Package: a4 Version: 1.18.0 Depends: a4Base, a4Preproc, a4Classif, a4Core, a4Reporting Suggests: MLP, nlcv, ALL, Cairo License: GPL-3 MD5sum: 34c649ed6f75d87215c2077ba91d80f1 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4_1.18.0.tgz vignettes: vignettes/a4/inst/doc/a4vignette.pdf vignetteTitles: a4vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: a4Base Version: 1.18.0 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: 956624bb9df6778a966cba2682d4daa4 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Base_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Base_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Base_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Base_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Classif Version: 1.18.0 Depends: methods, a4Core, a4Preproc, MLInterfaces, ROCR, pamr, glmnet, varSelRF Imports: a4Core Suggests: ALL License: GPL-3 MD5sum: 973dc010c3a3a69e6894a182703f3e33 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Classif_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Classif_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Classif_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Classif_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4 Package: a4Core Version: 1.18.0 Depends: methods, Biobase, glmnet License: GPL-3 MD5sum: 1ad94201a90eafabeb3e99c36e2e5098 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Core_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Core_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Core_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Core_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif importsMe: a4Classif Package: a4Preproc Version: 1.18.0 Depends: methods, AnnotationDbi Suggests: ALL, hgu95av2.db License: GPL-3 MD5sum: 1e8e2ffc5352dea9fc5409eb759bf77b 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Preproc_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Preproc_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Preproc_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Preproc_1.18.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4, a4Base, a4Classif Package: a4Reporting Version: 1.18.0 Depends: methods, annaffy Imports: xtable, utils License: GPL-3 MD5sum: f310cec49cd7722ca59fab049f13950d 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/a4Reporting_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/a4Reporting_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/a4Reporting_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/a4Reporting_1.18.0.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: f80c14dbc47d0f9fd44717aaac3470f4 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/3.2/ABAEnrichment_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/ABAEnrichment_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/ABAEnrichment_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ABAEnrichment_1.2.2.tgz vignettes: vignettes/ABAEnrichment/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ABAEnrichment/inst/doc/ABAEnrichment.html htmlTitles: "ABAEnrichment: gene expression enrichment in human brain regions" Package: ABarray Version: 1.38.0 Imports: Biobase, graphics, grDevices, methods, multtest, stats, tcltk, utils Suggests: limma, LPE License: GPL MD5sum: 848dfa2f28710dbe2b03c8a0379f29ec 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ABarray_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ABarray_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ABarray_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ABarray_1.38.0.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: 1.6.1 Depends: R (>= 2.10), methods Imports: locfit, limma License: GPL (>= 3) MD5sum: a0b7fcfbaca64a2729ecb6e062ae1cc3 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ABSSeq_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ABSSeq_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ABSSeq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ABSSeq_1.6.1.tgz vignettes: vignettes/ABSSeq/inst/doc/ABSSeq.pdf vignetteTitles: ABSSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: acde Version: 1.0.0 Depends: R(>= 3.2), boot(>= 1.3) Suggests: BiocGenerics, RUnit License: GPL-3 MD5sum: f38a7459ca7ce44f174c472f03ddebae 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/acde_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/acde_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/acde_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/acde_1.0.0.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 Package: aCGH Version: 1.48.0 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: e67abe3063855037ff215949839fbc83 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/aCGH_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/aCGH_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/aCGH_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/aCGH_1.48.0.tgz vignettes: vignettes/aCGH/inst/doc/aCGH.pdf vignetteTitles: aCGH Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CRImage importsMe: ADaCGH2, snapCGH suggestsMe: beadarraySNP Package: ACME Version: 2.26.0 Depends: R (>= 2.10), Biobase (>= 2.5.5), methods, BiocGenerics Imports: graphics, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: b053f9a155141989b9be7179e3ca422f 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ACME_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ACME_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ACME_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ACME_2.26.0.tgz vignettes: vignettes/ACME/inst/doc/ACME.pdf vignetteTitles: ACME hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oligo Package: ADaCGH2 Version: 2.10.0 Depends: R (>= 2.15.0), parallel, ff Imports: bit, ffbase, DNAcopy, tilingArray, GLAD, waveslim, cluster, aCGH, snapCGH Suggests: CGHregions, Cairo, limma Enhances: Rmpi License: GPL (>= 3) Archs: i386, x64 MD5sum: 0a99671e8a47623ce9e4711b87277f9f 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 source.ver: src/contrib/ADaCGH2_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ADaCGH2_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ADaCGH2_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ADaCGH2_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ADaCGH2_2.10.0.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: 1.40.0 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: 03469f4292c40d3a773da483cfea910d 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/adSplit_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/adSplit_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/adSplit_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/adSplit_1.40.0.tgz vignettes: vignettes/adSplit/inst/doc/tr_2005_02.pdf vignetteTitles: Annotation-Driven Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: affxparser Version: 1.42.0 Depends: R (>= 2.6.0) Suggests: R.oo (>= 1.19.0), R.utils (>= 2.1.0), AffymetrixDataTestFiles License: LGPL (>= 2) Archs: i386, x64 MD5sum: fba1cf756b6316c25f6394eb59bd5af2 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], 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affxparser_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affxparser_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affxparser_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affxparser_1.42.0.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: 1.48.0 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: 12e56364b33d2c72547d170ffb7284fc 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affy_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affy_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affy_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affy_1.48.0.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 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: 1.46.0 Depends: R (>= 2.13.0), methods, Biobase (>= 2.3.3) Suggests: splines, affycompData License: GPL (>= 2) MD5sum: ea6f9b2547c429cd511b2f4e1c2ddaae 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affycomp_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affycomp_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affycomp_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affycomp_1.46.0.tgz vignettes: vignettes/affycomp/inst/doc/affycomp.pdf vignetteTitles: affycomp primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: AffyCompatible Version: 1.30.0 Depends: R (>= 2.7.0), XML (>= 2.8-1), RCurl (>= 0.8-1), methods Imports: Biostrings License: Artistic-2.0 MD5sum: dd988093d48acc0e8530e2c4aca8710e 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyCompatible_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyCompatible_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyCompatible_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyCompatible_1.30.0.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 importsMe: IdMappingRetrieval Package: affyContam Version: 1.28.0 Depends: R (>= 2.7.0), tools, methods, utils, Biobase, affy, affydata License: Artistic-2.0 MD5sum: 361f1180213943cc5615ec47d704ac02 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyContam_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyContam_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyContam_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyContam_1.28.0.tgz vignettes: vignettes/affyContam/inst/doc/affyContam.pdf vignetteTitles: affy contamination tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: affycoretools Version: 1.42.0 Depends: Biobase Imports: affy, limma, GOstats, gcrma, splines, xtable, AnnotationDbi, ggplot2, gplots, oligoClasses, ReportingTools, hwriter, lattice Suggests: affydata, hgfocuscdf, BiocStyle, knitr, hgu95av2.db, rgl License: Artistic-2.0 MD5sum: 49cdb5d0699480de0ac76d954a59de0e 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affycoretools_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affycoretools_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affycoretools_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affycoretools_1.42.0.tgz vignettes: vignettes/affycoretools/inst/doc/RefactoredAffycoretools.pdf vignetteTitles: affycoretools,, refactored hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: AffyExpress Version: 1.36.0 Depends: R (>= 2.10), affy (>= 1.23.4), limma Suggests: simpleaffy, R2HTML, affyPLM, hgu95av2cdf, hgu95av2, test3cdf, genefilter, estrogen, annaffy, gcrma License: LGPL MD5sum: e87cdc88d96bdb6a674d97e433c7b5f3 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyExpress_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyExpress_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyExpress_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyExpress_1.36.0.tgz vignettes: vignettes/AffyExpress/inst/doc/AffyExpress.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: affyILM Version: 1.22.0 Depends: R (>= 2.10.0), methods, gcrma Imports: affxparser (>= 1.16.0), affy, graphics, methods, Biobase Suggests: AffymetrixDataTestFiles License: GPL version 3 MD5sum: e71658e411282ca455ff75e0e49831dc 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyILM_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyILM_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyILM_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyILM_1.22.0.tgz vignettes: vignettes/affyILM/inst/doc/affyILM.pdf vignetteTitles: affyILM1.3.0 hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: affyio Version: 1.40.0 Depends: R (>= 2.6.0) Imports: zlibbioc License: LGPL (>= 2) Archs: i386, x64 MD5sum: 7d6f9ffc936d768a3bbfd74942c9ab86 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyio_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyio_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyio_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyio_1.40.0.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: 1.44.0 Imports: limma, tcltk, affy, BiocInstaller, affyio, tkrplot, affyPLM, R2HTML, xtable, gcrma, AnnotationDbi License: GPL (>=2) MD5sum: 128ce1fc283d7e3003aee2b290ab6145 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 Microarray 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 , Keith Satterley URL: http://bioinf.wehi.edu.au/affylmGUI/ source.ver: src/contrib/affylmGUI_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affylmGUI_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affylmGUI_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affylmGUI_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affylmGUI_1.44.0.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 dependsOnMe: oneChannelGUI Package: affyPara Version: 1.30.0 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: e2721c8f5f1eced3efbb556b1c6ac01b 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyPara_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyPara_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyPara_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyPara_1.30.0.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 Package: affypdnn Version: 1.44.0 Depends: R (>= 2.13.0), affy (>= 1.5) Suggests: affydata, hgu95av2probe License: LGPL MD5sum: 4ba3a7c53bdbbd1f405e00dd015f77fb 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affypdnn_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affypdnn_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affypdnn_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affypdnn_1.44.0.tgz vignettes: vignettes/affypdnn/inst/doc/affypdnn.pdf vignetteTitles: affypdnn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: affyPLM Version: 1.46.0 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: 77fc5934354d1e90193399a4fb041dd4 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyPLM_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyPLM_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyPLM_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyPLM_1.46.0.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 dependsOnMe: RefPlus importsMe: affylmGUI, affyQCReport, arrayQualityMetrics suggestsMe: AffyExpress, arrayMvout, ArrayTools, BiocCaseStudies, BiocGenerics, ELBOW, frmaTools, metahdep, oneChannelGUI, piano Package: affyQCReport Version: 1.48.0 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: 2a7eadd06192f3ee56db402250fdf1ca 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/affyQCReport_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/affyQCReport_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/affyQCReport_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/affyQCReport_1.48.0.tgz vignettes: vignettes/affyQCReport/inst/doc/affyQCReport.pdf vignetteTitles: affyQCReport: Methods for Generating Affymetrix QC Reports hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: BiocCaseStudies Package: AffyRNADegradation Version: 1.16.0 Depends: R (>= 2.9.0), methods, affy Suggests: AmpAffyExample License: GPL-2 MD5sum: 873e10c29da25d9ba861b5284a92367e 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyRNADegradation_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyRNADegradation_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyRNADegradation_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyRNADegradation_1.16.0.tgz vignettes: vignettes/AffyRNADegradation/inst/doc/vignette.pdf vignetteTitles: AffyRNADegradation Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: AffyTiling Version: 1.28.0 Depends: R (>= 2.6) Imports: affxparser, affy (>= 1.16), stats, utils, preprocessCore License: GPL (>= 2) Archs: i386, x64 MD5sum: b58d221a5ac663e441cb2edcf4a7137b 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AffyTiling_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AffyTiling_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AffyTiling_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AffyTiling_1.28.0.tgz vignettes: vignettes/AffyTiling/inst/doc/AffyTiling.pdf vignetteTitles: AffyTiling hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: AGDEX Version: 1.18.0 Depends: R (>= 2.10), Biobase, GSEABase Imports: stats License: GPL Version 2 or later MD5sum: d21cdc3ad000d122ad3700c03b048385 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AGDEX_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AGDEX_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AGDEX_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AGDEX_1.18.0.tgz vignettes: vignettes/AGDEX/inst/doc/AGDEX.pdf vignetteTitles: AGDEX.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: agilp Version: 3.2.0 Depends: R (>= 2.14.0) License: GPL-3 MD5sum: 2d9337763f3ff5821aac2ca6a3684a99 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_3.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/agilp_3.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/agilp_3.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/agilp_3.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/agilp_3.2.0.