testthat, rmarkdown, knitr, R.rsp License: GPL-3 + file LICENSE MD5sum: 1821f671a46cc03876259e4964c98cd9 NeedsCompilation: no Title: debrowser: 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, user can easily discover different parts of the data that like never have been done before. Manually creating and looking these plots takes time. With this system 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 Author: Alper Kucukural , Nicholas Merowsky , Manuel Garber Maintainer: Alper Kucukural URL: https://github.com/UMMS-Biocore/debrowser VignetteBuilder: knitr, R.rsp BugReports: https://github.com/UMMS-Biocore/debrowser/issues/new source.ver: src/contrib/debrowser_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/3.3/debrowser_1.0.10.zip win64.binary.ver: bin/windows64/contrib/3.3/debrowser_1.0.10.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/debrowser_1.0.10.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/debrowser/inst/doc/DEBrowser.R htmlDocs: vignettes/debrowser/inst/doc/DEBrowser.html htmlTitles: DEBrowser Vignette Package: DECIPHER Version: 2.0.2 Depends: R (>= 3.3.0), Biostrings (>= 2.35.12), RSQLite (>= 1.0.0), stats, parallel Imports: methods, DBI, S4Vectors, IRanges, XVector LinkingTo: Biostrings, RSQLite, S4Vectors, IRanges, XVector License: GPL-3 Archs: i386, x64 MD5sum: e5df51b93e3801ab67d67e871d4386a8 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 Author: Erik Wright Maintainer: Erik Wright source.ver: src/contrib/DECIPHER_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DECIPHER_2.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DECIPHER_2.0.2.zip mac.binary.ver: bin/macosx/contrib/3.3/DECIPHER_1.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DECIPHER_2.0.2.tgz vignettes: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.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 vignetteTitles: The Art of Multiple Sequence Alignment in R, 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 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DECIPHER/inst/doc/ArtOfAlignmentInR.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 Package: DeconRNASeq Version: 1.14.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: 09999f72a58e6d41187d6cc8f3535879 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 source.ver: src/contrib/DeconRNASeq_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DeconRNASeq_1.13.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DeconRNASeq_1.13.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DeconRNASeq_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DeconRNASeq_1.14.0.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 Package: DEDS Version: 1.46.0 Depends: R (>= 1.7.0) License: LGPL Archs: i386, x64 MD5sum: cc6d8315dab34acf0c5e6d8123b01eed NeedsCompilation: yes Title: Differential Expression via Distance Summary for Microarray Data Description: This library contains functions that calculate various statistics of differential expression for microarray data, including t statistics, fold change, F statistics, SAM, moderated t and F statistics and B statistics. It also implements a new methodology called DEDS (Differential Expression via Distance Summary), which selects differentially expressed genes by integrating and summarizing a set of statistics using a weighted distance approach. biocViews: Microarray, DifferentialExpression Author: Yuanyuan Xiao , Jean Yee Hwa Yang . Maintainer: Yuanyuan Xiao source.ver: src/contrib/DEDS_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEDS_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DEDS_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DEDS_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEDS_1.46.0.tgz vignettes: vignettes/DEDS/inst/doc/DEDS.pdf vignetteTitles: DEDS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEDS/inst/doc/DEDS.R Package: deepSNV Version: 1.18.1 Depends: R (>= 2.13.0), methods, graphics, parallel, Rhtslib, IRanges, GenomicRanges, SummarizedExperiment, Biostrings, VGAM, VariantAnnotation (>= 1.13.44), Imports: Rhtslib LinkingTo: Rhtslib Suggests: RColorBrewer, knitr License: GPL-3 Archs: i386, x64 MD5sum: 2a270512876f209a228d85f4d4073a05 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], David Jones [ctb], Inigo Martincorena [ctb], Moritz Gerstung [aut, cre] Maintainer: Moritz Gerstung URL: http://github.com/mg14/deepSNV VignetteBuilder: knitr source.ver: src/contrib/deepSNV_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/deepSNV_1.18.1.zip win64.binary.ver: bin/windows64/contrib/3.3/deepSNV_1.18.1.zip mac.binary.ver: bin/macosx/contrib/3.3/deepSNV_1.15.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/deepSNV_1.18.1.tgz vignettes: vignettes/deepSNV/inst/doc/deepSNV.pdf, vignettes/deepSNV/inst/doc/shearwater.pdf vignetteTitles: An R package for detecting low frequency variants in deep sequencing experiments, Subclonal variant calling with multiple samples and prior knowledge using shearwater hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/deepSNV/inst/doc/shearwaterML.html htmlTitles: Shearwater ML suggestsMe: GenomicFiles Package: DEFormats Version: 1.0.2 Imports: checkmate, DESeq2, edgeR (>= 3.13.4), GenomicRanges, methods, stats, SummarizedExperiment Suggests: BiocStyle (>= 1.8.0), knitr, rmarkdown, testthat License: GPL-3 MD5sum: 2a9410974826bfdf84f46461b14923a1 NeedsCompilation: no Title: Differential gene expression data formats converter Description: Covert between different data formats used by differential gene expression analysis tools. biocViews: 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 source.ver: src/contrib/DEFormats_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEFormats_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DEFormats_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEFormats_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEFormats/inst/doc/DEFormats.R htmlDocs: vignettes/DEFormats/inst/doc/DEFormats.html htmlTitles: Differential gene expression data formats converter importsMe: regionReport Package: DEGraph Version: 1.24.0 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: 1984fa4b0b3f7a611aab9a44c8761006 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 source.ver: src/contrib/DEGraph_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEGraph_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DEGraph_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DEGraph_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEGraph_1.24.0.tgz 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 suggestsMe: ToPASeq Package: DEGreport Version: 1.8.2 Depends: R (>= 3.2.0) Imports: plyr, utils, ggplot2, edgeR, BiocGenerics Suggests: knitr, biomaRt, RUnit, BiocStyle, BiocParallel, coda, Nozzle.R1 License: GPL (>=2) MD5sum: 4006a779e33f3bb68308ba9f4a7d9f84 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 Author: Lorena Pantano Maintainer: Lorena Pantano VignetteBuilder: knitr source.ver: src/contrib/DEGreport_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEGreport_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DEGreport_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/DEGreport_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEGreport_1.8.2.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.pdf vignetteTitles: DEGreport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEGreport/inst/doc/DEGreport.R Package: DEGseq Version: 1.26.0 Depends: R (>= 2.8.0), qvalue, samr, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: ca96d3c178a8b5543d816b493a481cb9 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 Author: Likun Wang and Xi Wang . Maintainer: Likun Wang source.ver: src/contrib/DEGseq_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEGseq_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DEGseq_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DEGseq_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEGseq_1.26.0.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 Package: deltaGseg Version: 1.12.2 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 9fa6ecc0b5ffbbcd16381881be94a86c 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 source.ver: src/contrib/deltaGseg_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/deltaGseg_1.11.1.zip win64.binary.ver: bin/windows64/contrib/3.3/deltaGseg_1.11.1.zip mac.binary.ver: bin/macosx/contrib/3.3/deltaGseg_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/deltaGseg_1.12.2.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 Package: DeMAND Version: 1.2.1 Depends: R (>= 2.14.0), KernSmooth, methods License: file LICENSE MD5sum: 7d81cb17f8bffce247e97dc0f7b26860 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 source.ver: src/contrib/DeMAND_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/DeMAND_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/DeMAND_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.3/DeMAND_0.99.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DeMAND_1.2.1.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 Package: derfinder Version: 1.6.4 Depends: R(>= 3.2) Imports: AnnotationDbi (>= 1.27.9), BiocParallel, bumphunter (>= 1.9.2), derfinderHelper (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges (>= 1.17.40), Hmisc, IRanges (>= 2.3.23), qvalue (>= 1.99.0), Rsamtools, rtracklayer, S4Vectors (>= 0.9.38) Suggests: BiocStyle, biovizBase, devtools (>= 1.6), derfinderData (>= 0.99.0), derfinderPlot, DESeq2, ggplot2, knitcitations (>= 1.0.1), knitr (>= 1.6), limma, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 808704baaff060f6f32b4c78a7f76318 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 Author: Leonardo Collado-Torres [aut, cre], Alyssa C. Frazee [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Leonardo Collado-Torres URL: https://github.com/lcolladotor/derfinder VignetteBuilder: knitr BugReports: https://github.com/lcolladotor/derfinder/issues source.ver: src/contrib/derfinder_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/derfinder_1.6.4.zip win64.binary.ver: bin/windows64/contrib/3.3/derfinder_1.6.4.zip mac.binary.ver: bin/macosx/contrib/3.3/derfinder_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/derfinder_1.6.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinder/inst/doc/derfinder-quickstart.R, vignettes/derfinder/inst/doc/derfinder-users-guide.R htmlDocs: vignettes/derfinder/inst/doc/derfinder-quickstart.html, vignettes/derfinder/inst/doc/derfinder-users-guide.html htmlTitles: derfinder quick start guide, derfinder users guide importsMe: derfinderPlot, regionReport Package: derfinderHelper Version: 1.6.3 Depends: R(>= 3.2.2) Imports: IRanges (>= 1.99.27), Matrix, S4Vectors (>= 0.2.2) Suggests: devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), BiocStyle, rmarkdown (>= 0.3.3), testthat License: Artistic-2.0 MD5sum: 3383798f422e724359fb0d3c838f2229 NeedsCompilation: no Title: derfinder helper package Description: Helper package for speeding up the derfinder package when using multiple cores. biocViews: DifferentialExpression, Sequencing, RNASeq, Software 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://github.com/leekgroup/derfinderHelper/issues source.ver: src/contrib/derfinderHelper_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/derfinderHelper_1.6.3.zip win64.binary.ver: bin/windows64/contrib/3.3/derfinderHelper_1.6.3.zip mac.binary.ver: bin/macosx/contrib/3.3/derfinderHelper_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/derfinderHelper_1.6.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderHelper/inst/doc/derfinderHelper.R htmlDocs: vignettes/derfinderHelper/inst/doc/derfinderHelper.html htmlTitles: Introduction to derfinderHelper importsMe: derfinder Package: derfinderPlot Version: 1.6.3 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), GenomeInfoDb (>= 1.3.3), GenomicFeatures, GenomicRanges (>= 1.17.40), ggbio (>= 1.13.13), ggplot2, IRanges (>= 1.99.28), limma, plyr, RColorBrewer, reshape2, S4Vectors (>= 0.9.38), scales Suggests: biovizBase, bumphunter (>= 1.7.6), derfinderData (>= 0.99.0), devtools (>= 1.6), knitcitations (>= 1.0.1), knitr (>= 1.6), BiocStyle, org.Hs.eg.db, rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 540f271c758955a3d9fa00d06fac8ac3 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 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://github.com/leekgroup/derfinderPlot/issues source.ver: src/contrib/derfinderPlot_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/derfinderPlot_1.6.3.zip win64.binary.ver: bin/windows64/contrib/3.3/derfinderPlot_1.6.3.zip mac.binary.ver: bin/macosx/contrib/3.3/derfinderPlot_1.3.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/derfinderPlot_1.6.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/derfinderPlot/inst/doc/derfinderPlot.R htmlDocs: vignettes/derfinderPlot/inst/doc/derfinderPlot.html htmlTitles: Introduction to derfinderPlot suggestsMe: derfinder, regionReport Package: DESeq Version: 1.24.0 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) Archs: i386, x64 MD5sum: ba913f5613c8336f7071516e198a18be 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, ChIPSeq, RNASeq, SAGE, DifferentialExpression Author: Simon Anders, EMBL Heidelberg Maintainer: Simon Anders URL: http://www-huber.embl.de/users/anders/DESeq source.ver: src/contrib/DESeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DESeq_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DESeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DESeq_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DESeq_1.24.0.tgz vignettes: vignettes/DESeq/inst/doc/DESeq.pdf vignetteTitles: Analysing RNA-Seq data with the "DESeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq/inst/doc/DESeq.R dependsOnMe: DBChIP, metaseqR, Polyfit, SeqGSEA, TCC, tRanslatome importsMe: ampliQueso, ArrayExpressHTS, easyRNASeq, EDASeq, EDDA, gCMAP, HTSFilter, rnaSeqMap, ToPASeq suggestsMe: BitSeq, compcodeR, DESeq2, dexus, DiffBind, ELBOW, gage, genefilter, IHW, oneChannelGUI, regionReport, SSPA, XBSeq Package: DESeq2 Version: 1.12.4 Depends: S4Vectors (>= 0.9.25), IRanges, GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, locfit, geneplotter, ggplot2, Hmisc, Rcpp (>= 0.11.0) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, BiocStyle, vsn, pheatmap, RColorBrewer, airway, IHW, tximport, tximportData, readr, pasilla (>= 0.2.10), DESeq License: LGPL (>= 3) Archs: i386, x64 MD5sum: 08f115d77d4abe94f655be6c9f3144f8 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, ChIPSeq, RNASeq, SAGE, DifferentialExpression, GeneExpression, Transcription Author: Michael Love (HSPH Boston), Simon Anders, Wolfgang Huber (EMBL Heidelberg) Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/DESeq2_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/DESeq2_1.12.4.zip win64.binary.ver: bin/windows64/contrib/3.3/DESeq2_1.12.4.zip mac.binary.ver: bin/macosx/contrib/3.3/DESeq2_1.9.32.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DESeq2_1.12.4.tgz vignettes: vignettes/DESeq2/inst/doc/DESeq2.pdf vignetteTitles: Analyzing RNA-seq data with the "DESeq2" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DESeq2/inst/doc/DESeq2.R dependsOnMe: DChIPRep, DEXSeq, FourCSeq, MLSeq, rgsepd, TCC, XBSeq importsMe: debrowser, DEFormats, DiffBind, EnrichmentBrowser, FourCSeq, GenoGAM, Glimma, HTSFilter, isomiRs, JunctionSeq, pcaExplorer, regionReport, ReportingTools, SNPhood, systemPipeR, ToPASeq suggestsMe: biobroom, BiocGenerics, compcodeR, derfinder, gage, GenomicAlignments, GenomicRanges, IHW, oneChannelGUI, phyloseq, RUVSeq, scran, subSeq, tximport, variancePartition Package: destiny Version: 1.2.1 Depends: R (>= 3.2.0), methods, Biobase Imports: graphics, Rcpp (>= 0.10.3), RcppEigen, BiocGenerics, Matrix, Hmisc, FNN, VIM, proxy, igraph, scatterplot3d LinkingTo: Rcpp, RcppEigen Suggests: RColorBrewer, ggplot2, nbconvertR Enhances: rgl License: GPL Archs: i386, x64 MD5sum: af0caa87b775047834140f56e077abd4 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 SystemRequirements: C++11 VignetteBuilder: nbconvertR source.ver: src/contrib/destiny_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/destiny_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/destiny_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/destiny_1.2.1.tgz vignettes: vignettes/destiny/inst/doc/destiny.pdf vignetteTitles: destiny.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: cytofkit suggestsMe: scater Package: DEXSeq Version: 1.18.4 Depends: BiocParallel, Biobase, SummarizedExperiment, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), DESeq2 (>= 1.9.11), AnnotationDbi, RColorBrewer, S4Vectors Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr License: GPL (>= 3) MD5sum: 8c8b9e96cdd566f32eaab822bcef2b9d 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: Sequencing, RNASeq, DifferentialExpression Author: Simon Anders and Alejandro Reyes Maintainer: Alejandro Reyes VignetteBuilder: knitr source.ver: src/contrib/DEXSeq_1.18.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/DEXSeq_1.18.4.zip win64.binary.ver: bin/windows64/contrib/3.3/DEXSeq_1.18.4.zip mac.binary.ver: bin/macosx/contrib/3.3/DEXSeq_1.15.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DEXSeq_1.18.4.tgz vignettes: vignettes/DEXSeq/inst/doc/DEXSeq.pdf vignetteTitles: Analyzing RNA-seq data for differential exon usage with the "DEXSeq" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DEXSeq/inst/doc/DEXSeq.R suggestsMe: GenomicRanges, oneChannelGUI, subSeq Package: dexus Version: 1.12.1 Depends: R (>= 2.15), methods, BiocGenerics Suggests: parallel, statmod, stats, DESeq, RColorBrewer License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: ae7d981c6209041040c55b3025b3f983 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: Sequencing, RNASeq, GeneExpression, DifferentialExpression, CellBiology, Classification, QualityControl Author: Guenter Klambauer Maintainer: Guenter Klambauer source.ver: src/contrib/dexus_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/dexus_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/dexus_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.3/dexus_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dexus_1.12.1.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 Package: DFP Version: 1.30.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 7400505d22d26d5408aef8e07c81ac64 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 source.ver: src/contrib/DFP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DFP_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DFP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DFP_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DFP_1.30.0.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 Package: DiffBind Version: 2.0.9 Depends: R (>= 3.3.0), GenomicRanges, SummarizedExperiment Imports: RColorBrewer, amap, edgeR, gplots, grDevices, limma, GenomicAlignments, locfit, stats, utils, IRanges, zlibbioc, lattice, systemPipeR, tools, Rcpp, dplyr, BiocParallel, parallel, S4Vectors, Rsamtools, DESeq2 LinkingTo: Rsamtools (>= 1.19.38), Rcpp Suggests: DESeq, BiocStyle, testthat Enhances: rgl, XLConnect License: Artistic-2.0 Archs: i386, x64 MD5sum: 9d00cf8d809f138ce2a3b1e5620bbf51 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 Author: Rory Stark, Gord Brown Maintainer: Rory Stark source.ver: src/contrib/DiffBind_2.0.9.tar.gz win.binary.ver: bin/windows/contrib/3.3/DiffBind_2.0.9.zip win64.binary.ver: bin/windows64/contrib/3.3/DiffBind_2.0.9.zip mac.binary.ver: bin/macosx/contrib/3.3/DiffBind_1.15.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DiffBind_2.0.9.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 Package: diffGeneAnalysis Version: 1.54.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: a364291e08e3c329708ccace3b642bf3 NeedsCompilation: no Title: Performs differential gene expression Analysis Description: Analyze microarray data biocViews: Microarray, DifferentialExpression Author: Choudary Jagarlamudi Maintainer: Choudary Jagarlamudi source.ver: src/contrib/diffGeneAnalysis_1.54.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/diffGeneAnalysis_1.54.0.zip win64.binary.ver: bin/windows64/contrib/3.3/diffGeneAnalysis_1.54.0.zip mac.binary.ver: bin/macosx/contrib/3.3/diffGeneAnalysis_1.51.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diffGeneAnalysis_1.54.0.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 Package: diffHic Version: 1.4.3 Depends: R (>= 3.3.0), GenomicRanges, InteractionSet, SummarizedExperiment Imports: Rsamtools, Rhtslib, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics, grDevices, graphics, stats, utils LinkingTo: Rhtslib, zlibbioc Suggests: BSgenome.Ecoli.NCBI.20080805, Matrix License: GPL-3 Archs: i386, x64 MD5sum: 4826d412688660a9fc332d558e0c7dfa 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 Maintainer: Aaron Lun source.ver: src/contrib/diffHic_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/diffHic_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/diffHic_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/diffHic_1.1.15.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diffHic_1.4.3.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 Rfiles: vignettes/diffHic/inst/doc/bam2hdf.R Package: DiffLogo Version: 1.2.1 Depends: R (>= 1.8.0), stats, cba, Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: 8e53858d50a9fed26ecc46adcab8c144 NeedsCompilation: no Title: DiffLogo: A comparative visualisation of sequence motifs Description: DiffLogo is an easy-to-use tool to visualize motif differences. biocViews: Software, SequenceMatching, MultipleComparison, MotifAnnotation, Visualization Author: Martin Nettling [aut], Hendrik Treutler [aut, cre], Jan Grau [aut, ctb], Jens Keilwagen [aut, ctb], Stefan Posch [aut], Ivo Grosse [aut] Maintainer: Hendrik Treutler URL: https://github.com/mgledi/DiffLogo/ VignetteBuilder: knitr BugReports: https://github.com/mgledi/DiffLogo/issues source.ver: src/contrib/DiffLogo_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/DiffLogo_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/DiffLogo_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DiffLogo_1.2.1.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 Package: diffloop Version: 1.0.2 Imports: methods, GenomicRanges, foreach, plyr, dplyr, reshape2, ggplot2, matrixStats, Sushi, edgeR, locfit, statmod, biomaRt, GenomeInfoDb, S4Vectors, IRanges, grDevices, graphics, stats, utils, Biobase, readr Suggests: diffloopdata, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: e64f029841a7531b7de2655ac01c5873 NeedsCompilation: no Title: Differential DNA loop calling from ChIA-PET data Description: A suite of tools for subsetting, visualizing, annotating, and statistically analyzing the results of one or more ChIA-PET experiments. biocViews: Preprocessing, QualityControl, Visualization, DataImport, DataRepresentation, GO Author: Caleb Lareau [aut, cre], Martin Aryee [aut] Maintainer: Caleb Lareau VignetteBuilder: knitr source.ver: src/contrib/diffloop_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/diffloop_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/diffloop_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diffloop_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/diffloop/inst/doc/diffloop.R htmlDocs: vignettes/diffloop/inst/doc/diffloop.html htmlTitles: diffloop: Identifying differential DNA loops from ChIA-PET data Package: diggit Version: 1.5.2 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: file LICENSE MD5sum: b91f51775091e0748c5daeb5345c7711 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 source.ver: src/contrib/diggit_1.5.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/diggit_1.5.2.zip win64.binary.ver: bin/windows64/contrib/3.3/diggit_1.5.2.zip mac.binary.ver: bin/macosx/contrib/3.3/diggit_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/diggit_1.5.2.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 Package: DirichletMultinomial Version: 1.14.0 Depends: S4Vectors, IRanges Imports: stats4, methods, BiocGenerics Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: 31d4762053e048ef0ab83c5221ecfd19 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: Microbiome, Sequencing, Clustering, Classification, Metagenomics Author: Martin Morgan Maintainer: Martin Morgan SystemRequirements: gsl source.ver: src/contrib/DirichletMultinomial_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DirichletMultinomial_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DirichletMultinomial_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DirichletMultinomial_1.11.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DirichletMultinomial_1.14.0.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 Package: dks Version: 1.18.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 20984ccd17885b2d7265f574c3bbefba 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 source.ver: src/contrib/dks_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dks_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dks_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/dks_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dks_1.18.0.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 Package: DMRcaller Version: 1.4.2 Depends: R (>= 3.2), GenomicRanges, IRanges, S4Vectors Imports: parallel, Rcpp, RcppRoll Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: b1d55ed063fb2c4cf7ec9d3b296f2cd6 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 and Jonathan Michael Foonlan Tsang Maintainer: Nicolae Radu Zabet VignetteBuilder: knitr source.ver: src/contrib/DMRcaller_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DMRcaller_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DMRcaller_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/DMRcaller_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DMRcaller_1.4.2.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 Package: DMRcate Version: 1.8.6 Depends: R (>= 3.3.0), minfi, DSS, DMRcatedata Imports: limma, GenomicRanges, parallel, methods, graphics, plyr, Gviz, IRanges, stats, utils, S4Vectors Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19, IlluminaHumanMethylationEPICanno.ilm10b2.hg19 License: file LICENSE MD5sum: f888beac30a36c7394905df485b20d9d 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 Bisulphite 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 Author: Tim Peters Maintainer: Tim Peters VignetteBuilder: knitr source.ver: src/contrib/DMRcate_1.8.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/DMRcate_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/DMRcate_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.3/DMRcate_1.5.61.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DMRcate_1.8.6.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 importsMe: MEAL Package: DMRforPairs Version: 1.8.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 53f6970a5c217968791dc6fed5640bba 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/ source.ver: src/contrib/DMRforPairs_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DMRforPairs_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DMRforPairs_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DMRforPairs_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DMRforPairs_1.8.0.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 Package: DNABarcodes Version: 1.2.2 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: bcda2b6b9b9c8ed1fee26e2c5c79ac37 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 source.ver: src/contrib/DNABarcodes_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DNABarcodes_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DNABarcodes_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DNABarcodes_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNABarcodes/inst/doc/DNABarcodes.R htmlDocs: vignettes/DNABarcodes/inst/doc/DNABarcodes.html htmlTitles: DNABarcodes Package: DNAcopy Version: 1.46.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: 32c9f6f5d5d45d6aa9b6a2a9857f33dd 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 source.ver: src/contrib/DNAcopy_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DNAcopy_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DNAcopy_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DNAcopy_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DNAcopy_1.46.0.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, CopyNumber450k, CRImage, PureCN, snapCGH, SomatiCA importsMe: ADaCGH2, ArrayTV, ChAMP, Clonality, cn.farms, CNAnorm, CNVrd2, contiBAIT, conumee, CopywriteR, GWASTools, MEDIPS, MinimumDistance, QDNAseq, Repitools, snapCGH, SomatiCA suggestsMe: beadarraySNP, Clonality, cn.mops, fastseg, genoset Package: DNAshapeR Version: 1.0.2 Depends: R (>= 3.3), 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: 9c0f42589372d36d8bf59f306a7d5563 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 source.ver: src/contrib/DNAshapeR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DNAshapeR_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DNAshapeR_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DNAshapeR_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DNAshapeR/inst/doc/DNAshapeR.R htmlDocs: vignettes/DNAshapeR/inst/doc/DNAshapeR.html htmlTitles: DNAshapeR Package: domainsignatures Version: 1.32.0 Depends: R (>= 2.4.0), KEGG.db, prada, biomaRt, methods Imports: AnnotationDbi License: Artistic-2.0 MD5sum: fb8a5a311b2d83ccd9cce4b0c691c7fa NeedsCompilation: no Title: Geneset enrichment based on InterPro domain signatures Description: Find significantly enriched gene classifications in a list of functionally undescribed genes based on their InterPro domain structure. biocViews: Annotation, Pathways, GeneSetEnrichment Author: Florian Hahne, Tim Beissbarth Maintainer: Florian Hahne source.ver: src/contrib/domainsignatures_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/domainsignatures_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/domainsignatures_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/domainsignatures_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/domainsignatures_1.32.0.tgz vignettes: vignettes/domainsignatures/inst/doc/domainenrichment.pdf vignetteTitles: Gene set enrichment using InterPro domain signatures hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/domainsignatures/inst/doc/domainenrichment.R Package: doppelgangR Version: 1.0.2 Depends: R (>= 3.3), Biobase, BiocParallel Imports: sva, impute, digest, mnormt, methods, grDevices, graphics, stats, utils Suggests: BiocStyle, knitr, rmarkdown, curatedOvarianData, ROCR, pROC, RUnit, simulatorZ, proxy License: GPL (>=2.0) MD5sum: b51969324165d3b0f45c0a46f16d0a41 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: RNASeq, Microarray, GeneExpression, QualityControl Author: Levi Waldron, Markus Riester, Marcel Ramos Maintainer: Levi Waldron URL: https://github.com/lwaldron/doppelgangR VignetteBuilder: knitr BugReports: https://github.com/lwaldron/doppelgangR/issues source.ver: src/contrib/doppelgangR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/doppelgangR_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/doppelgangR_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/doppelgangR_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/doppelgangR/inst/doc/doppelgangR.R htmlDocs: vignettes/doppelgangR/inst/doc/doppelgangR.html htmlTitles: doppelgangR vignette Package: DOQTL Version: 1.8.0 Depends: R (>= 3.0.0), BSgenome.Mmusculus.UCSC.mm10, GenomicRanges, VariantAnnotation Imports: annotate, annotationTools, biomaRt, Biobase, BiocGenerics, corpcor, doParallel, foreach, fpc, hwriter, IRanges, iterators, mclust, QTLRel, regress, rhdf5, Rsamtools, RUnit, XML Suggests: MUGAExampleData, doMPI License: GPL-3 Archs: i386, x64 MD5sum: 18abe014447f3710d6ec407d89d1eb97 NeedsCompilation: yes Title: Genotyping and QTL Mapping in DO Mice Description: DOQTL is a quantitative trait locus (QTL) mapping pipeline designed for Diversity Outbred mice and other multi-parent outbred populations. The package reads in data from genotyping arrays and perform haplotype reconstruction using a hidden Markov model (HMM). The haplotype probabilities from the HMM are then used to perform linkage mapping. When founder sequences are available, DOQTL can use the haplotype reconstructions to impute the founder sequences onto DO genomes and perform association mapping. biocViews: GeneticVariability, SNP, Genetics, HiddenMarkovModel Author: Daniel Gatti, Karl Broman, Andrey Shabalin, Petr Simecek Maintainer: Daniel Gatti URL: http://do.jax.org source.ver: src/contrib/DOQTL_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DOQTL_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DOQTL_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DOQTL_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DOQTL_1.8.0.tgz vignettes: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.pdf vignetteTitles: QTL Mapping using Diversity Outbred Mice hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOQTL/inst/doc/QTL_Mapping_DO_Mice.R Package: DOSE Version: 2.10.7 Depends: R (>= 3.1.0) Imports: AnnotationDbi, DO.db, ggplot2, GOSemSim, graphics, grDevices, grid, igraph, methods, plyr, qvalue, reshape2, scales, stats4, utils Suggests: org.Hs.eg.db, clusterProfiler, knitr, BiocStyle License: Artistic-2.0 MD5sum: efedb464ed94567791746ecaa8d396aa 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 with contributions from Li-Gen Wang, Vladislav Petyuk and Giovanni Dall'Olio. Maintainer: Guangchuang Yu URL: http://guangchuangyu.github.io/DOSE VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues source.ver: src/contrib/DOSE_2.10.7.tar.gz win.binary.ver: bin/windows/contrib/3.3/DOSE_2.10.7.zip win64.binary.ver: bin/windows64/contrib/3.3/DOSE_2.10.7.zip mac.binary.ver: bin/macosx/contrib/3.3/DOSE_2.7.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DOSE_2.10.7.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DOSE/inst/doc/DOSE.R htmlDocs: vignettes/DOSE/inst/doc/DOSE.html htmlTitles: Disease Ontology Semantic and Enrichment analysis dependsOnMe: clusterProfiler, ReactomePA importsMe: ChIPseeker, debrowser, facopy suggestsMe: GOSemSim Package: DRIMSeq Version: 1.0.2 Depends: R (>= 3.3.0) Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, methods, BiocParallel, edgeR, utils, stats, grDevices, ggplot2, reshape2 Suggests: PasillaTranscriptExpr, GeuvadisTranscriptExpr, grid, BiocStyle, knitr, testthat License: GPL (>= 3) MD5sum: a6dc02b76d942c700273db712e735479 NeedsCompilation: no Title: Differential splicing and sQTL analyses with Dirichlet-multinomial model in RNA-Seq Description: The package provides two frameworks. One for the differential splicing analysis between different conditions and one for the sQTL analysis. Both are based on modeling the counts of genomic features (i.e., transcripts, exons or exonic bins) with Dirichlet-multinomial distribution. The package also makes available functions for visualization and exploration of the data and results. biocViews: SNP, AlternativeSplicing, DifferentialSplicing, Genetics, RNASeq, Sequencing, WorkflowStep, MultipleComparison, GeneExpression, DifferentialExpression Author: Malgorzata Nowicka [aut, cre] Maintainer: Malgorzata Nowicka VignetteBuilder: knitr source.ver: src/contrib/DRIMSeq_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DRIMSeq_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DRIMSeq_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DRIMSeq_1.0.2.tgz vignettes: vignettes/DRIMSeq/inst/doc/DRIMSeq.pdf vignetteTitles: Differential splicing and sQTL analyses in RNA-seq with 'DRIMSeq' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DRIMSeq/inst/doc/DRIMSeq.R Package: DriverNet Version: 1.12.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 403e9c689778eedd5079cbd62e3fc82a 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 source.ver: src/contrib/DriverNet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DriverNet_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DriverNet_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DriverNet_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DriverNet_1.12.0.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 Package: DrugVsDisease Version: 2.12.0 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: 0c70c9df38bd5bdb7b69737bc12ba8e8 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 source.ver: src/contrib/DrugVsDisease_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DrugVsDisease_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DrugVsDisease_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DrugVsDisease_2.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DrugVsDisease_2.12.0.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 Package: DSS Version: 2.12.0 Depends: Biobase, bsseq, splines, methods Suggests: BiocStyle License: GPL Archs: i386, x64 MD5sum: 1042b78816242f79057666ef61c65a01 NeedsCompilation: yes Title: Dispersion shrinakge 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, ChIPSeq, DNAMethylation,GeneExpression, DifferentialExpression,DifferentialMethylation Author: Hao Wu Maintainer: Hao Wu source.ver: src/contrib/DSS_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DSS_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DSS_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DSS_2.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DSS_2.12.0.tgz vignettes: vignettes/DSS/inst/doc/DSS.pdf vignetteTitles: Differential expression for RNA-seq data with dispersion shrinkage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DSS/inst/doc/DSS.R dependsOnMe: DMRcate Package: DTA Version: 2.18.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: 8a9808b7a31ef699d692a35a3efc7b93 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 source.ver: src/contrib/DTA_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DTA_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DTA_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DTA_2.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DTA_2.18.0.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 Package: dualKS Version: 1.32.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: 5b21537f64383d8e0f1bf70d74399056 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 source.ver: src/contrib/dualKS_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dualKS_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dualKS_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/dualKS_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dualKS_1.32.0.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 Package: DupChecker Version: 1.10.2 Imports: tools, R.utils, RCurl Suggests: knitr License: GPL (>= 2) MD5sum: 1e4f15a72db07bb232c433d73fa76dfc NeedsCompilation: no Title: a package for checking high-throughput genomic data redundancy in meta-analysis Description: Meta-analysis has become a popular approach for high-throughput genomic data analysis because it often can significantly increase power to detect biological signals or patterns in datasets. However, when using public-available databases for meta-analysis, duplication of samples is an often encountered problem, especially for gene expression data. Not removing duplicates would make study results questionable. We developed a Bioconductor package DupChecker that efficiently identifies duplicated samples by generating MD5 fingerprints for raw data. biocViews: Preprocessing Author: Quanhu Sheng, Yu Shyr, Xi Chen Maintainer: "Quanhu SHENG" VignetteBuilder: knitr source.ver: src/contrib/DupChecker_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/DupChecker_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/DupChecker_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/DupChecker_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DupChecker_1.10.2.tgz vignettes: vignettes/DupChecker/inst/doc/DupChecker.pdf vignetteTitles: Validate genomic data with "DupChecker" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/DupChecker/inst/doc/DupChecker.R Package: dupRadar Version: 1.2.2 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: 2c4527c61a83bdcaa9fb46c9865ae3a6 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 Author: Sergi Sayols , Holger Klein Maintainer: Sergi Sayols , Holger Klein VignetteBuilder: knitr source.ver: src/contrib/dupRadar_1.2.2.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dupRadar_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/dupRadar/inst/doc/dupRadar.R htmlDocs: vignettes/dupRadar/inst/doc/dupRadar.html htmlTitles: Using dupRadar Package: dyebias Version: 1.32.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: be6352ba71466cb5bac6feb39cf29a31 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 source.ver: src/contrib/dyebias_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/dyebias_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/dyebias_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/dyebias_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/dyebias_1.32.0.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 Package: DynDoc Version: 1.50.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 038c34335e06a87597b0134f85ee89f5 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 source.ver: src/contrib/DynDoc_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/DynDoc_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/DynDoc_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/DynDoc_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/DynDoc_1.50.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets Package: EasyqpcR Version: 1.14.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: 5b5509e88eff42c4a2e9bd873cf0d424 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 source.ver: src/contrib/EasyqpcR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EasyqpcR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EasyqpcR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/EasyqpcR_1.11.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EasyqpcR_1.14.0.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 Package: easyRNASeq Version: 2.8.2 Imports: Biobase (>= 2.31.3), BiocGenerics (>= 0.17.2), BiocParallel (>= 1.5.1), biomaRt (>= 2.27.2), Biostrings (>= 2.39.3), DESeq (>= 1.23.0), edgeR (>= 3.13.4), GenomeInfoDb (>= 1.7.3), genomeIntervals (>= 1.27.0), GenomicAlignments (>= 1.7.3), GenomicRanges (>= 1.23.16), SummarizedExperiment (>= 1.1.11), graphics, IRanges (>= 2.5.27), LSD (>= 3.0), locfit, methods, parallel, Rsamtools (>= 1.23.1), S4Vectors (>= 0.9.38), ShortRead (>= 1.29.1), utils Suggests: BiocStyle (>= 1.9.2), BSgenome (>= 1.39.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, GenomicFeatures (>= 1.23.15), knitr, rmarkdown, RnaSeqTutorial (>= 0.9.0), RUnit (>= 0.4.31) License: Artistic-2.0 MD5sum: 86357cd738af3d6da2bc76730df26b97 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 Author: Nicolas Delhomme, Ismael Padioleau, Bastian Schiffthaler, Niklas Maehler Maintainer: Nicolas Delhomme VignetteBuilder: knitr source.ver: src/contrib/easyRNASeq_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/easyRNASeq_2.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/easyRNASeq_2.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/easyRNASeq_2.5.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/easyRNASeq_2.8.2.tgz vignettes: vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf vignetteTitles: easyRNASeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/easyRNASeq/inst/doc/easyRNASeq.R, vignettes/easyRNASeq/inst/doc/simpleRNASeq.R htmlDocs: vignettes/easyRNASeq/inst/doc/simpleRNASeq.html htmlTitles: geneNetworkR suggestsMe: SeqGSEA Package: EBarrays Version: 2.36.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: a92a47b5188c411f700ce4d549793562 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 source.ver: src/contrib/EBarrays_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBarrays_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBarrays_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/EBarrays_2.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBarrays_2.36.0.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 Package: EBcoexpress Version: 1.16.0 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: 24011b4116c0f3aa897aa58ca4708d6e 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 source.ver: src/contrib/EBcoexpress_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBcoexpress_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBcoexpress_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/EBcoexpress_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBcoexpress_1.16.0.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 Package: EBImage Version: 4.14.2 Imports: BiocGenerics (>= 0.7.1), methods, graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7), utils Suggests: BiocStyle, digest, knitr, rmarkdown License: LGPL Archs: i386, x64 MD5sum: a21fa336c845f608b8659ebab8da7d5d 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 source.ver: src/contrib/EBImage_4.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBImage_4.14.2.zip win64.binary.ver: bin/windows64/contrib/3.3/EBImage_4.14.2.zip mac.binary.ver: bin/macosx/contrib/3.3/EBImage_4.11.14.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBImage_4.14.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBImage/inst/doc/EBImage-introduction.R htmlDocs: vignettes/EBImage/inst/doc/EBImage-introduction.html htmlTitles: Introduction to EBImage dependsOnMe: CRImage, flowcatchR, imageHTS importsMe: flowCHIC suggestsMe: ggtree, HilbertVis, tofsims Package: EBSEA Version: 1.0.0 Imports: edgeR, limma, gtools, graphics, stats License: GPL-2 MD5sum: 41f5a8bb7993b89300a964b1ed9c9381 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 source.ver: src/contrib/EBSEA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBSEA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBSEA_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBSEA_1.0.0.tgz vignettes: vignettes/EBSEA/inst/doc/EBSEA.pdf vignetteTitles: EBSEA: Exon Based Strategy for Expression Analysis of genes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EBSEA/inst/doc/EBSEA.R Package: EBSeq Version: 1.12.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 8d3aa1ef92ab87f20da41627110ffec1 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: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBSeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/EBSeq_1.9.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBSeq_1.12.0.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 suggestsMe: compcodeR Package: EBSeqHMM Version: 1.6.0 Depends: EBSeq License: Artistic-2.0 MD5sum: 9cfd86363d3030996bc7b8db78c172d9 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: StatisticalMethod, DifferentialExpression, MultipleComparison, RNASeq, Sequencing, GeneExpression, Bayesian, HiddenMarkovModel, TimeCourse Author: Ning Leng, Christina Kendziorski Maintainer: Ning Leng source.ver: src/contrib/EBSeqHMM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EBSeqHMM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EBSeqHMM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/EBSeqHMM_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EBSeqHMM_1.6.0.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 Package: ecolitk Version: 1.44.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: ab863a7f948b3f40519827e2468ea3a4 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 source.ver: src/contrib/ecolitk_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ecolitk_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ecolitk_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ecolitk_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ecolitk_1.44.0.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 Package: EDASeq Version: 2.6.2 Depends: Biobase (>= 2.15.1), ShortRead (>= 1.11.42) Imports: methods, graphics, BiocGenerics, IRanges (>= 1.13.9), DESeq, aroma.light, Rsamtools (>= 1.5.75), biomaRt, Biostrings, AnnotationDbi, GenomicFeatures, GenomicRanges Suggests: BiocStyle, knitr, yeastRNASeq, leeBamViews, edgeR, KernSmooth License: Artistic-2.0 MD5sum: 9e4bb224c69a77895caa1b53d76692f1 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: 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 source.ver: src/contrib/EDASeq_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/EDASeq_2.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/EDASeq_2.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/EDASeq_2.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EDASeq_2.6.2.tgz vignettes: vignettes/EDASeq/inst/doc/EDASeq.pdf vignetteTitles: EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EDASeq/inst/doc/EDASeq.R dependsOnMe: metaseqR, RUVSeq importsMe: EnrichmentBrowser, TCGAbiolinks suggestsMe: HTSFilter, oneChannelGUI Package: EDDA Version: 1.10.0 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: ad281e531ba4bbd6e0318c86b5c1aa14 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: 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 source.ver: src/contrib/EDDA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/EDDA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/EDDA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/EDDA_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EDDA_1.10.0.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: edge Version: 2.4.2 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: fa70cef6626c978114148fda3d1348cc 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 source.ver: src/contrib/edge_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/edge_2.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/edge_2.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/edge_2.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/edge_2.4.2.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 Package: edgeR Version: 3.14.0 Depends: R (>= 2.15.0), limma Imports: graphics, stats, utils, methods Suggests: MASS, statmod, splines, locfit, KernSmooth License: GPL (>=2) Archs: i386, x64 MD5sum: c2e95796f2635668a1b3b9132a0fe977 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 counts, including ChIP-seq, SAGE and CAGE. biocViews: GeneExpression, Transcription, AlternativeSplicing, Coverage, DifferentialExpression, DifferentialSplicing, GeneSetEnrichment, Genetics, Bayesian, Clustering, Regression, TimeCourse, SAGE, Sequencing, ChIPSeq, RNASeq, BatchEffect, MultipleComparison, Normalization, QualityControl Author: Yunshun Chen , Aaron Lun , Davis McCarthy , Xiaobei Zhou , Mark Robinson , Gordon Smyth Maintainer: Yunshun Chen , Aaron Lun , Mark Robinson , Davis McCarthy , Gordon Smyth URL: http://bioinf.wehi.edu.au/edgeR source.ver: src/contrib/edgeR_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/edgeR_3.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/edgeR_3.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/edgeR_3.11.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/edgeR_3.14.0.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: DBChIP, debrowser, manta, methylMnM, MLSeq, RUVSeq, TCC, tRanslatome importsMe: affycoretools, ampliQueso, ArrayExpressHTS, compcodeR, csaw, DEFormats, DEGreport, DiffBind, diffHic, diffloop, DRIMSeq, easyRNASeq, EBSEA, EDDA, EGSEA, EnrichmentBrowser, erccdashboard, Glimma, HTSFilter, MEDIPS, metaseqR, msmsTests, PROPER, Repitools, rnaSeqMap, scater, scde, scran, STATegRa, systemPipeR, TCGAbiolinks, ToPASeq, tweeDEseq suggestsMe: baySeq, biobroom, BitSeq, ClassifyR, clonotypeR, cqn, EDASeq, gage, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, missMethyl, oneChannelGUI, regionReport, SSPA, subSeq, tximport, variancePartition Package: EGAD Version: 1.0.4 Depends: R(>= 3.3) Imports: gplots, Biobase, GEOquery, limma, arrayQualityMetrics, impute, RColorBrewer, zoo, igraph, plyr, Matrix, MASS, RCurl Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: 3533298f9b6cb39bb6cc1077eaa4f535 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 Developer [aut, cre], Melanie Weber Developer [aut, ctb], Paul Pavlidis Author [aut], Jesse Gillis Author [aut, ctb] Maintainer: Sara Ballouz VignetteBuilder: knitr source.ver: src/contrib/EGAD_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/EGAD_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/EGAD_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EGAD_1.0.4.tgz vignettes: vignettes/EGAD/inst/doc/EGAD.pdf vignetteTitles: "EGAD user guide" hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EGAD/inst/doc/EGAD.R Package: EGSEA Version: 1.0.3 Depends: R (>= 3.3), 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, grDevices, graphics, utils, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, EGSEAdata Suggests: BiocStyle, knitr, testthat License: GPL-2 MD5sum: 49e76cec4ec1010ef017c210f4d4abcb 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: DifferentialExpression, GO, GeneExpression, GeneSetEnrichment, Genetics, Microarray, MultipleComparison, OneChannel, Pathways, RNASeq, Sequencing, Software, SystemsBiology, TwoChannel,Metabolomics, Proteomics, KEGG, GraphAndNetwork Author: Monther Alhamdoosh, Milica Ng and Matthew Ritchie Maintainer: Monther Alhamdoosh VignetteBuilder: knitr source.ver: src/contrib/EGSEA_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/EGSEA_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/EGSEA_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EGSEA_1.0.3.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 Package: eiR Version: 1.12.2 Depends: R (>= 2.10.0), ChemmineR (>= 2.15.15), methods, DBI Imports: snow, tools, snowfall, RUnit, methods, ChemmineR, RCurl, digest, BiocGenerics LinkingTo: BH Suggests: RCurl, snow, BiocStyle, knitcitations, knitr, knitrBootstrap License: Artistic-2.0 MD5sum: 687ee6eb24c44c4a23437e480ec85664 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 Author: Kevin Horan, Yiqun Cao and Tyler Backman Maintainer: Thomas Girke URL: https://github.com/girke-lab/eiR SystemRequirements: GSL (>=1.14) http://www.gnu.org/software/gsl/ VignetteBuilder: knitr source.ver: src/contrib/eiR_1.12.2.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/eiR_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/eiR_1.12.2.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/eiR/inst/doc/eiR.R htmlDocs: vignettes/eiR/inst/doc/eiR.html htmlTitles: eiR Package: eisa Version: 1.24.0 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: 7dce2a64df635e8e4ccb582e81769494 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 source.ver: src/contrib/eisa_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/eisa_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/eisa_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/eisa_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/eisa_1.24.0.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 Package: ELBOW Version: 1.8.0 Depends: R (>= 2.15.0) Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer License: file LICENSE License_is_FOSS: yes License_restricts_use: no MD5sum: a5a49b2acb29cc3a8a59139735b6de1f 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: 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 source.ver: src/contrib/ELBOW_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ELBOW_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ELBOW_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ELBOW_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ELBOW_1.8.0.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 Package: ELMER Version: 1.4.2 Depends: R (>= 3.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19, Homo.sapiens, ELMER.data Imports: methods,BiocGenerics,S4Vectors,IRanges,GenomeInfoDb,GenomicRanges,ggplot2,reshape,grid,gridExtra,minfi,GenomicFeatures Suggests: parallel, snow, BiocStyle, knitr, R.utils, downloader License: GPL-3 MD5sum: a43a17023802aa56a17be18609420924 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 Author: Lijing Yao [cre, aut], Ben Berman [aut], Peggy Farnham [aut], Hui Shen [ctb], Peter Laird [ctb], Simon Coetzee [ctb] Maintainer: Lijing Yao VignetteBuilder: knitr source.ver: src/contrib/ELMER_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ELMER_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ELMER_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ELMER_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ELMER_1.4.2.tgz vignettes: vignettes/ELMER/inst/doc/vignettes.pdf vignetteTitles: ELMER: Inferring Regulatory Element Landscapes and Transcription Factor Networks Using Methylomes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ELMER/inst/doc/vignettes.R Package: EMDomics Version: 2.2.2 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: d172a65d46ea1294a0441fb4360895b1 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 source.ver: src/contrib/EMDomics_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/EMDomics_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/EMDomics_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/EMDomics_1.1.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EMDomics_2.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EMDomics/inst/doc/EMDomics.R htmlDocs: vignettes/EMDomics/inst/doc/EMDomics.html htmlTitles: EMDomics Vignette Package: EmpiricalBrownsMethod Version: 1.0.2 Depends: R (>= 3.2.0) Suggests: BiocStyle, testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 4224482e169eece07a987b2cc8da27ae 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 source.ver: src/contrib/EmpiricalBrownsMethod_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/EmpiricalBrownsMethod_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/EmpiricalBrownsMethod_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EmpiricalBrownsMethod_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.R htmlDocs: vignettes/EmpiricalBrownsMethod/inst/doc/ebmVignette.html htmlTitles: Empirical Browns Method Package: ENCODExplorer Version: 1.4.3 Depends: R (>= 3.3) Imports: tools, jsonlite, RSQLite, parallel, RCurl Suggests: RUnit,BiocGenerics,knitr, curl, httr License: Artistic-2.0 MD5sum: 512ac0cdd4a114f9229abfa9c0918045 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 , Audrey Lemacon and Arnaud Droit Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/ENCODExplorer/issues source.ver: src/contrib/ENCODExplorer_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ENCODExplorer_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ENCODExplorer_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/ENCODExplorer_1.1.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ENCODExplorer_1.4.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ENCODExplorer/inst/doc/DataUpdate.R, vignettes/ENCODExplorer/inst/doc/DBmodel.R, vignettes/ENCODExplorer/inst/doc/ENCODExplorer.R htmlDocs: vignettes/ENCODExplorer/inst/doc/DataUpdate.html, vignettes/ENCODExplorer/inst/doc/DBmodel.html, vignettes/ENCODExplorer/inst/doc/ENCODExplorer.html htmlTitles: Data update, Database model, Introduction to ENCODExplorer Package: ENmix Version: 1.8.0 Depends: minfi,parallel,doParallel,Biobase (>= 2.17.8),foreach Imports: MASS,preprocessCore,wateRmelon,sva,geneplotter,impute Suggests: minfiData (>= 0.4.1), RPMM, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 0246a341b52827e0deebcf98414635da NeedsCompilation: no Title: Data preprocessing and quality control for Illumina HumanMethylation450 and MethylationEPIC BeadChip Description: Illumina Methylation BeadChip array measurements have intrinsic levels of background noise that degrade methylation measurement. The ENmix package provides an efficient data pre-processing tool designed to reduce background noise and improve signal for DNA methylation estimation. Several efficient novel methods were incorporated in the package: ENmix is a model based background correction method that can significantly improve accuracy and reproducibility of methylation measures; RCP taking advantage of the high spatial correlation of DNA methylation levels between nearby type I and II probe pairs to reduce probe type bias and improve data quality on type II probe measures.The data structure used by the ENmix package is compatible with several other related R packages, such as minfi, wateRmelon and ChAMP, providing straightforward integration of ENmix-corrected datasets for subsequent data analysis. The software is designed to support large scale data analysis, and provides multi-processor parallel computing wrappers for some commonly used but computation intensive data preprocessing methods. In addition ENmix package has selectable complementary functions for efficient data visualization (such as data distribution plotting), quality control (identification and filtering of low quality data points, samples, probes, and outliers, along with imputation of missing values), inter-array normalization (3 different quantile normalizations), identification of probes with multimodal distributions due to SNPs and other factors, and exploration of data variance structure using principal component regression analysis plots. Together these provide a set of flexible and transparent tools for preprocessing of EWAS data in a computationally-efficient and user-friendly package. biocViews: DNAMethylation, Preprocessing, QualityControl, TwoChannel, Microarray, OneChannel, MethylationArray, BatchEffect, Normalization, DataImport, Regression, PrincipalComponent Author: Zongli Xu [cre, aut], Liang Niu [aut], Leping Li [ctb], Jack Taylor [ctb] Maintainer: Zongli Xu source.ver: src/contrib/ENmix_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ENmix_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ENmix_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ENmix_1.1.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ENmix_1.8.0.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 Package: EnrichedHeatmap Version: 1.2.2 Depends: R (>= 3.1.2), grid, ComplexHeatmap (>= 1.9.7), GenomicRanges, IRanges, locfit Imports: methods, matrixStats, stats, GetoptLong Suggests: testthat (>= 0.3), knitr, markdown, circlize (>= 0.3.1) License: GPL (>= 2) MD5sum: 02ae2182885bbb7c6b7e280741e21a82 NeedsCompilation: no 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 source.ver: src/contrib/EnrichedHeatmap_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/EnrichedHeatmap_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/EnrichedHeatmap_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EnrichedHeatmap_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.R htmlDocs: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html htmlTitles: Make Enriched Heatmaps Package: EnrichmentBrowser Version: 2.2.3 Depends: R(>= 3.0.0), Biobase, GSEABase, pathview Imports: AnnotationDbi, ComplexHeatmap, DESeq2, EDASeq, GO.db, KEGGREST, KEGGgraph, MASS, ReportingTools, Rgraphviz, S4Vectors, SPIA, SummarizedExperiment, biocGraph, edgeR, geneplotter, graph, hwriter, limma, safe, topGO Suggests: ALL, BiocStyle, airway, hgu95av2.db License: Artistic-2.0 MD5sum: 92abad4677560c0e9030b5bab7649234 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: Microarray, RNASeq, GeneExpression, DifferentialExpression, Pathways, GraphAndNetwork, Network, GeneSetEnrichment, NetworkEnrichment, Visualization, ReportWriting Author: Ludwig Geistlinger, Gergely Csaba, Ralf Zimmer Maintainer: Ludwig Geistlinger source.ver: src/contrib/EnrichmentBrowser_2.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/EnrichmentBrowser_2.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/EnrichmentBrowser_2.2.3.zip mac.binary.ver: bin/macosx/contrib/3.3/EnrichmentBrowser_1.99.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EnrichmentBrowser_2.2.3.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 Package: ensembldb Version: 1.4.7 Depends: BiocGenerics (>= 0.15.10), GenomicRanges (>= 1.23.21), GenomicFeatures (>= 1.23.18) Imports: methods, RSQLite, DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors, AnnotationHub, Rsamtools, IRanges Suggests: BiocStyle, knitr, rmarkdown, EnsDb.Hsapiens.v75 (>= 0.99.7), RUnit, shiny, Gviz, BSgenome.Hsapiens.UCSC.hg19 License: LGPL MD5sum: 4badf537772a95e9054e1d0af4fb515a NeedsCompilation: no Title: Utilities to create and use an Ensembl based annotation database 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, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes. biocViews: Genetics, AnnotationData, Sequencing, Coverage Author: Johannes Rainer , Tim Triche Maintainer: Johannes Rainer URL: https://github.com/jotsetung/ensembldb VignetteBuilder: knitr BugReports: https://github.com/jotsetung/ensembldb/issues source.ver: src/contrib/ensembldb_1.4.7.tar.gz win.binary.ver: bin/windows/contrib/3.3/ensembldb_1.4.7.zip win64.binary.ver: bin/windows64/contrib/3.3/ensembldb_1.4.7.zip mac.binary.ver: bin/macosx/contrib/3.3/ensembldb_1.1.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ensembldb_1.4.7.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensembldb/inst/doc/ensembldb.R htmlDocs: vignettes/ensembldb/inst/doc/ensembldb.html htmlTitles: Generating an using Ensembl based annotation packages importsMe: biovizBase, ChIPpeakAnno, ggbio Package: ensemblVEP Version: 1.12.0 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: S4Vectors (>= 0.9.25), Biostrings, SummarizedExperiment Suggests: RUnit License: Artistic-2.0 MD5sum: d14bb91368ead47a95b13d243c426b57 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 Maintainer: Bioconductor Package Maintainer SystemRequirements: Ensembl VEP (API version 84) and the Perl package DBD::mysql must be installed. See the package README and Ensembl web site, http://www.ensembl.org/info/docs/tools/vep/index.html for installation instructions. source.ver: src/contrib/ensemblVEP_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/ensemblVEP_1.9.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ensemblVEP_1.12.0.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf vignetteTitles: ensemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ensemblVEP/inst/doc/ensemblVEP.R Package: ENVISIONQuery Version: 1.20.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: 880b8c4d559655f5e6a556dbf6b50b71 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 source.ver: src/contrib/ENVISIONQuery_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ENVISIONQuery_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ENVISIONQuery_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ENVISIONQuery_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ENVISIONQuery_1.20.0.tgz 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 importsMe: IdMappingRetrieval Package: epigenomix Version: 1.12.0 Depends: R (>= 3.2.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment Imports: BiocGenerics, MCMCpack, Rsamtools, parallel, GenomeInfoDb, beadarray License: LGPL-3 MD5sum: 295ed769d5bb3f394f7555315bf767d2 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 source.ver: src/contrib/epigenomix_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/epigenomix_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/epigenomix_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/epigenomix_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epigenomix_1.12.0.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 Package: epivizr Version: 2.2.4 Depends: R (>= 3.2.3), methods, Imports: epivizrServer (>= 1.0.1), epivizrData (>= 1.0.1), GenomicRanges, S4Vectors, IRanges Suggests: testthat, roxygen2, knitr, Biobase, SummarizedExperiment, antiProfilesData, hgu133plus2.db, Mus.musculus License: Artistic-2.0 MD5sum: 351377fb8253deb7f4de3b5d1c4881bf 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 Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA source.ver: src/contrib/epivizr_2.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizr_2.2.4.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizr_2.2.4.zip mac.binary.ver: bin/macosx/contrib/3.3/epivizr_1.7.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizr_2.2.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/epivizr/inst/doc/IntroToEpivizr.R htmlDocs: vignettes/epivizr/inst/doc/IntroToEpivizr.html htmlTitles: Introduction to epivizr dependsOnMe: epivizrStandalone Package: epivizrData Version: 1.0.3 Depends: R (>= 3.2.3), methods, epivizrServer (>= 1.0.1), Biobase Imports: S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0), OrganismDbi, GenomicFeatures, GenomeInfoDb, IRanges Suggests: testthat, roxygen2, bumphunter, hgu133plus2.db, Mus.musculus, TxDb.Mmusculus.UCSC.mm10.knownGene, rjson, knitr, rmarkdown License: MIT + file LICENSE MD5sum: eb769f4baf53b96a3f389e5ccdfbe3aa 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 source.ver: src/contrib/epivizrData_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizrData_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizrData_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizrData_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/epivizrData/inst/doc/epivizrData.R htmlDocs: vignettes/epivizrData/inst/doc/epivizrData.html htmlTitles: Vignette Title importsMe: epivizr Package: epivizrServer Version: 1.0.3 Depends: R (>= 3.2.3), methods Imports: httpuv (>= 1.3.0), R6 (>= 2.0.0), rjson, mime (>= 0.2) Suggests: testthat, knitr, rmarkdown License: MIT + file LICENSE MD5sum: 6fb46bf79708c53dbbcd442fd84a30c7 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 source.ver: src/contrib/epivizrServer_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizrServer_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizrServer_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizrServer_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/epivizrServer/inst/doc/epivizrServer.html htmlTitles: epivizrServer Usage dependsOnMe: epivizrData importsMe: epivizr, epivizrStandalone Package: epivizrStandalone Version: 1.0.4 Depends: R (>= 3.2.3), epivizr (>= 1.99.5), methods Imports: git2r, epivizrServer, GenomeInfoDb, BiocGenerics, GenomicFeatures, S4Vectors Suggests: testthat, knitr, rmarkdown, OrganismDbi (>= 1.13.9), Mus.musculus, Biobase License: MIT + file LICENSE MD5sum: 9e9812f69853068c94b698508561b083 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 source.ver: src/contrib/epivizrStandalone_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/epivizrStandalone_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/epivizrStandalone_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/epivizrStandalone_1.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/epivizrStandalone/inst/doc/EpivizrStandalone.html htmlTitles: Introduction to epivizrStandalone Package: erccdashboard Version: 1.6.0 Depends: R (>= 3.1), ggplot2 (>= 1.0.1), gridExtra (>= 2.0.0) Imports: edgeR, gplots, grid, gtools, limma, locfit, MASS, plyr, QuasiSeq, qvalue, reshape2, ROCR, scales, stringr License: GPL (>=2) MD5sum: 7ae5f05bb4d2dd7875f7b2adb009cdb1 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: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Genetics, Microarray, mRNAMicroarray, RNASeq, BatchEffect, MultipleComparison, QualityControl Author: Sarah Munro, Steve Lund Maintainer: Sarah Munro URL: http://www.nist.gov/mml/bbd/erccdashboard.cfm, https://github.com/usnistgov/erccdashboard, http://tinyurl.com/erccsrm BugReports: https://github.com/usnistgov/erccdashboard/issues source.ver: src/contrib/erccdashboard_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/erccdashboard_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/erccdashboard_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/erccdashboard_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/erccdashboard_1.6.0.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 Package: erma Version: 0.4.2 Depends: R (>= 3.1), methods, Homo.sapiens Imports: GenomicFiles (>= 1.5.2), rtracklayer, S4Vectors, BiocGenerics, GenomicRanges, SummarizedExperiment, ggplot2, Biobase, shiny, foreach, AnnotationDbi Suggests: rmarkdown, BiocStyle, knitr, GO.db, BiocParallel, png, DT, doParallel License: Artistic-2.0 MD5sum: 80103babbbf5dbc6ed74a035cef9536e 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 source.ver: src/contrib/erma_0.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/erma_0.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/erma_0.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/erma_0.1.27.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/erma_0.4.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/erma/inst/doc/erma.R htmlDocs: vignettes/erma/inst/doc/erma.html htmlTitles: ermaInteractive suggestsMe: gQTLBase Package: eudysbiome Version: 1.2.0 Depends: R (>= 3.1.0) Imports: plyr, Rsamtools, R.utils, Biostrings License: GPL-2 MD5sum: 05b79487ea85a5408b4512d28b04f21b 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 source.ver: src/contrib/eudysbiome_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/eudysbiome_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/eudysbiome_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/eudysbiome_0.99.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/eudysbiome_1.2.0.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 Package: EWCE Version: 1.0.2 Depends: R(>= 3.3) Imports: ggplot2, reshape2, biomaRt Suggests: knitr, BiocStyle License: Artistic-2.0 MD5sum: 48591946a1a7cf5827c03f23fbb6c5fb NeedsCompilation: no Title: Expression Weighted Celltype Enrichment Description: Used to determine which cell types are enriched within gene lists. The package provides tools for testing enrichments within simple gene lists (such as human disease associated genes) and those resulting from differential expression studies. The package does not depend upon any particular Single Cell Transcriptome dataset and user defined datasets can be loaded in and used in the analyses. biocViews: GeneExpression, Transcription, DifferentialExpression, GeneSetEnrichment, Genetics, Microarray, mRNAMicroarray, OneChannel, RNASeq, BiomedicalInformatics, Proteomics, Visualization, FunctionalGenomics Author: Dr Nathan Skene Maintainer: Nathan Skene VignetteBuilder: knitr source.ver: src/contrib/EWCE_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/EWCE_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/EWCE_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/EWCE_1.0.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/EWCE/inst/doc/EWCE.R htmlDocs: vignettes/EWCE/inst/doc/EWCE.html htmlTitles: Expression Weighted Celltype Enrichment with EWCE Package: ExiMiR Version: 2.14.0 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: eb698b594a09fe0af8c12e49c08d2f09 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 source.ver: src/contrib/ExiMiR_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExiMiR_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ExiMiR_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ExiMiR_2.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExiMiR_2.14.0.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 Package: exomeCopy Version: 1.18.0 Depends: IRanges (>= 2.5.27), GenomicRanges (>= 1.23.16), Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) Archs: i386, x64 MD5sum: 4e516218f6da32f9c25f4c523c6ae940 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 source.ver: src/contrib/exomeCopy_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/exomeCopy_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/exomeCopy_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/exomeCopy_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/exomeCopy_1.18.0.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: CNVPanelizer, contiBAIT, Rariant Package: exomePeak Version: 2.6.0 Depends: Rsamtools, GenomicFeatures (>= 1.14.5), rtracklayer, GenomicAlignments License: GPL-2 MD5sum: 90ad16afb2b89d176c487649206b320f NeedsCompilation: no Title: exome-based anlaysis of MeRIP-Seq data: peak calling and differential analysis Description: The package is developed for the analysis of affinity-based epitranscriptome shortgun sequencing data from MeRIP-seq (maA-seq). It was built on the basis of the exomePeak MATLAB package (Meng, Jia, et al. "Exome-based analysis for RNA epigenome sequencing data." Bioinformatics 29.12 (2013): 1565-1567.) with new functions for differential analysis of two experimental conditions to unveil the dynamics in post-transcriptional regulation of the RNA methylome. The exomePeak R-package accepts and statistically supports multiple biological replicates, internally removes PCR artifacts and multi-mapping reads, outputs exome-based binding sites (RNA methylation sites) and detects differential post-transcriptional RNA modification sites between two experimental conditions in term of percentage rather the absolute amount. The package is still under active development, and we welcome all biology and computation scientist for all kinds of collaborations and communications. Please feel free to contact Dr. Jia Meng if you have any questions. biocViews: Sequencing, HighThroughputSequencing, Methylseq, RNAseq Author: Lin Zhang , Lian Liu , Jia Meng Maintainer: Lin Zhang , Lian Liu , Jia Meng source.ver: src/contrib/exomePeak_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/exomePeak_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/exomePeak_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/exomePeak_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/exomePeak_2.6.0.tgz vignettes: vignettes/exomePeak/inst/doc/exomePeak-Overview.pdf vignetteTitles: An introduction to exomePeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/exomePeak/inst/doc/exomePeak-Overview.R Package: explorase Version: 1.36.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: 5e0155a1c644dfba7b874a46821efd9d 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 source.ver: src/contrib/explorase_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/explorase_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/explorase_1.36.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ExpressionAtlas Version: 1.0.3 Depends: R (>= 3.2.0), methods, Biobase, SummarizedExperiment, limma, S4Vectors, xml2 Imports: utils, XML, httr Suggests: knitr, testthat, rmarkdown License: GPL (>= 3) MD5sum: 7cdad0e502d12a1ea5d59da61022886a 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: Maria Keays VignetteBuilder: knitr source.ver: src/contrib/ExpressionAtlas_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExpressionAtlas_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/ExpressionAtlas_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExpressionAtlas_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.R htmlDocs: vignettes/ExpressionAtlas/inst/doc/ExpressionAtlas.html htmlTitles: ExpressionAtlas Package: ExpressionView Version: 1.24.0 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: f4f21845feb85170bab5c7532cca095b 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 source.ver: src/contrib/ExpressionView_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ExpressionView_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ExpressionView_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ExpressionView_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ExpressionView_1.24.0.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 Package: fabia Version: 2.18.1 Depends: R (>= 2.8.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 222eb660db79fb6faaac84442856f7ea 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: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/fabia/fabia.html source.ver: src/contrib/fabia_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/fabia_2.18.1.zip win64.binary.ver: bin/windows64/contrib/3.3/fabia_2.18.1.zip mac.binary.ver: bin/macosx/contrib/3.3/fabia_2.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fabia_2.18.1.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 Package: facopy Version: 1.6.0 Depends: R (>= 3.0), methods, cgdsr (>= 1.1.30), coin (>= 1.0), ggplot2, gridExtra, facopy.annot, grid Imports: annotate, data.table, DOSE, FactoMineR, GO.db, GOstats, graphite, igraph, S4Vectors, IRanges, MASS, nnet, reshape2, Rgraphviz, scales License: CC BY-NC 4.0 MD5sum: fed2a2b4737a827248c25fd270b0d976 NeedsCompilation: no Title: Feature-based association and gene-set enrichment for copy number alteration analysis in cancer Description: facopy is an R package for fine-tuned cancer CNA association modeling. Association is measured directly at the genomic features of interest and, in the case of genes, downstream gene-set enrichment analysis can be performed thanks to novel internal processing of the data. The software opens a way to systematically scrutinize the differences in CNA distribution across tumoral phenotypes, such as those that relate to tumor type, location and progression. Currently, the output format from 11 different methods that analyze data from whole-genome/exome sequencing and SNP microarrays, is supported. Multiple genomes, alteration types and variable types are also supported. biocViews: Software, CopyNumberVariation, GeneSetEnrichment, GenomicVariation, Genetics, Microarray, Sequencing, Visualization Author: David Mosen-Ansorena Maintainer: David Mosen-Ansorena source.ver: src/contrib/facopy_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/facopy_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/facopy_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/facopy_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/facopy_1.6.0.tgz vignettes: vignettes/facopy/inst/doc/facopy.pdf vignetteTitles: facopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/facopy/inst/doc/facopy.R Package: factDesign Version: 1.48.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: 38ffce783de62479d2b449d38895697e 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 source.ver: src/contrib/factDesign_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/factDesign_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/factDesign_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/factDesign_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/factDesign_1.48.0.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 Package: FamAgg Version: 1.0.2 Depends: methods, kinship2, igraph Imports: gap, BiocGenerics, Matrix, utils, survey Suggests: BiocStyle, knitr, RUnit, rmarkdown License: MIT + file LICENSE MD5sum: 6a6176208a01de201914a31bbf4d56c6 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/jotsetung/FamAgg VignetteBuilder: knitr BugReports: https://github.com/jotsetung/FamAgg/issues source.ver: src/contrib/FamAgg_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/FamAgg_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/FamAgg_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FamAgg_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/FamAgg/inst/doc/FamAgg.R htmlDocs: vignettes/FamAgg/inst/doc/FamAgg.html htmlTitles: Pedigree Analysis and Familial Aggregation Package: farms Version: 1.24.0 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: 363ad1ac89c5cbb2de8b2c06be63b30f 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 source.ver: src/contrib/farms_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/farms_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/farms_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/farms_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/farms_1.24.0.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 Package: fastLiquidAssociation Version: 1.8.0 Depends: methods, LiquidAssociation, parallel, stats, Hmisc Imports: WGCNA Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: 17ae43f8537d973306bff18e7b248bfd 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 source.ver: src/contrib/fastLiquidAssociation_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fastLiquidAssociation_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fastLiquidAssociation_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/fastLiquidAssociation_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fastLiquidAssociation_1.8.0.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 Package: fastseg Version: 1.18.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: methods, graphics, stats, BiocGenerics, S4Vectors, IRanges Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: d678d5673375e27b3d17181a9560490b 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 source.ver: src/contrib/fastseg_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fastseg_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fastseg_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/fastseg_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fastseg_1.18.0.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 Package: fCI Version: 1.2.2 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: d94fde6af589027daf4df2fa7f4e5446 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 source.ver: src/contrib/fCI_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/fCI_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/fCI_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/fCI_0.99.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fCI_1.2.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fCI/inst/doc/fCI.R htmlDocs: vignettes/fCI/inst/doc/fCI.html htmlTitles: fCI Package: fdrame Version: 1.44.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 324b989892ef4069971b39890ab8ca2b 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 source.ver: src/contrib/fdrame_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/fdrame_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/fdrame_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/fdrame_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fdrame_1.44.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: FEM Version: 2.8.0 Depends: AnnotationDbi,Matrix,marray,corrplot,igraph,impute,limma,org.Hs.eg.db,graph,BiocGenerics Imports: graph License: GPL (>=2) MD5sum: 659e351521f7131b1d21f8a0aa593ed0 NeedsCompilation: no Title: Identification of Functional Epigenetic Modules Description: The FEM package performs a systems-level integrative analysis of DNA methylation and gene expression data. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details, see Jiao et al Bioinformatics 2014. biocViews: SystemsBiology,NetworkEnrichment,DifferentialMethylation,DifferentialExpression Author: Andrew E. Teschendorff and Yinming Jiao Maintainer: Andrew E. Teschendorff source.ver: src/contrib/FEM_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FEM_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FEM_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/FEM_2.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FEM_2.8.0.tgz vignettes: vignettes/FEM/inst/doc/IntroDoFEM.pdf vignetteTitles: The FEM package performs a systems-level integrative analysis of DNA methylationa and gene expression. It seeks modules of functionally related genes which exhibit differential promoter DNA methylation and differential expression,, where an inverse association between promoter DNA methylation and gene expression is assumed. For full details,, see Jiao et al Bioinformatics 2014. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FEM/inst/doc/IntroDoFEM.R Package: ffpe Version: 1.16.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 4e9daf2a38af687dab00ff9bf2d79a76 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 source.ver: src/contrib/ffpe_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ffpe_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ffpe_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ffpe_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ffpe_1.16.0.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 Package: FGNet Version: 3.6.2 Depends: R (>= 2.15) Imports: igraph (>= 0.6), hwriter, R.utils, XML, plotrix, reshape2, RColorBrewer, png Suggests: RGtk2, RCurl, RDAVIDWebService, gage, topGO, KEGGprofile, GO.db, KEGG.db, reactome.db, RUnit, BiocGenerics, org.Sc.sgd.db, knitr, rmarkdown, AnnotationDbi License: GPL (>= 2) MD5sum: 7e365dffd3db2a07a9b0ae117537cc0a 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 source.ver: src/contrib/FGNet_3.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/FGNet_3.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/FGNet_3.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/FGNet_3.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FGNet_3.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FGNet/inst/doc/FGNet.R htmlDocs: vignettes/FGNet/inst/doc/FGNet.html htmlTitles: FGNet Package: FindMyFriends Version: 1.2.2 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: 0d9e04d000bbe9ed78e522dbe4c22951 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 source.ver: src/contrib/FindMyFriends_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/FindMyFriends_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/FindMyFriends_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FindMyFriends_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.R htmlDocs: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html htmlTitles: Creating pangenomes using FindMyFriends importsMe: PanVizGenerator Package: FISHalyseR Version: 1.6.2 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: b5efcfc60679c688f8c755db0545b7b6 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 source.ver: src/contrib/FISHalyseR_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/FISHalyseR_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/FISHalyseR_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/FISHalyseR_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FISHalyseR_1.6.2.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 Package: flagme Version: 1.28.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: 4bbf80cca7fec708f9906da2bf6b54b3 NeedsCompilation: yes Title: Analysis of Metabolomics GC/MS Data Description: Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data biocViews: DifferentialExpression, MassSpectrometry Author: Mark Robinson , Riccardo Romoli Maintainer: Mark Robinson , Riccardo Romoli source.ver: src/contrib/flagme_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flagme_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flagme_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flagme_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flagme_1.28.0.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 Package: flipflop Version: 1.10.0 Depends: R (>= 2.10.0) Imports: methods, Matrix, IRanges, GenomicRanges, parallel Suggests: GenomicFeatures License: GPL-3 Archs: i386, x64 MD5sum: 9694b1f451bf1663a2aa213e16885a77 NeedsCompilation: yes Title: Fast lasso-based isoform prediction as a flow problem Description: Flipflop discovers which isoforms of a gene are expressed in a given sample together with their abundances, based on RNA-Seq read data. It takes an alignment file in SAM format as input. It can also discover transcripts from several samples simultaneously, increasing statistical power. biocViews: RNASeq, RNASeqData, AlternativeSplicing, Regression Author: Elsa Bernard, Laurent Jacob, Julien Mairal and Jean-Philippe Vert Maintainer: Elsa Bernard URL: http://cbio.ensmp.fr/flipflop SystemRequirements: GNU make source.ver: src/contrib/flipflop_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flipflop_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flipflop_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flipflop_1.7.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flipflop_1.10.0.tgz vignettes: vignettes/flipflop/inst/doc/flipflop.pdf vignetteTitles: FlipFlop: Fast Lasso-based Isoform Prediction as a Flow Problem hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flipflop/inst/doc/flipflop.R Package: flowAI Version: 1.2.9 Depends: R (>= 3.2) Imports: ggplot2, flowCore, plyr, changepoint, knitr, reshape2, RColorBrewer, scales Suggests: testthat, shiny, rmarkdown License: GPL MD5sum: 1dd66dd6dde1a351bda75a00d6f69a85 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 Author: Gianni Monaco, Hao Chen Maintainer: Gianni Monaco VignetteBuilder: knitr source.ver: src/contrib/flowAI_1.2.9.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowAI_1.2.9.zip win64.binary.ver: bin/windows64/contrib/3.3/flowAI_1.2.9.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowAI_1.2.9.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowAI/inst/doc/flowAI.R htmlDocs: vignettes/flowAI/inst/doc/flowAI.html htmlTitles: Automatic and GUI methods to do quality control on Flow cytometry Data Package: flowBeads Version: 1.10.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: 9831e76d3b56d850d56bbef9c23f37da 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: Infrastructure, FlowCytometry, CellBasedAssays Author: Nikolas Pontikos Maintainer: Nikolas Pontikos source.ver: src/contrib/flowBeads_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowBeads_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowBeads_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowBeads_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowBeads_1.10.0.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 Package: flowBin Version: 1.8.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: ffd765b964f0dce22577b55d53dc6f60 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: CellBasedAssays, FlowCytometry Author: Kieran O'Neill Maintainer: Kieran O'Neill source.ver: src/contrib/flowBin_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowBin_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowBin_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowBin_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowBin_1.8.0.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 Package: flowcatchR Version: 1.6.2 Depends: R (>= 2.10), methods, EBImage Imports: rgl, colorRamps, abind, BiocParallel Suggests: BiocStyle, knitr, shiny License: BSD_3_clause + file LICENSE MD5sum: 3d09fe9e84a750eaf00a2996d1ae0143 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 SystemRequirements: ImageMagick VignetteBuilder: knitr BugReports: https://github.com/federicomarini/flowcatchR/issues source.ver: src/contrib/flowcatchR_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowcatchR_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/flowcatchR_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/flowcatchR_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowcatchR_1.6.2.tgz vignettes: vignettes/flowcatchR/inst/doc/flowcatchR-vignette.pdf 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 Package: flowCHIC Version: 1.6.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 528913d1f7fc822d087b0cc124c3443a 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: 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 source.ver: src/contrib/flowCHIC_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCHIC_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCHIC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowCHIC_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCHIC_1.6.0.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 Package: flowCL Version: 1.10.0 Depends: R (>= 3.0.2), Rgraphviz, SPARQL Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 6ec7232722edcde1b146e71714b0019f NeedsCompilation: no Title: Semantic labelling of flow cytometric cell populations Description: Semantic labelling of flow cytometric cell populations. biocViews: FlowCytometry Author: Justin Meskas, Radina Droumeva Maintainer: Justin Meskas source.ver: src/contrib/flowCL_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCL_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCL_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowCL_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCL_1.10.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCL/inst/doc/flowCL.R Package: flowClean Version: 1.10.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: 56bdc7cd8acd00440f963be8a02a1e68 NeedsCompilation: no Title: flowClean Description: A quality control tool for flow cytometry data based on compositional data analysis. biocViews: FlowCytometry, QualityControl Author: Kipper Fletez-Brant Maintainer: Kipper Fletez-Brant source.ver: src/contrib/flowClean_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowClean_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowClean_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowClean_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowClean_1.10.0.