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 Package: AgiMicroRna Version: 2.20.0 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: 42f163ba78fc04e56e9152ff03a9c414 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AgiMicroRna_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AgiMicroRna_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AgiMicroRna_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AgiMicroRna_2.20.0.tgz vignettes: vignettes/AgiMicroRna/inst/doc/AgiMicroRna.pdf vignetteTitles: AgiMicroRna hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: AIMS Version: 1.2.0 Depends: R (>= 2.10), e1071, Biobase Suggests: breastCancerVDX, hgu133a.db, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 1670ba2878de9d4372e7fbb997a846bb 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AIMS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AIMS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AIMS_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AIMS_1.2.0.tgz vignettes: vignettes/AIMS/inst/doc/AIMS.pdf vignetteTitles: AIMS An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: genefu Package: ALDEx2 Version: 1.2.0 Depends: methods, SummarizedExperiment Imports: S4Vectors, IRanges, GenomicRanges Suggests: parallel, BiocParallel License: file LICENSE MD5sum: 56161ea25ce2e98485c590179b56afdc 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ALDEx2_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ALDEx2_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ALDEx2_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ALDEx2_1.2.0.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 Package: AllelicImbalance Version: 1.8.3 Depends: R (>= 3.2.0), grid, GenomicRanges, SummarizedExperiment (>= 0.2.0), GenomicAlignments Imports: methods, BiocGenerics, AnnotationDbi, VariantAnnotation, Biostrings, S4Vectors, IRanges, Rsamtools, GenomicFeatures, Gviz, lattice, seqinr, GenomeInfoDb Suggests: testthat, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20120608, BiocStyle, knitr License: GPL-3 MD5sum: 225202a22efbbcc9fdad3f0d16a36c40 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_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/AllelicImbalance_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.2/AllelicImbalance_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.2/AllelicImbalance_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AllelicImbalance_1.8.3.tgz vignettes: vignettes/AllelicImbalance/inst/doc/AllelicImbalance-vignette.pdf vignetteTitles: AllelicImbalance-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: alsace Version: 1.6.0 Depends: R (>= 2.10), ALS, ptw (>= 1.0.6) Suggests: lattice License: GPL (>= 2) MD5sum: 4df47c4334f5fe15051e6c82b3745ec3 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/alsace_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/alsace_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/alsace_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/alsace_1.6.0.tgz vignettes: vignettes/alsace/inst/doc/alsace.pdf vignetteTitles: alsace hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: altcdfenvs Version: 2.32.0 Depends: R (>= 2.7), methods, BiocGenerics (>= 0.1.0), Biobase (>= 2.15.1), affy, makecdfenv, Biostrings, hypergraph Suggests: plasmodiumanophelescdf, hgu95acdf, hgu133aprobe, hgu133a.db, hgu133acdf, Rgraphviz, RColorBrewer License: GPL (>= 2) MD5sum: 69b4346737df070ca6fa541ff0adb36e 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_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/altcdfenvs_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/altcdfenvs_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/altcdfenvs_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/altcdfenvs_2.32.0.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 importsMe: Harshlight Package: ampliQueso Version: 1.8.0 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: a3869dc504676178d2e5842e1d96c0b2 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ampliQueso_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ampliQueso_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ampliQueso_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ampliQueso_1.8.0.tgz vignettes: vignettes/ampliQueso/inst/doc/ampliQueso.pdf vignetteTitles: ampliQueso primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: AnalysisPageServer Version: 1.4.1 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: 12392c91e7e784803da36115013dd47a 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnalysisPageServer_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/AnalysisPageServer_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/AnalysisPageServer_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnalysisPageServer_1.4.1.tgz vignettes: vignettes/AnalysisPageServer/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE 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: annaffy Version: 1.42.0 Depends: R (>= 2.5.0), methods, Biobase, GO.db, KEGG.db Imports: AnnotationDbi (>= 0.1.15) Suggests: hgu95av2.db, multtest, tcltk License: LGPL MD5sum: c5f365aedb56927d122d6833c82b13e1 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/annaffy_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/annaffy_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/annaffy_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annaffy_1.42.0.tgz vignettes: vignettes/annaffy/inst/doc/annaffy.pdf vignetteTitles: annaffy Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, a4Reporting, PGSEA, webbioc suggestsMe: AffyExpress, ArrayTools, BiocCaseStudies Package: annmap Version: 1.12.0 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: 2b18d9cd3001b724f87ac4a250609cd3 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_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/annmap_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annmap_1.12.0.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: 1.48.0 Depends: R (>= 2.10), AnnotationDbi (>= 1.27.5), XML Imports: Biobase, DBI, xtable, graphics, utils, stats, methods, BiocGenerics (>= 0.13.8) 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: 29865c0c27689498662a029b0f5c1388 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/annotate_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/annotate_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/annotate_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annotate_1.48.0.tgz vignettes: vignettes/annotate/inst/doc/annotate.pdf, vignettes/annotate/inst/doc/chromLoc.pdf, vignettes/annotate/inst/doc/GOusage.pdf, vignettes/annotate/inst/doc/prettyOutput.pdf, vignettes/annotate/inst/doc/query.pdf, vignettes/annotate/inst/doc/useDataPkgs.pdf, vignettes/annotate/inst/doc/useHomology.pdf, vignettes/annotate/inst/doc/useProbeInfo.pdf vignetteTitles: Annotation Overview, HowTo: use chromosomal information, Basic GO Usage, HowTo: Get HTML Output, HOWTO: Use the online query tools, Using Data Packages, Using the homology package, Using Affymetrix Probe Level Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ChromHeatMap, GeneAnswers, geneplotter, GOSim, GSEABase, idiogram, macat, MineICA, MLInterfaces, PCpheno, phenoTest, PREDA, RpsiXML, ScISI, SemDist importsMe: CAFE, Category, categoryCompare, codelink, 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: 1.32.3 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, 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, BiocStyle, knitr License: Artistic-2.0 MD5sum: f1d5c71bbdaaa0a9c224a4df0a5af4f8 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_1.32.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationDbi_1.32.3.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationDbi_1.32.3.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationDbi_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationDbi_1.32.3.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 dependsOnMe: a4Base, a4Preproc, annotate, AnnotationForge, AnnotationFuncs, attract, Category, chimera, ChromHeatMap, customProDB, eisa, ExpressionView, GenomicFeatures, GOFunction, goProfiles, miRNAtap, MLP, OrganismDbi, PAnnBuilder, pathRender, PGSEA, proBAMr, RpsiXML, safe, SemDist, topGO importsMe: adSplit, affycoretools, affylmGUI, AllelicImbalance, annaffy, AnnotationHub, AnnotationHubData, attract, beadarray, biomaRt, BioNet, biovizBase, bumphunter, CancerMutationAnalysis, Category, categoryCompare, ChIPpeakAnno, ChIPseeker, clusterProfiler, CoCiteStats, compEpiTools, csaw, customProDB, derfinder, domainsignatures, DOSE, EDASeq, EnrichmentBrowser, ensembldb, ExpressionView, FunciSNP, gage, gCMAP, gCMAPWeb, genefilter, geneplotter, GGBase, 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, mvGST, NanoStringQCPro, PADOG, PAnnBuilder, pathview, pcaGoPromoter, PCpheno, PGA, phenoTest, pwOmics, qpgraph, ReactomePA, REDseq, rgsepd, rTRM, ScISI, SGSeq, SLGI, SVM2CRM, tigre, ToPASeq, topGO, UniProt.ws, VariantAnnotation, VariantFiltering suggestsMe: BiocCaseStudies, BiocGenerics, FGNet, geecc, GeneAnswers, GeneRegionScan, GenomicRanges, GenoView, limma, miRLAB, MmPalateMiRNA, neaGUI, oligo, oneChannelGUI, piano, pRoloc, qcmetrics, R3CPET, sigPathway, SummarizedExperiment Package: AnnotationForge Version: 1.12.2 Depends: R (>= 2.7.0), methods, utils, BiocGenerics (>= 0.15.10), Biobase (>= 1.17.0), AnnotationDbi (>= 1.31.19), org.Hs.eg.db Imports: methods, utils, DBI, RSQLite, BiocGenerics, S4Vectors, Biobase Suggests: DBI (>= 0.2-4), RSQLite (>= 0.6-4), XML, RCurl, hgu95av2.db, human.db0, affy, Homo.sapiens, hom.Hs.inp.db, GO.db, BiocStyle, knitr License: Artistic-2.0 MD5sum: 23ec60f94fca10497bbfe1ecf3ab866f 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_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationForge_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationForge_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationForge_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationForge_1.12.2.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 htmlDocs: vignettes/AnnotationForge/inst/doc/MakingNewOrganismPackages.html htmlTitles: "Making New Organism Packages" importsMe: AnnotationHubData, GOstats suggestsMe: AnnotationDbi, AnnotationHub Package: AnnotationFuncs Version: 1.20.0 Depends: R (>= 2.7.0), AnnotationDbi Suggests: org.Bt.eg.db, GO.db, org.Hs.eg.db, hom.Hs.inp.db License: GPL-2 MD5sum: 11f34251f6180ea6ed33f04bda512459 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationFuncs_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationFuncs_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationFuncs_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationFuncs_1.20.0.tgz vignettes: vignettes/AnnotationFuncs/inst/doc/AnnotationFuncsUserguide.pdf vignetteTitles: Annotation mapping functions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: AnnotationHub Version: 2.2.5 Depends: BiocGenerics (>= 0.15.10) Imports: utils, methods, 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: e8332e8781c9c2a8e43d824ca2968cdd 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_2.2.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationHub_2.2.5.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationHub_2.2.5.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationHub_2.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationHub_2.2.5.tgz vignettes: vignettes/AnnotationHub/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/AnnotationHub/inst/doc/AnnotationHub-HOWTO.html, vignettes/AnnotationHub/inst/doc/AnnotationHub.html, vignettes/AnnotationHub/inst/doc/AnnotationHubRecipes.html htmlTitles: "AnnotationHub How-To’s", "AnnotationHub: Access the AnnotationHub Web Service", "AnnotationHub: How to write recipes for new resources for the AnnotationHub" dependsOnMe: AnnotationHubData, ProteomicsAnnotationHubData, RefNet importsMe: ensembldb, gwascat, pwOmics suggestsMe: dupRadar, GenomicRanges, Pbase Package: AnnotationHubData Version: 1.0.2 Depends: R (>= 3.2.2), methods, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges, AnnotationHub Imports: GenomicFeatures, Rsamtools, rtracklayer, RCurl, BiocGenerics, jsonlite, BiocInstaller, httr, AnnotationDbi, Biobase, Biostrings, DBI, GEOquery, GenomeInfoDb, OrganismDbi, RSQLite, rBiopaxParser, AnnotationForge, futile.logger (>= 1.3.0), XML, xml2 Suggests: RUnit, knitr,RMySQL, BiocStyle, grasp2db License: Artistic-2.0 MD5sum: a9dfbc43b8f89d3465e7270ca11a5885 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/AnnotationHubData_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/AnnotationHubData_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/AnnotationHubData_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AnnotationHubData_1.0.2.tgz vignettes: vignettes/AnnotationHubData/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/AnnotationHubData/inst/doc/AnnotationHubData.html htmlTitles: "The AnnotationHubData Package" Package: annotationTools Version: 1.44.0 Imports: Biobase, stats License: GPL MD5sum: bd8d7993a9caac476ec95eef2066b0af 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/annotationTools_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/annotationTools_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/annotationTools_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/annotationTools_1.44.0.tgz vignettes: vignettes/annotationTools/inst/doc/annotationTools.pdf vignetteTitles: annotationTools Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: DOQTL Package: anota Version: 1.18.0 Depends: qvalue Imports: multtest, qvalue License: GPL-3 MD5sum: ea101803b407c42f0f511c86957e345f 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/anota_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/anota_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/anota_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/anota_1.18.0.tgz vignettes: vignettes/anota/inst/doc/anota.pdf vignetteTitles: ANalysis Of Translational Activity (anota) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tRanslatome Package: antiProfiles Version: 1.10.0 Depends: R (>= 3.0), matrixStats (>= 0.5), methods (>= 2.14), locfit (>= 1.5) Suggests: antiProfilesData, RColorBrewer License: Artistic-2.0 MD5sum: c08b1c323b3b01260811ee89ab1906f2 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 source.ver: src/contrib/antiProfiles_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/antiProfiles_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/antiProfiles_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/antiProfiles_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/antiProfiles_1.10.0.tgz vignettes: vignettes/antiProfiles/inst/doc/antiProfiles.pdf vignetteTitles: Introduction to antiProfiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: apComplex Version: 2.36.0 Depends: R (>= 2.10), graph, RBGL Imports: Rgraphviz, stats, org.Sc.sgd.db License: LGPL MD5sum: df110f9577b1ac032b276e62d0fa6c41 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_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/apComplex_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/apComplex_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/apComplex_2.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/apComplex_2.36.0.tgz vignettes: vignettes/apComplex/inst/doc/apComplex.pdf vignetteTitles: apComplex hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ScISI suggestsMe: BiocCaseStudies Package: aroma.light Version: 3.0.0 Depends: R (>= 2.14.0) Imports: R.methodsS3 (>= 1.7.0), R.oo (>= 1.19.0), R.utils (>= 2.1.0), matrixStats (>= 0.14.2) Suggests: princurve (>= 1.1-12) License: GPL (>= 2) MD5sum: a19fc63844c10a66603c062a4ceee9be 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_3.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/aroma.light_3.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/aroma.light_3.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/aroma.light_3.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/aroma.light_3.0.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EDASeq suggestsMe: TIN Package: ArrayExpress Version: 1.30.1 Depends: R (>= 2.9.0), Biobase (>= 2.4.0) Imports: XML, oligo, limma Suggests: affy License: Artistic-2.0 MD5sum: e7f2bd62fbf63680749701f4db3620ac 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_1.30.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ArrayExpress_1.30.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ArrayExpress_1.30.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ArrayExpress_1.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayExpress_1.30.1.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 dependsOnMe: DrugVsDisease suggestsMe: gCMAPWeb Package: ArrayExpressHTS Version: 1.20.0 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: 9af4b29e73772bd9d77a105b2105f4a4 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_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/ArrayExpressHTS_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayExpressHTS_1.20.0.tgz vignettes: vignettes/ArrayExpressHTS/inst/doc/ArrayExpressHTS.pdf vignetteTitles: ArrayExpressHTS: RNA-Seq Pipeline for transcription profiling experiments hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayMvout Version: 1.28.0 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: 44b9dc2ce33518d8db1ebcf76a8c0da2 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/arrayMvout_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/arrayMvout_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/arrayMvout_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/arrayMvout_1.28.0.tgz vignettes: vignettes/arrayMvout/inst/doc/arrayMvout.pdf vignetteTitles: arrayMvout -- multivariate outlier algorithm for expression arrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQuality Version: 1.48.0 Depends: R (>= 2.2.0) Imports: graphics, grDevices, grid, gridBase, hexbin, limma, marray, methods, RColorBrewer, stats, utils Suggests: mclust, MEEBOdata, HEEBOdata License: LGPL MD5sum: 46668ba4799137bbba058f723f066330 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/arrayQuality_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/arrayQuality_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/arrayQuality_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/arrayQuality_1.