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 Package: flowClust Version: 3.10.1 Depends: R(>= 2.5.0),methods, Biobase, graph, RBGL,ellipse, flowViz, mnormt, corpcor, flowCore, clue Imports: BiocGenerics, MCMCpack Suggests: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 53d875717d88472d2bd99ef39dc98944 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: Clustering, Visualization, FlowCytometry Author: Raphael Gottardo , Kenneth Lo , Greg Finak Maintainer: Greg Finak , Mike Jiang SystemRequirements: GNU make source.ver: src/contrib/flowClust_3.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowClust_3.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/flowClust_3.10.1.zip mac.binary.ver: bin/macosx/contrib/3.3/flowClust_3.7.01.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowClust_3.10.1.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.pdf vignetteTitles: flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowClust/inst/doc/flowClust.R importsMe: flowTrans, flowType suggestsMe: BiocGenerics Package: flowCore Version: 1.38.2 Depends: R (>= 2.10.0) Imports: Biobase, BiocGenerics (>= 0.1.14), graph, graphics, methods, rrcov, stats, utils, stats4, corpcor, Rcpp, matrixStats LinkingTo: Rcpp, BH(>= 1.60.0-1) Suggests: Rgraphviz, flowViz, flowStats, testthat, flowWorkspace,flowWorkspaceData,openCyto License: Artistic-2.0 Archs: i386, x64 MD5sum: 6a70256cd2a98d00bfdcfe3780de42a9 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: Infrastructure, FlowCytometry, CellBasedAssays Author: B. Ellis, P. Haaland, F. Hahne, N. Le Meur, N. Gopalakrishnan, J. Spidlen, M. Jiang Maintainer: M.Jiang SystemRequirements: GNU make source.ver: src/contrib/flowCore_1.38.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCore_1.38.2.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCore_1.38.2.zip mac.binary.ver: bin/macosx/contrib/3.3/flowCore_1.35.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCore_1.38.2.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowCore/inst/doc/HowTo-flowCore.R dependsOnMe: flowBeads, flowBin, flowClean, flowClust, flowFP, flowMatch, flowStats, flowTrans, flowViz, flowVS, ggcyto, immunoClust, ncdfFlow, plateCore importsMe: cytofkit, flowAI, flowBeads, flowCHIC, flowDensity, flowFit, flowMeans, flowQ, flowQB, FlowSOM, flowStats, flowTrans, flowType, flowUtils, flowViz, plateCore, spade suggestsMe: COMPASS, FlowRepositoryR, RchyOptimyx Package: flowCyBar Version: 1.8.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: 5ad6a84309fbe8413aff462ca1a94735 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: 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 source.ver: src/contrib/flowCyBar_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowCyBar_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowCyBar_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowCyBar_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowCyBar_1.8.0.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 Package: flowDensity Version: 1.6.0 Depends: R (>= 2.10.0), methods Imports: flowCore, graphics, car, gplots, RFOC, GEOmap, methods, grDevices License: Artistic-2.0 MD5sum: 218ac368060dac9d1dff177e9766c4ee 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, StemCells, DensityGating Author: M. Jafar Taghiyar, Mehrnoush Malek Maintainer: Mehrnoush Malek source.ver: src/contrib/flowDensity_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowDensity_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowDensity_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowDensity_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowDensity_1.6.0.tgz vignettes: vignettes/flowDensity/inst/doc/flowDensityVignette.pdf vignetteTitles: Automated alternative to the current manual gating practice hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowDensity/inst/doc/flowDensityVignette.R Package: flowFit Version: 1.10.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, kza, methods, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 MD5sum: 7304b9147b335e633d975f61451588e2 NeedsCompilation: no 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: FlowCytometry, CellBasedAssays Author: Davide Rambaldi Maintainer: Davide Rambaldi BugReports: Davide Rambaldi source.ver: src/contrib/flowFit_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowFit_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowFit_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowFit_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowFit_1.10.0.tgz vignettes: vignettes/flowFit/inst/doc/HowTo-flowFit.pdf vignetteTitles: Fitting Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowFit/inst/doc/HowTo-flowFit.R Package: flowFP Version: 1.30.0 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: 06345d460fce5b09d01f53f8ee7c934c 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 source.ver: src/contrib/flowFP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowFP_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowFP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowFP_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowFP_1.30.0.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 Package: flowMap Version: 1.10.2 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: b64ea200447a11b1a26ce41620362ad7 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: MultipleComparison, FlowCytometry Author: Chiaowen Joyce Hsiao, Yu Qian, and Richard H. Scheuermann Maintainer: Chiaowen Joyce Hsiao VignetteBuilder: knitr source.ver: src/contrib/flowMap_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMap_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMap_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/flowMap_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMap_1.10.2.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 Package: flowMatch Version: 1.8.0 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: 289c4184e3af2f10b843610426d0a154 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: Clustering, FlowCytometry Author: Ariful Azad Maintainer: Ariful Azad source.ver: src/contrib/flowMatch_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMatch_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMatch_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowMatch_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMatch_1.8.0.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 Package: flowMeans Version: 1.32.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: a113e09032c579fe252e928c68de0059 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: FlowCytometry, CellBiology, Clustering Author: Nima Aghaeepour Maintainer: Nima Aghaeepour source.ver: src/contrib/flowMeans_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMeans_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMeans_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowMeans_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMeans_1.32.0.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: flowType Package: flowMerge Version: 2.20.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 1f809fbd07d9c26ba8d5fc6008ab496c 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: Clustering, FlowCytometry Author: Greg Finak , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowMerge_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowMerge_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowMerge_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowMerge_2.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowMerge_2.20.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowMerge.pdf vignetteTitles: flowMerge package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowMerge/inst/doc/flowMerge.R importsMe: flowType Package: flowPeaks Version: 1.14.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: 94bb6464bf510856ec975b7f2a2174a9 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: FlowCytometry, Clustering, Gating Author: Yongchao Ge Maintainer: Yongchao Ge source.ver: src/contrib/flowPeaks_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowPeaks_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowPeaks_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowPeaks_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowPeaks_1.14.0.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 Package: flowPlots Version: 1.20.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 7ffbdaed6105ab7d5819cea12cbc7740 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: FlowCytometry, CellBasedAssays, Visualization, DataRepresentation Author: N. Hawkins, S. Self Maintainer: N. Hawkins source.ver: src/contrib/flowPlots_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowPlots_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowPlots_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowPlots_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowPlots_1.20.0.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 Package: flowQ Version: 1.32.0 Depends: R (>= 2.10.0), methods, BiocGenerics, outliers, lattice, flowViz, mvoutlier, bioDist, parody, RColorBrewer, latticeExtra Imports: methods, BiocGenerics, geneplotter, flowCore, flowViz, IRanges Suggests: flowStats License: Artistic-2.0 MD5sum: 26f1e9d632336e993eec5ec23658eea7 NeedsCompilation: no Title: Quality control for flow cytometry Description: Provides quality control and quality assessment tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: R. Gentleman, F. Hahne, J. Kettman, N. Le Meur, N. Gopalakrishnan Maintainer: Mike Jiang SystemRequirements: ImageMagick source.ver: src/contrib/flowQ_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/flowQ_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowQ_1.32.0.tgz vignettes: vignettes/flowQ/inst/doc/DataQualityAssessment.pdf, vignettes/flowQ/inst/doc/Extending-flowQ.pdf vignetteTitles: Data Quality Assesment for Ungated Flow Cytometry Data, Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQ/inst/doc/DataQualityAssessment.R, vignettes/flowQ/inst/doc/Extending-flowQ.R Package: flowQB Version: 1.18.4 Imports: methods, flowCore (>= 1.32.0), stats, extremevalues Suggests: FlowRepositoryR, xlsx, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d6e1a3c20be400204a61aabbecdd2a37 NeedsCompilation: no Title: Automated Quadratic Characterization of Flow Cytometer Instrument Sensitivity: Q, B and CV instrinsic calculations Description: flowQB is a fully automated R Bioconductor package to calculate automatically the detector efficiency (Q), optical background (B) and intrinsic CV of the beads. biocViews: FlowCytometry, Regression, PeakDetection, QualityControl, MultiChannel, OneChannel Author: Josef Spidlen, Faysal El Khettabi, Wayne Moore, David Parks, Ryan Brinkman Maintainer: Josef Spidlen source.ver: src/contrib/flowQB_1.18.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowQB_1.18.4.zip win64.binary.ver: bin/windows64/contrib/3.3/flowQB_1.18.4.zip mac.binary.ver: bin/macosx/contrib/3.3/flowQB_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowQB_1.18.4.tgz vignettes: vignettes/flowQB/inst/doc/flowQBVignettes.pdf vignetteTitles: flowQB package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowQB/inst/doc/flowQBVignettes.R Package: FlowRepositoryR Version: 1.4.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools, utils, jsonlite Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 MD5sum: eaec86146397f67b32dbc53eef4ab8af 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: Infrastructure, FlowCytometry Author: Josef Spidlen [aut, cre] Maintainer: Josef Spidlen source.ver: src/contrib/FlowRepositoryR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FlowRepositoryR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FlowRepositoryR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/FlowRepositoryR_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FlowRepositoryR_1.4.0.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 suggestsMe: flowQB Package: FlowSOM Version: 1.4.0 Depends: R (>= 3.2), igraph Imports: flowCore, ConsensusClusterPlus, BiocGenerics, tsne, flowUtils, XML Suggests: BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: 4d3a04705098dea9594bc85b6e9a3cd2 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 source.ver: src/contrib/FlowSOM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FlowSOM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FlowSOM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/FlowSOM_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FlowSOM_1.4.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: Using SOMs for visualization of cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/FlowSOM/inst/doc/FlowSOM.R importsMe: cytofkit Package: flowStats Version: 3.30.1 Depends: R (>= 2.10), flowCore, fda (>= 2.2.6), cluster, flowWorkspace Imports: BiocGenerics, MASS, flowViz, flowCore, fda (>= 2.2.6), Biobase, methods, grDevices, graphics, stats, utils, KernSmooth, lattice,ks Suggests: xtable Enhances: RBGL,ncdfFlow,graph License: Artistic-2.0 MD5sum: 4197ef7cd97cfb595903149a0a777cf2 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: FlowCytometry, CellBasedAssays Author: Florian Hahne, Nishant Gopalakrishnan, Alireza Hadj Khodabakhshi, Chao-Jen Wong, Kyongryun Lee Maintainer: Greg Finak and Mike Jiang source.ver: src/contrib/flowStats_3.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowStats_3.30.1.zip win64.binary.ver: bin/windows64/contrib/3.3/flowStats_3.30.1.zip mac.binary.ver: bin/macosx/contrib/3.3/flowStats_3.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowStats_3.30.1.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 importsMe: plateCore suggestsMe: flowCore, flowQ, ggcyto Package: flowTrans Version: 1.24.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 294464188ef44b2efc757a9e51f18328 NeedsCompilation: no Title: Parameter Optimization for Flow Cytometry Data Transformation Description: Profile maximum likelihood estimation of parameters for flow cytometry data transformations. biocViews: FlowCytometry Author: Greg Finak , Juan Manuel-Perez , Raphael Gottardo Maintainer: Greg Finak source.ver: src/contrib/flowTrans_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowTrans_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowTrans_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowTrans_1.21.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowTrans_1.24.0.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 Package: flowType Version: 2.10.0 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 Archs: i386, x64 MD5sum: df198edf6c07c75cd460313cc09694da NeedsCompilation: yes Title: Phenotyping Flow Cytometry Assays Description: Phenotyping Flow Cytometry Assays using multidimentional expansion of single dimentional partitions. biocViews: FlowCytometry Author: Nima Aghaeepour, Kieran O'Neill, Adrin Jalali Maintainer: Nima Aghaeepour source.ver: src/contrib/flowType_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowType_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowType_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowType_2.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowType_2.10.0.tgz vignettes: vignettes/flowType/inst/doc/flowType.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowType/inst/doc/flowType.R importsMe: RchyOptimyx Package: flowUtils Version: 1.36.0 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: 786b764f5bba4b195dbdd953c734dc5a NeedsCompilation: no Title: Utilities for flow cytometry Description: Provides utilities for flow cytometry data. biocViews: 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 source.ver: src/contrib/flowUtils_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowUtils_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/flowUtils_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/flowUtils_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowUtils_1.36.0.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: FlowSOM Package: flowViz Version: 1.36.2 Depends: R (>= 2.7.0), flowCore, lattice Imports: stats4, Biobase, flowCore, graphics, grDevices, grid, KernSmooth, lattice, latticeExtra, MASS, methods, RColorBrewer, stats, utils, hexbin,IDPmisc Suggests: colorspace, flowStats,knitr License: Artistic-2.0 MD5sum: fb7f9d29795fa2df722ecd7cea72be64 NeedsCompilation: no Title: Visualization for flow cytometry Description: Provides visualization tools for flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays, Visualization Author: B. Ellis, R. Gentleman, F. Hahne, N. Le Meur, D. Sarkar, M. Jiang Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/flowViz_1.36.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowViz_1.36.2.zip win64.binary.ver: bin/windows64/contrib/3.3/flowViz_1.36.2.zip mac.binary.ver: bin/macosx/contrib/3.3/flowViz_1.33.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowViz_1.36.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/flowViz/inst/doc/filters.R htmlDocs: vignettes/flowViz/inst/doc/filters.html htmlTitles: Visualizing Gates with Flow Cytometry Data dependsOnMe: flowClust, flowFP, flowQ, flowVS, ncdfFlow, plateCore importsMe: flowFit, flowQ, flowStats, flowTrans suggestsMe: flowBeads, flowClean, flowCore, ggcyto, spade Package: flowVS Version: 1.4.2 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 49a78624e0de692677c63eaf46101bc5 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: FlowCytometry, CellBasedAssays, Microarray Author: Ariful Azad Maintainer: Ariful Azad VignetteBuilder: knitr source.ver: src/contrib/flowVS_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowVS_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/flowVS_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/flowVS_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowVS_1.4.2.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 Package: flowWorkspace Version: 3.18.11 Depends: R (>= 2.16.0),flowCore(>= 1.37.8),flowViz(>= 1.29.27),ncdfFlow(>= 2.17.4),gridExtra Imports: Biobase, BiocGenerics, graph, graphics, lattice, methods, stats, stats4, utils, RBGL, XML, tools, gridExtra, Rgraphviz, data.table, dplyr, latticeExtra, Rcpp, RColorBrewer, stringr, scales LinkingTo: Rcpp, BH(>= 1.60.0-1) Suggests: testthat, flowWorkspaceData, RSVGTipsDevice, knitr, ggcyto License: Artistic-2.0 Archs: i386, x64 MD5sum: a0e8c352e665b3dfcab7af6141b170bb NeedsCompilation: yes Title: Infrastructure for representing and interacting with the gated cytometry 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: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Greg Finak, Mike Jiang Maintainer: Greg Finak ,Mike Jiang SystemRequirements: xml2, GNU make VignetteBuilder: knitr source.ver: src/contrib/flowWorkspace_3.18.11.tar.gz win.binary.ver: bin/windows/contrib/3.3/flowWorkspace_3.18.11.zip win64.binary.ver: bin/windows64/contrib/3.3/flowWorkspace_3.18.11.zip mac.binary.ver: bin/macosx/contrib/3.3/flowWorkspace_3.15.15.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/flowWorkspace_3.18.11.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.R, vignettes/flowWorkspace/inst/doc/plotGate.R htmlDocs: vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html, vignettes/flowWorkspace/inst/doc/plotGate.html htmlTitles: How to merge GatingSets, How to plot gated data dependsOnMe: flowStats, ggcyto, openCyto suggestsMe: COMPASS Package: fmcsR Version: 1.14.2 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: 679d5631379501d025d4843544cfdef5 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 Author: Yan Wang, Tyler Backman, Kevin Horan, Thomas Girke Maintainer: Thomas Girke URL: https://github.com/girke-lab/fmcsR VignetteBuilder: knitr source.ver: src/contrib/fmcsR_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/fmcsR_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.3/fmcsR_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.3/fmcsR_1.11.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/fmcsR_1.14.2.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/fmcsR/inst/doc/fmcsR.R htmlDocs: vignettes/fmcsR/inst/doc/fmcsR.html htmlTitles: fmcsR importsMe: Rcpi suggestsMe: ChemmineR Package: focalCall Version: 1.6.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 5be8d05eb66ae9800981775ffb99a522 NeedsCompilation: no 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 source.ver: src/contrib/focalCall_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/focalCall_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/focalCall_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/focalCall_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/focalCall_1.6.0.tgz vignettes: vignettes/focalCall/inst/doc/focalCall.pdf vignetteTitles: focalCall hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/focalCall/inst/doc/focalCall.R Package: FourCSeq Version: 1.6.2 Depends: R (>= 3.0), GenomicRanges, ggplot2, DESeq2 (>= 1.9.11), splines, methods, LSD Imports: DESeq2, Biobase, Biostrings, GenomicRanges, SummarizedExperiment, Rsamtools, ggbio, reshape2, rtracklayer, fda, GenomicAlignments, gtools, Matrix Suggests: BiocStyle, knitr, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) MD5sum: 023188b109c086970a8ab8e851aee34f 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, EMBL Heidelberg Maintainer: Felix A. Klein VignetteBuilder: knitr source.ver: src/contrib/FourCSeq_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/FourCSeq_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/FourCSeq_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/FourCSeq_1.3.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FourCSeq_1.6.2.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 Package: FRGEpistasis Version: 1.8.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: 107d785a6ecc8fcc5699fc6198e7d9ed 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 source.ver: src/contrib/FRGEpistasis_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FRGEpistasis_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FRGEpistasis_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/FRGEpistasis_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FRGEpistasis_1.8.0.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 Package: frma Version: 1.24.0 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: 6da8e3147ac8b231b6e3c3a376e75eed 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 source.ver: src/contrib/frma_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/frma_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/frma_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/frma_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/frma_1.24.0.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 suggestsMe: frmaTools Package: frmaTools Version: 1.24.0 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: 6aa61489498776598aa45e231ac9c9e8 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 source.ver: src/contrib/frmaTools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/frmaTools_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/frmaTools_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/frmaTools_1.21.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/frmaTools_1.24.0.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 Package: FunciSNP Version: 1.14.0 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: a91c50575bc34a36524c22d14a5abdb2 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/ source.ver: src/contrib/FunciSNP_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/FunciSNP_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/FunciSNP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/FunciSNP_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/FunciSNP_1.14.0.tgz 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 Package: gaga Version: 2.18.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: bab9955ea0bdbeb21a55e222b1e03449 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: OneChannel, MassSpectrometry, MultipleComparison, DifferentialExpression, Classification Author: David Rossell . Maintainer: David Rossell source.ver: src/contrib/gaga_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaga_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gaga_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gaga_2.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaga_2.18.0.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 Package: gage Version: 2.22.0 Depends: R (>= 2.10) Imports: graph, KEGGREST, AnnotationDbi Suggests: pathview, gageData, GO.db, org.Hs.eg.db, hgu133a.db, GSEABase, Rsamtools, GenomicAlignments, TxDb.Hsapiens.UCSC.hg19.knownGene, DESeq, DESeq2, edgeR, limma License: GPL (>=2.0) MD5sum: 3188cde2910fe42a049a1654ae989908 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: http://www.biomedcentral.com/1471-2105/10/161 source.ver: src/contrib/gage_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gage_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gage_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gage_2.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gage_2.22.0.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 suggestsMe: FGNet, pathview Package: gaggle Version: 1.40.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: 909e1d12da502486e143a2f0180bb6e1 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/ source.ver: src/contrib/gaggle_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaggle_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gaggle_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gaggle_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaggle_1.40.0.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 Package: gaia Version: 2.16.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: ce91e60d05d2ee74c4bec266a2748532 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 source.ver: src/contrib/gaia_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaia_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gaia_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gaia_2.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaia_2.16.0.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 Package: garfield Version: 1.0.2 Suggests: knitr License: GPL-3 Archs: i386, x64 MD5sum: 0db8242b16fbb03e1f63743c2a2e4914 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 source.ver: src/contrib/garfield_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/garfield_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/garfield_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/garfield_1.0.2.tgz vignettes: vignettes/garfield/inst/doc/vignette.pdf vignetteTitles: garfield Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: gaucho Version: 1.8.2 Depends: R (>= 3.0.0), compiler, GA, graph, heatmap.plus, png, Rgraphviz Suggests: knitr License: GPL-3 MD5sum: 8d1b319bf4601cef114facd7804d30b0 NeedsCompilation: no Title: Genetic Algorithms for Understanding Clonal Heterogeneity and Ordering Description: Use genetic algorithms to determine the relationship between clones in heterogenous populations such as cancer sequencing samples biocViews: Software,Genetics,SNP,Sequencing,SomaticMutation Author: Alex Murison [aut, cre], Christopher Wardell [aut, cre] Maintainer: Alex Murison , Christopher Wardell VignetteBuilder: knitr source.ver: src/contrib/gaucho_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/gaucho_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/gaucho_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/gaucho_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gaucho_1.8.2.tgz vignettes: vignettes/gaucho/inst/doc/gaucho_vignette.pdf vignetteTitles: An introduction to gaucho hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gaucho/inst/doc/gaucho_vignette.R Package: gcatest Version: 1.2.2 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 9840a47e4f12108b990ff88a159b7d22 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 source.ver: src/contrib/gcatest_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/gcatest_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/gcatest_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gcatest_1.2.2.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 Package: gCMAP Version: 1.16.0 Depends: GSEABase, limma (>= 3.20.0) Imports: Biobase, methods, GSEAlm, Category, Matrix (>= 1.0.9), parallel, annotate, genefilter, AnnotationDbi, DESeq Suggests: BiocGenerics, KEGG.db, reactome.db, RUnit, GO.db, mgsa Enhances: bigmemory, bigmemoryExtras (>= 1.1.2) License: Artistic-2.0 MD5sum: 10979b52a1b8fb7de6283440dfccb783 NeedsCompilation: no Title: Tools for Connectivity Map-like analyses Description: The gCMAP package provides a toolkit for comparing differential gene expression profiles through gene set enrichment analysis. Starting from normalized microarray or RNA-seq gene expression values (stored in lists of ExpressionSet and CountDataSet objects) the package performs differential expression analysis using the limma or DESeq packages. Supplying a simple list of gene identifiers, global differential expression profiles or data from complete experiments as input, users can use a unified set of several well-known gene set enrichment analysis methods to retrieve experiments with similar changes in gene expression. To take into account the directionality of gene expression changes, gCMAPQuery introduces the SignedGeneSet class, directly extending GeneSet from the GSEABase package. To increase performance of large queries, multiple gene sets are stored as sparse incidence matrices within CMAPCollection eSets. gCMAP offers implementations of 1. Fisher's exact test (Fisher, J R Stat Soc, 1922) 2. The "connectivity map" method (Lamb et al, Science, 2006) 3. Parametric and non-parametric t-statistic summaries (Jiang & Gentleman, Bioinformatics, 2007) and 4. Wilcoxon / Mann-Whitney rank sum statistics (Wilcoxon, Biometrics Bulletin, 1945) as well as wrappers for the 5. camera (Wu & Smyth, Nucleic Acid Res, 2012) 6. mroast and romer (Wu et al, Bioinformatics, 2010) functions from the limma package and 7. wraps the gsea method from the mgsa package (Bauer et al, NAR, 2010). All methods return CMAPResult objects, an S4 class inheriting from AnnotatedDataFrame, containing enrichment statistics as well as annotation data and providing simple high-level summary plots. biocViews: Microarray, Software, Pathways, Annotation Author: Thomas Sandmann , Richard Bourgon and Sarah Kummerfeld Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAP_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gCMAP_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gCMAP_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gCMAP_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gCMAP_1.16.0.tgz vignettes: vignettes/gCMAP/inst/doc/diffExprAnalysis.pdf, vignettes/gCMAP/inst/doc/gCMAP.pdf vignetteTitles: Creating reference datasets, gCMAP classes and methods hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAP/inst/doc/diffExprAnalysis.R, vignettes/gCMAP/inst/doc/gCMAP.R dependsOnMe: gCMAPWeb Package: gCMAPWeb Version: 1.12.0 Depends: Biobase, gCMAP (>= 1.3.0), methods, R (>= 2.15.0), Rook Imports: brew, BiocGenerics, annotate, AnnotationDbi, grDevices, GSEABase, hwriter, parallel, yaml Suggests: affy, ArrayExpress, hgfocus.db, hgu133a.db, mgug4104a.db, org.Hs.eg.db, org.Mm.eg.db, RUnit Enhances: bigmemory, bigmemoryExtras License: Artistic-2.0 MD5sum: 9600e4fc0e71a9c2305822f4696f42a3 NeedsCompilation: no Title: A web interface for gene-set enrichment analyses Description: The gCMAPWeb R package provides a graphical user interface for the gCMAP package. gCMAPWeb uses the Rook package and can be used either on a local machine, leveraging R's internal web server, or run on a dedicated rApache web server installation. gCMAPWeb allows users to search their own data sources and instructions to generate reference datasets from public repositories are included with the package. The package supports three common types of analyses, specifically queries with 1. one or two sets of query gene identifiers, whose members are expected to show changes in gene expression in a consistent direction. For example, an up-regulated gene set might contain genes activated by a transcription factor, a down-regulated geneset targets repressed by the same factor. 2. a single set of query gene identifiers, whose members are expected to show divergent differential expression (non-directional query). For example, members of a particular signaling pathway, some of which may be up- some down-regulated in response to a stimulus. 3. a query with the complete results of a differential expression profiling experiment. For example, gene identifiers and z-scores from a previous perturbation experiment. gCMAPWeb accepts three types of identifiers: EntreIds, gene Symbols and microarray probe ids and can be configured to work with any species supported by Bioconductor. For each query submission, significantly similar reference datasets will be identified and reported in graphical and tabular form. biocViews: GUI, GeneSetEnrichment, Visualization Author: Thomas Sandmann Maintainer: Thomas Sandmann source.ver: src/contrib/gCMAPWeb_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gCMAPWeb_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gCMAPWeb_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gCMAPWeb_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gCMAPWeb_1.12.0.tgz vignettes: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.pdf, vignettes/gCMAPWeb/inst/doc/referenceDatasets.pdf vignetteTitles: gCMAPWeb configuration, Recreating the Broad Connectivity Map v1 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gCMAPWeb/inst/doc/gCMAPWeb.R, vignettes/gCMAPWeb/inst/doc/referenceDatasets.R Package: gcrma Version: 2.44.0 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, BiocInstaller Suggests: affydata, tools, splines, hgu95av2cdf, hgu95av2probe License: LGPL Archs: i386, x64 MD5sum: f61687243cd05736761673c1e4d1e8a6 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 source.ver: src/contrib/gcrma_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gcrma_2.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gcrma_2.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gcrma_2.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gcrma_2.44.0.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, limmaGUI, simpleaffy suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies, panp Package: gdsfmt Version: 1.8.3 Depends: R (>= 2.15.0) Imports: methods Suggests: parallel, digest, crayon, RUnit, knitr, BiocGenerics License: LGPL-3 Archs: i386, x64 MD5sum: 9bbfb17fbfb6333e03b6f67bbaa65727 NeedsCompilation: yes Title: R Interface to CoreArray Genomic Data Structure (GDS) Files Description: This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are 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 single genetic/genomic variant, 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: Software, 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 (for the included liblzma sources) Maintainer: Xiuwen Zheng URL: http://corearray.sourceforge.net/, http://github.com/zhengxwen/gdsfmt VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/gdsfmt/issues source.ver: src/contrib/gdsfmt_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/gdsfmt_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/gdsfmt_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.3/gdsfmt_1.5.13.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gdsfmt_1.8.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.R htmlDocs: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.html htmlTitles: Introduction to GDS Format dependsOnMe: SeqArray, SNPRelate importsMe: GENESIS, GWASTools, SeqVarTools suggestsMe: HIBAG Package: geecc Version: 1.6.0 Depends: R (>= 3.0.0), methods Imports: MASS, hypergea (>= 1.2.3), gplots Suggests: hgu133plus2.db, GO.db, AnnotationDbi License: GPL (>= 2) MD5sum: 702c6a88c6526ee28ac2ac9da3c1773b NeedsCompilation: no Title: Gene set Enrichment analysis Extended to Contingency Cubes Description: Use log-linear models to perform hypergeometric and chi-squared tests for gene set enrichments for two (based on contingency tables) or three categories (contingency cubes). Categories can be differentially expressed genes, GO terms, sequence length, GC content, chromosmal position, phylostrata, .... biocViews: BiologicalQuestion, GeneSetEnrichment, WorkflowStep, GO, StatisticalMethod, GeneExpression, Transcription, RNASeq, Microarray Author: Markus Boenn Maintainer: Markus Boenn source.ver: src/contrib/geecc_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geecc_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geecc_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/geecc_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geecc_1.6.0.tgz vignettes: vignettes/geecc/inst/doc/geecc.pdf vignetteTitles: geecc User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/geecc/inst/doc/geecc.R Package: genArise Version: 1.48.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 0f9273e1550435c30e74e171d35b1912 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 source.ver: src/contrib/genArise_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genArise_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genArise_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/genArise_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genArise_1.48.0.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 Package: genbankr Version: 1.0.4 Depends: methods Imports: BiocGenerics, IRanges, GenomicRanges(>= 1.23.24), GenomicFeatures, Biostrings, VariantAnnotation, rtracklayer, S4Vectors, GenomeInfoDb, Biobase Suggests: RUnit, rentrez, knitr License: Artistic-2.0 MD5sum: d241ca5e10066be5e10369019ced2536 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 source.ver: src/contrib/genbankr_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/genbankr_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/genbankr_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genbankr_1.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genbankr/inst/doc/genbankr.R htmlDocs: vignettes/genbankr/inst/doc/genbankr.html htmlTitles: genbankr Package: GENE.E Version: 1.12.2 Depends: R (>= 2.7.0), rhdf5 (>= 2.8.0), RCurl (>= 1.6-6) Imports: rhdf5, RCurl Suggests: RUnit, BiocGenerics, knitr, golubEsets (>= 1.0) License: GPL-2 MD5sum: 7f6b47631d7b4f6f44ba595edcdd3b3f NeedsCompilation: no Title: Interact with GENE-E from R Description: Interactive exploration of matrices in GENE-E. biocViews: ThirdPartyClient Author: Joshua Gould Maintainer: Joshua Gould URL: http://www.broadinstitute.org/cancer/software/GENE-E SystemRequirements: GENE-E software. VignetteBuilder: knitr source.ver: src/contrib/GENE.E_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GENE.E_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GENE.E_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.3/GENE.E_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GENE.E_1.12.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENE.E/inst/doc/GENE.E-vignette.R htmlDocs: vignettes/GENE.E/inst/doc/GENE.E-vignette.html htmlTitles: GENE.E Overview Package: GeneAnswers Version: 2.14.0 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: 9d25de35069e0023cba372307f9d1d36 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 source.ver: src/contrib/GeneAnswers_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneAnswers_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneAnswers_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneAnswers_2.11.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneAnswers_2.14.0.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 Package: GeneBreak Version: 1.2.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: c000331fb30ad663eb0278a2d6ac797d 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 source.ver: src/contrib/GeneBreak_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneBreak_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneBreak_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneBreak_1.2.0.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 Package: GeneExpressionSignature Version: 1.18.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: e7666939dda9208ae98544a111f44b3b 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 source.ver: src/contrib/GeneExpressionSignature_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneExpressionSignature_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneExpressionSignature_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneExpressionSignature_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneExpressionSignature_1.18.0.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 Package: genefilter Version: 1.54.2 Imports: S4Vectors (>= 0.9.42), AnnotationDbi, annotate, Biobase, graphics, methods, stats, survival Suggests: class, hgu95av2.db, tkWidgets, ALL, ROC, DESeq, pasilla, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: 2c34e32bdfa498eac9ffb0304df5df12 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 source.ver: src/contrib/genefilter_1.54.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/genefilter_1.54.2.zip win64.binary.ver: bin/windows64/contrib/3.3/genefilter_1.54.2.zip mac.binary.ver: bin/macosx/contrib/3.3/genefilter_1.51.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genefilter_1.54.2.tgz vignettes: vignettes/genefilter/inst/doc/howtogenefilter.pdf, vignettes/genefilter/inst/doc/howtogenefinder.pdf, vignettes/genefilter/inst/doc/independent_filtering_plots.pdf, vignettes/genefilter/inst/doc/independent_filtering.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), Diagnostics for independent filtering 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, vignettes/genefilter/inst/doc/independent_filtering.R dependsOnMe: a4Base, cellHTS2, charm, CNTools, GeneMeta, simpleaffy, sva importsMe: affyQCReport, annmap, arrayQualityMetrics, Category, cellHTS, DESeq, DESeq2, DEXSeq, eisa, gCMAP, GGBase, GSRI, JunctionSeq, methyAnalysis, methylumi, minfi, MLInterfaces, mogsa, pcaExplorer, PECA, phenoTest, Ringo, simpleaffy, TCGAbiolinks, tilingArray, XDE suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, categoryCompare, clusterStab, codelink, compcodeR, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, HDF5Array, logicFS, lumi, MCRestimate, npGSEA, oligo, oneChannelGUI, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, topGO, XDE Package: genefu Version: 2.4.2 Depends: survcomp, mclust, limma,biomaRt, iC10, AIMS, R (>= 2.10) Imports: amap Suggests: GeneMeta, breastCancerVDX, breastCancerMAINZ, breastCancerTRANSBIG, breastCancerUPP, breastCancerUNT, breastCancerNKI, rmeta, Biobase, xtable, knitr, caret, survival License: Artistic-2.0 MD5sum: fefdcc224afa1f6a93c72fe3e0e51e3e 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. Schroder, Laia Pare, Joel S. Parker, Aleix Prat, and Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains , Markus Schroeder URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr source.ver: src/contrib/genefu_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/genefu_2.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/genefu_2.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/genefu_2.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genefu_2.4.2.tgz vignettes: vignettes/genefu/inst/doc/genefu.pdf vignetteTitles: genefu An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genefu/inst/doc/genefu.R dependsOnMe: pbcmc Package: GeneGA Version: 1.22.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: a4e2bdf1065d85bbb551b794d9d2866c 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/ source.ver: src/contrib/GeneGA_1.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/GeneGA_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneGA_1.22.0.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 Package: GeneMeta Version: 1.44.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: df4b7a13dab822f271f57a1da891303c 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 source.ver: src/contrib/GeneMeta_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneMeta_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneMeta_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneMeta_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneMeta_1.44.0.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 suggestsMe: genefu, XDE Package: GeneNetworkBuilder Version: 1.14.4 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13) Imports: plyr, graph LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, Rgraphviz, XML, RBGL, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 059b6c5f9625d2fc8126c10eb4821462 NeedsCompilation: yes Title: Build Regulatory Network from ChIP-chip/ChIP-seq and 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 source.ver: src/contrib/GeneNetworkBuilder_1.14.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneNetworkBuilder_1.14.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneNetworkBuilder_1.14.4.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneNetworkBuilder_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneNetworkBuilder_1.14.4.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.pdf vignetteTitles: GeneNetworkBuilder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.R Package: GeneOverlap Version: 1.8.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 90ccbe5ee539cef98eace76804eb48f5 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, Mount Sinai Maintainer: Li Shen, Mount Sinai URL: http://shenlab-sinai.github.io/shenlab-sinai/ source.ver: src/contrib/GeneOverlap_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneOverlap_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneOverlap_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneOverlap_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneOverlap_1.8.0.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 Package: geneplotter Version: 1.50.0 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: f4a584445918efbc90f2dc44f07aefb8 NeedsCompilation: no Title: Graphics related functions for Bioconductor Description: Functions for plotting genomic data biocViews: Visualization Author: R. Gentleman, Biocore Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/geneplotter_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneplotter_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneplotter_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/geneplotter_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneplotter_1.50.0.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: HMMcopy importsMe: biocGraph, DESeq, DESeq2, DEXSeq, EnrichmentBrowser, flowQ, IsoGeneGUI, JunctionSeq, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, GOstats Package: geneRecommender Version: 1.44.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: 3945dbaee330f2bb98f6f6e29cf08030 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 source.ver: src/contrib/geneRecommender_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneRecommender_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneRecommender_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/geneRecommender_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneRecommender_1.44.0.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 Package: GeneRegionScan Version: 1.28.0 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: 954231f9f3d1ea51eb84c991d60b3a82 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 source.ver: src/contrib/GeneRegionScan_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneRegionScan_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneRegionScan_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneRegionScan_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneRegionScan_1.28.0.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 Package: geneRxCluster Version: 1.8.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 05a6561f5503fae6713b4b9723e5cc58 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 source.ver: src/contrib/geneRxCluster_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geneRxCluster_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geneRxCluster_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/geneRxCluster_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geneRxCluster_1.8.0.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 Package: GeneSelectMMD Version: 2.16.0 Depends: R (>= 2.13.2), Biobase Imports: Biobase, MASS, graphics, stats, survival, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: 2ba7b35654a84b1406842e75e7be7711 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 source.ver: src/contrib/GeneSelectMMD_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneSelectMMD_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneSelectMMD_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneSelectMMD_2.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneSelectMMD_2.16.0.tgz vignettes: vignettes/GeneSelectMMD/inst/doc/gsMMD.pdf vignetteTitles: Gene Selection based on a mixture of marginal distributions hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelectMMD/inst/doc/gsMMD.R importsMe: iCheck Package: GeneSelector Version: 2.22.0 Depends: R (>= 2.5.1), methods, stats, Biobase Imports: multtest, siggenes, samr, limma Suggests: multtest, siggenes, samr, limma License: GPL (>= 2) Archs: i386, x64 MD5sum: 3c2a8e6a80ed6d3c343ef9e53f1670d5 NeedsCompilation: yes Title: Stability and Aggregation of ranked gene lists Description: The term 'GeneSelector' refers to a filter selecting those genes which are consistently identified as differentially expressed using various statistical procedures. 'Selected' genes are those present at the top of the list in various ranking methods (currently 14). In addition, the stability of the findings can be taken into account in the final ranking by examining perturbed versions of the original data set, e.g. by leaving samples, swapping class labels, generating bootstrap replicates or adding noise. Given multiple ranked lists, one can use aggregation methods in order to find a synthesis. biocViews: StatisticalMethod, DifferentialExpression Author: Martin Slawski , Anne-Laure Boulesteix . Maintainer: Martin Slawski source.ver: src/contrib/GeneSelector_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneSelector_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneSelector_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneSelector_2.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneSelector_2.22.0.tgz vignettes: vignettes/GeneSelector/inst/doc/GeneSelector.pdf vignetteTitles: GeneSelector.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneSelector/inst/doc/GeneSelector.R Package: GENESIS Version: 2.2.7 Imports: GWASTools, SeqArray, SeqVarTools, Biobase, gdsfmt, graph, grDevices, graphics, stats, utils Suggests: SNPRelate, logistf, survey, CompQuadForm, RUnit, BiocGenerics, knitr License: GPL-3 MD5sum: f831431b375442248bec1522cedbe932 NeedsCompilation: no 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 and Timothy Thornton Maintainer: Matthew P. Conomos VignetteBuilder: knitr source.ver: src/contrib/GENESIS_2.2.7.tar.gz win.binary.ver: bin/windows/contrib/3.3/GENESIS_2.2.7.zip win64.binary.ver: bin/windows64/contrib/3.3/GENESIS_2.2.7.zip mac.binary.ver: bin/macosx/contrib/3.3/GENESIS_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GENESIS_2.2.7.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GENESIS/inst/doc/assoc_test.R, vignettes/GENESIS/inst/doc/pcair.R htmlDocs: vignettes/GENESIS/inst/doc/assoc_test.html, vignettes/GENESIS/inst/doc/pcair.html htmlTitles: Genetic Association Testing using the GENESIS Package, Population Structure and Relatedness Inference using the GENESIS Package Package: geNetClassifier Version: 1.12.0 Depends: R (>= 2.10.1), Biobase (>= 2.5.5), EBarrays, minet, methods Imports: e1071, graphics Suggests: leukemiasEset, RUnit, BiocGenerics Enhances: RColorBrewer, igraph, infotheo License: GPL (>= 2) MD5sum: 7b71f32110817b6a5de66ad958a61581 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 source.ver: src/contrib/geNetClassifier_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/geNetClassifier_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/geNetClassifier_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/geNetClassifier_1.9.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/geNetClassifier_1.12.0.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: canceR Package: GeneticsDesign Version: 1.40.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 MD5sum: 9e83c9e0dfa03d3139a8d449ec3a33b5 NeedsCompilation: no 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 source.ver: src/contrib/GeneticsDesign_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneticsDesign_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneticsDesign_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneticsDesign_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneticsDesign_1.40.0.tgz vignettes: vignettes/GeneticsDesign/inst/doc/GPC.pdf vignetteTitles: Power Calculation for Testing If Disease is Associated a Marker in a Case-Control Study Using the \Rpackage{GeneticsDesign} Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GeneticsDesign/inst/doc/GPC.R Package: GeneticsPed Version: 1.34.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: def314590247ce52b48cfac3ae23a3d5 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 source.ver: src/contrib/GeneticsPed_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GeneticsPed_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GeneticsPed_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GeneticsPed_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GeneticsPed_1.34.0.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 Package: genoCN Version: 1.24.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: 02b5f465434b634e63919c383ca90dc1 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 source.ver: src/contrib/genoCN_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genoCN_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genoCN_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/genoCN_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genoCN_1.24.0.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 Package: GenoGAM Version: 1.0.3 Depends: R (>= 3.3), Rsamtools (>= 1.18.2), SummarizedExperiment (>= 1.1.19), GenomicRanges (>= 1.23.16), methods Imports: BiocParallel (>= 1.5.17), data.table (>= 1.9.4), DESeq2 (>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb (>= 1.7.6), GenomicAlignments (>= 1.7.17), IRanges (>= 2.5.30), mgcv (>= 1.8), reshape2 (>= 1.4.1), S4Vectors (>= 0.9.34) Suggests: BiocStyle, chipseq (>= 1.21.2), testthat, knitr License: GPL-2 MD5sum: bbdbe22b6108b35a457a5f56726f3179 NeedsCompilation: no 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 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 source.ver: src/contrib/GenoGAM_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenoGAM_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/GenoGAM_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenoGAM_1.0.3.tgz vignettes: vignettes/GenoGAM/inst/doc/GenoGAM.pdf vignetteTitles: GenoGAM: Genome-wide generalized additive models hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenoGAM/inst/doc/GenoGAM.R Package: genomation Version: 1.4.2 Depends: R (>= 3.0.0),grid Imports: Biostrings, BSgenome, data.table, GenomeInfoDb, GenomicRanges (>= 1.23.26), GenomicAlignments, S4Vectors (>= 0.9.25), ggplot2, gridBase, impute, IRanges, matrixStats, methods, parallel, plotrix, plyr, readr, reshape2, Rsamtools, seqPattern, rtracklayer, Suggests: BiocGenerics, genomationData, knitr, knitrBootstrap, RColorBrewer, rmarkdown, RUnit License: Artistic-2.0 MD5sum: bf50c1b31248195130884d70f5e4fbf9 NeedsCompilation: no 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], Liz Ing-Simmons [ctb] Maintainer: Altuna Akalin , Vedran Franke URL: http://bioinformatics.mdc-berlin.de/genomation/ VignetteBuilder: knitr BugReports: https://github.com/BIMSBbioinfo/genomation/issues source.ver: src/contrib/genomation_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/genomation_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/genomation_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/genomation_1.1.16.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genomation_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genomation/inst/doc/GenomationManual-knitr.R htmlDocs: vignettes/genomation/inst/doc/GenomationManual-knitr.html htmlTitles: genomation importsMe: CexoR Package: GenomeGraphs Version: 1.32.0 Depends: R (>= 2.10), methods, biomaRt, grid License: Artistic-2.0 MD5sum: 89fe8db91a16cc829777833fafab6c34 NeedsCompilation: no Title: Plotting genomic information from Ensembl Description: Genomic data analyses requires integrated visualization of known genomic information and new experimental data. GenomeGraphs uses the biomaRt package to perform live annotation queries to Ensembl 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. Another strength of GenomeGraphs is to plot different data types such as array CGH, gene expression, sequencing and other data, together in one plot using the same genome coordinate system. biocViews: Visualization, Microarray Author: Steffen Durinck , James Bullard Maintainer: Steffen Durinck source.ver: src/contrib/GenomeGraphs_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomeGraphs_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomeGraphs_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomeGraphs_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomeGraphs_1.32.0.tgz vignettes: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.pdf vignetteTitles: The GenomeGraphs users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.R dependsOnMe: Genominator, waveTiling suggestsMe: oligo, rMAT, triplex Package: GenomeInfoDb Version: 1.8.7 Depends: R (>= 3.1), methods, stats4, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.9.25), IRanges (>= 1.99.26) Imports: methods, BiocGenerics, S4Vectors Suggests: GenomicRanges, Rsamtools, GenomicAlignments, BSgenome, GenomicFeatures, BSgenome.Scerevisiae.UCSC.sacCer2, BSgenome.Celegans.UCSC.ce2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Dmelanogaster.UCSC.dm3.ensGene, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: b8bf491b8b4c4aebaf11ce11937d3c53 NeedsCompilation: no Title: Utilities for manipulating chromosome and other 'seqname' identifiers 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 VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w source.ver: src/contrib/GenomeInfoDb_1.8.7.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomeInfoDb_1.8.7.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomeInfoDb_1.8.7.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomeInfoDb_1.5.15.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomeInfoDb_1.8.7.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: BSgenome, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, htSeqTools, methyAnalysis, Rsamtools, TitanCNA, VariantAnnotation importsMe: AllelicImbalance, AneuFinder, AnnotationHubData, ballgown, biovizBase, BiSeq, BSgenome, bsseq, casper, CexoR, ChIPpeakAnno, ChIPseeker, cn.mops, CNEr, CNPBayes, compEpiTools, consensusSeekeR, conumee, CopywriteR, CrispRVariants, csaw, customProDB, derfinder, derfinderPlot, diffHic, diffloop, easyRNASeq, ensembldb, epigenomix, epivizrData, epivizrStandalone, exomeCopy, genbankr, GenoGAM, genomation, genomeIntervals, GenomicFiles, GenomicInteractions, genoset, genotypeeval, ggbio, GGtools, GoogleGenomics, gQTLstats, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, HiTC, HTSeqGenie, InPAS, InteractionSet, IVAS, metagene, methylPipe, methylumi, minfi, MinimumDistance, mosaics, motifbreakR, myvariant, NarrowPeaks, podkat, prebs, ProteomicsAnnotationHubData, PureCN, qpgraph, QuasR, R3CPET, r3Cseq, RareVariantVis, Rariant, regioneR, regionReport, Repitools, RiboProfiling, roar, rtracklayer, SeqArray, seqplots, SGSeq, ShortRead, SNPchip, SNPhood, soGGi, SomaticSignatures, SplicingGraphs, STAN, SummarizedExperiment, TarSeqQC, TFBSTools, transcriptR, VanillaICE, VariantFiltering, VariantTools suggestsMe: AnnotationForge, AnnotationHub, gQTLBase, QDNAseq, recoup Package: genomeIntervals Version: 1.28.0 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: 566c98f82269669c23a8c3e78a85d28b 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 source.ver: src/contrib/genomeIntervals_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genomeIntervals_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genomeIntervals_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/genomeIntervals_1.25.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genomeIntervals_1.28.0.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 importsMe: easyRNASeq Package: genomes Version: 3.2.0 Depends: readr, curl License: GPL-3 MD5sum: 611488367e1a8c16cbca755c292198cc NeedsCompilation: no Title: Genome sequencing project metadata Description: Download genome and assembly reports from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben source.ver: src/contrib/genomes_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genomes_3.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genomes_3.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/genomes_2.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genomes_3.2.0.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 Package: GenomicAlignments Version: 1.8.4 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.9.40), IRanges (>= 2.5.36), GenomeInfoDb (>= 1.1.20), GenomicRanges (>= 1.24.2), SummarizedExperiment (>= 0.3.1), Biostrings (>= 2.37.1), Rsamtools (>= 1.21.4) 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: b86ada58fd88b0a74e2bcb2bb21927cd 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: Genetics, Infrastructure, DataImport, Sequencing, RNASeq, SNP, Coverage, Alignment Author: Hervé Pagès, Valerie Obenchain, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=2KqBSbkfhRo , https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicAlignments_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicAlignments_1.8.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicAlignments_1.8.4.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomicAlignments_1.5.13.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicAlignments_1.8.4.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, exomePeak, GoogleGenomics, groHMM, Guitar, hiReadsProcessor, prebs, recoup, RIPSeeker, rnaSeqMap, ShortRead, SplicingGraphs importsMe: AneuFinder, biovizBase, ChIPpeakAnno, ChIPQC, CNEr, CopywriteR, CoverageView, CrispRVariants, customProDB, derfinder, DiffBind, easyRNASeq, FourCSeq, GenoGAM, genomation, GenomicFiles, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, HTSeqGenie, INSPEcT, metagene, methylPipe, mosaics, PICS, QuasR, Repitools, RiboProfiling, RNAprobR, roar, Rqc, rtracklayer, SGSeq, similaRpeak, soGGi, SplicingGraphs, trackViewer, transcriptR suggestsMe: BiocParallel, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, IRanges, oneChannelGUI, Rsamtools, Streamer Package: GenomicFeatures Version: 1.24.5 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.9.47), IRanges (>= 2.3.21), GenomeInfoDb (>= 1.5.16), GenomicRanges (>= 1.21.32), AnnotationDbi (>= 1.33.15) Imports: methods, utils, stats, tools, DBI, RSQLite, RCurl, XVector, Biostrings (>= 2.23.3), rtracklayer (>= 1.29.24), biomaRt (>= 2.17.1), Biobase (>= 2.15.1) Suggests: org.Mm.eg.db, org.Hs.eg.db, BSgenome, BSgenome.Hsapiens.UCSC.hg19 (>= 1.3.17), BSgenome.Celegans.UCSC.ce2, BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.3.17), mirbase.db, FDb.UCSC.tRNAs, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene (>= 2.7.1), TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Hsapiens.UCSC.hg19.lincRNAsTranscripts, TxDb.Hsapiens.UCSC.hg38.knownGene, SNPlocs.Hsapiens.dbSNP141.GRCh38, Rsamtools, pasillaBamSubset (>= 0.0.5), GenomicAlignments, RUnit, BiocStyle, knitr License: Artistic-2.0 MD5sum: cf98a0b2efe88da6058218c7c479f2f6 NeedsCompilation: no Title: Tools for making and manipulating transcript centric annotations 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 Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/GenomicFeatures_1.24.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicFeatures_1.24.5.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicFeatures_1.24.5.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomicFeatures_1.21.21.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicFeatures_1.24.5.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, exomePeak, Guitar, InPAS, OrganismDbi, RNAprobR, SplicingGraphs importsMe: AllelicImbalance, AnnotationHubData, biovizBase, bumphunter, casper, ChIPpeakAnno, ChIPQC, ChIPseeker, compEpiTools, CompGO, csaw, customProDB, derfinder, derfinderPlot, EDASeq, epivizrData, epivizrStandalone, genbankr, GenVisR, ggbio, gmapR, gQTLstats, Gviz, gwascat, HTSeqGenie, INSPEcT, lumi, metagene, methyAnalysis, PGA, proBAMr, qpgraph, QuasR, RiboProfiling, SGSeq, SplicingGraphs, systemPipeR, TCGAbiolinks, trackViewer, transcriptR, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: AnnotationHub, biomvRCNS, Biostrings, chipseq, chromPlot, CrispRVariants, cummeRbund, DEXSeq, easyRNASeq, flipflop, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, IRanges, MiRaGE, RIPSeeker, Rsamtools, ShortRead, SummarizedExperiment Package: GenomicFiles Version: 1.8.0 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), GenomicRanges (>= 1.23.21), SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments (>= 1.7.7), Biobase, IRanges, S4Vectors (>= 0.9.25), VariantAnnotation, GenomeInfoDb Suggests: BiocStyle, RUnit, genefilter, deepSNV, RNAseqData.HNRNPC.bam.chr14, Biostrings, Homo.sapiens License: Artistic-2.0 MD5sum: 8a4557e5407b70aa44befeb9118fcf2d 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: Valerie Obenchain, Michael Love, Martin Morgan Maintainer: Bioconductor Package Maintainer Video: https://www.youtube.com/watch?v=3PK_jx44QTs source.ver: src/contrib/GenomicFiles_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicFiles_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicFiles_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomicFiles_1.5.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicFiles_1.8.0.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 importsMe: contiBAIT, derfinder, erma, gQTLBase, gQTLstats, QuasR, Rqc Package: GenomicInteractions Version: 1.6.2 Depends: R (>= 3.3), InteractionSet Imports: Rsamtools, rtracklayer, GenomicRanges, IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors, dplyr, Gviz, Biobase, graphics, stats, utils Suggests: knitr, BiocStyle, testthat License: GPL-3 MD5sum: bbe8115722f784135dee582bfe35d03c NeedsCompilation: no Title: R package for handling genomic interaction data Description: R package for handling Genomic interaction data, such as ChIA-PET/Hi-C, annotating genomic features with interaction information and producing various plots / statistics. biocViews: Software,Infrastructure,DataImport,DataRepresentation,HiC Author: Harmston, N., Ing-Simmons, E., Perry, M., Baresic, A., Lenhard, B. Maintainer: Malcolm Perry , Liz Ing-Simmons URL: https://github.com/ComputationalRegulatoryGenomicsICL/GenomicInteractions/ VignetteBuilder: knitr source.ver: src/contrib/GenomicInteractions_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicInteractions_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicInteractions_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomicInteractions_1.3.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicInteractions_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.R, vignettes/GenomicInteractions/inst/doc/hic_vignette.R htmlDocs: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.html, vignettes/GenomicInteractions/inst/doc/hic_vignette.html htmlTitles: chiapet_vignette.html, GenomicInteractions-HiC suggestsMe: Chicago Package: GenomicRanges Version: 1.24.3 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.17.5), S4Vectors (>= 0.9.47), IRanges (>= 2.5.36), GenomeInfoDb (>= 1.1.20) Imports: utils, stats, XVector LinkingTo: S4Vectors, IRanges Suggests: Biobase, AnnotationDbi (>= 1.21.1), annotate, Biostrings (>= 2.25.3), Rsamtools (>= 1.13.53), SummarizedExperiment (>= 0.1.5), Matrix, GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq2, DEXSeq, edgeR, KEGGgraph, BiocStyle, digest, RUnit, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2, KEGG.db, hgu95av2.db, org.Hs.eg.db, org.Mm.eg.db, org.Sc.sgd.db, pasilla, pasillaBamSubset, TxDb.Athaliana.BioMart.plantsmart22, TxDb.Dmelanogaster.UCSC.dm3.ensGene, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 Archs: i386, x64 MD5sum: ae40ba717b7c9a8fbc113b717e91aa30 NeedsCompilation: yes Title: Representation and manipulation of genomic intervals and variables defined along a genome 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, Sequencing, Annotation, Coverage, GenomeAnnotation Author: P. Aboyoun, H. Pagès, and M. Lawrence Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GenomicRanges_1.24.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicRanges_1.24.3.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicRanges_1.24.3.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomicRanges_1.21.21.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicRanges_1.24.3.tgz vignettes: vignettes/GenomicRanges/inst/doc/ExtendingGenomicRanges.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesHOWTOs.pdf, vignettes/GenomicRanges/inst/doc/GenomicRangesIntroduction.pdf, vignettes/GenomicRanges/inst/doc/GRanges_and_GRangesList_slides.pdf vignetteTitles: Extending GenomicRanges, GenomicRanges HOWTOs, An Introduction to the GenomicRanges Package, A quick introduction to GRanges and GRangesList objects 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 dependsOnMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData, Basic4Cseq, baySeq, biomvRCNS, BiSeq, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, casper, chimera, ChIPpeakAnno, ChIPQC, chipseq, chromPlot, CINdex, cleanUpdTSeq, cn.mops, CNPBayes, cnvGSA, CNVPanelizer, compEpiTools, consensusSeekeR, CSAR, csaw, deepSNV, DESeq2, DEXSeq, DiffBind, diffHic, DMRcaller, DMRforPairs, DNAshapeR, DOQTL, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, exomeCopy, fastseg, FourCSeq, GeneBreak, GenoGAM, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicTuples, genoset, gmapR, GOTHiC, GreyListChIP, groHMM, gtrellis, GUIDEseq, Guitar, Gviz, hiAnnotator, HilbertCurve, HiTC, htSeqTools, IdeoViz, InPAS, intansv, InteractionSet, isomiRs, MBASED, metagene, methyAnalysis, methylPipe, minfi, PGA, PING, podkat, QuasR, r3Cseq, Rariant, Rcade, recoup, regioneR, rfPred, rGREAT, riboSeqR, RIPSeeker, RnBeads, Rsamtools, RSVSim, rtracklayer, segmentSeq, seqbias, SGSeq, SICtools, SigFuge, SMITE, SNPhood, SomatiCA, SomaticSignatures, SummarizedExperiment, TarSeqQC, TitanCNA, trackViewer, TransView, VanillaICE, VariantAnnotation, VariantTools, vtpnet, wavClusteR importsMe: ALDEx2, ArrayExpressHTS, BadRegionFinder, ballgown, bamsignals, BBCAnalyzer, beadarray, BEAT, biovizBase, BiSeq, BSgenome, CAGEr, CexoR, chipenrich, ChIPseeker, chipseq, ChIPseqR, chromDraw, CNEr, CNPBayes, coMET, contiBAIT, conumee, copynumber, CopywriteR, CoverageView, CrispRVariants, customProDB, DChIPRep, debrowser, DEFormats, derfinder, derfinderPlot, diffloop, DMRcate, DRIMSeq, easyRNASeq, EDASeq, epivizr, epivizrData, erma, flipflop, FourCSeq, FunciSNP, genbankr, genomation, genomeIntervals, GenomicAlignments, GenomicInteractions, genotypeeval, GenVisR, GGBase, ggbio, GGtools, GoogleGenomics, gQTLBase, gQTLstats, gwascat, h5vc, hiReadsProcessor, HTSeqGenie, INSPEcT, IVAS, JunctionSeq, LOLA, lumi, M3D, MEAL, MEDIPS, methyAnalysis, MethylSeekR, methylumi, MinimumDistance, MMDiff2, mosaics, motifbreakR, MultiDataSet, NarrowPeaks, nucleR, oligoClasses, OrganismDbi, Pbase, pcaExplorer, pepStat, PICS, pqsfinder, prebs, proBAMr, PureCN, Pviz, pwOmics, QDNAseq, qpgraph, R3CPET, R453Plus1Toolbox, RareVariantVis, regioneR, regionReport, Repitools, RiboProfiling, RNAprobR, rnaSeqMap, roar, seq2pathway, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, simulatorZ, SNPchip, soGGi, SomatiCA, spliceR, SplicingGraphs, STAN, SVM2CRM, systemPipeR, TCGAbiolinks, TFBSTools, tracktables, transcriptR, triplex, VariantFiltering, waveTiling suggestsMe: AnnotationHub, biobroom, BiocGenerics, BiocParallel, Chicago, cummeRbund, GenomeInfoDb, HDF5Array, interactiveDisplay, IRanges, metaseqR, MiRaGE, NarrowPeaks, NGScopy, RTCGA, S4Vectors, SeqGSEA Package: GenomicTuples Version: 1.6.2 Depends: R (>= 3.3.0), GenomicRanges (>= 1.23.15), GenomeInfoDb (>= 1.7.2), S4Vectors (>= 0.9.38) Imports: methods, BiocGenerics (>= 0.17.0), Rcpp (>= 0.11.2), IRanges (>= 2.5.26), data.table LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 9a27d28d5c9dd3532873bedbf3b111ed 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 , with contributions from Marcin Cieslik and Herve Pages. Maintainer: Peter Hickey URL: www.github.com/PeteHaitch/GenomicTuples VignetteBuilder: knitr BugReports: https://github.com/PeteHaitch/GenomicTuples/issues source.ver: src/contrib/GenomicTuples_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenomicTuples_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GenomicTuples_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/GenomicTuples_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenomicTuples_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.R htmlDocs: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html htmlTitles: GenomicTuplesIntroduction Package: Genominator Version: 1.26.0 Depends: R (>= 2.10), methods, RSQLite, DBI (>= 0.2-5), BiocGenerics (>= 0.1.0), IRanges (>= 2.5.27), GenomeGraphs Imports: graphics, stats, utils Suggests: biomaRt, ShortRead, yeastRNASeq License: Artistic-2.0 MD5sum: 64cfb51ff0e2f1e58f396e970f1335f4 NeedsCompilation: no Title: Analyze, manage and store genomic data Description: Tools for storing, accessing, analyzing and visualizing genomic data. biocViews: Infrastructure Author: James Bullard, Kasper Daniel Hansen Maintainer: James Bullard source.ver: src/contrib/Genominator_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Genominator_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Genominator_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Genominator_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Genominator_1.26.0.tgz vignettes: vignettes/Genominator/inst/doc/Genominator.pdf, vignettes/Genominator/inst/doc/plotting.pdf, vignettes/Genominator/inst/doc/withShortRead.pdf vignetteTitles: The Genominator User Guide, Plotting with Genominator, Working with the ShortRead Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Genominator/inst/doc/Genominator.R, vignettes/Genominator/inst/doc/plotting.R, vignettes/Genominator/inst/doc/withShortRead.R suggestsMe: oneChannelGUI Package: genoset Version: 1.28.2 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), GenomicRanges (>= 1.17.19), SummarizedExperiment (>= 1.1.6) Imports: S4Vectors (>= 0.9.25), GenomeInfoDb (>= 1.1.3), IRanges (>= 2.5.12), methods, graphics Suggests: RUnit, knitr, BiocStyle, rmarkdown, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 22963a9a5246c60c5f4838b4ed9a6e8f 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 source.ver: src/contrib/genoset_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/genoset_1.28.2.zip win64.binary.ver: bin/windows64/contrib/3.3/genoset_1.28.2.zip mac.binary.ver: bin/macosx/contrib/3.3/genoset_1.23.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genoset_1.28.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/genoset/inst/doc/genoset.R htmlDocs: vignettes/genoset/inst/doc/genoset.html htmlTitles: genoset importsMe: methyAnalysis, VegaMC Package: genotypeeval Version: 1.2.2 Depends: R (>= 3.2.0), VariantAnnotation Imports: ggplot2, rtracklayer, BiocGenerics, GenomicRanges, GenomeInfoDb, IRanges, methods, BiocParallel Suggests: knitr, testthat, SNPlocs.Hsapiens.dbSNP141.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene License: file LICENSE MD5sum: 76d3699a8a3e8d28e27652f6707211ad 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: knitr source.ver: src/contrib/genotypeeval_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/genotypeeval_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/genotypeeval_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/genotypeeval_0.99.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genotypeeval_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.R htmlDocs: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html htmlTitles: genotypeeval_vignette Package: genphen Version: 1.0.0 Depends: R(>= 3.3), randomForest, e1071, ggplot2, effsize, Biostrings License: GPL (>= 2) MD5sum: c12ca7eb9641cc05be06bed06caa8a9d NeedsCompilation: no Title: A tool for computing genotype-phenotype associations using statistical learning techniques Description: Given a set of genetic polymorphisms in the form of single nucleotide poylmorphisms or single amino acid polymorphisms and a corresponding phenotype data, often we are interested to quantify their association such that we can identify the causal polymorphisms. Using statistical learning techniques such as random forests and support vector machines, this tool provides the means to estimate genotype-phenotype associations. It also provides visualization functions which enable the user to visually inspect the results of such genetic association study and conveniently select the genotypes which have the highest strenght ofassociation with the phenotype. biocViews: GenomeWideAssociation, Regression, Classification, SupportVectorMachine, Genetics, SequenceMatching Author: Simo Kitanovski Maintainer: Simo Kitanovski source.ver: src/contrib/genphen_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/genphen_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/genphen_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/genphen_1.0.0.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 Package: GenRank Version: 1.0.2 Depends: R (>= 3.2.3) Imports: matrixStats, reshape2, survcomp Suggests: knitr, rmarkdown, testthat License: Artistic-2.0 MD5sum: 254de4d1c560eb8127b720db811cc14d 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 source.ver: src/contrib/GenRank_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenRank_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GenRank_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenRank_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GenRank/inst/doc/GenRank_Vignette.R htmlDocs: vignettes/GenRank/inst/doc/GenRank_Vignette.html htmlTitles: Introduction to GenRank Package Package: GenVisR Version: 1.0.4 Depends: R (>= 3.3.0) Imports: AnnotationDbi, biomaRt, BiocGenerics, Biostrings, DBI, FField, GenomicFeatures, GenomicRanges, ggplot2 (>= 0.9.2), grid, gridExtra, gtable, gtools, IRanges, plyr (>= 1.8.3), reshape2, Rsamtools, scales, stats, utils, viridis Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, RMySQL, roxygen2, testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 + file LICENSE MD5sum: f1d6209e7e465b9f01fb11efd9c61366 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 source.ver: src/contrib/GenVisR_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GenVisR_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GenVisR_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GenVisR_1.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GenVisR/inst/doc/GenVisR_intro.R, vignettes/GenVisR/inst/doc/waterfall_introduction.R htmlDocs: vignettes/GenVisR/inst/doc/GenVisR_intro.html, vignettes/GenVisR/inst/doc/waterfall_introduction.html htmlTitles: GenVisR: An introduction, waterfall: function introduction Package: GEOmetadb Version: 1.32.2 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: 1b1ac07ee92378b1bbed7fa8709c9955 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 source.ver: src/contrib/GEOmetadb_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEOmetadb_1.32.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GEOmetadb_1.32.2.zip mac.binary.ver: bin/macosx/contrib/3.3/GEOmetadb_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOmetadb_1.32.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOmetadb/inst/doc/GEOmetadb.R htmlDocs: vignettes/GEOmetadb/inst/doc/GEOmetadb.html htmlTitles: GEOmetadb Package: GEOquery Version: 2.38.4 Depends: methods, Biobase Imports: XML, RCurl, httr Suggests: limma, knitr, rmarkdown, RUnit, BiocGenerics License: GPL-2 MD5sum: 54c96cb6d2b4eab9421b7d47180f26d6 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 source.ver: src/contrib/GEOquery_2.38.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEOquery_2.38.4.zip win64.binary.ver: bin/windows64/contrib/3.3/GEOquery_2.38.4.zip mac.binary.ver: bin/macosx/contrib/3.3/GEOquery_2.35.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOquery_2.38.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOquery/inst/doc/GEOquery.R htmlDocs: vignettes/GEOquery/inst/doc/GEOquery.html htmlTitles: Using GEOquery dependsOnMe: DrugVsDisease, SCAN.UPC importsMe: AnnotationHubData, ChIPXpress, EGAD, minfi, SRAdb suggestsMe: dyebias, ELBOW, multiClust, PGSEA, RGSEA, RnBeads, Rnits, skewr, TargetScore Package: GEOsearch Version: 1.2.2 Depends: R(>= 3.2) Imports: org.Hs.eg.db, org.Mm.eg.db Suggests: knitr, shiny, DT, 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.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(>=2) MD5sum: 3443d9dc6faa7946574cf61d8609859f NeedsCompilation: no Title: GEOsearch Description: GEOsearch is an extendable search engine for NCBI GEO (Gene Expression Omnibus). Instead of directly searching the term, GEOsearch can find all the gene names contained in the search term and search all the alias of the gene names simultaneously in GEO database. GEOsearch also provides other functions such as summarizing common biology keywords in the search results. biocViews: GUI,Software Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/GEOsearch_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEOsearch_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GEOsearch_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/GEOsearch_0.99.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOsearch_1.2.2.tgz vignettes: vignettes/GEOsearch/inst/doc/GEOsearch.pdf vignetteTitles: GEOsearch: Extendable Search Engine for Gene Expression Omnibus hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GEOsearch/inst/doc/GEOsearch.R Package: GEOsubmission Version: 1.24.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: 3d92e0aba83b91bf66b5d3f885ac6c5a 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 source.ver: src/contrib/GEOsubmission_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEOsubmission_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GEOsubmission_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GEOsubmission_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEOsubmission_1.24.0.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 Package: gespeR Version: 1.4.2 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: 230d5d6cf16d26e6caf3b92eed6c686d 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: CellBasedAssays, Preprocessing, GeneTarget, Regression, Visualization Author: Fabian Schmich Maintainer: Fabian Schmich URL: http://www.cbg.ethz.ch/software/gespeR VignetteBuilder: knitr source.ver: src/contrib/gespeR_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/gespeR_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/gespeR_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/gespeR_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gespeR_1.4.2.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 Package: GEWIST Version: 1.16.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: 940158d722bb0cc1fcb9c7f48b269067 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 source.ver: src/contrib/GEWIST_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GEWIST_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GEWIST_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GEWIST_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GEWIST_1.16.0.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 Package: GGBase Version: 3.34.0 Depends: R (>= 2.14), methods, snpStats Imports: limma, genefilter, Biobase, BiocGenerics, S4Vectors, IRanges, Matrix, AnnotationDbi, digest, GenomicRanges, SummarizedExperiment Suggests: GGtools, illuminaHumanv1.db License: Artistic-2.0 MD5sum: a6ff6c0cc081dde130ae72739b6a4f46 NeedsCompilation: no Title: GGBase infrastructure for genetics of gene expression package GGtools Description: infrastructure biocViews: Genetics, Infrastructure Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/GGBase_3.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GGBase_3.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GGBase_3.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GGBase_3.31.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GGBase_3.34.0.tgz vignettes: vignettes/GGBase/inst/doc/ggbase.pdf 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 Package: ggbio Version: 1.20.2 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.19.1), Biobase, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.21.10), SummarizedExperiment, Biostrings, Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), BSgenome, VariantAnnotation (>= 1.11.4), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.17.13), OrganismDbi, GGally, ensembldb (>= 1.3.8), AnnotationDbi Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, chipseq, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat, EnsDb.Hsapiens.v75 License: Artistic-2.0 MD5sum: 513fbcdb37fa3ffeb883be54f433e043 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 source.ver: src/contrib/ggbio_1.20.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ggbio_1.20.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ggbio_1.20.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ggbio_1.17.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ggbio_1.20.2.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: coMET, derfinderPlot, FourCSeq, gwascat, R3CPET, Rariant, ReportingTools, RiboProfiling, SomaticSignatures suggestsMe: beadarray, GoogleGenomics, gQTLstats, interactiveDisplay, regionReport, RnBeads Package: ggcyto Version: 1.0.6 Depends: methods, ggplot2(>= 2.0.0), flowCore, ncdfFlow(>= 2.17.1), flowWorkspace(>= 3.17.24) Imports: plyr, scales, data.table, RColorBrewer, gridExtra Suggests: testthat, flowWorkspaceData, knitr, rmarkdown, flowStats, openCyto, flowViz License: Artistic-2.0 MD5sum: 4d49e23292b84ea07b66f8a87436cb85 NeedsCompilation: no Title: Visualize Cytometry data with ggplot Description: With the dedicated fority 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: FlowCytometry, CellBasedAssays, Infrastructure, Visualization Author: Mike Jiang Maintainer: Mike Jiang URL: https://github.com/RGLab/ggcyto/issues VignetteBuilder: knitr source.ver: src/contrib/ggcyto_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/ggcyto_1.0.6.zip win64.binary.ver: bin/windows64/contrib/3.3/ggcyto_1.0.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ggcyto_1.0.6.tgz 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 htmlDocs: 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 htmlTitles: Quick plot for cytometry data, Visualize flowSet with ggcyto, Visualize GatingSet with ggcyto, Feature summary of ggcyto suggestsMe: flowWorkspace Package: GGtools Version: 5.8.0 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel, Homo.sapiens Imports: methods, utils, stats, BiocGenerics, snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, 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: ac5dca03758943bd30f9b154b46f1b30 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 source.ver: src/contrib/GGtools_5.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GGtools_5.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GGtools_5.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GGtools_5.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GGtools_5.8.0.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 suggestsMe: GGBase, gQTLBase, gwascat Package: ggtree Version: 1.4.20 Depends: R (>= 3.2.0), ggplot2 (>= 2.0.0) Imports: ape, grDevices, grid, jsonlite, magrittr, methods, stats4, tidyr, utils Suggests: Biostrings, colorspace, EBImage, emojifont, knitr, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: 0e87401239d610e3655de3c9640f33a7 NeedsCompilation: no Title: an R package for visualization and annotation of phylogenetic trees with their covariates and other associated data Description: ggtree extends the ggplot2 plotting system which implemented the grammar of graphics. ggtree is designed for visualizing phylogenetic tree and different types of associated annotation data. biocViews: Alignment, Annotation, Clustering, DataImport, MultipleSequenceAlignment, ReproducibleResearch, Software, Visualization Author: Guangchuang Yu and Tommy Tsan-Yuk Lam Maintainer: Guangchuang Yu URL: http://guangchuangyu.github.io/ggtree VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ggtree/issues source.ver: src/contrib/ggtree_1.4.20.tar.gz win.binary.ver: bin/windows/contrib/3.3/ggtree_1.4.20.zip win64.binary.ver: bin/windows64/contrib/3.3/ggtree_1.4.20.zip mac.binary.ver: bin/macosx/contrib/3.3/ggtree_1.1.19.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ggtree_1.4.20.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ggtree/inst/doc/advanceTreeAnnotation.R, vignettes/ggtree/inst/doc/ggtree.R, vignettes/ggtree/inst/doc/ggtreeUtilities.R, vignettes/ggtree/inst/doc/treeAnnotation.R, vignettes/ggtree/inst/doc/treeImport.R, vignettes/ggtree/inst/doc/treeManipulation.R, vignettes/ggtree/inst/doc/treeVisualization.R htmlDocs: vignettes/ggtree/inst/doc/advanceTreeAnnotation.html, vignettes/ggtree/inst/doc/ggtree.html, vignettes/ggtree/inst/doc/ggtreeUtilities.html, vignettes/ggtree/inst/doc/treeAnnotation.html, vignettes/ggtree/inst/doc/treeImport.html, vignettes/ggtree/inst/doc/treeManipulation.html, vignettes/ggtree/inst/doc/treeVisualization.html htmlTitles: 05 Advance Tree Annotation, 00 ggtree introduction, 06 ggtree utilities, 04 Tree Annotation, 01 Tree Data Import, 03 Tree Manipulation, 02 Tree Visualization Package: girafe Version: 1.24.0 Depends: R (>= 2.10.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.7.21), Rsamtools, intervals (>= 0.13.1), ShortRead (>= 1.3.21), genomeIntervals (>= 1.25.1), grid Imports: methods, Biobase, Biostrings, graphics, grDevices, stats, utils, IRanges (>= 2.3.23) Suggests: MASS, org.Mm.eg.db, RColorBrewer Enhances: genomeIntervals License: Artistic-2.0 Archs: i386, x64 MD5sum: a296732ce7e5a3739fd804888943eae3 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 source.ver: src/contrib/girafe_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/girafe_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/girafe_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/girafe_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/girafe_1.24.0.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 Package: GLAD Version: 2.36.0 Depends: R (>= 2.10) Suggests: aws, tcltk License: GPL-2 Archs: i386, x64 MD5sum: f80744d78221b401424dfb222ebe729f 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'. source.ver: src/contrib/GLAD_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GLAD_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GLAD_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GLAD_2.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GLAD_2.36.0.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, MANOR, seqCNA importsMe: ITALICS, MANOR, snapCGH Package: Glimma Version: 1.0.0 Depends: R (>= 3.2.0) Imports: DESeq2, edgeR, grDevices, methods, stats, utils Suggests: BiocStyle, limma License: GPL-3 | file LICENSE MD5sum: 7fda235b6e39ef2c4ceffc9d6d5084bc NeedsCompilation: no Title: Interactive HTML graphics for RNA-seq data Description: This package generates interactive visualisations of RNA-sequencing data based on output from limma, edgeR or DESeq2. Interactions are built on top of popular static displays from the limma package, providing users with access to gene IDs and sample information. Plots are generated using d3.js and displayed in HTML pages. biocViews: DifferentialExpression, ReportWriting, RNASeq, Visualization Author: Shian Su, Matthew E. Ritchie Maintainer: Shian Su URL: https://github.com/Shians/Glimma BugReports: https://github.com/Shians/Glimma/issues source.ver: src/contrib/Glimma_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Glimma_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Glimma_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Glimma_1.0.0.tgz vignettes: vignettes/Glimma/inst/doc/Glimma.pdf vignetteTitles: Glimma Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Glimma/inst/doc/Glimma.R Package: GlobalAncova Version: 3.40.0 Depends: methods, corpcor, globaltest Imports: annotate, AnnotationDbi Suggests: Biobase, annotate, GO.db, KEGG.db, golubEsets, hu6800.db, vsn, GSEABase, Rgraphviz License: GPL (>= 2) Archs: i386, x64 MD5sum: a26e026a763fe76de10fe2e7731135b8 NeedsCompilation: yes Title: Calculates a global test for differential gene expression between groups Description: 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. This work was supported by the NGFN grant 01 GR 0459, BMBF, Germany. biocViews: Microarray, OneChannel, DifferentialExpression, Pathways Author: U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel Maintainer: Manuela Hummel source.ver: src/contrib/GlobalAncova_3.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GlobalAncova_3.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GlobalAncova_3.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GlobalAncova_3.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GlobalAncova_3.40.0.tgz vignettes: vignettes/GlobalAncova/inst/doc/GlobalAncova.pdf, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.pdf vignetteTitles: GlobalAncova.pdf, GlobalAncovaDecomp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GlobalAncova/inst/doc/GlobalAncova.R, vignettes/GlobalAncova/inst/doc/GlobalAncovaDecomp.R Package: globalSeq Version: 1.0.2 Depends: R(>= 3.3.0) Suggests: knitr, testthat, SummarizedExperiment License: GPL-3 MD5sum: 1cd2ae1a5cded64beff43b4c779b25ed NeedsCompilation: no Title: Testing for association between RNA-Seq and high-dimensional data 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. biocViews: StatisticalMethod, GeneExpression, RNASeq, GenomeWideAssociation, DifferentialExpression Author: Armin Rauschenberger Maintainer: Armin Rauschenberger VignetteBuilder: knitr source.ver: src/contrib/globalSeq_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/globalSeq_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/globalSeq_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/globalSeq_1.0.2.tgz vignettes: vignettes/globalSeq/inst/doc/globalSeq.pdf vignetteTitles: globalSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/globalSeq/inst/doc/globalSeq.R Package: globaltest Version: 5.26.0 Depends: methods, survival Imports: Biobase, AnnotationDbi, annotate, graphics Suggests: vsn, golubEsets, KEGG.db, hu6800.db, Rgraphviz, GO.db, lungExpression, org.Hs.eg.db, GSEABase, penalized, gss, MASS, boot, rpart License: GPL (>= 2) MD5sum: da20c6ee7f0609065d61055aa38008de 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 and Aldo Solari Maintainer: Jelle Goeman source.ver: src/contrib/globaltest_5.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/globaltest_5.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/globaltest_5.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/globaltest_5.23.