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: arrayQualityMetrics Version: 3.26.1 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: bca4e50addd887fc96212bb12852bffc 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_3.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/arrayQualityMetrics_3.26.1.zip win64.binary.ver: bin/windows64/contrib/3.2/arrayQualityMetrics_3.26.1.zip mac.binary.ver: bin/macosx/contrib/3.2/arrayQualityMetrics_3.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/arrayQualityMetrics_3.26.1.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 Package: ArrayTools Version: 1.30.0 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: 037c150977ab165a4566e82f676ed14c 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ArrayTools_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ArrayTools_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ArrayTools_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayTools_1.30.0.tgz vignettes: vignettes/ArrayTools/inst/doc/ArrayTools.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ArrayTV Version: 1.8.0 Depends: R (>= 2.14) Imports: foreach, DNAcopy, methods, 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: 4ea15e1a1753a83ce982942219745861 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ArrayTV_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ArrayTV_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ArrayTV_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ArrayTV_1.8.0.tgz vignettes: vignettes/ArrayTV/inst/doc/ArrayTV.pdf vignetteTitles: ArrayTV Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: VanillaICE Package: ARRmNormalization Version: 1.10.0 Depends: R (>= 2.15.1), ARRmData License: Artistic-2.0 MD5sum: 236286e4466893b5ce51d1763dcfd34a 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ARRmNormalization_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ARRmNormalization_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ARRmNormalization_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ARRmNormalization_1.10.0.tgz vignettes: vignettes/ARRmNormalization/inst/doc/ARRmNormalization.pdf vignetteTitles: ARRmNormalization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ASEB Version: 1.14.0 Depends: R (>= 2.8.0), methods Imports: graphics, methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: b8b4d03fac7a915d5d95d0dcc1010136 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASEB_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ASEB_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ASEB_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASEB_1.14.0.tgz vignettes: vignettes/ASEB/inst/doc/ASEB.pdf vignetteTitles: ASEB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ASGSCA Version: 1.4.0 Imports: Matrix, MASS Suggests: BiocStyle License: GPL-3 MD5sum: d795186101b7afd5747556428550d934 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASGSCA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ASGSCA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ASGSCA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASGSCA_1.4.0.tgz vignettes: vignettes/ASGSCA/inst/doc/ASGSCA.pdf vignetteTitles: Association Studies using Generalized Structured Equation Models. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ASSET Version: 1.8.0 Depends: MASS, msm, rmeta Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 2ebe30e8ec628be0095aecf6134b9436 NeedsCompilation: no Title: An R package for subset-based association analysis of heterogeneous traits and subtypes Description: An R package for subset-based analysis of heterogeneous traits and subtypes biocViews: Software Author: Samsiddhi Bhattacharjee, Nilanjan Chatterjee and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/ASSET_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASSET_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ASSET_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ASSET_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASSET_1.8.0.tgz vignettes: vignettes/ASSET/inst/doc/vignette.pdf vignetteTitles: ASSET Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: ASSIGN Version: 1.6.0 Depends: Rlab, msm, gplots Imports: graphics, grDevices, stats, utils License: MIT MD5sum: 9d0af83c3ad0c5cebaa608470f766c23 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ASSIGN_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ASSIGN_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ASSIGN_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ASSIGN_1.6.0.tgz vignettes: vignettes/ASSIGN/inst/doc/ASSIGN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: AtlasRDF Version: 1.6.0 Depends: R (>= 2.10), hash, SPARQL, methods License: Apache License 2.0 MD5sum: 0fb7efb07f9a4f47278c0bf2fac18439 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/AtlasRDF_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/AtlasRDF_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/AtlasRDF_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/AtlasRDF_1.6.0.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 Package: attract Version: 1.22.0 Depends: R (>= 2.10.0), AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats Imports: Biobase, AnnotationDbi, KEGG.db, limma, cluster, GOstats, graphics, methods, stats Suggests: illuminaHumanv1.db License: LGPL (>= 2.0) MD5sum: e9f370e2e4cca2a9b041fc123228573b 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: StatisticalMethod, GeneExpression, KEGG Author: Jessica Mar Maintainer: Jessica Mar source.ver: src/contrib/attract_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/attract_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/attract_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/attract_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/attract_1.22.0.tgz vignettes: vignettes/attract/inst/doc/attract.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BAC Version: 1.30.0 Depends: R (>= 2.10) License: Artistic-2.0 Archs: i386, x64 MD5sum: ce99eb9af4db66a417bbe7b802986f21 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BAC_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BAC_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BAC_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BAC_1.30.0.tgz vignettes: vignettes/BAC/inst/doc/BAC.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BADER Version: 1.8.0 Suggests: pasilla (>= 0.2.10) License: GPL-2 Archs: i386, x64 MD5sum: 4724d68e743f4dfa7fbb12f44dcc4d21 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BADER_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BADER_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BADER_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BADER_1.8.0.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 Package: BAGS Version: 2.10.0 Depends: R (>= 2.10), breastCancerVDX, Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 3af82b4b2c149bf0f124bb63f385574e 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BAGS_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BAGS_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BAGS_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BAGS_2.10.0.tgz vignettes: vignettes/BAGS/inst/doc/BAGS.pdf vignetteTitles: BAGS: A Bayesian Approach for Geneset Selection. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ballgown Version: 2.2.0 Depends: R (>= 3.1.1), methods Imports: GenomicRanges (>= 1.17.25), IRanges (>= 1.99.22), S4Vectors (>= 0.1.2), RColorBrewer, splines, sva, limma, rtracklayer (>= 1.29.25), Biobase (>= 2.25.0), GenomeInfoDb Suggests: testthat, knitr License: Artistic-2.0 MD5sum: 717570395edef570a4606858bafc6edb 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: Alyssa C. Frazee [aut, cre], Leonardo Collado-Torres [aut], Andrew E. Jaffe [aut], Jeffrey T. Leek [aut, ths] Maintainer: Alyssa Frazee VignetteBuilder: knitr BugReports: https://github.com/alyssafrazee/ballgown/issues source.ver: src/contrib/ballgown_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ballgown_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ballgown_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ballgown_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ballgown_2.2.0.tgz vignettes: vignettes/ballgown/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ballgown/inst/doc/ballgown.html htmlTitles: "Flexible isoform-level differential expression analysis with Ballgown" suggestsMe: polyester Package: bamsignals Version: 1.2.1 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: aa5858de28d99e75a6669f67b657573e 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/bamsignals_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/bamsignals_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/bamsignals_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bamsignals_1.2.1.tgz vignettes: vignettes/bamsignals/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/bamsignals/inst/doc/bamsignals.html htmlTitles: "Introduction to the bamsignals package" Package: BaseSpaceR Version: 1.14.0 Depends: R (>= 2.15.0), RCurl, RJSONIO Imports: methods Suggests: RUnit, IRanges, Rsamtools License: Apache License 2.0 MD5sum: caf1a654f3a2a9de6a331dd6f2b002ad 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BaseSpaceR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BaseSpaceR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BaseSpaceR_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BaseSpaceR_1.14.0.tgz vignettes: vignettes/BaseSpaceR/inst/doc/BaseSpaceR.pdf vignetteTitles: BaseSpaceR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Basic4Cseq Version: 1.6.0 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: 5041f06e19d1ffa197f621403ae38c4b 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Basic4Cseq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Basic4Cseq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Basic4Cseq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Basic4Cseq_1.6.0.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 Package: BayesPeak Version: 1.22.0 Depends: R (>= 2.14), IRanges Imports: IRanges, graphics Suggests: BiocStyle, parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 2495a6d6a8c0a27e0640e05674377092 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BayesPeak_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BayesPeak_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BayesPeak_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BayesPeak_1.22.0.tgz vignettes: vignettes/BayesPeak/inst/doc/BayesPeak.pdf vignetteTitles: BayesPeak Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: baySeq Version: 2.4.1 Depends: R (>= 2.3.0), methods, GenomicRanges, abind, perm Suggests: edgeR, BiocStyle, BiocGenerics License: GPL-3 MD5sum: 8c217ffcd5669a271c57c7dc218414e2 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_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/baySeq_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/baySeq_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/baySeq_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/baySeq_2.4.1.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 dependsOnMe: Rcade, segmentSeq, TCC importsMe: EDDA, metaseqR suggestsMe: compcodeR, oneChannelGUI, riboSeqR Package: BBCAnalyzer Version: 1.0.0 Imports: VariantAnnotation, Rsamtools Suggests: BSgenome.Hsapiens.UCSC.hg19 License: LGPL-3 MD5sum: 6fe260e8a283ee58bc37f0b44fbe5b4f 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BBCAnalyzer_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BBCAnalyzer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BBCAnalyzer_0.99.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BBCAnalyzer_1.0.0.tgz vignettes: vignettes/BBCAnalyzer/inst/doc/BBCAnalyzer.pdf vignetteTitles: Using BBCAnalyzer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: BCRANK Version: 1.32.0 Depends: methods Imports: Biostrings Suggests: seqLogo License: GPL-2 Archs: i386, x64 MD5sum: 079c6df6a1b24632639da22fd53824ab 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BCRANK_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BCRANK_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BCRANK_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BCRANK_1.32.0.tgz vignettes: vignettes/BCRANK/inst/doc/BCRANK.pdf vignetteTitles: BCRANK hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: beadarray Version: 2.20.1 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: abb0cc58d4ac60e28161142daabd198f 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_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/beadarray_2.20.1.zip win64.binary.ver: bin/windows64/contrib/3.2/beadarray_2.20.1.zip mac.binary.ver: bin/macosx/contrib/3.2/beadarray_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/beadarray_2.20.1.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 importsMe: arrayQualityMetrics, blima, epigenomix suggestsMe: beadarraySNP, lumi Package: beadarraySNP Version: 1.36.0 Depends: methods, Biobase (>= 2.14), quantsmooth Suggests: aCGH, affy, limma, snapCGH, beadarray, DNAcopy License: GPL-2 MD5sum: a202d6e041e8eb4491591e4dbf9d71c8 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/beadarraySNP_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/beadarraySNP_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/beadarraySNP_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/beadarraySNP_1.36.0.tgz vignettes: vignettes/beadarraySNP/inst/doc/beadarraySNP.pdf vignetteTitles: beadarraySNP.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BeadDataPackR Version: 1.22.0 Suggests: BiocStyle, knitr License: GPL-2 Archs: i386, x64 MD5sum: a33115fab79e3d80266e42d91a7a4330 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BeadDataPackR_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BeadDataPackR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BeadDataPackR_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BeadDataPackR_1.22.0.tgz vignettes: vignettes/BeadDataPackR/inst/doc/BeadDataPackR.pdf vignetteTitles: BeadDataPackR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: beadarray Package: BEAT Version: 1.8.0 Depends: R (>= 2.13.0) Imports: GenomicRanges, ShortRead, Biostrings, BSgenome License: LGPL (>= 3.0) MD5sum: ec8470bcf91fd633197d82505c8446ec 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BEAT_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BEAT_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BEAT_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BEAT_1.8.0.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 Package: BEclear Version: 1.2.0 Depends: snowfall, Matrix License: GPL-2 MD5sum: fedf8864fbe02142578437a89b9762af 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BEclear_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BEclear_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BEclear_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BEclear_1.2.0.tgz vignettes: vignettes/BEclear/inst/doc/BEclear.pdf vignetteTitles: BEclear tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: betr Version: 1.26.0 Depends: R(>= 2.6.0) Imports: Biobase (>= 2.5.5), limma, mvtnorm, methods, stats Suggests: Biobase License: LGPL MD5sum: 4c4b9447d094f947234c10fd9b0d9472 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/betr_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/betr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/betr_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/betr_1.26.0.tgz vignettes: vignettes/betr/inst/doc/betr.pdf vignetteTitles: BETR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bgafun Version: 1.32.0 Depends: made4, seqinr,ade4 License: Artistic-2.0 MD5sum: 7215db70cb90159e33f05c3adf5525ad 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bgafun_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bgafun_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bgafun_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bgafun_1.32.0.tgz vignettes: vignettes/bgafun/inst/doc/bgafun.pdf vignetteTitles: bgafun.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BGmix Version: 1.30.0 Depends: R (>= 2.3.1), KernSmooth License: GPL-2 MD5sum: ff69459c6dcb6132763e30e6876ea6f9 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_1.30.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/BGmix_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BGmix_1.30.0.tgz vignettes: vignettes/BGmix/inst/doc/BGmix.pdf vignetteTitles: BGmix Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bgx Version: 1.36.0 Depends: R (>= 2.0.1), Biobase, affy (>= 1.5.0), gcrma (>= 2.4.1) Suggests: affydata, hgu95av2cdf License: GPL-2 Archs: i386, x64 MD5sum: e5140899aed3d4c2f2e953b889c37182 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bgx_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bgx_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bgx_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bgx_1.36.0.tgz vignettes: vignettes/bgx/inst/doc/bgx.pdf vignetteTitles: HowTo BGX hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BHC Version: 1.22.0 License: GPL-3 Archs: i386, x64 MD5sum: 306c66a977f918d4a17a721fa6823fe5 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BHC_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BHC_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BHC_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BHC_1.22.0.tgz vignettes: vignettes/BHC/inst/doc/bhc.pdf vignetteTitles: Bayesian Hierarchical Clustering hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BicARE Version: 1.28.0 Depends: R (>= 1.8.0), Biobase (>= 2.5.5), multtest, GSEABase License: GPL-2 Archs: i386, x64 MD5sum: 8a5069d4aeb5b18aa3cab5ba6c06cf3c 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BicARE_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BicARE_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BicARE_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BicARE_1.28.0.tgz vignettes: vignettes/BicARE/inst/doc/BicARE.pdf vignetteTitles: BicARE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiGGR Version: 1.6.0 Depends: R (>= 2.14.0), rsbml, hyperdraw, LIM,stringr Imports: hypergraph License: file LICENSE MD5sum: 27fc2bb40bc943605861a46d6feb61d5 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiGGR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiGGR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiGGR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiGGR_1.6.0.tgz vignettes: vignettes/BiGGR/inst/doc/BiGGR.pdf vignetteTitles: BiGGR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: bigmemoryExtras Version: 1.14.2 Depends: R (>= 2.12), bigmemory (>= 4.3) Imports: methods, Biobase Suggests: RUnit, BiocGenerics, BiocStyle, knitr License: Artistic-2.0 OS_type: unix MD5sum: ac3542583fd814fca4679b28cbd08d2d 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_1.14.2.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/bigmemoryExtras_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bigmemoryExtras_1.14.2.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: bioassayR Version: 1.8.37 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: 7106c35bdccd12a2c493c085ba0a3d81 NeedsCompilation: no Title: Cross-target analysis of small molecule bioactivity Description: Despite a large and rapidly growing body of small molecule bioactivity data, systematic leverage of these data as a reference for identifying compounds with a desired bioactivity, and assessing the drugability of protein targets are limited by informatics challenges stemming from the large data volume, heterogenous experimental designs, sparseness, and noise. This tool addresses these issues to enable simultaneous analysis of thousands of bioassay experiments performed over a diverse and sparse set of compounds and biological targets. biocViews: MicrotitrePlateAssay, CellBasedAssays, Visualization, Infrastructure, DataImport, Bioinformatics, Proteomics 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_1.8.37.tar.gz win.binary.ver: bin/windows/contrib/3.2/bioassayR_1.8.37.zip win64.binary.ver: bin/windows64/contrib/3.2/bioassayR_1.8.37.zip mac.binary.ver: bin/macosx/contrib/3.2/bioassayR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bioassayR_1.8.37.tgz vignettes: vignettes/bioassayR/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/bioassayR/inst/doc/bioassayR.html htmlTitles: "Introduction and Examples" Package: Biobase Version: 2.30.0 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: 697d3b5504f899c38a5e6e784ca0e33f 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Biobase_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Biobase_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Biobase_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Biobase_2.30.0.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 dependsOnMe: a4Base, a4Core, ACME, affy, affycomp, affyContam, affycoretools, affyPLM, affyQCReport, AGDEX, AIMS, altcdfenvs, annaffy, AnnotationDbi, AnnotationForge, ArrayExpress, arrayMvout, ArrayTools, BAGS, beadarray, beadarraySNP, bgx, BicARE, BiocCaseStudies, BioMVCClass, birta, BrainStars, CAMERA, cancerclass, Cardinal, casper, Category, categoryCompare, cellHTS2, CGHbase, CGHcall, CGHregions, charm, chimera, chroGPS, ClassifyR, clippda, clusterStab, CMA, cn.farms, cn.mops, codelink, convert, copa, CopyNumber450k, DESeq, destiny, DEXSeq, DFP, diggit, DSS, dualKS, dyebias, EBarrays, EDASeq, edge, eisa, EnrichmentBrowser, epigenomix, epivizr, ExiMiR, fabia, factDesign, fastseg, flowBeads, flowClust, frma, gaga, gCMAPWeb, GeneAnswers, GeneExpressionSignature, GeneMeta, geneplotter, geneRecommender, GeneRegionScan, GeneSelectMMD, GeneSelector, geNetClassifier, genoset, GEOquery, GOexpress, GOFunction, goProfiles, GOstats, GSEABase, GSEAlm, GWASTools, hapFabia, HCsnip, HELP, hopach, HTqPCR, htSeqTools, HTSFilter, HybridMTest, iCheck, IdeoViz, idiogram, InPAS, inSilicoDb, inSilicoMerging, INSPEcT, isobar, iterativeBMA, LMGene, lumi, macat, mAPKL, maSigPro, massiR, MEAL, MergeMaid, metagenomeFeatures, metagenomeSeq, methyAnalysis, methylumi, Mfuzz, MiChip, MIMOSA, MineICA, minfi, MiRaGE, miRcomp, MLInterfaces, MLSeq, MmPalateMiRNA, monocle, MSnbase, Mulcom, multtest, NanoStringDiff, NOISeq, nondetects, NormqPCR, oligo, oneChannelGUI, OrderedList, OTUbase, OutlierD, PAnnBuilder, panp, pcaMethods, pcot2, pdInfoBuilder, pdmclass, pepStat, PGSEA, phenoTest, PLPE, plrs, prada, PREDA, PROMISE, qpcrNorm, R453Plus1Toolbox, RbcBook1, rbsurv, rcellminer, ReadqPCR, reb, RefPlus, rHVDM, Ringo, Risa, Rmagpie, rMAT, RNAinteract, rnaSeqMap, Rnits, Roleswitch, RpsiXML, RTopper, RUVSeq, safe, SCAN.UPC, SeqGSEA, sigaR, SigCheck, siggenes, simpleaffy, simulatorZ, SpeCond, SPEM, spkTools, splicegear, stepwiseCM, SummarizedExperiment, TDARACNE, tigre, tilingArray, topGO, TPP, tRanslatome, tspair, twilight, UNDO, variancePartition, VegaMC, viper, vsn, waveTiling, webbioc, xcms, XDE importsMe: ABarray, aCGH, adSplit, affyILM, affyQCReport, AgiMicroRna, AnalysisPageServer, annmap, annotate, AnnotationDbi, AnnotationForge, AnnotationHubData, annotationTools, ArrayExpressHTS, arrayQualityMetrics, ArrayTools, attract, ballgown, betr, bigmemoryExtras, biobroom, biocViews, BioNet, BioSeqClass, biosvd, birte, BiSeq, blima, BrainStars, bsseq, BubbleTree, CAFE, canceR, Category, cellHTS, CGHnormaliter, charm, ChIPpeakAnno, ChIPQC, ChIPXpress, ChromHeatMap, clipper, cogena, ConsensusClusterPlus, crlmm, cummeRbund, cycle, cytofkit, ddCt, DESeq2, DOQTL, easyRNASeq, EBarrays, ecolitk, ensembldb, erma, ExiMiR, farms, ffpe, FindMyFriends, flowCore, flowFP, flowMatch, flowMeans, flowQB, flowStats, flowType, flowUtils, flowViz, flowWorkspace, FourCSeq, frma, frmaTools, gCMAP, gcrma, genefilter, GeneMeta, geneRecommender, GeneRegionScan, GeneSelectMMD, GenomicFeatures, GenomicTuples, GEOsubmission, gespeR, GGBase, ggbio, GGtools, girafe, globaltest, gmapR, GOFunction, GOstats, gQTLstats, GSRI, GSVA, Gviz, Harshlight, HEM, HTqPCR, IdMappingAnalysis, imageHTS, IsoGeneGUI, lapmix, LiquidAssociation, lumi, LVSmiRNA, maanova, makecdfenv, maSigPro, mBPCR, MCRestimate, MeSHDbi, metaArray, methyAnalysis, MethylAid, methylumi, MiChip, MinimumDistance, MiPP, mmnet, MmPalateMiRNA, mogsa, MoPS, MSnID, multiscan, mzR, NanoStringQCPro, npGSEA, nucleR, OGSA, oligoClasses, oposSOM, OrderedList, OrganismDbi, PAnnBuilder, panp, Pbase, PCpheno, phyloseq, piano, plateCore, plethy, plgem, plier, podkat, ppiStats, prada, prebs, pRoloc, PROMISE, ProteomicsAnnotationHubData, PSEA, puma, pvac, pvca, pwOmics, qcmetrics, QDNAseq, qpgraph, quantro, QuasR, qusage, randPack, ReadqPCR, RGalaxy, Rmagpie, rMAT, rols, rqubic, Rtreemix, RUVnormalize, SAGx, SeqVarTools, ShortRead, sigsquared, SimBindProfiles, simpleaffy, SLGI, SNPchip, SomaticSignatures, spade, spkTools, splicegear, STATegRa, subSeq, synapter, TCGAbiolinks, TEQC, TFBSTools, timecourse, ToPASeq, topGO, TSSi, twilight, VanillaICE, VariantAnnotation, VariantFiltering, VariantTools, XBSeq, XDE suggestsMe: betr, BiocCaseStudies, BiocCheck, BiocGenerics, BSgenome, Category, DART, farms, genefu, GenomicRanges, GlobalAncova, GSAR, Heatplus, interactiveDisplay, kebabs, les, limma, messina, msa, nem, OSAT, pkgDepTools, ROC, survcomp, TargetScore, tkWidgets, TypeInfo, vbmp, widgetTools Package: biobroom Version: 1.2.0 Depends: R (>= 3.0.0), broom Imports: dplyr, tidyr, Biobase Suggests: limma, DESeq2, airway, ggplot2, plyr, GenomicRanges, edgeR, qvalue, knitr, data.table, MSnbase, SummarizedExperiment License: LGPL MD5sum: 6866b952f88bb0111f01ab15bdb0d68a 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: David G. Robinson, Andrew J. Bass, 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biobroom_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biobroom_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/biobroom_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biobroom_1.2.0.tgz vignettes: vignettes/biobroom/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/biobroom/inst/doc/biobroom_vignette.html htmlTitles: "biobroom Vignette" Package: BiocCaseStudies Version: 1.32.0 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: 15b72522a2c518613d5cf435f66cf29c 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocCaseStudies_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocCaseStudies_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocCaseStudies_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocCaseStudies_1.32.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiocCheck Version: 1.6.1 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: 95ef3b54e33336131d582df507ab61e0 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocCheck_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocCheck_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocCheck_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocCheck_1.6.1.tgz vignettes: vignettes/BiocCheck/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/BiocCheck/inst/doc/BiocCheck.html htmlTitles: "BiocCheck" Package: BiocGenerics Version: 0.16.1 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: c2148ffd86fc6f1f819c7f68eb2c744f 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_0.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocGenerics_0.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocGenerics_0.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocGenerics_0.16.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocGenerics_0.16.1.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, cn.mops, codelink, copynumber, CopyNumber450k, CRISPRseek, cummeRbund, DESeq, dexus, ensembldb, ensemblVEP, flowQ, FlowSOM, geneplotter, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicRanges, GenomicTuples, Genominator, genoset, ggbio, girafe, graphite, GSEABase, GUIDEseq, htSeqTools, interactiveDisplay, interactiveDisplayBase, IRanges, MBASED, MeSHDbi, meshr, methyAnalysis, MineICA, minfi, MLInterfaces, MotifDb, MotIV, MSnbase, multtest, oligo, OrganismDbi, Pbase, PICS, plethy, ProtGenerics, PSICQUIC, PWMEnrich, RareVariantVis, REDseq, Repitools, rMAT, RNAprobR, RnBeads, rsbml, S4Vectors, scsR, shinyMethyl, ShortRead, simpleaffy, simulatorZ, soGGi, TEQC, tigre, topGO, UNDO, UniProt.ws, VanillaICE, VariantAnnotation, VariantFiltering, xcms, XVector importsMe: affyPLM, AllelicImbalance, annmap, annotate, AnnotationDbi, AnnotationForge, AnnotationHubData, ArrayExpressHTS, bamsignals, biocGraph, Biostrings, biosvd, biovizBase, BiSeq, blima, BrowserViz, BrowserVizDemo, BSgenome, BubbleTree, bumphunter, casper, Category, cghMCR, ChemmineOB, ChemmineR, ChIPpeakAnno, ChIPQC, ChIPseeker, chipseq, CNPBayes, cobindR, compEpiTools, CoverageView, crlmm, cummeRbund, ddCt, DESeq2, destiny, DEXSeq, diffHic, DOQTL, DrugVsDisease, easyRNASeq, EBImage, EDASeq, eiR, eisa, epigenomix, erma, fastseg, ffpe, flowBin, flowClust, flowCore, flowFP, flowQ, flowStats, flowWorkspace, fmcsR, frma, gCMAPWeb, GenomeInfoDb, GenomicAlignments, GenomicInteractions, genotypeeval, GGBase, GGtools, GOTHiC, gQTLBase, gQTLstats, graph, GSVA, Gviz, gwascat, hiReadsProcessor, hopach, HTSeqGenie, INSPEcT, intansv, IONiseR, IVAS, KCsmart, LVSmiRNA, MEAL, metaMS, MethylAid, methylPipe, methylumi, MinimumDistance, MiRaGE, mogsa, monocle, motifbreakR, msa, mzR, NarrowPeaks, npGSEA, nucleR, oligoClasses, parglms, pcaMethods, pdInfoBuilder, phyloseq, piano, PING, plrs, podkat, prada, ProCoNA, pRoloc, pwOmics, QuasR, R453Plus1Toolbox, RCyjs, RCytoscape, REDseq, RefNet, ReportingTools, RGalaxy, RGSEA, RiboProfiling, Ringo, rMAT, Rqc, rqubic, Rsamtools, rsbml, rtracklayer, S4Vectors, SGSeq, simpleaffy, SLGI, snpStats, SplicingGraphs, Streamer, SummarizedExperiment, systemPipeR, TarSeqQC, TCGAbiolinks, TFBSTools, triform, TSSi, unifiedWMWqPCR, VariantTools, XDE, XVector suggestsMe: acde, AIMS, ArrayTV, ASSET, baySeq, bigmemoryExtras, BiocCheck, BiocInstaller, BiocParallel, BiocStyle, biocViews, BiRewire, CAFE, CAMERA, CAnD, CausalR, ccrepe, CellNOptR, CexoR, ChIPXpress, clipper, clonotypeR, CNEr, CNORfeeder, CNORfuzzy, CNVPanelizer, coMET, cosmiq, COSNet, cpvSNP, cytofkit, dagLogo, DAPAR, DBChIP, DEGreport, DMRcaller, DMRcate, ENmix, FGNet, flowCL, FlowRepositoryR, focalCall, gCMAP, gdsfmt, GENE.E, GeneNetworkBuilder, GeneOverlap, geneRxCluster, GENESIS, geNetClassifier, genomation, GEOquery, GOstats, GraphPAC, GreyListChIP, GWASTools, h5vc, hiAnnotator, hierGWAS, hypergraph, iClusterPlus, illuminaio, InPAS, INPower, inSilicoMerging, kebabs, KEGGREST, ldblock, LOLA, M3D, mAPKL, massiR, MatrixRider, mdgsa, Metab, metagene, metagenomeSeq, metaseqR, miRcomp, mirIntegrator, miRLAB, Mirsynergy, motifStack, MSnID, MultiMed, netbenchmark, NetSAM, nondetects, PAA, Path2PPI, PathNet, pathview, pepXMLTab, PGA, PhenStat, Prize, proBAMr, pRolocGUI, qpgraph, quantro, QuartPAC, RBGL, rBiopaxParser, Rcade, rcellminer, rCGH, Rcpi, RCy3, Rgraphviz, rgsepd, riboSeqR, roar, ROntoTools, ropls, RTN, rTRM, sangerseqR, SANTA, sapFinder, segmentSeq, SeqArray, seqPattern, seqTools, SeqVarTools, SICtools, sigsquared, SIMAT, similaRpeak, SNPRelate, SpacePAC, specL, STATegRa, STRINGdb, TCC, TIN, ToPASeq, trackViewer, traseR, TRONCO Package: biocGraph Version: 1.32.0 Depends: Rgraphviz, graph Imports: Rgraphviz, geneplotter, graph, BiocGenerics, methods Suggests: fibroEset, geneplotter, hgu95av2.db License: Artistic-2.0 MD5sum: 0c9c4cf0f85d6e3c322abad931a4765e 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biocGraph_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biocGraph_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/biocGraph_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biocGraph_1.32.0.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 importsMe: EnrichmentBrowser suggestsMe: BiocCaseStudies Package: BiocInstaller Version: 1.20.3 Depends: R (>= 3.2.0) Suggests: devtools, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 796a9d65cfb9de9884ad8ba341a6005c 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_1.20.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocInstaller_1.20.3.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocInstaller_1.20.3.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocInstaller_1.20.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocInstaller_1.20.3.