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/globaltest_5.26.0.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 suggestsMe: topGO Package: gmapR Version: 1.14.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.12), Rsamtools (>= 1.17.8) Imports: S4Vectors (>= 0.9.25), IRanges, rtracklayer (>= 1.31.2), GenomicFeatures (>= 1.17.13), Biostrings, VariantAnnotation (>= 1.11.4), tools, Biobase, BSgenome, GenomicAlignments (>= 1.1.9), 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: 280f600cf161e289d9e995b802eeeea7 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 source.ver: src/contrib/gmapR_1.14.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gmapR_1.14.0.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: VariantTools Package: GMRP Version: 1.0.0 Depends: R(>= 3.3.0),stats,utils,graphics, grDevices, diagram, plotrix, base,GenomicRanges Suggests: BiocStyle, BiocGenerics, VariantAnnotation License: GPL (>= 2) MD5sum: 5373a989e074a2dde9fd3ab71bf1de00 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 and Dajiang Liu Maintainer: Yuan-De Tan source.ver: src/contrib/GMRP_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GMRP_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GMRP_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GMRP_1.0.0.tgz vignettes: vignettes/GMRP/inst/doc/GMRP.pdf vignetteTitles: 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 Package: GOexpress Version: 1.6.1 Depends: R (>= 3.0.2), grid, Biobase (>= 2.22.0), VennDiagram (>= 1.6.5) Imports: biomaRt (>= 2.18.0), stringr (>= 0.6.2), ggplot2 (>= 0.9.0), RColorBrewer (>= 1.0), gplots (>= 2.13.0), randomForest (>= 4.6) Suggests: RCurl (>= 1.95), BiocStyle License: GPL (>= 3) MD5sum: de0c2131f2af384a70b62a0951c756e2 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 Author: Kevin Rue-Albrecht [aut, cre], 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 source.ver: src/contrib/GOexpress_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOexpress_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GOexpress_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.3/GOexpress_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOexpress_1.6.1.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 Package: GOFunction Version: 1.20.0 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) MD5sum: 40e1f56d5d828dead98905dc50f8c68a NeedsCompilation: no 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 source.ver: src/contrib/GOFunction_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOFunction_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOFunction_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GOFunction_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOFunction_1.20.0.tgz vignettes: vignettes/GOFunction/inst/doc/GOFunction.pdf vignetteTitles: GO-function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOFunction/inst/doc/GOFunction.R Package: GoogleGenomics Version: 1.4.2 Depends: R (>= 3.1.0), GenomicAlignments (>= 1.0.1), VariantAnnotation Imports: Biostrings, GenomeInfoDb, GenomicRanges, IRanges, httr, rjson, Rsamtools, S4Vectors (>= 0.9.25) Suggests: BiocStyle, httpuv, knitr, rmarkdown, testthat, ggbio, ggplot2, BSgenome.Hsapiens.UCSC.hg19, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: Apache License (== 2.0) | file LICENSE MD5sum: 6bf69224f474e8a8050b3827761cf015 NeedsCompilation: no Title: R Client for Google Genomics API Description: Provides an R package to interact with the Google Genomics API. biocViews: DataImport, ThirdPartyClient Author: Cassie Doll [aut], Nicole Deflaux [aut], Siddhartha Bagaria [aut, cre] Maintainer: Siddhartha Bagaria URL: https://cloud.google.com/genomics/ VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/GoogleGenomics/issues source.ver: src/contrib/GoogleGenomics_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GoogleGenomics_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GoogleGenomics_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/GoogleGenomics_1.1.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GoogleGenomics_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GoogleGenomics/inst/doc/AnnotatingVariants.R, vignettes/GoogleGenomics/inst/doc/PlottingAlignments.R, vignettes/GoogleGenomics/inst/doc/VariantAnnotation-comparison-test.R htmlDocs: vignettes/GoogleGenomics/inst/doc/AnnotatingVariants.html, vignettes/GoogleGenomics/inst/doc/PlottingAlignments.html, vignettes/GoogleGenomics/inst/doc/VariantAnnotation-comparison-test.html htmlTitles: Annotating Variants, Plotting Alignments, Reproducing Variant Annotation Results Package: goProfiles Version: 1.34.0 Depends: Biobase, AnnotationDbi, GO.db Suggests: org.Hs.eg.db License: GPL-2 MD5sum: 34805809699ce250d507ee6ee4c35750 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 source.ver: src/contrib/goProfiles_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/goProfiles_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/goProfiles_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/goProfiles_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/goProfiles_1.34.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/goProfiles/inst/doc/goProfiles.R Package: GOSemSim Version: 1.30.3 Depends: R (>= 3.2.0) Imports: Rcpp, AnnotationDbi, GO.db, utils LinkingTo: Rcpp Suggests: DOSE, clusterProfiler, org.Hs.eg.db, knitr, BiocStyle, BiocInstaller License: Artistic-2.0 Archs: i386, x64 MD5sum: db34559c6d9a0f092604e976ef26b801 NeedsCompilation: yes Title: GO-terms Semantic Similarity Measures Description: Implemented five methods proposed by Resnik, Schlicker, Jiang, Lin and Wang respectively for estimating GO semantic similarities. Support many species, including Anopheles, Arabidopsis, Bovine, Canine, Chicken, Chimp, Coelicolor, E coli strain K12 and Sakai, Fly, Gondii, Human, Malaria, Mouse, Pig, Rhesus, Rat, Worm, Xenopus, Yeast, and Zebrafish. biocViews: Annotation, GO, Clustering, Pathways, Network, Software Author: Guangchuang Yu with contributions from Alexey Stukalov and Chuanle Xiao. Maintainer: Guangchuang Yu URL: http://guangchuangyu.github.io/GOSemSim VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/GOSemSim/issues source.ver: src/contrib/GOSemSim_1.30.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOSemSim_1.30.3.zip win64.binary.ver: bin/windows64/contrib/3.3/GOSemSim_1.30.3.zip mac.binary.ver: bin/macosx/contrib/3.3/GOSemSim_1.27.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOSemSim_1.30.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/GOSemSim/inst/doc/GOSemSim.R htmlDocs: vignettes/GOSemSim/inst/doc/GOSemSim.html htmlTitles: An introduction to GOSemSim dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, Rcpi, ReactomePA suggestsMe: SemDist Package: goseq Version: 1.24.0 Depends: R (>= 2.11.0), BiasedUrn, geneLenDataBase Imports: mgcv, graphics, stats, utils, AnnotationDbi, GO.db,BiocGenerics Suggests: edgeR, org.Hs.eg.db, rtracklayer License: LGPL (>= 2) MD5sum: 6731dcb3b02d5c3b2b5d33083142c645 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: Sequencing, GO, GeneExpression, Transcription, RNASeq Author: Matthew Young Maintainer: Nadia Davidson , Anthony Hawkins source.ver: src/contrib/goseq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/goseq_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/goseq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/goseq_1.21.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/goseq_1.24.0.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: SMITE suggestsMe: oneChannelGUI Package: GOSim Version: 1.10.0 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: 121950b5856a54a873c29ddd0f1aa336 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 source.ver: src/contrib/GOSim_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOSim_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOSim_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GOSim_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOSim_1.10.0.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 Package: GOstats Version: 2.38.1 Depends: R (>= 2.10), Biobase (>= 1.15.29), Category (>= 2.3.26), graph Imports: methods, stats, stats4, AnnotationDbi (>= 0.0.89), Biobase (>= 1.15.29), Category (>= 2.3.26), GO.db (>= 1.13.0), RBGL, annotate (>= 1.13.2), graph (>= 1.15.15), AnnotationForge Suggests: hgu95av2.db (>= 1.13.0), ALL, GO.db (>= 1.13.0), annotate, multtest, genefilter, RColorBrewer, Rgraphviz, xtable, SparseM, GSEABase, geneplotter, org.Hs.eg.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 684862c904e7b5f4731bd27d09f9ce43 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: R. Gentleman and S. Falcon Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/GOstats_2.38.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOstats_2.38.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GOstats_2.38.1.zip mac.binary.ver: bin/macosx/contrib/3.3/GOstats_2.35.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOstats_2.38.1.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, RDAVIDWebService importsMe: affycoretools, attract, categoryCompare, facopy, mvGST, pcaExplorer, ProCoNA, systemPipeR suggestsMe: BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, HTSanalyzeR, interactiveDisplay, MineICA, miRLAB, MLP, MmPalateMiRNA, oneChannelGUI, phenoDist, qpgraph, RnBeads, safe Package: GOsummaries Version: 2.6.0 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: 06c8b7d5c2a6a790595bd9f732c5df0e 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 source.ver: src/contrib/GOsummaries_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOsummaries_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GOsummaries_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GOsummaries_2.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOsummaries_2.6.0.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 Package: GOTHiC Version: 1.8.1 Depends: R (>= 2.15.1), methods, utils, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors (>= 0.9.38), IRanges, Rsamtools, ShortRead, rtracklayer, ggplot2 Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: 547d0eab92cbeaa91f603c3bb968c308 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: Sequencing, Preprocessing, Epigenetics, HiC Author: Borbala Mifsud and Robert Sugar Maintainer: Borbala Mifsud source.ver: src/contrib/GOTHiC_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GOTHiC_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GOTHiC_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.3/GOTHiC_1.5.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GOTHiC_1.8.1.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 Package: goTools Version: 1.46.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 7ad68af404557a79b0012df9b987233b 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 source.ver: src/contrib/goTools_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/goTools_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/goTools_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/goTools_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/goTools_1.46.0.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 Package: gpls Version: 1.44.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: 23a0ff239d9cf60588a1f0d0cd9f5674 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 source.ver: src/contrib/gpls_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gpls_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gpls_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gpls_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gpls_1.44.0.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: MCRestimate, MLInterfaces Package: gprege Version: 1.16.0 Depends: R (>= 2.8.0), gptk Suggests: spam License: AGPL-3 MD5sum: afc261ad335d7f86b41b26c9b5127ff7 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, DifferentialExpression, TimeCourse Author: Alfredo Kalaitzis Maintainer: Alfredo Kalaitzis BugReports: alkalait@gmail.com source.ver: src/contrib/gprege_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/gprege_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/gprege_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/gprege_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gprege_1.16.0.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 Package: gQTLBase Version: 1.4.2 Imports: GenomicRanges, methods, BatchJobs, BBmisc, S4Vectors, BiocGenerics, foreach, doParallel, bit, ff, rtracklayer, ffbase, GenomicFiles, SummarizedExperiment Suggests: geuvStore2, knitr, rmarkdown, BiocStyle, RUnit, GGtools, Homo.sapiens, IRanges, erma, GenomeInfoDb, gwascat, geuvPack License: Artistic-2.0 MD5sum: c28baf63aa8b94394dabdcce03564bba 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 source.ver: src/contrib/gQTLBase_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/gQTLBase_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/gQTLBase_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/gQTLBase_1.1.18.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gQTLBase_1.4.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLBase/inst/doc/gQTLBase.R htmlDocs: vignettes/gQTLBase/inst/doc/gQTLBase.html htmlTitles: gQTLBase infrastructure for eQTL archives importsMe: gQTLstats Package: gQTLstats Version: 1.4.3 Depends: R (>= 3.1.0) 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 Suggests: geuvPack, geuvStore2, Rsamtools, knitr, rmarkdown, ggbio, BiocStyle, Homo.sapiens, RUnit, multtest License: Artistic-2.0 MD5sum: cb55f554d1aa6465497ec9d0cb8bf117 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 source.ver: src/contrib/gQTLstats_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/gQTLstats_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/gQTLstats_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/gQTLstats_1.1.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gQTLstats_1.4.3.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gQTLstats/inst/doc/gQTLstats.R htmlDocs: vignettes/gQTLstats/inst/doc/gQTLstats.html htmlTitles: gQTLstats: statistics for genetics of genomic features importsMe: gwascat Package: graph Version: 1.50.0 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: 3e65e4281e39a9b93caec2c4d8ae297c 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 source.ver: src/contrib/graph_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/graph_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/graph_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/graph_1.47.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/graph_1.50.0.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, CellNOptR, clipper, CNORfeeder, ddgraph, flowClust, gaggle, gaucho, GOFunction, GOstats, GraphAT, GSEABase, hypergraph, maigesPack, MineICA, NCIgraph, NetSAM, pathRender, pkgDepTools, RbcBook1, RBGL, RBioinf, RCy3, RCyjs, RCytoscape, RDAVIDWebService, Rgraphviz, ROntoTools, RpsiXML, SRAdb, ToPASeq, topGO, vtpnet importsMe: AnalysisPageServer, BgeeDB, BiocCheck, biocGraph, biocViews, CAMERA, Category, categoryCompare, ChIPpeakAnno, CHRONOS, DEGraph, EnrichmentBrowser, FEM, flowCore, flowUtils, flowWorkspace, gage, GeneNetworkBuilder, GENESIS, GOFunction, GOSim, GOstats, GraphAT, graphite, gwascat, HTSanalyzeR, hyperdraw, KEGGgraph, keggorthology, mvGST, NCIgraph, nem, netresponse, OncoSimulR, OrganismDbi, pathview, PCpheno, pkgDepTools, ppiStats, qpgraph, RchyOptimyx, RGraph2js, rsbml, Rtreemix, SplicingGraphs, Streamer, VariantFiltering suggestsMe: AnnotationDbi, BiocCaseStudies, DEGraph, EBcoexpress, ecolitk, GeneAnswers, mmnet, MmPalateMiRNA, netbenchmark, NetPathMiner, rBiopaxParser, rTRM, SPIA, VariantTools Package: GraphAlignment Version: 1.36.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 2c6da755ac781623e315ce940fd15fe0 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/ source.ver: src/contrib/GraphAlignment_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GraphAlignment_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GraphAlignment_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GraphAlignment_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GraphAlignment_1.36.0.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 Package: GraphAT Version: 1.44.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: 567d28529bab70dd25b3cd5c08df0711 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 source.ver: src/contrib/GraphAT_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GraphAT_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GraphAT_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GraphAT_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GraphAT_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: graphite Version: 1.18.1 Depends: R (>= 2.10), BiocGenerics, methods Imports: AnnotationDbi, graph, stats, utils, rappdirs Suggests: BiocStyle, hgu133plus2.db, org.Hs.eg.db, SPIA (>= 2.2), topologyGSA (>= 1.4.0), clipper, ALL, testthat Enhances: DEGraph, RCytoscape, RCy3 License: AGPL-3 MD5sum: bc6dd1ebf52b5f42da43c6ada98091b9 NeedsCompilation: no Title: GRAPH Interaction from pathway Topological Environment Description: Graph objects from pathway topology derived from Biocarta, HumanCyc, KEGG, NCI, Panther, Reactome and SPIKE databases. biocViews: Pathways, ThirdPartyClient, GraphAndNetwork, Network, Reactome, KEGG, BioCarta Author: Gabriele Sales , Enrica Calura , Chiara Romualdi Maintainer: Gabriele Sales source.ver: src/contrib/graphite_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/graphite_1.18.1.zip win64.binary.ver: bin/windows64/contrib/3.3/graphite_1.18.1.zip mac.binary.ver: bin/macosx/contrib/3.3/graphite_1.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/graphite_1.18.1.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/graphite/inst/doc/graphite.R dependsOnMe: ToPASeq importsMe: facopy, mogsa, ReactomePA suggestsMe: clipper Package: GraphPAC Version: 1.14.0 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 753037449f2ccfe6204484385f123f8b 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 source.ver: src/contrib/GraphPAC_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GraphPAC_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GraphPAC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GraphPAC_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GraphPAC_1.14.0.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 Package: GRENITS Version: 1.24.0 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: ea3be8b714fa9ee84020c1a89d02bceb 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 source.ver: src/contrib/GRENITS_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GRENITS_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GRENITS_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GRENITS_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GRENITS_1.24.0.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 Package: GreyListChIP Version: 1.4.1 Depends: R (>= 3.1), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb, SummarizedExperiment Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: 7d8a27b716df379faf571c6e4dbbd6ab 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 source.ver: src/contrib/GreyListChIP_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GreyListChIP_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GreyListChIP_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.3/GreyListChIP_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GreyListChIP_1.4.1.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 Package: groHMM Version: 1.6.0 Depends: R (>= 3.0.2), MASS, parallel, S4Vectors (>= 0.9.25), IRanges (>= 2.5.27), GenomeInfoDb, GenomicRanges (>= 1.23.16), GenomicAlignments, rtracklayer Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 4a58d5f05fe77209f1be49981c01b0ea 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 , Venkat Malladi , Tulip Nandu , W. Lee Kraus URL: https://github.com/Kraus-Lab/groHMM BugReports: https://github.com/Kraus-Lab/groHMM/issues source.ver: src/contrib/groHMM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/groHMM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/groHMM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/groHMM_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/groHMM_1.6.0.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 Package: GSALightning Version: 1.0.2 Depends: R (>= 3.3.0) Imports: Matrix, data.table Suggests: knitr, rmarkdown License: GPL (>=2) MD5sum: b5a421964a94f4dc902cdc201cb82e66 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 VignetteBuilder: knitr source.ver: src/contrib/GSALightning_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSALightning_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/GSALightning_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSALightning_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/GSALightning/inst/doc/vignette.R htmlDocs: vignettes/GSALightning/inst/doc/vignette.html htmlTitles: Vignette Title Package: GSAR Version: 1.6.0 Depends: R (>= 3.0.1), igraph (>= 0.7.0) Suggests: MASS, GSVAdata, ALL, tweeDEseqCountData, GSEABase, annotate, org.Hs.eg.db, Biobase, genefilter, hgu95av2.db, edgeR, BiocStyle License: GPL (>=2) MD5sum: 5ac3c1ac80ffd8baf27f6ec66d53c29e 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 source.ver: src/contrib/GSAR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSAR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSAR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GSAR_1.3.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSAR_1.6.0.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 Package: GSCA Version: 2.2.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: dcc48d39b7f5eb0dd9022addb0977ba6 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 source.ver: src/contrib/GSCA_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSCA_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSCA_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GSCA_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSCA_2.2.0.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 Package: GSEABase Version: 1.34.1 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 License: Artistic-2.0 MD5sum: 26f0a512fddadcb36162dd80fcf8a2d4 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 source.ver: src/contrib/GSEABase_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSEABase_1.34.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GSEABase_1.34.1.zip mac.binary.ver: bin/macosx/contrib/3.3/GSEABase_1.31.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSEABase_1.34.1.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, cpvSNP, EnrichmentBrowser, gCMAP, npGSEA, PROMISE, splineTCDiffExpr, splineTimeR importsMe: canceR, Category, categoryCompare, cellHTS2, clusterProfiler, gCMAPWeb, GSRI, GSVA, HTSanalyzeR, mogsa, oppar, PCpheno, phenoTest, PROMISE, ReportingTools suggestsMe: BiocCaseStudies, gage, GlobalAncova, globaltest, GOstats, GSAR, PGSEA, phenoTest Package: GSEAlm Version: 1.32.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: 65f14a8950ce2819a0cc15ba79d25da9 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 source.ver: src/contrib/GSEAlm_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSEAlm_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSEAlm_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GSEAlm_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSEAlm_1.32.0.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 importsMe: canceR, gCMAP Package: GSReg Version: 1.6.0 Depends: R (>= 2.13.1) Suggests: GSBenchMark License: GPL-2 Archs: i386, x64 MD5sum: 03cbc3f9e6f924dfed38a1fa63205eb9 NeedsCompilation: yes Title: Gene Set Regulation (GS-Reg) Description: A package for gene set analysis based on the variability of expressions. It implements DIfferential RAnk Conservation (DIRAC) and gene set Expression Variation Analysis (EVA) methods. biocViews: GeneRegulation, Pathways, GeneExpression, GeneticVariability, GeneSetEnrichment Author: Bahman Afsari , Elana J. Fertig Maintainer: Bahman Afsari source.ver: src/contrib/GSReg_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSReg_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSReg_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GSReg_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSReg_1.6.0.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 Package: GSRI Version: 2.20.0 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: 2635f126c2cd0a994a845c6c6d044415 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 source.ver: src/contrib/GSRI_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSRI_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSRI_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GSRI_2.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSRI_2.20.0.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 Package: GSVA Version: 1.20.0 Depends: R (>= 2.13.0) Imports: methods, BiocGenerics, Biobase, GSEABase (>= 1.17.4) Suggests: limma, RColorBrewer, genefilter, mclust, edgeR, snow, parallel, GSVAdata License: GPL (>= 2) Archs: i386, x64 MD5sum: 60a156251843bbdb0a842ff81b52db0f 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 with contributions from Robert Castelo Maintainer: Justin Guinney URL: http://www.sagebase.org source.ver: src/contrib/GSVA_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GSVA_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GSVA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GSVA_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GSVA_1.20.0.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 importsMe: EGSEA, oppar Package: gtrellis Version: 1.4.2 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: circlize (>= 0.3.3), GetoptLong Suggests: testthat (>= 1.0.0), knitr, RColorBrewer, markdown, ComplexHeatmap (>= 1.9.7) License: GPL (>= 2) MD5sum: c1d7faa19d79f8f6ce0193b3bb2d4d89 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 source.ver: src/contrib/gtrellis_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/gtrellis_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/gtrellis_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/gtrellis_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gtrellis_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gtrellis/inst/doc/gtrellis.R htmlDocs: vignettes/gtrellis/inst/doc/gtrellis.html htmlTitles: Make Genome-level Trellis Graph Package: GUIDEseq Version: 1.2.1 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 Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19 License: GPL (>= 2) MD5sum: 4627beca9e8b4e6f8c1246a0669a9d69 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: GeneRegulation, Sequencing, WorkflowStep Author: Lihua Julie Zhu, Michael Lawrence, Ankit Gupta, Alper Kucukural, Manuel Garber, Scot A. Wolfe Maintainer: Lihua Julie Zhu VignetteBuilder: knitr source.ver: src/contrib/GUIDEseq_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/GUIDEseq_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/GUIDEseq_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GUIDEseq_1.2.1.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 Package: Guitar Version: 1.10.0 Depends: Rsamtools, GenomicFeatures, rtracklayer, GenomicAlignments, GenomicRanges, ggplot2, grid, IRanges License: GPL-2 MD5sum: b84b18f311859f87e6343c0f4c0b674f 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, Coverage Author: Jia Meng Maintainer: Jia Meng source.ver: src/contrib/Guitar_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Guitar_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Guitar_1.10.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Guitar_1.10.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Sample Guitar workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Guitar/inst/doc/Guitar-Overview.R Package: Gviz Version: 1.16.5 Depends: R (>= 2.10.0), 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), 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) Suggests: xtable, BSgenome.Hsapiens.UCSC.hg19, BiocStyle License: Artistic-2.0 MD5sum: 81f61d0d12c823f91eff0a69d14b3943 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 Author: Florian Hahne, Steffen Durinck, Robert Ivanek, Arne Mueller, Steve Lianoglou, Ge Tan , Lance Parsons , Shraddha Pai Maintainer: Florian Hahne source.ver: src/contrib/Gviz_1.16.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/Gviz_1.16.5.zip win64.binary.ver: bin/windows64/contrib/3.3/Gviz_1.16.5.zip mac.binary.ver: bin/macosx/contrib/3.3/Gviz_1.13.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Gviz_1.16.5.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.pdf vignetteTitles: Gviz users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Gviz/inst/doc/Gviz.R dependsOnMe: biomvRCNS, coMET, cummeRbund, DMRforPairs, Pbase, Pviz importsMe: AllelicImbalance, DMRcate, GenomicInteractions, GGtools, gwascat, InPAS, methyAnalysis, methylPipe, motifbreakR, PING, STAN, trackViewer, VariantFiltering suggestsMe: annmap, CNEr, ensembldb, GenomicRanges, interactiveDisplay, pqsfinder, QuasR, RnBeads, SplicingGraphs Package: gwascat Version: 2.4.2 Depends: R (>= 3.0.0), Homo.sapiens Imports: methods, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, snpStats, Biostrings, Rsamtools, rtracklayer, gQTLstats, Gviz, VariantAnnotation, AnnotationHub, AnnotationDbi, GenomicFeatures, graph, ggbio, ggplot2, SummarizedExperiment Suggests: DO.db, DT, utils, knitr, RBGL, RUnit, GGtools Enhances: SNPlocs.Hsapiens.dbSNP144.GRCh37 License: Artistic-2.0 MD5sum: bb0ac0e525d4cadc1455bf57fc86a61f 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: utils, knitr source.ver: src/contrib/gwascat_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/gwascat_2.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/gwascat_2.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/gwascat_2.1.18.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/gwascat_2.4.2.tgz vignettes: vignettes/gwascat/inst/doc/gwascat.pdf vignetteTitles: gwascat -- exploring NHGRI GWAS catalog hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/gwascat/inst/doc/gwascat.R, vignettes/gwascat/inst/doc/gwascatOnt.R htmlDocs: vignettes/gwascat/inst/doc/gwascatOnt.html htmlTitles: gwascat: exploring GWAS results using the experimental factor ontology dependsOnMe: vtpnet suggestsMe: gQTLBase Package: GWASTools Version: 1.18.0 Depends: Biobase Imports: methods, ncdf4, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth Suggests: GWASdata, BiocGenerics, RUnit, SNPRelate, snpStats, VariantAnnotation License: Artistic-2.0 MD5sum: b0e52cfc09764572690168e1ccefa2cd 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, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar, Adrienne Stilp, Sarah Nelson Maintainer: Stephanie M. Gogarten , Adrienne Stilp source.ver: src/contrib/GWASTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/GWASTools_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/GWASTools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/GWASTools_1.15.14.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/GWASTools_1.18.0.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 importsMe: GENESIS suggestsMe: podkat Package: h5vc Version: 2.6.3 Depends: grid, gridExtra, ggplot2 Imports: rhdf5, reshape, S4Vectors, IRanges, Biostrings, Rsamtools (>= 1.19.38), methods, GenomicRanges, abind, BiocParallel, BatchJobs, h5vcData, GenomeInfoDb LinkingTo: Rsamtools Suggests: knitr, locfit, BSgenome.Hsapiens.UCSC.hg19, bit64, biomaRt, BSgenome.Hsapiens.NCBI.GRCh38, RUnit, BiocGenerics License: GPL (>= 3) Archs: i386, x64 MD5sum: 5baf57fb640ffecdf615cddd4900b69b 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. For detail see the webpage at http://www.ebi.ac.uk/~pyl/h5vc. Author: Paul Theodor Pyl Maintainer: Paul Theodor Pyl VignetteBuilder: knitr source.ver: src/contrib/h5vc_2.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/h5vc_2.6.3.zip win64.binary.ver: bin/windows64/contrib/3.3/h5vc_2.6.3.zip mac.binary.ver: bin/macosx/contrib/3.3/h5vc_2.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/h5vc_2.6.3.tgz 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 htmlDocs: vignettes/h5vc/inst/doc/h5vc.simple.genome.browser.html, vignettes/h5vc/inst/doc/h5vc.tour.html htmlTitles: Building a minimal genome browser with h5vc and shiny, h5vc -- Tour Package: hapFabia Version: 1.14.1 Depends: R (>= 2.12.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 48782e3206d9bd20625e372908d4e85a 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: Sepp Hochreiter URL: http://www.bioinf.jku.at/software/hapFabia/hapFabia.html source.ver: src/contrib/hapFabia_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/hapFabia_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/hapFabia_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.3/hapFabia_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hapFabia_1.14.1.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 Package: Harman Version: 1.0.2 Depends: R (>= 3.3) Imports: Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: HarmanData, BiocGenerics, BiocStyle, knitr, rmarkdown, RUnit, missMethyl, RColorBrewer, bladderbatch, limma, minfi, lumi, msmsEDA, affydata, minfiData License: GPL-3 + file LICENCE Archs: i386, x64 MD5sum: 66794ff847117ee4e7e871cecda02c43 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 source.ver: src/contrib/Harman_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Harman_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Harman_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Harman_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Harman/inst/doc/IntroductionToHarman.R htmlDocs: vignettes/Harman/inst/doc/IntroductionToHarman.html htmlTitles: IntroductionToHarman Package: Harshlight Version: 1.44.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 3c7b2f7a5239363a704902ca13615bf8 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/ source.ver: src/contrib/Harshlight_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Harshlight_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Harshlight_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Harshlight_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Harshlight_1.44.0.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 Package: HCsnip Version: 1.12.0 Depends: R(>= 2.10.0), survival, coin, fpc, clusterRepro, impute, randomForestSRC, sm, sigaR, Biobase License: GPL (>= 2) MD5sum: e7d6ce862f8703a7db6a2abb62cf73e4 NeedsCompilation: no Title: Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree Description: Decompose given hierarchical clustering tree into non-overlapping clusters in a semi-supervised way by using available patients follow-up information as guidance. Contains functions for snipping HC tree, various cluster quality evaluation criteria, assigning new patients to one of the two given HC trees, testing the significance of clusters with permutation argument and clusters visualization using sample's molecular entropy. biocViews: Microarray, aCGH, GeneExpression, Clustering Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/HCsnip_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HCsnip_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HCsnip_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HCsnip_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HCsnip_1.12.0.tgz vignettes: vignettes/HCsnip/inst/doc/HCsnip.pdf vignetteTitles: HCsnip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HCsnip/inst/doc/HCsnip.R Package: HDF5Array Version: 1.0.2 Depends: R (>= 3.2), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.9.43) Imports: stats, IRanges (>= 2.5.17), rhdf5 Suggests: h5vcData, SummarizedExperiment, GenomicRanges, genefilter, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 089269b5dcbba1999e81586daab1eb24 NeedsCompilation: no Title: An array-like container for convenient access and manipulation of HDF5 datasets Description: This package implements the HDF5Array class for convenient access and manipulation of HDF5 datasets. In order to reduce memory usage and optimize performance, operations on an HDF5Array object are either delayed or executed using a block processing mechanism. The delaying and block processing mechanisms are independent of the on-disk backend and implemented via the DelayedArray class. They even work on ordinary arrays where they can sometimes improve performance. biocViews: Infrastructure, DataRepresentation, Sequencing, Annotation, Coverage, GenomeAnnotation Author: Hervé Pagès Maintainer: Hervé Pagès VignetteBuilder: knitr source.ver: src/contrib/HDF5Array_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/HDF5Array_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/HDF5Array_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HDF5Array_1.0.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: HDTD Version: 1.6.0 Imports: stats License: GPL-3 MD5sum: e2f33b4cfcdbb6e7baa82e060e2c9cac NeedsCompilation: no 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, John C. Marioni and Simon Tavare Maintainer: Anestis Touloumis source.ver: src/contrib/HDTD_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HDTD_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HDTD_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HDTD_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HDTD_1.6.0.tgz vignettes: vignettes/HDTD/inst/doc/Manual.pdf vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HDTD/inst/doc/Manual.R Package: Heatplus Version: 2.18.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: 1600646f45d915d6045ef693caa14d1a 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 source.ver: src/contrib/Heatplus_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Heatplus_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Heatplus_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Heatplus_2.15.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Heatplus_2.18.0.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 Package: HELP Version: 1.30.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: 12962f076e52a701343b178f1ee7b4fd 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 source.ver: src/contrib/HELP_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HELP_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HELP_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HELP_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HELP_1.30.0.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 Package: HEM Version: 1.44.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: eac57f74e2293d7b1d6646e803b9dd89 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/ source.ver: src/contrib/HEM_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HEM_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HEM_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HEM_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HEM_1.44.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hiAnnotator Version: 1.6.2 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: fac7d129aebae1cbefcc99ffe840b362 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 source.ver: src/contrib/hiAnnotator_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/hiAnnotator_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/hiAnnotator_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/hiAnnotator_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hiAnnotator_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiAnnotator/inst/doc/Intro.R htmlDocs: vignettes/hiAnnotator/inst/doc/Intro.html htmlTitles: Using hiAnnotator dependsOnMe: hiReadsProcessor Package: HIBAG Version: 1.8.3 Depends: R (>= 3.2.0) Imports: methods Suggests: parallel, knitr, gdsfmt (>= 1.2.2), SNPRelate (>= 1.1.6) License: GPL-3 Archs: i386, x64 MD5sum: 7e0850d7a7374981e3901776b82bf387 NeedsCompilation: yes Title: HLA Genotype Imputation with Attribute Bagging Description: It is a software package for imputing HLA types using SNP data, and 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://www.biostat.washington.edu/~bsweir/HIBAG/, http://github.com/zhengxwen/HIBAG VignetteBuilder: knitr source.ver: src/contrib/HIBAG_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/HIBAG_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/HIBAG_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.3/HIBAG_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HIBAG_1.8.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.R htmlDocs: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.html htmlTitles: HIBAG vignette html Package: hierGWAS Version: 1.2.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 1ac72a7763105f4496b17cf85fd07fbe 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 source.ver: src/contrib/hierGWAS_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hierGWAS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hierGWAS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/hierGWAS_0.99.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hierGWAS_1.2.0.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 Package: HilbertCurve Version: 1.2.2 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: methods, HilbertVis, png, grDevices, circlize (>= 0.3.3) Suggests: knitr, testthat (>= 1.0.0), ComplexHeatmap (>= 1.7.0), markdown, RColorBrewer, RCurl, GetoptLong License: GPL (>= 2) MD5sum: f4bade1561cef1f32fcbf9d17eaf3a92 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 source.ver: src/contrib/HilbertCurve_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/HilbertCurve_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/HilbertCurve_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/HilbertCurve_0.99.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HilbertCurve_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HilbertCurve/inst/doc/HilbertCurve.R htmlDocs: vignettes/HilbertCurve/inst/doc/HilbertCurve.html htmlTitles: Making 2D Hilbert Curve suggestsMe: ComplexHeatmap Package: HilbertVis Version: 1.30.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: i386, x64 MD5sum: 95fa59d5101318ef401b4ef7ed75d1aa 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 source.ver: src/contrib/HilbertVis_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HilbertVis_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HilbertVis_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HilbertVis_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HilbertVis_1.30.0.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 Package: HilbertVisGUI Version: 1.30.0 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) MD5sum: fd94c867c4b1f33dfb9b7ea12776c6f5 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 source.ver: src/contrib/HilbertVisGUI_1.30.0.tar.gz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Package: hiReadsProcessor Version: 1.8.2 Depends: Biostrings, GenomicAlignments, xlsx, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, BiocGenerics, GenomicRanges (>= 1.23.16), rSFFreader Suggests: knitr, testthat License: GPL-3 MD5sum: c174bf21bba5d5369e1e8f0208f71f72 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 source.ver: src/contrib/hiReadsProcessor_1.8.2.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/hiReadsProcessor_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hiReadsProcessor_1.8.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hiReadsProcessor/inst/doc/Tutorial.R htmlDocs: vignettes/hiReadsProcessor/inst/doc/Tutorial.html htmlTitles: Using hiReadsProcessor Package: HiTC Version: 1.16.0 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: 879cab59b2f8aa2b221fd27cdf047edc 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 source.ver: src/contrib/HiTC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HiTC_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HiTC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HiTC_1.13.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HiTC_1.16.0.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 Package: HMMcopy Version: 1.14.0 Depends: R (>= 2.10.0), IRanges (>= 1.4.16), geneplotter (>= 1.24.0) License: GPL-3 Archs: i386, x64 MD5sum: 4d54c832b0a36691c1497e2fd9e8fbf8 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 , Gavin Ha , Sohrab Shah source.ver: src/contrib/HMMcopy_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HMMcopy_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HMMcopy_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HMMcopy_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HMMcopy_1.14.0.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 Package: hopach Version: 2.32.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 886cbd3dd8cc56edede36a6c288e9d1c 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/ source.ver: src/contrib/hopach_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hopach_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hopach_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/hopach_2.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hopach_2.32.0.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 suggestsMe: BiocCaseStudies Package: hpar Version: 1.14.2 Depends: R (>= 2.15) Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 6e13e09ca4a0c152f19b1afdf579920c NeedsCompilation: no Title: Human Protein Atlas in R Description: A simple interface to and data from the Human Protein Atlas project. biocViews: Proteomics, Homo_sapiens, CellBiology Author: Laurent Gatto Maintainer: Laurent Gatto VignetteBuilder: knitr source.ver: src/contrib/hpar_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/hpar_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.3/hpar_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.3/hpar_1.11.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hpar_1.14.2.tgz vignettes: vignettes/hpar/inst/doc/hpar.pdf vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/hpar/inst/doc/hpar.R suggestsMe: pRoloc Package: HTqPCR Version: 1.26.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 668859ac7a0bebea9490665f9263f087 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 source.ver: src/contrib/HTqPCR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HTqPCR_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HTqPCR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HTqPCR_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HTqPCR_1.26.0.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 Package: HTSanalyzeR Version: 2.24.0 Depends: R (>= 2.15), igraph, methods Imports: graph, igraph, GSEABase, BioNet, cellHTS2, AnnotationDbi, biomaRt, RankProd Suggests: KEGG.db, GO.db, org.Dm.eg.db, GOstats, org.Ce.eg.db, org.Mm.eg.db, org.Rn.eg.db, org.Hs.eg.db, snow License: Artistic-2.0 MD5sum: fba407a48a65b728eb21c6035d1006eb NeedsCompilation: no Title: Gene set over-representation, enrichment and network analyses for high-throughput screens Description: This package provides classes and methods for gene set over-representation, enrichment and network analyses on high-throughput screens. The over-representation analysis is performed based on hypergeometric tests. The enrichment analysis is based on the GSEA algorithm (Subramanian et al. PNAS 2005). The network analysis identifies enriched subnetworks based on algorithms from the BioNet package (Beisser et al., Bioinformatics 2010). A pipeline is also specifically designed for cellHTS2 object to perform integrative network analyses of high-throughput RNA interference screens. The users can build their own analysis pipeline for their own data set based on this package. biocViews: CellBasedAssays, MultipleComparison Author: Xin Wang , Camille Terfve , John C. Rose , Florian Markowetz Maintainer: Xin Wang source.ver: src/contrib/HTSanalyzeR_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HTSanalyzeR_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HTSanalyzeR_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HTSanalyzeR_2.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HTSanalyzeR_2.24.0.tgz vignettes: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.pdf vignetteTitles: Main vignette:Gene set enrichment and network analysis of high-throughput RNAi screen data using HTSanalyzeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSanalyzeR/inst/doc/HTSanalyzeR-Vignette.R importsMe: phenoTest suggestsMe: RTN Package: HTSeqGenie Version: 4.2.0 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: 9ababe21efde83ed6ad0d5e48f4b1e4e NeedsCompilation: no Title: A NGS analysis pipeline. Description: Libraries to perform NGS analysis. Author: Gregoire Pau, Jens Reeder Maintainer: Jens Reeder source.ver: src/contrib/HTSeqGenie_4.2.0.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 Package: htSeqTools Version: 1.18.0 Depends: R (>= 2.12.2), methods, BiocGenerics (>= 0.1.0), Biobase, S4Vectors, IRanges, methods, MASS, BSgenome, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.11) Enhances: parallel,multicore License: GPL (>=2) MD5sum: bdc3c4dd24d5a548e53004e732b1c475 NeedsCompilation: no Title: Quality Control, Visualization and Processing for High-Throughput Sequencing data Description: We provide efficient, easy-to-use tools for High-Throughput Sequencing (ChIP-seq, RNAseq etc.). These include MDS plots (analogues to PCA), detecting inefficient immuno-precipitation or over-amplification artifacts, tools to identify and test for genomic regions with large accumulation of reads, and visualization of coverage profiles. biocViews: Sequencing, QualityControl Author: Evarist Planet, Camille Stephan-Otto, Oscar Reina, Oscar Flores, David Rossell Maintainer: Oscar Reina source.ver: src/contrib/htSeqTools_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/htSeqTools_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/htSeqTools_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/htSeqTools_1.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/htSeqTools_1.18.0.tgz vignettes: vignettes/htSeqTools/inst/doc/htSeqTools.pdf vignetteTitles: Manual for the htSeqTools library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/htSeqTools/inst/doc/htSeqTools.R Package: HTSFilter Version: 1.12.0 Depends: methods, Biobase (>= 2.27.3), R (>= 3.2) Imports: DESeq (>= 1.19.0), edgeR (>= 3.9.14), DESeq2 (>= 1.6.3) Suggests: EDASeq (>= 2.1.4), BiocStyle License: Artistic-2.0 MD5sum: e5a116db48af9abeaa67360844f87166 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 Author: Andrea Rau, Melina Gallopin, Gilles Celeux, and Florence Jaffrezic Maintainer: Andrea Rau source.ver: src/contrib/HTSFilter_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HTSFilter_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HTSFilter_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HTSFilter_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HTSFilter_1.12.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.pdf vignetteTitles: HTSFilter Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/HTSFilter/inst/doc/HTSFilter.R Package: HybridMTest Version: 1.16.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 5618571930cf816d92540bc298b87cc6 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 source.ver: src/contrib/HybridMTest_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/HybridMTest_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/HybridMTest_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/HybridMTest_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/HybridMTest_1.16.0.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 Package: hyperdraw Version: 1.24.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 52f93477b9b46cd0989adc158cfc03b0 NeedsCompilation: no Title: Visualizing Hypergaphs Description: Functions for visualizing hypergraphs. biocViews: Visualization, GraphAndNetwork Author: Paul Murrell Maintainer: Paul Murrell SystemRequirements: graphviz source.ver: src/contrib/hyperdraw_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hyperdraw_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hyperdraw_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/hyperdraw_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hyperdraw_1.24.0.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 Package: hypergraph Version: 1.44.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 1ac5c12abf4d2dc348df58debdf4e6f0 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 source.ver: src/contrib/hypergraph_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/hypergraph_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/hypergraph_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/hypergraph_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/hypergraph_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw Package: iASeq Version: 1.16.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 527204b35b11a784da4301e108af765c 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: SNP, RNASeq, ChIPSeq Author: Yingying Wei, Hongkai Ji Maintainer: Yingying Wei source.ver: src/contrib/iASeq_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iASeq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iASeq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iASeq_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iASeq_1.16.0.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 Package: iBBiG Version: 1.16.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: i386, x64 MD5sum: 8b858a2fb734aeed10e7a45da1178201 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/ source.ver: src/contrib/iBBiG_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iBBiG_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iBBiG_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iBBiG_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iBBiG_1.16.0.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 Package: ibh Version: 1.20.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: c3131f8fe07a517bc31a52586f43c5ed 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 source.ver: src/contrib/ibh_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ibh_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ibh_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ibh_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ibh_1.20.0.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 Package: iBMQ Version: 1.12.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: 5862f84e720da8fcb3aae3d920aa85ce 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 source.ver: src/contrib/iBMQ_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iBMQ_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iBMQ_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iBMQ_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iBMQ_1.12.0.tgz 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 Package: iCARE Version: 1.0.0 Depends: R (>= 3.3.0) Suggests: RUnit, BiocGenerics License: GPL-3 + file LICENSE Archs: i386, x64 MD5sum: 8ad214f95915de8aa76e8beae052cc16 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, Nilanjan Chatterjee and William Wheeler Maintainer: Bill Wheeler source.ver: src/contrib/iCARE_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iCARE_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iCARE_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iCARE_1.0.0.tgz vignettes: vignettes/iCARE/inst/doc/vignette.pdf vignetteTitles: iCARE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iCARE/inst/doc/vignette.R Package: Icens Version: 1.44.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: f1ed869e99082c56f5d8b9fceed0d450 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 source.ver: src/contrib/Icens_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Icens_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Icens_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Icens_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Icens_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess importsMe: PROcess Package: iCheck Version: 1.2.0 Depends: R (>= 3.2.0), Biobase, lumi, gplots Imports: stats, graphics, preprocessCore, grDevices, randomForest, affy, limma, parallel, vsn, GeneSelectMMD, rgl, MASS, lmtest, scatterplot3d License: GPL (>= 2) MD5sum: 9c54d034523c9d68580df69cdcfcf78d 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 , Brandon Guo Christopher Anderson , Barbara Klanderman , Vincent Carey , Benjamin Raby Maintainer: Weiliang Qiu source.ver: src/contrib/iCheck_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iCheck_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iCheck_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iCheck_1.2.0.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 Package: iChip Version: 1.26.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: f66f801782ea47ec26910029954c5c40 NeedsCompilation: yes Title: Bayesian Modeling of ChIP-chip Data Through Hidden Ising Models Description: This package uses hidden Ising models to identify enriched genomic regions in ChIP-chip data. It 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 source.ver: src/contrib/iChip_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iChip_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iChip_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iChip_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iChip_1.26.0.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 Package: iClusterPlus Version: 1.8.0 Depends: R (>= 2.15.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: a587dd8a0ec976bbe2941d994f9ecc2c 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 source.ver: src/contrib/iClusterPlus_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iClusterPlus_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iClusterPlus_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iClusterPlus_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iClusterPlus_1.8.0.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 Package: iCOBRA Version: 1.0.2 Depends: R (>= 3.2) Imports: shiny (>= 0.9.1.9008), shinydashboard, shinyBS, reshape2, ggplot2, scales, ROCR, dplyr, DT, limma, methods, UpSetR Suggests: knitr, testthat License: GPL (>=2) MD5sum: 8221fd519da32525f39a4444144bbcfe 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 VignetteBuilder: knitr source.ver: src/contrib/iCOBRA_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/iCOBRA_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/iCOBRA_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iCOBRA_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iCOBRA/inst/doc/iCOBRA.R htmlDocs: vignettes/iCOBRA/inst/doc/iCOBRA.html htmlTitles: iCOBRA User Guide Package: IdeoViz Version: 1.6.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 8ae04d7ca471441f51d18d409f6c6d8d 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 source.ver: src/contrib/IdeoViz_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IdeoViz_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IdeoViz_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/IdeoViz_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IdeoViz_1.6.0.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: FALSE Rfiles: vignettes/IdeoViz/inst/doc/Vignette.R Package: idiogram Version: 1.48.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 05b2d7d53648010bdcb4f4dbbbf2b82e NeedsCompilation: no Title: idiogram Description: A package for plotting genomic data by chromosomal location biocViews: Visualization Author: Karl J. Dykema Maintainer: Karl J. Dykema source.ver: src/contrib/idiogram_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/idiogram_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/idiogram_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/idiogram_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/idiogram_1.48.0.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 dependsOnMe: reb Package: IdMappingAnalysis Version: 1.16.0 Depends: R (>= 2.14), R.oo (>= 1.13.0), rChoiceDialogs Imports: boot, mclust, RColorBrewer, Biobase License: GPL-2 MD5sum: 8d862e5989a152ab2cb5e010a2b001d1 NeedsCompilation: no Title: ID Mapping Analysis Description: Identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingAnalysis_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IdMappingAnalysis_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IdMappingAnalysis_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/IdMappingAnalysis_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IdMappingAnalysis_1.16.0.tgz vignettes: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.pdf vignetteTitles: Critically comparing identifier maps retrieved from bioinformatics annotation resources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingAnalysis/inst/doc/IdMappingAnalysis.R Package: IdMappingRetrieval Version: 1.20.0 Depends: R.oo, XML, RCurl, rChoiceDialogs Imports: biomaRt, ENVISIONQuery, AffyCompatible, R.methodsS3, utils License: GPL-2 MD5sum: 77716be02277ae45a7da05a1e6ad48bf NeedsCompilation: no Title: ID Mapping Data Retrieval Description: Data retrieval for identifier mapping performance analysis biocViews: Annotation, MultipleComparison Author: Alex Lisovich, Roger Day Maintainer: Alex Lisovich , Roger Day source.ver: src/contrib/IdMappingRetrieval_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IdMappingRetrieval_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IdMappingRetrieval_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/IdMappingRetrieval_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IdMappingRetrieval_1.20.0.tgz vignettes: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.pdf vignetteTitles: Collection and subsequent fast retrieval of identifier mapping related information from various online sources. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IdMappingRetrieval/inst/doc/IdMappingRetrieval.R Package: iGC Version: 1.2.2 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: 45d93d209702b5c96d913b6f8a9d8bde 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 source.ver: src/contrib/iGC_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/iGC_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/iGC_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iGC_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/iGC/inst/doc/Introduction.R htmlDocs: vignettes/iGC/inst/doc/Introduction.html htmlTitles: Introduction to iGC Package: IHW Version: 1.0.2 Depends: R (>= 3.3.0) Imports: methods, slam, lpsymphony, fdrtool, BiocGenerics Suggests: ggplot2, scales, DESeq, DESeq2, airway, testthat, Matrix, BiocStyle, knitr, rmarkdown, devtools License: Artistic-2.0 MD5sum: e9eef8e6f351d8bfba316049d7269e1a 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: MultipleComparison, RNASeq Author: Nikos Ignatiadis [aut, cre], Wolfgang Huber [aut] Maintainer: Nikos Ignatiadis VignetteBuilder: knitr source.ver: src/contrib/IHW_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/IHW_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/IHW_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IHW_1.0.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IHW/inst/doc/introduction_to_ihw.R htmlDocs: vignettes/IHW/inst/doc/introduction_to_ihw.html htmlTitles: "Introduction to IHW" suggestsMe: DESeq2 Package: illuminaio Version: 0.14.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: f148b585d595bcdb3763bd5f4b893eba 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 source.ver: src/contrib/illuminaio_0.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/illuminaio_0.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/illuminaio_0.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/illuminaio_0.11.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/illuminaio_0.14.0.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 importsMe: beadarray, crlmm, methylumi, minfi suggestsMe: limma Package: imageHTS Version: 1.22.0 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: c59608dfb7c2e13ba46974dee35842d6 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: Software, CellBasedAssays, Preprocessing, Visualization Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber Maintainer: Joseph Barry source.ver: src/contrib/imageHTS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/imageHTS_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/imageHTS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/imageHTS_1.19.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/imageHTS_1.22.0.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 dependsOnMe: phenoDist Package: Imetagene Version: 1.2.2 Depends: R (>= 3.2.0), metagene, shiny Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2 Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: d83368f94220978b91ce4b1596f9d609 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 source.ver: src/contrib/Imetagene_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Imetagene_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Imetagene_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Imetagene_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Imetagene/inst/doc/imetagene.R htmlDocs: vignettes/Imetagene/inst/doc/imetagene.html htmlTitles: Presentation of Imetagene Package: ImmuneSpaceR Version: 1.0.2 Imports: methods, data.table, RCurl, Rlabkey (>= 2.1.127), Biobase, pheatmap, ggplot2, scales, stats, gtools, gplots, reshape2 Suggests: knitr, rmarkdown, testthat License: GPL-2 MD5sum: d79e926501096b1da75c3733936ce75c 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, Renan Sauteraud, Mike Jiang, Gil Guday Maintainer: Renan Sauteraud URL: https://github.com/RGLab/ImmuneSpaceR VignetteBuilder: knitr BugReports: https://github.com/RGLab/ImmuneSpaceR/issues source.ver: src/contrib/ImmuneSpaceR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ImmuneSpaceR_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ImmuneSpaceR_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ImmuneSpaceR_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ImmuneSpaceR/inst/doc/arrays.R, vignettes/ImmuneSpaceR/inst/doc/getDataset.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.R, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.R, vignettes/ImmuneSpaceR/inst/doc/Using_RImmuneSpace.R htmlDocs: vignettes/ImmuneSpaceR/inst/doc/arrays.html, vignettes/ImmuneSpaceR/inst/doc/getDataset.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY144.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY180.html, vignettes/ImmuneSpaceR/inst/doc/report_SDY269.html, vignettes/ImmuneSpaceR/inst/doc/Using_RImmuneSpace.html htmlTitles: Handling expression matrices with ImmuneSpaceR, Downloading tables with getDataset, Reproducing an online report using ImmuneSpaceR: Correlation of HAI/virus neutralizition titer and cell counts in SDY144, Reproducing an online report using ImmuneSpaceR: Plasmablast abundance in SDY180, Reproducing an online report using ImmuneSpaceR: Correlation between HAI and flow cytometry in SDY269, An introduction to the ImmuneSpaceR package Package: immunoClust Version: 1.4.0 Depends: R(>= 3.2), methods, stats, graphics, grid, lattice, flowCore Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: ddcea98c47fcfbb24e489ff4c0f79d9d NeedsCompilation: yes Title: immunoClust - Automated Pipeline for Population Detection in Flow Cytometry Description: Model based clustering and meta-clustering of Flow Cytometry Data biocViews: Clustering, FlowCytometry, CellBasedAssays Author: Till Soerensen Maintainer: Till Soerensen source.ver: src/contrib/immunoClust_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/immunoClust_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/immunoClust_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/immunoClust_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/immunoClust_1.4.0.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 Package: IMPCdata Version: 1.6.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 086e62a3c6eb8587adc2ce594ea2a346 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 source.ver: src/contrib/IMPCdata_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IMPCdata_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IMPCdata_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/IMPCdata_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IMPCdata_1.6.0.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 Package: impute Version: 1.46.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 3a2c0b122de4c22bbcedb666c217b2ad 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 source.ver: src/contrib/impute_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/impute_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/impute_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/impute_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/impute_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, HCsnip, TIN importsMe: ChAMP, doppelgangR, EGAD, genomation, metaX, miRLAB, MSnbase, Rnits suggestsMe: BioNet, MethPed, RnBeads Package: InPAS Version: 1.4.4 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: f0a4697f0a15e247ff24a0784e89703c NeedsCompilation: no Title: Identification of Novel alternative PolyAdenylation Sites (PAS) 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 from RNAseq data. It leverages cleanUpdTSeq to fine tune identified APA sites. biocViews: RNASeq, Sequencing, AlternativeSplicing, Coverage, DifferentialSplicing, GeneRegulation, Transcription Author: Jianhong Ou, Sung Mi Park, Michael R. Green and Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/InPAS_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/InPAS_1.4.4.zip win64.binary.ver: bin/windows64/contrib/3.3/InPAS_1.4.4.zip mac.binary.ver: bin/macosx/contrib/3.3/InPAS_1.1.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/InPAS_1.4.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InPAS/inst/doc/InPAS.R htmlDocs: vignettes/InPAS/inst/doc/InPAS.html htmlTitles: InPAS Vignette Package: INPower Version: 1.8.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 8de65d8e73698cdc95a3048006f21260 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 source.ver: src/contrib/INPower_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/INPower_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/INPower_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/INPower_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/INPower_1.8.0.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 Package: inSilicoDb Version: 2.7.0 Depends: R (>= 3.0.0), rjson, Biobase, RCurl Suggests: limma License: GPL-2 MD5sum: dbeb9a81b8df8ec0bf3f905c26d277a9 NeedsCompilation: no Title: Access to the InSilico Database Description: Access expert curated and normalized microarray eSet datasets from the InSilico Database. biocViews: Microarray, DataImport Author: Jaro Vanderheijden [ctb], Quentin De Clerck [ctb], Jonatan Taminau [cre] Maintainer: InSilico DB URL: https://insilicodb.com source.ver: src/contrib/inSilicoDb_2.7.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/inSilicoDb_2.7.0.zip win64.binary.ver: bin/windows64/contrib/3.3/inSilicoDb_2.7.0.zip mac.binary.ver: bin/macosx/contrib/3.3/inSilicoDb_2.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/inSilicoDb_2.7.0.tgz vignettes: vignettes/inSilicoDb/inst/doc/inSilicoDb.pdf, vignettes/inSilicoDb/inst/doc/inSilicoDb2.pdf vignetteTitles: Using the inSilicoDb package, Using the inSilicoDb v2 package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inSilicoDb/inst/doc/inSilicoDb.R, vignettes/inSilicoDb/inst/doc/inSilicoDb2.R suggestsMe: inSilicoMerging Package: inSilicoMerging Version: 1.15.0 Depends: R (>= 2.11.1), Biobase Suggests: BiocGenerics, inSilicoDb License: GPL-2 MD5sum: 155e89ec259d8f9b7ea018f3fd905894 NeedsCompilation: no Title: Collection of Merging Techniques for Gene Expression Data Description: Collection of techniques to remove inter-study bias when combining gene expression data originating from different studies. biocViews: Microarray Author: Jaro Vanderheijden [ctb], Quentin De Clerck [ctb], Jonatan Taminau [cre] Maintainer: InSilico DB URL: http://insilicodb.com/ source.ver: src/contrib/inSilicoMerging_1.15.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/inSilicoMerging_1.13.0.tgz vignettes: vignettes/inSilicoMerging/inst/doc/inSilicoMerging.pdf vignetteTitles: Using the inSilicoMerging package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/inSilicoMerging/inst/doc/inSilicoMerging.R Package: INSPEcT Version: 1.2.2 Depends: R (>= 3.2), methods, Biobase, BiocParallel Imports: pROC, deSolve, rootSolve, compiler, preprocessCore, GenomicFeatures, GenomicRanges, IRanges, BiocGenerics, GenomicAlignments, Rsamtools, S4Vectors Suggests: BiocStyle, knitr, TxDb.Mmusculus.UCSC.mm9.knownGene License: GPL-2 MD5sum: 4fd6e71d08edebb9204df8855a34c666 NeedsCompilation: no Title: Analysis of 4sU-seq and RNA-seq time-course data Description: INSPEcT (INference of Synthesis, Processing and dEgradation rates in Time-Course experiments) analyses 4sU-seq and RNA-seq time-course data in order to evaluate synthesis, processing and degradation rates and asses via modeling the rates that determines changes in mature mRNA levels. biocViews: Sequencing, RNASeq, GeneRegulation, TimeCourse, SystemsBiology Author: Stefano de Pretis Maintainer: Stefano de Pretis VignetteBuilder: knitr source.ver: src/contrib/INSPEcT_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/INSPEcT_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/INSPEcT_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/INSPEcT_0.99.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/INSPEcT_1.2.2.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT.pdf vignetteTitles: INSPEcT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/INSPEcT/inst/doc/INSPEcT.R Package: intansv Version: 1.10.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: Artistic-2.0 MD5sum: c987fae2fde1cd19b89131b9b718aad8 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 source.ver: src/contrib/intansv_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/intansv_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/intansv_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/intansv_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/intansv_1.10.0.tgz vignettes: vignettes/intansv/inst/doc/intansvOverview.pdf vignetteTitles: An Introduction to intansv hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/intansv/inst/doc/intansvOverview.R Package: InteractionSet Version: 1.0.4 Depends: R (>= 3.3.0), GenomicRanges, SummarizedExperiment (>= 1.1.6) Imports: IRanges, S4Vectors (>= 0.9.24), GenomeInfoDb, BiocGenerics, methods, Matrix Suggests: testthat, knitr, rmarkdown, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: 9c083c4543dab3cc90529c82f396f026 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 , Malcolm Perry , Liz Ing-Simmons Maintainer: Aaron Lun VignetteBuilder: knitr source.ver: src/contrib/InteractionSet_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/InteractionSet_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/InteractionSet_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/InteractionSet_1.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/InteractionSet/inst/doc/interactions.R htmlDocs: vignettes/InteractionSet/inst/doc/interactions.html htmlTitles: Interacting with InteractionSet classes for genomic interaction data dependsOnMe: diffHic, GenomicInteractions Package: interactiveDisplay Version: 1.10.2 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: a915c590ccd29f1b3d794dee4570f350 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 source.ver: src/contrib/interactiveDisplay_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/interactiveDisplay_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/interactiveDisplay_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/interactiveDisplay_1.7.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/interactiveDisplay_1.10.2.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 Package: interactiveDisplayBase Version: 1.10.3 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny Suggests: knitr Enhances: rstudioapi License: Artistic-2.0 MD5sum: cb07285377eee3619722af45f094b748 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, Marc Carlson Maintainer: Shawn Balcome VignetteBuilder: knitr source.ver: src/contrib/interactiveDisplayBase_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/interactiveDisplayBase_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.3/interactiveDisplayBase_1.10.3.zip mac.binary.ver: bin/macosx/contrib/3.3/interactiveDisplayBase_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/interactiveDisplayBase_1.10.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.R htmlDocs: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html htmlTitles: Using interactiveDisplayBase for Bioconductor object visualization and modification importsMe: AnnotationHub, interactiveDisplay Package: inveRsion Version: 1.20.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 76b5e9374429db572a7e1c5a00d9eaf8 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 source.ver: src/contrib/inveRsion_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/inveRsion_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/inveRsion_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/inveRsion_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/inveRsion_1.20.0.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 Package: IONiseR Version: 1.2.3 Depends: R (>= 3.2) Imports: rhdf5, dplyr, magrittr, tidyr, data.table, ShortRead, Biostrings, ggplot2, methods, BiocGenerics, XVector Suggests: BiocStyle, knitr, rmarkdown, gridExtra, testthat, minionSummaryData License: MIT + file LICENSE MD5sum: 58c58251e6fb58df81f6cc355d93640a 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 source.ver: src/contrib/IONiseR_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/IONiseR_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/IONiseR_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.3/IONiseR_0.99.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IONiseR_1.2.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/IONiseR/inst/doc/IONiseR.R htmlDocs: vignettes/IONiseR/inst/doc/IONiseR.html htmlTitles: Quality assessment tools for nanopore data Package: iontree Version: 1.18.0 Depends: methods, rJava, RSQLite, XML Suggests: iontreeData License: GPL-2 MD5sum: d651b8338760901c61d93b5ed5508322 NeedsCompilation: no Title: Data management and analysis of ion trees from ion-trap mass spectrometry Description: Ion fragmentation provides structural information for metabolite identification. This package provides utility functions to manage and analyse MS2/MS3 fragmentation data from ion trap mass spectrometry. It was designed for high throughput metabolomics data with many biological samples and a large numer of ion trees collected. Tests have been done with data from low-resolution mass spectrometry but could be readily extended to precursor ion based fragmentation data from high resoultion mass spectrometry. biocViews: Metabolomics, MassSpectrometry Author: Mingshu Cao Maintainer: Mingshu Cao source.ver: src/contrib/iontree_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iontree_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iontree_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iontree_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iontree_1.18.0.tgz vignettes: vignettes/iontree/inst/doc/iontree_doc.pdf vignetteTitles: MSn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/iontree/inst/doc/iontree_doc.R Package: iPAC Version: 1.16.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 8d23b2e6fb8985322dcf786fd5428946 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 source.ver: src/contrib/iPAC_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iPAC_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iPAC_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iPAC_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iPAC_1.16.0.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 Package: IPPD Version: 1.20.0 Depends: R (>= 2.12.0), MASS, Matrix, XML, digest, bitops Imports: methods, stats, graphics License: GPL (version 2 or later) Archs: i386, x64 MD5sum: 77c18951ad726fb4e2368a21891828c7 NeedsCompilation: yes Title: Isotopic peak pattern deconvolution for Protein Mass Spectrometry by template matching Description: The package provides functionality to extract isotopic peak patterns from raw mass spectra. This is done by fitting a large set of template basis functions to the raw spectrum using either nonnegative least squares or least absolute deviation fittting. The package offers a flexible function which tries to estimate model parameters in a way tailored to the peak shapes in the data. The package also provides functionality to process LCMS runs. biocViews: Proteomics Author: Martin Slawski , Rene Hussong , Andreas Hildebrandt , Matthias Hein Maintainer: Martin Slawski source.ver: src/contrib/IPPD_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IPPD_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IPPD_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/IPPD_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IPPD_1.20.0.tgz vignettes: vignettes/IPPD/inst/doc/IPPD.pdf vignetteTitles: IPPD Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IPPD/inst/doc/IPPD.R Package: IRanges Version: 2.6.1 Depends: R (>= 3.1.0), methods, utils, stats, BiocGenerics (>= 0.15.10), S4Vectors (>= 0.9.48) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, GenomicFeatures, GenomicAlignments, BSgenome.Celegans.UCSC.ce2, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: cd76442a0a5535862e43fa8d7b1b1fdc NeedsCompilation: yes Title: Infrastructure for manipulating intervals on sequences Description: The package provides efficient low-level and highly reusable S4 classes for storing ranges of integers, RLE vectors (Run-Length Encoding), and, more generally, data that can be organized sequentially (formally defined as Vector objects), as well as views on these Vector objects. Efficient list-like classes are also provided for storing big collections of instances of the basic classes. All classes in the package use consistent naming and share the same rich and consistent "Vector API" as much as possible. biocViews: Infrastructure, DataRepresentation Author: H. Pagès, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/IRanges_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/IRanges_2.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/IRanges_2.6.1.zip mac.binary.ver: bin/macosx/contrib/3.3/IRanges_2.3.19.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IRanges_2.6.1.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Introduction to IRanges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/IRanges/inst/doc/IRangesOverview.R dependsOnMe: AnnotationDbi, AnnotationHubData, BayesPeak, biomvRCNS, Biostrings, BiSeq, BSgenome, BubbleTree, bumphunter, CAFE, casper, CexoR, ChIPpeakAnno, chipseq, chroGPS, CODEX, consensusSeekeR, CSAR, customProDB, deepSNV, DESeq2, DEXSeq, DirichletMultinomial, DMRcaller, EnrichedHeatmap, epigenomix, exomeCopy, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, Genominator, groHMM, gtrellis, Guitar, Gviz, HilbertCurve, HiTC, HMMcopy, htSeqTools, IdeoViz, isomiRs, methyAnalysis, MotifDb, motifRG, oneChannelGUI, OTUbase, pepStat, PGA, PING, proBAMr, PSICQUIC, RefNet, rfPred, rGADEM, rGREAT, RIPSeeker, rMAT, scsR, segmentSeq, SGSeq, SICtools, SomatiCA, TEQC, TitanCNA, triform, triplex, VariantTools, XVector importsMe: ALDEx2, AllelicImbalance, AneuFinder, annmap, AnnotationDbi, ArrayExpressHTS, ballgown, bamsignals, BayesPeak, BBCAnalyzer, beadarray, Biostrings, biovizBase, BiSeq, BitSeq, BSgenome, bsseq, CAGEr, charm, chipenrich, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, CINdex, cleaver, clusterProfiler, cn.mops, CNEr, CNPBayes, CNVPanelizer, CNVrd2, cobindR, coMET, compEpiTools, contiBAIT, conumee, copynumber, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, customProDB, debrowser, DECIPHER, derfinder, derfinderHelper, derfinderPlot, DiffBind, diffHic, diffloop, DMRcate, DOQTL, DRIMSeq, easyRNASeq, EDASeq, ensembldb, epivizr, epivizrData, facopy, fastseg, FindMyFriends, flipflop, flowQ, FunciSNP, genbankr, GenoGAM, genomation, genomeIntervals, GenomicAlignments, GenomicFiles, GenomicInteractions, GenomicTuples, genoset, genotypeeval, GenVisR, GGBase, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GOTHiC, gQTLstats, GUIDEseq, gwascat, h5vc, HDF5Array, HTSeqGenie, InPAS, INSPEcT, intansv, InteractionSet, IVAS, JunctionSeq, LOLA, M3D, MatrixRider, MEAL, MEDIPS, metagene, methVisual, methyAnalysis, methylPipe, MethylSeekR, methylumi, minfi, MinimumDistance, mosaics, motifbreakR, MotIV, msa, MSnbase, MultiDataSet, NarrowPeaks, nucleoSim, nucleR, oligoClasses, OrganismDbi, Pbase, pcaExplorer, pdInfoBuilder, PICS, PING, plethy, podkat, polyester, pqsfinder, prebs, PureCN, Pviz, QDNAseq, qpgraph, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RareVariantVis, Rariant, REDseq, regioneR, Repitools, ReportingTools, rGADEM, RiboProfiling, rMAT, rnaSeqMap, RnBeads, roar, Rolexa, Rqc, Rsamtools, rSFFreader, RSVSim, RTN, rtracklayer, SCAN.UPC, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, simulatorZ, skewr, SMITE, SNPchip, SNPhood, soGGi, SomatiCA, SomaticSignatures, spliceR, SplicingGraphs, STAN, SummarizedExperiment, SVM2CRM, TarSeqQC, TCGAbiolinks, TFBSTools, tracktables, trackViewer, transcriptR, TransView, triform, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, waveTiling, XVector suggestsMe: AnnotationHub, BaseSpaceR, BiocGenerics, Chicago, ClassifyR, gQTLBase, HilbertVis, HilbertVisGUI, MiRaGE, regionReport, RTCGA, S4Vectors Package: iSeq Version: 1.24.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: ae168e8cc9ab873cd21a8f4490f62141 NeedsCompilation: yes Title: Bayesian Hierarchical Modeling of ChIP-seq Data Through Hidden Ising Models Description: This package uses Bayesian hidden Ising models to identify IP-enriched genomic regions from ChIP-seq data. It can be used to analyze ChIP-seq data with and without controls and replicates. biocViews: ChIPSeq, Sequencing Author: Qianxing Mo Maintainer: Qianxing Mo source.ver: src/contrib/iSeq_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iSeq_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iSeq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iSeq_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iSeq_1.24.0.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 Package: isobar Version: 1.18.0 Depends: R (>= 2.10.0), Biobase, stats, methods Imports: distr, plyr Suggests: MSnbase, OrgMassSpecR, XML, biomaRt, ggplot2, RJSONIO, Hmisc, gplots, RColorBrewer, gridExtra, limma, boot, distr, DBI, MASS License: LGPL-2 MD5sum: 740b92233933d60eb4653e5c838236f1 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: 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 source.ver: src/contrib/isobar_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/isobar_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/isobar_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/isobar_1.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/isobar_1.18.0.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 Package: IsoGeneGUI Version: 2.8.0 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: 5d9d9a77c2fdf27e5868e099edb5547b 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 source.ver: src/contrib/IsoGeneGUI_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IsoGeneGUI_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IsoGeneGUI_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/IsoGeneGUI_2.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IsoGeneGUI_2.8.0.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 Package: ISoLDE Version: 1.0.2 Depends: R (>= 3.3.0),graphics,grDevices,stats,utils License: GPL (>= 2.0) Archs: i386, x64 MD5sum: 99fa0666848deb4edf8c9db38a0676fa 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: 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 source.ver: src/contrib/ISoLDE_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ISoLDE_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ISoLDE_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ISoLDE_1.0.2.tgz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: isomiRs Version: 1.0.3 Depends: R (>= 3.2), DiscriMiner, IRanges, S4Vectors, GenomicRanges, SummarizedExperiment (>= 0.2.0) Imports: BiocGenerics (>= 0.7.5), DESeq2, plyr, dplyr, RColorBrewer, gplots, methods, ggplot2, GGally Suggests: knitr, RUnit, BiocStyle License: MIT + file LICENSE MD5sum: 36669b2e85ce69a0f168afe55e11c928 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 Author: Lorena Pantano, Georgia Escaramis Maintainer: Lorena Pantano VignetteBuilder: knitr source.ver: src/contrib/isomiRs_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/isomiRs_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/isomiRs_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/isomiRs_1.0.3.tgz vignettes: vignettes/isomiRs/inst/doc/isomiRs-intro.pdf vignetteTitles: isomiRs hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/isomiRs/inst/doc/isomiRs-intro.R Package: ITALICS Version: 2.32.0 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: a7ac8c9e664bf422b36d8b1436347112 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 source.ver: src/contrib/ITALICS_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ITALICS_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ITALICS_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ITALICS_2.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ITALICS_2.32.0.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 Package: iterativeBMA Version: 1.30.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 828d567f9e911deec9aa5ee006980f4d 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 source.ver: src/contrib/iterativeBMA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iterativeBMA_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iterativeBMA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iterativeBMA_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iterativeBMA_1.30.0.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 Package: iterativeBMAsurv Version: 1.30.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: 2ce6bac0f4ced7f944c35dc42873bc9c 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 source.ver: src/contrib/iterativeBMAsurv_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/iterativeBMAsurv_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/iterativeBMAsurv_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/iterativeBMAsurv_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/iterativeBMAsurv_1.30.0.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 Package: IVAS Version: 1.4.0 Depends: R (> 3.0.0),GenomicFeatures Imports: doParallel, lme4, Matrix, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb Suggests: BiocStyle License: GPL-2 MD5sum: 7a75082c47b53b8326374d2edbed4bc5 NeedsCompilation: no Title: Identification of genetic Variants affecting Alternative Splicing Description: Identification of genetic variants affecting alternative splicing. biocViews: AlternativeSplicing, DifferentialExpression, DifferentialSplicing, GeneExpression, GeneRegulation, Regression, RNASeq, Sequencing, SNP, Software, Transcription Author: Seonggyun Han, Sangsoo Kim Maintainer: Seonggyun Han source.ver: src/contrib/IVAS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/IVAS_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/IVAS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/IVAS_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/IVAS_1.4.0.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 Package: jmosaics Version: 1.11.0 Depends: R (>= 2.15.2), mosaics License: GPL (>= 2) MD5sum: 947f06e9c256591620a37c88fa559ea2 NeedsCompilation: no Title: Joint analysis of multiple ChIP-Seq data sets Description: jmosaics detects enriched regions of ChIP-seq data sets jointly. biocViews: ChIPSeq, Sequencing, Transcription, Genetics Author: Xin Zeng Maintainer: Xin Zeng source.ver: src/contrib/jmosaics_1.11.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/jmosaics_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/jmosaics_1.11.0.tgz vignettes: vignettes/jmosaics/inst/doc/jmosaics.pdf vignetteTitles: jMOSAiCS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/jmosaics/inst/doc/jmosaics.R Package: joda Version: 1.20.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) MD5sum: 4d585cc957b80edc37ce3e45f91e0aa2 NeedsCompilation: no 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 source.ver: src/contrib/joda_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/joda_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/joda_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/joda_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/joda_1.20.0.tgz vignettes: vignettes/joda/inst/doc/JodaVignette.pdf vignetteTitles: Introduction to joda hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/joda/inst/doc/JodaVignette.R Package: JunctionSeq Version: 1.2.4 Depends: R (>= 3.3), methods, SummarizedExperiment (>= 0.2.0) Imports: DESeq2 (>= 1.10.0), statmod, Hmisc, plotrix, stringr, Biobase (>= 2.30.0), locfit, BiocGenerics (>= 0.7.5), BiocParallel, genefilter, geneplotter, S4Vectors, IRanges, GenomicRanges Suggests: MASS, knitr, JctSeqData, BiocStyle Enhances: Cairo, pryr License: file LICENSE MD5sum: 7002b2f96f52fda34c5be0544fe9ac23 NeedsCompilation: no 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: 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 source.ver: src/contrib/JunctionSeq_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/JunctionSeq_1.2.4.zip win64.binary.ver: bin/windows64/contrib/3.3/JunctionSeq_1.2.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/JunctionSeq_1.2.4.tgz vignettes: vignettes/JunctionSeq/inst/doc/JunctionSeq.pdf vignetteTitles: JunctionSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: KCsmart Version: 2.30.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 42aa438b48da8ff9a628f943a48aa9c6 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 source.ver: src/contrib/KCsmart_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/KCsmart_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/KCsmart_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/KCsmart_2.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KCsmart_2.30.0.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 Package: kebabs Version: 1.6.2 Depends: R (>= 3.2.0), Biostrings (>= 2.35.5), kernlab Imports: methods, stats, Rcpp (>= 0.11.2), Matrix, XVector (>= 0.7.3), S4Vectors (>= 0.5.11), 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: bb70cfd9ca8d4162f6a6da1f510fff5d 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/ VignetteBuilder: knitr source.ver: src/contrib/kebabs_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/kebabs_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/kebabs_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/kebabs_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/kebabs_1.6.2.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 Package: KEGGgraph Version: 1.30.0 Imports: methods, XML (>= 2.3-0), graph Suggests: Rgraphviz, RBGL, RUnit, RColorBrewer, KEGG.db, org.Hs.eg.db, hgu133plus2.db, SPIA License: GPL (>= 2) MD5sum: 106470d29b455239d7b2d3a86952e97e 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 source.ver: src/contrib/KEGGgraph_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/KEGGgraph_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/KEGGgraph_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/KEGGgraph_1.27.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KEGGgraph_1.30.0.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, NCIgraph, pathview, ToPASeq suggestsMe: DEGraph, GenomicRanges Package: keggorthology Version: 2.24.0 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: 7c69cd6275cb0e9609a9d9035808087c 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 source.ver: src/contrib/keggorthology_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/keggorthology_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/keggorthology_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/keggorthology_2.