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: affy, affylmGUI, AnnotationHub, AnnotationHubData, BiocCheck, ChIPpeakAnno, gcrma, limmaGUI, oligoClasses, OrganismDbi, ProteomicsAnnotationHubData, QuasR, webbioc suggestsMe: BSgenome, GOSemSim, metaseqR, pkgDepTools Package: BiocParallel Version: 1.4.3 Depends: methods Imports: futile.logger, parallel, snow Suggests: BiocGenerics, tools, foreach, BatchJobs, BBmisc, doParallel, Rmpi, GenomicRanges, RNAseqData.HNRNPC.bam.chr14, Rsamtools, GenomicAlignments, ShortRead, codetools, RUnit, BiocStyle, knitr License: GPL-2 | GPL-3 MD5sum: 7954a6ae6d148ac6f1179c47fde8d7d4 NeedsCompilation: no Title: Bioconductor facilities for parallel evaluation Description: This package provides modified versions and novel implementation of functions for parallel evaluation, tailored to use with Bioconductor objects. biocViews: Infrastructure Author: Bioconductor Package Maintainer [cre], Martin Morgan [aut], Valerie Obenchain [aut], Michel Lang [aut], Ryan Thompson [aut] Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr source.ver: src/contrib/BiocParallel_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocParallel_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocParallel_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocParallel_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocParallel_1.4.3.tgz vignettes: vignettes/BiocParallel/inst/doc/Errors_Logs_And_Debugging.pdf, vignettes/BiocParallel/inst/doc/Introduction_To_BiocParallel.pdf vignetteTitles: Errors,, Logs and Debugging, Introduction to BiocParallel hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ClassifyR, CopywriteR, DEXSeq, GenomicFiles, hiReadsProcessor, INSPEcT, MBASED, metagene, MSnbase, Oscope, pRoloc, Rqc, ShortRead, SigCheck importsMe: ChIPQC, cpvSNP, CRISPRseek, derfinder, DESeq2, easyRNASeq, EMDomics, FindMyFriends, flowcatchR, GenomicAlignments, genotypeeval, gmapR, GUIDEseq, h5vc, HTSeqGenie, InPAS, LowMACA, MethylAid, motifbreakR, qpgraph, QuasR, Rsamtools, RUVcorr, soGGi, synapter, TarSeqQC, TFBSTools, VariantFiltering, VariantTools suggestsMe: ALDEx2, chimera, DEGreport, erma, oneChannelGUI, specL, systemPipeR Package: BiocStyle Version: 1.8.0 Suggests: knitr (>= 1.7), rmarkdown, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: f8fb61345f5c3044bde6d1f39126f8b8 NeedsCompilation: no 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Package vignettes illustrate use and functionality. biocViews: Software Author: Martin Morgan, Andrzej Oleś, Wolfgang Huber Maintainer: Bioconductor Package Maintainer URL: https://github.com/Bioconductor/BiocStyle VignetteBuilder: knitr BugReports: https://github.com/Bioconductor/BiocStyle/issues source.ver: src/contrib/BiocStyle_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiocStyle_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiocStyle_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiocStyle_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiocStyle_1.8.0.tgz vignettes: vignettes/BiocStyle/inst/doc/LatexStyle.pdf vignetteTitles: Bioconductor LaTeX Style hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Rfiles: vignettes/BiocStyle/inst/doc/LatexStyle.R htmlDocs: vignettes/BiocStyle/inst/doc/HtmlStyle.html htmlTitles: "Bioconductor style for HTML documents" importsMe: BubbleTree, Rqc suggestsMe: ABAEnrichment, affycoretools, AllelicImbalance, AnnotationDbi, AnnotationForge, AnnotationHub, AnnotationHubData, arrayQualityMetrics, ASGSCA, bamsignals, BayesPeak, baySeq, beadarray, BeadDataPackR, bigmemoryExtras, bioassayR, BiocParallel, BitSeq, blima, BrowserViz, BrowserVizDemo, bsseq, CAFE, CAnD, Cardinal, ccrepe, CexoR, ChemmineOB, ChemmineR, ChIPpeakAnno, ChIPQC, ChIPseeker, ClassifyR, cleanUpdTSeq, cleaver, clipper, clusterProfiler, CNEr, CNPBayes, coMET, compcodeR, conumee, CopywriteR, CoRegNet, cosmiq, cpvSNP, CRISPRseek, dagLogo, DAPAR, DChIPRep, DEGreport, derfinder, derfinderHelper, derfinderPlot, DESeq2, DEXSeq, DiffBind, DNABarcodes, DOSE, DSS, dupRadar, easyRNASeq, EBImage, EDASeq, eiR, ELMER, EnrichmentBrowser, ensembldb, erma, fCI, flowcatchR, flowMap, FlowSOM, fmcsR, FourCSeq, genefilter, GeneOverlap, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicInteractions, GenomicRanges, GenomicTuples, genoset, ggbio, GOexpress, GoogleGenomics, GOSemSim, gQTLBase, gQTLstats, graphite, GreyListChIP, groHMM, GSAR, GUIDEseq, Gviz, HIBAG, HiTC, hpar, HTSFilter, iGC, illuminaio, imageHTS, Imetagene, immunoClust, InPAS, INSPEcT, IONiseR, IVAS, LowMACA, M3D, mAPKL, MatrixRider, MBASED, mdgsa, MEAL, MEDIPS, messina, metagene, metaX, MethylAid, MethylMix, miRcomp, missMethyl, mogsa, motifbreakR, motifStack, mQTL.NMR, MSnbase, MSnID, mygene, myvariant, mzR, NanoStringDiff, NanoStringQCPro, NarrowPeaks, nethet, nondetects, npGSEA, oligo, omicade4, OmicsMarkeR, OncoSimulR, OperaMate, Oscope, PAA, Path2PPI, paxtoolsr, Pbase, PGA, plethy, Polyfit, pRoloc, Prostar, ProteomicsAnnotationHubData, proteoQC, PSEA, PWMEnrich, qcmetrics, qpgraph, quantro, QuasR, R3CPET, rain, Rcade, rcellminer, rCGH, RCyjs, ReactomePA, RefNet, regioneR, regionReport, ReQON, rfPred, RGSEA, Rhtslib, RiboProfiling, riboSeqR, RNAprobR, rnaseqcomp, RnaSeqSampleSize, Rnits, rols, ropls, rpx, Rsamtools, RTCGAToolbox, RUVcorr, RUVSeq, sangerseqR, sapFinder, sbgr, segmentSeq, seqPattern, seqplots, SeqVarTools, SGSeq, shinyMethyl, ShortRead, SigCheck, SigFuge, simulatorZ, sincell, SNPhood, soGGi, specL, SSPA, STAN, STATegRa, SummarizedExperiment, sva, synapter, systemPipeR, TCGAbiolinks, TFBSTools, tigre, TPP, tracktables, trackViewer, TRONCO, TurboNorm, variancePartition, VariantAnnotation, VariantFiltering, XBSeq Package: biocViews Version: 1.38.1 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: 41f56d52a15c5a2ac24159addb9e7018 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_1.38.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/biocViews_1.38.1.zip win64.binary.ver: bin/windows64/contrib/3.2/biocViews_1.38.1.zip mac.binary.ver: bin/macosx/contrib/3.2/biocViews_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biocViews_1.38.1.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 dependsOnMe: Risa importsMe: BiocCheck Package: bioDist Version: 1.42.0 Depends: R (>= 2.0), methods, Biobase,KernSmooth Suggests: locfit License: Artistic-2.0 MD5sum: b0646e3ab9d844aeaa56dbcf86f0d18e 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bioDist_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bioDist_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bioDist_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bioDist_1.42.0.tgz vignettes: vignettes/bioDist/inst/doc/bioDist.pdf vignetteTitles: bioDist Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: flowQ suggestsMe: BiocCaseStudies Package: biomaRt Version: 2.26.1 Depends: methods Imports: utils, XML, RCurl, AnnotationDbi Suggests: annotate License: Artistic-2.0 MD5sum: 3adddc512c9763cf8527cc592e05204b 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_2.26.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/biomaRt_2.26.1.zip win64.binary.ver: bin/windows64/contrib/3.2/biomaRt_2.26.1.zip mac.binary.ver: bin/macosx/contrib/3.2/biomaRt_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biomaRt_2.26.1.tgz vignettes: vignettes/biomaRt/inst/doc/biomaRt.pdf vignetteTitles: The biomaRt users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: coMET, customProDB, dagLogo, domainsignatures, DrugVsDisease, genefu, GenomeGraphs, MineICA, PSICQUIC, Roleswitch, VegaMC importsMe: ArrayExpressHTS, ChIPpeakAnno, cobindR, customProDB, DEXSeq, DOQTL, easyRNASeq, EDASeq, EnrichmentBrowser, GenomicFeatures, gespeR, GOexpress, Gviz, HTSanalyzeR, IdMappingRetrieval, MEDIPS, metaseqR, methyAnalysis, oposSOM, Pbase, PGA, phenoTest, pRoloc, pwOmics, R453Plus1Toolbox, rgsepd, RNAither, seq2pathway, SeqGSEA, TCGAbiolinks suggestsMe: bioassayR, BiocCaseStudies, DEGreport, GeneAnswers, Genominator, h5vc, isobar, massiR, MineICA, MiRaGE, oligo, oneChannelGUI, OrganismDbi, paxtoolsr, piano, R3CPET, Rcade, RIPSeeker, RnBeads, rTANDEM, rTRM, ShortRead, SIM, sincell, systemPipeR, trackViewer Package: BioMVCClass Version: 1.38.0 Depends: R (>= 2.1.0), methods, MVCClass, Biobase, graph, Rgraphviz License: LGPL MD5sum: 429bc45cdd843dd10a80f058b9734844 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BioMVCClass_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BioMVCClass_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BioMVCClass_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BioMVCClass_1.38.0.tgz vignettes: vignettes/BioMVCClass/inst/doc/BioMVCClass.pdf vignetteTitles: BioMVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: biomvRCNS Version: 1.10.0 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: 3a76d4a4778e63aef07067804d69d9b9 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biomvRCNS_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biomvRCNS_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/biomvRCNS_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biomvRCNS_1.10.0.tgz vignettes: vignettes/biomvRCNS/inst/doc/biomvRCNS.pdf vignetteTitles: biomvRCNS package introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: BioNet Version: 1.30.0 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: b66d78a69131e7066d7ba0b5943aa7e6 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BioNet_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BioNet_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BioNet_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BioNet_1.30.0.tgz vignettes: vignettes/BioNet/inst/doc/Tutorial.pdf vignetteTitles: BioNet Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: HTSanalyzeR suggestsMe: SANTA Package: BioSeqClass Version: 1.28.0 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: 4f7a3e28c6038d907d8de930218ad2d8 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BioSeqClass_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BioSeqClass_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BioSeqClass_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BioSeqClass_1.28.0.tgz vignettes: vignettes/BioSeqClass/inst/doc/BioSeqClass.pdf vignetteTitles: Using the BioSeqClass Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Biostrings Version: 2.38.4 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.6), S4Vectors (>= 0.7.1), IRanges (>= 2.4.7), XVector (>= 0.9.3) 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: 1671ad0a1c200ed1dc23386bc53f1640 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. Pages, P. Aboyoun, R. Gentleman, and S. DebRoy Maintainer: H. Pages source.ver: src/contrib/Biostrings_2.38.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/Biostrings_2.38.4.zip win64.binary.ver: bin/windows64/contrib/3.2/Biostrings_2.38.4.zip mac.binary.ver: bin/macosx/contrib/3.2/Biostrings_2.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Biostrings_2.38.4.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 dependsOnMe: altcdfenvs, Basic4Cseq, BRAIN, BSgenome, ChIPpeakAnno, ChIPsim, cleaver, CRISPRseek, DECIPHER, deepSNV, GeneRegionScan, genomes, GenomicAlignments, GOTHiC, hiReadsProcessor, iPAC, kebabs, MethTargetedNGS, methVisual, minfi, MotifDb, motifRG, motifStack, msa, muscle, oligo, oneChannelGUI, pcaGoPromoter, PGA, qrqc, R453Plus1Toolbox, REDseq, rGADEM, RiboProfiling, Roleswitch, rRDP, Rsamtools, RSVSim, sangerseqR, SCAN.UPC, scsR, SELEX, seqbias, ShortRead, SICtools, systemPipeR, triplex, waveTiling importsMe: AffyCompatible, AllelicImbalance, AnnotationHubData, ArrayExpressHTS, BCRANK, BEAT, BioSeqClass, biovizBase, BSgenome, charm, ChIPseqR, ChIPsim, CNEr, cobindR, compEpiTools, customProDB, dagLogo, diffHic, easyRNASeq, EDASeq, ensemblVEP, FindMyFriends, FourCSeq, gcrma, GeneRegionScan, genomation, GenomicAlignments, GenomicFeatures, ggbio, GGtools, ggtree, girafe, gmapR, GoogleGenomics, GUIDEseq, Gviz, gwascat, h5vc, HiTC, HTSeqGenie, IONiseR, KEGGREST, LowMACA, MatrixRider, MEDIPS, MEDME, metagenomeFeatures, methVisual, methylPipe, microRNA, motifbreakR, MotIV, oligoClasses, OTUbase, Pbase, pdInfoBuilder, phyloseq, podkat, polyester, proBAMr, ProteomicsAnnotationHubData, Pviz, qrqc, QuasR, r3Cseq, Rcpi, REDseq, Repitools, rGADEM, RNAprobR, Rolexa, Rqc, rSFFreader, rtracklayer, SeqArray, seqPattern, seqplots, SGSeq, SNPhood, soGGi, SomaticSignatures, synapter, TFBSTools, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: annotate, AnnotationHub, CSAR, exomeCopy, GenomicFiles, GenomicRanges, genoset, methylumi, microRNA, MiRaGE, procoil, rpx, rTRM, XVector Package: biosvd Version: 2.6.0 Depends: R (>= 3.1.0) Imports: BiocGenerics, Biobase, methods, grid, graphics, NMF License: Artistic-2.0 MD5sum: 24326fdce372b89d471f68e8935550f2 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biosvd_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biosvd_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/biosvd_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biosvd_2.6.0.tgz vignettes: vignettes/biosvd/inst/doc/biosvd.pdf vignetteTitles: biosvd hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: biovizBase Version: 1.18.0 Depends: R (>= 2.10), methods Imports: grDevices, stats, scales, Hmisc, RColorBrewer, dichromat, BiocGenerics, S4Vectors (>= 0.2.4), IRanges (>= 1.99.28), GenomeInfoDb (>= 1.5.14), GenomicRanges (>= 1.17.19), SummarizedExperiment, Biostrings (>= 2.33.11), Rsamtools (>= 1.17.28), GenomicAlignments (>= 1.1.16), GenomicFeatures (>= 1.21.19), AnnotationDbi, VariantAnnotation (>= 1.11.4) Suggests: BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome, rtracklayer License: Artistic-2.0 Archs: i386, x64 MD5sum: eec20b53f9fc03ced8de933797ab26e6 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, Michael Lawrence, Dianne Cook Maintainer: Tengfei Yin source.ver: src/contrib/biovizBase_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/biovizBase_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/biovizBase_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/biovizBase_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/biovizBase_1.18.0.tgz vignettes: vignettes/biovizBase/inst/doc/intro.pdf vignetteTitles: An Introduction to biovizBase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, qrqc importsMe: BubbleTree, GenoView, ggbio, Gviz, Pviz, qrqc, Rqc suggestsMe: derfinder, derfinderPlot, R3CPET, regionReport Package: BiRewire Version: 3.0.1 Depends: igraph, slam, tsne, Matrix Suggests: RUnit, BiocGenerics License: GPL-3 Archs: i386, x64 MD5sum: 725dd3b386012f947e154092da8728db 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], Davide Albanese [cbt], Francesco Iorio [cbt], Giuseppe Jurman [cbt], Julio Saez-Rodriguez [cbt] . Maintainer: Andrea Gobbi URL: http://www.ebi.ac.uk/~iorio/BiRewire source.ver: src/contrib/BiRewire_3.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiRewire_3.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/BiRewire_3.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/BiRewire_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiRewire_3.0.1.tgz vignettes: vignettes/BiRewire/inst/doc/BiRewire.pdf vignetteTitles: BiRewire hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: birta Version: 1.14.0 Depends: limma, MASS, R(>= 2.10), Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 35e0cdddf3626bb413ff9c9c0f629783 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/birta_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/birta_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/birta_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/birta_1.14.0.tgz vignettes: vignettes/birta/inst/doc/birta.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: birte Version: 1.6.1 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: ac57b2410de82031ece4334edd7ea5fb 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/birte_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/birte_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/birte_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/birte_1.6.1.tgz vignettes: vignettes/birte/inst/doc/birte.pdf vignetteTitles: Bayesian Inference of Regulation of Transcriptional Activity hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BiSeq Version: 1.10.0 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: e674f31b9b17e6bbc67f2827649bcc1c 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BiSeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BiSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BiSeq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BiSeq_1.10.0.tgz vignettes: vignettes/BiSeq/inst/doc/BiSeq.pdf vignetteTitles: An Introduction to BiSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: M3D Package: BitSeq Version: 1.14.0 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: a7e5cb40281a62671baa082ef827f860 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BitSeq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BitSeq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BitSeq_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BitSeq_1.14.0.tgz vignettes: vignettes/BitSeq/inst/doc/BitSeq.pdf vignetteTitles: BitSeq User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: blima Version: 1.4.0 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: adeee10af8f4fdd22f26f0e5f13c2495 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/blima_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/blima_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/blima_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/blima_1.4.0.tgz vignettes: vignettes/blima/inst/doc/blima.pdf vignetteTitles: blima.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: BRAIN Version: 1.16.0 Depends: R (>= 2.8.1), PolynomF, Biostrings, lattice License: GPL-2 MD5sum: 5838873e6ef797f3295c2eef4ec77a09 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BRAIN_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BRAIN_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BRAIN_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BRAIN_1.16.0.tgz vignettes: vignettes/BRAIN/inst/doc/BRAIN-vignette.pdf vignetteTitles: BRAIN Usage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: cleaver Package: BrainStars Version: 1.14.0 Depends: RCurl, Biobase, methods Imports: RJSONIO, Biobase License: Artistic-2.0 MD5sum: 99fd003734cc7c8bed43b65b7ea8c900 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BrainStars_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BrainStars_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BrainStars_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BrainStars_1.14.0.tgz vignettes: vignettes/BrainStars/inst/doc/BrainStars.pdf vignetteTitles: BrainStars hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bridge Version: 1.34.0 Depends: R (>= 1.9.0), rama License: GPL (>= 2) Archs: i386, x64 MD5sum: afde6b8358919b42b1faf16e5686adda 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bridge_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bridge_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bridge_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bridge_1.34.0.tgz vignettes: vignettes/bridge/inst/doc/bridge.pdf vignetteTitles: bridge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BridgeDbR Version: 1.4.0 Depends: R (>= 2.0.0), rJava Imports: RCurl License: AGPL-3 MD5sum: 061e50b0d3cedb5eecadf262c1e2ec18 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BridgeDbR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BridgeDbR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BridgeDbR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BridgeDbR_1.4.0.tgz vignettes: vignettes/BridgeDbR/inst/doc/tutorial.pdf vignetteTitles: tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: BrowserViz Version: 1.2.3 Depends: R (>= 3.2.1), Rcpp (>= 0.11.5), jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: 1d396db63c201010b4d6c29679b37f35 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_1.2.3.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/BrowserViz_1.1.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BrowserViz_1.2.1.tgz vignettes: vignettes/BrowserViz/inst/doc/BrowserViz.pdf vignetteTitles: BrowserViz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BrowserVizDemo, RCyjs Package: BrowserVizDemo Version: 1.2.3 Depends: R (>= 3.1.2), BrowserViz, jsonlite (>= 0.9.15), httpuv(>= 1.3.2) Imports: methods, BiocGenerics Suggests: RUnit, BiocStyle License: GPL-2 MD5sum: 71d4db89e2123a818a75a8ff45eac96e 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_1.2.3.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/BrowserVizDemo_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BrowserVizDemo_1.2.1.tgz vignettes: vignettes/BrowserVizDemo/inst/doc/BrowserVizDemo.pdf vignetteTitles: BrowserVizDemo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: BSgenome Version: 1.38.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.5.10), IRanges (>= 2.1.33), GenomeInfoDb (>= 1.3.19), GenomicRanges (>= 1.19.23), Biostrings (>= 2.35.3), rtracklayer (>= 1.25.8) Imports: methods, 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: dd73af0d97993ca6122bf5383731d580 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: Herve Pages Maintainer: H. Pages source.ver: src/contrib/BSgenome_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BSgenome_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BSgenome_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BSgenome_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BSgenome_1.38.0.tgz vignettes: vignettes/BSgenome/inst/doc/BSgenomeForge.pdf, vignettes/BSgenome/inst/doc/GenomeSearching.pdf vignetteTitles: How to forge a BSgenome data package, Efficient genome searching with Biostrings and the BSgenome data packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAGEr, cleanUpdTSeq, GOTHiC, htSeqTools, MEDIPS, motifRG, REDseq, regioneR, rGADEM importsMe: BEAT, charm, ChIPpeakAnno, cobindR, CRISPRseek, diffHic, genomation, ggbio, gmapR, GreyListChIP, GUIDEseq, Gviz, hiAnnotator, InPAS, MethylSeekR, 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, rtracklayer, spliceR, waveTiling Package: bsseq Version: 1.6.0 Depends: R (>= 2.15), methods, BiocGenerics, IRanges (>= 2.1.10), GenomicRanges (>= 1.19.6), SummarizedExperiment (>= 0.1.1), parallel, GenomeInfoDb Imports: scales, stats, graphics, Biobase, locfit, gtools, data.table, S4Vectors, R.utils (>= 2.0.0), matrixStats Suggests: RUnit, bsseqData, BiocStyle License: Artistic-2.0 MD5sum: 003a689ea137577fb704dc417679b503 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 BugReports: https://github.com/kasperdanielhansen/bsseq/issues source.ver: src/contrib/bsseq_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bsseq_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bsseq_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bsseq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bsseq_1.6.0.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 dependsOnMe: DSS Package: BubbleTree Version: 2.0.1 Depends: R (>= 3.2.1), IRanges, GenomicRanges, plyr, dplyr, magrittr Imports: BiocGenerics (>= 0.7.5), BiocStyle, Biobase, ggplot2, WriteXLS, gtools, RColorBrewer, limma, scales, rgl, grid, gtable, gridExtra, biovizBase, rainbow Suggests: methods, knitr, rmarkdown License: LGPL (>= 3) MD5sum: d5e320cc8a6043e4021681ec9cd1ed3c 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: Wei Zhu VignetteBuilder: knitr source.ver: src/contrib/BubbleTree_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/BubbleTree_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/BubbleTree_2.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/BubbleTree_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BubbleTree_2.0.1.tgz vignettes: vignettes/BubbleTree/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/BubbleTree/inst/doc/BubbleTree-vignette.html htmlTitles: "BubbleTree Tutorial" Package: BufferedMatrix Version: 1.34.0 Depends: R (>= 2.6.0), methods License: LGPL (>= 2) Archs: i386, x64 MD5sum: 5fb2e881165fd8a2a0b769e17d68f9c7 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BufferedMatrix_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BufferedMatrix_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BufferedMatrix_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BufferedMatrix_1.34.0.tgz vignettes: vignettes/BufferedMatrix/inst/doc/BufferedMatrix.pdf vignetteTitles: BufferedMatrix: Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BufferedMatrixMethods Package: BufferedMatrixMethods Version: 1.34.0 Depends: R (>= 2.6.0), BufferedMatrix (>= 1.3.0), methods LinkingTo: BufferedMatrix Suggests: affyio, affy License: GPL (>= 2) Archs: i386, x64 MD5sum: 854fec907019c179f0586eabe3348cee 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BufferedMatrixMethods_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BufferedMatrixMethods_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BufferedMatrixMethods_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BufferedMatrixMethods_1.34.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: bumphunter Version: 1.10.0 Depends: R (>= 2.10), S4Vectors (>= 0.7.20), 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: 54058ab3878f25e347458c11cec0a03e 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/bumphunter_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/bumphunter_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/bumphunter_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/bumphunter_1.10.0.tgz vignettes: vignettes/bumphunter/inst/doc/bumphunter.pdf vignetteTitles: The bumphunter user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: minfi importsMe: derfinder, regionReport suggestsMe: derfinderPlot Package: BUS Version: 1.26.0 Depends: R (>= 2.3.0), minet Imports: stats, infotheo License: GPL-3 Archs: i386, x64 MD5sum: eb6215f9a80ba8ce95c250033c4fc7a2 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/BUS_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/BUS_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/BUS_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/BUS_1.26.0.tgz vignettes: vignettes/BUS/inst/doc/bus.pdf vignetteTitles: bus.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CAFE Version: 1.6.0 Depends: R (>= 2.10), biovizBase, GenomicRanges, IRanges, ggbio Imports: affy, ggplot2, annotate, grid, gridExtra, tcltk, Biobase Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: e9901e34f3912210aec1b21a2228b566 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAFE_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CAFE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CAFE_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAFE_1.6.0.tgz vignettes: vignettes/CAFE/inst/doc/CAFE-manual.pdf vignetteTitles: Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CAGEr Version: 1.12.0 Depends: methods, R (>= 2.15.0), BSgenome Imports: utils, Rsamtools, GenomicRanges, IRanges, data.table, beanplot, rtracklayer, som, VGAM Suggests: BSgenome.Drerio.UCSC.danRer7, FANTOM3and4CAGE Enhances: parallel License: GPL-3 MD5sum: d38f04097808a16a52694f6e9e835130 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, HighThroughputSequencing, Transcription, Clustering, Visualization Author: Vanja Haberle, Department of Biology, University of Bergen, Norway Maintainer: Vanja Haberle source.ver: src/contrib/CAGEr_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAGEr_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CAGEr_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CAGEr_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAGEr_1.12.0.tgz vignettes: vignettes/CAGEr/inst/doc/CAGEr.pdf vignetteTitles: CAGEr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: seqPattern Package: CALIB Version: 1.36.0 Depends: R (>= 2.10), limma, methods Imports: limma, methods, graphics, stats, utils License: LGPL Archs: i386, x64 MD5sum: 96a75cebb2153f9562d5cc4f8334b08c 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CALIB_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CALIB_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CALIB_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CALIB_1.36.0.tgz vignettes: vignettes/CALIB/inst/doc/quickstart.pdf vignetteTitles: CALIB Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CAMERA Version: 1.26.0 Depends: R (>= 2.1.0), methods, Biobase, xcms (>= 1.13.5), igraph 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: c2d33e87b3e51c7e10647d8ba870b053 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAMERA_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CAMERA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CAMERA_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAMERA_1.26.0.tgz vignettes: vignettes/CAMERA/inst/doc/CAMERA.pdf vignetteTitles: Molecule Identification with CAMERA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: flagme, MAIT, metaMS importsMe: metaX suggestsMe: RMassBank Package: canceR Version: 1.2.0 Depends: R (>= 3.0.0), tcltk, tcltk2, cgdsr Imports: GSEABase,GSEAlm,tkrplot, geNetClassifier,RUnit, Formula, rpart,survival, Biobase, phenoTest, Formula, rpart, Biobase, circlize, plyr License: GPL-2 MD5sum: 6744a89303225513e898f2804a9fabda 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 Department. Nuclear Science Center of Tunisia. Maintainer: Karim Mezhoud source.ver: src/contrib/canceR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/canceR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/canceR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/canceR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/canceR_1.2.0.tgz vignettes: vignettes/canceR/inst/doc/canceR.pdf vignetteTitles: canceR.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: cancerclass Version: 1.14.0 Depends: R (>= 2.14.0), Biobase, binom, methods, stats Suggests: cancerdata License: GPL 3 Archs: i386, x64 MD5sum: 01d3bc4690d3add1cb3b39862343b4e3 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cancerclass_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cancerclass_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cancerclass_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cancerclass_1.14.0.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 Package: CancerMutationAnalysis Version: 1.13.0 Depends: R (>= 2.10.0), qvalue Imports: AnnotationDbi, limma, methods, stats Suggests: KEGG.db License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 7cec17e3db630a4818da6613a4820f6e 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_1.13.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CancerMutationAnalysis_1.13.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CancerMutationAnalysis_1.13.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CancerMutationAnalysis_1.13.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CancerMutationAnalysis_1.13.0.tgz vignettes: vignettes/CancerMutationAnalysis/inst/doc/CancerMutationAnalysis.pdf vignetteTitles: CancerMutationAnalysisTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: CAnD Version: 1.2.1 Imports: methods, ggplot2, reshape Suggests: RUnit, BiocGenerics, BiocStyle License: Artistic-2.0 MD5sum: 2549dddea7b020be40d17019afb832ed 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/CAnD_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/CAnD_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/CAnD_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CAnD_1.2.1.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 Package: caOmicsV Version: 1.0.0 Depends: R (>= 3.2), igraph (>= 0.7.1), bc3net (>= 1.0.2) License: GPL (>=2.0) MD5sum: 1a38a5444c7609ab77ebe1089b46c53b 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/caOmicsV_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/caOmicsV_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/caOmicsV_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/caOmicsV_1.0.0.tgz vignettes: vignettes/caOmicsV/inst/doc/Introduction_to_caOmicsV.pdf vignetteTitles: Intrudoction_to_caOmicsV hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Cardinal Version: 1.2.1 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: 929b0b229f5d5fafdc68d8c235c56b42 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/Cardinal_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/Cardinal_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/Cardinal_1.1.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Cardinal_1.2.1.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 Package: casper Version: 2.4.0 Depends: R (>= 2.14.1), Biobase, IRanges, methods, GenomicRanges Imports: BiocGenerics, coda, EBarrays, gaga, gtools, GenomeInfoDb, GenomicFeatures, limma, mgcv, Rsamtools, rtracklayer, S4Vectors, sqldf, survival, VGAM Enhances: parallel License: GPL (>=2) Archs: i386, x64 MD5sum: 869bc453fe9f428a222da8683343d2d4 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/casper_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/casper_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/casper_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/casper_2.4.0.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 Package: Category Version: 2.36.0 Depends: methods, stats4, Matrix, BiocGenerics (>= 0.13.8), AnnotationDbi, Biobase, GO.db Imports: methods, utils, stats, stats4, BiocGenerics, graph, Biobase, AnnotationDbi, RBGL, GSEABase (>= 1.19.3), genefilter, annotate (>= 1.15.6) Suggests: EBarrays, ALL, Rgraphviz, RColorBrewer, xtable (>= 1.4-6), hgu95av2.db, KEGG.db, SNPchip, geneplotter, limma, lattice, graph, Biobase, genefilter, methods, RUnit, org.Sc.sgd.db, GOstats License: Artistic-2.0 MD5sum: 54d7d14521c22a8e16627b57974910ff 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_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Category_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Category_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Category_2.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Category_2.36.0.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 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: 1.14.0 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: a19c2f0386883be9eb017ab5c5a0bb2c 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/categoryCompare_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/categoryCompare_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/categoryCompare_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/categoryCompare_1.14.0.tgz vignettes: vignettes/categoryCompare/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/categoryCompare/inst/doc/categoryCompare_vignette.html htmlTitles: "categoryCompare: High-throughput data meta-analysis using feature annotations" Package: CausalR Version: 1.0.2 Depends: R (>= 3.2) Imports: igraph Suggests: knitr, RUnit, BiocGenerics License: GPL (>= 2) MD5sum: e5331c61e97e9330caeeef7ab925dafc 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/CausalR_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/CausalR_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/CausalR_0.99.12.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CausalR_1.0.2.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 Package: ccrepe Version: 1.6.0 Imports: infotheo (>= 1.1) Suggests: knitr, BiocStyle, BiocGenerics, testthat License: MIT + file LICENSE MD5sum: 8cdcfa369ce1007805222e16efa467d1 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ccrepe_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ccrepe_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ccrepe_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ccrepe_1.6.0.tgz vignettes: vignettes/ccrepe/inst/doc/ccrepe.pdf vignetteTitles: ccrepe hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: cellGrowth Version: 1.14.0 Depends: R (>= 2.12.0), locfit (>= 1.5-4) Imports: lattice License: Artistic-2.0 MD5sum: c7b50cfaeb038457188d7a9ff3716fa2 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cellGrowth_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cellGrowth_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cellGrowth_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cellGrowth_1.14.0.tgz vignettes: vignettes/cellGrowth/inst/doc/cellGrowth.pdf vignetteTitles: Overview of the cellGrowth package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: cellHTS Version: 1.40.2 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: f44e1f4a2833042a55266a50d82838c4 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_1.40.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/cellHTS_1.40.2.zip win64.binary.ver: bin/windows64/contrib/3.2/cellHTS_1.40.2.zip mac.binary.ver: bin/macosx/contrib/3.2/cellHTS_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cellHTS_1.40.2.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 suggestsMe: prada Package: cellHTS2 Version: 2.34.1 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: 9364ad34d035e4fc36fee36a1c4d3914 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_2.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/cellHTS2_2.34.1.zip win64.binary.ver: bin/windows64/contrib/3.2/cellHTS2_2.34.1.zip mac.binary.ver: bin/macosx/contrib/3.2/cellHTS2_2.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cellHTS2_2.34.1.