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/keggorthology_2.24.0.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 Package: KEGGprofile Version: 1.14.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt License: GPL (>= 2) MD5sum: 3d97118c8c2b0c867eff8612afa02461 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 source.ver: src/contrib/KEGGprofile_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/KEGGprofile_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/KEGGprofile_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/KEGGprofile_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KEGGprofile_1.14.0.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: FGNet Package: KEGGREST Version: 1.12.3 Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: f5fae4878d569ab5d5e38dfd9b49a4be NeedsCompilation: no Title: Client-side REST access to KEGG Description: A package that provides a client interface to the 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 Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/KEGGREST_1.12.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/KEGGREST_1.12.3.zip win64.binary.ver: bin/windows64/contrib/3.3/KEGGREST_1.12.3.zip mac.binary.ver: bin/macosx/contrib/3.3/KEGGREST_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/KEGGREST_1.12.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.R htmlDocs: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html htmlTitles: Accessing the KEGG REST API dependsOnMe: PAPi, ROntoTools importsMe: attract, EnrichmentBrowser, gage, mmnet, pathview Package: kimod Version: 1.0.0 Depends: R(>= 3.3),methods Imports: cluster, graphics, Biobase License: GPL (>=2) MD5sum: 18a701a78e33dd066cb9d94460422550 NeedsCompilation: no Title: A k-tables approach to integrate multiple Omics-Data Description: This package allows to work with mixed omics data (transcriptomics, proteomics, microarray-chips, rna-seq data), introducing the following improvements: distance options (for numeric and/or categorical variables) for each of the tables, bootstrap resampling techniques on the residuals matrices for all methods, that enable perform confidence ellipses for the projection of individuals, variables and biplot methodology to project variables (gene expression) on the compromise. Since the main purpose of the package is to use these techniques to omic data analysis, it includes an example data from four different microarray platforms (i.e.,Agilent, Affymetrix HGU 95, Affymetrix HGU 133 and Affymetrix HGU 133plus 2.0) on the NCI-60 cell lines.NCI60_4arrays is a list containing the NCI-60 microarray data with only few hundreds of genes randomly selected in each platform to keep the size of the package small. The data are the same that the package omicade4 used to implement the co-inertia analysis. The references in packages follow the style of the APA-6th norm. biocViews: Microarray, Visualization, GeneExpression, ExperimentData, Proteomics Author: Maria Laura Zingaretti, Johanna Altair Demey-Zambrano, Jose Luis Vicente-Villardon, Jhonny Rafael Demey Maintainer: M L Zingaretti source.ver: src/contrib/kimod_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/kimod_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/kimod_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/kimod_1.0.0.tgz vignettes: vignettes/kimod/inst/doc/kimod-vignette.pdf vignetteTitles: kimod A K-tables approach to integrate multiple Omics-Data in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/kimod/inst/doc/kimod-vignette.R Package: lapmix Version: 1.38.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: d1013a9017a383004352c1c3bad31f8e 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 source.ver: src/contrib/lapmix_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lapmix_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lapmix_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/lapmix_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lapmix_1.38.0.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 Package: LBE Version: 1.40.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 6edf11cb8cbe7d7a98e1c0cc656d92b6 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 source.ver: src/contrib/LBE_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LBE_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LBE_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/LBE_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LBE_1.40.0.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 Package: ldblock Version: 1.2.2 Depends: R (>= 3.1), methods Imports: Matrix, snpStats Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 8f113bed5d79e7b21edb21679dcdfdde 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 source.ver: src/contrib/ldblock_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ldblock_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ldblock_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ldblock_0.99.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ldblock_1.2.2.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.pdf vignetteTitles: LD block import and manipulation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ldblock/inst/doc/ldblock.R Package: LEA Version: 1.4.0 Depends: R (>= 3.0.2), methods, stats, utils License: GPL-3 Archs: i386, x64 MD5sum: d73d14fb20280839d4677b8c2ef043e5 NeedsCompilation: yes Title: LEA: an R package for Landscape and Ecological Association Studies Description: LEA is an R package dedicated to landscape genomics and ecological association tests. LEA can run analyses of population structure and genome scans for local adaptation. It includes statistical methods for estimating ancestry coefficients from large genotypic matrices and evaluating the number of ancestral populations (snmf, pca); and identifying genetic polymorphisms that exhibit high correlation with some environmental gradient or with the variables used as proxies for ecological pressures (lfmm), and controlling the false discovery rate. LEA is mainly based on optimized C programs that can scale with the dimension of very large data sets. biocViews: Software, StatisticalMethod, Clustering, Regression Author: Eric Frichot , Olivier Francois Maintainer: Eric Frichot URL: http://membres-timc.imag.fr/Olivier.Francois/lea.html source.ver: src/contrib/LEA_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LEA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LEA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/LEA_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LEA_1.4.0.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 Package: LedPred Version: 1.6.1 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, ggplot2, irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 1e67877967b2f8ab207aae0601b0c17c 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 source.ver: src/contrib/LedPred_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/LedPred_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/LedPred_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.3/LedPred_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LedPred_1.6.1.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 Package: les Version: 1.22.0 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 444a0c730f1f5833b5070e0ec19df1af 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 source.ver: src/contrib/les_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/les_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/les_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/les_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/les_1.22.0.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 Package: lfa Version: 1.2.2 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: e3fa1880a6c23669523748c9334e3a3e 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 source.ver: src/contrib/lfa_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/lfa_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/lfa_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lfa_1.2.2.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 Package: limma Version: 3.28.21 Depends: R (>= 2.3.0) Imports: grDevices, graphics, stats, utils, methods Suggests: affy, AnnotationDbi, BiasedUrn, Biobase, ellipse, GO.db, illuminaio, locfit, MASS, org.Hs.eg.db, splines, statmod (>= 1.2.2), vsn License: GPL (>=2) Archs: i386, x64 MD5sum: a097aec2cc0153f8abf778dfd5ba2dfa 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, Genetics, Bayesian, Clustering, Regression, TimeCourse, Microarray, microRNAArray, mRNAMicroarray, OneChannel, ProprietaryPlatforms, TwoChannel, RNASeq, BatchEffect, MultipleComparison, Normalization, Preprocessing, QualityControl 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 source.ver: src/contrib/limma_3.28.21.tar.gz win.binary.ver: bin/windows/contrib/3.3/limma_3.28.21.zip win64.binary.ver: bin/windows64/contrib/3.3/limma_3.28.21.zip mac.binary.ver: bin/macosx/contrib/3.3/limma_3.25.15.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/limma_3.28.21.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: a4Base, AffyExpress, birta, bsseq, CALIB, cghMCR, codelink, convert, Cormotif, coRNAi, DrugVsDisease, edgeR, ExiMiR, ExpressionAtlas, gCMAP, HTqPCR, maigesPack, marray, metagenomeSeq, metaseqR, MLSeq, MmPalateMiRNA, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, snapCGH, splineTCDiffExpr, splineTimeR, SSPA, tRanslatome, TurboNorm, wateRmelon importsMe: ABSSeq, affycoretools, affylmGUI, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, attract, ballgown, BatchQC, beadarray, betr, birte, BubbleTree, bumphunter, CALIB, CancerMutationAnalysis, casper, charm, ChIPpeakAnno, compcodeR, CountClust, csaw, derfinderPlot, DiffBind, diffHic, DMRcate, EBSEA, EGAD, EGSEA, EnrichmentBrowser, erccdashboard, explorase, flowBin, GeneSelectMMD, GeneSelector, GGBase, GOsummaries, gQTLstats, HTqPCR, iCheck, iChip, iCOBRA, InPAS, limmaGUI, Linnorm, lmdme, LVSmiRNA, mAPKL, maSigPro, MEAL, minfi, miRLAB, missMethyl, MmPalateMiRNA, monocle, MSstats, nem, nethet, nondetects, OGSA, OLIN, PAA, PADOG, pbcmc, pcaExplorer, PECA, pepStat, phenoTest, polyester, Ringo, RNAinteract, RNAither, RTN, RTopper, scater, scran, SimBindProfiles, snapCGH, STATegRa, systemPipeR, TCGAbiolinks, timecourse, ToPASeq, tweeDEseq, variancePartition, vsn suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocCaseStudies, BioNet, Category, categoryCompare, ClassifyR, CMA, coGPS, derfinder, dyebias, ELBOW, gage, GeneSelector, GEOquery, Glimma, GSRI, GSVA, Harman, Heatplus, inSilicoDb, isobar, les, lumi, mdgsa, methylumi, MLP, npGSEA, oligo, oneChannelGUI, oppar, paxtoolsr, PGSEA, piano, plw, PREDA, puma, Rcade, RTopper, rtracklayer, subSeq, sva, tximport Package: limmaGUI Version: 1.48.0 Imports: limma, tcltk, BiocInstaller, tkrplot, R2HTML, xtable, gcrma, AnnotationDbi License: GPL (>=2) MD5sum: 9411aa48d2357f8f0d263c5c393f77b9 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], Yifang Hu [ctb] Maintainer: Yifang Hu , Gordon Smyth , Keith Satterley URL: http://bioinf.wehi.edu.au/limmaGUI/ source.ver: src/contrib/limmaGUI_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/limmaGUI_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/limmaGUI_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/limmaGUI_1.45.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/limmaGUI_1.48.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf, vignettes/limmaGUI/inst/doc/LinModIntro.pdf vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette, LinModIntro.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/limmaGUI/inst/doc/limmaGUI.R htmlDocs: 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 htmlTitles: about.html, CustMenu.html, import.html, index.html, InputFiles.html, lgDevel.html, windowsFocus.html Package: Linnorm Version: 1.0.6 Imports: Rcpp (>= 0.12.2), RcppArmadillo, MASS, limma, stats, utils, statmod LinkingTo: Rcpp, RcppArmadillo Suggests: BiocStyle, knitr, rmarkdown, seqc, gplots, RColorBrewer License: MIT + file LICENSE Archs: i386, x64 MD5sum: 72f59d8d3cbde5d19ce4323e837ec405 NeedsCompilation: yes Title: Linear model and normality based transformation method (Linnorm) Description: Linnorm is an R package for the analysis of RNA-seq, scRNA-seq, ChIP-seq count data or any large scale count data. Its main function is to normalize and transform these datasets for parametric tests. Examples of parametric tests include using limma for differential expression analysis or differential peak detection, or calculating Pearson correlation coefficient for gene correlation study. Linnorm can work with raw count, CPM, RPKM, FPKM and TPM. Additionally, Linnorm provides the RnaXSim function for the simulation of RNA-seq raw counts for the evaluation of differential expression analysis methods. RnaXSim can simulate RNA-seq dataset in Gamma, Log Normal, Negative Binomial or Poisson distributions. biocViews: Sequencing, ChIPSeq, RNASeq, DifferentialExpression, GeneExpression, Genetics, Normalization, Software, Transcription, BatchEffect Author: Shun Hang Yip , Panwen Wang , Jean-Pierre Kocher , Pak Chung Sham , Junwen Wang Maintainer: Ken Shun Hang Yip URL: http://www.jjwanglab.org/Linnorm/ VignetteBuilder: knitr source.ver: src/contrib/Linnorm_1.0.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/Linnorm_1.0.6.zip win64.binary.ver: bin/windows64/contrib/3.3/Linnorm_1.0.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Linnorm_1.0.6.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 Package: LiquidAssociation Version: 1.26.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 1dd9bfaf23008b02fd3e929ed4046de1 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 source.ver: src/contrib/LiquidAssociation_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LiquidAssociation_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LiquidAssociation_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/LiquidAssociation_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LiquidAssociation_1.26.0.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 Package: lmdme Version: 1.14.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 643a52a88dbad410f8d0160f6ef9c5b7 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 source.ver: src/contrib/lmdme_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lmdme_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lmdme_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/lmdme_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lmdme_1.14.0.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 Package: LMGene Version: 2.28.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), multtest, survival, affy Suggests: affydata License: LGPL MD5sum: 546a7bf273a32e28c4af6bd770c7b1a3 NeedsCompilation: no Title: LMGene Software for Data Transformation and Identification of Differentially Expressed Genes in Gene Expression Arrays Description: LMGene package for analysis of microarray data using a linear model and glog data transformation biocViews: Microarray, DifferentialExpression, Preprocessing Author: David Rocke, Geun Cheol Lee, John Tillinghast, Blythe Durbin-Johnson, and Shiquan Wu Maintainer: Blythe Durbin-Johnson URL: http://dmrocke.ucdavis.edu/software.html source.ver: src/contrib/LMGene_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LMGene_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LMGene_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/LMGene_2.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LMGene_2.28.0.tgz vignettes: vignettes/LMGene/inst/doc/LMGene.pdf vignetteTitles: LMGene User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LMGene/inst/doc/LMGene.R Package: logicFS Version: 1.42.0 Depends: LogicReg, mcbiopi Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 3c15f83fb79abd858503216153b5912a 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 Maintainer: Holger Schwender source.ver: src/contrib/logicFS_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/logicFS_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/logicFS_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/logicFS_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/logicFS_1.42.0.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 Package: logitT Version: 1.30.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) Archs: i386, x64 MD5sum: 8a8a2fe56047c4aabb2dc08e5d7d6018 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 source.ver: src/contrib/logitT_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/logitT_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/logitT_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/logitT_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/logitT_1.30.0.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 Package: lol Version: 1.20.0 Depends: penalized, Matrix Imports: Matrix, penalized, graphics, grDevices, stats License: GPL-2 MD5sum: 10b40da6a373682a3816a9b7fc2ebe98 NeedsCompilation: no Title: Lots Of Lasso Description: Various optimization methods for Lasso inference with matrix warpper biocViews: StatisticalMethod Author: Yinyin Yuan Maintainer: Yinyin Yuan source.ver: src/contrib/lol_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lol_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lol_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/lol_1.17.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lol_1.20.0.tgz vignettes: vignettes/lol/inst/doc/lol.pdf vignetteTitles: An introduction to the lol package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lol/inst/doc/lol.R Package: LOLA Version: 1.2.2 Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, data.table Suggests: knitr, parallel, testthat Enhances: simpleCache, qvalue License: GPL-3 MD5sum: 5ba049f4092e024c2503bd3482f1bbcb NeedsCompilation: no Title: Location 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 make is 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 [cre] Maintainer: Nathan Sheffield URL: http://databio.org/lola VignetteBuilder: knitr BugReports: http://github.com/sheffien/LOLA source.ver: src/contrib/LOLA_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/LOLA_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/LOLA_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LOLA_1.2.2.tgz 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 htmlDocs: vignettes/LOLA/inst/doc/choosingUniverse.html, vignettes/LOLA/inst/doc/gettingStarted.html, vignettes/LOLA/inst/doc/usingLOLACore.html htmlTitles: Choosing a LOLA Universe, Getting Started with LOLA, Using LOLA Core Package: LowMACA Version: 1.4.2 Depends: R (>= 2.10) Imports: cgdsr, parallel, stringr, reshape2, data.table, RColorBrewer, methods, LowMACAAnnotation, BiocParallel, motifStack, Biostrings Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 7aa432fb9af06aac079c66a94083c0d8 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 source.ver: src/contrib/LowMACA_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/LowMACA_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/LowMACA_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/LowMACA_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LowMACA_1.4.2.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.pdf vignetteTitles: LowMACA hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LowMACA/inst/doc/LowMACA.R Package: LPE Version: 1.46.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: 80f28fc1045167d27599cb38d4e967f9 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/ source.ver: src/contrib/LPE_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LPE_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LPE_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/LPE_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LPE_1.46.0.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 Package: LPEadj Version: 1.32.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: 63a3d4be9aaffaa476df7c70a9516684 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 source.ver: src/contrib/LPEadj_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LPEadj_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LPEadj_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/LPEadj_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LPEadj_1.32.0.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 Package: lpNet Version: 2.4.0 Depends: lpSolve, nem License: Artistic License 2.0 MD5sum: e50452e5b2e03d7ed778c973aee1d554 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 source.ver: src/contrib/lpNet_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lpNet_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lpNet_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/lpNet_2.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lpNet_2.4.0.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 Package: lpsymphony Version: 1.0.2 Depends: R (>= 3.0.0) Suggests: BiocStyle, knitr Enhances: slam License: EPL Archs: i386, x64 MD5sum: 38380d5b37fec99398b7193c82a6f44e 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/ VignetteBuilder: knitr source.ver: src/contrib/lpsymphony_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/lpsymphony_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/lpsymphony_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lpsymphony_1.0.2.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 Package: lumi Version: 2.24.0 Depends: R (>= 2.10), Biobase (>= 2.5.5) Imports: affy (>= 1.23.4), methylumi (>= 2.3.2), GenomicFeatures, GenomicRanges, annotate, Biobase (>= 2.5.5), 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: 2e578da4559be7a10f56b9d3ce3b1ebd 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: Pan Du source.ver: src/contrib/lumi_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/lumi_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/lumi_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/lumi_2.21.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/lumi_2.24.0.tgz vignettes: vignettes/lumi/inst/doc/IlluminaAnnotation.pdf, vignettes/lumi/inst/doc/lumi_VST_evaluation.pdf, vignettes/lumi/inst/doc/lumi.pdf, vignettes/lumi/inst/doc/methylationAnalysis.pdf vignetteTitles: Resolve the inconsistency of Illumina identifiers through nuID, Evaluation of VST algorithm in lumi package, Using lumi A package processing Illumina Microarray, Analyze Illumina Infinium methylation microarray data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/lumi/inst/doc/IlluminaAnnotation.R, vignettes/lumi/inst/doc/lumi_VST_evaluation.R, vignettes/lumi/inst/doc/lumi.R, vignettes/lumi/inst/doc/methylationAnalysis.R dependsOnMe: arrayMvout, iCheck, wateRmelon importsMe: ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, Harman, methylumi, tigre Package: LVSmiRNA Version: 1.22.0 Depends: R (>= 3.1.0), methods, splines Imports: BiocGenerics, stats4, graphics, stats, utils, MASS, Biobase, quantreg, limma, affy, SparseM, vsn, zlibbioc Enhances: parallel,snow, Rmpi License: GPL-2 Archs: i386, x64 MD5sum: caccf35ae36c542c0722f2509c8900fd NeedsCompilation: yes Title: LVS normalization for Agilent miRNA data Description: Normalization of Agilent miRNA arrays. biocViews: Microarray,AgilentChip,OneChannel,Preprocessing Author: Stefano Calza, Suo Chen, Yudi Pawitan Maintainer: Stefano Calza source.ver: src/contrib/LVSmiRNA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/LVSmiRNA_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/LVSmiRNA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/LVSmiRNA_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LVSmiRNA_1.22.0.tgz vignettes: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.pdf vignetteTitles: LVSmiRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.R Package: LymphoSeq Version: 1.0.2 Depends: R (>= 3.2), LymphoSeqDB Imports: data.table, plyr, dplyr, reshape, VennDiagram, ggplot2, ineq, RColorBrewer, circlize, grid, utils, stats Suggests: knitr, pheatmap, wordcloud License: Artistic-2.0 MD5sum: 335de4e78870c169ede01cea27846c39 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 Author: David Coffey Maintainer: David Coffey VignetteBuilder: knitr source.ver: src/contrib/LymphoSeq_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/LymphoSeq_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/LymphoSeq_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/LymphoSeq_1.0.2.tgz vignettes: vignettes/LymphoSeq/inst/doc/LymphoSeq.pdf vignetteTitles: Analyze 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 Package: M3D Version: 1.6.2 Depends: R (>= 3.0.0) Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment, BiSeq, parallel Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: Artistic License 2.0 MD5sum: cd976c3eab4faf599f1bd2622126a6cb NeedsCompilation: no Title: Identifies differentially methylated regions across testing groups Description: This package identifies statistically significantly differentially methylated regions of CpGs. It uses kernel methods (the Maximum Mean Discrepancy) to measure differences in methylation profiles, and relates these to inter-replicate changes, whilst accounting for variation in coverage profiles. biocViews: DNAMethylation, DifferentialMethylation, Coverage, CpGIsland Author: Tom Mayo Maintainer: Tom Mayo VignetteBuilder: knitr source.ver: src/contrib/M3D_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/M3D_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/M3D_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/M3D_1.3.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/M3D_1.6.2.tgz vignettes: vignettes/M3D/inst/doc/M3D_vignette.pdf vignetteTitles: An Introduction to the M$^3$D method hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/M3D/inst/doc/M3D_vignette.R Package: maanova Version: 1.42.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) Archs: i386, x64 MD5sum: 98aa3910be702d366250c478c62bac66 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 source.ver: src/contrib/maanova_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maanova_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maanova_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/maanova_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maanova_1.42.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: macat Version: 1.46.0 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: 7ef010ee813dd40320dca33cb896eef4 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 source.ver: src/contrib/macat_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/macat_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/macat_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/macat_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/macat_1.46.0.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 Package: maCorrPlot Version: 1.42.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: 8c79e1b2eaf0413bfc2a193f1774771a 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 source.ver: src/contrib/maCorrPlot_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maCorrPlot_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maCorrPlot_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/maCorrPlot_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maCorrPlot_1.42.0.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 Package: made4 Version: 1.46.0 Depends: ade4, RColorBrewer,gplots,scatterplot3d Suggests: affy License: Artistic-2.0 MD5sum: 3782d03e68c8c0ccb24cee7fe157b945 NeedsCompilation: no Title: Multivariate analysis of microarray data using ADE4 Description: Multivariate data analysis and graphical display of microarray data. Functions include between group analysis and coinertia analysis. It contains functions that require ADE4. biocViews: Clustering, Classification, MultipleComparison Author: Aedin Culhane Maintainer: Aedin Culhane URL: http://www.hsph.harvard.edu/aedin-culhane/ source.ver: src/contrib/made4_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/made4_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/made4_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/made4_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/made4_1.46.0.tgz vignettes: vignettes/made4/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/made4/inst/doc/introduction.R dependsOnMe: bgafun importsMe: omicade4 Package: maigesPack Version: 1.36.0 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: 9af79f032b360e9351d794eee910c587 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/ source.ver: src/contrib/maigesPack_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maigesPack_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maigesPack_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/maigesPack_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maigesPack_1.36.0.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 Package: MAIT Version: 1.6.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Enhances: rgl License: GPL-2 MD5sum: dfbd2d1f20d9d63a7b9353e2584e5e76 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: MassSpectrometry, Metabolomics, Software Author: Francesc Fernandez-Albert, Rafael Llorach, Cristina Andres-LaCueva, Alexandre Perera Maintainer: Francesc Fernandez-Albert source.ver: src/contrib/MAIT_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MAIT_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MAIT_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MAIT_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MAIT_1.6.0.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 Package: makecdfenv Version: 1.48.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: fc548dd4bb0a416647236ae9715ca4bb 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 source.ver: src/contrib/makecdfenv_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/makecdfenv_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/makecdfenv_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/makecdfenv_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/makecdfenv_1.48.0.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 Package: MANOR Version: 1.44.0 Depends: R (>= 2.10), GLAD Imports: GLAD, graphics, grDevices, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: 8bc69dcfa8f73a05b15509bf962dfc0f 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 Author: Pierre Neuvial , Philippe Hupe Maintainer: Pierre Neuvial URL: http://bioinfo.curie.fr/projects/manor/index.html source.ver: src/contrib/MANOR_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MANOR_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MANOR_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MANOR_1.41.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MANOR_1.44.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.pdf vignetteTitles: MANOR overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MANOR/inst/doc/MANOR.R Package: manta Version: 1.18.0 Depends: R (>= 1.8.0), methods, edgeR (>= 2.5.13) Imports: Hmisc, caroline(>= 0.6.6) Suggests: RSQLite, plotrix License: Artistic-2.0 MD5sum: 59b0b6fa0ed1e05254e1bfabffca7835 NeedsCompilation: no Title: Microbial Assemblage Normalized Transcript Analysis Description: Tools for robust comparative metatranscriptomics. biocViews: DifferentialExpression, RNASeq, Genetics, GeneExpression, Sequencing, QualityControl, DataImport, Visualization Author: Ginger Armbrust, Adrian Marchetti Maintainer: Chris Berthiaume , Adrian Marchetti URL: http://manta.ocean.washington.edu/ source.ver: src/contrib/manta_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/manta_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/manta_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/manta_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/manta_1.18.0.tgz vignettes: vignettes/manta/inst/doc/manta.pdf vignetteTitles: manta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/manta/inst/doc/manta.R Package: MantelCorr Version: 1.42.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 55497c9d5b3f6c554d5c1759c7190acf 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 source.ver: src/contrib/MantelCorr_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MantelCorr_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MantelCorr_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MantelCorr_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MantelCorr_1.42.0.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 Package: mAPKL Version: 1.4.2 Depends: R (>= 3.2.0), 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: f7d9c3b2209b2714272f02692bea0856 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 source.ver: src/contrib/mAPKL_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/mAPKL_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/mAPKL_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/mAPKL_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mAPKL_1.4.2.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 Package: maPredictDSC Version: 1.10.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: 74698910222eadeb569c0b561fb2d0f4 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 source.ver: src/contrib/maPredictDSC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maPredictDSC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maPredictDSC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/maPredictDSC_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maPredictDSC_1.10.0.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 Package: marray Version: 1.50.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: 32c11e22e20003bc1c422832f5dae7bc 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/ source.ver: src/contrib/marray_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/marray_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/marray_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/marray_1.47.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/marray_1.50.0.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 importsMe: arrayQuality, ChAMP, methylPipe, MSstats, nnNorm, OLIN, OLINgui, piano, plrs, sigaR, stepNorm, timecourse suggestsMe: DEGraph, Mfuzz Package: maSigPro Version: 1.44.0 Depends: R (>= 2.3.1), stats, Biobase, MASS Imports: Biobase, graphics, grDevices, limma, Mfuzz, stats, utils, MASS License: GPL (>= 2) MD5sum: 794cd030fe24c97618b91954d2485233 NeedsCompilation: no Title: Significant Gene Expression Profile Differences in Time Course Microarray 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 experiments. biocViews: Microarray, DifferentialExpression, TimeCourse Author: Ana Conesa , Maria Jose Nueda Maintainer: Maria Jose Nueda URL: http://bioinfo.cipf.es/ source.ver: src/contrib/maSigPro_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maSigPro_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maSigPro_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/maSigPro_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maSigPro_1.44.0.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 suggestsMe: oneChannelGUI Package: maskBAD Version: 1.16.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe License: GPL version 2 or newer MD5sum: f1525e696d51fcffd11993ee7e0bf665 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 source.ver: src/contrib/maskBAD_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/maskBAD_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/maskBAD_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/maskBAD_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/maskBAD_1.16.0.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 Package: MassArray Version: 1.24.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, methods, stats, utils License: GPL (>=2) MD5sum: 56ca0c6f0368c2c81cff3ff76d64606a 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: DNAMethylation, SNP, MassSpectrometry, Genetics, DataImport, Visualization Author: Reid F. Thompson , John M. Greally Maintainer: Reid F. Thompson source.ver: src/contrib/MassArray_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MassArray_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MassArray_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MassArray_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MassArray_1.24.0.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 Package: massiR Version: 1.8.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: dd78101a9a098850d4113a2f157e84e1 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 source.ver: src/contrib/massiR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/massiR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/massiR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/massiR_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/massiR_1.8.0.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 Package: MassSpecWavelet Version: 1.38.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) Archs: i386, x64 MD5sum: 0ebefb7e8abba382e03a161bec62f311 NeedsCompilation: yes Title: Mass spectrum processing by wavelet-based algorithms Description: Processing Mass Spectrometry spectrum by using wavelet based algorithm biocViews: MassSpectrometry, Proteomics Author: Pan Du, Warren Kibbe, Simon Lin Maintainer: Pan Du source.ver: src/contrib/MassSpecWavelet_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MassSpecWavelet_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MassSpecWavelet_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MassSpecWavelet_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MassSpecWavelet_1.38.0.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 suggestsMe: xcms Package: matchBox Version: 1.14.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 00640537cc626ec45a81d491c400437a 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 source.ver: src/contrib/matchBox_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/matchBox_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/matchBox_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/matchBox_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/matchBox_1.14.0.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 Package: MatrixRider Version: 1.4.0 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: 0071903e5ca83eaf65c8c70ee87ce791 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 source.ver: src/contrib/MatrixRider_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MatrixRider_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MatrixRider_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MatrixRider_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MatrixRider_1.4.0.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 Package: MBAmethyl Version: 1.6.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: 044db38e426457c056dec7f3523f9dc1 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 source.ver: src/contrib/MBAmethyl_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBAmethyl_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBAmethyl_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MBAmethyl_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBAmethyl_1.6.0.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 Package: MBASED Version: 1.6.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 90ef4c810863dd83a298d4fab48fee19 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 source.ver: src/contrib/MBASED_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBASED_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBASED_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MBASED_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBASED_1.6.0.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 Package: MBCB Version: 1.26.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 9bd657f699c461642687aa1c8a9b37e9 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 source.ver: src/contrib/MBCB_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBCB_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBCB_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MBCB_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBCB_1.26.0.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 Package: mBPCR Version: 1.26.0 Depends: oligoClasses, SNPchip Imports: Biobase Suggests: xtable License: GPL (>= 2) MD5sum: 4c7e5184ac5ef578a2e25ff14f56cbc7 NeedsCompilation: no Title: Bayesian Piecewise Constant Regression for DNA copy number estimation Description: Estimates the DNA copy number profile using mBPCR to detect 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 source.ver: src/contrib/mBPCR_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mBPCR_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mBPCR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mBPCR_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mBPCR_1.26.0.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 Package: MBttest Version: 1.0.0 Depends: R (>= 3.3.0), stats, gplots, gtools,graphics,base, utils,grDevices Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: 955da042171970242280d28ce9b12226 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 source.ver: src/contrib/MBttest_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MBttest_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MBttest_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MBttest_1.0.0.tgz vignettes: vignettes/MBttest/inst/doc/MBttest.pdf vignetteTitles: Analysing RNA-Seq count data with the "MBttest" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MBttest/inst/doc/MBttest.R Package: mcaGUI Version: 1.20.0 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: f9b19ca55ab118bf0b66cdbb75a39c28 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 source.ver: src/contrib/mcaGUI_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mcaGUI_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mcaGUI_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mcaGUI_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mcaGUI_1.20.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MCRestimate Version: 2.28.0 Depends: R (>= 2.7.2), golubEsets (>= 1.4.6) Imports: e1071 (>= 1.5-12), pamr (>= 1.22), randomForest (>= 3.9-6), RColorBrewer (>= 0.1-3), Biobase (>= 2.5.5), graphics, grDevices, stats, utils Suggests: xtable (>= 1.2-1), ROC (>= 1.8.0), genefilter (>= 1.12.0), gpls (>= 1.6.0) License: GPL (>= 2) MD5sum: c1f142ed47463f4069e1fb89416eaa20 NeedsCompilation: no Title: Misclassification error estimation with cross-validation Description: This package includes a function for combining preprocessing and classification methods to calculate misclassification errors biocViews: Classification Author: Marc Johannes, Markus Ruschhaupt, Holger Froehlich, Ulrich Mansmann, Andreas Buness, Patrick Warnat, Wolfgang Huber, Axel Benner, Tim Beissbarth Maintainer: Marc Johannes source.ver: src/contrib/MCRestimate_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MCRestimate_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MCRestimate_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MCRestimate_2.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MCRestimate_2.28.0.tgz vignettes: vignettes/MCRestimate/inst/doc/UsingMCRestimate.pdf vignetteTitles: HOW TO use MCRestimate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MCRestimate/inst/doc/UsingMCRestimate.R Package: mdgsa Version: 1.4.2 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: 7eab84a9bb2b70fe59e732c53056b0dd 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 source.ver: src/contrib/mdgsa_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/mdgsa_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/mdgsa_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/mdgsa_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mdgsa_1.4.2.tgz vignettes: vignettes/mdgsa/inst/doc/mdgsa_vignette.pdf vignetteTitles: mdgsa_vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mdgsa/inst/doc/mdgsa_vignette.R Package: mdqc Version: 1.34.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 793f4c846c18cd7ac2e6d40c9ca5bc38 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 source.ver: src/contrib/mdqc_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mdqc_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mdqc_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mdqc_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mdqc_1.34.0.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 Package: MEAL Version: 1.2.3 Depends: R (>= 3.2.0), Biobase, MultiDataSet Imports: GenomicRanges, SNPassoc, limma, DMRcate, snpStats, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, doParallel, parallel, ggplot2 (>= 2.0.0), sva, permute Suggests: testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, MEALData, BiocStyle License: Artistic-2.0 MD5sum: 8aa0fd4a5e2f09d4b58e8cca0cd0a7b9 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 [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzlez [aut] Maintainer: Carlos Ruiz VignetteBuilder: knitr source.ver: src/contrib/MEAL_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEAL_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/MEAL_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEAL_1.2.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MEAL/inst/doc/caseExample.R, vignettes/MEAL/inst/doc/MEAL.R htmlDocs: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html htmlTitles: MEAL case example, Introduction to MEAL Package: MeasurementError.cor Version: 1.44.0 License: LGPL MD5sum: b982d90bd42e92dc06aca1b46938f2d7 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 source.ver: src/contrib/MeasurementError.cor_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MeasurementError.cor_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MeasurementError.cor_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MeasurementError.cor_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MeasurementError.cor_1.44.0.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 Package: MEDIPS Version: 1.22.0 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: 409d68aea0fbab4b45005e206ce5b55e 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 source.ver: src/contrib/MEDIPS_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEDIPS_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MEDIPS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MEDIPS_1.19.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEDIPS_1.22.0.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 Package: MEDME Version: 1.32.0 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: 4a0bb302bd483ba5bbd54805627ea840 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 source.ver: src/contrib/MEDME_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEDME_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MEDME_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MEDME_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEDME_1.32.0.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 Package: MEIGOR Version: 1.6.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR License: GPL-3 MD5sum: f4f99c5ee03f75279a8e516f52437a7a NeedsCompilation: no Title: MEIGO - MEtaheuristics for bIoinformatics Global Optimization Description: Global Optimization biocViews: SystemsBiology Author: Jose Egea, David Henriques, Alexandre Fdez. Villaverde, Thomas Cokelaer Maintainer: Jose Egea source.ver: src/contrib/MEIGOR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MEIGOR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MEIGOR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MEIGOR_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MEIGOR_1.6.0.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: FALSE Rfiles: vignettes/MEIGOR/inst/doc/MEIGOR-vignette.R Package: MergeMaid Version: 2.44.0 Depends: R (>= 2.10.0), survival, Biobase, MASS, methods License: GPL (>= 2) MD5sum: 69b53deb735a134d755b4916c11da0ad NeedsCompilation: no Title: Merge Maid Description: The functions in this R extension are intended for cross-study comparison of gene expression array data. Required from the user is gene expression matrices, their corresponding gene-id vectors and other useful information, and they could be 'list','matrix', or 'ExpressionSet'. The main function is 'mergeExprs' which transforms the input objects into data in the merged format, such that common genes in different datasets can be easily found. And the function 'intcor' calculate the correlation coefficients. Other functions use the output from 'modelOutcome' to graphically display the results and cross-validate associations of gene expression data with survival. biocViews: Microarray, DifferentialExpression, Visualization Author: Xiaogang Zhong Leslie Cope Elizabeth Garrett Giovanni Parmigiani Maintainer: Xiaogang Zhong URL: http://astor.som.jhmi.edu/MergeMaid source.ver: src/contrib/MergeMaid_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MergeMaid_2.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MergeMaid_2.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MergeMaid_2.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MergeMaid_2.44.0.tgz vignettes: vignettes/MergeMaid/inst/doc/MergeMaid.pdf vignetteTitles: MergeMaid primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: metaArray, XDE suggestsMe: oneChannelGUI Package: Mergeomics Version: 1.0.0 Depends: R (>= 3.0.1) Suggests: RUnit, BiocGenerics License: GPL (>= 2) MD5sum: 7a88da8a0f47a657e2c47aad23b25ae4 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 source.ver: src/contrib/Mergeomics_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mergeomics_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Mergeomics_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mergeomics_1.0.0.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 Package: MeSHDbi Version: 1.8.0 Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: RUnit License: Artistic-2.0 MD5sum: 2ae7c8afb9394c22bedf818aa37568bd 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 source.ver: src/contrib/MeSHDbi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MeSHDbi_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MeSHDbi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MeSHDbi_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MeSHDbi_1.8.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: meshr Package: meshr Version: 1.8.0 Depends: R (>= 3.0.1), fdrtool, Category, BiocGenerics, methods, cummeRbund, org.Hs.eg.db, 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, S4Vectors License: Artistic-2.0 MD5sum: 1242276a4836a0c4e42a7ae72ad0c54e 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: Itoshi Nikaido, Koki Tsuyuzaki, Gota Morota Maintainer: Koki Tsuyuzaki source.ver: src/contrib/meshr_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/meshr_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/meshr_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/meshr_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/meshr_1.8.0.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 Package: MeSHSim Version: 1.4.0 Depends: R(>= 3.0.0) Imports: XML, RCurl License: GPL-2 MD5sum: a0a920a18f5afc175d8d72ea71511a83 NeedsCompilation: no Title: MeSH(Medical Subject Headings) Semantic Similarity Measures Description: Provide for measuring semantic similarity over MeSH headings and MEDLINE documents biocViews: Clustering, Software Author: Jing Zhou, Yuxuan Shui Maintainer: Jing ZHou <12210240050@fudan.edu.cn> source.ver: src/contrib/MeSHSim_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MeSHSim_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MeSHSim_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MeSHSim_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MeSHSim_1.4.0.tgz vignettes: vignettes/MeSHSim/inst/doc/MeSHSim.pdf vignetteTitles: MeSHSim Quick Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MeSHSim/inst/doc/MeSHSim.R Package: messina Version: 1.8.2 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: 5fa94400c29ef59fad065f81ad732cfe 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 source.ver: src/contrib/messina_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/messina_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/messina_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/messina_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/messina_1.8.2.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 Package: metaArray Version: 1.50.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 Archs: i386, x64 MD5sum: b49e5e340e180808987e4ffe3505e2d2 NeedsCompilation: yes 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 source.ver: src/contrib/metaArray_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaArray_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaArray_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/metaArray_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaArray_1.50.0.tgz vignettes: vignettes/metaArray/inst/doc/metaArray.pdf vignetteTitles: metaArray Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaArray/inst/doc/metaArray.R suggestsMe: oneChannelGUI Package: Metab Version: 1.6.0 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: daec540421b9a05af22c82a68188a4ec 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: Metabolomics, MassSpectrometry, AMDIS, GCMS Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/Metab_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Metab_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Metab_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Metab_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Metab_1.6.0.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 Package: metabomxtr Version: 1.6.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest Suggests: xtable, ggplot2, reshape2 License: GPL-2 MD5sum: 711629e46d326fe77db298d9a243a9ef 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: Metabolomics, MassSpectrometry Author: Michael Nodzenski, Anna Reisetter, Denise Scholtens Maintainer: Michael Nodzenski source.ver: src/contrib/metabomxtr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metabomxtr_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metabomxtr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/metabomxtr_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metabomxtr_1.6.0.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 Package: metaCCA Version: 1.0.2 Suggests: knitr License: MIT + file LICENSE MD5sum: 23e697893c5e6fdbe620a1ffc2b1347e 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: http://biorxiv.org/content/early/2015/07/16/022665 VignetteBuilder: knitr source.ver: src/contrib/metaCCA_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaCCA_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/metaCCA_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaCCA_1.0.2.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 Package: metagene Version: 2.4.4 Depends: R (>= 3.3.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, DBChIP, matrixStats Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown, similaRpeak License: Artistic-2.0 | file LICENSE MD5sum: e14667b7e81c3230c126cff6dab04988 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 , Astrid Louise Deschenes and Arnaud Droit . Maintainer: Charles Joly Beauparlant VignetteBuilder: knitr BugReports: https://github.com/CharlesJB/metagene/issues source.ver: src/contrib/metagene_2.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/metagene_2.4.4.zip win64.binary.ver: bin/windows64/contrib/3.3/metagene_2.4.4.zip mac.binary.ver: bin/macosx/contrib/3.3/metagene_2.1.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metagene_2.4.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/metagene/inst/doc/metagene.R htmlDocs: vignettes/metagene/inst/doc/metagene.html htmlTitles: Introduction to metagene dependsOnMe: Imetagene Package: metagenomeFeatures Version: 1.1.0 Depends: R (>= 3.2), Biobase (>= 2.17.8) Imports: Biostrings (>= 2.36.4), ShortRead (>= 1.26.0), dplyr (>= 0.4.3), stringr (>= 1.0.0), lazyeval (>= 0.1.10), RSQLite (>= 1.0.0), magrittr (>= 1.5), methods, lattice Suggests: knitr (>= 1.11), msd16s (>= 0.102.0), testthat (>= 0.10.0) License: Artistic-2.0 MD5sum: 55049a03430dea1ba1bfcfb69b3140e1 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: 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 source.ver: src/contrib/metagenomeFeatures_1.1.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metagenomeFeatures_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/metagenomeFeatures_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metagenomeFeatures_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/metagenomeFeatures/inst/doc/Example_16S_Annotation_Workflow.html, vignettes/metagenomeFeatures/inst/doc/Exploring_a_MgDb.html htmlTitles: Vignette Title, Vignette Title Package: metagenomeSeq Version: 1.14.2 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots Suggests: annotate, BiocGenerics, biomformat, knitr, gss, RUnit, vegan, interactiveDisplay License: Artistic-2.0 MD5sum: 10153082ca3d204e530f8bddcc94b78b 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: Classification, Clustering, GeneticVariability, DifferentialExpression, Microbiome, Metagenomics, Normalization, Visualization, MultipleComparison, Sequencing, Software Author: Joseph Nathaniel Paulson, 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 source.ver: src/contrib/metagenomeSeq_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/metagenomeSeq_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.3/metagenomeSeq_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.3/metagenomeSeq_1.11.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metagenomeSeq_1.14.2.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 suggestsMe: interactiveDisplay, phyloseq Package: metahdep Version: 1.30.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: i386, x64 MD5sum: 0a9d8b6ce9bb5c20c6f61276b8200c71 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 source.ver: src/contrib/metahdep_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metahdep_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metahdep_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/metahdep_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metahdep_1.30.0.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 Package: metaMS Version: 1.8.0 Depends: R (>= 2.10), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: 7d5b340efa9590da9fa59350e3b451a1 NeedsCompilation: no Title: MS-based metabolomics annotation pipeline Description: MS-based metabolomics data processing and compound annotation pipeline. biocViews: MassSpectrometry, Metabolomics Author: Ron Wehrens [aut, cre] (author of GC-MS part), 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) Maintainer: Ron Wehrens source.ver: src/contrib/metaMS_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaMS_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaMS_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/metaMS_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaMS_1.8.0.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 Package: metaSeq Version: 1.12.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: c7eddf80ad0b2b202fa1e543d4c2e0e1 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 Author: Koki Tsuyuzaki, Itoshi Nikaido Maintainer: Koki Tsuyuzaki source.ver: src/contrib/metaSeq_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaSeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/metaSeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/metaSeq_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaSeq_1.12.0.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 Package: metaseqR Version: 1.12.2 Depends: R (>= 2.13.0), 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, BiocInstaller, BSgenome, RSQLite Enhances: parallel, TCC, RMySQL License: GPL (>= 3) MD5sum: c210d4e0634d65a5079c1dbaad483b01 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: Software, GeneExpression, DifferentialExpression, WorkflowStep, Preprocessing, QualityControl, Normalization, ReportWriting, RNASeq Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: http://www.fleming.gr VignetteBuilder: knitr source.ver: src/contrib/metaseqR_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaseqR_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/metaseqR_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.3/metaseqR_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaseqR_1.12.2.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 Package: metaX Version: 1.4.2 Depends: R (>= 3.2.0),VennDiagram,pROC,SSPA,methods Imports: Nozzle.R1, ggplot2, parallel, pcaMethods, reshape2, plyr, BBmisc, mixOmics, preprocessCore, vsn, pls, impute, missForest, doParallel, DiscriMiner, xcms, ape, scatterplot3d, pheatmap, bootstrap, boot, caret, dplyr, stringr, RColorBrewer, DiffCorr, RCurl, lattice, faahKO, data.table, CAMERA, igraph, tidyr, scales Suggests: knitr, BiocStyle, R.utils, RUnit,BiocGenerics License: LGPL-2 MD5sum: 9c975539f895a2d008757fa0ecada2ed NeedsCompilation: no Title: An R package for metabolomic data analysis Description: The package provides a integrated pipeline for mass spectrometry- based metabolomic data analysis. It includes the stages peak detection, data preprocessing, normalization, missing value imputation, univariate statistical analysis, multivariate statistical analysis such as PCA and PLS-DA, metabolite identification, pathway analysis, power analysis, feature selection and modeling, data quality assessment. biocViews: Metabolomics, MassSpectrometry, QualityControl Author: Bo Wen Maintainer: Bo Wen URL: http://wenbostar.github.io/metaX/ VignetteBuilder: knitr BugReports: https://github.com/wenbostar/metaX/issues PackageStatus: Deprecated source.ver: src/contrib/metaX_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/metaX_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/metaX_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/metaX_0.99.16.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/metaX_1.4.2.tgz vignettes: vignettes/metaX/inst/doc/metaX.pdf vignetteTitles: metaX tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/metaX/inst/doc/metaX.R Package: MethPed Version: 1.0.2 Depends: R (>= 3.0.0), Biobase Imports: randomForest, grDevices, graphics, stats Suggests: BiocStyle, knitr, markdown, impute License: GPL-2 MD5sum: 1df4495ed3db93d43aa47273e45d7e45 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: 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 source.ver: src/contrib/MethPed_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethPed_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MethPed_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethPed_1.0.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethPed/inst/doc/MethPed-vignette.R htmlDocs: vignettes/MethPed/inst/doc/MethPed-vignette.html htmlTitles: MethPed User Guide Package: MethTargetedNGS Version: 1.4.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: c9d1393f1c529c519deb69aff7c9dda6 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 source.ver: src/contrib/MethTargetedNGS_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethTargetedNGS_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethTargetedNGS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MethTargetedNGS_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethTargetedNGS_1.4.0.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 Package: methVisual Version: 1.24.0 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) MD5sum: e6de7a897729a21d6343d0c5bd64ab25 NeedsCompilation: no 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 source.ver: src/contrib/methVisual_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methVisual_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methVisual_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/methVisual_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methVisual_1.24.0.tgz vignettes: vignettes/methVisual/inst/doc/methVisual.pdf vignetteTitles: Introduction to methVisual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/methVisual/inst/doc/methVisual.R Package: methyAnalysis Version: 1.14.0 Depends: R (>= 2.10), grid, BiocGenerics, IRanges, GenomeInfoDb, GenomicRanges, Biobase (>= 2.5.5), org.Hs.eg.db Imports: lumi, methylumi, Gviz, genoset, SummarizedExperiment, GenomicRanges, VariantAnnotation, IRanges, rtracklayer, 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: 049927fa3eff20bd235c65b1c99b5474 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: Pan Du source.ver: src/contrib/methyAnalysis_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methyAnalysis_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methyAnalysis_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/methyAnalysis_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methyAnalysis_1.14.0.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 Package: MethylAid Version: 1.6.2 Depends: R (>= 3.0) Imports: Biobase, BiocParallel, BiocGenerics, ggplot2, grid, gridBase, grDevices, graphics, hexbin, matrixStats, minfi (>= 1.17.9), methods, RColorBrewer, shiny, stats, utils Suggests: BiocStyle, knitr, MethylAidData, minfiData, RUnit License: GPL (>= 2) MD5sum: a4750662d4fd7e58317d349d89fc5d34 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: M. van Iterson URL: https://github.com/mvaniterson/methylaid VignetteBuilder: knitr BugReports: https://github.com/mvaniterson/methylaid/issues source.ver: src/contrib/MethylAid_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethylAid_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MethylAid_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/MethylAid_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethylAid_1.6.2.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 Package: MethylMix Version: 1.6.0 Depends: R (>= 3.1.1) Imports: foreach,parallel,doParallel,RColorBrewer,optimx,RPMM Suggests: BiocStyle License: GPL-2 MD5sum: 9f7f7b6fb4a8fc9fe86c9c820469bad8 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. biocViews: DNAMethylation,StatisticalMethod,DifferentialMethylation,GeneRegulation,GeneExpression,MethylationArray, DifferentialExpression, Pathways, Network Author: Olivier Gevaert Maintainer: Olivier Gevaert source.ver: src/contrib/MethylMix_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethylMix_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethylMix_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MethylMix_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethylMix_1.6.0.tgz vignettes: vignettes/MethylMix/inst/doc/MethylMix.pdf vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MethylMix/inst/doc/MethylMix.R Package: methylMnM Version: 1.10.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: i386, x64 MD5sum: 5e966bcee05e635d18c75192213e2fb8 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 source.ver: src/contrib/methylMnM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/methylMnM_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/methylMnM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/methylMnM_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methylMnM_1.10.0.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 Package: methylPipe Version: 1.6.2 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: 4aa5bcbaa4ecd949276587eccc97c893 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 source.ver: src/contrib/methylPipe_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/methylPipe_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/methylPipe_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/methylPipe_1.3.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methylPipe_1.6.2.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 importsMe: compEpiTools Package: MethylSeekR Version: 1.12.0 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: 88d6cc103b6d4f7592846b13198ff025 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 source.ver: src/contrib/MethylSeekR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MethylSeekR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MethylSeekR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MethylSeekR_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MethylSeekR_1.12.0.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 Package: methylumi Version: 2.18.2 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: 793d35720d313b23edd0b373cc81e03b 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 source.ver: src/contrib/methylumi_2.18.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/methylumi_2.18.2.zip win64.binary.ver: bin/windows64/contrib/3.3/methylumi_2.18.2.zip mac.binary.ver: bin/macosx/contrib/3.3/methylumi_2.15.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/methylumi_2.18.2.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: RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, methyAnalysis, missMethyl suggestsMe: MultiDataSet Package: Mfuzz Version: 2.32.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: b56982d33af6355f8fab8e63bdc213a6 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/ source.ver: src/contrib/Mfuzz_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mfuzz_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Mfuzz_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Mfuzz_2.29.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mfuzz_2.32.0.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 importsMe: maSigPro suggestsMe: pwOmics Package: MGFM Version: 1.6.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 57ed95add114d36aa4909c963f1e5509 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 source.ver: src/contrib/MGFM_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MGFM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MGFM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MGFM_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MGFM_1.6.0.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 Package: mgsa Version: 1.20.0 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: 9d158d22048bd088508a41e140b79b5f 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 source.ver: src/contrib/mgsa_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mgsa_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mgsa_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mgsa_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mgsa_1.20.0.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 suggestsMe: gCMAP Package: MiChip Version: 1.26.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: e9a76feb0116af3a9b804bd76058a843 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 source.ver: src/contrib/MiChip_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MiChip_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MiChip_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MiChip_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MiChip_1.26.0.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 Package: microRNA Version: 1.30.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) Suggests: Biostrings (>= 2.11.32) Enhances: Rlibstree License: Artistic-2.0 MD5sum: cd81565db012f16ef492fe7fdea1b306 NeedsCompilation: no 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" source.ver: src/contrib/microRNA_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/microRNA_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/microRNA_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/microRNA_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/microRNA_1.30.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Roleswitch suggestsMe: MmPalateMiRNA, rtracklayer Package: MIMOSA Version: 1.10.2 Depends: R (>= 3.0.2), MASS, plyr, reshape, Biobase, ggplot2 Imports: methods, Formula, data.table, pracma, MCMCpack, coda, modeest, testthat, Rcpp, scales, Kmisc LinkingTo: Rcpp, RcppArmadillo Suggests: parallel, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: e167d44fa1367e8b1127587e5f6d59c8 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: FlowCytometry, CellBasedAssays Author: Greg Finak Maintainer: Greg Finak VignetteBuilder: knitr source.ver: src/contrib/MIMOSA_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MIMOSA_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MIMOSA_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/MIMOSA_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MIMOSA_1.10.2.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MIMOSA/inst/doc/MIMOSA.R Package: MineICA Version: 1.12.0 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 Enhances: doMC License: GPL-2 MD5sum: 1dac6e210fd849f44f1b271dbf0fc77e 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 source.ver: src/contrib/MineICA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MineICA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MineICA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MineICA_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MineICA_1.12.0.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 Package: minet Version: 3.30.0 Imports: infotheo License: file LICENSE Archs: i386, x64 MD5sum: 252fc2743a84c52bdc4d183b963aee28 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 source.ver: src/contrib/minet_3.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/minet_3.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/minet_3.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/minet_3.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/minet_3.30.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: netbenchmark, RTN suggestsMe: CNORfeeder, predictionet Package: minfi Version: 1.18.6 Depends: methods, BiocGenerics (>= 0.15.3), Biobase (>= 2.17.8), lattice, GenomicRanges, SummarizedExperiment (>= 1.1.6), Biostrings, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, IRanges, beanplot, RColorBrewer, nor1mix, siggenes, limma, preprocessCore, illuminaio, matrixStats (>= 0.50.0), mclust, genefilter, nlme, reshape, MASS, quadprog, data.table, GEOquery, stats, grDevices, graphics, utils Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.4.1), FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, BiocStyle, knitr, rmarkdown License: Artistic-2.0 MD5sum: 2be23b7b574cf2792f752595063fbb70 NeedsCompilation: no Title: Analyze Illumina's methylation arrays Description: Tools for analyzing and visualizing Illumina's methylation array data. biocViews: DNAMethylation, Microarray, TwoChannel, DataImport, 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] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/minfi VignetteBuilder: knitr BugReports: https://github.com/kasperdanielhansen/minfi/issues source.ver: src/contrib/minfi_1.18.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/minfi_1.18.6.zip win64.binary.ver: bin/windows64/contrib/3.3/minfi_1.18.6.zip mac.binary.ver: bin/macosx/contrib/3.3/minfi_1.15.11.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/minfi_1.18.6.tgz vignettes: vignettes/minfi/inst/doc/minfi.pdf vignetteTitles: minfi User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/minfi/inst/doc/minfi.R dependsOnMe: ChAMP, conumee, CopyNumber450k, DMRcate, methylumi, shinyMethyl importsMe: MEAL, MethylAid, methylumi, missMethyl, MultiDataSet, quantro, skewr suggestsMe: Harman, RnBeads Package: MinimumDistance Version: 1.16.0 Depends: R (>= 3.0.1), VanillaICE (>= 1.31.3) Imports: methods, BiocGenerics, Biobase, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 0.2.0), oligoClasses, DNAcopy, ff, foreach, matrixStats, lattice, data.table, grid, stats, utils Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, SNPchip, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: c452728d044a3a0a46e56100ca0b7c8e 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 B Scharpf source.ver: src/contrib/MinimumDistance_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MinimumDistance_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MinimumDistance_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MinimumDistance_1.13.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MinimumDistance_1.16.0.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 Package: MiPP Version: 1.44.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: b9813c8af855250bcd6c3a02ba80d9c8 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/ source.ver: src/contrib/MiPP_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MiPP_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MiPP_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MiPP_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MiPP_1.44.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MiRaGE Version: 1.14.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: BiocGenerics, S4Vectors, AnnotationDbi 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: 5e4390e5320872c7b4ad8406f2b9f290 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: Microarray, GeneExpression, RNASeq, Sequencing, SAGE Author: Y-h. Taguchi Maintainer: Y-h. Taguchi source.ver: src/contrib/MiRaGE_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MiRaGE_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MiRaGE_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MiRaGE_1.11.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MiRaGE_1.14.0.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 Package: miRcomp Version: 1.2.2 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods, graphics, KernSmooth, stats Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL-3 | file LICENSE MD5sum: 9f663172fadce4bd940fc2fb89a3a6c5 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 Maintainer: Matthew N. McCall VignetteBuilder: knitr source.ver: src/contrib/miRcomp_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRcomp_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/miRcomp_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRcomp_1.2.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/miRcomp/inst/doc/miRcomp.R htmlDocs: vignettes/miRcomp/inst/doc/miRcomp.html htmlTitles: Assessment and comparison of miRNA expression estimation methods (miRcomp) Package: mirIntegrator Version: 1.2.0 Depends: R (>= 3.2) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: eedc3f64ec8b625fb296b084f290ea5c 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 and Sorin Draghici Maintainer: Diana Diaz URL: http://vortex.cs.wayne.edu/ source.ver: src/contrib/mirIntegrator_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mirIntegrator_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mirIntegrator_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mirIntegrator_0.99.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mirIntegrator_1.2.0.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 Package: miRLAB Version: 1.2.2 Imports: RCurl, httr, stringr, Hmisc, energy, entropy, Roleswitch, gplots, glmnet, impute, limma, pcalg Suggests: knitr, RUnit, BiocGenerics, AnnotationDbi, org.Hs.eg.db, GOstats, Category License: GPL (>=2) MD5sum: d9ed75c76d6daa77d997a52fa0d14832 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 Maintainer: Thuc Duy Le VignetteBuilder: knitr source.ver: src/contrib/miRLAB_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRLAB_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/miRLAB_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/miRLAB_0.99.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRLAB_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRLAB/inst/doc/miRLAB-vignette.R htmlDocs: vignettes/miRLAB/inst/doc/miRLAB-vignette.html htmlTitles: miRLAB Package: miRNAmeConverter Version: 1.0.2 Depends: miRBaseVersions.db Imports: DBI, AnnotationDbi Suggests: methods, testthat, knitr, rmarkdown License: Artistic-2.0 MD5sum: 78964643dd568e02d546f65c28761063 NeedsCompilation: no Title: Convert miRNA Names to Different miRBase Versions Description: Package containing an S4 class for translating mature miRNA names to different miRBase versions, 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 Haunsberger VignetteBuilder: knitr source.ver: src/contrib/miRNAmeConverter_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRNAmeConverter_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/miRNAmeConverter_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRNAmeConverter_1.0.2.tgz vignettes: vignettes/miRNAmeConverter/inst/doc/miRNAmeConverter-vignette.pdf vignetteTitles: miRNAmeConverter-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAmeConverter/inst/doc/miRNAmeConverter-vignette.R Package: miRNApath Version: 1.32.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: b11dba6c52029a1ef1c73ec4817993bf 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 source.ver: src/contrib/miRNApath_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRNApath_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/miRNApath_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/miRNApath_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRNApath_1.32.0.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 suggestsMe: oneChannelGUI Package: miRNAtap Version: 1.6.0 Depends: R (>= 3.2.0), AnnotationDbi Imports: DBI, RSQLite, stringr, sqldf, plyr, methods Suggests: topGO, org.Hs.eg.db, miRNAtap.db, testthat License: GPL-2 MD5sum: 298ffd2343c2c5865e17d0bff8d28269 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: Maciej Pajak source.ver: src/contrib/miRNAtap_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/miRNAtap_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/miRNAtap_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/miRNAtap_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/miRNAtap_1.6.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/miRNAtap/inst/doc/miRNAtap.R importsMe: SpidermiR suggestsMe: oneChannelGUI Package: Mirsynergy Version: 1.8.1 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 MD5sum: aac7307a261d194a039363709038ac86 NeedsCompilation: no 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 source.ver: src/contrib/Mirsynergy_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mirsynergy_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/Mirsynergy_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.3/Mirsynergy_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mirsynergy_1.8.1.tgz vignettes: vignettes/Mirsynergy/inst/doc/Mirsynergy.pdf vignetteTitles: Mirsynergy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mirsynergy/inst/doc/Mirsynergy.R Package: missMethyl Version: 1.6.2 Depends: R (>= 2.3.0) Imports: limma, minfi, methylumi, IlluminaHumanMethylation450kmanifest, statmod, ruv, stringr, IlluminaHumanMethylation450kanno.ilmn12.hg19, org.Hs.eg.db, AnnotationDbi, BiasedUrn Suggests: minfiData, BiocStyle, knitr, edgeR, tweeDEseqCountData License: GPL-2 MD5sum: ed9337f449991e3378a52b6d5e82abda NeedsCompilation: no Title: Analysis of methylation array data Description: Normalisation and testing for differential variability and differential methylation for data from Illumina's Infinium HumanMethylation450 array. 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. biocViews: Normalization, DNAMethylation, MethylationArray, GenomicVariation, GeneticVariability, DifferentialMethylation, GeneSetEnrichment Author: Belinda Phipson and Jovana Maksimovic Maintainer: Belinda Phipson , Jovana Maksimovic VignetteBuilder: knitr source.ver: src/contrib/missMethyl_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/missMethyl_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/missMethyl_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/missMethyl_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/missMethyl_1.6.2.tgz vignettes: vignettes/missMethyl/inst/doc/missMethyl.pdf vignetteTitles: missMethyl: analysing data from Illumina's HumanMethylation450 array hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/missMethyl/inst/doc/missMethyl.R suggestsMe: Harman Package: mitoODE Version: 1.10.0 Depends: R (>= 2.14.0), minpack.lm, MASS, parallel, mitoODEdata, KernSmooth License: LGPL Archs: i386, x64 MD5sum: 55d12ddcf3b9df90afb504c340f18ada NeedsCompilation: yes Title: Implementation of the differential equation model described in "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" Description: The package contains the methods to fit a cell-cycle model on cell count data and the code to reproduce the results shown in our paper "Dynamical modelling of phenotypes in a genome-wide RNAi live-cell imaging assay" by Pau, G., Walter, T., Neumann, B., Heriche, J.-K., Ellenberg, J., & Huber, W., BMC Bioinformatics (2013), 14(1), 308. doi:10.1186/1471-2105-14-308 biocViews: ExperimentData, TimeCourse, CellBasedAssays, Preprocessing Author: Gregoire Pau Maintainer: Gregoire Pau SystemRequirements: source.ver: src/contrib/mitoODE_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mitoODE_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mitoODE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mitoODE_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mitoODE_1.10.0.tgz vignettes: vignettes/mitoODE/inst/doc/mitoODE-introduction.pdf vignetteTitles: mitoODE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mitoODE/inst/doc/mitoODE-introduction.R Package: MLInterfaces Version: 1.52.0 Depends: R (>= 2.9), methods, BiocGenerics (>= 0.13.11), Biobase, annotate, cluster Imports: gdata, pls, sfsmisc, MASS, rpart, rda, genefilter, fpc, ggvis, shiny, rgl, gbm, RColorBrewer, hwriter, threejs (>= 0.2.2), mlbench, stats4 Suggests: class, e1071, ipred, randomForest, gpls, pamr, nnet, ALL, hgu95av2.db, som, hu6800.db, lattice, caret (>= 5.07), golubEsets, ada, keggorthology, kernlab, mboost, party Enhances: parallel License: LGPL MD5sum: 42895f81fc08d053c6d980fb1e406d97 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 source.ver: src/contrib/MLInterfaces_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MLInterfaces_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MLInterfaces_1.52.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MLInterfaces_1.49.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MLInterfaces_1.52.0.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: a4Classif, pRoloc, SigCheck suggestsMe: BiocCaseStudies Package: MLP Version: 1.20.0 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: 549b2ce1bcb314f2ab8f8522f7f4dc3a 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 source.ver: src/contrib/MLP_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MLP_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MLP_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MLP_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MLP_1.20.0.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 suggestsMe: a4 Package: MLSeq Version: 1.12.2 Depends: R (>= 3.0.0), caret, DESeq2, Biobase, limma, randomForest, edgeR Imports: methods Suggests: knitr, e1071, kernlab, earth, ellipse, fastICA, gam, ipred, klaR, MASS, mda, mgcv, mlbench, nnet, party, pls, pROC, proxy, RANN, spls, affy License: GPL(>=2) MD5sum: a5c09a3d650778949e463956752b10d7 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: Sequencing, RNASeq, Classification, Clustering Author: Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver, Ahmet Ozturk Maintainer: Gokmen Zararsiz VignetteBuilder: knitr source.ver: src/contrib/MLSeq_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MLSeq_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MLSeq_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.3/MLSeq_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MLSeq_1.12.2.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: MLSeq hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MLSeq/inst/doc/MLSeq.R Package: MMDiff Version: 1.12.0 Depends: R (>= 2.14.0),GenomicRanges,parallel,DiffBind,GMD,Rsamtools Imports: IRanges,Biobase Suggests: MMDiffBamSubset License: Artistic-2.0 MD5sum: 1e5ecc9ce9b2e7707714d489493c227e NeedsCompilation: no Title: Statistical Testing for ChIP-Seq data sets Description: This package detects statistically significant difference between read enrichment profiles in different ChIP-Seq samples. To take advantage of shape differences it uses Kernel methods (Maximum Mean Discrepancy, MMD). WARNING: This package is deprecated, please use MMDiff2 instead. biocViews: ChIPSeq, MultipleComparison Author: Gabriele Schweikert Maintainer: Gabriele Schweikert PackageStatus: Deprecated source.ver: src/contrib/MMDiff_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MMDiff_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MMDiff_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MMDiff_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MMDiff_1.12.0.tgz vignettes: vignettes/MMDiff/inst/doc/MMDiff.pdf vignetteTitles: Analysing ChIP-Seq data with the "MMDiff" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MMDiff/inst/doc/MMDiff.R Package: MMDiff2 Version: 1.0.2 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: dd497b2e461150552844d75cbc988374 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 source.ver: src/contrib/MMDiff2_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MMDiff2_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MMDiff2_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MMDiff2_1.0.2.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 Package: mmnet Version: 1.10.2 Depends: R (>= 2.14), igraph, biom Imports: Biobase, RJSONIO, stringr, reshape2, ggplot2, KEGGREST, plyr, XML, RCurl, flexmix, Matrix, methods, tools Suggests: RCytoscape, graph, knitr License: GPL (>= 2) MD5sum: 7d1c1a53252162b2b3a90944b10e1b87 NeedsCompilation: no Title: A metagenomic pipeline for systems biology Description: This package gives the implementations microbiome metabolic network constructing and analyzing. It introduces a unique metagenomic systems biology approach, mapping metagenomic data to the KEGG global metabolic pathway and constructing a systems-level network. The system-level network and the next topological analysis will be of great help to analysis the various functional properties, including regulation and metabolic functionality of the metagenome. biocViews: GraphsAndNetwork, Sequencing, Pathways, Microbiome, SystemsBiology Author: Yang Cao, Fei Li Maintainer: Yang Cao , Fei Li VignetteBuilder: knitr source.ver: src/contrib/mmnet_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/mmnet_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/mmnet_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/mmnet_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mmnet_1.10.2.tgz vignettes: vignettes/mmnet/inst/doc/mmnet.pdf vignetteTitles: mmnet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mmnet/inst/doc/mmnet.R Package: MmPalateMiRNA Version: 1.22.0 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 MD5sum: 944dce167bf715f91cab62ac5634616c NeedsCompilation: no 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 source.ver: src/contrib/MmPalateMiRNA_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MmPalateMiRNA_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MmPalateMiRNA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MmPalateMiRNA_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MmPalateMiRNA_1.22.0.tgz vignettes: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.pdf vignetteTitles: Palate miRNA Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.R Package: mogsa Version: 1.6.4 Depends: R (>= 3.2.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster Suggests: BiocStyle, knitr License: GPL-2 MD5sum: f2252c5b7a67268a02319299a2f2cbb4 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 source.ver: src/contrib/mogsa_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/mogsa_1.6.4.zip win64.binary.ver: bin/windows64/contrib/3.3/mogsa_1.6.4.zip mac.binary.ver: bin/macosx/contrib/3.3/mogsa_1.1.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mogsa_1.6.4.tgz vignettes: vignettes/mogsa/inst/doc/moCluster.pdf, vignettes/mogsa/inst/doc/mogsa.pdf vignetteTitles: mogsa: gene set analysis on 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.R, vignettes/mogsa/inst/doc/mogsa.R Package: monocle Version: 1.6.2 Depends: R (>= 2.7.0), HSMMSingleCell (>= 0.101.5), Biobase, ggplot2(>= 0.9.3.1), splines, VGAM (>= 0.9-5), igraph(>= 0.7.0), plyr Imports: BiocGenerics, cluster, combinat, fastICA, grid, irlba, matrixStats, methods, parallel, reshape2, stats, utils, limma Suggests: knitr, Hmisc License: Artistic-2.0 MD5sum: 537a5b98db2177ede0f26583c0c7d7f4 NeedsCompilation: no Title: Analysis tools for single-cell expression experiments. 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: Sequencing, RNASeq, GeneExpression, DifferentialExpression, Infrastructure, DataImport, DataRepresentation, Visualization, Clustering, MultipleComparison, QualityControl Author: Cole Trapnell Maintainer: Cole Trapnell VignetteBuilder: knitr source.ver: src/contrib/monocle_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/monocle_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/monocle_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/monocle_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/monocle_1.6.2.tgz vignettes: vignettes/monocle/inst/doc/monocle-vignette.pdf vignetteTitles: Monocle: Differential expression and time-series analysis for single-cell RNA-Seq and qPCR experiments. hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/monocle/inst/doc/monocle-vignette.R suggestsMe: scater, scran, sincell Package: MoPS Version: 1.6.0 Imports: Biobase License: GPL-3 MD5sum: b20042219f187945da21f0d4d8df5817 NeedsCompilation: no Title: MoPS - Model-based Periodicity Screening Description: Identification and characterization of periodic fluctuations in time-series data. biocViews: GeneRegulation,Classification,TimeCourse,Regression Author: Philipp Eser, Achim Tresch Maintainer: Philipp Eser source.ver: src/contrib/MoPS_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MoPS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MoPS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MoPS_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MoPS_1.6.0.tgz vignettes: vignettes/MoPS/inst/doc/MoPS.pdf vignetteTitles: Model-based Periodicity Screening hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MoPS/inst/doc/MoPS.R Package: mosaics Version: 2.10.0 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: 6b5d8f04573463fa39dda84bd0708e94 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 source.ver: src/contrib/mosaics_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mosaics_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mosaics_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mosaics_2.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mosaics_2.10.0.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 dependsOnMe: jmosaics Package: motifbreakR Version: 1.2.2 Depends: R (>= 3.2), 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 Suggests: BSgenome.Hsapiens.UCSC.hg19, SNPlocs.Hsapiens.dbSNP.20120608, SNPlocs.Hsapiens.dbSNP142.GRCh37, knitr, rmarkdown, BSgenome.Drerio.UCSC.danRer7, BiocStyle License: GPL-2 MD5sum: a11f4396fd736bafb91466f0bc80c3e7 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 source.ver: src/contrib/motifbreakR_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/motifbreakR_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/motifbreakR_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/motifbreakR_1.2.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.R htmlDocs: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html htmlTitles: motifbreakR: an Introduction Package: MotifDb Version: 1.14.0 Depends: R (>= 2.15.0), methods, BiocGenerics, S4Vectors, IRanges, Biostrings Imports: rtracklayer Suggests: RUnit, seqLogo, MotIV License: Artistic-2.0 | file LICENSE License_is_FOSS: no License_restricts_use: yes MD5sum: c6c0620d7f1d061b5c976bb7e9d2e72c NeedsCompilation: no Title: An Annotated Collection of Protein-DNA Binding Sequence Motifs Description: More than 2000 annotated position frequency matrices from nine public sources, for multiple organisms. biocViews: MotifAnnotation Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/MotifDb_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MotifDb_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MotifDb_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MotifDb_1.11.