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 dependsOnMe: coRNAi, imageHTS, staRank importsMe: gespeR, HTSanalyzeR, RNAinteract suggestsMe: bioassayR Package: CellNOptR Version: 1.16.0 Depends: R (>= 2.15.0), RBGL, graph, methods, hash, ggplot2, RCurl, Rgraphviz, XML Suggests: RUnit, BiocGenerics, igraph License: GPL-3 Archs: i386, x64 MD5sum: 066a863adcd91ba63ddbc36918aee5ed 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CellNOptR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CellNOptR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CellNOptR_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CellNOptR_1.16.0.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 dependsOnMe: CNORdt, CNORfeeder, CNORfuzzy, CNORode suggestsMe: MEIGOR Package: CexoR Version: 1.8.0 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: 6b163bc5a8104f3cf9685ee165ea9b7f 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CexoR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CexoR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CexoR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CexoR_1.8.0.tgz vignettes: vignettes/CexoR/inst/doc/CexoR.pdf vignetteTitles: CexoR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: CFAssay Version: 1.4.0 Depends: R (>= 2.10.0) License: LGPL MD5sum: e984a2cf7d69688d49f9861b915be5af 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CFAssay_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CFAssay_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CFAssay_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CFAssay_1.4.0.tgz vignettes: vignettes/CFAssay/inst/doc/cfassay.pdf vignetteTitles: CFAssay hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CGEN Version: 3.6.2 Depends: R (>= 2.10.1), survival, mvtnorm Suggests: cluster License: GPL-2 + file LICENSE Archs: i386, x64 MD5sum: b5e245785a935a10045cdb38863a4983 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, Minsun Song and William Wheeler Maintainer: William Wheeler source.ver: src/contrib/CGEN_3.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGEN_3.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/CGEN_3.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/CGEN_3.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGEN_3.6.2.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 Package: CGHbase Version: 1.30.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), marray License: GPL MD5sum: 6437bcdfc277d174ccc39af990e420e0 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 source.ver: src/contrib/CGHbase_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHbase_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHbase_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHbase_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHbase_1.30.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, CGHnormaliter, CGHregions, GeneBreak, sigaR importsMe: CGHnormaliter, plrs, QDNAseq Package: CGHcall Version: 2.32.0 Depends: R (>= 2.0.0), impute(>= 1.8.0), DNAcopy (>= 1.6.0), methods, Biobase, CGHbase (>= 1.15.1), snowfall License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: ac7ab75f7bad539cd5e16c8fa984ae30 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_2.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHcall_2.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHcall_2.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHcall_2.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHcall_2.32.0.tgz vignettes: vignettes/CGHcall/inst/doc/CGHcall.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHnormaliter, focalCall, GeneBreak importsMe: CGHnormaliter, QDNAseq Package: cghMCR Version: 1.28.0 Depends: methods, DNAcopy, CNTools, limma Imports: BiocGenerics (>= 0.1.6), stats4 License: LGPL MD5sum: 6bd4411f803c937d896bd7b3c5de5401 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cghMCR_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cghMCR_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cghMCR_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cghMCR_1.28.0.tgz vignettes: vignettes/cghMCR/inst/doc/findMCR.pdf vignetteTitles: cghMCR findMCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: TCGAbiolinks Package: CGHnormaliter Version: 1.24.0 Depends: CGHcall (>= 2.17.0), CGHbase (>= 1.15.0) Imports: Biobase, CGHbase, CGHcall, methods, stats, utils License: GPL (>= 3) MD5sum: d57f9979b97db423d48ee3e65421818d 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHnormaliter_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHnormaliter_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHnormaliter_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHnormaliter_1.24.0.tgz vignettes: vignettes/CGHnormaliter/inst/doc/CGHnormaliter.pdf vignetteTitles: CGHnormaliter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CGHregions Version: 1.28.0 Depends: R (>= 2.0.0), methods, Biobase, CGHbase License: GPL (http://www.gnu.org/copyleft/gpl.html) MD5sum: 359eed2641d40cf87a2f94389c10a261 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CGHregions_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CGHregions_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CGHregions_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CGHregions_1.28.0.tgz vignettes: vignettes/CGHregions/inst/doc/CGHregions.pdf vignetteTitles: CGHcall hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: ADaCGH2 Package: ChAMP Version: 1.8.2 Depends: R (>= 3.0.1), minfi, ChAMPdata, Illumina450ProbeVariants.db Imports: sva, IlluminaHumanMethylation450kmanifest, limma, RPMM, DNAcopy, preprocessCore, impute, marray, wateRmelon, plyr, IRanges, GenomicRanges License: GPL-3 MD5sum: fc5494a35f0259c5642f1e3683701302 NeedsCompilation: no Title: Chip Analysis Methylation Pipeline for Illumina HumanMethylation450 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 aberrations. In addition their is a method to help calculate hmC using BS and oxBS samples. biocViews: Microarray, MethylationArray, Normalization, TwoChannel, CopyNumber Author: Tiffany Morris [cre, aut], Lee Butcher [ctb], Andrew Feber [ctb], Andrew Teschendorff [ctb], Ankur Chakravarthy [ctb] Maintainer: Tiffany Morris source.ver: src/contrib/ChAMP_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChAMP_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/ChAMP_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/ChAMP_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChAMP_1.8.2.tgz vignettes: vignettes/ChAMP/inst/doc/ChAMP.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: charm Version: 2.16.1 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: 3c4c5811f2f955cde2f4df7130815c54 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_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/charm_2.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/charm_2.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/charm_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/charm_2.16.1.tgz vignettes: vignettes/charm/inst/doc/charm.pdf vignetteTitles: charm Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ChemmineOB Version: 1.8.2 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: 82b57b7f0b85805d5d4afc5196b2c6d0 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_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChemmineOB_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/ChemmineOB_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/ChemmineOB_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChemmineOB_1.8.2.tgz vignettes: vignettes/ChemmineOB/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/ChemmineOB/inst/doc/ChemmineOB.html htmlTitles: "ChemmineOB" suggestsMe: ChemmineR Package: ChemmineR Version: 2.22.3 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: b92f04fe405df669798a3d5a73803efd 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_2.22.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChemmineR_2.22.3.zip win64.binary.ver: bin/windows64/contrib/3.2/ChemmineR_2.22.3.zip mac.binary.ver: bin/macosx/contrib/3.2/ChemmineR_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChemmineR_2.22.3.tgz vignettes: vignettes/ChemmineR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ChemmineR/inst/doc/ChemmineR.html htmlTitles: "ChemmineR" dependsOnMe: eiR, fmcsR importsMe: bioassayR, eiR, fmcsR, Rchemcpp, Rcpi suggestsMe: ChemmineOB Package: chimera Version: 1.12.0 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: 3614e7f219dbada083fb563514c6af93 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chimera_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chimera_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chimera_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chimera_1.12.0.tgz vignettes: vignettes/chimera/inst/doc/chimera.pdf vignetteTitles: chimera hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: oneChannelGUI Package: ChIPComp Version: 1.0.0 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: 611e23ce50417fe321ebb3212c58b680 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPComp_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPComp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPComp_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPComp_1.0.0.tgz vignettes: vignettes/ChIPComp/inst/doc/ChIPComp.pdf vignetteTitles: ChIPComp hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: chipenrich Version: 1.8.0 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: 984f749ab82b04c77df94b58e42c8883 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chipenrich_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chipenrich_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chipenrich_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chipenrich_1.8.0.tgz vignettes: vignettes/chipenrich/inst/doc/chipenrich.pdf vignetteTitles: ChIP-Enrich Vignette/Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ChIPpeakAnno Version: 3.4.6 Depends: R (>= 3.1), methods, grid, IRanges, Biostrings, GenomicRanges, S4Vectors, VennDiagram Imports: BiocGenerics (>= 0.15.1), GO.db, biomaRt, BSgenome, GenomicFeatures, GenomeInfoDb, matrixStats, AnnotationDbi, limma, multtest, RBGL, graph, BiocInstaller, stats, regioneR, DBI, ensembldb, Biobase Suggests: reactome.db, BSgenome.Ecoli.NCBI.20080805, 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, RUnit, BiocStyle, rtracklayer, knitr License: GPL (>= 2) MD5sum: 4c8885d345432cfcd149492574dcaf6d 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 is to facilitate the downstream analysis for ChIP-seq experiments. It includes functions to find the nearest gene, exon, miRNA or custom features such as the most conserved elements and other transcription factor binding sites supplied by users, retrieve the sequences around the peak, obtain enriched Gene Ontology (GO) terms or pathways. 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). Starting 3.4, we also implement functions for permutation test to determine the association between two sets of peaks, and to plot heatmaps for given feature/peak ranges. 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_3.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPpeakAnno_3.4.6.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPpeakAnno_3.4.6.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPpeakAnno_3.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPpeakAnno_3.4.6.tgz vignettes: vignettes/ChIPpeakAnno/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ChIPpeakAnno/inst/doc/ChIPpeakAnno.html htmlTitles: "The ChIPpeakAnno user’s guide" dependsOnMe: REDseq importsMe: FunciSNP, GUIDEseq, REDseq suggestsMe: oneChannelGUI, R3CPET, RIPSeeker Package: ChIPQC Version: 1.6.1 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 Suggests: BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, TxDb.Hsapiens.UCSC.hg18.knownGene, TxDb.Mmusculus.UCSC.mm10.knownGene, TxDb.Mmusculus.UCSC.mm9.knownGene, TxDb.Rnorvegicus.UCSC.rn4.ensGene, TxDb.Celegans.UCSC.ce6.ensGene, TxDb.Dmelanogaster.UCSC.dm3.ensGene License: GPL (>= 3) MD5sum: 6cc74e31560ceba7a60bee7dd221e514 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPQC_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPQC_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPQC_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPQC_1.6.1.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: 1.6.7 Depends: R (>= 3.1.0) Imports: BiocGenerics, boot, AnnotationDbi, IRanges, GenomeInfoDb, GenomicRanges, GenomicFeatures, ggplot2, gplots, graphics, grDevices, grid, gridBase, gtools, methods, plotrix, dplyr, parallel, plyr, magrittr, RColorBrewer, rtracklayer, S4Vectors, TxDb.Hsapiens.UCSC.hg19.knownGene, UpSetR Suggests: clusterProfiler, DOSE, ReactomePA, org.Hs.eg.db, knitr, BiocStyle, rmarkdown License: Artistic-2.0 MD5sum: 1d41a2e4b5b495de69b0ecd9125fe98d 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 and Herve Pages. Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/ChIPseeker VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ChIPseeker/issues source.ver: src/contrib/ChIPseeker_1.6.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPseeker_1.6.7.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPseeker_1.6.7.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPseeker_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPseeker_1.6.7.tgz vignettes: vignettes/ChIPseeker/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ChIPseeker/inst/doc/ChIPseeker.html htmlTitles: "ChIPseeker: an R package for ChIP peak Annotation,, Comparison and Visualization" Package: chipseq Version: 1.20.0 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.1.0), S4Vectors (>= 0.0.1), 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: 4b87f1d6b98c10decb43579905f613f6 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chipseq_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chipseq_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chipseq_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chipseq_1.20.0.tgz vignettes: vignettes/chipseq/inst/doc/Workflow.pdf vignetteTitles: A Sample ChIP-Seq analysis workflow hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PING importsMe: ChIPQC, CopywriteR, HTSeqGenie, soGGi suggestsMe: ggbio, oneChannelGUI Package: ChIPseqR Version: 1.24.1 Depends: R (>= 2.10.0), methods, BiocGenerics, S4Vectors Imports: Biostrings, fBasics, GenomicRanges, IRanges (>= 2.3.13), graphics, grDevices, HilbertVis, ShortRead, stats, timsac, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 40f0166dd9975f6b4be430746b919569 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_1.24.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPseqR_1.24.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPseqR_1.24.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPseqR_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPseqR_1.24.1.tgz vignettes: vignettes/ChIPseqR/inst/doc/Introduction.pdf vignetteTitles: Introduction to ChIPseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ChIPsim Version: 1.24.0 Depends: Biostrings (>= 2.29.2) Imports: IRanges, XVector, Biostrings, ShortRead, graphics, methods, stats, utils Suggests: actuar, zoo License: GPL (>= 2) MD5sum: b416860329564c69c6af85d1df8f385b 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChIPsim_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ChIPsim_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ChIPsim_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPsim_1.24.0.tgz vignettes: vignettes/ChIPsim/inst/doc/ChIPsimIntro.pdf vignetteTitles: Simulating ChIP-seq experiments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ChIPXpress Version: 1.12.0 Depends: R (>= 2.10), ChIPXpressData Imports: Biobase, GEOquery, frma, affy, bigmemory, biganalytics Suggests: mouse4302frmavecs, mouse4302.db, mouse4302cdf, RUnit, BiocGenerics License: GPL(>=2) MD5sum: 55bd17977acd70b77e2b7e0763ce4f35 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_1.12.