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MotifDb_1.14.0.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.pdf vignetteTitles: %%MotifDb Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MotifDb/inst/doc/MotifDb.R dependsOnMe: motifbreakR importsMe: rTRMui suggestsMe: DiffLogo, MMDiff2, motifStack, profileScoreDist, PWMEnrich, rTRM, vtpnet Package: motifRG Version: 1.16.0 Depends: R (>= 2.15), Biostrings (>= 2.26), IRanges, seqLogo, parallel, methods, grid, graphics, BSgenome, XVector, BSgenome.Hsapiens.UCSC.hg19 Imports: Biostrings,IRanges,seqLogo,parallel,methods,grid,graphics,XVector License: Artistic-2.0 MD5sum: cd246bccfa44fa9903c74e3882b193ab NeedsCompilation: no Title: A package for discriminative motif discovery, designed for high throughput sequencing dataset Description: Tools for discriminative motif discovery using regression methods biocViews: Transcription,MotifDiscovery Author: Zizhen Yao Maintainer: Zizhen Yao source.ver: src/contrib/motifRG_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/motifRG_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/motifRG_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/motifRG_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/motifRG_1.16.0.tgz vignettes: vignettes/motifRG/inst/doc/motifRG.pdf vignetteTitles: motifRG: regression-based discriminative motif discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifRG/inst/doc/motifRG.R Package: motifStack Version: 1.16.2 Depends: R (>= 2.15.1), methods, grImport, grid, MotIV, ade4, Biostrings Imports: XML, scales Suggests: RUnit, BiocGenerics, MotifDb, RColorBrewer, BiocStyle, knitr License: GPL (>= 2) MD5sum: 38b603d24e903b73e5b6a2f63d270393 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 source.ver: src/contrib/motifStack_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/motifStack_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.3/motifStack_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.3/motifStack_1.13.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/motifStack_1.16.2.tgz vignettes: vignettes/motifStack/inst/doc/motifStack.pdf vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/motifStack/inst/doc/motifStack_HTML.R, vignettes/motifStack/inst/doc/motifStack.R htmlDocs: vignettes/motifStack/inst/doc/motifStack_HTML.html htmlTitles: motifStack Vignette dependsOnMe: dagLogo importsMe: LowMACA, motifbreakR suggestsMe: ChIPpeakAnno Package: MotIV Version: 1.28.0 Depends: R (>= 2.10), BiocGenerics (>= 0.1.0) Imports: graphics, grid, methods, S4Vectors, IRanges (>= 1.13.5), Biostrings (>= 1.24.0), lattice, rGADEM, utils Suggests: rtracklayer License: GPL-2 Archs: i386, x64 MD5sum: a6067a6cb683f5129e570b96d1e65dff NeedsCompilation: yes 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/) source.ver: src/contrib/MotIV_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MotIV_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MotIV_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MotIV_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MotIV_1.28.0.tgz vignettes: vignettes/MotIV/inst/doc/MotIV.pdf vignetteTitles: The MotIV users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MotIV/inst/doc/MotIV.R dependsOnMe: motifStack suggestsMe: MotifDb Package: MPFE Version: 1.8.0 License: GPL (>= 3) MD5sum: d8cd01ee7555b2802dcde22cb16d2f8a 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 source.ver: src/contrib/MPFE_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MPFE_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MPFE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MPFE_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MPFE_1.8.0.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 Package: mQTL.NMR Version: 1.6.0 Depends: R (>= 2.15.0) Imports: qtl, GenABEL, MASS, outliers, graphics, stats, utils Suggests: BiocStyle License: Artistic-2.0 MD5sum: c8a6a7fbe5bb997ace1ed896d1361a6f NeedsCompilation: yes Title: Metabolomic Quantitative Trait Locus Mapping for 1H NMR data Description: mQTL.NMR provides a complete mQTL analysis pipeline for 1H NMR data. Distinctive features include normalisation using most-used approaches, peak alignment using RSPA approach, dimensionality reduction using SRV and binning approaches, and mQTL analysis for animal and human cohorts. biocViews: Cheminformatics, Metabolomics, Genetics, SNP Author: Lyamine Hedjazi and Jean-Baptiste Cazier Maintainer: Lyamine Hedjazi URL: http://www.ican-institute.org/tools/ source.ver: src/contrib/mQTL.NMR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mQTL.NMR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mQTL.NMR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mQTL.NMR_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mQTL.NMR_1.6.0.tgz vignettes: vignettes/mQTL.NMR/inst/doc/FAQ.pdf, vignettes/mQTL.NMR/inst/doc/mQTLUse.pdf vignetteTitles: Frequently Asked Questions, How to use the mQTL.NMR package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mQTL.NMR/inst/doc/FAQ.R, vignettes/mQTL.NMR/inst/doc/mQTLUse.R Package: msa Version: 1.4.5 Depends: R (>= 3.1.0), methods, Biostrings (>= 2.30.0) Imports: Rcpp (>= 0.11.1), BiocGenerics, IRanges (>= 1.20.0), S4Vectors, tools LinkingTo: Rcpp Suggests: Biobase, knitr, seqinr License: GPL (>= 2) Archs: i386, x64 MD5sum: e2e7bceb9763768abe30b7bc113a8cbc 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 source.ver: src/contrib/msa_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/msa_1.4.5.zip win64.binary.ver: bin/windows64/contrib/3.3/msa_1.4.5.zip mac.binary.ver: bin/macosx/contrib/3.3/msa_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/msa_1.4.5.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: odseq Package: MSGFgui Version: 1.6.2 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 05d21d3db6c608ece8af02762ba7c5b0 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: MassSpectrometry, Proteomics, GUI, Visualization Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/MSGFgui_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSGFgui_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MSGFgui_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/MSGFgui_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSGFgui_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFgui/inst/doc/Using_MSGFgui.R htmlDocs: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html htmlTitles: Using MSGFgui Package: MSGFplus Version: 1.6.2 Depends: methods Imports: mzID Suggests: gWidgets, knitr, testthat License: GPL (>= 2) MD5sum: 2cee7571e54780584adc0f281edcf7bc NeedsCompilation: no Title: An interface between R and MS-GF+ Description: This package contains function to perform peptide identification using MS-GF+ biocViews: MassSpectrometry, Proteomics Author: Thomas Lin Pedersen Maintainer: Thomas Lin Pedersen SystemRequirements: Java (>= 1.7) VignetteBuilder: knitr source.ver: src/contrib/MSGFplus_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSGFplus_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MSGFplus_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/MSGFplus_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSGFplus_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSGFplus/inst/doc/Using_MSGFplus.R htmlDocs: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html htmlTitles: Using MSGFgui importsMe: MSGFgui Package: msmsEDA Version: 1.10.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: 46ba3b64dfb0d3c791adfc4e8251e019 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: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori source.ver: src/contrib/msmsEDA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/msmsEDA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/msmsEDA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/msmsEDA_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/msmsEDA_1.10.0.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 Package: msmsTests Version: 1.10.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 9e7d694257f961d26433f84b5b439b2d 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: Software, MassSpectrometry, Proteomics Author: Josep Gregori, Alex Sanchez, and Josep Villanueva Maintainer: Josep Gregori i Font source.ver: src/contrib/msmsTests_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/msmsTests_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/msmsTests_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/msmsTests_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/msmsTests_1.10.0.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 suggestsMe: MSnID Package: MSnbase Version: 1.20.7 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR, BiocParallel, ProtGenerics (>= 1.3.1) Imports: plyr, IRanges, preprocessCore, vsn, grid, reshape2, stats4, affy, impute, pcaMethods, mzID (>= 1.5.2), MALDIquant (>= 1.12), digest, lattice, ggplot2, S4Vectors, Rcpp LinkingTo: Rcpp Suggests: testthat, zoo, knitr (>= 1.1.0), rols, Rdisop, pRoloc, pRolocdata (>= 1.7.1), msdata, roxygen2, rgl, BiocStyle, imputeLCMD, norm, gplots, shiny License: Artistic-2.0 Archs: i386, x64 MD5sum: dad9749b96050c1d5e1484e2d76fdf63 NeedsCompilation: yes Title: Base Functions and Classes for MS-based Proteomics Description: Basic plotting, data manipulation and processing of MS-based Proteomics data. biocViews: Infrastructure, Proteomics, MassSpectrometry, QualityControl, DataImport Author: Laurent Gatto with contributions from Guangchuang Yu, Samuel Wieczorek, Vasile-Cosmin Lazar, Vladislav Petyuk, Thomas Naake, Richie Cotton, Martina Fisher and Sebastian Gibb. Maintainer: Laurent Gatto URL: https://github.com/lgatto/MSnbase VignetteBuilder: knitr BugReports: https://github.com/lgatto/MSnbase/issues source.ver: src/contrib/MSnbase_1.20.7.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSnbase_1.20.7.zip win64.binary.ver: bin/windows64/contrib/3.3/MSnbase_1.20.7.zip mac.binary.ver: bin/macosx/contrib/3.3/MSnbase_1.17.14.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSnbase_1.20.7.tgz vignettes: vignettes/MSnbase/inst/doc/MSnbase-demo.pdf, vignettes/MSnbase/inst/doc/MSnbase-development.pdf, vignettes/MSnbase/inst/doc/MSnbase-io.pdf vignetteTitles: Base Functions and Classes for MS-based Proteomics, MSnbase development, MSnbase IO capabilities hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnbase/inst/doc/MSnbase-demo.R, vignettes/MSnbase/inst/doc/MSnbase-development.R, vignettes/MSnbase/inst/doc/MSnbase-io.R dependsOnMe: msmsEDA, msmsTests, ProCoNA, pRoloc, pRolocGUI, proteoQC, synapter importsMe: DAPAR, MSnID, MSstats, Pbase, ProteomicsAnnotationHubData suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, qcmetrics, rpx Package: MSnID Version: 1.6.0 Depends: R (>= 2.10), Rcpp Imports: MSnbase (>= 1.12.1), mzID (>= 1.3.5), R.cache, foreach, doParallel, parallel, reshape2, methods, iterators, data.table, Biobase, ProtGenerics Suggests: BiocStyle, msmsTests, ggplot2, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 6acc5df1fc22b953210629df3fd99b3d 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 Author: Vlad Petyuk with contributions from Laurent Gatto Maintainer: Vlad Petyuk source.ver: src/contrib/MSnID_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSnID_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MSnID_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MSnID_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSnID_1.6.0.tgz vignettes: vignettes/MSnID/inst/doc/msnid_vignette.pdf vignetteTitles: MSnID Package for Handling MS/MS Identifications hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/MSnID/inst/doc/msnid_vignette.R Package: MSstats Version: 3.4.0 Depends: R (>= 3.2), ggplot2 (>= 2.0.0), Rcpp, grid, reshape2 Imports: lme4, marray, limma, gplots, ggrepel, preprocessCore, data.table, MSnbase, reshape, survival, minpack.lm License: Artistic-2.0 MD5sum: 5f4dcfa929a33dc097a47c66710c96e9 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. Author: Meena Choi , Lin-Yang Cheng , Tsung-Heng Tsai , Ching-Yun Chang , Olga Vitek Maintainer: Meena Choi URL: http://msstats.org BugReports: https://groups.google.com/forum/#!forum/msstats source.ver: src/contrib/MSstats_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MSstats_3.3.7.zip win64.binary.ver: bin/windows64/contrib/3.3/MSstats_3.3.7.zip mac.binary.ver: bin/macosx/contrib/3.3/MSstats_2.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MSstats_3.3.11.tgz vignettes: vignettes/MSstats/inst/doc/MSstats-manual.pdf vignetteTitles: MSstats-manual.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: SWATH2stats Package: Mulcom Version: 1.22.0 Depends: R (>= 2.10), fields, Biobase Imports: graphics, grDevices, stats, methods License: GPL-2 Archs: i386, x64 MD5sum: c3338678cfdb411f559d515ebcadf87b 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 source.ver: src/contrib/Mulcom_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Mulcom_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Mulcom_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Mulcom_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Mulcom_1.22.0.tgz vignettes: vignettes/Mulcom/inst/doc/MulcomVignette.pdf vignetteTitles: Mulcom Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Mulcom/inst/doc/MulcomVignette.R Package: multiClust Version: 1.0.2 Imports: mclust, ctc, survival, cluster, dendextend, amap, graphics, grDevices Suggests: knitr, gplots, RUnit, BiocGenerics, preprocessCore, Biobase, GEOquery License: GPL (>= 2) MD5sum: 7f3d4c1b3cc812fb506c7e5a7dce5c1a NeedsCompilation: no Title: A collection of gene feature selection and clustering analysis algorithms Description: Whole transcriptomic profiles are useful for studying the expression levels of thousands of genes across samples. Clustering algorithms are used to identify patterns in these profiles to determine clinically relevant subgroups. Feature selection is a critical 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 the appropriate methods is difficult as recent work demonstrates that no method is the clear winner. Hence, we present an R-package called `multiClust` that allows researchers to experiment with the choice of combination of methods for gene selection and clustering with ease. In addition, using multiClust, we present the merit of gene selection and clustering methods in the context of clinical relevance of clustering, specifically clinical outcome. Our integrative R- package contains: 1. A function to read in gene expression data and format appropriately for analysis in R. 2. Four different ways to select the number of genes a. Fixed b. Percent c. Poly d. GMM 3. Four gene ranking options that order genes based on different statistical criteria a. CV_Rank b. CV_Guided c. SD_Rank d. Poly 4. Two ways to determine the cluster number a. Fixed b. Gap Statistic 5. Two clustering algorithms a. Hierarchical clustering b. K-means clustering 6. A function to calculate average gene expression in each sample cluster 7. A function to correlate sample clusters with clinical outcome Order of Function use: 1. input_file, a function to read-in the gene expression file and assign gene probe names as the rownames. 2. number_probes, a function to determine the number of probes to select for in the gene feature selection process. 3. probe_ranking, a function to select for gene probes using one of the available gene probe ranking options. 4. number_clusters, a function to determine the number of clusters to be used to cluster genes and samples. 5. cluster_analysis, a function to perform Kmeans or Hierarchical clustering analysis of the selected gene expression data. 6. avg_probe_exp, a function to produce a matrix containing the average expression of each gene probe within each sample cluster. 7. surv_analysis, a function to produce Kaplan-Meier Survival Plots of selected gene expression data. 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 source.ver: src/contrib/multiClust_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/multiClust_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/multiClust_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/multiClust_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/multiClust/inst/doc/multiClust.R htmlDocs: vignettes/multiClust/inst/doc/multiClust.html htmlTitles: "A Guide to multiClust" Package: MultiDataSet Version: 1.0.2 Depends: R (>= 3.3), Biobase Imports: BiocGenerics, GenomicRanges, IRanges, minfi, S4Vectors, SummarizedExperiment Suggests: MEALData, minfiData, knitr, rmarkdown, testthat, methylumi License: file LICENSE MD5sum: dbc49fe3f5899631797ba3e6b38fd993 NeedsCompilation: no Title: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and MethylationSet Description: Implementation of the BRGE's (Bioinformatic Research Group in Epidemiology from Center for Research in Environmental Epidemiology) MultiDataSet and MethylationSet. MultiDataSet is designed for integrating multi omics data sets and MethylationSet to contain normalized methylation data. These package contains base classes for MEAL and rexposome packages. biocViews: Software, DataRepresentation Author: Carlos Ruiz-Arenas [aut, cre], Carles Hernandez-Ferrer [aut], Juan R. Gonzlez [aut] Maintainer: Carlos Ruiz-Arenas VignetteBuilder: knitr source.ver: src/contrib/MultiDataSet_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/MultiDataSet_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/MultiDataSet_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MultiDataSet_1.0.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/MultiDataSet/inst/doc/MultiDataSet.R htmlDocs: vignettes/MultiDataSet/inst/doc/MultiDataSet.html htmlTitles: Introduction to MultiDataSet dependsOnMe: MEAL Package: MultiMed Version: 1.6.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: 4150ca53fe8c9baeb1eda2a51537745d NeedsCompilation: no Title: Testing multiple biological mediators simultaneously Description: Implements permutation method with joint correction for testing multiple mediators biocViews: MultipleComparison, StatisticalMethod, Software Author: Simina M. Boca, Joshua N. Sampson Maintainer: Simina M. Boca source.ver: src/contrib/MultiMed_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MultiMed_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MultiMed_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MultiMed_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MultiMed_1.6.0.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 Package: multiscan Version: 1.32.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 463343782c94c22d0ad6ffffffe5f143 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 source.ver: src/contrib/multiscan_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/multiscan_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/multiscan_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/multiscan_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/multiscan_1.32.0.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 Package: multtest Version: 2.28.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: i386, x64 MD5sum: a82a1564e15fdc5afe6efc4c33a33bbc 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 source.ver: src/contrib/multtest_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/multtest_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/multtest_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/multtest_2.25.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/multtest_2.28.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, aCGH, BicARE, iPAC, KCsmart, LMGene, PREDA, rain, REDseq, SAGx, siggenes, webbioc importsMe: ABarray, aCGH, adSplit, anota, ChIPpeakAnno, GeneSelector, IsoGeneGUI, mAPKL, metabomxtr, nethet, OCplus, phyloseq, REDseq, RTopper, synapter, webbioc suggestsMe: annaffy, BiocCaseStudies, ecolitk, factDesign, GeneSelector, GGtools, GOstats, gQTLstats, GSEAlm, maigesPack, MmPalateMiRNA, oneChannelGUI, pcot2, topGO, xcms Package: muscle Version: 3.14.0 Depends: Biostrings License: Unlimited Archs: i386, x64 MD5sum: 35198d1516a9c374b6c5419b83394595 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/ source.ver: src/contrib/muscle_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/muscle_3.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/muscle_3.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/muscle_3.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/muscle_3.14.0.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 Package: MVCClass Version: 1.46.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 7c69a7fc0b72e16b3fa677e06ec9d997 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 source.ver: src/contrib/MVCClass_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/MVCClass_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/MVCClass_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/MVCClass_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/MVCClass_1.46.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass Package: mvGST Version: 1.6.0 Depends: R(>= 2.10.0), GO.db, Rgraphviz Imports: gProfileR, stringr, topGO, GOstats, annotate, AnnotationDbi, graph Suggests: hgu133plus2.db, org.Hs.eg.db License: GPL-3 MD5sum: 85435f259c44aee2bdfdd332a2aaadb4 NeedsCompilation: no Title: Multivariate and directional gene set testing Description: mvGST provides platform-independent tools to identify GO terms (gene sets) that are differentially active (up or down) in multiple contrasts of interest. Given a matrix of one-sided p-values (rows for genes, columns for contrasts), mvGST uses meta-analytic methods to combine p-values for all genes annotated to each gene set, and then classify each gene set as being significantly more active (1), less active (-1), or not significantly differentially active (0) in each contrast of interest. With multiple contrasts of interest, each gene set is assigned to a profile (across contrasts) of differential activity. Tools are also provided for visualizing (in a GO graph) the gene sets classified to a given profile. biocViews: Microarray, OneChannel, RNASeq, DifferentialExpression, GO, Pathways, GeneSetEnrichment, GraphAndNetwork Author: John R. Stevens and Dennis S. Mecham Maintainer: John R. Stevens source.ver: src/contrib/mvGST_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mvGST_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mvGST_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mvGST_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mvGST_1.6.0.tgz vignettes: vignettes/mvGST/inst/doc/mvGST.pdf vignetteTitles: mvGST Tutorial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/mvGST/inst/doc/mvGST.R Package: mygene Version: 1.8.0 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: 6ad5d1ec3e8ae4afc8b066945208b2d1 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, Chunlei Wu Maintainer: Adam Mark, Chunlei Wu source.ver: src/contrib/mygene_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/mygene_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/mygene_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/mygene_1.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mygene_1.8.0.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 Package: myvariant Version: 1.2.0 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: 2f76e83f4207f016d6db18cb439ab530 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 source.ver: src/contrib/myvariant_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/myvariant_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/myvariant_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/myvariant_0.99.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/myvariant_1.2.0.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 Package: mzID Version: 1.10.2 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 25818e75dccd58a9ca4ab07cb0b13bcc 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: DataImport, MassSpectrometry, Proteomics Author: Thomas Lin Pedersen, Vladislav A Petyuk with contributions from Laurent Gatto and Sebastian Gibb. Maintainer: Thomas Lin Pedersen VignetteBuilder: knitr source.ver: src/contrib/mzID_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/mzID_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/mzID_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/mzID_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mzID_1.10.2.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 importsMe: MSGFgui, MSGFplus, MSnbase, MSnID, Pbase suggestsMe: mzR Package: mzR Version: 2.6.3 Depends: Rcpp (>= 0.10.1), methods, utils Imports: Biobase, BiocGenerics (>= 0.13.6), ProtGenerics LinkingTo: Rcpp, zlibbioc Suggests: msdata (>= 0.3.5), RUnit, mzID, BiocStyle, knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: c36c4a9ec2ec82642f5ffae56d9a08ea 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: Infrastructure, DataImport, Proteomics, Metabolomics, MassSpectrometry Author: Bernd Fischer, Steffen Neumann, Laurent Gatto, Qiang Kou Maintainer: Bernd Fischer , Steffen Neumann , Laurent Gatto , Qiang Kou URL: https://github.com/sneumann/mzR/ SystemRequirements: C++11, GNU make, NetCDF VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/ source.ver: src/contrib/mzR_2.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/mzR_2.6.3.zip win64.binary.ver: bin/windows64/contrib/3.3/mzR_2.6.3.zip mac.binary.ver: bin/macosx/contrib/3.3/mzR_2.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/mzR_2.6.3.tgz vignettes: vignettes/mzR/inst/doc/mzR.pdf 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, xcms importsMe: Pbase, ProteomicsAnnotationHubData, SIMAT suggestsMe: AnnotationHub, qcmetrics Package: NanoStringDiff Version: 1.2.0 Depends: Biobase Imports: matrixStats, methods Suggests: testthat, BiocStyle License: GPL MD5sum: 07aa3def6a2f2d9437ecb7aaf782734f NeedsCompilation: no 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 , chi wang Maintainer: hong wang source.ver: src/contrib/NanoStringDiff_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NanoStringDiff_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NanoStringDiff_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NanoStringDiff_0.99.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NanoStringDiff_1.2.0.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 Package: NanoStringQCPro Version: 1.4.0 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 License: Artistic-2.0 MD5sum: 3a3739f6cadcbedc655bd91e7b34d28f 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 source.ver: src/contrib/NanoStringQCPro_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NanoStringQCPro_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NanoStringQCPro_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NanoStringQCPro_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NanoStringQCPro_1.4.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: NanoStringQCPro overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.R Package: NarrowPeaks Version: 1.16.0 Depends: R (>= 2.10.0), splines Imports: BiocGenerics, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, fda, CSAR, ICSNP Suggests: rtracklayer, BiocStyle, GenomicRanges, CSAR License: Artistic-2.0 Archs: i386, x64 MD5sum: 3e1397690761fe1184f4afa82c7740ff NeedsCompilation: yes 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 source.ver: src/contrib/NarrowPeaks_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NarrowPeaks_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NarrowPeaks_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NarrowPeaks_1.13.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NarrowPeaks_1.16.0.tgz vignettes: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.pdf vignetteTitles: NarrowPeaks Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.R Package: ncdfFlow Version: 2.18.0 Depends: R (>= 2.14.0), flowCore(>= 1.37.15), flowViz, RcppArmadillo, methods, BH Imports: Biobase,BiocGenerics,flowCore,flowViz,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH Suggests: testthat,parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 2481ec8ee827149b7fb77d1fe9b3cd8b 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: FlowCytometry Author: Mike Jiang,Greg Finak,N. Gopalakrishnan Maintainer: Mike Jiang SystemRequirements: hdf5 (>= 1.8.0) source.ver: src/contrib/ncdfFlow_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ncdfFlow_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ncdfFlow_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ncdfFlow_2.15.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ncdfFlow_2.18.0.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 suggestsMe: COMPASS Package: NCIgraph Version: 1.20.0 Depends: graph, R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCytoscape, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: ef4f1f124d195cb74409c87540b22e04 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 source.ver: src/contrib/NCIgraph_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NCIgraph_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NCIgraph_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NCIgraph_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NCIgraph_1.20.0.tgz 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 Package: neaGUI Version: 1.9.0 Depends: tcltk Imports: hwriter Suggests: AnnotationDbi, org.Hs.eg.db, KEGG.db, GO.db, reactome.db, RUnit, GOstats,hwriter License: GPL-2 MD5sum: 1b52d9a715d0d25fb166aa08a7a0afc7 NeedsCompilation: no Title: An R package to perform the network enrichment analysis (NEA). Description: neaGUI is an easy to use R package developed to perform the network enrichment analysis (NEA) proposed by Alexeyenko et al. (2012). The NEA method extends the overlap statistics in GSEA to network links between genes in the experimental set and those in the functional categories by exploiting biological information in terms of gene interaction network. The neaGUI requires the following R packages: tcltk, KEGG.db, GO.db, reactome.db, org.Hs.eg.db, AnnotationDbi, and hwriter. biocViews: Microarray, DifferentialExpression, GUI, GeneSetEnrichment, NetworkEnrichment, Pathways, Reactome, Network, GO, KEGG Author: Setia Pramana, Woojoo Lee, Yudi Pawitan Maintainer: Setia Pramana source.ver: src/contrib/neaGUI_1.9.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/neaGUI_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/neaGUI_1.9.0.tgz vignettes: vignettes/neaGUI/inst/doc/neaGUI_vignette.pdf vignetteTitles: neaGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/neaGUI/inst/doc/neaGUI_vignette.R Package: nem Version: 2.46.0 Depends: R (>= 3.0) Imports: boot, e1071, graph, graphics, grDevices, methods, RBGL (>= 1.8.1), RColorBrewer, stats, utils, Rgraphviz, statmod, plotrix, limma Suggests: Biobase (>= 1.10) Enhances: doMC, snow, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: e71f298f27e1ee08366c95233eeefabf NeedsCompilation: yes Title: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies Description: The package 'nem' allows to reconstruct features of pathways from the nested structure of perturbation effects. It takes as input (1.) a set of pathway components, which were perturbed, and (2.) phenotypic readout of these perturbations (e.g. gene expression, protein expression). The output is a directed graph representing the phenotypic hierarchy. biocViews: Microarray, Bioinformatics, GraphsAndNetworks, Pathways, SystemsBiology, NetworkInference Author: Holger Froehlich, Florian Markowetz, Achim Tresch, Theresa Niederberger, Christian Bender, Matthias Maneck, Claudio Lottaz, Tim Beissbarth Maintainer: Holger Froehlich URL: http://www.bioconductor.org source.ver: src/contrib/nem_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nem_2.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nem_2.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/nem_2.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nem_2.46.0.tgz vignettes: vignettes/nem/inst/doc/markowetz-thesis-2006.pdf, vignettes/nem/inst/doc/nem.pdf vignetteTitles: markowetz-thesis-2006.pdf, Nested Effects Models - An example in Drosophila immune response hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nem/inst/doc/nem.R dependsOnMe: lpNet importsMe: birte suggestsMe: rBiopaxParser Package: netbenchmark Version: 1.4.2 Depends: grndata (>= 0.99.3) Imports: Rcpp (>= 0.11.0), minet, randomForest, c3net, PCIT, GeneNet, tools, pracma, Matrix, corpcor, fdrtool LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, knitr, graph License: CC BY-NC-SA 4.0 Archs: i386, x64 MD5sum: ba373f129ab6a06eb37e8d26476cc3ec NeedsCompilation: yes 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, with contributions from Alexandre Irrthum Maintainer: Pau Bellot URL: https://imatge.upc.edu/netbenchmark/ VignetteBuilder: knitr source.ver: src/contrib/netbenchmark_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/netbenchmark_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/netbenchmark_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/netbenchmark_1.1.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/netbenchmark_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/netbenchmark/inst/doc/netbenchmark.R htmlDocs: vignettes/netbenchmark/inst/doc/netbenchmark.html htmlTitles: Netbenchmark Package: netbiov Version: 1.6.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: 0bf6fc8dd6cb05944070bf17a244c3aa 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 source.ver: src/contrib/netbiov_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/netbiov_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/netbiov_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/netbiov_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/netbiov_1.6.0.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 Package: nethet Version: 1.4.2 Imports: glasso, mvtnorm, parcor, GeneNet, huge, CompQuadForm, ggm, mclust, parallel, GSA, limma, multtest, ICSNP, glmnet, network, ggplot2 Suggests: knitr, xtable, BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: 3fb49643aeceef518930fce6311190fd 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 source.ver: src/contrib/nethet_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/nethet_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/nethet_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/nethet_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nethet_1.4.2.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 Package: NetPathMiner Version: 1.8.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, RCytoscape, graph License: GPL (>= 2) Archs: i386, x64 MD5sum: ec3a597bbbe5ac949c04b3a3609c6eca 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) source.ver: src/contrib/NetPathMiner_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NetPathMiner_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NetPathMiner_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NetPathMiner_1.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NetPathMiner_1.8.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.pdf vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NetPathMiner/inst/doc/NPMVignette.R Package: netresponse Version: 1.32.2 Depends: R (>= 2.15.1), Rgraphviz, methods, minet, mclust, reshape2 Imports: dmt, ggplot2, graph, igraph, parallel, plyr, qvalue, RColorBrewer License: GPL (>=2) Archs: i386, x64 MD5sum: d6cda920eca09913067288bfa3e482d9 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, Transcription Author: Leo Lahti, Olli-Pekka Huovilainen, Antonio Gusmao and Juuso Parkkinen Maintainer: Leo Lahti URL: https://github.com/antagomir/netresponse BugReports: https://github.com/antagomir/netresponse/issues source.ver: src/contrib/netresponse_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/netresponse_1.32.2.zip win64.binary.ver: bin/windows64/contrib/3.3/netresponse_1.32.2.zip mac.binary.ver: bin/macosx/contrib/3.3/netresponse_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/netresponse_1.32.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NetSAM Version: 1.12.0 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: f99f6263a70a654f169abd8757a6a5a8 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 source.ver: src/contrib/NetSAM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NetSAM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NetSAM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NetSAM_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NetSAM_1.12.0.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 Package: networkBMA Version: 1.14.0 Depends: R (>= 2.15.0), stats, utils, BMA, Rcpp (>= 0.10.3), RcppArmadillo (>= 0.3.810.2), RcppEigen (>= 0.3.1.2.1) LinkingTo: Rcpp, RcppArmadillo, RcppEigen License: GPL (>= 2) Archs: i386, x64 MD5sum: 8044faf6b56e13f7bffd6ae8584e9665 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, Ka Yee Yeung, Adrian Raftery (with contributions from Kenneth Lo) Maintainer: Ka Yee Yeung source.ver: src/contrib/networkBMA_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/networkBMA_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/networkBMA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/networkBMA_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/networkBMA_1.14.0.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 Package: NGScopy Version: 1.6.0 Depends: R (>= 3.1.0) Imports: methods, parallel, Xmisc (>= 0.2.1), rbamtools (>= 2.6.0), changepoint (>= 2.1.1) Suggests: RUnit, NGScopyData, GenomicRanges License: GPL (>=2) MD5sum: b78dfaace90504649b31ab1a9b2ee438 NeedsCompilation: no Title: NGScopy: Detection of Copy Number Variations in Next Generation Sequencing sequencing Description: NGScopy provides a quantitative caller for detecting copy number variations in next generation sequencing (NGS), including whole genome sequencing (WGS), whole exome sequencing (WES) and targeted panel sequencing (TPS). The caller can be parallelized by chromosomes to use multiple processors/cores on one computer. biocViews: CopyNumberVariation, DNASeq, TargetedResequencing, ExomeSeq, WholeGenome, Sequencing Author: Xiaobei Zhao [aut, cre, cph] Maintainer: Xiaobei Zhao source.ver: src/contrib/NGScopy_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NGScopy_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NGScopy_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NGScopy_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NGScopy_1.6.0.tgz vignettes: vignettes/NGScopy/inst/doc/NGScopy-vignette.pdf vignetteTitles: NGScopy: Detection of copy number variations in next generation sequencing (User's Guide) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/NGScopy/inst/doc/NGScopy-vignette.R Package: nnNorm Version: 2.36.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: d738c8764b793767fffa91a625ebc1cb 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/ source.ver: src/contrib/nnNorm_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nnNorm_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nnNorm_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/nnNorm_2.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nnNorm_2.36.0.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 Package: NOISeq Version: 2.16.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: 726142db8daa3e407b91bf1b60fd1bd2 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: RNASeq, DifferentialExpression, Visualization, Sequencing Author: Sonia Tarazona, Pedro Furio-Tari, Maria Jose Nueda, Alberto Ferrer and Ana Conesa Maintainer: Sonia Tarazona source.ver: src/contrib/NOISeq_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NOISeq_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NOISeq_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NOISeq_2.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NOISeq_2.16.0.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 Package: nondetects Version: 2.2.0 Depends: R (>= 3.2), Biobase (>= 2.22.0) Imports: limma, mvtnorm, utils, methods, HTqPCR (>= 1.16.0) Suggests: BiocStyle (>= 1.0.0), RUnit, BiocGenerics (>= 0.8.0) License: GPL-3 MD5sum: 7b214077b5b253aef0196cc5557fb14c 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 source.ver: src/contrib/nondetects_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nondetects_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nondetects_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/nondetects_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nondetects_2.2.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: normalize450K Version: 1.0.0 Depends: R (>= 3.3), Biobase, illuminaio, quadprog Imports: utils License: BSD_2_clause + file LICENSE MD5sum: 86595cec1a18f43d0880f0c2ff830e68 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 source.ver: src/contrib/normalize450K_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/normalize450K_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/normalize450K_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/normalize450K_1.0.0.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 Package: NormqPCR Version: 1.18.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: 8dbbdd08fce50ffe52b9794c95db5b9f 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 source.ver: src/contrib/NormqPCR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NormqPCR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NormqPCR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NormqPCR_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NormqPCR_1.18.0.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 Package: npGSEA Version: 1.8.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: f534164dfc06d5311aa52862e65cc2dc 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 source.ver: src/contrib/npGSEA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/npGSEA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/npGSEA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/npGSEA_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/npGSEA_1.8.0.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 Package: NTW Version: 1.22.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: c7c2d4f7c411867a483873253abef8d9 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 source.ver: src/contrib/NTW_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NTW_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NTW_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NTW_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NTW_1.22.0.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 Package: nucleoSim Version: 1.0.2 Imports: stats, IRanges, S4Vectors, graphics Suggests: BiocStyle, BiocGenerics, knitr, rmarkdown, RUnit License: Artistic-2.0 MD5sum: 18e9b62291b80cb8210ee7a0c43760d5 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 source.ver: src/contrib/nucleoSim_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/nucleoSim_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/nucleoSim_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nucleoSim_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleoSim/inst/doc/nucleoSim.R htmlDocs: vignettes/nucleoSim/inst/doc/nucleoSim.html htmlTitles: Generate synthetic nucleosome maps Package: nucleR Version: 2.4.0 Depends: ShortRead Imports: methods, BiocGenerics, S4Vectors (>= 0.9.39), IRanges (>= 2.5.27), Biobase, GenomicRanges (>= 1.23.16), Rsamtools, stats, graphics, parallel Suggests: Starr License: LGPL (>= 3) MD5sum: 0365baf75e25b74b479bda53f784a2b2 NeedsCompilation: no Title: Nucleosome positioning package for R Description: Nucleosome positioning for Tiling Arrays and NGS experiments. biocViews: ChIPSeq, Microarray, Sequencing, Genetics Author: Oscar Flores, Ricard Illa Maintainer: Ricard Illa source.ver: src/contrib/nucleR_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nucleR_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nucleR_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/nucleR_2.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nucleR_2.4.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.pdf vignetteTitles: nucleR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nucleR/inst/doc/nucleR.R Package: nudge Version: 1.38.0 Imports: stats License: GPL-2 MD5sum: 748d79a6842cafc6218ac9b1732e556b NeedsCompilation: no Title: Normal Uniform Differential Gene Expression detection Description: Package for normalizing microarray data in single and multiple replicate experiments and fitting a normal-uniform mixture to detect differentially expressed genes in the cases where the two samples are being compared directly or indirectly (via a common reference sample) biocViews: Microarray, TwoChannel, DifferentialExpression Author: N. Dean and A. E. Raftery Maintainer: N. Dean source.ver: src/contrib/nudge_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/nudge_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/nudge_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/nudge_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/nudge_1.38.0.tgz vignettes: vignettes/nudge/inst/doc/nudge.vignette.pdf vignetteTitles: nudge Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/nudge/inst/doc/nudge.vignette.R Package: NuPoP Version: 1.22.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: ea1cee899af701f06641ebad88d84832 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 source.ver: src/contrib/NuPoP_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/NuPoP_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/NuPoP_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/NuPoP_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/NuPoP_1.22.0.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 Package: occugene Version: 1.32.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: 9b14b03cf5bfc225de0a57f3bc2ced6d 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 source.ver: src/contrib/occugene_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/occugene_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/occugene_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/occugene_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/occugene_1.32.0.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 Package: OCplus Version: 1.46.0 Depends: R (>= 2.1.0), akima Imports: multtest (>= 1.7.3), graphics, grDevices, stats License: LGPL MD5sum: 80bac963d3671106edc69b69b4e6781e 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 source.ver: src/contrib/OCplus_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OCplus_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OCplus_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OCplus_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OCplus_1.46.0.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 Package: odseq Version: 1.0.2 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: 59385700ae617b66eee6552038e85fb6 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 source.ver: src/contrib/odseq_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/odseq_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/odseq_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/odseq_1.0.2.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 Package: OGSA Version: 1.2.0 Depends: R (>= 3.2.0) Imports: gplots(>= 2.8.0), limma(>= 3.18.13), Biobase License: GPL (== 2) MD5sum: a2755d87b89768bc2039ea753e09829c 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 source.ver: src/contrib/OGSA_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OGSA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OGSA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OGSA_0.99.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OGSA_1.2.0.tgz vignettes: vignettes/OGSA/inst/doc/OGSAUsersManual.pdf vignetteTitles: OGSA Users Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OGSA/inst/doc/OGSAUsersManual.R Package: oligo Version: 1.36.1 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, GenomeGraphs, RCurl, ACME, biomaRt, AnnotationDbi, GenomeGraphs, RCurl Enhances: ff, doMC, doMPI License: LGPL (>= 2) Archs: i386, x64 MD5sum: 97d2fd60acfa961deb0865b204f8b606 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 source.ver: src/contrib/oligo_1.36.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/oligo_1.36.1.zip win64.binary.ver: bin/windows64/contrib/3.3/oligo_1.36.1.zip mac.binary.ver: bin/macosx/contrib/3.3/oligo_1.33.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oligo_1.36.1.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, waveTiling importsMe: ArrayExpress, charm, cn.farms, frma, ITALICS suggestsMe: BiocGenerics, fastseg, frmaTools Package: oligoClasses Version: 1.34.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.5.17), GenomicRanges (>= 1.23.7), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), ff, foreach, BiocInstaller, utils, S4Vectors (>= 0.9.25), RSQLite 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, SNPchip, VanillaICE, crlmm Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: GPL (>= 2) MD5sum: 9a53b7e07009d6354b332a5f7935e1b8 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 source.ver: src/contrib/oligoClasses_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oligoClasses_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oligoClasses_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/oligoClasses_1.31.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oligoClasses_1.34.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, waveTiling importsMe: affycoretools, ArrayTV, charm, frma, ITALICS, MinimumDistance, pdInfoBuilder, puma, SNPchip, VanillaICE suggestsMe: BiocGenerics, CNPBayes Package: OLIN Version: 1.50.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 37cea6713e4e81b5c49cdc03b54016ac 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 source.ver: src/contrib/OLIN_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OLIN_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OLIN_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OLIN_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OLIN_1.50.0.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 Package: OLINgui Version: 1.46.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: 87cfef66d8f6dc43e96f29d26d3564ee 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 source.ver: src/contrib/OLINgui_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OLINgui_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OLINgui_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OLINgui_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OLINgui_1.46.0.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 Package: omicade4 Version: 1.12.0 Depends: R (>= 3.0.0), ade4 Imports: made4 Suggests: BiocStyle License: GPL-2 MD5sum: 61e9e20803c1e0ba700e336cf336c96e NeedsCompilation: no Title: Multiple co-inertia analysis of omics datasets Description: Multiple co-inertia analysis of omics datasets biocViews: Software, Clustering, Classification, MultipleComparison Author: Chen Meng, Aedin Culhane, Amin M. Gholami. Maintainer: Chen Meng source.ver: src/contrib/omicade4_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/omicade4_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/omicade4_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/omicade4_1.9.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/omicade4_1.12.0.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 Package: OmicCircos Version: 1.10.0 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: ef09e944ab7f23b0fc13fbd14372e748 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 source.ver: src/contrib/OmicCircos_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OmicCircos_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OmicCircos_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OmicCircos_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OmicCircos_1.10.0.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 Package: OmicsMarkeR Version: 1.4.2 Depends: R (>= 3.2.0) Imports: 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 MD5sum: 3bff901ae396c1404b19b5511de34c69 NeedsCompilation: no 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 source.ver: src/contrib/OmicsMarkeR_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/OmicsMarkeR_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/OmicsMarkeR_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/OmicsMarkeR_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OmicsMarkeR_1.4.2.tgz vignettes: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.pdf vignetteTitles: A Short Introduction to the OmicMarkeR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.R Package: OncoScore Version: 1.0.2 Depends: R (>= 3.3), Imports: biomaRt, grDevices, graphics, utils, Suggests: BiocGenerics, BiocStyle, testthat, License: GPL-3 MD5sum: a70058e07a850d289bfb7ee3f63f9775 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 frequency in biomedical literature. The score is evaluated from PubMed literature by dynamically updatable web queries. biocViews: BiomedicalInformatics Author: Daniele Ramazzotti [aut, cre], Luca De Sano [aut], Roberta Spinelli [ctb], Carlo Gambacorti Passerini [ctb], Rocco Piazza [ctb] Maintainer: Daniele Ramazzotti URL: https://github.com/danro9685/OncoScore BugReports: https://github.com/danro9685/OncoScore source.ver: src/contrib/OncoScore_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/OncoScore_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/OncoScore_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OncoScore_1.0.2.tgz vignettes: vignettes/OncoScore/inst/doc/vignette.pdf vignetteTitles: OncoScore hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoScore/inst/doc/vignette.R Package: OncoSimulR Version: 2.2.2 Depends: R (>= 3.1.0) Imports: Rcpp (>= 0.11.1), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods, RColorBrewer, grDevices LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat License: GPL (>= 3) Archs: i386, x64 MD5sum: 1088bd9a8800f19dde06779088ee70eb NeedsCompilation: yes Title: Forward Genetic Simulation of Cancer Progresion 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. 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 Tress, Conjunctive Bayesian Networks, and other tumor progression models, and for plotting and sampling from single or multiple realizations of the simulations, including single-cell sampling, as well as functions for plotting the true phylogenetic relationships of the clones. biocViews: BiologicalQuestion, SomaticMutation Author: Ramon Diaz-Uriarte [aut, cre], Mark Taylor [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 source.ver: src/contrib/OncoSimulR_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/OncoSimulR_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/OncoSimulR_2.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OncoSimulR_2.2.2.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.pdf vignetteTitles: OncoSimulR Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OncoSimulR/inst/doc/OncoSimulR.R Package: oneChannelGUI Version: 1.38.0 Depends: Biobase, affylmGUI, tkrplot, tkWidgets, IRanges, Rsamtools (>= 1.13.1), Biostrings, siggenes, chimera Suggests: annotate, genefilter, maSigPro, pamr, pdmclass, ChIPpeakAnno, chipseq, BSgenome, Rgraphviz, affy ,annaffy, affyPLM, multtest, ssize, sizepower, RankProd, org.Hs.eg.db, org.Mm.eg.db, org.Rn.eg.db, edgeR, metaArray, MergeMaid, biomaRt, GenomeGraphs,AffyCompatible, rtracklayer, Genominator, EDASeq, limma, DESeq, DEXSeq, goseq, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, ragene10sttranscriptcluster.db, GOstats, AnnotationDbi, preprocessCore, baySeq, HuExExonProbesetLocation, MoExExonProbesetLocation, RaExExonProbesetLocation, snow, RmiR, RmiR.Hs.miRNA, BSgenome.Hsapiens.UCSC.hg19, R.utils, cummeRbund, BSgenome.Mmusculus.UCSC.mm9, BSgenome.Rnorvegicus.UCSC.rn4, DESeq2, GenomicAlignments, BiocParallel, KEGG.db, miRNApath, miRNAtap, miRNAtap.db License: Artistic-2.0 MD5sum: 8552d5afc6a941c994cc91743edb2e75 NeedsCompilation: no Title: A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops Description: This package was developed to simplify the use of Bioconductor tools for beginners having limited or no experience in writing R code. This library provides a graphical interface for microarray gene and exon level analysis as well as miRNA/mRNA-seq data analysis biocViews: Sequencing, RNASeq, Microarray, OneChannel, DataImport, QualityControl, Preprocessing, StatisticalMethod, DifferentialExpression, GUI, MultipleComparison Author: Raffale A Calogero, Bioinformatics and Genomics Unit, Molecular Biotechnology Center, Torino (Italy) Maintainer: Raffaele A Calogero URL: http://www.bioinformatica.unito.it/oneChannelGUI/ source.ver: src/contrib/oneChannelGUI_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oneChannelGUI_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oneChannelGUI_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/oneChannelGUI_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oneChannelGUI_1.38.0.tgz vignettes: vignettes/oneChannelGUI/inst/doc/Exon-level.analysis.pdf, vignettes/oneChannelGUI/inst/doc/gene-level.analysis.pdf, vignettes/oneChannelGUI/inst/doc/install.pdf, vignettes/oneChannelGUI/inst/doc/RNAseq.pdf, vignettes/oneChannelGUI/inst/doc/standAloneFunctions.pdf vignetteTitles: oneChannelGUI microarray exon-level data analysis overview, oneChannelGUI microarray gene-level data analysis overview, oneChannelGUI Installation, oneChannelGUI miRNA and RNA-seq data analysis overview, oneChannelGUI Stand Alone Functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oneChannelGUI/inst/doc/install.R Package: ontoCAT Version: 1.24.0 Depends: rJava, methods License: Apache License 2.0 MD5sum: a19e90dda2d576f9127053f6a1769ad7 NeedsCompilation: no Title: Ontology traversal and search Description: The ontoCAT R package provides a simple interface to ontologies described in widely used standard formats, stored locally in the filesystem or accessible online. biocViews: Classification, DataRepresentation Author: Natalja Kurbatova, Tomasz Adamusiak, Pavel Kurnosov, Morris Swertz, Misha Kapushevsky Maintainer: Natalja Kurbatova source.ver: src/contrib/ontoCAT_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ontoCAT_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ontoCAT_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ontoCAT_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ontoCAT_1.24.0.tgz vignettes: vignettes/ontoCAT/inst/doc/ontoCAT.pdf vignetteTitles: ontoCAT package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/ontoCAT/inst/doc/ontoCAT.R suggestsMe: RMassBank Package: openCyto Version: 1.10.3 Depends: flowWorkspace(>= 3.17.43) Imports: methods,Biobase,gtools,flowCore(>= 1.31.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace,flowStats(>= 3.29.1),flowClust,MASS,clue,plyr,RBGL,graph,data.table,ks,RColorBrewer,lattice,rrcov,R.utils LinkingTo: Rcpp Suggests: flowWorkspaceData, knitr, testthat, utils, tools, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 3c24d44ee405d9c0d72cf3ff6e017334 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: FlowCytometry, DataImport, Preprocessing, DataRepresentation Author: Mike Jiang, John Ramey, Greg Finak, Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/openCyto_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/openCyto_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.3/openCyto_1.10.3.zip mac.binary.ver: bin/macosx/contrib/3.3/openCyto_1.7.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/openCyto_1.10.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.R, vignettes/openCyto/inst/doc/openCytoVignette.R htmlDocs: vignettes/openCyto/inst/doc/HowToWriteCSVTemplate.html, vignettes/openCyto/inst/doc/openCytoVignette.html htmlTitles: How to write a csv gating template, An Introduction to the openCyto package suggestsMe: ggcyto Package: OperaMate Version: 1.4.0 Depends: R (>= 3.2.0),stats,methods,grDevices Imports: pheatmap,grid,ggplot2,fBasics,gProfileR,gridExtra,reshape2,stabledist Suggests: BiocStyle License: GPL (>= 3) MD5sum: a8ae24310ee8858cc8f12b5472cc14a8 NeedsCompilation: no Title: An R package of Data Importing, Processing and Analysis for Opera High Content Screening System Description: OperaMate is a flexible R package dealing with the data generated by PerkinElmer's Opera High Content Screening System. The functions include the data importing, normalization and quality control, hit detection and function analysis. biocViews: Preprocessing, CellBasedAssays, Normalization, QualityControl Author: Chenglin Liu Maintainer: Chenglin Liu source.ver: src/contrib/OperaMate_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OperaMate_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OperaMate_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OperaMate_0.99.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OperaMate_1.4.0.tgz vignettes: vignettes/OperaMate/inst/doc/OperaMate-manual.pdf, vignettes/OperaMate/inst/doc/OperaMate-vignette.pdf vignetteTitles: OperaMate-manual.pdf, An introduction to OperaMate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/OperaMate/inst/doc/OperaMate-vignette.R Package: oposSOM Version: 1.10.0 Depends: R (>= 3.0), igraph (>= 1.0.0) Imports: som, fastICA, scatterplot3d, pixmap, fdrtool, ape, KernSmooth, biomaRt, Biobase License: GPL (>=2) MD5sum: e6e840ae9a056914aa4354188f18b9d3 NeedsCompilation: no 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 and Martin Kalcher Maintainer: Henry Loeffler-Wirth URL: http://som.izbi.uni-leipzig.de source.ver: src/contrib/oposSOM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/oposSOM_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/oposSOM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/oposSOM_1.5.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oposSOM_1.10.0.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 Package: oppar Version: 1.0.2 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: 66292dfb956b62ac436f7237f4d4f41b 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 source.ver: src/contrib/oppar_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/oppar_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/oppar_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/oppar_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/oppar/inst/doc/oppar.R htmlDocs: vignettes/oppar/inst/doc/oppar.html htmlTitles: OPPAR: Outlier Profile and Pathway Analysis in R Package: OrderedList Version: 1.44.0 Depends: R (>= 2.1.0), Biobase (>= 1.5.12), twilight (>= 1.9.2), methods Imports: Biobase, graphics, methods, stats, twilight License: GPL (>= 2) MD5sum: 336f0485b13781bc6e961d784b8a6cc2 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/index.shtml source.ver: src/contrib/OrderedList_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OrderedList_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OrderedList_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OrderedList_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OrderedList_1.44.0.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 Package: OrganismDbi Version: 1.14.1 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.33.15), GenomicFeatures (>= 1.23.31) Imports: Biobase, BiocInstaller, GenomicRanges, graph, IRanges, RBGL, RSQLite, S4Vectors (>= 0.9.25), stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, AnnotationHub, FDb.UCSC.tRNAs, rtracklayer, biomaRt, RUnit License: Artistic-2.0 MD5sum: d71faed4e6a8c573960bcb48b8f433e4 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, Herve Pages, Martin Morgan, Valerie Obenchain Maintainer: Biocore Data Team source.ver: src/contrib/OrganismDbi_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/OrganismDbi_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.3/OrganismDbi_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.3/OrganismDbi_1.11.42.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OrganismDbi_1.14.1.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 importsMe: AnnotationHubData, epivizrData, ggbio suggestsMe: epivizrStandalone Package: OSAT Version: 1.20.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 5ad3f10c8ccd9809e20dacf198668da7 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 source.ver: src/contrib/OSAT_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OSAT_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OSAT_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OSAT_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OSAT_1.20.0.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 Package: Oscope Version: 1.2.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: dd87f2b98e77e0a9018d51a0501e0998 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: StatisticalMethod,RNASeq, Sequencing, GeneExpression Author: Ning Leng Maintainer: Ning Leng source.ver: src/contrib/Oscope_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Oscope_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Oscope_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Oscope_0.99.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Oscope_1.2.0.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 Package: OTUbase Version: 1.22.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: 7d59c62fcf812801fe0603a50e2825ea 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 source.ver: src/contrib/OTUbase_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OTUbase_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OTUbase_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OTUbase_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OTUbase_1.22.0.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 Package: OutlierD Version: 1.36.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: c81f830ae825fb9cf8fc583ec3085ff0 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/ source.ver: src/contrib/OutlierD_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/OutlierD_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/OutlierD_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/OutlierD_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/OutlierD_1.36.0.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 Package: PAA Version: 1.7.1 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: 1a4efa0cf86059b6a16c6d1f9316e563 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 source.ver: src/contrib/PAA_1.7.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/PAA_1.7.1.zip win64.binary.ver: bin/windows64/contrib/3.3/PAA_1.7.1.zip mac.binary.ver: bin/macosx/contrib/3.3/PAA_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PAA_1.7.1.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 Package: PADOG Version: 1.14.0 Depends: R (>= 3.0.0), KEGGdzPathwaysGEO, methods,Biobase Imports: limma, AnnotationDbi, GSA, foreach, doRNG, hgu133plus2.db, hgu133a.db, KEGG.db, nlme Suggests: doParallel, parallel License: GPL (>= 2) MD5sum: 50408c1e3167a6b30f1dce086ec67e08 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 source.ver: src/contrib/PADOG_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PADOG_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PADOG_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PADOG_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PADOG_1.14.0.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 importsMe: EGSEA Package: paircompviz Version: 1.10.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: d13db24206ab53babe7061d5b1566c22 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 source.ver: src/contrib/paircompviz_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/paircompviz_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/paircompviz_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/paircompviz_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/paircompviz_1.10.0.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 Package: pandaR Version: 1.4.2 Depends: R (>= 3.0.0), methods, Biobase, BiocGenerics, Imports: matrixStats, igraph, ggplot2, grid, reshape, plyr, RUnit, hexbin Suggests: rmarkdown License: GPL-2 MD5sum: d4336e9a4b148b8971d704b7a183e444 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, Albert Young, Joseph N. Paulson Maintainer: Joseph N. Paulson , Dan Schlauch VignetteBuilder: rmarkdown source.ver: src/contrib/pandaR_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/pandaR_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/pandaR_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/pandaR_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pandaR_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: PAnnBuilder Version: 1.36.0 Depends: R (>= 2.7.0), methods, utils, RSQLite, Biobase (>= 1.17.0), AnnotationDbi (>= 1.3.12) Imports: methods, utils, Biobase, DBI, RSQLite, AnnotationDbi Suggests: org.Hs.ipi.db License: LGPL (>= 2.0) MD5sum: f30a9dea83f664119f020ffbda85280e NeedsCompilation: no Title: Protein annotation data package builder Description: Processing annotation data from public data repositories and building protein-centric annotation data packages. biocViews: Annotation, Proteomics Author: Li Hong lihong@sibs.ac.cn Maintainer: Li Hong URL: http://www.biosino.org/PAnnBuilder source.ver: src/contrib/PAnnBuilder_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PAnnBuilder_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PAnnBuilder_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PAnnBuilder_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PAnnBuilder_1.36.0.tgz vignettes: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.pdf vignetteTitles: Using the PAnnBuilder Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.R Package: panp Version: 1.42.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 34553ec0deac3d617651d29f4935a7a7 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 source.ver: src/contrib/panp_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/panp_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/panp_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/panp_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/panp_1.42.0.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 Package: PANR Version: 1.18.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: 1d8fa0f06251c5ccf768645a3382d313 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: NetworkInference, Visualization, GraphAndNetwork, Clustering, CellBasedAssays Author: Xin Wang Maintainer: Xin Wang source.ver: src/contrib/PANR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PANR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PANR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PANR_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PANR_1.18.0.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 suggestsMe: RedeR Package: PanVizGenerator Version: 1.0.3 Depends: methods Imports: shiny, tools, jsonlite, pcaMethods, FindMyFriends, igraph, stats, utils Suggests: BiocStyle, knitr, rmarkdown, testthat, digest License: GPL (>= 2) MD5sum: 6fedd81e18f73d628e67926b4f979c97 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 source.ver: src/contrib/PanVizGenerator_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/PanVizGenerator_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/PanVizGenerator_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PanVizGenerator_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PanVizGenerator/inst/doc/panviz_howto.R htmlDocs: vignettes/PanVizGenerator/inst/doc/panviz_howto.html htmlTitles: Creating PanViz visualizations with PanVizGenerator Package: PAPi Version: 1.12.0 Depends: R (>= 2.15.2), svDialogs, KEGGREST License: GPL(>= 2) MD5sum: 5cc9ea968188c8b62008f65051b7e14f NeedsCompilation: no Title: Predict metabolic pathway activity based on metabolomics data Description: The Pathway Activity Profiling - PAPi - is an R package for predicting the activity of metabolic pathways based solely on a metabolomics data set containing a list of metabolites identified and their respective abundances in different biological samples. PAPi generates hypothesis that improves the final biological interpretation. See Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics. biocViews: MassSpectrometry, Metabolomics Author: Raphael Aggio Maintainer: Raphael Aggio source.ver: src/contrib/PAPi_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PAPi_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PAPi_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PAPi_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PAPi_1.12.0.tgz vignettes: vignettes/PAPi/inst/doc/PAPi.pdf, vignettes/PAPi/inst/doc/PAPiPackage.pdf vignetteTitles: PAPi.pdf, Applying PAPi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PAPi/inst/doc/PAPiPackage.R Package: parglms Version: 1.4.2 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS License: Artistic-2.0 MD5sum: 18ced692f0f43dfc9eeccf8364121b08 NeedsCompilation: no Title: support for parallelized estimation of GLMs/GEEs Description: support for parallelized estimation of GLMs/GEEs, catering for dispersed data Author: VJ Carey Maintainer: VJ Carey source.ver: src/contrib/parglms_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/parglms_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/parglms_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/parglms_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/parglms_1.4.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: parody Version: 1.30.0 Depends: R (>= 2.5.0), methods, tools, utils License: Artistic-2.0 MD5sum: 843e7b5ae0a4043ca5b2d0b1d9e452a3 NeedsCompilation: no Title: Parametric And Resistant Outlier DYtection Description: 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 source.ver: src/contrib/parody_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/parody_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/parody_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/parody_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/parody_1.30.0.tgz vignettes: vignettes/parody/inst/doc/parody.pdf vignetteTitles: parody: parametric and resistant outlier detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/parody/inst/doc/parody.R dependsOnMe: arrayMvout, flowQ Package: Path2PPI Version: 1.2.2 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 87cc039fc352adf592e9380b1c590026 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 source.ver: src/contrib/Path2PPI_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Path2PPI_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Path2PPI_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/Path2PPI_0.99.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Path2PPI_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.R htmlDocs: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html htmlTitles: Path2PPI - A brief tutorial Package: pathifier Version: 1.10.0 Imports: R.oo, princurve License: Artistic-1.0 MD5sum: b5c0ada1362586e3032329a99fb1b8e5 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 source.ver: src/contrib/pathifier_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathifier_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathifier_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pathifier_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathifier_1.10.0.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 Package: PathNet Version: 1.12.0 Depends: R (>= 1.14.0) Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: 5d8d0121d41e7ad424aea534263f2fdc 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: Jason B. Smith source.ver: src/contrib/PathNet_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PathNet_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PathNet_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PathNet_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PathNet_1.12.0.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 Package: pathRender Version: 1.40.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 1bfcd2f5fe4f4bbb0e8c3ecb30b6d393 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 source.ver: src/contrib/pathRender_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathRender_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathRender_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pathRender_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathRender_1.40.0.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 Package: pathVar Version: 1.2.0 Depends: R (>= 3.2.2), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table License: LGPL (>= 2.0) MD5sum: 9b6631587fe850e05b9e3ac70c5d0efc 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 source.ver: src/contrib/pathVar_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathVar_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathVar_1.2.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathVar_1.2.0.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 Package: pathview Version: 1.12.0 Depends: R (>= 2.10), org.Hs.eg.db Imports: KEGGgraph, XML, Rgraphviz, graph, png, AnnotationDbi, KEGGREST, methods, utils Suggests: gage, org.Mm.eg.db, RUnit, BiocGenerics License: GPL (>=3.0) MD5sum: 6b5878fa39fb42f8b81e576578d00718 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: http://pathview.uncc.edu/ source.ver: src/contrib/pathview_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pathview_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pathview_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pathview_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pathview_1.12.0.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: EGSEA, EnrichmentBrowser importsMe: CompGO suggestsMe: clusterProfiler, gage Package: paxtoolsr Version: 1.6.3 Depends: R (>= 3.2), rJava (>= 0.9-4), XML Imports: httr, igraph, plyr, rjson, R.utils, data.table Suggests: testthat, knitr, BiocStyle, rmarkdown, RColorBrewer, biomaRt, estrogen, affy, hgu95av2, hgu95av2cdf, limma, foreach, doSNOW, parallel License: LGPL-3 MD5sum: d102506a86711be3049cbbc1139fb9fc 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 source.ver: src/contrib/paxtoolsr_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/paxtoolsr_1.6.3.zip win64.binary.ver: bin/windows64/contrib/3.3/paxtoolsr_1.6.3.zip mac.binary.ver: bin/macosx/contrib/3.3/paxtoolsr_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/paxtoolsr_1.6.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.R htmlDocs: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html htmlTitles: Using PaxtoolsR Package: Pbase Version: 0.12.2 Depends: R (>= 2.10), methods, BiocGenerics, Rcpp, Gviz Imports: cleaver (>= 1.3.6), Biobase, Biostrings, IRanges, S4Vectors, mzID, mzR (>= 1.99.1), MSnbase (>= 1.15.5), Pviz, biomaRt, GenomicRanges, rtracklayer Suggests: testthat (>= 0.8), ggplot2, BSgenome.Hsapiens.NCBI.GRCh38, TxDb.Hsapiens.UCSC.hg38.knownGene, AnnotationHub, knitr, rmarkdown, BiocStyle License: GPL-3 MD5sum: cbd210e4b2397a6a77ba5624e13e303d NeedsCompilation: no Title: Manipulating and exploring protein and proteomics data Description: A set of classes and functions to investigate and understand protein sequence data in the context of a proteomics experiment. biocViews: Infrastructure, Proteomics, MassSpectrometry, Visualization, DataImport, DataRepresentation Author: Laurent Gatto [aut], Sebastian Gibb [aut, cre] Maintainer: Sebastian Gibb , Laurent Gatto URL: https://github.com/ComputationalProteomicsUnit/Pbase VignetteBuilder: knitr BugReports: https://github.com/ComputationalProteomicsUnit/Pbase/issues source.ver: src/contrib/Pbase_0.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Pbase_0.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Pbase_0.12.2.zip mac.binary.ver: bin/macosx/contrib/3.3/Pbase_0.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Pbase_0.12.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Pbase/inst/doc/ensucsc.R, vignettes/Pbase/inst/doc/mapping.R, vignettes/Pbase/inst/doc/Pbase-data.R htmlDocs: vignettes/Pbase/inst/doc/ensucsc.html, vignettes/Pbase/inst/doc/mapping.html, vignettes/Pbase/inst/doc/Pbase-data.html htmlTitles: Ensembl and UCSC mapping, mapping, Pbase-data Package: pbcmc Version: 1.0.0 Depends: R (>= 3.3.0), genefu Imports: Biobase, BiocGenerics, BiocParallel (>= 1.3.13), parallel, reshape2, grid, utils, cowplot, methods, limma, ggplot2, gridExtra, grDevices, stats Suggests: breastCancerUPP, breastCancerNKI, breastCancerVDX, breastCancerTRANSBIG, breastCancerMAINZ, breastCancerUNT License: GPL (>=2) MD5sum: b8aff70f64a9e6b1f30a38bc1a8170ca NeedsCompilation: no Title: Permutation-Based Confidence for Molecular Classification Description: The pbcmc package characterizes uncertainty assessment on gene expression classifiers, a. k. a. molecular signatures, based on a permutation test. In order to achieve this goal, synthetic simulated subjects are obtained by permutations of gene labels. Then, each synthetic subject is tested against the corresponding subtype classifier to build the null distribution. Thus, classification confidence measurement can be provided for each subject, to assist physician therapy choice. At present, it is only available for PAM50 implementation in genefu package but it can easily be extend to other molecular signatures. biocViews: Classification, GeneExpression, Microarray, MultipleComparison, QualityControl, Normalization, Clustering, mRNAMicroarray, OneChannel, TwoChannel, RNASeq, KEGG, DifferentialExpression Author: Cristobal Fresno, German A. Gonzalez, Andrea S. Llera and Elmer A. Fernandez Maintainer: Cristobal Fresno URL: http://www.bdmg.com.ar/ source.ver: src/contrib/pbcmc_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pbcmc_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pbcmc_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pbcmc_1.0.0.tgz vignettes: vignettes/pbcmc/inst/doc/pbcmc-vignette.pdf vignetteTitles: PermutationBased Confidence for Molecular Class hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pbcmc/inst/doc/pbcmc-vignette.R Package: pcaExplorer Version: 1.0.2 Imports: DESeq2, SummarizedExperiment, GenomicRanges, IRanges, S4Vectors, genefilter, ggplot2 (>= 2.0.0), d3heatmap, scales, NMF, plyr, topGO, limma, GOstats, GO.db, AnnotationDbi, shiny (>= 0.12.0), shinydashboard, shinyBS, ggrepel, DT, grDevices, methods Suggests: knitr, testthat, BiocStyle, rmarkdown, airway, org.Hs.eg.db License: MIT + file LICENSE MD5sum: 039dd798eed2e4f0f6f7478e39e4869b 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: Visualization, RNASeq, DimensionReduction, PrincipalComponent, QualityControl, GUI Author: Federico Marini [aut, cre] Maintainer: Federico Marini URL: https://github.com/federicomarini/pcaExplorer VignetteBuilder: knitr BugReports: https://github.com/federicomarini/pcaExplorer/issues source.ver: src/contrib/pcaExplorer_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcaExplorer_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/pcaExplorer_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcaExplorer_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pcaExplorer/inst/doc/pcaExplorer.R htmlDocs: vignettes/pcaExplorer/inst/doc/pcaExplorer.html htmlTitles: pcaExplorer User Guide Package: pcaGoPromoter Version: 1.16.0 Depends: R (>= 2.14.0), ellipse, Biostrings Imports: AnnotationDbi Suggests: Rgraphviz, GO.db, hgu133plus2.db, mouse4302.db, rat2302.db, hugene10sttranscriptcluster.db, mogene10sttranscriptcluster.db, pcaGoPromoter.Hs.hg19, pcaGoPromoter.Mm.mm9, pcaGoPromoter.Rn.rn4, serumStimulation, parallel License: GPL (>= 2) MD5sum: bde8cbd4a69a1c3c56939aa559368633 NeedsCompilation: no Title: pcaGoPromoter is used to analyze DNA micro array data Description: This package contains functions to ease the analyses of DNA micro arrays. It utilizes principal component analysis as the initial multivariate analysis, followed by functional interpretation of the principal component dimensions with overrepresentation analysis for GO terms and regulatory interpretations using overrepresentation analysis of predicted transcription factor binding sites with the primo algorithm. biocViews: GeneExpression, Microarray, GO , Visualization Author: Morten Hansen, Jorgen Olsen Maintainer: Morten Hansen source.ver: src/contrib/pcaGoPromoter_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcaGoPromoter_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pcaGoPromoter_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pcaGoPromoter_1.13.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcaGoPromoter_1.16.0.tgz vignettes: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.pdf vignetteTitles: pcaGoPromoter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.R Package: pcaMethods Version: 1.64.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice, ggplot2 License: GPL (>= 3) Archs: i386, x64 MD5sum: 6d9a2260670193960184776a9d11dbb8 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 source.ver: src/contrib/pcaMethods_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcaMethods_1.64.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pcaMethods_1.64.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pcaMethods_1.59.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcaMethods_1.64.0.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: TRUE 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 importsMe: CompGO, metaX, MSnbase, PanVizGenerator, scde, SomaticSignatures Package: PCAN Version: 1.0.2 Depends: R (>= 3.3), BiocParallel Imports: grDevices, stats Suggests: BiocStyle, knitr, rmarkdown, reactome.db, STRINGdb License: CC BY-NC-ND 4.0 MD5sum: 4f9f554a03633a5123937e577f926f32 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 source.ver: src/contrib/PCAN_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/PCAN_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/PCAN_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PCAN_1.0.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PCAN/inst/doc/PCAN.R htmlDocs: vignettes/PCAN/inst/doc/PCAN.html htmlTitles: Assessing gene relevance for a set of phenotypes Package: pcot2 Version: 1.40.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) MD5sum: 9514ed955fb05cbd5303ef0482865321 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 source.ver: src/contrib/pcot2_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pcot2_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pcot2_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pcot2_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pcot2_1.40.0.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 Package: PCpheno Version: 1.34.0 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: 3b78f8cf28300bfd6bbdccca3ccf2cec 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 source.ver: src/contrib/PCpheno_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PCpheno_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PCpheno_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PCpheno_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PCpheno_1.34.0.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 Package: pdInfoBuilder Version: 1.36.0 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: 4c3b5d9f1c55a58ccdc6eda5b6da2c5d 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 source.ver: src/contrib/pdInfoBuilder_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pdInfoBuilder_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pdInfoBuilder_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pdInfoBuilder_1.33.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pdInfoBuilder_1.36.0.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 Package: pdmclass Version: 1.44.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), fibroEset, mda License: Artistic-2.0 MD5sum: 72e6f9c10ebc8078b16f7c77f4372e01 NeedsCompilation: no Title: Classification of Microarray Samples using Penalized Discriminant Methods Description: This package can be used to classify microarray data using one of three penalized regression methods; partial least squares, principal components regression, or ridge regression. biocViews: Classification Author: James W. MacDonald, Debashis Ghosh, based in part on pls code of Mike Denham Maintainer: James W. MacDonald source.ver: src/contrib/pdmclass_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pdmclass_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pdmclass_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pdmclass_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pdmclass_1.44.0.tgz vignettes: vignettes/pdmclass/inst/doc/pdmclass.pdf vignetteTitles: pdmclass Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pdmclass/inst/doc/pdmclass.R suggestsMe: oneChannelGUI Package: PECA Version: 1.8.0 Depends: R (>= 3.3) Imports: ROTS, limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: d7fab3b35dab288352cc9997481b8598 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 source.ver: src/contrib/PECA_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PECA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PECA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PECA_1.5.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PECA_1.8.0.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 Package: pepStat Version: 1.6.2 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: 2437d72bdfa112b17cf1ca313c730d15 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 source.ver: src/contrib/pepStat_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/pepStat_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/pepStat_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/pepStat_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pepStat_1.6.2.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 Package: pepXMLTab Version: 1.6.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 7dc069a4482feea79c0eb549b10470a5 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: Proteomics, MassSpectrometry Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/pepXMLTab_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pepXMLTab_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pepXMLTab_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pepXMLTab_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pepXMLTab_1.6.0.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 Package: PGA Version: 1.2.2 Depends: R (>= 3.0.1), 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 (>= 1.7.0), pheatmap Suggests: BSgenome.Hsapiens.UCSC.hg19, RUnit, BiocGenerics, BiocStyle, knitr, R.utils License: GPL-2 MD5sum: 40951659466c824d245e10dc3a814820 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, MassSpectrometry, Software, ReportWriting, RNASeq, Sequencing Author: Shaohang Xu, Bo Wen Maintainer: Bo Wen , Shaohang Xu VignetteBuilder: knitr source.ver: src/contrib/PGA_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/PGA_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/PGA_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/PGA_0.99.10.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PGA_1.2.2.tgz 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 Package: PGSEA Version: 1.46.0 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 MD5sum: 264be8c69372e5f5632a9e6dc3ab2108 NeedsCompilation: no Title: Parametric Gene Set Enrichment Analysis Description: Parametric Analysis of Gene Set Enrichment biocViews: Microarray Author: Kyle Furge and Karl Dykema Maintainer: Karl Dykema source.ver: src/contrib/PGSEA_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PGSEA_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PGSEA_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PGSEA_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PGSEA_1.46.0.tgz vignettes: vignettes/PGSEA/inst/doc/PGSEA.pdf, vignettes/PGSEA/inst/doc/PGSEA2.pdf vignetteTitles: HOWTO: PGSEA, HOWTO: PGSEA Example Workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PGSEA/inst/doc/PGSEA.R, vignettes/PGSEA/inst/doc/PGSEA2.R dependsOnMe: GeneExpressionSignature Package: phenoDist Version: 1.20.0 Depends: R (>= 2.9.0), imageHTS, e1071 Suggests: GOstats, MASS License: LGPL-2.1 MD5sum: 0e15fa52544692c97bcf5d4c2ee37927 NeedsCompilation: no Title: Phenotypic distance measures Description: PhenoDist is designed for measuring phenotypic distance in image-based high-throughput screening, in order to identify strong phenotypes and to group treatments into functional clusters. biocViews: CellBasedAssays Author: Xian Zhang, Gregoire Pau, Wolfgang Huber, Michael Boutros Maintainer: Xian Zhang URL: http://www.dkfz.de/signaling, http://www.embl.de/research/units/genome_biology/huber/ source.ver: src/contrib/phenoDist_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/phenoDist_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/phenoDist_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/phenoDist_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/phenoDist_1.20.0.tgz vignettes: vignettes/phenoDist/inst/doc/phenoDist.pdf vignetteTitles: Introduction to phenoDist hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/phenoDist/inst/doc/phenoDist.R Package: phenoTest Version: 1.20.0 Depends: R (>= 2.12.0), Biobase, methods, annotate, Heatplus, BMA, ggplot2 Imports: survival, limma, Hmisc, gplots, Category, AnnotationDbi, hopach, biomaRt, GSEABase, genefilter, xtable, annotate, mgcv, SNPchip, hgu133a.db, HTSanalyzeR, 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: 2b8c256ae5d09f092d59c030098e0d0c 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 source.ver: src/contrib/phenoTest_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/phenoTest_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/phenoTest_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/phenoTest_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/phenoTest_1.20.0.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 Package: PhenStat Version: 2.6.0 Depends: R (>= 2.3.0) Imports: methods, car, nlme, nortest, MASS, logistf Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: fb4bb21c85e13c25b58f65a994cdc866 NeedsCompilation: no Title: Statistical analysis of phenotypic data Description: Package contains methods for statistical analysis of phenotypic data. Author: Natalja Kurbatova, Natasha Karp, Jeremy Mason Maintainer: Natasha Karp source.ver: src/contrib/PhenStat_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PhenStat_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PhenStat_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PhenStat_2.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PhenStat_2.6.0.tgz vignettes: vignettes/PhenStat/inst/doc/PhenStat.pdf, vignettes/PhenStat/inst/doc/PhenStatUsersGuide.pdf vignetteTitles: PhenStat Vignette, PhenStatUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/PhenStat/inst/doc/PhenStat.R Package: phyloseq Version: 1.16.2 Depends: R (>= 3.2.0) Imports: BiocGenerics (>= 0.14.0), ade4 (>= 1.7.2), ape (>= 3.1.1), biomformat (>= 0.4.0), Biostrings (>= 2.28.0), cluster (>= 1.14.4), data.table (>= 1.9.6), foreach (>= 1.4.2), ggplot2 (>= 2.1.0), igraph (>= 0.7.0), methods (>= 3.1.0), multtest (>= 2.16.0), plyr (>= 1.8), reshape2 (>= 1.2.2), scales (>= 0.3.0), vegan (>= 2.0.10), Biobase Suggests: BiocStyle (>= 1.6), DESeq2 (>= 1.8), genefilter (>= 1.50), testthat (>= 0.10), knitr (>= 1.10), metagenomeSeq (>= 1.10), rmarkdown (>= 0.7) Enhances: doParallel (>= 1.0.1) License: AGPL-3 MD5sum: f2975a42a005d6537820d8ff67bceba6 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: 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 source.ver: src/contrib/phyloseq_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/phyloseq_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.3/phyloseq_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.3/phyloseq_1.13.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/phyloseq_1.16.2.tgz 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 htmlDocs: 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 htmlTitles: analysis vignette, phyloseq basics vignette, phyloseq Frequently Asked Questions (FAQ), phyloseq and DESeq2 on Colorectal Cancer Data dependsOnMe: RPA Package: piano Version: 1.12.1 Depends: R (>= 2.14.0) Imports: BiocGenerics, Biobase, gplots, igraph, relations, marray Suggests: yeast2.db, rsbml, plotrix, limma, affy, plier, affyPLM, gtools, biomaRt, snowfall, AnnotationDbi License: GPL (>=2) MD5sum: 38972b1001c9c336cb0e2f6b5368c832 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 and Intawat Nookaew Maintainer: Leif Varemo URL: http://www.sysbio.se/piano source.ver: src/contrib/piano_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/piano_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/piano_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.3/piano_1.9.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/piano_1.12.1.tgz vignettes: vignettes/piano/inst/doc/piano-vignette.pdf vignetteTitles: Piano - Platform for Integrative Analysis of Omics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/piano/inst/doc/piano-vignette.R importsMe: saps Package: pickgene Version: 1.44.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 1ede10d54187dda8c620e7fbcc8f890e 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 source.ver: src/contrib/pickgene_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pickgene_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pickgene_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pickgene_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pickgene_1.44.0.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 Package: PICS Version: 2.16.0 Depends: R (>= 2.14.0), BiocGenerics (>= 0.1.3) Imports: methods, stats4, IRanges, GenomicRanges, graphics, grDevices, stats, Rsamtools, GenomicAlignments, S4Vectors Suggests: ShortRead, rtracklayer, parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 0a325a07e80598e4742635b7171c8dae 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 source.ver: src/contrib/PICS_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PICS_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PICS_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PICS_2.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PICS_2.16.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.pdf vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PICS/inst/doc/PICS.R importsMe: PING Package: PING Version: 2.16.0 Depends: R(>= 2.15.0), chipseq, IRanges, GenomicRanges Imports: methods, PICS, graphics, grDevices, stats, Gviz, fda, BSgenome, stats4, BiocGenerics, IRanges, S4Vectors Suggests: parallel, ShortRead, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: a882a849be66f6fab71e7a8ec6fee015 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 source.ver: src/contrib/PING_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PING_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PING_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PING_2.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PING_2.16.0.tgz vignettes: vignettes/PING/inst/doc/PING-PE.pdf, vignettes/PING/inst/doc/PING.pdf vignetteTitles: Using PING with paired-end sequencing data, The PING users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PING/inst/doc/PING-PE.R, vignettes/PING/inst/doc/PING.R Package: pint Version: 1.22.0 Depends: mvtnorm, methods, graphics, Matrix, dmt License: BSD_2_clause + file LICENSE MD5sum: 3b5ab26fb41aaa6e55f52b6f0756cefc NeedsCompilation: no Title: Pairwise INTegration of functional genomics data Description: Pairwise data integration for functional genomics, including tools for DNA/RNA/miRNA dependency screens. biocViews: aCGH, GeneExpression, Genetics, DifferentialExpression, Microarray Author: Olli-Pekka Huovilainen and Leo Lahti Maintainer: Olli-Pekka Huovilainen URL: https://github.com/antagomir/pint BugReports: https://github.com/antagomir/pint/issues source.ver: src/contrib/pint_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pint_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pint_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pint_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pint_1.22.0.tgz vignettes: vignettes/pint/inst/doc/depsearch.pdf vignetteTitles: pint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pint/inst/doc/depsearch.R Package: pkgDepTools Version: 1.38.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocInstaller License: GPL-2 MD5sum: 38e39e3dc9a641501488c0359a60c4bc 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 Maintainer: Seth Falcon source.ver: src/contrib/pkgDepTools_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pkgDepTools_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pkgDepTools_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pkgDepTools_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pkgDepTools_1.38.0.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 Package: plateCore Version: 1.30.0 Depends: R (>= 2.10), flowCore, flowViz, lattice, latticeExtra Imports: Biobase, flowCore, graphics, grDevices, lattice, MASS, methods, robustbase, stats, utils, flowStats Suggests: gplots License: Artistic-2.0 MD5sum: 7e47b81d8ed4132ca3c97e77ec799d77 NeedsCompilation: no Title: Statistical tools and data structures for plate-based flow cytometry Description: Provides basic S4 data structures and routines for analyzing plate based flow cytometry data. biocViews: FlowCytometry, Infrastructure, CellBasedAssays Author: Errol Strain, Florian Hahne, and Perry Haaland Maintainer: Errol Strain URL: http://www.bioconductor.org source.ver: src/contrib/plateCore_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plateCore_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plateCore_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/plateCore_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plateCore_1.30.0.tgz vignettes: vignettes/plateCore/inst/doc/plateCoreVig.pdf vignetteTitles: An R Package for Analysis of High Throughput Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plateCore/inst/doc/plateCoreVig.R Package: plethy Version: 1.10.0 Depends: R (>= 3.1.0), methods, BiocGenerics, S4Vectors Imports: Streamer, DBI, RSQLite (>= 1.0.0), IRanges, reshape2, plyr, RColorBrewer,ggplot2, Biobase Suggests: RUnit, BiocStyle License: GPL-3 MD5sum: 04975ccf13c92c8d656a8c5c42a1aade 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 source.ver: src/contrib/plethy_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plethy_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plethy_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/plethy_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plethy_1.10.0.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 Package: plgem Version: 1.44.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS License: GPL-2 MD5sum: b7a102437ca15dd6b1277e4b028c7557 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: Microarray, DifferentialExpression, Proteomics, GeneExpression, MassSpectrometry Author: Mattia Pelizzola and Norman Pavelka Maintainer: Norman Pavelka URL: http://www.genopolis.it source.ver: src/contrib/plgem_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plgem_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plgem_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/plgem_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plgem_1.44.0.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 Package: plier Version: 1.42.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: e30980d029c2260e9cad406701e3de18 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 source.ver: src/contrib/plier_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plier_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plier_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/plier_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plier_1.42.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano Package: PLPE Version: 1.32.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: 1b8e5c5734f54c075f98475a9d9fb723 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/ source.ver: src/contrib/PLPE_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PLPE_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PLPE_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PLPE_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PLPE_1.32.0.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 Package: plrs Version: 1.12.0 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) MD5sum: 4c1ec89c8e4b78bdbc001cf34151a675 NeedsCompilation: no 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 source.ver: src/contrib/plrs_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plrs_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plrs_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/plrs_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plrs_1.12.0.tgz vignettes: vignettes/plrs/inst/doc/plrs_vignette.pdf vignetteTitles: plrs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plrs/inst/doc/plrs_vignette.R Package: plw Version: 1.32.0 Depends: R (>= 2.10), affy (>= 1.23.4) Imports: MASS, affy, graphics, splines, stats Suggests: limma License: GPL-2 Archs: i386, x64 MD5sum: 26e0c2366cf0d8d38f53d1609bfbd9ef NeedsCompilation: yes Title: Probe level Locally moderated Weighted t-tests. Description: Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). biocViews: Microarray, OneChannel, TwoChannel, DifferentialExpression Author: Magnus Astrand Maintainer: Magnus Astrand source.ver: src/contrib/plw_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/plw_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/plw_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/plw_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/plw_1.32.0.tgz vignettes: vignettes/plw/inst/doc/HowToPLW.pdf vignetteTitles: HowTo plw hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/plw/inst/doc/HowToPLW.R Package: pmm Version: 1.4.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: aeef54baf2833477e9a23710317e8f99 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 source.ver: src/contrib/pmm_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pmm_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pmm_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pmm_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pmm_1.4.0.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 Package: podkat Version: 1.4.2 Depends: R (>= 3.2.0), methods, Rsamtools, GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, grDevices, utils, Biobase, BiocGenerics, Matrix, GenomeInfoDb, IRanges, Biostrings, BSgenome (>= 1.32.0) LinkingTo: Rcpp, Rsamtools Suggests: BSgenome.Hsapiens.UCSC.hg38.masked, TxDb.Hsapiens.UCSC.hg38.knownGene, BSgenome.Mmusculus.UCSC.mm10.masked, GWASTools (>= 1.13.24), VariantAnnotation, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: aab8f44ee1d0e1e2c317f9d9d08f3506 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/ VignetteBuilder: knitr source.ver: src/contrib/podkat_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/podkat_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/podkat_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/podkat_1.1.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/podkat_1.4.2.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 Package: polyester Version: 1.8.3 Depends: R (>= 3.0.0) Imports: BiocGenerics, Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 7429b0437f35c338bc37cf13c4d5f551 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, Jeffrey T. Leek Maintainer: Alyssa Frazee , Jeff Leek VignetteBuilder: knitr source.ver: src/contrib/polyester_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/polyester_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/polyester_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.3/polyester_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/polyester_1.8.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/polyester/inst/doc/polyester.R htmlDocs: vignettes/polyester/inst/doc/polyester.html htmlTitles: The Polyester package for simulating RNA-seq reads Package: Polyfit Version: 1.6.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) MD5sum: b58d2ec37a932148bda6f254d287c083 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: DifferentialExpression, Sequencing, RNASeq, GeneExpression Author: Conrad Burden Maintainer: Conrad Burden source.ver: src/contrib/Polyfit_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Polyfit_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Polyfit_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Polyfit_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Polyfit_1.6.0.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 Package: ppiStats Version: 1.38.0 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: 5fd89f4af1927157c48d4135cc342c02 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 source.ver: src/contrib/ppiStats_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ppiStats_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ppiStats_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ppiStats_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ppiStats_1.38.0.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 Package: pqsfinder Version: 1.0.2 Depends: Biostrings Imports: Rcpp (>= 0.12.3), GenomicRanges, IRanges, S4Vectors, methods LinkingTo: Rcpp, BH, flowCore Suggests: BiocStyle, knitr, Gviz, rtracklayer, biomaRt, BSgenome.Hsapiens.UCSC.hg38 License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: e8df1c9acd0907efcd9fafca60541c20 NeedsCompilation: yes Title: Identification of potential quadruplex forming sequences Description: The main functionality of the this package is to detect DNA sequence patterns that are likely to fold into an intramolecular G-quadruplex (G4). Unlike many other approaches, this package is able to detect sequences responsible for G4s folded from imperfect G-runs containing bulges or mismatches and as such is more sensitive than competing algorithms. biocViews: MotifDiscovery, SequenceMatching, GeneRegulation Author: Jiri Hon, Matej Lexa and Tomas Martinek Maintainer: Jiri Hon SystemRequirements: GNU make VignetteBuilder: knitr source.ver: src/contrib/pqsfinder_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/pqsfinder_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/pqsfinder_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pqsfinder_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/pqsfinder/inst/doc/pqsfinder.R htmlDocs: vignettes/pqsfinder/inst/doc/pqsfinder.html htmlTitles: pqsfinder: User Guide Package: prada Version: 1.48.0 Depends: R (>= 2.10), Biobase, RColorBrewer, grid, methods, rrcov Imports: Biobase, BiocGenerics, graphics, grDevices, grid, MASS, methods, RColorBrewer, rrcov, stats4, utils Suggests: cellHTS, tcltk License: LGPL Archs: i386, x64 MD5sum: a83b8402e9b33b252c577552757c6aa2 NeedsCompilation: yes 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: CellBasedAssays, Visualization Author: Florian Hahne , Wolfgang Huber , Markus Ruschhaupt, Joern Toedling , Joseph Barry Maintainer: Florian Hahne source.ver: src/contrib/prada_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/prada_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/prada_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/prada_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/prada_1.48.0.tgz vignettes: vignettes/prada/inst/doc/norm2.pdf, vignettes/prada/inst/doc/prada2cellHTS.pdf vignetteTitles: Removal of contaminants from FACS data, Combining prada output and cellHTS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prada/inst/doc/norm2.R, vignettes/prada/inst/doc/prada2cellHTS.R dependsOnMe: domainsignatures, RNAither importsMe: cellHTS, cellHTS2 Package: prebs Version: 1.12.0 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: be0d5afa19e00670e0924e13fe3120d4 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: Microarray, RNASeq, Sequencing, GeneExpression, Preprocessing Author: Karolis Uziela and Antti Honkela Maintainer: Karolis Uziela source.ver: src/contrib/prebs_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/prebs_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/prebs_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/prebs_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/prebs_1.12.0.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 Package: PREDA Version: 1.18.0 Depends: R (>= 2.9.0), Biobase, lokern (>= 1.0.9), multtest, stats, methods, annotate Suggests: quantsmooth, qvalue, samr, limma, caTools, affy, PREDAsampledata Enhances: Rmpi, rsprng License: GPL-2 MD5sum: 896b21aebeeb25799e417578b45aec26 NeedsCompilation: no Title: Position RElated Data Anlysis Description: Package for the position related analysis of quantitative functional genomics data. biocViews: Software, CopyNumberVariation, GeneExpression, Genetics Author: Francesco Ferrari Maintainer: Francesco Ferrari source.ver: src/contrib/PREDA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PREDA_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PREDA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PREDA_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PREDA_1.18.0.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 Package: predictionet Version: 1.18.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: fb52c8d1c11862df218acc3b7e5197ab 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 source.ver: src/contrib/predictionet_1.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/predictionet_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/predictionet_1.18.0.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 Package: preprocessCore Version: 1.34.0 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: 1135fb620afece97fa5cf1e42cb3ec36 NeedsCompilation: yes Title: A collection of pre-processing functions Description: A library of core preprocessing routines biocViews: Infrastructure Author: Benjamin Milo Bolstad Maintainer: Benjamin Milo Bolstad URL: https://github.com/bmbolstad/preprocessCore source.ver: src/contrib/preprocessCore_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/preprocessCore_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/preprocessCore_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/preprocessCore_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/preprocessCore_1.34.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: affyPLM, CopyNumber450k, cqn, crlmm, RefPlus importsMe: affy, AffyTiling, ChAMP, charm, cn.farms, EMDomics, ExiMiR, frma, frmaTools, iCheck, InPAS, INSPEcT, lumi, MBCB, MEDIPS, metaX, minfi, MSnbase, MSstats, oligo, PECA, soGGi, waveTiling suggestsMe: multiClust, oneChannelGUI Package: Prize Version: 1.2.0 Imports: diagram, stringr, ggplot2, reshape2, grDevices, matrixcalc, stats, gplots, methods, utils, graphics Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d928f519e19ce4217ca17f56ae5f6c85 NeedsCompilation: no 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: Software, MultipleComparison, GeneExpression, CellBiology, RNASeq Author: Daryanaz Dargahi Maintainer: Daryanaz Dargahi source.ver: src/contrib/Prize_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Prize_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Prize_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Prize_0.99.16.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Prize_1.2.0.tgz vignettes: vignettes/Prize/inst/doc/Prize.pdf vignetteTitles: Prize: an R package for prioritization estimation based on analytic hierarchy process hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prize/inst/doc/Prize.R Package: proBAMr Version: 1.6.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: c61f8feaf0b6fa21207fb50d5d0004b7 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: Proteomics, MassSpectrometry, Software, Visualization Author: Xiaojing Wang Maintainer: Xiaojing Wang source.ver: src/contrib/proBAMr_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/proBAMr_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/proBAMr_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/proBAMr_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/proBAMr_1.6.0.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 Package: PROcess Version: 1.48.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: c73b7068414fb86b773a8ace5187dc20 NeedsCompilation: no Title: Ciphergen SELDI-TOF Processing Description: A package for processing protein mass spectrometry data. biocViews: MassSpectrometry, Proteomics Author: Xiaochun Li Maintainer: Xiaochun Li source.ver: src/contrib/PROcess_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PROcess_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PROcess_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PROcess_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PROcess_1.48.0.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 Package: procoil Version: 2.0.2 Depends: R (>= 3.3.0), kebabs Imports: methods, stats, graphics, S4Vectors, Biostrings, utils Suggests: knitr License: GPL (>= 2) MD5sum: 8cd01df3fe84f9bc2ed49361d8d27549 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/ VignetteBuilder: knitr source.ver: src/contrib/procoil_2.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/procoil_2.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/procoil_2.0.2.zip mac.binary.ver: bin/macosx/contrib/3.3/procoil_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/procoil_2.0.2.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 Package: ProCoNA Version: 1.10.0 Depends: R (>= 2.10), methods, WGCNA, MSnbase, flashClust Imports: BiocGenerics, GOstats Suggests: RUnit License: GPL (>= 2) MD5sum: c4f26094d4d3c3091487e03812c3fe76 NeedsCompilation: no Title: Protein co-expression network analysis (ProCoNA). Description: Protein co-expression network construction using peptide level data, with statisical analysis. (Journal of Clinical Bioinformatics 2013, 3:11 doi:10.1186/2043-9113-3-11) biocViews: GraphAndNetwork, Software, Proteomics Author: David L Gibbs Maintainer: David L Gibbs source.ver: src/contrib/ProCoNA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ProCoNA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ProCoNA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ProCoNA_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ProCoNA_1.10.0.tgz vignettes: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.pdf vignetteTitles: De Novo Peptide Network Example hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.R Package: profileScoreDist Version: 1.0.2 Depends: R(>= 3.3) Imports: Rcpp, BiocGenerics, methods, graphics LinkingTo: Rcpp Suggests: BiocStyle, knitr, MotifDb License: MIT + file LICENSE Archs: i386, x64 MD5sum: df622d256b2ae1343c6f2044cf49dda5 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 source.ver: src/contrib/profileScoreDist_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/profileScoreDist_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/profileScoreDist_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/profileScoreDist_1.0.2.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 Package: pRoloc Version: 1.12.4 Depends: R (>= 2.15), MSnbase (>= 1.19.20), MLInterfaces (>= 1.37.1), methods, Rcpp (>= 0.10.3), BiocParallel Imports: Biobase, mclust (>= 4.3), caret, e1071, sampling, class, kernlab, lattice, nnet, randomForest, proxy, FNN, BiocGenerics, stats, RColorBrewer, scales, MASS, knitr, mvtnorm, gtools, plyr, ggplot2, biomaRt, utils, grDevices, graphics LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, pRolocdata (>= 1.9.4), roxygen2, synapter, xtable, tsne, BiocStyle, hpar, dplyr, GO.db, AnnotationDbi License: GPL-2 Archs: i386, x64 MD5sum: db81476a5beb765a7cd6ffecf06278a3 NeedsCompilation: yes Title: A unifying bioinformatics framework for spatial proteomics Description: This package implements pattern recognition techniques on quantitiative mass spectrometry data to infer protein sub-cellular localisation. biocViews: Proteomics, MassSpectrometry, Classification, Clustering, QualityControl Author: Laurent Gatto 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 source.ver: src/contrib/pRoloc_1.12.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/pRoloc_1.12.4.zip win64.binary.ver: bin/windows64/contrib/3.3/pRoloc_1.12.4.zip mac.binary.ver: bin/macosx/contrib/3.3/pRoloc_1.9.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pRoloc_1.12.4.tgz vignettes: vignettes/pRoloc/inst/doc/HUPO_2011_poster.pdf, vignettes/pRoloc/inst/doc/HUPO_2014_poster.pdf, vignettes/pRoloc/inst/doc/pRoloc-ml.pdf, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.pdf, vignettes/pRoloc/inst/doc/pRoloc-tutorial.pdf vignetteTitles: HUPO 2011 poster: pRoloc -- A unifying bioinformatics framework for organelle proteomics, HUPO 2014 poster: A state-of-the-art machine learning pipeline for the analysis of spatial proteomics data, Machine learning techniques available in pRoloc, A transfer learning algorithm for spatial proteomics, pRoloc tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRoloc/inst/doc/HUPO_2011_poster.R, vignettes/pRoloc/inst/doc/HUPO_2014_poster.R, vignettes/pRoloc/inst/doc/pRoloc-goannotations.R, vignettes/pRoloc/inst/doc/pRoloc-transfer-learning.R htmlDocs: vignettes/pRoloc/inst/doc/pRoloc-goannotations.html htmlTitles: Annotating spatial proteomics data dependsOnMe: pRolocGUI suggestsMe: MSnbase Package: pRolocGUI Version: 1.6.2 Depends: R (>= 3.1.0), pRoloc (>= 1.11.1), MSnbase (>= 1.13.11), methods Imports: shiny (>= 0.9.1), scales, dplyr, DT, utils, graphics Suggests: pRolocdata, knitr, BiocStyle, rmarkdown, devtools License: GPL-2 MD5sum: f13b37016cd86c03cddcfac0daed61ed 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 M Breckels, Thomas Naake and Laurent Gatto Maintainer: Laurent Gatto , Lisa M Breckels URL: http://ComputationalProteomicsUnit.github.io/pRolocGUI/ VignetteBuilder: knitr Video: https://www.youtube.com/playlist?list=PLvIXxpatSLA2loV5Srs2VBpJIYUlVJ4ow BugReports: https://github.com/ComputationalProteomicsUnit/pRolocGUI/issues source.ver: src/contrib/pRolocGUI_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/pRolocGUI_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/pRolocGUI_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/pRolocGUI_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pRolocGUI_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pRolocGUI/inst/doc/pRolocGUI.R htmlDocs: vignettes/pRolocGUI/inst/doc/pRolocGUI.html htmlTitles: pRolocGUI - Interactive visualisation of spatial proteomics data Package: PROMISE Version: 1.24.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: 71e83b6bce35a30b00e70390398955d6 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 source.ver: src/contrib/PROMISE_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PROMISE_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PROMISE_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PROMISE_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PROMISE_1.24.0.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 Package: PROPER Version: 1.4.2 Depends: R (>= 2.10) Imports: edgeR Suggests: BiocStyle,DESeq,DSS,knitr License: GPL MD5sum: ac076f5add6545c8f46635f36d2843ea 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: Sequencing, RNASeq, DifferentialExpression Author: Hao Wu Maintainer: Hao Wu VignetteBuilder: knitr source.ver: src/contrib/PROPER_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/PROPER_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/PROPER_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/PROPER_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PROPER_1.4.2.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 Package: Prostar Version: 1.4.12 Depends: R (>= 3.3) Imports: DAPAR (>= 1.4.6), DAPARdata, rhandsontable, data.table, shinyjs, DT, shiny, shinyAce Suggests: BiocStyle License: Artistic-2.0 MD5sum: 0fa6e456f2d55ecc6e74fb65113c800b NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: MassSpectrometry, Normalization, Preprocessing, Proteomics, GUI Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut] Maintainer: Samuel Wieczorek source.ver: src/contrib/Prostar_1.4.12.tar.gz win.binary.ver: bin/windows/contrib/3.3/Prostar_1.4.12.zip win64.binary.ver: bin/windows64/contrib/3.3/Prostar_1.4.12.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Prostar_1.4.12.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_Tutorial.pdf, vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar tutorial, Prostar user manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Prostar/inst/doc/Prostar_Tutorial.R, vignettes/Prostar/inst/doc/Prostar_UserManual.R suggestsMe: DAPAR Package: prot2D Version: 1.10.0 Depends: R (>= 2.15),fdrtool,st,samr,Biobase,limma,Mulcom,impute,MASS,qvalue Suggests: made4,affy License: GPL (>= 2) MD5sum: 42ad887bfc0b901e9ea19b902668fa64 NeedsCompilation: no Title: Statistical Tools for volume data from 2D Gel Electrophoresis Description: The purpose of this package is to analyze (i.e. Normalize and select significant spots) data issued from 2D GEl experiments biocViews: DifferentialExpression, MultipleComparison, Proteomics Author: Sebastien Artigaud Maintainer: Sebastien Artigaud source.ver: src/contrib/prot2D_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/prot2D_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/prot2D_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/prot2D_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/prot2D_1.10.0.tgz vignettes: vignettes/prot2D/inst/doc/prot2D.pdf vignetteTitles: prot2D hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/prot2D/inst/doc/prot2D.R Package: proteinProfiles Version: 1.12.1 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 99cafa076930bd305ace11232b9da0d6 NeedsCompilation: no Title: Protein Profiling Description: Significance assessment for distance measures of time-course protein profiles Author: Julian Gehring Maintainer: Julian Gehring source.ver: src/contrib/proteinProfiles_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/proteinProfiles_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/proteinProfiles_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.3/proteinProfiles_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/proteinProfiles_1.12.1.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 Package: ProteomicsAnnotationHubData Version: 1.2.2 Depends: AnnotationHub (>= 2.1.45), AnnotationHubData, Imports: mzR (>= 2.3.2), MSnbase, Biostrings, GenomeInfoDb, utils, Biobase, BiocInstaller, RCurl Suggests: knitr, BiocStyle, rmarkdown, testthat License: Artistic-2.0 MD5sum: 8bfe5764923b3d33db6d754bd24c8b66 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 source.ver: src/contrib/ProteomicsAnnotationHubData_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ProteomicsAnnotationHubData_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ProteomicsAnnotationHubData_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ProteomicsAnnotationHubData_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.R htmlDocs: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html htmlTitles: Proteomics Data in Annotation Hub Package: proteoQC Version: 1.8.2 Depends: R (>= 3.0.0), XML, VennDiagram, MSnbase Imports: rTANDEM, plyr, seqinr, Nozzle.R1, ggplot2, reshape2, parallel, Rcpp (>= 0.11.1) LinkingTo: Rcpp Suggests: RforProteomics, knitr, BiocStyle, rpx, R.utils, RUnit,BiocGenerics License: LGPL-2 Archs: i386, x64 MD5sum: da6afea7429e6f7ad79a0defe8292d53 NeedsCompilation: yes Title: An R package for proteomics data quality control Description: This package creates a HTML format QC report for MS/MS-based proteomics data. The report is intended to allow the user to quickly assess the quality of proteomics data. biocViews: Proteomics, MassSpectrometry, QualityControl, Visualization, ReportWriting Author: Bo Wen , Laurent Gatto Maintainer: Bo Wen VignetteBuilder: knitr source.ver: src/contrib/proteoQC_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/proteoQC_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/proteoQC_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/proteoQC_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/proteoQC_1.8.2.tgz vignettes: vignettes/proteoQC/inst/doc/proteoQC.pdf vignetteTitles: proteoQC tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/proteoQC/inst/doc/proteoQC.R Package: ProtGenerics Version: 1.4.0 Depends: methods License: Artistic-2.0 MD5sum: 8162ad42480096fc860860c4db61a598 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 source.ver: src/contrib/ProtGenerics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ProtGenerics_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ProtGenerics_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ProtGenerics_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ProtGenerics_1.4.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, tofsims, xcms importsMe: MSnID, mzID, mzR Package: PSEA Version: 1.6.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: a11cf3a399765214b26c4390c40ef579 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 source.ver: src/contrib/PSEA_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PSEA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PSEA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PSEA_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PSEA_1.6.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PSEA/inst/doc/PSEA.R Package: PSICQUIC Version: 1.10.0 Depends: R (>= 3.2.2), methods, IRanges, biomaRt, BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: fbe2f7106ed7b1901e871941f751b756 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 source.ver: src/contrib/PSICQUIC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/PSICQUIC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/PSICQUIC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/PSICQUIC_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PSICQUIC_1.10.0.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 dependsOnMe: RefNet Package: psygenet2r Version: 1.4.0 Depends: R (>= 3.3) Imports: stringr, RCurl, igraph, ggplot2, reshape2, grid, parallel, biomaRt, BgeeDB, topGO Suggests: testthat License: MIT + file LICENSE MD5sum: 8554706f0664cad4ed6c45faadcb6ea1 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], Carles Hernandez-Ferrer [cre] Maintainer: Alba Gutierrez-Sacristan source.ver: src/contrib/psygenet2r_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/psygenet2r_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/psygenet2r_1.4.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/psygenet2r_1.4.0.tgz vignettes: vignettes/psygenet2r/inst/doc/caseStudy.pdf, vignettes/psygenet2r/inst/doc/generalOverview.pdf 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/caseStudy.R, vignettes/psygenet2r/inst/doc/generalOverview.R Package: puma Version: 3.14.0 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: 7f1d84babe9bc21470785f7c7f033472 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 source.ver: src/contrib/puma_3.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/puma_3.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/puma_3.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/puma_3.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/puma_3.14.0.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 Package: PureCN Version: 1.0.4 Depends: R (>= 3.3), DNAcopy, VariantAnnotation (>= 1.14.1) Imports: GenomicRanges (>= 1.20.3), IRanges (>= 2.2.1), RColorBrewer, S4Vectors, data.table, grDevices, graphics, stats, utils, SummarizedExperiment, GenomeInfoDb Suggests: PSCBS, RUnit, BiocStyle, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 4b1c79f3973142b0df15861c257fcc3f NeedsCompilation: no Title: Estimating tumor purity, ploidy, LOH, and SNV status using hybrid capture NGS data Description: This package estimates tumor purity, copy number, loss of heterozygosity (LOH), and status of single nucleotide variants (SNVs). PureCN is designed for hybrid capture sequencing data, integrates well with standard somatic variant detection pipelines, and has support for tumor samples without matching normal samples. biocViews: CopyNumberVariation, Software, Sequencing, VariantAnnotation, VariantDetection Author: Markus Riester Maintainer: Markus Riester VignetteBuilder: knitr source.ver: src/contrib/PureCN_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/PureCN_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/PureCN_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PureCN_1.0.4.tgz vignettes: vignettes/PureCN/inst/doc/PureCN.pdf vignetteTitles: PureCN hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/PureCN/inst/doc/PureCN.R Package: pvac Version: 1.20.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: f5d69fc92da7b764f8a90f3ed15ea6a7 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 source.ver: src/contrib/pvac_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pvac_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pvac_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pvac_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pvac_1.20.0.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 Package: pvca Version: 1.12.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, stats, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: e9a91172e04fc51eb29f50ce475440a6 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 source.ver: src/contrib/pvca_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pvca_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pvca_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pvca_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pvca_1.12.0.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 Package: Pviz Version: 1.6.2 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: bb746dbe9c3314a00a5d260427757e83 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 source.ver: src/contrib/Pviz_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Pviz_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Pviz_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/Pviz_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Pviz_1.6.2.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 importsMe: Pbase suggestsMe: pepStat Package: PWMEnrich Version: 4.8.2 Depends: methods, grid, BiocGenerics, Biostrings, Imports: seqLogo, gdata, evd Suggests: MotifDb, BSgenome.Dmelanogaster.UCSC.dm3, PWMEnrich.Dmelanogaster.background, testthat, gtools, parallel, PWMEnrich.Hsapiens.background, PWMEnrich.Mmusculus.background, BiocStyle, knitr License: LGPL (>= 2) MD5sum: 097df59d0c8ff638d1fa41158e710d3d 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: Robert Stojnic VignetteBuilder: knitr source.ver: src/contrib/PWMEnrich_4.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/PWMEnrich_4.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/PWMEnrich_4.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/PWMEnrich_4.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/PWMEnrich_4.8.2.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 suggestsMe: rTRM Package: pwOmics Version: 1.4.0 Depends: R (>= 3.2) Imports: data.table, rBiopaxParser, igraph, STRINGdb, graphics, gplots, Biobase, BiocGenerics, AnnotationDbi, biomaRt, AnnotationHub, GenomicRanges Suggests: ebdbNet, longitudinal, Mfuzz License: GPL (>= 2) MD5sum: b1426a067e6dc83ef74d04345b19c93c 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 proteins. A separate downstream analysis of proteomic data including pathway identification and enrichment analysis, 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 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. biocViews: SystemsBiology, Transcription, GeneTarget Author: Astrid Wachter Maintainer: Astrid Wachter source.ver: src/contrib/pwOmics_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/pwOmics_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/pwOmics_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/pwOmics_1.1.14.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/pwOmics_1.4.0.tgz vignettes: vignettes/pwOmics/inst/doc/pwOmics.pdf vignetteTitles: pwOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/pwOmics/inst/doc/pwOmics.R Package: qcmetrics Version: 1.10.2 Depends: R (>= 2.10) 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: e038e96155c225d35a1bc7483c56b460 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: Software, Bioinformatics, QualityControl, Proteomics, Microarray, MassSpectrometry, Visualisation, ReportWriting Author: Laurent Gatto Maintainer: Laurent Gatto URL: https://github.com/lgatto/qcmetrics VignetteBuilder: knitr source.ver: src/contrib/qcmetrics_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/qcmetrics_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/qcmetrics_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/qcmetrics_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qcmetrics_1.10.2.tgz vignettes: vignettes/qcmetrics/inst/doc/qcmetrics.pdf vignetteTitles: The 'qcmetrics' infrastructure for quality control and reporting hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/qcmetrics/inst/doc/qcmetrics.R Package: QDNAseq Version: 1.8.1 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 (>= 0.15.0), R.utils (>= 2.2.0), Rsamtools (>= 1.20), Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), digest (>= 0.6.8), GenomeInfoDb (>= 1.6.0), future (>= 0.12.0), R.cache (>= 0.12.0) License: GPL MD5sum: 2950ec6385cfeb06524bf356ef7f3864 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] Maintainer: Daoud Sie URL: https://github.com/ccagc/QDNAseq BugReports: https://github.com/ccagc/QDNAseq/issues source.ver: src/contrib/QDNAseq_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/QDNAseq_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.3/QDNAseq_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.3/QDNAseq_1.5.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QDNAseq_1.8.1.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 Package: qpcrNorm Version: 1.30.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: 58fe224700135f5de70831f48532a559 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 source.ver: src/contrib/qpcrNorm_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/qpcrNorm_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/qpcrNorm_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/qpcrNorm_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qpcrNorm_1.30.0.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 Package: qpgraph Version: 2.6.1 Depends: R (>= 3.0.0) 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: 85fbec7dcfa849d526f0fe298b685796 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: http://functionalgenomics.upf.edu/qpgraph source.ver: src/contrib/qpgraph_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/qpgraph_2.6.1.zip win64.binary.ver: bin/windows64/contrib/3.3/qpgraph_2.6.1.zip mac.binary.ver: bin/macosx/contrib/3.3/qpgraph_2.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qpgraph_2.6.1.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, ToPASeq Package: qrqc Version: 1.26.0 Depends: reshape, ggplot2, Biostrings, biovizBase, brew, xtable, Rsamtools (>= 1.19.38), testthat Imports: reshape, ggplot2, Biostrings, biovizBase, graphics, methods, plyr, stats LinkingTo: Rsamtools License: GPL (>=2) Archs: i386, x64 MD5sum: ddd9308b01bb76c424175204c4d546fb 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 source.ver: src/contrib/qrqc_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/qrqc_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/qrqc_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/qrqc_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qrqc_1.26.0.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 Package: QUALIFIER Version: 1.16.1 Depends: R (>= 2.14.0),flowCore,flowViz,ncdfFlow,flowWorkspace, data.table,reshape Imports: MASS,hwriter,lattice,stats4,flowCore,flowViz,methods,flowWorkspace,latticeExtra,grDevices,tools, Biobase,XML,grid Suggests: RSVGTipsDevice, knitr License: Artistic-2.0 MD5sum: 57ce13caa9d81aca01ce0dac7e6bbd68 NeedsCompilation: no Title: Quality Control of Gated Flow Cytometry Experiments Description: Provides quality control and quality assessment tools for gated flow cytometry data. biocViews: Infrastructure, FlowCytometry, CellBasedAssays Author: Mike Jiang,Greg Finak,Raphael Gottardo Maintainer: Mike Jiang VignetteBuilder: knitr source.ver: src/contrib/QUALIFIER_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/QUALIFIER_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.3/QUALIFIER_1.16.1.zip mac.binary.ver: bin/macosx/contrib/3.3/QUALIFIER_1.13.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QUALIFIER_1.16.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: quantro Version: 1.6.2 Depends: R (>= 3.1.3) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: c02dc02e6e8f46d60b66fdf4719d10b3 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 and Rafael Irizarry Maintainer: Stephanie Hicks VignetteBuilder: knitr source.ver: src/contrib/quantro_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/quantro_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/quantro_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/quantro_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/quantro_1.6.2.tgz vignettes: vignettes/quantro/inst/doc/quantro-vignette.pdf vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/quantro/inst/doc/quantro-vignette.R Package: quantsmooth Version: 1.38.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 7698afa9942863f20871d7eb54edaa7c 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 source.ver: src/contrib/quantsmooth_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/quantsmooth_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/quantsmooth_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/quantsmooth_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/quantsmooth_1.38.0.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 Package: QuartPAC Version: 1.4.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 9bf5a5423ffbd68ddc76eff418061e34 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 source.ver: src/contrib/QuartPAC_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/QuartPAC_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/QuartPAC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/QuartPAC_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QuartPAC_1.4.0.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 Package: QuasR Version: 1.12.0 Depends: parallel, GenomicRanges (>= 1.13.3), Rbowtie Imports: methods, grDevices, graphics, utils, zlibbioc, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, BiocInstaller, Biobase, Biostrings, BSgenome, Rsamtools (>= 1.19.38), GenomicFeatures (>= 1.17.13), ShortRead (>= 1.19.1), GenomicAlignments, BiocParallel, GenomeInfoDb, rtracklayer, GenomicFiles LinkingTo: Rsamtools Suggests: Gviz, RUnit, BiocStyle License: GPL-2 Archs: x64 MD5sum: e596fa3b33b0db2b6e85e990c6d295ef 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 Author: Anita Lerch, Dimos Gaiditzis and Michael Stadler Maintainer: Michael Stadler source.ver: src/contrib/QuasR_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/QuasR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/QuasR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/QuasR_1.9.13.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QuasR_1.12.0.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.pdf vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/QuasR/inst/doc/QuasR.R Package: QuaternaryProd Version: 1.0.2 Depends: R (>= 3.2.0), Rcpp (>= 0.11.3) LinkingTo: Rcpp Suggests: readr, org.Hs.eg.db, dplyr, stringr, knitr, fdrtool License: GPL (>=3) Archs: i386, x64 MD5sum: 7edae8d03d43b1bcc5814906e75d59d4 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 String-db. QuaternaryProd is a free alternative to commercial products such as Quiagen and 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. 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 String-db. biocViews: GraphAndNetwork, GeneExpression, Transcription Author: Carl Tony Fakhry [cre, aut], Ping Chen [ths], Kourosh Zarringhalam [aut, ths] Maintainer: Carl Tony Fakhry VignetteBuilder: knitr source.ver: src/contrib/QuaternaryProd_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/QuaternaryProd_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/QuaternaryProd_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QuaternaryProd_1.0.2.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 Package: QUBIC Version: 1.0.3 Depends: R (>= 3.1), biclust Imports: Rcpp (>= 0.11.0), methods, Matrix LinkingTo: Rcpp, RcppArmadillo Suggests: QUBICdata, qgraph, knitr, rmarkdown Enhances: RColorBrewer, fields License: CC BY-NC-ND 4.0 + file LICENSE Archs: i386, x64 MD5sum: 92d424923bf9dd24b1116a370c1f9db2 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 source.ver: src/contrib/QUBIC_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/QUBIC_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/QUBIC_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/QUBIC_1.0.3.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 Package: qusage Version: 2.4.0 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, lsmeans License: GPL (>= 2) MD5sum: 24159c52dc66369145f4535074700116 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. For questions, contact Chris Bolen (cbolen1@gmail.com) or Steven Kleinstein (steven.kleinstein@yale.edu) biocViews: GeneSetEnrichment, Microarray, RNASeq, Software 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 source.ver: src/contrib/qusage_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/qusage_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/qusage_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/qusage_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qusage_2.4.0.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 suggestsMe: SigCheck Package: qvalue Version: 2.4.2 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: ad6d0d6b3bc4435797ac195b114be27d 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 with contributions from Andrew J. Bass, Alan Dabney and David Robinson Maintainer: John D. Storey , Andrew J. Bass URL: http://github.com/jdstorey/qvalue VignetteBuilder: knitr source.ver: src/contrib/qvalue_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/qvalue_2.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/qvalue_2.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/qvalue_2.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/qvalue_2.4.2.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, SSPA, webbioc importsMe: anota, clusterProfiler, derfinder, DOSE, edge, erccdashboard, msmsTests, netresponse, Rnits, sRAP, subSeq, synapter, trigger, webbioc suggestsMe: biobroom, LBE, maanova, PREDA Package: R3CPET Version: 1.4.2 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: methods, parallel, clues, ggplot2, pheatmap, clValid, igraph, data.table, reshape2, Hmisc, RCurl, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, 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: 4add723bd51a43f18ab2a3a9e87df755 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 Author: Djekidel MN, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues source.ver: src/contrib/R3CPET_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/R3CPET_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/R3CPET_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/R3CPET_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/R3CPET_1.4.2.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 Package: r3Cseq Version: 1.18.0 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: 82de692eea51af31a4047aedff659dd2 NeedsCompilation: no Title: Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq) Description: This package is an implementation of data analysis for the long-range interactions from 3C-seq assay. biocViews: Preprocessing, Sequencing Author: Supat Thongjuea, MRC Molecular Haematology Unit, Weatherall Institute of Molecular Medicine, University of Oxford, UK Maintainer: Supat Thongjuea URL: http://r3cseq.genereg.net source.ver: src/contrib/r3Cseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/r3Cseq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/r3Cseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/r3Cseq_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/r3Cseq_1.18.0.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 Package: R453Plus1Toolbox Version: 1.22.0 Depends: R (>= 2.12.0), methods, VariantAnnotation, Biostrings, Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors (>= 0.9.25), IRanges, XVector, GenomicRanges, SummarizedExperiment, biomaRt, BSgenome, Rsamtools, ShortRead Suggests: rtracklayer, BSgenome.Hsapiens.UCSC.hg19, BSgenome.Scerevisiae.UCSC.sacCer2 License: LGPL-3 Archs: i386, x64 MD5sum: 2673b575deec1be3a787b196ed25ddae 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 source.ver: src/contrib/R453Plus1Toolbox_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/R453Plus1Toolbox_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/R453Plus1Toolbox_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/R453Plus1Toolbox_1.19.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/R453Plus1Toolbox_1.22.0.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 Package: R4RNA Version: 1.0.0 Depends: R (>= 3.2.0), Biostrings (>= 2.38.0) License: GPL-3 MD5sum: 86c2ad1643d7800af3b5950b349e0108 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/ source.ver: src/contrib/R4RNA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/R4RNA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/R4RNA_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/R4RNA_1.0.0.tgz vignettes: vignettes/R4RNA/inst/doc/R4RNA.pdf vignetteTitles: R4RNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/R4RNA/inst/doc/R4RNA.R Package: rain Version: 1.6.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: af093d403acc954226d9bddb63e4fe7c 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 source.ver: src/contrib/rain_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rain_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rain_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rain_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rain_1.6.0.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 Package: rama Version: 1.46.0 Depends: R(>= 2.5.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 7c8a1d38edae14ba5c32e8d9193eb0d5 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 source.ver: src/contrib/rama_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rama_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rama_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rama_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rama_1.46.0.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 Package: RamiGO Version: 1.18.0 Depends: gsubfn,methods Imports: igraph,RCurl,png,RCytoscape,graph License: Artistic-2.0 MD5sum: 33c90c98dbf8f559b27974dd16ec4bc0 NeedsCompilation: no Title: AmiGO visualize R interface Description: R interface sending requests to AmiGO visualize, retrieving DAG GO trees, parsing GraphViz DOT format files and exporting GML files for Cytoscape. Also uses RCytoscape to interactively display AmiGO trees in Cytoscape. biocViews: GO, Visualization, GraphAndNetwork, Classification, ThirdPartyClient Author: Markus Schroeder, Daniel Gusenleitner, John Quackenbush, Aedin Culhane, Benjamin Haibe-Kains Maintainer: Markus Schroeder source.ver: src/contrib/RamiGO_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RamiGO_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RamiGO_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RamiGO_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RamiGO_1.18.0.tgz vignettes: vignettes/RamiGO/inst/doc/RamiGO.pdf vignetteTitles: RamiGO: An Introduction (HowTo) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RamiGO/inst/doc/RamiGO.R Package: randPack Version: 1.18.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: 848f391405c77d35066b9721309b47c4 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 source.ver: src/contrib/randPack_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/randPack_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/randPack_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/randPack_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/randPack_1.18.0.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 Package: RankProd Version: 2.44.0 Depends: R (>= 1.9.0) Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: b69d309360a5f7b52c94592648a7f8d9 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 Author: Fangxin Hong and Ben Wittner with contribution from Rainer Breitling , Colin Smith , and Florian Battke Maintainer: Fangxin Hong source.ver: src/contrib/RankProd_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RankProd_2.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RankProd_2.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RankProd_2.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RankProd_2.44.0.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: HTSanalyzeR, synlet suggestsMe: oneChannelGUI Package: RareVariantVis Version: 1.6.2 Depends: BiocGenerics, VariantAnnotation, googleVis Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Suggests: knitr, AshkenazimSonChr21 License: Artistic-2.0 MD5sum: b039dbb9c2943dc73eab5070707694cd NeedsCompilation: no Title: Visualization of rare variants in whole genome sequencing data Description: Genomic variants can be analyzed and visualized using many tools. Unfortunately, number of tools for global interrogation of variants is limited. Package RareVariantVis aims to present genomic variants (especially rare ones) in a global, per chromosome way. Visualization 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. biocViews: GenomicVariation, Sequencing, WholeGenome Author: Tomasz Stokowy Maintainer: Tomasz Stokowy VignetteBuilder: knitr source.ver: src/contrib/RareVariantVis_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RareVariantVis_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RareVariantVis_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/RareVariantVis_1.0.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RareVariantVis_1.6.2.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 Package: Rariant Version: 1.8.3 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: methods, 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: 37b1d4d462c2e95536148e0db92726df 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 source.ver: src/contrib/Rariant_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rariant_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/Rariant_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.3/Rariant_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rariant_1.8.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rariant/inst/doc/Rariant-vignette.R htmlDocs: vignettes/Rariant/inst/doc/Rariant-vignette.html htmlTitles: Rariant Package: RbcBook1 Version: 1.40.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: bee5078c0628cfcdd22e71f2e684442d 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 source.ver: src/contrib/RbcBook1_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RbcBook1_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RbcBook1_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RbcBook1_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RbcBook1_1.40.0.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 Package: RBGL Version: 1.48.1 Depends: graph, methods Imports: methods Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: e012e1022b4392c9dd90677ac69798c6 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 source.ver: src/contrib/RBGL_1.48.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/RBGL_1.48.1.zip win64.binary.ver: bin/windows64/contrib/3.3/RBGL_1.48.1.zip mac.binary.ver: bin/macosx/contrib/3.3/RBGL_1.45.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RBGL_1.48.1.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, joda, pkgDepTools, RpsiXML importsMe: biocViews, CAMERA, Category, ChIPpeakAnno, CHRONOS, clipper, DEGraph, flowWorkspace, GeneAnswers, GOSim, GOstats, NCIgraph, nem, OrganismDbi, pkgDepTools, predictionet, RDAVIDWebService, Streamer, ToPASeq, VariantFiltering suggestsMe: BiocCaseStudies, DEGraph, GeneNetworkBuilder, graph, gwascat, KEGGgraph, rBiopaxParser, VariantTools Package: RBioinf Version: 1.32.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: af474b29fc686d07e43979c5d25fef7f 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 source.ver: src/contrib/RBioinf_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RBioinf_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RBioinf_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RBioinf_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RBioinf_1.32.0.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 Package: rBiopaxParser Version: 2.10.0 Depends: R (>= 3.0.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, nem, RBGL License: GPL (>= 2) MD5sum: 0b8e1b02ffa1c2ea268342cfaf27522b 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/rBiopaxParser source.ver: src/contrib/rBiopaxParser_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rBiopaxParser_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rBiopaxParser_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rBiopaxParser_2.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rBiopaxParser_2.10.0.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: AnnotationHubData, pwOmics suggestsMe: AnnotationHub, NetPathMiner Package: RBM Version: 1.4.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: 5d51285a6738f2ddb3b910e028bcb179 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 source.ver: src/contrib/RBM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RBM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RBM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RBM_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RBM_1.4.0.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 Package: Rbowtie Version: 1.12.0 Suggests: parallel License: Artistic-1.0 | file LICENSE Archs: x64 MD5sum: edb80b7a1a8e55148432e19a42d8c929 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 source.ver: src/contrib/Rbowtie_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rbowtie_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rbowtie_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rbowtie_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rbowtie_1.12.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.pdf vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.R dependsOnMe: QuasR Package: rbsurv Version: 2.30.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 042d7569f67c97b4e897ac1cc4cb2a14 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/ source.ver: src/contrib/rbsurv_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rbsurv_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rbsurv_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rbsurv_2.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rbsurv_2.30.0.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 Package: Rcade Version: 1.14.0 Depends: R (>= 2.14.0), methods, GenomicRanges, baySeq, Rsamtools Imports: graphics, S4Vectors, rgl, plotrix Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: ce92ce2a13689614ccf2f16312efd1c6 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 source.ver: src/contrib/Rcade_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rcade_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rcade_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rcade_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rcade_1.14.0.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 Package: RCASPAR Version: 1.18.0 License: GPL (>=3) MD5sum: 946c51d83515f1b69a935cfdf63586e3 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 source.ver: src/contrib/RCASPAR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RCASPAR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RCASPAR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RCASPAR_1.15.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RCASPAR_1.18.0.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 Package: rcellminer Version: 1.4.2 Depends: R (>= 3.2), Biobase, rcdk, fingerprint, rcellminerData Imports: stringr, gplots, methods, shiny Suggests: knitr, RColorBrewer, sqldf, BiocGenerics, testthat, BiocStyle, jsonlite License: LGPL-3 MD5sum: a4481fd8f7856e8a6cc4b70eb233d555 NeedsCompilation: no Title: rcellminer: Molecular Profiles and Drug Response 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 URL: http://discover.nci.nih.gov/cellminer/ VignetteBuilder: knitr source.ver: src/contrib/rcellminer_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rcellminer_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rcellminer_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/rcellminer_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rcellminer_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rcellminer/inst/doc/rcellminerUsage.R htmlDocs: vignettes/rcellminer/inst/doc/rcellminerUsage.html htmlTitles: Using rcellminer Package: rCGH Version: 1.2.2 Depends: R (>= 3.2.1),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: f254e777ffc12c25b462a175b510b9cf 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 is in a suitable format. This package takes over all the steps required for individual genomic profiles analysis, from reading files to segmenting and annotating genes. This package 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 source.ver: src/contrib/rCGH_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rCGH_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rCGH_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/rCGH_0.99.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rCGH_1.2.2.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 Package: Rchemcpp Version: 2.10.0 Depends: R (>= 2.15.0) Imports: Rcpp (>= 0.11.1), methods, ChemmineR LinkingTo: Rcpp Suggests: apcluster, kernlab License: GPL (>= 2.1) Archs: i386, x64 MD5sum: fdbb06e2d09db37f077320165b2b8f7e NeedsCompilation: yes Title: Similarity measures for chemical compounds Description: The Rchemcpp package implements the marginalized graph kernel and extensions, Tanimoto kernels, graph kernels, pharmacophore and 3D kernels suggested for measuring the similarity of molecules. biocViews: Bioinformatics, CellBasedAssays, Clustering, DataImport, Infrastructure, MicrotitrePlateAssay, Proteomics, Software, Visualization Author: Michael Mahr, Guenter Klambauer Maintainer: Guenter Klambauer URL: http://www.bioinf.jku.at/software/Rchemcpp SystemRequirements: GNU make source.ver: src/contrib/Rchemcpp_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rchemcpp_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rchemcpp_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rchemcpp_2.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rchemcpp_2.10.0.tgz vignettes: vignettes/Rchemcpp/inst/doc/Rchemcpp.pdf vignetteTitles: Rchemcpp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rchemcpp/inst/doc/Rchemcpp.R Package: RchyOptimyx Version: 2.12.0 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: 5d91af5e48a14cf6ba8fc701ee1c3dda 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 source.ver: src/contrib/RchyOptimyx_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RchyOptimyx_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RchyOptimyx_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RchyOptimyx_2.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RchyOptimyx_2.12.0.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 Package: Rcpi Version: 1.8.0 Imports: RCurl, rjson, rcdk, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR Suggests: RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 MD5sum: 3ec1d03e558d348809d5cdc75fef9824 NeedsCompilation: no Title: Toolkit for Compound-Protein Interaction in Drug Discovery Description: The Rcpi package offers an R/Bioconductor package emphasizing the comprehensive integration of bioinformatics and chemoinformatics into a molecular informatics platform for drug discovery. biocViews: Software, DataImport, DataRepresentation, FeatureExtraction, Cheminformatics, BiomedicalInformatics, Proteomics, GO, GraphAndNetwork, SystemsBiology Author: Nan Xiao , Dongsheng Cao , Qingsong Xu Maintainer: Nan Xiao URL: https://github.com/road2stat/Rcpi BugReports: https://github.com/road2stat/Rcpi/issues source.ver: src/contrib/Rcpi_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rcpi_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rcpi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rcpi_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rcpi_1.8.0.tgz vignettes: vignettes/Rcpi/inst/doc/Rcpi-quickref.pdf, vignettes/Rcpi/inst/doc/Rcpi.pdf vignetteTitles: Rcpi Quick Reference Card, Rcpi: R/Bioconductor Package as an Integrated Informatics Platform in Drug Discovery hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rcpi/inst/doc/Rcpi-quickref.R, vignettes/Rcpi/inst/doc/Rcpi.R Package: RCy3 Version: 1.2.0 Depends: R (>= 3.2), graph (>= 1.48.0) Imports: httr, methods, RCurl, RJSONIO Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 290f876f7945eba6ce8b98bde152889f NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape >= 3.3.0 Description: Vizualize, analyze and explore graphs, connecting R to Cytoscape (>= 3.3.0). biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Tanja Muetze, Georgi Kolishovski, Paul Shannon Maintainer: Tanja Muetze , Georgi Kolishovski , Paul Shannon SystemRequirements: Cytoscape (>= 3.3.0), CyREST (>= 3.3.1), Java (>=8) source.ver: src/contrib/RCy3_1.2.0.tar.gz vignettes: vignettes/RCy3/inst/doc/RCy3.pdf vignetteTitles: RCy3 Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCy3/inst/doc/RCy3.R Package: RCyjs Version: 1.4.0 Depends: R (>= 3.2.0), BrowserViz (>= 1.1.7), graph (>= 1.44.0) Imports: methods, httpuv (>= 1.3.2), Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), BiocGenerics, igraph Suggests: RUnit, BiocStyle, RefNet License: GPL-2 MD5sum: 7cd562da5a5102c32163a9745bc56a72 NeedsCompilation: no Title: Display and manipulate graphs in cytoscape.js Description: Interactive viewing and exploration of graphs, connecting R to Cytoscape.js. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: http://rcytoscape.systemsbiology.net source.ver: src/contrib/RCyjs_1.4.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/RCyjs_1.1.6.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.pdf vignetteTitles: RCyjs hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCyjs/inst/doc/RCyjs.R Package: RCytoscape Version: 1.21.1 Depends: R (>= 2.14.0), graph (>= 1.31.0), XMLRPC (>= 0.2.4) Imports: methods, XMLRPC, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: 535dfd48fce44c49a5b1699d9061f1ab NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape Description: Interactvive viewing and exploration of graphs, connecting R to Cytoscape. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient Author: Paul Shannon Maintainer: Paul Shannon URL: http://rcytoscape.systemsbiology.net source.ver: src/contrib/RCytoscape_1.21.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/RCytoscape_1.21.1.zip win64.binary.ver: bin/windows64/contrib/3.3/RCytoscape_1.21.1.zip mac.binary.ver: bin/macosx/contrib/3.3/RCytoscape_1.19.0.tgz vignettes: vignettes/RCytoscape/inst/doc/RCytoscape.pdf vignetteTitles: RCytoscape Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RCytoscape/inst/doc/RCytoscape.R importsMe: categoryCompare, NCIgraph suggestsMe: clipper, mmnet, NetPathMiner Package: RDAVIDWebService Version: 1.10.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: 14b48394f0fa777a386d7496f4a21e24 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/ source.ver: src/contrib/RDAVIDWebService_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RDAVIDWebService_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RDAVIDWebService_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RDAVIDWebService_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RDAVIDWebService_1.10.0.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 Package: Rdisop Version: 1.32.0 Depends: R (>= 2.0.0), RcppClassic LinkingTo: RcppClassic, Rcpp Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: 07ca477e451be38c06eb8cedcf1224de 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: 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 source.ver: src/contrib/Rdisop_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rdisop_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rdisop_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rdisop_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rdisop_1.32.0.tgz vignettes: vignettes/Rdisop/inst/doc/Rdisop.pdf vignetteTitles: Molecule Identification with Rdisop hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE suggestsMe: MSnbase Package: RDRToolbox Version: 1.22.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: c35eaeca047f73de2d649d576fa13ce0 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 source.ver: src/contrib/RDRToolbox_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RDRToolbox_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RDRToolbox_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RDRToolbox_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RDRToolbox_1.22.0.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 Package: ReactomePA Version: 1.16.2 Depends: R (>= 3.2.0), DOSE (>= 2.9.6) Imports: AnnotationDbi, reactome.db, igraph, graphite, GOSemSim Suggests: BiocStyle, clusterProfiler, knitr, org.Hs.eg.db License: GPL-2 MD5sum: f7f85285a939fe10d4033ee783d2aea7 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 with contributions from Vladislav Petyuk Maintainer: Guangchuang Yu URL: http://guangchuangyu.github.io/ReactomePA VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ReactomePA/issues source.ver: src/contrib/ReactomePA_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReactomePA_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ReactomePA_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ReactomePA_1.13.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReactomePA_1.16.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ReactomePA/inst/doc/ReactomePA.R htmlDocs: vignettes/ReactomePA/inst/doc/ReactomePA.html htmlTitles: An R package for Reactome Pathway Analysis importsMe: debrowser suggestsMe: ChIPseeker, CINdex, clusterProfiler Package: ReadqPCR Version: 1.18.0 Depends: R(>= 2.14.0), Biobase, methods, affy Imports: Biobase Suggests: qpcR License: LGPL-3 MD5sum: 45249acd9335b4e0be9c085287f19587 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 source.ver: src/contrib/ReadqPCR_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReadqPCR_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ReadqPCR_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ReadqPCR_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReadqPCR_1.18.0.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 Package: reb Version: 1.50.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 Archs: i386, x64 MD5sum: 255f41a29e4bf2b66193710d6c9dd8ca NeedsCompilation: yes 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 source.ver: src/contrib/reb_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/reb_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/reb_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/reb_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/reb_1.50.0.tgz vignettes: vignettes/reb/inst/doc/reb.pdf vignetteTitles: Smoothing of Microarray Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/reb/inst/doc/reb.R Package: recoup Version: 1.0.2 Depends: GenomicRanges, GenomicAlignments, ggplot2, ComplexHeatmap Imports: BiocGenerics, biomaRt, circlize, rtracklayer, plyr Suggests: grid, GenomeInfoDb, Rsamtools, BiocStyle, knitr, rmarkdown, zoo, RUnit, BiocInstaller, BSgenome, RSQLite, RMySQL Enhances: parallel License: GPL (>= 3) MD5sum: 4c733528010be026498f473d1a962b46 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: Software, GeneExpression, Preprocessing, QualityControl, RNASeq, ChIPSeq, Sequencing, Coverage Author: Panagiotis Moulos Maintainer: Panagiotis Moulos URL: https://github.com/pmoulos/recoup VignetteBuilder: knitr source.ver: src/contrib/recoup_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/recoup_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/recoup_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/recoup_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/recoup/inst/doc/recoup_intro.R htmlDocs: vignettes/recoup/inst/doc/recoup_intro.html htmlTitles: Introduction to the recoup package Package: RedeR Version: 1.20.0 Depends: R (>= 2.15), methods, igraph Imports: RCurl, XML, pvclust Suggests: PANR License: GPL (>= 2) MD5sum: 4bdeb847efea580a22461320155d8275 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) source.ver: src/contrib/RedeR_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RedeR_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RedeR_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RedeR_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RedeR_1.20.0.tgz vignettes: vignettes/RedeR/inst/doc/RedeR.pdf vignetteTitles: Main vignette: interactive visualization and manipulation of nested networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RedeR/inst/doc/RedeR.R importsMe: PANR, RTN Package: REDseq Version: 1.18.0 Depends: R (>= 2.15.0), BiocGenerics (>= 0.1.0), BSgenome.Celegans.UCSC.ce2, multtest, Biostrings, BSgenome, ChIPpeakAnno Imports: BiocGenerics, AnnotationDbi, Biostrings, ChIPpeakAnno, graphics, IRanges (>= 1.13.5), multtest, stats, utils License: GPL (>=2) MD5sum: cfee1836d613d0876c59596ed022ebe0 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 source.ver: src/contrib/REDseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/REDseq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/REDseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/REDseq_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/REDseq_1.18.0.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 Package: RefNet Version: 1.8.0 Depends: R (>= 2.15.0), methods, IRanges, PSICQUIC, AnnotationHub, RCurl, shiny Imports: BiocGenerics Suggests: RUnit, BiocStyle, org.Hs.eg.db License: Artistic-2.0 MD5sum: 30ca35f830029bd55931be139379f6f9 NeedsCompilation: no Title: A queryable collection of molecular interactions, from many sources Description: Molecular interactions with metadata, some archived, some dynamically obtained biocViews: GraphAndNetwork Author: Paul Shannon Maintainer: Paul Shannon source.ver: src/contrib/RefNet_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RefNet_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RefNet_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RefNet_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RefNet_1.8.0.tgz vignettes: vignettes/RefNet/inst/doc/RefNet.pdf vignetteTitles: RefNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RefNet/inst/doc/RefNet.R suggestsMe: RCyjs Package: RefPlus Version: 1.42.0 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: 8889650e13d19a2a703549e90f389abb 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 source.ver: src/contrib/RefPlus_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RefPlus_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RefPlus_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RefPlus_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RefPlus_1.42.0.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 Package: regioneR Version: 1.4.2 Depends: memoise, GenomicRanges, BSgenome, rtracklayer, parallel Imports: memoise, GenomicRanges, BSgenome, rtracklayer, parallel, graphics, stats, utils, GenomeInfoDb, IRanges Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 51f47e76b61a75be1389cdb58e0673d3 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 source.ver: src/contrib/regioneR_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/regioneR_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/regioneR_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/regioneR_1.1.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/regioneR_1.4.2.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.pdf vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regioneR/inst/doc/regioneR.R importsMe: ChIPpeakAnno Package: regionReport Version: 1.6.5 Depends: R(>= 3.2) Imports: derfinder (>= 1.1.0), DEFormats, DESeq2, GenomeInfoDb, GenomicRanges, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), RefManageR, rmarkdown (>= 0.9.5), S4Vectors, SummarizedExperiment Suggests: BiocStyle, biovizBase, bumphunter (>= 1.7.6), Cairo, derfinderPlot (>= 1.3.2), devtools (>= 1.6), DT, DESeq, edgeR, ggbio (>= 1.13.13), ggplot2, grid, gridExtra, IRanges, mgcv, pasilla, pheatmap, RColorBrewer, TxDb.Hsapiens.UCSC.hg19.knownGene, whisker License: Artistic-2.0 MD5sum: e016eab4648bab06107e159292208362 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 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://github.com/leekgroup/regionReport/issues source.ver: src/contrib/regionReport_1.6.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/regionReport_1.6.5.zip win64.binary.ver: bin/windows64/contrib/3.3/regionReport_1.6.5.zip mac.binary.ver: bin/macosx/contrib/3.3/regionReport_1.3.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/regionReport_1.6.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/regionReport/inst/doc/bumphunterExample.R, vignettes/regionReport/inst/doc/bumphunterExampleOutput.R, vignettes/regionReport/inst/doc/regionReport.R htmlDocs: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/bumphunterExampleOutput.html, vignettes/regionReport/inst/doc/regionReport.html htmlTitles: Example report using bumphunter results, Basic genomic regions exploration, Introduction to regionReport Package: Repitools Version: 1.18.3 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: parallel, S4Vectors (>= 0.9.25), IRanges (>= 1.20.0), GenomeInfoDb, GenomicRanges, Biostrings, Rsamtools, GenomicAlignments, rtracklayer, BSgenome, gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, aroma.affymetrix, Rsolnp, cluster Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) Archs: i386, x64 MD5sum: 0943d751fd399b1da7c33fba24db53a7 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 source.ver: src/contrib/Repitools_1.18.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/Repitools_1.18.3.zip win64.binary.ver: bin/windows64/contrib/3.3/Repitools_1.18.3.zip mac.binary.ver: bin/macosx/contrib/3.3/Repitools_1.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Repitools_1.18.3.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 Package: ReportingTools Version: 2.12.2 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, License: Artistic-2.0 MD5sum: 5c3cb4cd894d6d639602157bc86ac36b 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: 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 source.ver: src/contrib/ReportingTools_2.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReportingTools_2.12.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ReportingTools_2.12.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ReportingTools_2.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReportingTools_2.12.2.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 vignetteTitles: ReportingTools basics, Reporting on microarray differential expression, Reporting on RNA-seq differential expression, ReportingTools shiny 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 htmlDocs: vignettes/ReportingTools/inst/doc/knitr.html htmlTitles: Knitr and ReportingTools importsMe: affycoretools, EnrichmentBrowser suggestsMe: cpvSNP, GSEABase, npGSEA Package: ReQON Version: 1.18.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: c50c502b41e1ad16b2a0ff84f96e9838 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 source.ver: src/contrib/ReQON_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ReQON_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ReQON_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ReQON_1.15.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ReQON_1.18.0.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 Package: rfPred Version: 1.10.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) Archs: i386, x64 MD5sum: 71e5bbdf1778bf36f775ae612bc97cf1 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 source.ver: src/contrib/rfPred_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rfPred_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rfPred_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rfPred_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rfPred_1.10.0.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 Package: rGADEM Version: 2.20.0 Depends: R (>= 2.11.0), Biostrings, IRanges, BSgenome, methods, seqLogo Imports: Biostrings, IRanges, methods, graphics, seqLogo Suggests: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 Archs: i386, x64 MD5sum: 3a60709193b7e61134f6eb43a3739881 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 source.ver: src/contrib/rGADEM_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rGADEM_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rGADEM_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rGADEM_2.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rGADEM_2.20.0.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: MotIV Package: RGalaxy Version: 1.16.2 Depends: XML, methods, tools, optparse, digest, Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: e216e4659a8ad886543d144e9a986eae 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 source.ver: src/contrib/RGalaxy_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RGalaxy_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RGalaxy_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.3/RGalaxy_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RGalaxy_1.16.2.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.R htmlDocs: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html htmlTitles: Introduction to RGalaxy Package: RGraph2js Version: 1.0.0 Imports: utils, whisker, rjson, digest, graph Suggests: RUnit, BiocStyle, BiocGenerics, xtable, sna License: GPL-2 MD5sum: 758eabd65d17c92c39eed7eec29a29bd 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). source.ver: src/contrib/RGraph2js_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RGraph2js_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RGraph2js_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RGraph2js_1.0.0.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 Package: Rgraphviz Version: 2.16.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: b89b15d2e37f5bdbbd171c1130efc249 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) source.ver: src/contrib/Rgraphviz_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rgraphviz_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rgraphviz_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rgraphviz_2.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rgraphviz_2.16.0.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, gaucho, GOFunction, MineICA, mvGST, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, ToPASeq importsMe: apComplex, biocGraph, CompGO, DEGraph, EnrichmentBrowser, facopy, flowWorkspace, GOFunction, hyperdraw, mirIntegrator, nem, OncoSimulR, paircompviz, pathview, qpgraph, RchyOptimyx, SplicingGraphs, TRONCO suggestsMe: altcdfenvs, annotate, BiocCaseStudies, Category, CNORfeeder, CNORfuzzy, ddgraph, DEGraph, flowCore, GeneNetworkBuilder, geneplotter, GlobalAncova, globaltest, GOstats, GSEABase, KEGGgraph, MLP, NCIgraph, oneChannelGUI, pcaGoPromoter, pkgDepTools, RBGL, RBioinf, rBiopaxParser, RDAVIDWebService, Rtreemix, safe, SPIA, SRAdb, Streamer, topGO, vtpnet Package: rGREAT Version: 1.4.2 Depends: R (>= 3.1.2), GenomicRanges, IRanges, methods Imports: rjson, GetoptLong (>= 0.0.9), RCurl, utils Suggests: testthat (>= 0.3), knitr, circlize License: GPL (>= 2) MD5sum: 71372fde031a7804416a487ea22210ae 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 VignetteBuilder: knitr source.ver: src/contrib/rGREAT_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rGREAT_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rGREAT_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/rGREAT_1.1.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rGREAT_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rGREAT/inst/doc/rGREAT.R htmlDocs: vignettes/rGREAT/inst/doc/rGREAT.html htmlTitles: Analyze with GREAT Package: RGSEA Version: 1.6.2 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 0c861d83b1d0a317db955af272c1c032 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 source.ver: src/contrib/RGSEA_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RGSEA_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RGSEA_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/RGSEA_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RGSEA_1.6.2.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 Package: rgsepd Version: 1.4.2 Depends: R (>= 3.3.0), DESeq2, goseq (>= 1.17) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, SummarizedExperiment, hash, AnnotationDbi Suggests: boot, tools, RUnit, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: f78fc013447412b64e8a7dcaa904290f 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 RefSeq 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: Software, DifferentialExpression, GeneSetEnrichment, RNASeq Author: Karl Stamm Maintainer: Karl Stamm VignetteBuilder: knitr source.ver: src/contrib/rgsepd_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rgsepd_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rgsepd_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/rgsepd_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rgsepd_1.4.2.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rgsepd/inst/doc/rgsepd.R Package: rhdf5 Version: 2.16.0 Depends: methods Imports: zlibbioc Suggests: bit64,BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 4ddc772693eff2382076dc2bd678e46b NeedsCompilation: yes Title: HDF5 interface to R Description: This R/Bioconductor 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, Gregoire Pau Maintainer: Bernd Fischer SystemRequirements: GNU make source.ver: src/contrib/rhdf5_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rhdf5_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rhdf5_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rhdf5_2.13.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rhdf5_2.16.0.tgz vignettes: vignettes/rhdf5/inst/doc/rhdf5.pdf vignetteTitles: rhdf5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rhdf5/inst/doc/rhdf5.R dependsOnMe: GENE.E, GSCA importsMe: biomformat, diffHic, DOQTL, GENE.E, h5vc, HDF5Array, IONiseR, scater suggestsMe: SummarizedExperiment Package: Rhtslib Version: 1.4.3 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) Archs: i386, x64 MD5sum: 99a2cbdbac6f1589439002d3067bb0e7 NeedsCompilation: yes Title: HTSlib high-throughput sequencing library as an R package Description: This package provides version 1.1 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 [aut], Martin Morgan [aut], Bioconductor Package Maintainer [cre] Maintainer: Bioconductor Package Maintainer URL: https://github.com/nhayden/Rhtslib, http://www.htslib.org/ VignetteBuilder: knitr BugReports: https://github.com/nhayden/Rhtslib source.ver: src/contrib/Rhtslib_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rhtslib_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/Rhtslib_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/Rhtslib_1.1.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rhtslib_1.4.3.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rhtslib/inst/doc/Rhtslib.R htmlDocs: vignettes/Rhtslib/inst/doc/Rhtslib.html htmlTitles: Motivation and Use of Rhtslib dependsOnMe: deepSNV importsMe: csaw, deepSNV, diffHic Package: rHVDM Version: 1.38.0 Depends: R (>= 2.10), R2HTML (>= 1.5), affy (>= 1.23.4), minpack.lm (>= 1.0-5), Biobase (>= 2.5.5) License: GPL-2 MD5sum: a82541b2899cc53d3a5d69fbc38d10e2 NeedsCompilation: no Title: Hidden Variable Dynamic Modeling Description: A R implementation of HVDM (Genome Biol 2006, V7(3) R25) biocViews: Microarray, GraphAndNetwork, Transcription, Classification, NetworkInference Author: Martino Barenco Maintainer: Martino Barenco source.ver: src/contrib/rHVDM_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rHVDM_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rHVDM_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rHVDM_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rHVDM_1.38.0.tgz vignettes: vignettes/rHVDM/inst/doc/rHVDM.pdf vignetteTitles: rHVDM primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rHVDM/inst/doc/rHVDM.R Package: RiboProfiling Version: 1.2.1 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: 8ad69afe585bd24665ba7d0ff78c6137 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 source.ver: src/contrib/RiboProfiling_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/RiboProfiling_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RiboProfiling_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RiboProfiling_1.2.1.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 Package: riboSeqR Version: 1.6.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Suggests: baySeq, BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: ff5e86dac90733c40f318a5b27180b8a 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 source.ver: src/contrib/riboSeqR_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/riboSeqR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/riboSeqR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/riboSeqR_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/riboSeqR_1.6.0.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 Package: RImmPort Version: 1.0.2 Imports: plyr, dplyr, DBI, data.table, reshape2, methods, sqldf, tools, utils, RSQLite Suggests: knitr License: GPL-3 MD5sum: 8dfd538a5b07630d848305db0dfdb0b2 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: Ravi Shankar URL: http://bioconductor.org/packages/RImmPort/ VignetteBuilder: knitr source.ver: src/contrib/RImmPort_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RImmPort_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RImmPort_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RImmPort_1.0.2.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 Package: Ringo Version: 1.36.0 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: b2aec587ebabc41cb5df257298440e10 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 source.ver: src/contrib/Ringo_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Ringo_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Ringo_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Ringo_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Ringo_1.36.0.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, Starr importsMe: Repitools Package: RIPSeeker Version: 1.12.0 Depends: R (>= 2.15), methods, S4Vectors (>= 0.9.25), IRanges, GenomicRanges, SummarizedExperiment, Rsamtools, GenomicAlignments, rtracklayer Suggests: biomaRt, ChIPpeakAnno, parallel, GenomicFeatures License: GPL-2 MD5sum: 2f045c6e565f7bcd267b063699cf4860 NeedsCompilation: no Title: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments Description: Infer and discriminate RIP peaks from RIP-seq alignments using two-state HMM with negative binomial emission probability. While RIPSeeker is specifically tailored for RIP-seq data analysis, it also provides a suite of bioinformatics tools integrated within this self-contained software package comprehensively addressing issues ranging from post-alignments processing to visualization and annotation. biocViews: Sequencing, RIPSeq Author: Yue Li Maintainer: Yue Li URL: http://www.cs.utoronto.ca/~yueli/software.html source.ver: src/contrib/RIPSeeker_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RIPSeeker_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RIPSeeker_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RIPSeeker_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RIPSeeker_1.12.0.tgz vignettes: vignettes/RIPSeeker/inst/doc/RIPSeeker.pdf vignetteTitles: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RIPSeeker/inst/doc/RIPSeeker.R Package: Risa Version: 1.14.0 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: c5f8c9282494d7fa0944ec43f402c64d 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: source.ver: src/contrib/Risa_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Risa_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Risa_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Risa_1.11.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Risa_1.14.0.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 Package: RLMM Version: 1.34.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 4bf930d394e11937d4b9c9782529186f 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) source.ver: src/contrib/RLMM_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RLMM_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RLMM_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RLMM_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RLMM_1.34.0.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 Package: Rmagpie Version: 1.28.0 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: f7336000bd61266abdbe363b1ab4b5f9 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/ source.ver: src/contrib/Rmagpie_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rmagpie_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rmagpie_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rmagpie_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rmagpie_1.28.0.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 Package: RMassBank Version: 2.0.0 Depends: Rcpp Imports: XML,RCurl,rjson,S4Vectors,digest, rcdk,yaml,mzR,methods,Biobase,MSnbase Suggests: gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, ontoCAT, RUnit, enviPat License: Artistic-2.0 MD5sum: be347ebbb098adbad6288081b0acc0e4 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: Bioinformatics, MassSpectrometry, Metabolomics, Software Author: Michael Stravs, Emma Schymanski, Steffen Neumann, Erik Mueller, with contributions from Tobias Schulze Maintainer: RMassBank at Eawag SystemRequirements: OpenBabel source.ver: src/contrib/RMassBank_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RMassBank_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RMassBank_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RMassBank_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RMassBank_2.0.0.tgz vignettes: vignettes/RMassBank/inst/doc/RMassBank.pdf, vignettes/RMassBank/inst/doc/RMassBankNonstandard.pdf, vignettes/RMassBank/inst/doc/RMassBankXCMS.pdf vignetteTitles: RMassBank walkthrough, RMassBank non-standard usage, RMassBank using XCMS walkthrough hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RMassBank/inst/doc/RMassBank.R, vignettes/RMassBank/inst/doc/RMassBankNonstandard.R, vignettes/RMassBank/inst/doc/RMassBankXCMS.R Package: rMAT Version: 3.22.0 Depends: R(>= 2.9.0), BiocGenerics (>= 0.1.3), IRanges (>= 1.13.10), Biobase (>= 2.15.1), affxparser Imports: stats, methods, BiocGenerics, IRanges, Biobase, affxparser, stats4 Suggests: GenomeGraphs, rtracklayer License: Artistic-2.0 MD5sum: d9826a66c5cadda8de020cdf917bdc1f NeedsCompilation: yes Title: R implementation from MAT program to normalize and analyze tiling arrays and ChIP-chip data. Description: This package is an R version of the package MAT and contains functions to parse and merge Affymetrix BPMAP and CEL tiling array files (using C++ based Fusion SDK and Bioconductor package affxparser), normalize tiling arrays using sequence specific models, detect enriched regions from ChIP-chip experiments. Note: users should have GSL and GenomeGraphs installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'. Snow Leopard users can take advantage of increase speed with Grand Central Dispatch! biocViews: Microarray, Preprocessing Author: Charles Cheung and Arnaud Droit and Raphael Gottardo Maintainer: Arnaud Droit and Raphael Gottardo URL: http://www.rglab.org source.ver: src/contrib/rMAT_3.22.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/rMAT_3.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rMAT_3.22.0.tgz vignettes: vignettes/rMAT/inst/doc/rMAT.pdf vignetteTitles: The rMAT users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rMAT/inst/doc/rMAT.R Package: RmiR Version: 1.28.0 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: 1d61e3ae11484f308c1e41441905d012 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 source.ver: src/contrib/RmiR_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RmiR_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RmiR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RmiR_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RmiR_1.28.0.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 suggestsMe: oneChannelGUI Package: RNAinteract Version: 1.20.0 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: 48d0e0e9a5bb74fd16a27ea96fd34e69 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: CellBasedAssays, QualityControl, Preprocessing, Visualization Author: Bernd Fischer Maintainer: Bernd Fischer source.ver: src/contrib/RNAinteract_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNAinteract_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNAinteract_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RNAinteract_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNAinteract_1.20.0.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 Package: RNAither Version: 2.20.0 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: 2e1daecc92f4be509c0745ba9b43fe83 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 Author: Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany Maintainer: Lars Kaderali source.ver: src/contrib/RNAither_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNAither_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNAither_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RNAither_2.17.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNAither_2.20.0.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 Package: RNAprobR Version: 1.4.0 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), Rsamtools(>= 1.16.1), rtracklayer(>= 1.24.2), GenomicAlignments(>= 1.5.12) Suggests: BiocStyle License: GPL (>=2) MD5sum: 9f8dd3759c1f230168121ef8b5f4173e 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 source.ver: src/contrib/RNAprobR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNAprobR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNAprobR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RNAprobR_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNAprobR_1.4.0.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 Package: rnaseqcomp Version: 1.2.2 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: a5cdd7214dfab51e22ae6e9037840330 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 nessasery meta information should be organizd 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 source.ver: src/contrib/rnaseqcomp_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rnaseqcomp_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rnaseqcomp_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/rnaseqcomp_0.99.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rnaseqcomp_1.2.2.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.pdf vignetteTitles: The rnaseqcomp user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.R Package: rnaSeqMap Version: 2.30.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Archs: i386, x64 MD5sum: 283c36d89b253fa9cec250a442ffdd01 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: Annotation, ReportWriting, Transcription, GeneExpression, DifferentialExpression, Sequencing, RNASeq, SAGE, Visualization Author: Anna Lesniewska ; Michal Okoniewski Maintainer: Michal Okoniewski source.ver: src/contrib/rnaSeqMap_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rnaSeqMap_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rnaSeqMap_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rnaSeqMap_2.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rnaSeqMap_2.30.0.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 dependsOnMe: ampliQueso Package: RNASeqPower Version: 1.12.0 License: LGPL (>=2) MD5sum: 8ccbfe5cc59c2aba866ab6af1d2c3937 NeedsCompilation: no Title: Sample size for RNAseq studies Description: RNA-seq, sample size biocViews: RNASeq Author: Terry M Therneau [aut, cre], Hart Stephen [ctb] Maintainer: Terry M Therneau source.ver: src/contrib/RNASeqPower_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RNASeqPower_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RNASeqPower_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RNASeqPower_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RNASeqPower_1.12.0.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 Package: RnaSeqSampleSize Version: 1.4.2 Depends: R (>= 2.10), RnaSeqSampleSizeData Imports: biomaRt,edgeR,heatmap3,matlab,KEGGREST,Rcpp (>= 0.11.2) LinkingTo: Rcpp Suggests: BiocStyle, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: d0ab74d6e9d43b822627ec7ef398bec0 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 biocViews: ExperimentalDesign, Sequencing, RNASeq, GeneExpression, DifferentialExpression Author: Shilin Zhao, Chung-I Li, Yan Guo, Quanhu Sheng, Yu Shyr Maintainer: Shilin Zhao VignetteBuilder: knitr source.ver: src/contrib/RnaSeqSampleSize_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RnaSeqSampleSize_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RnaSeqSampleSize_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/RnaSeqSampleSize_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RnaSeqSampleSize_1.4.2.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 Package: RnBeads Version: 1.4.0 Depends: R (>= 3.0.0), BiocGenerics, S4Vectors (>= 0.9.25), GenomicRanges, MASS, RColorBrewer, cluster, ff, fields, ggplot2 (>= 0.9.2), gplots, gridExtra, limma, matrixStats, methods, illuminaio, methylumi, plyr Imports: IRanges Suggests: Category, GEOquery, GOstats, Gviz, IlluminaHumanMethylation450kmanifest, RPMM, RefFreeEWAS, RnBeads.hg19, 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, sva, wateRmelon, wordcloud, argparse, glmnet, impute License: GPL-3 MD5sum: 3c68ce5ba6fe23bdfff86e06a5a01456 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, TwoChannel, DataImport Author: Yassen Assenov [aut], Pavlo Lutsik [aut], Fabian Mueller [aut, cre] Maintainer: Fabian Mueller source.ver: src/contrib/RnBeads_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RnBeads_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RnBeads_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RnBeads_1.1.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RnBeads_1.4.0.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 Package: Rnits Version: 1.6.2 Depends: R (>= 3.1.0), Biobase, ggplot2, limma, methods Imports: affy, boot, impute, splines, graphics, qvalue, reshape2 Suggests: BiocStyle, knitr, GEOquery, stringr License: GPL-3 MD5sum: e5f12512dc2ad8d774b280dd656fc1af 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 source.ver: src/contrib/Rnits_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rnits_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Rnits_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/Rnits_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rnits_1.6.2.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 Package: roar Version: 1.8.0 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: 35505c629a9b8139df909be08497e9b9 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/ source.ver: src/contrib/roar_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/roar_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/roar_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/roar_1.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/roar_1.8.0.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 Package: ROC Version: 1.48.0 Depends: R (>= 1.9.0), utils, methods Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 57d1b4ebb1a05c7d2a1e223eb32696f1 NeedsCompilation: yes Title: utilities for ROC, with uarray focus Description: utilities for ROC, with uarray focus biocViews: DifferentialExpression Author: Vince Carey , Henning Redestig for C++ language enhancements Maintainer: Vince Carey URL: http://www.bioconductor.org source.ver: src/contrib/ROC_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ROC_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ROC_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ROC_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ROC_1.48.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.pdf vignetteTitles: ROC notes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ROC/inst/doc/ROCnotes.R dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter, MCRestimate Package: Roleswitch Version: 1.10.0 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 MD5sum: 61329bed5f6a6b4cc19d692a81c15918 NeedsCompilation: no 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 source.ver: src/contrib/Roleswitch_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Roleswitch_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Roleswitch_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Roleswitch_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Roleswitch_1.10.0.tgz vignettes: vignettes/Roleswitch/inst/doc/Roleswitch.pdf vignetteTitles: Roleswitch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Roleswitch/inst/doc/Roleswitch.R importsMe: miRLAB Package: Rolexa Version: 1.27.0 Depends: R (>= 2.9.0), graphics, grDevices, methods, ShortRead Imports: mclust, Biostrings, graphics, grDevices, IRanges, methods, ShortRead, stats Enhances: fork License: GPL-2 MD5sum: a7edc20256b7fc37446056b496c0d0c1 NeedsCompilation: no Title: Statistical analysis of Solexa sequencing data Description: Provides probabilistic base calling, quality checks and diagnostic plots for Solexa sequencing data biocViews: Sequencing, DataImport, Preprocessing, QualityControl Author: Jacques Rougemont, Arnaud Amzallag, Christian Iseli, Laurent Farinelli, Ioannis Xenarios, Felix Naef Maintainer: Jacques Rougemont source.ver: src/contrib/Rolexa_1.27.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/Rolexa_1.25.0.tgz vignettes: vignettes/Rolexa/inst/doc/Rolexa-vignette.pdf vignetteTitles: Rolexa hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rolexa/inst/doc/Rolexa-vignette-batchplot.R, vignettes/Rolexa/inst/doc/Rolexa-vignette-tileimage.R, vignettes/Rolexa/inst/doc/Rolexa-vignette.R Package: rols Version: 2.0.3 Depends: methods Imports: httr, progress, jsonlite, utils, Biobase Suggests: GO.db, knitr (>= 1.1.0), BiocStyle, testthat, lubridate, DT, rmarkdown License: GPL-2 MD5sum: 541f9cf5c85191ea19330db5e1ddd470 NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: An interface to the Ontology Lookup Service (OLS) to access and query hundred of ontolgies directly from R. biocViews: Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto , with contributions from Tiago Chedraoui Silva. Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr BugReports: https://github.com/lgatto/rols/issues source.ver: src/contrib/rols_2.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/rols_2.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/rols_2.0.3.zip mac.binary.ver: bin/macosx/contrib/3.3/rols_1.11.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rols_2.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rols/inst/doc/rols.R htmlDocs: vignettes/rols/inst/doc/rols.html htmlTitles: The rols interface to the Ontology Lookup Service suggestsMe: MSnbase Package: ROntoTools Version: 2.0.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: b4e8886c333840fe7d47f3280c300478 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 source.ver: src/contrib/ROntoTools_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ROntoTools_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ROntoTools_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ROntoTools_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ROntoTools_2.0.0.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 Package: ropls Version: 1.4.6 Depends: methods Suggests: RUnit, BiocGenerics, BiocStyle, faahKO, xcms, CAMERA License: CeCILL MD5sum: 154f1326c23bd38ec544a718601e577b 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 Author: Etienne A. Thevenot Maintainer: Etienne A. Thevenot URL: http://dx.doi.org/10.1021/acs.jproteome.5b00354 source.ver: src/contrib/ropls_1.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/ropls_1.4.6.zip win64.binary.ver: bin/windows64/contrib/3.3/ropls_1.4.6.zip mac.binary.ver: bin/macosx/contrib/3.3/ropls_1.1.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ropls_1.4.6.tgz vignettes: vignettes/ropls/inst/doc/ropls.pdf vignetteTitles: PCA,, PLS and OPLS with \emph{ropls} hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ropls/inst/doc/ropls.R dependsOnMe: biosigner Package: ROTS Version: 1.0.0 Depends: R (>= 3.3) Imports: Rcpp, stats, Biobase, methods LinkingTo: Rcpp License: GPL (>= 2) Archs: i386, x64 MD5sum: 58bd358791548d7aee1a69f13cb389c6 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 Author: Fatemeh Seyednasrollah, Tomi Suomi, Laura L. Elo Maintainer: Fatemeh Seyednasrollah source.ver: src/contrib/ROTS_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ROTS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ROTS_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ROTS_1.0.0.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 Package: RPA Version: 1.28.0 Depends: R (>= 3.1.1), affy, BiocGenerics, methods, phyloseq Suggests: affydata, parallel License: BSD_2_clause + file LICENSE MD5sum: 221518631d3d2a889ac2db7d72eea9cd 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. Lahti et al. "Probabilistic Analysis of Probe Reliability in Differential Gene Expression Studies with Short Oligonucleotide Arrays", TCBB/IEEE, 2011. http://doi.ieeecomputersociety.org/10.1109/TCBB.2009.38 biocViews: GeneExpression, Microarray, Preprocessing, QualityControl Author: Leo Lahti Maintainer: Leo Lahti URL: https://github.com/antagomir/RPA BugReports: https://github.com/antagomir/RPA source.ver: src/contrib/RPA_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RPA_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RPA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RPA_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RPA_1.28.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs Package: RpsiXML Version: 2.14.0 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: 044069f9ea3ad6c4c02e774f4ef71c60 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 source.ver: src/contrib/RpsiXML_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RpsiXML_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RpsiXML_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RpsiXML_2.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RpsiXML_2.14.0.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 Package: rpx Version: 1.8.3 Depends: methods Imports: XML, RCurl, utils Suggests: MSnbase, Biostrings, BiocStyle, testthat, knitr License: GPL-2 MD5sum: af92e8f61d20b6c6de977d3059176738 NeedsCompilation: no Title: R Interface to the ProteomeXchange Repository Description: This package implements an interface to proteomics data submitted to the ProteomeXchange consortium. biocViews: 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 source.ver: src/contrib/rpx_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/rpx_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.3/rpx_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.3/rpx_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rpx_1.8.3.tgz vignettes: vignettes/rpx/inst/doc/rpx.pdf vignetteTitles: An interface to proteomics data repositories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rpx/inst/doc/rpx.R suggestsMe: proteoQC Package: Rqc Version: 1.6.2 Depends: BiocParallel, ShortRead, ggplot2 Imports: BiocGenerics, Biostrings, IRanges, methods, S4Vectors, knitr (>= 1.7), BiocStyle, plyr, markdown, grid, reshape2, digest, Rcpp (>= 0.11.6), biovizBase, shiny, Rsamtools, GenomicAlignments, GenomicFiles LinkingTo: Rcpp Suggests: testthat License: GPL (>= 2) Archs: i386, x64 MD5sum: 74ad1d5a27efb49952d6892e0af71815 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 source.ver: src/contrib/Rqc_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rqc_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/Rqc_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/Rqc_1.3.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rqc_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rqc/inst/doc/Rqc.R htmlDocs: vignettes/Rqc/inst/doc/Rqc.html htmlTitles: Using Rqc Package: rqubic Version: 1.18.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Archs: i386, x64 MD5sum: d45adfa5f0190cca5c4e59602e1de2d8 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: Microarray, Clustering Author: Jitao David Zhang, with inputs from Laura Badi and Martin Ebeling Maintainer: Jitao David Zhang source.ver: src/contrib/rqubic_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rqubic_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rqubic_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rqubic_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rqubic_1.18.0.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 Package: rRDP Version: 1.6.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: f8226f45eb78b864565295f77b9a744a NeedsCompilation: no Title: Interface to the RDP Classifier Description: Seamlessly interfaces RDP classifier (version 2.9). biocViews: Genetics, Sequencing, Infrastructure, Classification, Microbiome Author: Michael Hahsler, Anurag Nagar Maintainer: Michael Hahsler SystemRequirements: Java source.ver: src/contrib/rRDP_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rRDP_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rRDP_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rRDP_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rRDP_1.6.0.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 Package: RRHO Version: 1.12.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 8e55e2bf0e98cfbcf7c4d0315f6ade50 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 source.ver: src/contrib/RRHO_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RRHO_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RRHO_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RRHO_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RRHO_1.12.0.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 Package: Rsamtools Version: 1.24.0 Depends: methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.21.6), Biostrings (>= 2.37.1) Imports: utils, BiocGenerics (>= 0.1.3), S4Vectors (>= 0.7.11), IRanges (>= 2.3.7), XVector (>= 0.9.1), zlibbioc, bitops, BiocParallel LinkingTo: 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, pasillaBamSubset, RUnit, BiocStyle License: Artistic-2.0 | file LICENSE Archs: i386, x64 MD5sum: a640f51170f24fa02bc429e5bb7b622e 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 (see 'LICENCE') 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, Herv\'e Pag\`es, Valerie Obenchain, Nathaniel Hayden Maintainer: Bioconductor Package Maintainer URL: http://bioconductor.org/packages/release/bioc/html/Rsamtools.html Video: https://www.youtube.com/watch?v=Rfon-DQYbWA&list=UUqaMSQd_h-2EDGsU6WDiX0Q source.ver: src/contrib/Rsamtools_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rsamtools_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rsamtools_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rsamtools_1.21.17.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rsamtools_1.24.0.tgz vignettes: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.pdf, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.pdf vignetteTitles: An introduction to Rsamtools, Using samtools C libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/Rsamtools/inst/doc/Rsamtools-Overview.R, vignettes/Rsamtools/inst/doc/Rsamtools-UsingCLibraries.R dependsOnMe: ArrayExpressHTS, BitSeq, chimera, CODEX, contiBAIT, CoverageView, exomeCopy, exomePeak, GenoGAM, GenomicAlignments, GenomicFiles, girafe, gmapR, Guitar, MEDIPS, methylPipe, MMDiff2, oneChannelGUI, podkat, qrqc, r3Cseq, Rcade, ReQON, rfPred, RIPSeeker, rnaSeqMap, SGSeq, ShortRead, SICtools, SNPhood, systemPipeR, TarSeqQC, TEQC, TitanCNA, VariantAnnotation, wavClusteR importsMe: AllelicImbalance, AneuFinder, annmap, AnnotationHubData, ArrayExpressHTS, BadRegionFinder, BBCAnalyzer, biovizBase, BSgenome, CAGEr, casper, CexoR, ChIPQC, cn.mops, CNVPanelizer, CNVrd2, compEpiTools, CopywriteR, CrispRVariants, csaw, customProDB, derfinder, DEXSeq, DiffBind, diffHic, DOQTL, easyRNASeq, EDASeq, ensembldb, epigenomix, eudysbiome, FourCSeq, FunciSNP, genomation, GenomicAlignments, GenomicInteractions, GenVisR, ggbio, GGtools, GoogleGenomics, GOTHiC, GreyListChIP, GUIDEseq, Gviz, gwascat, h5vc, HTSeqGenie, INSPEcT, metagene, mosaics, nucleR, PGA, PICS, QDNAseq, QuasR, R453Plus1Toolbox, Rariant, Repitools, RiboProfiling, RNAprobR, Rqc, rtracklayer, similaRpeak, soGGi, SplicingGraphs, tracktables, trackViewer, transcriptR, TransView, VariantFiltering, VariantTools suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocParallel, biomvRCNS, Chicago, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, gQTLstats, metaseqR, recoup, seqbias, SigFuge, Streamer Package: rsbml Version: 2.30.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: 03e0f32a6bce66507e222b156a53db33 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) source.ver: src/contrib/rsbml_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rsbml_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rsbml_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rsbml_2.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rsbml_2.30.0.tgz vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rsbml/inst/doc/quick-start.R dependsOnMe: BiGGR suggestsMe: piano, SBMLR Package: rSFFreader Version: 0.20.0 Depends: ShortRead (>= 1.23.17) Imports: methods, Biostrings, IRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: xtable License: Artistic-2.0 MD5sum: 426c157408eda89d8bb0a84626712d26 NeedsCompilation: yes Title: rSFFreader reads in sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers Description: rSFFreader reads sequence, qualities and clip point values from sff files generated by Roche 454 and Life Sciences Ion Torrent sequencers into similar classes as are present for fastq files. biocViews: DataImport, Sequencing Author: Matt Settles , Sam Hunter, Brice Sarver, Ilia Zhbannikov, Kyu-Chul Cho Maintainer: Matt Settles source.ver: src/contrib/rSFFreader_0.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/rSFFreader_0.17.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rSFFreader_0.20.0.tgz vignettes: vignettes/rSFFreader/inst/doc/rSFFreader.pdf vignetteTitles: An introduction to rSFFreader hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/rSFFreader/inst/doc/rSFFreader.R importsMe: hiReadsProcessor Package: Rsubread Version: 1.22.3 License: GPL-3 MD5sum: 9a91cd8004c5c3173e09a46fee075226 NeedsCompilation: yes Title: Subread sequence alignment for R Description: Provides powerful and easy-to-use tools for analyzing next-gen sequencing read data. Includes quality assessment of sequence reads, read alignment, read summarization, exon-exon junction detection, fusion detection, detection of short and long indels, absolute expression calling and SNP calling. Can be used with reads generated from any of the major sequencing platforms including Illumina GA/HiSeq/MiSeq, Roche GS-FLX, ABI SOLiD and LifeTech Ion PGM/Proton sequencers. biocViews: Sequencing, Alignment, SequenceMatching, RNASeq, ChIPSeq, GeneExpression, GeneRegulation, Genetics, SNP, GeneticVariability, Preprocessing, QualityControl, GenomeAnnotation, Software Author: Wei Shi and Yang Liao with contributions from Jenny Zhiyin Dai and Timothy Triche, Jr. Maintainer: Wei Shi URL: http://bioconductor.org/packages/release/bioc/html/Rsubread.html source.ver: src/contrib/Rsubread_1.22.3.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/Rsubread_1.19.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rsubread_1.22.3.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf vignetteTitles: Rsubread Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Rsubread/inst/doc/Rsubread.R importsMe: dupRadar Package: RSVSim Version: 1.12.0 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: 69e31938be882a3760be1a0f6bc06b94 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 source.ver: src/contrib/RSVSim_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RSVSim_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RSVSim_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RSVSim_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RSVSim_1.12.0.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 Package: rTANDEM Version: 1.12.0 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE Archs: i386, x64 MD5sum: 49b623c079de4619337dd6938a176d54 NeedsCompilation: yes 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: 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. source.ver: src/contrib/rTANDEM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rTANDEM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rTANDEM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rTANDEM_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rTANDEM_1.12.0.tgz vignettes: vignettes/rTANDEM/inst/doc/rTANDEM.pdf vignetteTitles: The rTANDEM users guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rTANDEM/inst/doc/rTANDEM.R dependsOnMe: PGA, shinyTANDEM importsMe: proteoQC Package: RTCA Version: 1.24.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: cb3c25a73c0d1620882eb63b12e81b5c 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: 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 source.ver: src/contrib/RTCA_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTCA_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTCA_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RTCA_1.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTCA_1.24.0.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 Package: RTCGA Version: 1.2.5 Depends: R (>= 3.3) 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: 2578d99bfef251a21a3bf4642954c715 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: 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 source.ver: src/contrib/RTCGA_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTCGA_1.2.5.zip win64.binary.ver: bin/windows64/contrib/3.3/RTCGA_1.2.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTCGA_1.2.5.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/RTCGA/inst/doc/RTCGA_Workflow.html htmlTitles: Integrating TCGA Data - RTCGA Workflow Package: RTCGAToolbox Version: 2.2.2 Depends: R (>= 3.2.0) Imports: methods,XML,limma (>= 3.18),survival,RCircos,data.table (>= 1.9.4),RCurl,RJSONIO Suggests: BiocStyle, knitr, rmarkdown, Homo.sapiens License: GPL (>= 2) MD5sum: d12e2d145ec231ed6ac796a47767c9e9 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: Sequencing, DifferentialExpression, GeneExpression Author: Mehmet Kemal Samur Maintainer: Mehmet Kemal Samur VignetteBuilder: knitr source.ver: src/contrib/RTCGAToolbox_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTCGAToolbox_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RTCGAToolbox_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/RTCGAToolbox_1.99.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTCGAToolbox_2.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.R htmlDocs: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html htmlTitles: Vignette Title Package: RTN Version: 1.10.0 Depends: R (>= 2.15), methods, igraph Imports: RedeR, minet, snow, limma, data.table, ff, car, IRanges Suggests: HTSanalyzeR, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 14ea117d162c9b7205b0753b4bb1fea3 NeedsCompilation: no Title: Reconstruction of transcriptional networks and analysis of master regulators Description: This package provides classes and methods for transcriptional network inference and analysis. Modulators of transcription factor activity are assessed by conditional mutual information, and master regulators are mapped to phenotypes using different strategies, e.g., gene set enrichment, shadow and synergy analyses. Additionally, master regulators can be linked to genetic markers using eQTL/VSE analysis, taking advantage of the haplotype block structure mapped to the human genome in order to explore risk-associated SNPs identified in GWAS studies. biocViews: NetworkInference, NetworkAnalysis, NetworkEnrichment, GeneRegulation, GeneExpression, GraphAndNetwork, GeneSetEnrichment,GeneticVariability,SNP Author: Mauro Castro, Xin Wang, Michael Fletcher, Florian Markowetz and Kerstin Meyer Maintainer: Mauro Castro URL: http://dx.doi.org/10.1038/ncomms3464 source.ver: src/contrib/RTN_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTN_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTN_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RTN_1.7.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTN_1.10.0.tgz vignettes: vignettes/RTN/inst/doc/RTN.pdf vignetteTitles: Main vignette: reconstruction and analysis of transcriptional networks in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RTN/inst/doc/RTN.R Package: RTopper Version: 1.18.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: f9df397da9a410c7c813e614f4b7efd6 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 source.ver: src/contrib/RTopper_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RTopper_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RTopper_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RTopper_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RTopper_1.18.0.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 Package: rtracklayer Version: 1.32.2 Depends: R (>= 3.3), methods, GenomicRanges (>= 1.21.20) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.13.8), S4Vectors (>= 0.9.33), IRanges (>= 2.3.7), XVector (>= 0.9.4), GenomeInfoDb (>= 1.3.14), Biostrings (>= 2.37.1), zlibbioc, RCurl (>= 1.4-2), Rsamtools (>= 1.17.8), GenomicAlignments (>= 1.5.4), tools LinkingTo: S4Vectors, IRanges, XVector Suggests: BSgenome (>= 1.33.4), humanStemCell, microRNA (>= 1.1.1), genefilter, limma, org.Hs.eg.db, hgu133plus2.db, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: effefd65ead5e53c75d23938a5e1601c NeedsCompilation: yes Title: R interface to genome browsers and their annotation tracks 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 source.ver: src/contrib/rtracklayer_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rtracklayer_1.32.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rtracklayer_1.32.2.zip mac.binary.ver: bin/macosx/contrib/3.3/rtracklayer_1.29.25.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rtracklayer_1.32.2.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/rtracklayer/inst/doc/rtracklayer.R dependsOnMe: BSgenome, CoverageView, cummeRbund, exomePeak, GenomicFiles, groHMM, Guitar, MethylSeekR, r3Cseq, regioneR, RIPSeeker, spliceR importsMe: AnnotationHubData, ballgown, BiSeq, BSgenome, CAGEr, casper, CexoR, ChIPseeker, ChromHeatMap, CNEr, coMET, CompGO, consensusSeekeR, contiBAIT, conumee, customProDB, derfinder, ensembldb, erma, FourCSeq, FunciSNP, genbankr, GenomicFeatures, GenomicInteractions, genotypeeval, ggbio, GGtools, gmapR, GOTHiC, gQTLBase, GreyListChIP, Gviz, gwascat, hiAnnotator, HiTC, HTSeqGenie, MEDIPS, metagene, methyAnalysis, motifbreakR, MotifDb, Pbase, PGA, proBAMr, QuasR, recoup, regioneR, Repitools, RiboProfiling, RNAprobR, roar, seqplots, SGSeq, similaRpeak, soGGi, TFBSTools, trackViewer, transcriptR, VariantAnnotation, VariantTools, wavClusteR suggestsMe: AnnotationHub, biovizBase, ChIPpeakAnno, CINdex, compEpiTools, CrispRVariants, GenomicAlignments, GenomicRanges, goseq, InPAS, interactiveDisplay, metaseqR, methylumi, MotIV, NarrowPeaks, oneChannelGUI, OrganismDbi, PICS, PING, pqsfinder, R453Plus1Toolbox, Ringo, rMAT, RnBeads, RSVSim, triplex, TSSi Package: Rtreemix Version: 1.34.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: i386, x64 MD5sum: ee9ca96e7dfb7e9e4323c3f4dbcf343e 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 source.ver: src/contrib/Rtreemix_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Rtreemix_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Rtreemix_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Rtreemix_1.31.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Rtreemix_1.34.0.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 Package: rTRM Version: 1.10.2 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: 7e99a8e14c3692434f6d83d91757c30b 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 source.ver: src/contrib/rTRM_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/rTRM_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/rTRM_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/rTRM_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rTRM_1.10.2.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 Package: rTRMui Version: 1.10.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: b95ba28088b3342c87c0ac33c9de8f90 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 source.ver: src/contrib/rTRMui_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/rTRMui_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/rTRMui_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/rTRMui_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/rTRMui_1.10.0.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 Package: RUVcorr Version: 1.4.2 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2 Suggests: knitr, BiocStyle, hgu133a2.db License: GPL-2 MD5sum: c75e46866f357d04be06c2cf3c81960b 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 source.ver: src/contrib/RUVcorr_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RUVcorr_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RUVcorr_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/RUVcorr_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RUVcorr_1.4.2.tgz vignettes: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.pdf vignetteTitles: RUVcorr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.R Package: RUVnormalize Version: 1.6.0 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: 098b06a914bde0e42e8308613aa5c76d 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 source.ver: src/contrib/RUVnormalize_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/RUVnormalize_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/RUVnormalize_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/RUVnormalize_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RUVnormalize_1.6.0.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 Package: RUVSeq Version: 1.6.2 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq2 License: Artistic-2.0 MD5sum: 7f2829797833123dd5e69e7e4b918405 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: 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 source.ver: src/contrib/RUVSeq_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/RUVSeq_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/RUVSeq_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/RUVSeq_1.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/RUVSeq_1.6.2.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 Package: RWebServices Version: 1.36.0 Depends: SJava (>= 0.85), TypeInfo, methods, tools (>= 2.10.0), R (>= 2.5.0) Imports: RCurl License: file LICENSE License_restricts_use: no MD5sum: cd2f2e9904847b4345ae2b4af06b273f NeedsCompilation: yes Title: Expose R functions as web services through Java/Axis/Apache Description: This package provides mechanisms for automatic function prototyping and exposure of R functionality in a web services environment. biocViews: Infrastructure Author: Nianhua Li, MT Morgan Maintainer: Martin Morgan PackageStatus: Deprecated source.ver: src/contrib/RWebServices_1.36.0.tar.gz vignettes: vignettes/RWebServices/inst/doc/EnablingPackages.pdf, vignettes/RWebServices/inst/doc/InstallingAndTesting.pdf, vignettes/RWebServices/inst/doc/LessonsLearned.pdf, vignettes/RWebServices/inst/doc/RelatedWork.pdf, vignettes/RWebServices/inst/doc/RToJava.pdf vignetteTitles: Enabling packages as web services, Installing and testing RWebServices and enabled packages, Lessons learned exposing web services, RelatedWork, From R to Java hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/RWebServices/inst/doc/EnablingPackages.R, vignettes/RWebServices/inst/doc/InstallingAndTesting.R, vignettes/RWebServices/inst/doc/RToJava.R Package: S4Vectors Version: 0.10.3 Depends: R (>= 3.3.0), methods, utils, stats, stats4, BiocGenerics (>= 0.15.10) Suggests: IRanges, GenomicRanges, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: c119114d52fea32dcf27d94f477036a3 NeedsCompilation: yes Title: S4 implementation of vectors and lists 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 source.ver: src/contrib/S4Vectors_0.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/S4Vectors_0.10.3.zip win64.binary.ver: bin/windows64/contrib/3.3/S4Vectors_0.10.3.zip mac.binary.ver: bin/macosx/contrib/3.3/S4Vectors_0.7.16.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/S4Vectors_0.10.3.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf vignetteTitles: Rle Tips and Tricks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/S4Vectors/inst/doc/RleTricks.R dependsOnMe: altcdfenvs, AnnotationHubData, Biostrings, BiSeq, BSgenome, bumphunter, CexoR, ChIPpeakAnno, chipseq, ChIPseqR, CSAR, DESeq2, DEXSeq, DirichletMultinomial, DMRcaller, epigenomix, ExpressionAtlas, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, girafe, groHMM, Gviz, HDF5Array, htSeqTools, InPAS, IRanges, isomiRs, meshr, MotifDb, OTUbase, plethy, RIPSeeker, RnBeads, segmentSeq, triplex, VariantTools, XVector importsMe: affycoretools, ALDEx2, AllelicImbalance, AneuFinder, AnnotationDbi, AnnotationForge, AnnotationHub, ArrayTV, BadRegionFinder, ballgown, biovizBase, BiSeq, BitSeq, BSgenome, bsseq, casper, ChIPQC, ChIPseeker, cleaver, clusterProfiler, cn.mops, CNEr, CNPBayes, CNVPanelizer, coMET, compEpiTools, consensusSeekeR, contiBAIT, copynumber, CopywriteR, CoverageView, CRISPRseek, CrispRVariants, csaw, cummeRbund, customProDB, DChIPRep, debrowser, DECIPHER, derfinder, derfinderHelper, derfinderPlot, DiffBind, diffHic, diffloop, DMRcate, DRIMSeq, easyRNASeq, EnrichmentBrowser, ensembldb, ensemblVEP, epivizr, epivizrData, epivizrStandalone, erma, facopy, fastseg, FindMyFriends, FunciSNP, genbankr, genefilter, GeneRegionScan, GenoGAM, genomation, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFiles, GenomicInteractions, genoset, GGBase, ggbio, GGtools, gmapR, GoogleGenomics, GOTHiC, gQTLBase, gQTLstats, GUIDEseq, gwascat, h5vc, HTSeqGenie, INSPEcT, InteractionSet, IVAS, JunctionSeq, kebabs, LOLA, M3D, MEAL, methylPipe, methylumi, minfi, MinimumDistance, MiRaGE, MMDiff2, mosaics, motifbreakR, MotIV, msa, MSnbase, MultiDataSet, mygene, myvariant, NarrowPeaks, nucleoSim, nucleR, oligoClasses, OrganismDbi, Pbase, pcaExplorer, pdInfoBuilder, PGA, PICS, PING, polyester, pqsfinder, prebs, procoil, PureCN, qcmetrics, qpgraph, QuasR, R3CPET, R453Plus1Toolbox, RareVariantVis, Rariant, Rcade, regionReport, Repitools, RiboProfiling, roar, Rqc, Rsamtools, rtracklayer, SeqArray, seqplots, SeqVarTools, sevenbridges, SGSeq, ShortRead, simulatorZ, SMITE, SNPchip, SNPhood, soGGi, SomaticSignatures, SplicingGraphs, STAN, SummarizedExperiment, TarSeqQC, TCGAbiolinks, TFBSTools, transcriptR, TransView, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, XVector suggestsMe: BiocGenerics, RTCGA, scran Package: safe Version: 3.12.0 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: ff85344d7375d224ec871066de21829d 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: William T. Barry source.ver: src/contrib/safe_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/safe_3.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/safe_3.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/safe_3.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/safe_3.12.0.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 importsMe: EGSEA, EnrichmentBrowser Package: sagenhaft Version: 1.42.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, methods, SparseM, stats, utils License: GPL (>= 2) MD5sum: 33dfde9b437c96f853ecd50ffb7fa4f7 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 and Lavinia Hyde . Maintainer: Tim Beissbarth URL: http://tagcalling.mbgproject.org source.ver: src/contrib/sagenhaft_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sagenhaft_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sagenhaft_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sagenhaft_1.39.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sagenhaft_1.42.0.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 Package: SAGx Version: 1.46.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Archs: i386, x64 MD5sum: 1ed14d93883672023bc45d8bb5b564b2 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 source.ver: src/contrib/SAGx_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SAGx_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SAGx_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SAGx_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SAGx_1.46.0.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 Package: SamSPECTRAL Version: 1.26.0 Depends: R (>= 2.10) Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: d4244cac034b89c65f0a11f3ee136109 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 sample. For instructions on manual installation, refer to the PDF file provided in the following documentation. biocViews: FlowCytometry, CellBiology, Clustering, Cancer, FlowCytometry, StemCells, HIV Author: Habil Zare and Parisa Shooshtari Maintainer: Habil Zare source.ver: src/contrib/SamSPECTRAL_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SamSPECTRAL_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SamSPECTRAL_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SamSPECTRAL_1.23.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SamSPECTRAL_1.26.0.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 Package: sangerseqR Version: 1.8.2 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: edead842f3f2d663cf5295f093e0b05d 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 source.ver: src/contrib/sangerseqR_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/sangerseqR_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.3/sangerseqR_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.3/sangerseqR_1.5.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sangerseqR_1.8.2.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 suggestsMe: CrispRVariants Package: SANTA Version: 2.10.2 Depends: R (>= 2.14), igraph Imports: Matrix, snow Suggests: RUnit, BiocGenerics, knitr, knitcitations, formatR, org.Sc.sgd.db, BioNet, DLBCL, msm License: GPL (>= 2) Archs: i386, x64 MD5sum: 4d1c7853509d6a1243c7d082dfd24e09 NeedsCompilation: yes Title: Spatial Analysis of Network Associations Description: This package provides methods for measuring the strength of association between a network and a phenotype. It does this by measuring clustering of the phenotype across the network (Knet). Vertices can also be individually ranked by their strength of association with high-weight vertices (Knode). biocViews: Network, NetworkEnrichment, Clustering Author: Alex J. Cornish and Florian Markowetz Maintainer: Alex J. Cornish VignetteBuilder: knitr source.ver: src/contrib/SANTA_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/SANTA_2.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/SANTA_2.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/SANTA_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SANTA_2.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SANTA/inst/doc/SANTA-vignette.R htmlDocs: vignettes/SANTA/inst/doc/SANTA-vignette.html htmlTitles: Introduction to SANTA Package: sapFinder Version: 1.10.0 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: i386, x64 MD5sum: 55218c1fb1c9b16cd352dc06f09073d6 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: MassSpectrometry, Proteomics, SNP, RNASeq, Visualization, ReportWriting Author: Shaohang Xu, Bo Wen Maintainer: Shaohang Xu , Bo Wen source.ver: src/contrib/sapFinder_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sapFinder_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sapFinder_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sapFinder_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sapFinder_1.10.0.tgz 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 Package: saps Version: 2.4.2 Depends: R (>= 2.14.0), survival Imports: piano, survcomp, reshape2 Suggests: snowfall, knitr License: MIT + file LICENSE MD5sum: 9006f3668b382e8bc4e007924b5f2c42 NeedsCompilation: no Title: Significance Analysis of Prognostic Signatures Description: Functions implementing the Significance Analysis of Prognostic Signatures method (SAPS). SAPS provides a robust method for identifying biologically significant gene sets associated with patient survival. Three basic statistics are computed. First, patients are clustered into two survival groups based on differential expression of a candidate gene set. P_pure is calculated as the probability of no survival difference between the two groups. Next, the same procedure is applied to randomly generated gene sets, and P_random is calculated as the proportion achieving a P_pure as significant as the candidate gene set. Finally, a pre-ranked Gene Set Enrichment Analysis (GSEA) is performed by ranking all genes by concordance index, and P_enrich is computed to indicate the degree to which the candidate gene set is enriched for genes with univariate prognostic significance. A SAPS_score is calculated to summarize the three statistics, and optionally a Q-value is computed to estimate the significance of the SAPS_score by calculating SAPS_scores for random gene sets. biocViews: BiomedicalInformatics, GeneExpression, GeneSetEnrichment, DifferentialExpression, Survival Author: Daniel Schmolze [aut, cre], Andrew Beck [aut], Benjamin Haibe-Kains [aut] Maintainer: Daniel Schmolze VignetteBuilder: knitr source.ver: src/contrib/saps_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/saps_2.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/saps_2.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/saps_2.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/saps_2.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/saps/inst/doc/saps.R htmlDocs: vignettes/saps/inst/doc/saps.html htmlTitles: SAPS Vignette Package: savR Version: 1.10.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: 6183ad6f04edbc006ad6811824d2138f 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 source.ver: src/contrib/savR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/savR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/savR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/savR_1.7.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/savR_1.10.0.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 Package: sbgr Version: 1.1.1 Depends: methods, utils, stats Imports: httr, jsonlite, objectProperties, stringr Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 0dd5b071154c46d66bbee26dae34f138 NeedsCompilation: no Title: R Client for Seven Bridges Genomics API Description: R client for Seven Bridges Genomics API. biocViews: Software, DataImport, ThirdPartyClient Author: Nan Xiao , Tengfei Yin Maintainer: Nan Xiao URL: https://www.sbgenomics.com VignetteBuilder: knitr BugReports: https://github.com/road2stat/sbgr/issues source.ver: src/contrib/sbgr_1.1.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/sbgr_0.99.8.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/sbgr/inst/doc/easy_api_v2.html, vignettes/sbgr/inst/doc/easy_api.html, vignettes/sbgr/inst/doc/sbgr.html htmlTitles: Easy API V2: A user-friendly cascading API, Easy API: A user-friendly cascading API, Running the FASTQC Pipeline with sbgr Package: SBMLR Version: 1.68.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 9bf00faf280d365b1ece1b1999323050 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 source.ver: src/contrib/SBMLR_1.68.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SBMLR_1.68.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SBMLR_1.68.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SBMLR_1.65.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SBMLR_1.68.0.tgz 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 Package: SC3 Version: 1.1.4 Depends: R(>= 3.3) Imports: graphics, stats, utils, methods, RSelenium, e1071, parallel, foreach, doParallel, doRNG, shiny, ggplot2, pheatmap (>= 1.0.8), RColorBrewer, colorspace, ROCR, robustbase, rrcov, cluster, WriteXLS, Rtsne Suggests: knitr, rmarkdown, testthat License: GPL-3 MD5sum: 01a418405d53d00cfa5c1abedcdac410 NeedsCompilation: no Title: Single-Cell Consensus Clustering Description: Interactive tool for clustering and analysis of single cell RNA-Seq data. biocViews: Classification, Clustering, DimensionReduction, SupportVectorMachine, RNASeq, Visualization, Transcriptomics, DataRepresentation, GUI, DifferentialExpression, GeneSetEnrichment, Transcription Author: Vladimir Kiselev Maintainer: Vladimir Kiselev URL: https://github.com/hemberg-lab/SC3 VignetteBuilder: knitr BugReports: https://github.com/hemberg-lab/SC3/issues source.ver: src/contrib/SC3_1.1.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/SC3_1.1.4.zip win64.binary.ver: bin/windows64/contrib/3.3/SC3_1.1.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SC3_1.1.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SC3/inst/doc/my-vignette.R htmlDocs: vignettes/SC3/inst/doc/my-vignette.html htmlTitles: SC3 manual Package: SCAN.UPC Version: 2.14.0 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: b2a81d44816e5cfb0add922fe56b45a2 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: 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 source.ver: src/contrib/SCAN.UPC_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SCAN.UPC_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SCAN.UPC_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SCAN.UPC_2.11.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SCAN.UPC_2.14.0.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 Package: scater Version: 1.0.4 Depends: R (>= 3.3), Biobase, ggplot2, methods Imports: biomaRt, BiocGenerics, data.table, dplyr, edgeR, grid, limma, matrixStats, parallel, plyr, reshape2, rhdf5, rjson, shiny, shinydashboard, tximport, viridis Suggests: BiocStyle, cowplot, cluster, destiny, knitr, monocle, mvoutlier, rmarkdown, Rtsne, testthat, magrittr License: GPL (>= 2) MD5sum: f50bfe74ff18d831fe50b1fec5e85f5e 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. biocViews: RNASeq, QualityControl, Preprocessing, Normalization, Visualization, DimensionReduction, Transcriptomics, GeneExpression, Sequencing, Software, DataImport, DataRepresentation, Infrastructure Author: Davis McCarthy Maintainer: Davis McCarthy URL: https://github.com/davismcc/scater VignetteBuilder: knitr BugReports: https://github.com/davismcc/scater/issues source.ver: src/contrib/scater_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/scater_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/scater_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scater_1.0.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scater/inst/doc/vignette.R htmlDocs: vignettes/scater/inst/doc/vignette.html htmlTitles: An introduction to the scater package dependsOnMe: scran Package: scde Version: 2.0.1 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: fe963b272a41619e8e55716ceb9c5cd0 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: 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 source.ver: src/contrib/scde_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/scde_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/scde_2.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scde_2.0.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ScISI Version: 1.44.0 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: 23f676b6d99af737feb110724c65dfad NeedsCompilation: no Title: In Silico Interactome Description: Package to create In Silico Interactomes biocViews: GraphAndNetwork, Proteomics, NetworkInference, DecisionTree Author: Tony Chiang Maintainer: Tony Chiang source.ver: src/contrib/ScISI_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ScISI_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ScISI_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ScISI_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ScISI_1.44.0.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 Package: scran Version: 1.0.4 Depends: R (>= 3.3.0), BiocParallel, scater Imports: dynamicTreeCut, zoo, edgeR, limma, stats, BiocGenerics, methods, Biobase, utils, Matrix Suggests: limSolve, testthat, knitr, BiocStyle, org.Mm.eg.db, DESeq2, monocle, S4Vectors License: GPL-3 Archs: i386, x64 MD5sum: 881bd54268e1f7f4c0d5d0b0caa43e8f NeedsCompilation: yes Title: Methods for Single-Cell RNA-Seq Data Analysis Description: This package implements a variety of low-level analyses of single-cell RNA-seq data. Methods are provided for normalization of cell-specific biases, assignment of cell cycle phase, and detection of highly variable and significantly correlated genes. biocViews: Normalization, Sequencing, RNASeq, Software, GeneExpression, Transcriptomics Author: Aaron Lun [aut, cre], Karsten Bach [aut], Jong Kyoung Kim [ctb], Antonio Scialdone [ctb] Maintainer: Aaron Lun VignetteBuilder: knitr source.ver: src/contrib/scran_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/scran_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.3/scran_1.0.4.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scran_1.0.4.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scran/inst/doc/scran.R htmlDocs: vignettes/scran/inst/doc/scran.html htmlTitles: Food for the mind: using scran to perform basic analyses of single-cell RNA-seq data Package: scsR Version: 1.8.0 Depends: R (>= 2.14.0), STRINGdb, methods, BiocGenerics, Biostrings, IRanges, plyr, tcltk Imports: sqldf, hash, ggplot2, graphics,grDevices, RColorBrewer Suggests: RUnit License: GPL-2 MD5sum: f171e181dd47488965db61bc3799ec7f NeedsCompilation: no 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 source.ver: src/contrib/scsR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/scsR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/scsR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/scsR_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/scsR_1.8.0.tgz vignettes: vignettes/scsR/inst/doc/scsR.pdf vignetteTitles: scsR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/scsR/inst/doc/scsR.R Package: segmentSeq Version: 2.6.0 Depends: R (>= 2.3.0), methods, baySeq (>= 1.99.0), ShortRead, GenomicRanges, IRanges, S4Vectors Imports: graphics, grDevices, utils Suggests: BiocStyle, BiocGenerics License: GPL-3 MD5sum: c28e559593d4e667d88371ca90751790 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 source.ver: src/contrib/segmentSeq_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/segmentSeq_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/segmentSeq_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/segmentSeq_2.3.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/segmentSeq_2.6.0.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 Package: SELEX Version: 1.4.0 Depends: R (>= 2.7.0), rJava (>= 0.5-0), Biostrings (>= 2.26.0) License: GPL (>=2) MD5sum: 7f96a1688c0fa0822b09e051edf42405 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, and Harmen Bussemaker Maintainer: Harmen Bussemaker URL: http://bussemakerlab.org/software/SELEX/ SystemRequirements: Java (>= 1.5) source.ver: src/contrib/SELEX_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SELEX_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SELEX_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SELEX_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SELEX_1.4.0.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 Package: SemDist Version: 1.6.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 3f07f5d77089fc45db7888cfcce429d9 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 source.ver: src/contrib/SemDist_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SemDist_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SemDist_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SemDist_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SemDist_1.6.0.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 Package: SEPA Version: 1.2.2 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, topGO, segmented, reshape2, org.Hs.eg.db, org.Mm.eg.db Suggests: knitr License: GPL(>=2) MD5sum: 1e7f67beac35d57b890a7508e0249ade NeedsCompilation: no Title: SEPA Description: Given single-cell RNA-seq data and true experiment time of cells or pseudo-time cell ordering, SEPA provides convenient functions for users to assign genes into different gene expression patterns such as constant, monotone increasing and increasing then decreasing. SEPA then performs GO enrichment analysis to analysis the functional roles of genes with same or similar patterns. biocViews: GeneExpression, Visualization, GUI, GO Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/SEPA_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/SEPA_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/SEPA_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/SEPA_0.99.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SEPA_1.2.2.tgz vignettes: vignettes/SEPA/inst/doc/SEPA.pdf vignetteTitles: SEPA: Single-Cell Gene Expression Pattern Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SEPA/inst/doc/SEPA.R Package: seq2pathway Version: 1.4.0 Depends: R (>= 2.10.0) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: 355f350caae95148e425006d06f3a72a 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 Lorenzo Pesce and Ana Marija Sokovic source.ver: src/contrib/seq2pathway_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seq2pathway_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seq2pathway_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/seq2pathway_1.1.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seq2pathway_1.4.0.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 Package: SeqArray Version: 1.12.9 Depends: R (>= 3.3.0), gdsfmt (>= 1.8.0) Imports: methods, parallel, S4Vectors, IRanges, GenomicRanges, GenomeInfoDb, SummarizedExperiment, Biostrings, VariantAnnotation LinkingTo: gdsfmt Suggests: BiocParallel, digest, crayon, RUnit, Biobase, BiocGenerics, knitr, Rcpp, SNPRelate, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 8e7cfae10171e49e60e274585d405710 NeedsCompilation: yes Title: Big Data Management of Whole-genome Sequence Variant Calls Description: Big data management of whole-genome sequencing variant calls with thousands of individuals: genotypic data (e.g., SNVs, indels and structural variation calls) and annotations in SeqArray 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 source.ver: src/contrib/SeqArray_1.12.9.tar.gz win.binary.ver: bin/windows/contrib/3.3/SeqArray_1.12.9.zip win64.binary.ver: bin/windows64/contrib/3.3/SeqArray_1.12.9.zip mac.binary.ver: bin/macosx/contrib/3.3/SeqArray_1.9.15.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SeqArray_1.12.9.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqArray/inst/doc/R_Integration.R, vignettes/SeqArray/inst/doc/SeqArrayTutorial.R htmlDocs: vignettes/SeqArray/inst/doc/OverviewSlides.html, vignettes/SeqArray/inst/doc/R_Integration.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html htmlTitles: SeqArray Overview, R Integration, SeqArray Data Format and Access dependsOnMe: SeqVarTools importsMe: GENESIS Package: seqbias Version: 1.20.0 Depends: R (>= 2.13.0), GenomicRanges (>= 0.1.0), Biostrings (>= 2.15.0), methods Imports: zlibbioc LinkingTo: Rsamtools (>= 1.19.38) Suggests: Rsamtools, ggplot2 License: LGPL-3 Archs: i386, x64 MD5sum: 3cf58a658aaebf4afda27d756f6d29a6 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 source.ver: src/contrib/seqbias_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqbias_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqbias_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/seqbias_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqbias_1.20.0.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 Package: seqCNA Version: 1.18.0 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: d804ef380c32e0e6c819d89c2501f7fc 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 source.ver: src/contrib/seqCNA_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqCNA_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqCNA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/seqCNA_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqCNA_1.18.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf vignetteTitles: seqCNA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqCNA/inst/doc/seqCNA.R Package: SeqGSEA Version: 1.12.0 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) MD5sum: 7f0d10606d3c5200927b9fd3ea76360c 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 Author: Xi Wang Maintainer: Xi Wang source.ver: src/contrib/SeqGSEA_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SeqGSEA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SeqGSEA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SeqGSEA_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SeqGSEA_1.12.0.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 Package: seqLogo Version: 1.38.0 Depends: methods, grid Imports: stats4 License: LGPL (>= 2) MD5sum: f492093b97ac613828f4d2e682619c02 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 Maintainer: Oliver Bembom source.ver: src/contrib/seqLogo_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqLogo_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqLogo_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/seqLogo_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqLogo_1.38.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.pdf vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqLogo/inst/doc/seqLogo.R dependsOnMe: motifRG, rGADEM importsMe: PWMEnrich, rGADEM, TFBSTools suggestsMe: BCRANK, DiffLogo, MotifDb Package: seqPattern Version: 1.4.0 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: 623d735dd8c88407de0bc2583258496f 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 source.ver: src/contrib/seqPattern_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqPattern_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqPattern_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/seqPattern_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqPattern_1.4.0.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 Package: seqplots Version: 1.10.2 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 Suggests: testthat, BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 18d0e1f1529743e181543d00d7e7cee6 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: ChIPSeq, RNASeq, Sequencing, Software, Visualization Author: Przemyslaw Stempor Maintainer: Przemyslaw Stempor URL: http://github.com/przemol/seqplots VignetteBuilder: knitr BugReports: http://github.com/przemol/seqplots/issues source.ver: src/contrib/seqplots_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqplots_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/seqplots_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/seqplots_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqplots_1.10.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/seqplots/inst/doc/QuickStart.R, vignettes/seqplots/inst/doc/SeqPlotsGUI.R htmlDocs: vignettes/seqplots/inst/doc/QuickStart.html, vignettes/seqplots/inst/doc/SeqPlotsGUI.html htmlTitles: Vignette Title, Vignette Title Package: seqTools Version: 1.6.0 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 831d5023bb26b049b2ab36b95fe6488e 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 source.ver: src/contrib/seqTools_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/seqTools_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/seqTools_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/seqTools_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/seqTools_1.6.0.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 Package: SeqVarTools Version: 1.10.1 Depends: SeqArray (>= 1.11.17) Imports: methods, stringr, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, VariantAnnotation, Biobase, logistf Suggests: BiocGenerics, BiocStyle, RUnit License: GPL-3 MD5sum: 087228ad14a9959a3db3f4052ad7de27 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 , Xiuwen Zheng , Adrienne Stilp source.ver: src/contrib/SeqVarTools_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SeqVarTools_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SeqVarTools_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.3/SeqVarTools_1.7.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SeqVarTools_1.10.1.tgz vignettes: vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SeqVarTools/inst/doc/SeqVarTools.R importsMe: GENESIS Package: sevenbridges Version: 1.2.8 Depends: methods, utils, stats Imports: httr, jsonlite, yaml, objectProperties, stringr, S4Vectors, docopt, curl, liftr, rstudioapi, uuid, dplyr Suggests: BiocStyle, knitr, rmarkdown, testthat, readr, clipr License: Apache License 2.0 MD5sum: 6b52baa6d7b6e18fe06f9118fb59d030 NeedsCompilation: no Title: R Client for Seven Bridges Platform API and CWL 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: Tengfei Yin , Dusan Randjelovic , Nan Xiao Maintainer: Tengfei Yin URL: https://www.sevenbridges.com VignetteBuilder: knitr BugReports: https://github.com/sbg/sevenbridges-r/issues source.ver: src/contrib/sevenbridges_1.2.8.tar.gz win.binary.ver: bin/windows/contrib/3.3/sevenbridges_1.1.17.zip win64.binary.ver: bin/windows64/contrib/3.3/sevenbridges_1.1.17.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sevenbridges_1.2.8.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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-sparql.R, vignettes/sevenbridges/inst/doc/docker.R, vignettes/sevenbridges/inst/doc/rstudio.R htmlDocs: vignettes/sevenbridges/inst/doc/api.html, vignettes/sevenbridges/inst/doc/apps.html, vignettes/sevenbridges/inst/doc/bioc-workflow.html, vignettes/sevenbridges/inst/doc/cgc-sparql.html, vignettes/sevenbridges/inst/doc/docker.html, vignettes/sevenbridges/inst/doc/rstudio.html htmlTitles: Complete Guide for API R Client, Describe CWL Tools/Workflows and Execution, Master Tutorial: use R for Cancer Genomics Cloud, Find Data on CGC via Data Exploerer,, SPARQL and Data API, Creating Your Docker Container and Command Line Interface (with docopt), IDE container: Rstudio and Shiny server and more Package: SGSeq Version: 1.6.12 Depends: IRanges, GenomicRanges (>= 1.23.21), Rsamtools, SummarizedExperiment, methods Imports: AnnotationDbi, BiocGenerics, Biostrings, GenomicAlignments, GenomicFeatures, GenomeInfoDb, RUnit, S4Vectors (>= 0.9.39), grDevices, graphics, igraph, parallel, rtracklayer, stats Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, knitr, rmarkdown License: Artistic-2.0 MD5sum: 16f9f86af20e0ab51532acabc71a1193 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 sequence reads mapped to a reference genome in BAM format. Genes are represented as a genome-wide splice graph, which can be obtained from existing annotation or can be predicted from the data. 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 package includes functions for splice event prediction, quantification, visualization and interpretation. biocViews: AlternativeSplicing, RNASeq, Transcription Author: Leonard Goldstein Maintainer: Leonard Goldstein VignetteBuilder: knitr source.ver: src/contrib/SGSeq_1.6.12.tar.gz win.binary.ver: bin/windows/contrib/3.3/SGSeq_1.6.12.zip win64.binary.ver: bin/windows64/contrib/3.3/SGSeq_1.6.12.zip mac.binary.ver: bin/macosx/contrib/3.3/SGSeq_1.3.20.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SGSeq_1.6.12.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SGSeq/inst/doc/SGSeq.R htmlDocs: vignettes/SGSeq/inst/doc/SGSeq.html htmlTitles: SGSeq Package: shinyMethyl Version: 1.6.2 Depends: methods, BiocGenerics (>= 0.3.2), shiny (>= 0.9.1), minfi (>= 1.6.0), IlluminaHumanMethylation450kmanifest, matrixStats, R (>= 3.0.0) Imports: RColorBrewer Suggests: shinyMethylData, minfiData, BiocStyle, RUnit, digest, knitr License: Artistic-2.0 MD5sum: a75ebdfeb0191ce718ba6bb4281a5187 NeedsCompilation: no Title: Interactive visualization for Illumina's 450k methylation arrays Description: Interactive tool for visualizing Illumina's 450k array data biocViews: DNAMethylation, Microarray, TwoChannel, Preprocessing, QualityControl Author: Jean-Philippe Fortin [cre, aut], Kasper Daniel Hansen [aut] Maintainer: Jean-Philippe Fortin VignetteBuilder: knitr source.ver: src/contrib/shinyMethyl_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/shinyMethyl_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/shinyMethyl_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/shinyMethyl_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/shinyMethyl_1.6.2.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 Package: shinyTANDEM Version: 1.10.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: c74de357d7b193a692077521ab625710 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: MassSpectrometry, Proteomics Author: Frederic Fournier , Arnaud Droit Maintainer: Frederic Fournier source.ver: src/contrib/shinyTANDEM_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/shinyTANDEM_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/shinyTANDEM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/shinyTANDEM_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/shinyTANDEM_1.10.0.tgz vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ShortRead Version: 1.30.0 Depends: BiocGenerics (>= 0.11.3), BiocParallel, Biostrings (>= 2.37.1), Rsamtools (>= 1.21.4), GenomicAlignments (>= 1.5.4) Imports: Biobase, S4Vectors (>= 0.7.1), IRanges (>= 2.3.7), GenomeInfoDb (>= 1.1.19), GenomicRanges (>= 1.21.6), hwriter, methods, zlibbioc, lattice, latticeExtra, LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, RUnit, biomaRt, GenomicFeatures, yeastNagalakshmi License: Artistic-2.0 Archs: i386, x64 MD5sum: ab549461deb0c38d9f42af31f963efc1 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 source.ver: src/contrib/ShortRead_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ShortRead_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ShortRead_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ShortRead_1.27.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ShortRead_1.30.0.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, girafe, HTSeqGenie, nucleR, OTUbase, Rolexa, Rqc, rSFFreader, segmentSeq, systemPipeR importsMe: ArrayExpressHTS, BEAT, chipseq, ChIPseqR, ChIPsim, dada2, easyRNASeq, GOTHiC, IONiseR, metagenomeFeatures, QuasR, R453Plus1Toolbox, Rolexa, RSVSim suggestsMe: BiocParallel, CSAR, DBChIP, GenomicAlignments, Genominator, PICS, PING, Repitools, Rsamtools Package: SICtools Version: 1.2.2 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: 93da04a0477d9ff9ecbc359a562a752a 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 source.ver: src/contrib/SICtools_1.2.2.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SICtools_1.2.2.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 Package: sigaR Version: 1.15.0 Depends: Biobase, CGHbase, methods, mvtnorm, penalized Imports: corpcor (>= 1.6.2), graphics, igraph, marray, MASS, mvtnorm, quadprog, penalized (>= 0.9-39), snowfall, stats License: GPL (>= 2) MD5sum: 061194ede58e763198ef69e965d92965 NeedsCompilation: no Title: statistics for integrative genomics analyses in R Description: Facilites 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 source.ver: src/contrib/sigaR_1.15.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/sigaR_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sigaR_1.15.0.tgz vignettes: vignettes/sigaR/inst/doc/sigaR.pdf vignetteTitles: sigaR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sigaR/inst/doc/sigaR.R dependsOnMe: HCsnip Package: SigCheck Version: 2.4.0 Depends: R (>= 3.2.0), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: fefea62ecc882f66d6e54f54bb0e2859 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 source.ver: src/contrib/SigCheck_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SigCheck_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SigCheck_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SigCheck_2.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SigCheck_2.4.0.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 Package: SigFuge Version: 1.10.0 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: ed25f2f59a42fbf0d380b254bda513cb NeedsCompilation: no Title: SigFuge Description: Algorithm for testing significance of clustering in RNA-seq data. biocViews: Clustering, Visualization, RNASeq Author: Patrick Kimes, Christopher Cabanski Maintainer: Patrick Kimes source.ver: src/contrib/SigFuge_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SigFuge_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SigFuge_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SigFuge_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SigFuge_1.10.0.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 Package: siggenes Version: 1.46.0 Depends: methods, Biobase, multtest, splines, graphics Imports: stats4 Suggests: affy, annotate, genefilter, KernSmooth, scrime (>= 1.2.5) License: LGPL (>= 2) MD5sum: 5d79048d0cae6e0edb1dc4f4c77c66ae 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 source.ver: src/contrib/siggenes_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/siggenes_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/siggenes_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/siggenes_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/siggenes_1.46.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf vignetteTitles: siggenes Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/siggenes/inst/doc/siggenes.R dependsOnMe: KCsmart, oneChannelGUI importsMe: charm, GeneSelector, minfi suggestsMe: GeneSelector, logicFS, trio, XDE Package: sigPathway Version: 1.40.0 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: aa5ef4fb0db3f8ef40a7295467f0be76 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 source.ver: src/contrib/sigPathway_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sigPathway_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sigPathway_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sigPathway_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sigPathway_1.40.0.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 Package: sigsquared Version: 1.4.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: f1f8f85c9cbb5af30ed7c264d8157c92 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 source.ver: src/contrib/sigsquared_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sigsquared_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sigsquared_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sigsquared_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sigsquared_1.4.0.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 Package: SIM Version: 1.42.0 Depends: R (>= 2.4), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: fea34b7b1deb8e85a806c3fcd5746e10 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 source.ver: src/contrib/SIM_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SIM_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SIM_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SIM_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SIM_1.42.0.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 Package: SIMAT Version: 1.4.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2 Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 4aec8379b01cbadc832c5357ce32ed3c 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: Software, Metabolomics, MassSpectrometry Author: Mo R. Nezami Ranjbar Maintainer: Mo R. Nezami Ranjbar URL: http://omics.georgetown.edu/SIMAT.html source.ver: src/contrib/SIMAT_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SIMAT_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SIMAT_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SIMAT_1.1.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SIMAT_1.4.0.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 Package: SimBindProfiles Version: 1.10.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: 855bf8ab41b67b23330c3779b3886326 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 source.ver: src/contrib/SimBindProfiles_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SimBindProfiles_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SimBindProfiles_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SimBindProfiles_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SimBindProfiles_1.10.0.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 Package: similaRpeak Version: 1.4.2 Depends: R6 (>= 2.0) Imports: rtracklayer, GenomicAlignments, Rsamtools Suggests: RUnit, BiocGenerics, knitr, BiocStyle License: Artistic-2.0 MD5sum: 9a34c7b7fbf21954691e0f5a3deb609d 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 Louise Deschenes URL: https://github.com/adeschen/similaRpeak VignetteBuilder: knitr BugReports: https://github.com/adeschen/similaRpeak/issues source.ver: src/contrib/similaRpeak_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/similaRpeak_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/similaRpeak_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/similaRpeak_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/similaRpeak_1.4.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/similaRpeak/inst/doc/similaRpeak.R htmlDocs: vignettes/similaRpeak/inst/doc/similaRpeak.html htmlTitles: Similarity between two ChIP-Seq profiles suggestsMe: metagene Package: simpleaffy Version: 2.48.0 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: d1c871947902f2f41e178a627bd93d54 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/ source.ver: src/contrib/simpleaffy_2.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/simpleaffy_2.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/simpleaffy_2.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/simpleaffy_2.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/simpleaffy_2.48.0.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 Package: simulatorZ Version: 1.6.0 Depends: R (>= 3.1), methods, BiocGenerics, Biobase, SummarizedExperiment, survival, CoxBoost Imports: graphics, stats, gbm, Hmisc, S4Vectors, IRanges, GenomicRanges Suggests: RUnit, BiocStyle, curatedOvarianData, parathyroidSE, superpc License: Artistic-2.0 Archs: i386, x64 MD5sum: ac7d7ffe187295232990ffc2bd34a475 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 source.ver: src/contrib/simulatorZ_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/simulatorZ_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/simulatorZ_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/simulatorZ_1.3.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/simulatorZ_1.6.0.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 suggestsMe: doppelgangR Package: sincell Version: 1.4.2 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: 1a05deee1f0caa1e29aab49732366b6a 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: 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 source.ver: src/contrib/sincell_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/sincell_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/sincell_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/sincell_1.1.01.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sincell_1.4.2.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 Package: SISPA Version: 1.2.1 Depends: R (>= 3.2),GSVA,changepoint,data.table,ggplot2,plyr Suggests: knitr License: GPL-2 MD5sum: 312e691600386a16218951454a383c7f NeedsCompilation: no Title: SISPA: Method for Sample Integrated Set Profile Analysis Description: Sample Integrated Gene Set Analysis (SISPA) is a method designed to define sample groups with similar gene set enrichment profiles. biocViews: GeneSetEnrichment,GenomeWideAssociation Author: Bhakti Dwivedi & Jeanne Kowalski Maintainer: Bhakti Dwivedi VignetteBuilder: knitr source.ver: src/contrib/SISPA_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SISPA_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SISPA_1.2.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SISPA_1.2.1.tgz vignettes: vignettes/SISPA/inst/doc/SISPA.pdf vignetteTitles: SISPA:Method for Sample Integrated Set Profile Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sizepower Version: 1.42.0 Depends: stats License: LGPL MD5sum: b415b751893717ceddc31cb4e5c5a47c 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 source.ver: src/contrib/sizepower_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sizepower_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sizepower_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sizepower_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sizepower_1.42.0.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 suggestsMe: oneChannelGUI Package: SJava Version: 0.98.0 Depends: R (>= 2.10.0), methods License: GPL (>= 2) MD5sum: 51b547aca3df64fc3b37903340d63fd1 NeedsCompilation: yes Title: The Omegahat interface for R and Java Description: An interface from R to Java to create and call Java objects and methods. biocViews: Infrastructure Author: Duncan Temple Lang, John Chambers Maintainer: Martin Morgan PackageStatus: Deprecated source.ver: src/contrib/SJava_0.98.0.tar.gz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RWebServices Package: skewr Version: 1.4.2 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, IRanges, RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: 37d0aa253a677aa359792167a59e64a2 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 source.ver: src/contrib/skewr_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/skewr_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.3/skewr_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.3/skewr_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/skewr_1.4.2.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 Package: SLGI Version: 1.32.0 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: 95cb760c02a72f797a46e893d7f67f65 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 source.ver: src/contrib/SLGI_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SLGI_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SLGI_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SLGI_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SLGI_1.32.0.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 Package: SLqPCR Version: 1.38.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: 0d8de6b510cf835df9ddb82f788376d2 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 source.ver: src/contrib/SLqPCR_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SLqPCR_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SLqPCR_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SLqPCR_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SLqPCR_1.38.0.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 Package: SMAP Version: 1.36.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: 05727e40303d29d1ccb102256a7a3d17 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 source.ver: src/contrib/SMAP_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SMAP_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SMAP_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SMAP_1.33.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SMAP_1.36.0.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 Package: SMITE Version: 1.0.2 Depends: R (>= 3.3), GenomicRanges Imports: scales, plyr, Hmisc, AnnotationDbi, org.Hs.eg.db, ggplot2, reactome.db, KEGG.db, BioNet, goseq, methods, IRanges, igraph, Biobase,tools, S4Vectors, geneLenDataBase, grDevices, graphics, stats, utils Suggests: knitr License: GPL (>=2) MD5sum: d17326b68f6af93a4749c1a66e9ca52d 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: 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 source.ver: src/contrib/SMITE_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/SMITE_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/SMITE_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SMITE_1.0.2.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 Package: SNAGEE Version: 1.12.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: eb8006fea1fabed4acc5ddc1472d079c 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/ source.ver: src/contrib/SNAGEE_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNAGEE_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SNAGEE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SNAGEE_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNAGEE_1.12.0.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 Package: snapCGH Version: 1.42.0 Depends: limma, DNAcopy, methods Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: d4b32215685675364b6887e3f3a2294f 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 source.ver: src/contrib/snapCGH_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/snapCGH_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.3/snapCGH_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.3/snapCGH_1.39.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/snapCGH_1.42.0.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 Package: snm Version: 1.20.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 57931268df9fbb0334e2328066cb6d43 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 source.ver: src/contrib/snm_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/snm_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/snm_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/snm_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/snm_1.20.0.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 Package: SNPchip Version: 2.18.0 Depends: R (>= 2.14.0) Imports: methods, graphics, lattice, grid, foreach, utils, Biobase, S4Vectors (>= 0.9.25), IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, oligoClasses (>= 1.31.1) Suggests: crlmm (>= 1.17.14), RUnit Enhances: doSNOW, VanillaICE (>= 1.21.24), RColorBrewer License: LGPL (>= 2) MD5sum: 5d7746f01428ac64b2d62cc0593a110f NeedsCompilation: no Title: Visualizations for copy number alterations Description: This package defines methods for visualizing high-throughput genomic data biocViews: CopyNumberVariation, SNP, GeneticVariability, Visualization Author: Robert Scharpf and Ingo Ruczinski Maintainer: Robert Scharpf URL: http://www.biostat.jhsph.edu/~iruczins/software/snpchip.html source.ver: src/contrib/SNPchip_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNPchip_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SNPchip_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SNPchip_2.15.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNPchip_2.18.0.tgz vignettes: vignettes/SNPchip/inst/doc/PlottingIdiograms.pdf vignetteTitles: Plotting Idiograms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPchip/inst/doc/PlottingIdiograms.R dependsOnMe: mBPCR importsMe: crlmm, phenoTest suggestsMe: Category, MinimumDistance, oligoClasses, VanillaICE Package: SNPhood Version: 1.2.3 Depends: R (>= 3.2), GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2, cluster, ggplot2, lattice, GenomeInfoDb, BiocParallel, VariantAnnotation, BiocGenerics, IRanges, methods, SummarizedExperiment, RColorBrewer, Biostrings, grDevices,gridExtra,stats,grid,utils, graphics, reshape2, scales, S4Vectors Suggests: BiocStyle, knitr, rmarkdown, SNPhoodData, corrplot, pryr License: LGPL (>= 3) MD5sum: 21d17450d1fc5cd5eee536c0e03f6729 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 VignetteBuilder: knitr BugReports: christian.arnold@embl.de source.ver: src/contrib/SNPhood_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNPhood_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/SNPhood_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNPhood_1.2.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.R, vignettes/SNPhood/inst/doc/workflow.R htmlDocs: vignettes/SNPhood/inst/doc/IntroductionToSNPhood.html, vignettes/SNPhood/inst/doc/workflow.html htmlTitles: Introduction and Methodological Details, Workflow example Package: SNPRelate Version: 1.6.6 Depends: R (>= 2.15), gdsfmt (>= 1.8.3) LinkingTo: gdsfmt Suggests: parallel, RUnit, knitr, MASS, BiocGenerics Enhances: SeqArray (>= 1.11.12) License: GPL-3 Archs: i386, x64 MD5sum: a450b98335255f51abc449556ab6a414 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. 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, http://corearray.sourceforge.net/tutorials/SNPRelate/ VignetteBuilder: knitr BugReports: http://github.com/zhengxwen/SNPRelate/issues source.ver: src/contrib/SNPRelate_1.6.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/SNPRelate_1.6.6.zip win64.binary.ver: bin/windows64/contrib/3.3/SNPRelate_1.6.6.zip mac.binary.ver: bin/macosx/contrib/3.3/SNPRelate_1.3.9.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SNPRelate_1.6.6.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.R htmlDocs: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.html htmlTitles: SNPRelate Tutorial suggestsMe: GENESIS, GWASTools, HIBAG, SeqArray Package: snpStats Version: 1.22.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: cd88d17354ab9a5739d3912fcd062d13 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 source.ver: src/contrib/snpStats_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/snpStats_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/snpStats_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/snpStats_1.19.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/snpStats_1.22.0.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 importsMe: FunciSNP, GGtools, gQTLstats, gwascat, ldblock, MEAL suggestsMe: crlmm, GWASTools, VariantAnnotation Package: soGGi Version: 1.4.4 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: 78d8b6d643bf90aa01516090aefb1023 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, Tom Carroll Maintainer: Tom Carroll VignetteBuilder: knitr source.ver: src/contrib/soGGi_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/soGGi_1.4.4.zip win64.binary.ver: bin/windows64/contrib/3.3/soGGi_1.4.4.zip mac.binary.ver: bin/macosx/contrib/3.3/soGGi_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/soGGi_1.4.4.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: DChIPRep Package: SomatiCA Version: 2.2.1 Depends: R (>= 2.14.0), lars, DNAcopy, foreach, methods, rebmix, GenomicRanges, IRanges, doParallel Imports: foreach, lars, sn, DNAcopy, methods, rebmix, GenomicRanges, IRanges Enhances: sn, SomatiCAData License: GPL (>=2) MD5sum: 64765908f46eb46f707e8ce89057f3c5 NeedsCompilation: no Title: SomatiCA: identifying, characterizing, and quantifying somatic copy number aberrations from cancer genome sequencing Description: SomatiCA is a software suite that is capable of identifying, characterizing, and quantifying somatic CNAs from cancer genome sequencing. First, it uses read depths and lesser allele frequencies (LAF) from mapped short sequence reads to segment the genome and identify candidate CNAs. Second, SomatiCA estimates the admixture rate from the relative copy-number profile of tumor-normal pair by a Bayesian finite mixture model. Third, SomatiCA quantifies absolute somatic copy-number and subclonality for each genomic segment to guide its characterization. Results from SomatiCA can be further integrated with single nucleotide variations (SNVs) to get a better understanding of the tumor evolution. biocViews: Sequencing, CopyNumberVariation Author: Mengjie Chen , Hongyu Zhao Maintainer: Mengjie Chen PackageStatus: Deprecated source.ver: src/contrib/SomatiCA_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/SomatiCA_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.3/SomatiCA_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.3/SomatiCA_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SomatiCA_2.2.1.tgz vignettes: vignettes/SomatiCA/inst/doc/SomatiCA.pdf, vignettes/SomatiCA/inst/doc/SomatiCAUserGuide.pdf vignetteTitles: SomatiCA Vignette, SomatiCAUserGuide.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SomatiCA/inst/doc/SomatiCA.R Package: SomaticSignatures Version: 2.8.4 Depends: R (>= 3.1.0), VariantAnnotation, GenomicRanges 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: 53092e837a3f0389c057b6fc9afa2705 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 source.ver: src/contrib/SomaticSignatures_2.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/SomaticSignatures_2.8.4.zip win64.binary.ver: bin/windows64/contrib/3.3/SomaticSignatures_2.8.4.zip mac.binary.ver: bin/macosx/contrib/3.3/SomaticSignatures_2.5.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SomaticSignatures_2.8.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.R htmlDocs: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html htmlTitles: SomaticSignatures importsMe: Rariant Package: SpacePAC Version: 1.10.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 6f8ffd4c20e5548df3e76fdb6f7cf1d5 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 source.ver: src/contrib/SpacePAC_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SpacePAC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SpacePAC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SpacePAC_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SpacePAC_1.10.0.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 Package: spade Version: 1.20.0 Depends: R (>= 2.11), igraph, Rclusterpp Imports: Biobase, flowCore Suggests: flowViz License: GPL-2 Archs: i386, x64 MD5sum: bce035b8307aa0b3870392a866df2514 NeedsCompilation: yes Title: SPADE -- An analysis and visualization tool for Flow Cytometry Description: SPADE, or Spanning tree Progression of Density normalized Events, is an analysis and visualization tool for high dimensional flow cytometry data that organizes cells into hierarchies of related phenotypes. biocViews: FlowCytometry, GraphAndNetwork, GUI, Visualization, Clustering Author: M. Linderman, P. Qiu, E. Simonds, Z. Bjornson Maintainer: Zach Bjornson URL: http://cytospade.org PackageStatus: Deprecated source.ver: src/contrib/spade_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spade_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spade_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/spade_1.17.0.tgz vignettes: vignettes/spade/inst/doc/SPADE.pdf vignetteTitles: spade package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/spade/inst/doc/SPADE.R Package: specL Version: 1.6.2 Depends: R (>= 3.2), methods, DBI, RSQLite, seqinr, protViz (>= 0.2.5), LinkingTo: Rcpp (>= 0.9.9) Suggests: RUnit, BiocGenerics, BiocStyle, plotrix, knitr, msqc1 (>= 0.99.7) License: GPL-3 Archs: i386, x64 MD5sum: 7b62138c442e7f722d069f743bfa1726 NeedsCompilation: yes Title: specL - Prepare Peptide Spectrum Matches for Use in Targeted Proteomics Description: specL provides a function for generating spectra libraries which 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. biocViews: MassSpectrometry, Proteomics Author: Christian Trachsel , Christian Panse , Jonas Grossmann , Witold E. Wolski Maintainer: Christian Panse , Witold E. Wolski URL: https://github.com/fgcz/specL VignetteBuilder: knitr BugReports: https://github.com/fgcz/specL/issues source.ver: src/contrib/specL_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/specL_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/specL_1.6.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/specL_1.6.2.tgz vignettes: vignettes/specL/inst/doc/specL.pdf vignetteTitles: Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/specL/inst/doc/cdsw.R, vignettes/specL/inst/doc/specL.R, vignettes/specL/inst/doc/ssrc.R htmlDocs: vignettes/specL/inst/doc/cdsw.html, vignettes/specL/inst/doc/ssrc.html htmlTitles: Computing Dynamic SWATH Windows, Retention Time Prediction using the ssrc Method Package: SpeCond Version: 1.26.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: 2d0f14df0d40908d083a45ac57fc27d2 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 source.ver: src/contrib/SpeCond_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SpeCond_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SpeCond_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SpeCond_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SpeCond_1.26.0.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 Package: SPEM Version: 1.12.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 29c1056136a3c44a2fca7ecc7b503855 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 source.ver: src/contrib/SPEM_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SPEM_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SPEM_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SPEM_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SPEM_1.12.0.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 Package: SPIA Version: 2.24.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: 73a50f216be5d875e25cbb8b79a8b892 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 source.ver: src/contrib/SPIA_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SPIA_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SPIA_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SPIA_2.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SPIA_2.24.0.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 Package: SpidermiR Version: 1.2.5 Depends: R (>= 3.0.0) Imports: networkD3, httr, igraph, utils, stats, miRNAtap, miRNAtap.db, AnnotationDbi, org.Hs.eg.db, ggplot2, gridExtra, gplots, grDevices, lattice, latticeExtra, visNetwork, TCGAbiolinks Suggests: BiocStyle, knitr, rmarkdown, testthat, devtools, roxygen2 License: GPL (>= 3) MD5sum: dce1db6872fe8784772fd4d3ac5e0ee7 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, miR2Disease,miRTar, miRandola,Pharmaco-miR,DIANA, Miranda, PicTar and TargetScan) and the use of standard analysis (igraph) and visualization methods (networkD3). 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 source.ver: src/contrib/SpidermiR_1.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.3/SpidermiR_1.2.5.zip win64.binary.ver: bin/windows64/contrib/3.3/SpidermiR_1.2.5.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SpidermiR_1.2.5.tgz vignettes: vignettes/SpidermiR/inst/doc/SpidermiR_pdf.pdf vignetteTitles: SpidermiR_pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SpidermiR/inst/doc/SpidermiR_pdf.R, vignettes/SpidermiR/inst/doc/SpidermiR.R htmlDocs: vignettes/SpidermiR/inst/doc/SpidermiR.html htmlTitles: Vignette Title Package: spikeLI Version: 2.32.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: eeb82e8f0d109af353d02da794f93e20 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 source.ver: src/contrib/spikeLI_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spikeLI_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spikeLI_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/spikeLI_2.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spikeLI_2.32.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: spkTools Version: 1.28.0 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: f32b3dcabad453a667a6704270d459a5 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 source.ver: src/contrib/spkTools_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spkTools_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spkTools_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/spkTools_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spkTools_1.28.0.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 Package: splicegear Version: 1.44.0 Depends: R (>= 2.6.0), methods, Biobase(>= 2.5.5) Imports: annotate, Biobase, graphics, grDevices, grid, methods, utils, XML License: LGPL MD5sum: ece69d585abe874bb18c3af82ee7acd3 NeedsCompilation: no Title: splicegear Description: A set of tools to work with alternative splicing biocViews: Infrastructure, Transcription Author: Laurent Gautier Maintainer: Laurent Gautier source.ver: src/contrib/splicegear_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/splicegear_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/splicegear_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/splicegear_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/splicegear_1.44.0.tgz vignettes: vignettes/splicegear/inst/doc/splicegear.pdf vignetteTitles: splicegear Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splicegear/inst/doc/splicegear.R Package: spliceR Version: 1.14.0 Depends: R (>= 2.15.0), methods, cummeRbund, rtracklayer, VennDiagram, RColorBrewer, plyr Imports: GenomicRanges, IRanges Suggests: BSgenome.Hsapiens.UCSC.hg19, BSgenome License: GPL (>=2) Archs: i386, x64 MD5sum: 3a3d4ff5fefab7a0c8955043bd54ab72 NeedsCompilation: yes Title: Classification of alternative splicing and prediction of coding potential from RNA-seq data. Description: An R package for classification of alternative splicing and prediction of coding potential from RNA-seq data. biocViews: GeneExpression, Transcription, AlternativeSplicing, DifferentialExpression, DifferentialSplicing, Sequencing, Visualization Author: Johannes Waage , Kristoffer Vitting-Seerup Maintainer: Johannes Waage , Kristoffer Vitting-Seerup source.ver: src/contrib/spliceR_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spliceR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spliceR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/spliceR_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spliceR_1.14.0.tgz vignettes: vignettes/spliceR/inst/doc/spliceR.pdf vignetteTitles: spliceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceR/inst/doc/spliceR.R Package: spliceSites Version: 1.20.0 Depends: methods,rbamtools (>= 2.14.3),refGenome (>= 1.6.0),Biobase,Biostrings (>= 2.28.0) Imports: BiocGenerics,doBy,seqLogo,IRanges License: GPL-2 Archs: i386, x64 MD5sum: 1dc6314adee18eeb8660ed61535ae595 NeedsCompilation: yes Title: A bioconductor package for exploration of alignment gap positions from RNA-seq data Description: Performs splice centered analysis on RNA-seq data. biocViews: RNAseq,GeneExpression,DifferentialExpression,Proteomics Author: Wolfgang Kaisers Maintainer: Wolfgang Kaisers source.ver: src/contrib/spliceSites_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spliceSites_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spliceSites_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/spliceSites_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spliceSites_1.20.0.tgz vignettes: vignettes/spliceSites/inst/doc/spliceSites.pdf vignetteTitles: spliceSites hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/spliceSites/inst/doc/spliceSites.R Package: SplicingGraphs Version: 1.12.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, igraph, BiocGenerics, S4Vectors (>= 0.9.25), IRanges (>= 2.3.21), 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: efa7ccc458679f79d4df697d861d4405 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 Author: D. Bindreither, M. Carlson, M. Morgan, H. Pages Maintainer: H. Pages source.ver: src/contrib/SplicingGraphs_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SplicingGraphs_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SplicingGraphs_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SplicingGraphs_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SplicingGraphs_1.12.0.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 Package: splineTCDiffExpr Version: 0.99.4 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: a1b9326942dc1caecb908c0b850f44c1 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 , Agata Michna VignetteBuilder: knitr source.ver: src/contrib/splineTCDiffExpr_0.99.4.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/splineTCDiffExpr_0.99.4.tgz vignettes: vignettes/splineTCDiffExpr/inst/doc/splineTCDiffExpr.pdf vignetteTitles: splineTCDiffExpr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTCDiffExpr/inst/doc/splineTCDiffExpr.R Package: splineTimeR Version: 1.0.1 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: 4777df66c11eb9cc6858bd2cb4180f6b 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 , Agata Michna VignetteBuilder: knitr source.ver: src/contrib/splineTimeR_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/splineTimeR_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.3/splineTimeR_1.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/splineTimeR_1.0.1.tgz vignettes: vignettes/splineTimeR/inst/doc/splineTimeR.pdf vignetteTitles: splineTimeR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/splineTimeR/inst/doc/splineTimeR.R Package: splots Version: 1.38.0 Imports: grid, RColorBrewer License: LGPL MD5sum: 2b41fd619c5f210c537632edac237f85 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 source.ver: src/contrib/splots_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/splots_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/splots_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/splots_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/splots_1.38.0.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 importsMe: RNAinteract, RNAither Package: spotSegmentation Version: 1.46.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) MD5sum: 52ca12fd8a26b37a3eff5be6109f0f6e NeedsCompilation: no 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 source.ver: src/contrib/spotSegmentation_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/spotSegmentation_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/spotSegmentation_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/spotSegmentation_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/spotSegmentation_1.46.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SQUADD Version: 1.22.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: e19f1da155f86c4ae20f1b9a9340912c 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 source.ver: src/contrib/SQUADD_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SQUADD_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SQUADD_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SQUADD_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SQUADD_1.22.0.tgz vignettes: vignettes/SQUADD/inst/doc/SQUADD_ERK.pdf, vignettes/SQUADD/inst/doc/SQUADD.pdf vignetteTitles: SQUADD package, SQUADD package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SQUADD/inst/doc/SQUADD_ERK.R, vignettes/SQUADD/inst/doc/SQUADD.R Package: SRAdb Version: 1.30.0 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: 8bd479d74f05df12209fadb1947aa7be 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 source.ver: src/contrib/SRAdb_1.30.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/SRAdb_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SRAdb_1.30.0.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 Package: sRAP Version: 1.12.0 Depends: WriteXLS Imports: gplots, pls, ROCR, qvalue License: GPL-3 MD5sum: 20635a9e7a6c7c4f4064ece58abb5f87 NeedsCompilation: no Title: Simplified RNA-Seq Analysis Pipeline Description: This package provides a pipeline for gene expression analysis (primarily for RNA-Seq data). The normalization function is specific for RNA-Seq analysis, but all other functions (Quality Control Figures, Differential Expression and Visualization, and Functional Enrichment via BD-Func) will work with any type of gene expression data. biocViews: GeneExpression, RNAseq, Microarray, Preprocessing, QualityControl, Statistics, DifferentialExpression, Visualization, GeneSetEnrichment, GO Author: Charles Warden Maintainer: Charles Warden source.ver: src/contrib/sRAP_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sRAP_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sRAP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sRAP_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sRAP_1.12.0.tgz vignettes: vignettes/sRAP/inst/doc/sRAP.pdf vignetteTitles: sRAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/sRAP/inst/doc/sRAP.R Package: sscore Version: 1.44.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: c86d87480b158a7552110c3213d92812 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 source.ver: src/contrib/sscore_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sscore_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sscore_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sscore_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sscore_1.44.0.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 Package: sscu Version: 1.0.2 Depends: R (>= 3.3) Imports: Biostrings (>= 2.36.4), seqinr (>= 3.1-3), BiocGenerics (>= 0.16.1) Suggests: knitr, rmarkdown License: GPL (>= 2) MD5sum: f560ea722ae4bf0387fa8f95915ff2f9 NeedsCompilation: no Title: Strength of Selected Codon Usage Description: The package can calculate the selection in codon usage in bacteria species. First and most important, the package can calculate the strength of selected codon usage bias (sscu) based on Paul Sharp's method. The method take into account of background mutation rate, and focus only on codons with universal translational advantages in all bacterial species. Thus the sscu index is comparable among different species. In addition, detainled optimal codons (selected codons) information can be calculated by optimal_codons function, so the users will have a more accurate selective scheme for each codons. Furthermore, we added one more function optimal_index in the package. The function has similar mathematical formula as s index, but focus on the estimates the amount of GC-ending optimal codon for the highly expressed genes in the four and six codon boxes. The function takes into account of background mutation rate, and it is comparable with the s index. However, since the set of GC-ending optimal codons are likely to be different among different species, the index can not be compared among different species. biocViews: Genetics, GeneExpression, WholeGenome Author: Yu Sun Maintainer: Yu Sun VignetteBuilder: knitr source.ver: src/contrib/sscu_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/sscu_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/sscu_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sscu_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sSeq Version: 1.10.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: da7d9f0547ebc1891dc72f7086f75100 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: RNASeq Author: Danni Yu , Wolfgang Huber and Olga Vitek Maintainer: Danni Yu source.ver: src/contrib/sSeq_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sSeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sSeq_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sSeq_1.10.0.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 Package: ssize Version: 1.46.0 Depends: gdata, xtable License: LGPL MD5sum: f7a4447a77ba3e1324105d3b4a1faa28 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 source.ver: src/contrib/ssize_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ssize_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ssize_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ssize_1.43.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ssize_1.46.0.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 suggestsMe: oneChannelGUI Package: SSPA Version: 2.12.0 Depends: R (>= 2.12), methods, qvalue, lattice, limma Imports: graphics, stats Suggests: BiocStyle, genefilter, edgeR, DESeq License: GPL (>= 2) Archs: i386, x64 MD5sum: f16b5e2b37d08ff1de5f889e627210c4 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: GeneExpression, RNASeq, Microarray, StatisticalMethod Author: Maarten van Iterson Maintainer: Maarten van Iterson URL: http://www.humgen.nl/MicroarrayAnalysisGroup.html source.ver: src/contrib/SSPA_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SSPA_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SSPA_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SSPA_2.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SSPA_2.12.0.tgz vignettes: vignettes/SSPA/inst/doc/SSPA.pdf vignetteTitles: SSPA Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SSPA/inst/doc/SSPA.R Package: ssviz Version: 1.6.2 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer Suggests: knitr License: GPL-2 MD5sum: ef488831753d1fc7b63fe42f288bd026 NeedsCompilation: no Title: A small RNA-seq visualizer and analysis toolkit Description: Small RNA sequencing viewer biocViews: Sequencing,RNASeq,Visualization,MultipleComparison,Genetics Author: Diana Low Maintainer: Diana Low VignetteBuilder: knitr source.ver: src/contrib/ssviz_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/ssviz_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/ssviz_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/ssviz_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ssviz_1.6.2.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 Package: STAN Version: 2.0.3 Depends: methods, poilog, parallel Imports: GenomicRanges, IRanges, S4Vectors, BiocGenerics, GenomeInfoDb, Gviz, Rsolnp Suggests: BiocStyle, gplots, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 22641659f1a99c1eb01e35fc3cb90d2b 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 Author: Benedikt Zacher, Julia Ertl, Julien Gagneur, Achim Tresch Maintainer: Benedikt Zacher VignetteBuilder: knitr source.ver: src/contrib/STAN_2.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/STAN_2.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/STAN_2.0.3.zip mac.binary.ver: bin/macosx/contrib/3.3/STAN_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/STAN_2.0.3.tgz vignettes: vignettes/STAN/inst/doc/STAN.pdf vignetteTitles: The genomic STate ANnotation package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STAN/inst/doc/STAN.R Package: staRank Version: 1.14.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: e8ee66f469049a9cda3b77e68fbd8614 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: MultipleComparison, CellBiology, CellBasedAssays, MicrotitrePlateAssay Author: Juliane Siebourg, Niko Beerenwinkel Maintainer: Juliane Siebourg source.ver: src/contrib/staRank_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/staRank_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/staRank_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/staRank_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/staRank_1.14.0.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 Package: Starr Version: 1.28.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 Archs: i386, x64 MD5sum: d763879fbd6cd61f3a2502b4e7adc651 NeedsCompilation: yes 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 source.ver: src/contrib/Starr_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Starr_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Starr_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Starr_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Starr_1.28.0.tgz vignettes: vignettes/Starr/inst/doc/Starr.pdf vignetteTitles: Simple tiling array analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Starr/inst/doc/Starr.R suggestsMe: nucleR Package: STATegRa Version: 1.6.2 Depends: R (>= 2.10) Imports: Biobase, gridExtra, ggplot2, methods, grid, MASS, calibrate, gplots, edgeR, limma, foreach, affy Suggests: RUnit, BiocGenerics, knitr (>= 1.6), rmarkdown, BiocStyle (>= 1.3), roxygen2, doSNOW License: GPL-2 MD5sum: 2318929f928e81b9613c20db7a690c15 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 , Patricia Sebastián-León , Gordon Ball VignetteBuilder: knitr source.ver: src/contrib/STATegRa_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/STATegRa_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/STATegRa_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/STATegRa_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/STATegRa_1.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/STATegRa/inst/doc/STATegRa.R htmlDocs: vignettes/STATegRa/inst/doc/STATegRa.html htmlTitles: STATegRa User's Guide Package: stepNorm Version: 1.44.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: 0927c2f5ba9c50740df50cbc8d121eb2 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/ source.ver: src/contrib/stepNorm_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/stepNorm_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/stepNorm_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/stepNorm_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/stepNorm_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: stepwiseCM Version: 1.18.0 Depends: R (>= 2.14), randomForest, MAclinical, tspair, pamr, snowfall, glmpath, penalized, e1071, Biobase License: GPL (>2) MD5sum: 131e50bd2bf0c866aa8864e216ce4f40 NeedsCompilation: no Title: Stepwise Classification of Cancer Samples using High-dimensional Data Sets Description: Stepwise classification of cancer samples using multiple data sets. This package implements the classification strategy using two heterogeneous data sets without actually combining them. Package uses the data type for which full measurements are available at the first stage, and the data type for which only partial measurements are available at the second stage. For incoming new samples package quantifies how much improvement will be obtained if covariates of new samples for the data types at the second stage are measured. This packages suits for the application where study goal is not only obtain high classification accuracy, but also requires economically cheap classifier. biocViews: Classification, Microarray Author: Askar Obulkasim Maintainer: Askar Obulkasim source.ver: src/contrib/stepwiseCM_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/stepwiseCM_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/stepwiseCM_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/stepwiseCM_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/stepwiseCM_1.18.0.tgz vignettes: vignettes/stepwiseCM/inst/doc/stepwiseCM.pdf vignetteTitles: stepwiseCM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/stepwiseCM/inst/doc/stepwiseCM.R Package: Streamer Version: 1.18.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: eecb5751992466a6aee5dfcc7439a90d 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 source.ver: src/contrib/Streamer_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Streamer_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Streamer_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Streamer_1.15.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Streamer_1.18.0.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 Package: STRINGdb Version: 1.12.0 Depends: R (>= 2.14.0) Imports: png, sqldf, plyr, igraph, RCurl, methods, RColorBrewer, gplots, hash, plotrix Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 2693f0b9424f51c91101491ac254809f 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 (http://www.string-db.org). biocViews: Network Author: Andrea Franceschini Maintainer: Alexander Roth source.ver: src/contrib/STRINGdb_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/STRINGdb_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/STRINGdb_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/STRINGdb_1.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/STRINGdb_1.12.0.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: scsR importsMe: pwOmics suggestsMe: PCAN Package: subSeq Version: 1.2.2 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: a04d76c7dc091b0dc994cea84f304fb4 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: 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 source.ver: src/contrib/subSeq_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/subSeq_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/subSeq_1.2.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/subSeq_1.2.2.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 Package: SummarizedExperiment Version: 1.2.3 Depends: R (>= 3.2), methods, GenomicRanges (>= 1.23.15), Biobase Imports: utils, stats, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.9.36), IRanges (>= 2.5.26), GenomeInfoDb Suggests: annotate, AnnotationDbi, hgu95av2.db, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, BiocStyle, knitr, rmarkdown, digest, jsonlite, rhdf5, airway License: Artistic-2.0 MD5sum: 8fd73332db0a76803a902bbd62486576 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 VignetteBuilder: knitr source.ver: src/contrib/SummarizedExperiment_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/SummarizedExperiment_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/SummarizedExperiment_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.3/SummarizedExperiment_0.3.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SummarizedExperiment_1.2.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.R htmlDocs: vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html htmlTitles: SummarizedExperiment dependsOnMe: ALDEx2, AllelicImbalance, BiSeq, bsseq, csaw, deepSNV, DESeq2, DEXSeq, DiffBind, diffHic, epigenomix, ExpressionAtlas, GenoGAM, GenomicAlignments, GenomicFiles, genoset, InteractionSet, isomiRs, JunctionSeq, MBASED, methylPipe, minfi, RIPSeeker, SGSeq, simulatorZ, soGGi, VanillaICE, VariantAnnotation importsMe: BBCAnalyzer, biovizBase, BiSeq, ChIPpeakAnno, CNPBayes, DChIPRep, debrowser, DEFormats, easyRNASeq, EnrichmentBrowser, ensemblVEP, epivizrData, erma, FourCSeq, GGBase, ggbio, gQTLBase, gQTLstats, GreyListChIP, gwascat, HTSeqGenie, M3D, methyAnalysis, methylumi, MinimumDistance, MultiDataSet, oligoClasses, pcaExplorer, PureCN, R453Plus1Toolbox, regionReport, rgsepd, roar, SeqArray, SNPchip, SNPhood, systemPipeR, TCGAbiolinks, ToPASeq, VariantFiltering suggestsMe: AnnotationHub, biobroom, epivizr, GenomicRanges, globalSeq, HDF5Array, interactiveDisplay, RiboProfiling Package: supraHex Version: 1.10.0 Depends: R (>= 3.0.2), hexbin Imports: ape, MASS License: GPL-2 MD5sum: d107efb64b90b10ab49e5eff03ea1d12 NeedsCompilation: no Title: 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 (see http://www.ncbi.nlm.nih.gov/ pubmed/24309102). biocViews: Bioinformatics, Clustering, Visualization, GeneExpression Author: Hai Fang and Julian Gough Maintainer: Hai Fang URL: http://suprahex.r-forge.r-project.org source.ver: src/contrib/supraHex_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/supraHex_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/supraHex_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/supraHex_1.7.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/supraHex_1.10.0.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 importsMe: TCGAbiolinks Package: survcomp Version: 1.22.0 Depends: survival, prodlim, R (>= 2.10) Imports: ipred, SuppDists, KernSmooth, survivalROC, bootstrap, grid, rmeta Suggests: Hmisc, CPE, clinfun, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 36fccec69535e6e196dc15f2c30ea57a NeedsCompilation: yes Title: Performance Assessment and Comparison for Survival Analysis Description: R package providing functions to assess and to compare the performance of risk prediction (survival) models. biocViews: GeneExpression, DifferentialExpression, Visualization Author: Benjamin Haibe-Kains, Markus Schroeder, Catharina Olsen, Christos Sotiriou, Gianluca Bontempi, John Quackenbush Maintainer: Benjamin Haibe-Kains , Markus Schroeder , Catharina Olsen URL: http://www.pmgenomics.ca/bhklab/ source.ver: src/contrib/survcomp_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/survcomp_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/survcomp_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/survcomp_1.19.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/survcomp_1.22.0.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: GenRank, saps suggestsMe: metaseqR Package: Sushi Version: 1.10.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: e3d9f62df8f6dd7fca307e7107126218 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 source.ver: src/contrib/Sushi_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Sushi_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Sushi_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Sushi_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Sushi_1.10.0.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 Package: sva Version: 3.20.0 Depends: R (>= 2.8), mgcv, genefilter Suggests: limma, pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 01480009f7ea91c0f2c52cb9611cee3e 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: Microarray, StatisticalMethod, Preprocessing, MultipleComparison, Sequencing, RNASeq, BatchEffect, Normalization Author: Jeffrey T. Leek , W. Evan Johnson , Hilary S. Parker , Elana J. Fertig , Andrew E. Jaffe , John D. Storey Maintainer: Jeffrey T. Leek , John D. Storey , W. Evan Johnson source.ver: src/contrib/sva_3.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/sva_3.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/sva_3.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/sva_3.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/sva_3.20.0.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 importsMe: ballgown, BatchQC, ChAMP, charm, doppelgangR, edge, MEAL, PAA, trigger suggestsMe: RnBeads, SomaticSignatures Package: SVM2CRM Version: 1.4.0 Depends: R (>= 3.2.0), LiblineaR, SVM2CRMdata Imports: AnnotationDbi, mclust, GenomicRanges, IRanges, zoo, squash, pls,rtracklayer,ROCR,verification License: GPL-3 MD5sum: a4ea4666b491931563294364f5d1e8dd NeedsCompilation: no Title: SVM2CRM: support vector machine for cis-regulatory elements detections Description: Detection of cis-regulatory elements using svm implemented in LiblineaR. biocViews: ChIPSeq, SupportVectorMachine, Software, Preprocessing, ChipOnChip Author: Guidantonio Malagoli Tagliazucchi Maintainer: Guidantonio Malagoli Tagliazucchi source.ver: src/contrib/SVM2CRM_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SVM2CRM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SVM2CRM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SVM2CRM_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SVM2CRM_1.4.0.tgz vignettes: vignettes/SVM2CRM/inst/doc/SVM2CRM.pdf vignetteTitles: The \Rpackage{SVM2CRM} Package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/SVM2CRM/inst/doc/SVM2CRM.R Package: SWATH2stats Version: 1.2.3 Depends: R(>= 2.10.0) Imports: data.table, reshape2, grid, ggplot2, stats Suggests: testthat, MSstats, aLFQ, knitr Enhances: imsbInfer License: GPL-3 MD5sum: a447529451a5252ca5baccf843cd49dd 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 Author: Peter Blattmann, Moritz Heusel and Ruedi Aebersold Maintainer: Peter Blattmann VignetteBuilder: knitr source.ver: src/contrib/SWATH2stats_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/SWATH2stats_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.3/SWATH2stats_1.2.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SWATH2stats_1.2.3.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 Package: SwathXtend Version: 1.0.0 Depends: e1071, openxlsx, VennDiagram, lattice License: GPL-2 MD5sum: 297bbf2865d55d4c443bf5b00e33b649 NeedsCompilation: no Title: SWATH extended library generation and satistical data analysis Description: It 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 source.ver: src/contrib/SwathXtend_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SwathXtend_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SwathXtend_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SwathXtend_1.0.0.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 Package: SwimR Version: 1.10.0 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: 315e424b92d6f409381b10146df6824b 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 source.ver: src/contrib/SwimR_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/SwimR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/SwimR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/SwimR_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/SwimR_1.10.0.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 Package: switchBox Version: 1.6.0 Depends: R (>= 2.13.1) License: GPL-2 Archs: i386, x64 MD5sum: 68a1e7d61ace72ab26665f3ed7f5ba27 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 Maintainer: Bahman Afsari , Luigi Marchionni source.ver: src/contrib/switchBox_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/switchBox_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/switchBox_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/switchBox_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/switchBox_1.6.0.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 Package: synapter Version: 1.14.2 Depends: R (>= 2.15), methods, MSnbase Imports: hwriter, RColorBrewer, lattice, qvalue, multtest, utils, Biobase, knitr, Biostrings, cleaver, BiocParallel Suggests: synapterdata, xtable, tcltk, BiocStyle License: GPL-2 MD5sum: 20db8f74396195c8de6de7c208b0a232 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: MassSpectrometry, Proteomics, GUI Author: Laurent Gatto, Nick J. Bond and Pavel V. Shliaha and Sebastian Gibb. Maintainer: Laurent Gatto and Sebastian Gibb URL: http://lgatto.github.com/synapter/ VignetteBuilder: knitr source.ver: src/contrib/synapter_1.14.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/synapter_1.14.2.zip win64.binary.ver: bin/windows64/contrib/3.3/synapter_1.14.2.zip mac.binary.ver: bin/macosx/contrib/3.3/synapter_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/synapter_1.14.2.tgz vignettes: vignettes/synapter/inst/doc/synapter.pdf vignetteTitles: Combining HDMSe/MSe data using 'synapter' to optimise identification and quantitation hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synapter/inst/doc/synapter.R suggestsMe: pRoloc Package: synlet Version: 1.2.2 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat License: GPL-3 MD5sum: 13354ca0b64870ef5c5f0b14d1fbf1c3 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: CellBasedAssays, QualityControl, Preprocessing, Visualization, FeatureExtraction Author: Chunxuan Shao Maintainer: Chunxuan Shao VignetteBuilder: knitr source.ver: src/contrib/synlet_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/synlet_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/synlet_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/synlet_0.99.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/synlet_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/synlet/inst/doc/synlet-vignette.R htmlDocs: vignettes/synlet/inst/doc/synlet-vignette.html htmlTitles: A working Demo for synlet Package: systemPipeR Version: 1.6.4 Depends: Rsamtools, Biostrings, ShortRead, methods Imports: BiocGenerics, GenomicRanges, GenomicFeatures, SummarizedExperiment, VariantAnnotation, rjson, ggplot2, grid, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, BatchJobs Suggests: ape, RUnit, BiocStyle, knitr, rmarkdown, biomaRt, BiocParallel License: Artistic-2.0 MD5sum: f52162b29502280411bd9d84b9279580 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 Author: Thomas Girke Maintainer: Thomas Girke URL: https://github.com/tgirke/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 source.ver: src/contrib/systemPipeR_1.6.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/systemPipeR_1.6.4.zip win64.binary.ver: bin/windows64/contrib/3.3/systemPipeR_1.6.4.zip mac.binary.ver: bin/macosx/contrib/3.3/systemPipeR_1.3.36.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/systemPipeR_1.6.4.tgz vignettes: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeRIBOseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.pdf, vignettes/systemPipeR/inst/doc/systemPipeVARseq.pdf vignetteTitles: ChIP-Seq Workflow Template, Ribo-Seq Workflow Template, RNA-Seq Workflow Template, VAR-Seq Workflow Template hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/systemPipeR/inst/doc/systemPipeChIPseq.R, vignettes/systemPipeR/inst/doc/systemPipeR.R, vignettes/systemPipeR/inst/doc/systemPipeRIBOseq.R, vignettes/systemPipeR/inst/doc/systemPipeRNAseq.R, vignettes/systemPipeR/inst/doc/systemPipeVARseq.R htmlDocs: vignettes/systemPipeR/inst/doc/systemPipeR.html htmlTitles: Overview Vignette importsMe: DiffBind Package: TargetScore Version: 1.10.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: d828a76ce2a4164abf3cf7c85f18f7f8 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 source.ver: src/contrib/TargetScore_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TargetScore_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TargetScore_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TargetScore_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TargetScore_1.10.0.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 Package: TargetSearch Version: 1.28.1 Depends: ncdf4 Imports: graphics, grDevices, methods, stats, tcltk, utils Suggests: TargetSearchData License: GPL (>= 2) Archs: i386, x64 MD5sum: 9278150a129461a7366afce6079d2e39 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 Author: Alvaro Cuadros-Inostroza , Jan Lisec , Henning Redestig , Matt Hannah Maintainer: Alvaro Cuadros-Inostroza source.ver: src/contrib/TargetSearch_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/TargetSearch_1.28.1.zip win64.binary.ver: bin/windows64/contrib/3.3/TargetSearch_1.28.1.zip mac.binary.ver: bin/macosx/contrib/3.3/TargetSearch_1.25.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TargetSearch_1.28.1.tgz vignettes: vignettes/TargetSearch/inst/doc/RICorrection.pdf, vignettes/TargetSearch/inst/doc/TargetSearch.pdf vignetteTitles: RI correction, The TargetSearch Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TargetSearch/inst/doc/RICorrection.R, vignettes/TargetSearch/inst/doc/TargetSearch.R Package: TarSeqQC Version: 1.2.0 Depends: R (>= 3.2.1), methods, GenomicRanges, Rsamtools (>= 1.20.4), ggplot2, plyr, openxlsx Imports: S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, cowplot, Biostrings Suggests: RUnit License: GPL (>=2) MD5sum: 7617b102c18f9104e87bbd4124469103 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 and Elmer A. Fernandez Maintainer: Gabriela Merino URL: http://www.bdmg.com.ar source.ver: src/contrib/TarSeqQC_1.2.0.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TarSeqQC_1.2.0.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 Package: TCC Version: 1.12.1 Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC Imports: samr Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: 2cc805101a162380adb332ae25e45a4f 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: Sequencing, DifferentialExpression, RNASeq Author: Jianqiang Sun, Tomoaki Nishiyama, Kentaro Shimizu, and Koji Kadota Maintainer: Jianqiang Sun , Tomoaki Nishiyama source.ver: src/contrib/TCC_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/TCC_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/TCC_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.3/TCC_1.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TCC_1.12.1.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 Package: TCGAbiolinks Version: 2.0.13 Depends: R (>= 3.2) Imports: downloader (>= 0.4), GGally, grDevices, graphics, GenomicRanges, XML (>= 3.98.0), Biobase, affy, xtable, data.table, EDASeq (>= 2.0.0), edgeR (>= 3.0.0), jsonlite (>= 1.0.0), plyr, knitr, biomaRt, coin, gplots, ggplot2, ggthemes, survival, stringr (>= 1.0.0), IRanges, scales, rvest (>= 0.3.0), stats, utils, dnet, igraph, supraHex, S4Vectors, ComplexHeatmap (>= 1.10.2), R.utils, SummarizedExperiment (>= 1.2.3), BiocGenerics, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, limma, genefilter, ConsensusClusterPlus, readr, RColorBrewer, doParallel, dplyr, parallel, xml2, reshape2, httr (>= 1.2.1), parmigene, matlab, circlize, ggrepel Suggests: testthat, png, BiocStyle, rmarkdown, devtools License: GPL (>= 3) MD5sum: 23de055a288052f3373e403aec023813 NeedsCompilation: no Title: TCGAbiolinks: An R/Bioconductor package for integrative analysis with TCGA data Description: The aim of TCGAbiolinks is : i) facilitate the TCGA 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) allow the user to download a specific version of the data and thus 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, Survival 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: Antonio Colaprico , Tiago Chedraoui Silva URL: https://github.com/BioinformaticsFMRP/TCGAbiolinks VignetteBuilder: knitr BugReports: https://github.com/BioinformaticsFMRP/TCGAbiolinks/issues source.ver: src/contrib/TCGAbiolinks_2.0.13.tar.gz win.binary.ver: bin/windows/contrib/3.3/TCGAbiolinks_2.0.13.zip win64.binary.ver: bin/windows64/contrib/3.3/TCGAbiolinks_2.0.13.zip mac.binary.ver: bin/macosx/contrib/3.3/TCGAbiolinks_0.99.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TCGAbiolinks_2.0.13.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.R htmlDocs: vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html htmlTitles: Vignette Title importsMe: SpidermiR Package: TDARACNE Version: 1.22.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: 691cdf73305e7da9ec06c0c24e0def43 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 source.ver: src/contrib/TDARACNE_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TDARACNE_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TDARACNE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TDARACNE_1.19.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TDARACNE_1.22.0.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 Package: TEQC Version: 3.12.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 52f2eae3b94ca996915e53ca395b5b9f 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 source.ver: src/contrib/TEQC_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TEQC_3.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TEQC_3.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TEQC_3.9.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TEQC_3.12.0.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 Package: ternarynet Version: 1.16.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: 6c19e1a70a02021b7bfd35202aa31d79 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 Maintainer: Matthew N. McCall source.ver: src/contrib/ternarynet_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ternarynet_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ternarynet_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ternarynet_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ternarynet_1.16.0.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 Package: TFBSTools Version: 1.10.4 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, 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) Suggests: BiocStyle(>= 1.7.7), JASPAR2014(>= 1.4.0), knitr(>= 1.11), testthat, JASPAR2016(>= 1.0.0) License: GPL-2 Archs: i386, x64 MD5sum: 261dbf87a2bf61a6043d0d17e0d81799 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 source.ver: src/contrib/TFBSTools_1.10.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/TFBSTools_1.10.4.zip win64.binary.ver: bin/windows64/contrib/3.3/TFBSTools_1.10.4.zip mac.binary.ver: bin/macosx/contrib/3.3/TFBSTools_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TFBSTools_1.10.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TFBSTools/inst/doc/TFBSTools.R htmlDocs: vignettes/TFBSTools/inst/doc/TFBSTools.html htmlTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package importsMe: MatrixRider Package: tigre Version: 1.26.0 Depends: R (>= 2.11.0), BiocGenerics, Biobase Imports: methods, AnnotationDbi, gplots, graphics, stats, utils, annotate, DBI, RSQLite Suggests: drosgenome1.db, puma, lumi, BiocStyle License: AGPL-3 Archs: i386, x64 MD5sum: f1d3f8176129b486a8e2b9ba232f10c8 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 source.ver: src/contrib/tigre_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tigre_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tigre_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.3/tigre_1.23.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tigre_1.26.0.tgz vignettes: vignettes/tigre/inst/doc/tigre_quick.pdf, vignettes/tigre/inst/doc/tigre.pdf vignetteTitles: tigre Quick Guide, tigre User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tigre/inst/doc/tigre_quick.R, vignettes/tigre/inst/doc/tigre.R Package: tilingArray Version: 1.50.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: 00d4a0a2b8f1da79c13cd4c19b31e8df 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 source.ver: src/contrib/tilingArray_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tilingArray_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tilingArray_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/tilingArray_1.47.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tilingArray_1.50.0.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 importsMe: ADaCGH2, snapCGH Package: timecourse Version: 1.44.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 162f37be96ae2a034ae88e557ba64814 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 source.ver: src/contrib/timecourse_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/timecourse_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/timecourse_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/timecourse_1.41.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/timecourse_1.44.0.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 Package: TIN Version: 1.4.1 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: 7afdfc4f0be8af1ca2500103c221bc46 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 source.ver: src/contrib/TIN_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/TIN_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.3/TIN_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.3/TIN_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TIN_1.4.1.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 Package: TitanCNA Version: 1.10.0 Depends: R (>= 3.2.0), foreach (>= 1.4.2), IRanges (>= 2.2.4), GenomicRanges (>= 1.20.5), Rsamtools (>= 1.20.4), GenomeInfoDb (>= 1.4.0) License: GPL-3 Archs: i386, x64 MD5sum: d7873fdc86147911dd0196009bb5d33d 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 prevalenece of clonal clusters in tumour whole genome sequencing data. biocViews: Sequencing, WholeGenome, DNASeq, ExomeSeq, StatisticalMethod, CopyNumberVariation, HiddenMarkovModel, Genetics, GenomicVariation Author: Gavin Ha, Sohrab P Shah Maintainer: Gavin Ha , Sohrab P Shah URL: https://github.com/gavinha/TitanCNA source.ver: src/contrib/TitanCNA_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TitanCNA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TitanCNA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TitanCNA_1.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TitanCNA_1.10.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf vignetteTitles: TitanCNA hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TitanCNA/inst/doc/TitanCNA.R Package: tkWidgets Version: 1.50.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: 6fd569be0c36298b9d890515a1e817cc 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 source.ver: src/contrib/tkWidgets_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tkWidgets_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tkWidgets_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/tkWidgets_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tkWidgets_1.50.0.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 dependsOnMe: oneChannelGUI importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray Package: tofsims Version: 1.0.2 Depends: R (>= 3.3.0), methods, utils, ProtGenerics Imports: Rcpp (>= 0.11.2), ALS, ChemometricsWithR, 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: c23dd6f3574474941b8a7197b542589f 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: Infrastructure, DataImport, MassSpectrometry, ImagingMassSpectrometry, Proteomics, Metabolomics Author: Lorenz Gerber, Viet Mai Hoang Maintainer: Lorenz Gerber VignetteBuilder: knitr source.ver: src/contrib/tofsims_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/tofsims_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/tofsims_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tofsims_1.0.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tofsims/inst/doc/workflow.R htmlDocs: vignettes/tofsims/inst/doc/workflow.html htmlTitles: Workflow with the `tofsims` package Package: ToPASeq Version: 1.6.0 Depends: graphite (>= 1.16), gRbase, graph, locfit, Rgraphviz Imports: R.utils, methods, Biobase, parallel, edgeR, DESeq2, SummarizedExperiment, RBGL, DESeq, fields, limma, TeachingDemos, KEGGgraph, qpgraph, clipper, AnnotationDbi, doParallel LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, gageData, DEGraph, plotrix, org.Hs.eg.db License: AGPL-3 Archs: i386, x64 MD5sum: 0853b0a0836998947492c22f92a33d36 NeedsCompilation: yes Title: Package for Topology-based Pathway Analysis of RNASeq data Description: Implementation of seven methods for topology-based pathway analysis of both RNASeq and microarray data: SPIA, DEGraph, TopologyGSA, TAPPA, PRS, PWEA and a visualization tool for a single pathway. biocViews: Software, GeneExpression, NetworkEnrichment, GraphAndNetwork, RNASeq, Visualization, Microarray, Pathways, DifferentialExpression, Author: Ivana Ihnatova, Eva Budinska Maintainer: Ivana Ihnatova source.ver: src/contrib/ToPASeq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/ToPASeq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.3/ToPASeq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.3/ToPASeq_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/ToPASeq_1.6.0.tgz vignettes: vignettes/ToPASeq/inst/doc/ToPASeq.pdf vignetteTitles: An R Package for topology-based pathway analysis of microaray and RNA-Seq data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/ToPASeq/inst/doc/ToPASeq.R Package: topGO Version: 2.24.0 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: 45b08c9cdac910a05bd130e518063357 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 source.ver: src/contrib/topGO_2.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/topGO_2.24.0.zip win64.binary.ver: bin/windows64/contrib/3.3/topGO_2.24.0.zip mac.binary.ver: bin/macosx/contrib/3.3/topGO_2.21.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/topGO_2.24.0.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, RNAither, tRanslatome importsMe: cellity, clusterProfiler, EnrichmentBrowser, GOSim, mvGST, pcaExplorer, psygenet2r, SEPA suggestsMe: FGNet, miRNAtap, Ringo Package: TPP Version: 2.2.6 Depends: R (>= 3.2.0), Biobase, openxlsx (>= 2.4.0), ggplot2 (>= 2.0.0) Imports: VGAM, reshape2, nls2, foreach, grid, gridExtra, doParallel, parallel, RColorBrewer, RCurl, plyr, VennDiagram Suggests: BiocStyle, knitr, testthat License: Artistic-2.0 MD5sum: d5164d116b5d510cd1551b1d00811c71 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: Proteomics, MassSpectrometry Author: Dorothee Childs, Holger Franken, Mikhail Savitski and Wolfgang Huber Maintainer: Dorothee Childs VignetteBuilder: knitr source.ver: src/contrib/TPP_2.2.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/TPP_2.2.6.zip win64.binary.ver: bin/windows64/contrib/3.3/TPP_2.2.6.zip mac.binary.ver: bin/macosx/contrib/3.3/TPP_1.1.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TPP_2.2.6.tgz vignettes: vignettes/TPP/inst/doc/TPP_introduction.pdf vignetteTitles: TPP_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TPP/inst/doc/TPP_introduction.R Package: tracktables Version: 1.6.2 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, tractor.base, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: 11994bbf91d2434afbaea0165ef900f2 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 source.ver: src/contrib/tracktables_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/tracktables_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/tracktables_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/tracktables_1.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tracktables_1.6.2.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 Package: trackViewer Version: 1.8.4 Depends: R (>= 3.1.0), methods, GenomicRanges, grid Imports: GenomicAlignments, GenomicFeatures, Gviz, pbapply, Rsamtools, rtracklayer, scales, tools, IRanges, AnnotationDbi, grImport Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, org.Hs.eg.db, BiocGenerics, BiocStyle, knitr, VariantAnnotation License: GPL (>= 2) MD5sum: 9dd5348450ef9404345a19690ac01618 NeedsCompilation: no Title: A bioconductor package with minimalist design for drawing elegant tracks or lollipop plot 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, Yong-Xu Wang, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/trackViewer_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/trackViewer_1.8.4.zip win64.binary.ver: bin/windows64/contrib/3.3/trackViewer_1.8.4.zip mac.binary.ver: bin/macosx/contrib/3.3/trackViewer_1.5.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/trackViewer_1.8.4.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/trackViewer/inst/doc/trackViewer.R htmlDocs: vignettes/trackViewer/inst/doc/trackViewer.html htmlTitles: trackViewer Vignette importsMe: coMET Package: transcriptR Version: 1.0.2 Depends: methods, R (>= 3.3) Imports: BiocGenerics, caret, chipseq, e1071, GenomicAlignments, GenomicRanges, GenomicFeatures, GenomeInfoDb, ggplot2, graphics, grDevices, IRanges, pROC, reshape2, Rsamtools, rtracklayer, S4Vectors, stats, utils Suggests: BiocStyle, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene, testthat License: GPL-3 MD5sum: 9dec4fd2178b533799c5b5e506629160 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: Transcription, Software, Sequencing, RNASeq, Coverage Author: Armen R. Karapetyan Maintainer: Armen R. Karapetyan VignetteBuilder: knitr source.ver: src/contrib/transcriptR_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/transcriptR_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.3/transcriptR_1.0.2.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/transcriptR_1.0.2.tgz vignettes: vignettes/transcriptR/inst/doc/transcriptR.pdf 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 Package: tRanslatome Version: 1.10.0 Depends: R (>= 2.15.0), methods, limma, sigPathway, samr, anota, DESeq, edgeR, RankProd, topGO, org.Hs.eg.db, GOSemSim, Heatplus, gplots, plotrix, Biobase License: GPL-3 MD5sum: 2f5c18ed64dd71678991363f6257b6b3 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, SAM, 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 source.ver: src/contrib/tRanslatome_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tRanslatome_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tRanslatome_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/tRanslatome_1.7.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tRanslatome_1.10.0.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 Package: TransView Version: 1.16.0 Depends: methods, GenomicRanges Imports: BiocGenerics, S4Vectors (>= 0.9.25), IRanges, Rsamtools (>= 1.19.38), zlibbioc, gplots LinkingTo: Rsamtools Suggests: RUnit, pasillaBamSubset License: GPL-3 Archs: i386, x64 MD5sum: 31ba6233685757ae2929ab104d829810 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: 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 source.ver: src/contrib/TransView_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TransView_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TransView_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TransView_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TransView_1.16.0.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 Package: traseR Version: 1.2.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: a6c4c56eafc89785ff4ec0f28eab0c5e 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 source.ver: src/contrib/traseR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/traseR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.3/traseR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.3/traseR_0.99.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/traseR_1.2.0.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 Package: triform Version: 1.14.0 Depends: R (>= 2.11.0), IRanges, yaml Imports: BiocGenerics, IRanges (>= 2.5.27), yaml Suggests: RUnit License: GPL-2 MD5sum: 769687f6c54bc428dce69240ca18274d NeedsCompilation: no Title: Triform finds enriched regions (peaks) in transcription factor ChIP-sequencing data Description: The Triform algorithm uses model-free statistics to identify peak-like distributions of TF ChIP sequencing reads, taking advantage of an improved peak definition in combination with known profile characteristics. biocViews: Sequencing, ChIPSeq Author: Karl Kornacker Developer [aut], Tony Handstad Developer [aut, cre] Maintainer: Thomas Carroll source.ver: src/contrib/triform_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/triform_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/triform_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/triform_1.11.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/triform_1.14.0.tgz vignettes: vignettes/triform/inst/doc/triform.pdf vignetteTitles: Triform users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/triform/inst/doc/triform.R Package: trigger Version: 1.18.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 Archs: i386, x64 MD5sum: cee5d2827925d0ff62669d6fcf9a6e68 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 source.ver: src/contrib/trigger_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/trigger_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/trigger_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/trigger_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/trigger_1.18.0.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 Package: trio Version: 3.10.0 Depends: R (>= 3.0.1) Suggests: survival, haplo.stats, mcbiopi, siggenes, splines, LogicReg (>= 1.5.3), logicFS (>= 1.28.1), KernSmooth, VariantAnnotation License: LGPL-2 MD5sum: 4263ddc68067ca1c24860364fe1a5883 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 source.ver: src/contrib/trio_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/trio_3.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/trio_3.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/trio_3.7.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/trio_3.10.0.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 Package: triplex Version: 1.12.0 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, GenomeGraphs License: BSD_2_clause + file LICENSE Archs: i386, x64 MD5sum: db5582516b4f89726cc5382c6915d6d3 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/ source.ver: src/contrib/triplex_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/triplex_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/triplex_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/triplex_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/triplex_1.12.0.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 Package: TRONCO Version: 2.4.3 Depends: R (>= 3.3), Imports: bnlearn, Rgraphviz, gtools, parallel, foreach, doParallel, iterators, RColorBrewer, circlize, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales, R.matlab, gRapHD, grDevices, graphics, stats, utils, Suggests: BiocGenerics, BiocStyle, testthat, knitr, License: file LICENSE MD5sum: b379a4406e5adf367a0f466c87d035d4 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. biocViews: BiomedicalInformatics, Bayesian, GraphAndNetwork, SomaticMutation, NetworkInference, Network, Clustering, DataImport 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: BIMIB Group URL: https://sites.google.com/site/troncopackage/ VignetteBuilder: knitr BugReports: https://github.com/BIMIB-DISCo/TRONCO source.ver: src/contrib/TRONCO_2.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/TRONCO_2.4.3.zip win64.binary.ver: bin/windows64/contrib/3.3/TRONCO_2.4.3.zip mac.binary.ver: bin/macosx/contrib/3.3/TRONCO_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TRONCO_2.4.3.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/TRONCO/inst/doc/vignette.R Package: TSCAN Version: 1.10.2 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots Suggests: knitr License: GPL(>=2) MD5sum: 5182f32e3709b86ffd83a113a032e75a NeedsCompilation: no Title: TSCAN: Tools for Single-Cell ANalysis Description: TSCAN enables users to easily construct and tune pseudotemporal cell ordering as well as analyzing differentially expressed genes. TSCAN comes with a user-friendly GUI written in shiny. More features will come in the future. biocViews: GeneExpression, Visualization, GUI Author: Zhicheng Ji, Hongkai Ji Maintainer: Zhicheng Ji VignetteBuilder: knitr source.ver: src/contrib/TSCAN_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/TSCAN_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.3/TSCAN_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.3/TSCAN_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TSCAN_1.10.2.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSCAN/inst/doc/TSCAN.R Package: tspair Version: 1.30.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Archs: i386, x64 MD5sum: 8d39921ce2995cc70226c5ba004a4fec 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 source.ver: src/contrib/tspair_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tspair_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tspair_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/tspair_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tspair_1.30.0.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 dependsOnMe: stepwiseCM Package: TSSi Version: 1.18.0 Depends: R (>= 2.13.2) Imports: methods, BiocGenerics (>= 0.3.2), S4Vectors, Hmisc, minqa, stats, Biobase (>= 0.3.2), plyr, IRanges Suggests: rtracklayer Enhances: parallel License: GPL-3 Archs: i386, x64 MD5sum: f965468d9930b31b8c5ac961838bde80 NeedsCompilation: yes Title: Transcription Start Site Identification Description: Identify and normalize transcription start sites in high-throughput sequencing data. biocViews: Sequencing, RNASeq, Genetics, Preprocessing Author: Julian Gehring, Clemens Kreutz Maintainer: Julian Gehring source.ver: src/contrib/TSSi_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TSSi_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TSSi_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TSSi_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TSSi_1.18.0.tgz vignettes: vignettes/TSSi/inst/doc/TSSi.pdf vignetteTitles: Introduction to the TSSi package: Identification of Transcription Start Sites hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/TSSi/inst/doc/TSSi.R Package: TurboNorm Version: 1.20.0 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: 063d43ee8e719f528a3317d979a08756 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 source.ver: src/contrib/TurboNorm_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TurboNorm_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TurboNorm_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TurboNorm_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TurboNorm_1.20.0.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 Package: tweeDEseq Version: 1.18.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: 2603087f628a2a698ba85100e264dabf 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: 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 source.ver: src/contrib/tweeDEseq_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/tweeDEseq_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/tweeDEseq_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/tweeDEseq_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tweeDEseq_1.18.0.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 Package: twilight Version: 1.48.0 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: b972cf2fbbb76e42bc780c7dd1c301a4 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 source.ver: src/contrib/twilight_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/twilight_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/twilight_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/twilight_1.45.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/twilight_1.48.0.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 importsMe: OrderedList Package: tximport Version: 1.0.3 Imports: utils Suggests: knitr, testthat, tximportData, TxDb.Hsapiens.UCSC.hg19.knownGene, readr (>= 0.2.2), limma, edgeR, DESeq2 (>= 1.11.6) License: GPL (>=2) MD5sum: c6e0a617f255af19fae34fe8e8096204 NeedsCompilation: no Title: Import and summarize transcript-level estimates for 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: RNASeq, Transcription, GeneExpression, DataImport Author: Michael Love, Charlotte Soneson, Mark Robinson Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/tximport_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.3/tximport_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.3/tximport_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/tximport_1.0.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/tximport/inst/doc/tximport.R htmlDocs: vignettes/tximport/inst/doc/tximport.html htmlTitles: tximport importsMe: scater suggestsMe: DESeq2, variancePartition Package: TypeInfo Version: 1.38.0 Depends: methods Suggests: Biobase License: BSD MD5sum: 508a3a1d7b1a568629b0b1d3f80c0405 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 source.ver: src/contrib/TypeInfo_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/TypeInfo_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/TypeInfo_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/TypeInfo_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/TypeInfo_1.38.0.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 dependsOnMe: RWebServices Package: UNDO Version: 1.14.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: e34b10fbdad95d97db0204e9978b52e8 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 source.ver: src/contrib/UNDO_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/UNDO_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/UNDO_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/UNDO_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/UNDO_1.14.0.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 Package: unifiedWMWqPCR Version: 1.8.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: 00f449fc75ebee5df2a5530aba41a0ac 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 source.ver: src/contrib/unifiedWMWqPCR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/unifiedWMWqPCR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.3/unifiedWMWqPCR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.3/unifiedWMWqPCR_1.5.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/unifiedWMWqPCR_1.8.0.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 Package: UniProt.ws Version: 2.12.0 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi Suggests: RUnit License: Artistic License 2.0 MD5sum: 19785ccf5776552fd1a553c4b14427b1 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 Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/UniProt.ws_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/UniProt.ws_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/UniProt.ws_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/UniProt.ws_2.9.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/UniProt.ws_2.12.0.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 suggestsMe: cleaver, dagLogo Package: Uniquorn Version: 1.0.8 Depends: R (>= 3.3) Imports: DBI, stringr, RSQLite, R.utils, WriteXLS Suggests: testthat, knitr, rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 35ca3173418e49ec9b85be92624b4f14 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. biocViews: Software, StatisticalMethod, WholeGenome Author: Raik Otto Maintainer: 'Raik Otto' VignetteBuilder: knitr source.ver: src/contrib/Uniquorn_1.0.8.tar.gz win.binary.ver: bin/windows/contrib/3.3/Uniquorn_1.0.6.zip win64.binary.ver: bin/windows64/contrib/3.3/Uniquorn_1.0.6.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Uniquorn_1.0.8.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Uniquorn/inst/doc/Uniquorn.R htmlDocs: vignettes/Uniquorn/inst/doc/Uniquorn.html htmlTitles: Vignette Title Package: VanillaICE Version: 1.34.0 Depends: R (>= 3.0.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.19.47), SummarizedExperiment (>= 0.2.0) Imports: Biobase, S4Vectors (>= 0.9.25), IRanges (>= 1.14.0), oligoClasses (>= 1.31.1), foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb, crlmm, tools, stats, utils, BSgenome.Hsapiens.UCSC.hg18 Suggests: RUnit, SNPchip, human610quadv1bCrlmm, ArrayTV Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 Archs: i386, x64 MD5sum: 2025819d9a98cedf6a123c07b422d463 NeedsCompilation: yes Title: A Hidden Markov Model for high throughput genotyping arrays Description: Hidden Markov Models for characterizing chromosomal alterations in high throughput SNP arrays. biocViews: CopyNumberVariation Author: Robert Scharpf , Kevin Scharpf, and Ingo Ruczinski Maintainer: Robert Scharpf source.ver: src/contrib/VanillaICE_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/VanillaICE_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.3/VanillaICE_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.3/VanillaICE_1.31.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VanillaICE_1.34.0.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: CNPBayes, oligoClasses Package: variancePartition Version: 1.2.11 Depends: ggplot2, foreach, Biobase, methods Imports: lme4 (>= 1.1-10), MASS, colorRamps, gplots, reshape2, pbkrtest, iterators, doParallel, limma, grDevices, graphics, utils, stats Suggests: edgeR, dendextend, tximport, tximportData, ballgown, DESeq2, readr, knitr, BiocStyle License: GPL (>= 2) MD5sum: 14390c5cc3871ee3e605e2fe273323b6 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 linear mixed model to quantify variation in gene expression attributable to individual, tissue, time point, or technical variables. biocViews: RNASeq, GeneExpression, Regression, Software Author: Gabriel E. Hoffman Maintainer: Gabriel E. Hoffman VignetteBuilder: knitr source.ver: src/contrib/variancePartition_1.2.11.tar.gz win.binary.ver: bin/windows/contrib/3.3/variancePartition_1.2.11.zip win64.binary.ver: bin/windows64/contrib/3.3/variancePartition_1.2.11.zip mac.binary.ver: bin/macosx/contrib/3.3/variancePartition_0.99.7.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/variancePartition_1.2.11.tgz vignettes: vignettes/variancePartition/inst/doc/additional_visualization.pdf, vignettes/variancePartition/inst/doc/variancePartition.pdf vignetteTitles: 2) Additional visualizations, 1) Tutorial on using variancePartition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/variancePartition/inst/doc/additional_visualization.R, vignettes/variancePartition/inst/doc/variancePartition.R Package: VariantAnnotation Version: 1.18.7 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.3), GenomeInfoDb (>= 1.7.1), GenomicRanges (>= 1.19.47), SummarizedExperiment (>= 0.3.1), Rsamtools (>= 1.23.10) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.9.47), IRanges (>= 2.3.25), XVector (>= 0.5.6), Biostrings (>= 2.33.5), AnnotationDbi (>= 1.27.9), BSgenome (>= 1.37.6), rtracklayer (>= 1.25.16), GenomicFeatures (>= 1.19.17) LinkingTo: S4Vectors, IRanges, XVector, Biostrings, Rsamtools Suggests: RUnit, AnnotationHub, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20110815, SNPlocs.Hsapiens.dbSNP.20101109, SIFT.Hsapiens.dbSNP132, SIFT.Hsapiens.dbSNP137, PolyPhen.Hsapiens.dbSNP131, snpStats, ggplot2, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: b08ecdc2e11c3c001def9c8a5bf85b78 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: Valerie Obenchain [aut, cre], Martin Morgan [aut], Michael Lawrence [aut], Stephanie Gogarten [ctb] Maintainer: Valerie Obenchain Video: https://www.youtube.com/watch?v=Ro0lHQ_J--I&list=UUqaMSQd_h-2EDGsU6WDiX0Q source.ver: src/contrib/VariantAnnotation_1.18.7.tar.gz win.binary.ver: bin/windows/contrib/3.3/VariantAnnotation_1.18.7.zip win64.binary.ver: bin/windows64/contrib/3.3/VariantAnnotation_1.18.7.zip mac.binary.ver: bin/macosx/contrib/3.3/VariantAnnotation_1.15.28.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VariantAnnotation_1.18.7.tgz vignettes: vignettes/VariantAnnotation/inst/doc/filterVcf.pdf, vignettes/VariantAnnotation/inst/doc/VariantAnnotation.pdf vignetteTitles: filterVcf Overview, 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, DOQTL, ensemblVEP, genotypeeval, GoogleGenomics, HTSeqGenie, myvariant, PureCN, R453Plus1Toolbox, RareVariantVis, Rariant, SomaticSignatures, VariantFiltering, VariantTools importsMe: AllelicImbalance, BadRegionFinder, BBCAnalyzer, biovizBase, customProDB, FunciSNP, genbankr, GenomicFiles, ggbio, GGtools, gmapR, gQTLstats, gwascat, methyAnalysis, motifbreakR, PGA, SeqArray, SeqVarTools, SNPhood, systemPipeR suggestsMe: AnnotationHub, CrispRVariants, GenomicRanges, GMRP, GWASTools, podkat, trackViewer, trio, vtpnet Package: VariantFiltering Version: 1.8.6 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.13.8), VariantAnnotation (>= 1.13.29) Imports: utils, DBI, RSQLite (>= 1.0.0), 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, Gviz, shiny 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.phase3.hs37d5, MafDb.ExAC.r0.3.1.snvs.hs37d5, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: 55001c21f036b6315062626338cde6aa 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] Maintainer: Robert Castelo URL: https://github.com/rcastelo/VariantFiltering BugReports: https://github.com/rcastelo/VariantFiltering/issues source.ver: src/contrib/VariantFiltering_1.8.6.tar.gz win.binary.ver: bin/windows/contrib/3.3/VariantFiltering_1.8.6.zip win64.binary.ver: bin/windows64/contrib/3.3/VariantFiltering_1.8.6.zip mac.binary.ver: bin/macosx/contrib/3.3/VariantFiltering_1.5.17.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VariantFiltering_1.8.6.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 Package: VariantTools Version: 1.14.1 Depends: S4Vectors (>= 0.9.25), IRanges (>= 1.99.2), GenomicRanges (>= 1.17.7), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.17.6), BiocGenerics, Biostrings, parallel, gmapR (>= 1.13.4), GenomicFeatures (>= 1.17.13), Matrix, rtracklayer (>= 1.25.3), BiocParallel, GenomeInfoDb, BSgenome, Biobase Suggests: RUnit, LungCancerLines (>= 0.0.6), RBGL, graph License: Artistic-2.0 MD5sum: 7ffb34e73e1825f029088072431ebc49 NeedsCompilation: no Title: Tools for Working with Genetic Variants Description: Tools for detecting, filtering, calling, comparing and plotting variants. biocViews: Genetics, GeneticVariability, Sequencing Author: Michael Lawrence, Jeremiah Degenhardt, Robert Gentleman Maintainer: Michael Lawrence source.ver: src/contrib/VariantTools_1.14.1.tar.gz vignettes: vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/VariantTools/inst/doc/VariantTools.R importsMe: HTSeqGenie Package: vbmp Version: 1.40.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: 313e9fc44c2744f5ad3c1c6d4db912bd 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 source.ver: src/contrib/vbmp_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/vbmp_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/vbmp_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/vbmp_1.37.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/vbmp_1.40.0.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 Package: Vega Version: 1.20.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 1b6f130c5daaf6339c847711f0d0daf0 NeedsCompilation: yes Title: An R package for copy number data segmentation Description: Vega (Variational Estimator for Genomic Aberrations) is an algorithm that adapts a very popular variational model (Mumford and Shah) used in image segmentation so that chromosomal aberrant regions can be efficiently detected. biocViews: aCGH, CopyNumberVariation Author: Sandro Morganella Maintainer: Sandro Morganella source.ver: src/contrib/Vega_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/Vega_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.3/Vega_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.3/Vega_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/Vega_1.20.0.tgz vignettes: vignettes/Vega/inst/doc/Vega.pdf vignetteTitles: Vega hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/Vega/inst/doc/Vega.R Package: VegaMC Version: 3.10.0 Depends: R (>= 2.10.0), biomaRt, Biobase Imports: methods, genoset License: GPL-2 Archs: i386, x64 MD5sum: 5e90c38285bc5626241577f46c7b0b28 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 source.ver: src/contrib/VegaMC_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/VegaMC_3.10.0.zip win64.binary.ver: bin/windows64/contrib/3.3/VegaMC_3.10.0.zip mac.binary.ver: bin/macosx/contrib/3.3/VegaMC_3.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/VegaMC_3.10.0.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 Package: viper Version: 1.7.4 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: file LICENSE MD5sum: 878d84817f14b82b29b763249c3bae36 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 source.ver: src/contrib/viper_1.7.4.tar.gz win.binary.ver: bin/windows/contrib/3.3/viper_1.7.4.zip win64.binary.ver: bin/windows64/contrib/3.3/viper_1.7.4.zip mac.binary.ver: bin/macosx/contrib/3.3/viper_1.5.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/viper_1.7.4.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 importsMe: diggit Package: vsn Version: 3.40.0 Depends: R (>= 2.10), Biobase Imports: methods, affy, limma, lattice, ggplot2 (>= 2.0.0), hexbin Suggests: affydata, hgu95av2cdf License: Artistic-2.0 Archs: i386, x64 MD5sum: 574df3e064cd45222083e87a4513c9b7 NeedsCompilation: yes Title: Variance stabilization and calibration for microarray data Description: The package implements a method for normalising microarray intensities, both between colours within array, and between arrays. The method uses a robust variant of the maximum-likelihood estimator for the stochastic model of microarray data described in the references (see vignette). The model incorporates data calibration (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 source.ver: src/contrib/vsn_3.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/vsn_3.40.0.zip win64.binary.ver: bin/windows64/contrib/3.3/vsn_3.40.0.zip mac.binary.ver: bin/macosx/contrib/3.3/vsn_3.37.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/vsn_3.40.0.tgz vignettes: vignettes/vsn/inst/doc/convergence2.pdf, vignettes/vsn/inst/doc/likelihoodcomputations.pdf, vignettes/vsn/inst/doc/vsn.pdf vignetteTitles: Verifying and assessing the performance with simulated data, Likelihood Calculations for vsn, Introduction to vsn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/vsn/inst/doc/vsn.R dependsOnMe: affyPara, cellHTS2, MmPalateMiRNA, webbioc importsMe: arrayQualityMetrics, iCheck, imageHTS, LVSmiRNA, metaseqR, metaX, MSnbase, pvca, Ringo, tilingArray suggestsMe: adSplit, beadarray, BiocCaseStudies, cellHTS, DESeq, DESeq2, ggbio, GlobalAncova, globaltest, limma, lumi, PAA, twilight Package: vtpnet Version: 0.12.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 1e4b783b68a3267f4aa2614e20f75850 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 source.ver: src/contrib/vtpnet_0.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/vtpnet_0.12.0.zip win64.binary.ver: bin/windows64/contrib/3.3/vtpnet_0.12.0.zip mac.binary.ver: bin/macosx/contrib/3.3/vtpnet_0.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/vtpnet_0.12.0.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 Package: wateRmelon Version: 1.17.0 Depends: R (>= 2.10), Biobase, limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19, illuminaio Imports: Biobase Suggests: RPMM Enhances: minfi License: GPL-3 MD5sum: febacacc7e256ca77b86fc6cc2226e90 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 source.ver: src/contrib/wateRmelon_1.17.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/wateRmelon_1.17.0.zip win64.binary.ver: bin/windows64/contrib/3.3/wateRmelon_1.17.0.zip mac.binary.ver: bin/macosx/contrib/3.3/wateRmelon_1.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/wateRmelon_1.17.0.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: skewr importsMe: ChAMP suggestsMe: RnBeads Package: wavClusteR Version: 2.6.2 Depends: R (>= 3.2), GenomicRanges (>= 1.23.16), Rsamtools Imports: methods, BiocGenerics, S4Vectors (>= 0.9.25), IRanges (>= 2.5.27), Biostrings, foreach, GenomicFeatures, ggplot2, Hmisc, mclust, rtracklayer, seqinr, stringr, wmtsa Suggests: BiocStyle, knitr, rmarkdown, BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: 447b80151a7480241419d11a26964069 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: Sequencing, Technology, RIPSeq, RNASeq, Bayesian Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio VignetteBuilder: knitr source.ver: src/contrib/wavClusteR_2.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/wavClusteR_2.6.2.zip win64.binary.ver: bin/windows64/contrib/3.3/wavClusteR_2.6.2.zip mac.binary.ver: bin/macosx/contrib/3.3/wavClusteR_2.3.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/wavClusteR_2.6.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/wavClusteR/inst/doc/wavCluster_vignette.R htmlDocs: vignettes/wavClusteR/inst/doc/wavCluster_vignette.html htmlTitles: wavClusteR: a workflow for PAR-CLIP data analysis Package: waveTiling Version: 1.14.0 Depends: oligo, oligoClasses, Biobase, Biostrings, GenomeGraphs Imports: methods, affy, preprocessCore, GenomicRanges, waveslim, IRanges Suggests: BSgenome, BSgenome.Athaliana.TAIR.TAIR9, waveTilingData, pd.atdschip.tiling, TxDb.Athaliana.BioMart.plantsmart22 License: GPL (>=2) Archs: i386, x64 MD5sum: 79d01c7d678ebe4094609749509b3d13 NeedsCompilation: yes Title: Wavelet-Based Models for Tiling Array Transcriptome Analysis Description: This package is designed to conduct transcriptome analysis for tiling arrays based on fast wavelet-based functional models. biocViews: Microarray, DifferentialExpression, TimeCourse, GeneExpression Author: Kristof De Beuf , Peter Pipelers and Lieven Clement Maintainer: Kristof De Beuf URL: https://r-forge.r-project.org/projects/wavetiling/ source.ver: src/contrib/waveTiling_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/waveTiling_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.3/waveTiling_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.3/waveTiling_1.11.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/waveTiling_1.14.0.tgz vignettes: vignettes/waveTiling/inst/doc/waveTiling-vignette.pdf vignetteTitles: The waveTiling package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/waveTiling/inst/doc/waveTiling-vignette.R Package: weaver Version: 1.38.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: 5f78bf83eaa77adeb2aa25e7b2c6a29c 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 source.ver: src/contrib/weaver_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/weaver_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.3/weaver_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.3/weaver_1.35.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/weaver_1.38.0.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 Package: webbioc Version: 1.44.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocInstaller License: GPL (>= 2) MD5sum: 85034bbf81d5e6088ecb7d17bf1d0f39 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 source.ver: src/contrib/webbioc_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/webbioc_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.3/webbioc_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.3/webbioc_1.41.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/webbioc_1.44.0.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 Package: widgetTools Version: 1.50.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: e394154dabbe5a69d5978b3efec66ca0 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 source.ver: src/contrib/widgetTools_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/widgetTools_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.3/widgetTools_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.3/widgetTools_1.47.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/widgetTools_1.50.0.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 suggestsMe: affy Package: XBSeq Version: 1.2.2 Depends: DESeq2, R (>= 3.2.0) Imports: pracma, matrixStats, locfit, ggplot2, methods, Biobase, dplyr, Delaporte, magrittr Suggests: knitr, DESeq, rmarkdown, BiocStyle, testthat License: GPL (>=3) MD5sum: b3e4927dce1b9911637cec3f908274a2 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: RNASeq, DifferentialExpression, Sequencing, Software, ExperimentalDesign Author: Yuanhang Liu Maintainer: Yuanhang Liu URL: https://github.com/Liuy12/XBSeq VignetteBuilder: knitr source.ver: src/contrib/XBSeq_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.3/XBSeq_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.3/XBSeq_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.3/XBSeq_0.99.8.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/XBSeq_1.2.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/XBSeq/inst/doc/XBSeq.R htmlDocs: vignettes/XBSeq/inst/doc/XBSeq.html htmlTitles: Differential expression analysis of count data using XBSeq package Package: xcms Version: 1.48.0 Depends: R (>= 2.14.0), methods, mzR (>= 1.1.6), BiocGenerics, ProtGenerics, Biobase Imports: lattice, RColorBrewer Suggests: faahKO, msdata, ncdf4, multtest, rgl, MassSpecWavelet (>= 1.5.2), RANN, RUnit, parallel Enhances: Rgraphviz, Rmpi, XML License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: aa197a2a3268716adc8e78b3ecdf3614 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: MassSpectrometry, Metabolomics Author: Colin A. Smith , Ralf Tautenhahn , Steffen Neumann , Paul Benton , Christopher Conley , Johannes Rainer Maintainer: Steffen Neumann URL: http://metlin.scripps.edu/download/ and https://github.com/sneumann/xcms BugReports: https://github.com/sneumann/xcms/issues/new source.ver: src/contrib/xcms_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/xcms_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.3/xcms_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.3/xcms_1.45.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/xcms_1.48.0.tgz vignettes: vignettes/xcms/inst/doc/xcmsDirect.pdf, vignettes/xcms/inst/doc/xcmsInstall.pdf, vignettes/xcms/inst/doc/xcmsMSn.pdf, vignettes/xcms/inst/doc/xcmsPreprocess.pdf vignetteTitles: Grouping FTICR-MS data with xcms, Installation Instructions for xcms, Processing Tandem-MS and MS$^n$ data with xcms, LC/MS Preprocessing and Analysis with xcms hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Rfiles: vignettes/xcms/inst/doc/xcmsDirect.R, vignettes/xcms/inst/doc/xcmsMSn.R, vignettes/xcms/inst/doc/xcmsPreprocess.R dependsOnMe: CAMERA, flagme, Metab, metaMS importsMe: CAMERA, cosmiq, metaX, Risa suggestsMe: MassSpecWavelet, RMassBank, ropls Package: XDE Version: 2.18.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), methods, graphics Imports: Biobase, BiocGenerics, genefilter, graphics, grDevices, gtools, MergeMaid, methods, stats, utils, mvtnorm Suggests: siggenes, genefilter, MASS, RColorBrewer, GeneMeta, RUnit Enhances: coda License: LGPL-2 Archs: i386, x64 MD5sum: 563078f32a8ab49c429ff3fc6c435e5f 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 source.ver: src/contrib/XDE_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/XDE_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/XDE_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/XDE_2.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/XDE_2.18.0.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 Package: xmapbridge Version: 1.30.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: 6c316781acae4ca98029f643ac47f190 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 source.ver: src/contrib/xmapbridge_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/xmapbridge_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.3/xmapbridge_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.3/xmapbridge_1.27.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/xmapbridge_1.30.0.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 Package: xps Version: 1.32.0 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) MD5sum: 51dad1e1af392ca7564245ca7b8e0574 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_v5.34.05 - See README file for installation instructions. source.ver: src/contrib/xps_1.32.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.3/xps_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/xps_1.32.0.tgz 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: FALSE 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 Package: XVector Version: 0.12.1 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.9.29), IRanges (>= 2.5.27) Imports: methods, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 64929cb068b3d1d4b7316aaf3d630b7f NeedsCompilation: yes Title: Representation and manpulation of external sequences Description: Memory efficient S4 classes for storing sequences "externally" (behind an R external pointer, or on disk). biocViews: Infrastructure, DataRepresentation Author: Hervé Pagès and Patrick Aboyoun Maintainer: Hervé Pagès source.ver: src/contrib/XVector_0.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.3/XVector_0.12.1.zip win64.binary.ver: bin/windows64/contrib/3.3/XVector_0.12.1.zip mac.binary.ver: bin/macosx/contrib/3.3/XVector_0.9.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/XVector_0.12.1.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, motifRG, triplex importsMe: Biostrings, BSgenome, ChIPsim, CNEr, compEpiTools, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, IONiseR, kebabs, MatrixRider, R453Plus1Toolbox, Rsamtools, rtracklayer, TFBSTools, tracktables, VariantAnnotation suggestsMe: IRanges Package: yaqcaffy Version: 1.32.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: eed45103b5be40e10920d49c9b1f8907 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 source.ver: src/contrib/yaqcaffy_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/yaqcaffy_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.3/yaqcaffy_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.3/yaqcaffy_1.29.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/yaqcaffy_1.32.0.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 Package: zlibbioc Version: 1.18.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: e22aab6169a16d8f7dc151d2e818d64a 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: http://bioconductor.org/packages/release/bioc/html/Zlibbioc.html source.ver: src/contrib/zlibbioc_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.3/zlibbioc_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.3/zlibbioc_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.3/zlibbioc_1.15.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.3/zlibbioc_1.18.0.tgz vignettes: vignettes/zlibbioc/inst/doc/UsingZlibbioc.pdf vignetteTitles: Using zlibbioc C libraries hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BitSeq importsMe: affy, affyio, affyPLM, bamsignals, ChemmineOB, DiffBind, LVSmiRNA, makecdfenv, oligo, QuasR, rhdf5, Rhtslib, Rsamtools, rtracklayer, seqbias, ShortRead, snpStats, Starr, TransView, VariantAnnotation, XVector