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/ChIPXpress_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChIPXpress_1.12.0.tgz vignettes: vignettes/ChIPXpress/inst/doc/ChIPXpress.pdf vignetteTitles: ChIPXpress hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: chopsticks Version: 1.34.0 Depends: R(>= 2.10.0), survival, methods Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: ae61314e84c251e023dc04ed1318c8d8 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chopsticks_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chopsticks_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chopsticks_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chopsticks_1.34.0.tgz vignettes: vignettes/chopsticks/inst/doc/chopsticks-vignette.pdf vignetteTitles: snpMatrix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: chroGPS Version: 1.14.0 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: 20647afef1d11074dddf5ae94ee961bf 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chroGPS_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chroGPS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chroGPS_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chroGPS_1.14.0.tgz vignettes: vignettes/chroGPS/inst/doc/chroGPS.pdf vignetteTitles: Manual for the chroGPS library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: chromDraw Version: 1.2.0 Depends: R (>= 3.0.0) Imports: Rcpp (>= 0.11.1), GenomicRanges (>= 1.17.46) LinkingTo: Rcpp License: GPL-3 Archs: i386, x64 MD5sum: 46fdd33bd1d3e556b4a855821f1df5cc NeedsCompilation: yes Title: chromDraw an R package for visualization of linear and circular karyotypes. Description: Package chromDraw is a simple package for linear and circular type of karyotype visualization. The linear type of visualization is usually used for demonstrating chromosomes structures in karyotype and the circular type of visualization is used for comparing of karyotypes between each other. This tool has own input data format or genomicRanges structure can be used as input. Each chromosome containing definition of blocks and centromere position. Output file formats 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/chromDraw_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/chromDraw_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/chromDraw_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/chromDraw_1.2.0.tgz vignettes: vignettes/chromDraw/inst/doc/chromDraw.pdf vignetteTitles: chromDraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ChromHeatMap Version: 1.24.0 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: c82047993e4951dabb9c7f8945e189ce 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ChromHeatMap_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ChromHeatMap_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ChromHeatMap_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ChromHeatMap_1.24.0.tgz vignettes: vignettes/ChromHeatMap/inst/doc/ChromHeatMap.pdf vignetteTitles: Plotting expression data with ChromHeatMap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cisPath Version: 1.10.0 Depends: R (>= 2.10.0) Imports: methods, utils License: GPL (>= 3) Archs: i386, x64 MD5sum: 9f339312d2aed931781fcc9c0ac4ae17 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cisPath_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cisPath_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cisPath_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cisPath_1.10.0.tgz vignettes: vignettes/cisPath/inst/doc/cisPath.pdf vignetteTitles: cisPath hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ClassifyR Version: 1.4.15 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: 306333674f9c33383ffae8f43d374a4f 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_1.4.15.tar.gz win.binary.ver: bin/windows/contrib/3.2/ClassifyR_1.4.15.zip win64.binary.ver: bin/windows64/contrib/3.2/ClassifyR_1.4.15.zip mac.binary.ver: bin/macosx/contrib/3.2/ClassifyR_1.4.5.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ClassifyR_1.4.15.tgz vignettes: vignettes/ClassifyR/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ClassifyR/inst/doc/ClassifyR.html htmlTitles: "An Introduction to ClassifyR" Package: cleanUpdTSeq Version: 1.8.0 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: 80f23d75ec9df9c8ebe631e48ca8de75 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cleanUpdTSeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cleanUpdTSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cleanUpdTSeq_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cleanUpdTSeq_1.8.0.tgz vignettes: vignettes/cleanUpdTSeq/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/cleanUpdTSeq/inst/doc/cleanUpdTSeq.html htmlTitles: "The cleanUpdTSeq user’s guide" importsMe: InPAS Package: cleaver Version: 1.8.0 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: 428336de44a78c2b2c2764c61c4e19c2 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cleaver_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cleaver_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cleaver_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cleaver_1.8.0.tgz vignettes: vignettes/cleaver/inst/doc/cleaver.pdf vignetteTitles: in-silico cleavage of polypeptides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: Pbase, synapter Package: clippda Version: 1.20.0 Depends: R (>= 2.13.1),limma, statmod, rgl, lattice, scatterplot3d, graphics, grDevices, stats, utils, Biobase, tools, methods License: GPL (>=2) MD5sum: 24c1f843d2012a63a35bcb112f604ae3 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clippda_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clippda_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clippda_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clippda_1.20.0.tgz vignettes: vignettes/clippda/inst/doc/clippda.pdf vignetteTitles: Sample Size Calculation hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: clipper Version: 1.10.0 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: 6f3eacafa14271d23c89facdedfa88a7 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clipper_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clipper_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clipper_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clipper_1.10.0.tgz vignettes: vignettes/clipper/inst/doc/clipper.pdf vignetteTitles: clipper hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: ToPASeq suggestsMe: graphite Package: Clomial Version: 1.6.0 Depends: R (>= 2.10), matrixStats Imports: methods, permute License: GPL (>= 2) MD5sum: 85ddb6d8600ce9f00ac1603430029d2a 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Clomial_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Clomial_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Clomial_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Clomial_1.6.0.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 Package: Clonality Version: 1.18.0 Depends: R (>= 2.12.2), DNAcopy Imports: DNAcopy, grDevices, graphics, stats, utils Suggests: gdata, DNAcopy License: GPL-3 MD5sum: 2e1db9abbf5790e6e9919075f09e2033 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Clonality_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Clonality_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Clonality_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Clonality_1.18.0.tgz vignettes: vignettes/Clonality/inst/doc/Clonality.pdf vignetteTitles: Clonality hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: clonotypeR Version: 1.8.0 Imports: methods Suggests: BiocGenerics, edgeR, knitr, pvclust, RUnit, vegan License: file LICENSE MD5sum: 7a1581ef664d4cb6f12e81e8c6031148 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 VignetteBuilder: knitr BugReports: http://clonotyper.branchable.com/Bugs/ source.ver: src/contrib/clonotypeR_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clonotypeR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clonotypeR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clonotypeR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clonotypeR_1.8.0.tgz vignettes: vignettes/clonotypeR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/clonotypeR/inst/doc/clonotypeR.html htmlTitles: "clonotypeR User's Guide" Package: clst Version: 1.18.0 Depends: R (>= 2.10) Imports: ROC, lattice Suggests: RUnit License: GPL-3 MD5sum: e524eedc6eecf77b4d3c428e94a65258 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clst_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clst_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clst_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clst_1.18.0.tgz vignettes: vignettes/clst/inst/doc/clstDemo.pdf vignetteTitles: clst hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: clstutils Package: clstutils Version: 1.18.0 Depends: R (>= 2.10), clst, rjson, ape Imports: lattice, RSQLite Suggests: RUnit, RSVGTipsDevice License: GPL-3 MD5sum: b637196a3ab66bed57779cc8eafaa7de 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clstutils_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clstutils_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clstutils_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clstutils_1.18.0.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 Package: clusterProfiler Version: 2.4.3 Depends: R (>= 3.1.0) Imports: DOSE, GOSemSim, AnnotationDbi, methods, stats4, plyr, ggplot2, GO.db, KEGGREST, magrittr, qvalue, topGO Suggests: BiocStyle, KEGG.db, knitr, org.Hs.eg.db, pathview, ReactomePA, RDAVIDWebService License: Artistic-2.0 MD5sum: 3db664f806e380de993bbb671fe6077a NeedsCompilation: no Title: statistical analysis and visulization 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: https://github.com/GuangchuangYu/clusterProfiler VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/clusterProfiler/issues source.ver: src/contrib/clusterProfiler_2.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/clusterProfiler_2.4.3.zip win64.binary.ver: bin/windows64/contrib/3.2/clusterProfiler_2.4.3.zip mac.binary.ver: bin/macosx/contrib/3.2/clusterProfiler_2.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clusterProfiler_2.4.3.tgz vignettes: vignettes/clusterProfiler/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/clusterProfiler/inst/doc/clusterProfiler.html htmlTitles: "Statistical analysis and visualization of functional profiles for gene and gene clusters" suggestsMe: ChIPseeker, DOSE, GOSemSim, ReactomePA Package: clusterStab Version: 1.42.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), methods Suggests: fibroEset, genefilter License: Artistic-2.0 MD5sum: 0d7c282e6a6b0828a3f404a88169b7b2 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/clusterStab_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/clusterStab_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/clusterStab_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/clusterStab_1.42.0.tgz vignettes: vignettes/clusterStab/inst/doc/clusterStab.pdf vignetteTitles: clusterStab Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CMA Version: 1.28.1 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: 180a0f1da3cef9da9733c5077d4d6935 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_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/CMA_1.28.1.zip win64.binary.ver: bin/windows64/contrib/3.2/CMA_1.28.1.zip mac.binary.ver: bin/macosx/contrib/3.2/CMA_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CMA_1.28.1.tgz vignettes: vignettes/CMA/inst/doc/CMA_vignette.pdf vignetteTitles: CMA_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cn.farms Version: 1.18.0 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: 239bfa1818f7c7bb323eea7c4a19e739 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cn.farms_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cn.farms_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cn.farms_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cn.farms_1.18.0.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 Package: cn.mops Version: 1.16.2 Depends: R (>= 2.12), BiocGenerics, Biobase, IRanges, GenomicRanges Imports: methods, graphics, Rsamtools, parallel Suggests: DNAcopy License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: a46119b3cf298fb773ca9f7aa8ef34ac 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_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/cn.mops_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.2/cn.mops_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.2/cn.mops_1.16.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cn.mops_1.16.2.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 Package: CNAnorm Version: 1.16.0 Depends: R (>= 2.10.1), methods Imports: DNAcopy License: GPL-2 Archs: i386, x64 MD5sum: 383b0a2b6743299a77f2211b93847610 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNAnorm_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNAnorm_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNAnorm_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNAnorm_1.16.0.tgz vignettes: vignettes/CNAnorm/inst/doc/CNAnorm.pdf vignetteTitles: CNAnorm.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CNEr Version: 1.6.2 Depends: R (>= 3.0.2) Imports: Biostrings(>= 2.33.4), RSQLite(>= 0.11.4), GenomeInfoDb(>= 1.1.3), GenomicRanges(>= 1.17.11), rtracklayer(>= 1.25.5), XVector(>= 0.5.4), DBI(>= 0.2-7), GenomicAlignments(>= 1.1.9), methods, S4Vectors(>= 0.0.4), IRanges(>= 1.99.6) LinkingTo: S4Vectors, IRanges, XVector Suggests: Gviz(>= 1.7.4), RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL-2 | file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: f032105b0dcb88309db051295152cec7 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_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNEr_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/CNEr_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/CNEr_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNEr_1.6.2.tgz vignettes: vignettes/CNEr/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/CNEr/inst/doc/CNEr.html htmlTitles: "CNE identification and visualisation" importsMe: TFBSTools Package: CNORdt Version: 1.12.0 Depends: R (>= 1.8.0), CellNOptR (>= 0.99), abind License: GPL-2 Archs: i386, x64 MD5sum: 1b59f456a9e07383033262910b54e841 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORdt_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORdt_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORdt_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORdt_1.12.0.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 Package: CNORfeeder Version: 1.10.0 Depends: R (>= 2.15.0), CellNOptR (>= 1.4.0), graph Suggests: minet, catnet, Rgraphviz, RUnit, BiocGenerics, igraph License: GPL-3 MD5sum: 16ed5a7948cbcd0efcb3e289e06fbdf5 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORfeeder_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORfeeder_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORfeeder_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORfeeder_1.10.0.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 Package: CNORfuzzy Version: 1.12.0 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: 04fdd9d01efd6f602b1e173dd2f1cb09 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORfuzzy_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORfuzzy_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORfuzzy_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORfuzzy_1.12.0.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 Package: CNORode Version: 1.12.0 Depends: CellNOptR (>= 1.5.14), genalg Enhances: MEIGOR License: GPL-2 Archs: i386, x64 MD5sum: 15b4dd4229476fff67d08fb4998ba742 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNORode_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNORode_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNORode_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNORode_1.12.0.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 dependsOnMe: MEIGOR Package: CNPBayes Version: 1.0.0 Depends: foreach, GenomicRanges Imports: Rcpp (>= 0.12.1), S4Vectors, matrixStats, RColorBrewer, gtools, oligoClasses, combinat, GenomeInfoDb, IRanges, methods, BiocGenerics LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, VanillaICE (>= 1.31.3), License: Artistic-2.0 Archs: i386, x64 MD5sum: 41e572cd14b98e4ead26ce64aa45f449 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNPBayes_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNPBayes_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNPBayes_1.0.0.tgz vignettes: vignettes/CNPBayes/inst/doc/FindCNPs.pdf, vignettes/CNPBayes/inst/doc/Implementation.pdf, vignettes/CNPBayes/inst/doc/Overview.pdf vignetteTitles: FindCNPs.pdf, Implementation.pdf, Overview.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CNTools Version: 1.26.0 Depends: R (>= 2.10), methods, tools, stats, genefilter License: LGPL Archs: i386, x64 MD5sum: e969cdf270f63d863f5db6f39b6e60cb 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNTools_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNTools_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNTools_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNTools_1.26.0.tgz vignettes: vignettes/CNTools/inst/doc/HowTo.pdf vignetteTitles: NCTools HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cghMCR importsMe: TCGAbiolinks Package: cnvGSA Version: 1.14.0 Depends: brglm, doParallel, foreach, GenomicRanges, methods, splitstackshape Suggests: cnvGSAdata, org.Hs.eg.db License: LGPL MD5sum: d72f4026899c6f028b83ee9a8b708475 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cnvGSA_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cnvGSA_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cnvGSA_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cnvGSA_1.14.0.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: 1.0.0 Depends: R (>= 3.2.0), GenomicRanges Imports: NOISeq, IRanges, Rsamtools, exomeCopy, foreach, ggplot2, plyr, xlsx Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: 4c48b6976ef054998d51d094e8a4d5d7 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], Volker Endris [ctb], Nicole Pfarr [ctb], Wilko Weichert [ths] Maintainer: Thomas Wolf VignetteBuilder: knitr source.ver: src/contrib/CNVPanelizer_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNVPanelizer_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNVPanelizer_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNVPanelizer_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNVPanelizer_1.0.0.tgz vignettes: vignettes/CNVPanelizer/inst/doc/CNVPanelizer.pdf vignetteTitles: CNVPanelizer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CNVrd2 Version: 1.8.1 Depends: R (>= 3.0.0), methods, VariantAnnotation, parallel, rjags, ggplot2, gridExtra Imports: DNAcopy, IRanges, Rsamtools Suggests: knitr License: GPL-2 MD5sum: 73ade6c91af91ae0b4cfdb01935bcab9 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNVrd2_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/CNVrd2_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/CNVrd2_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNVrd2_1.8.1.tgz vignettes: vignettes/CNVrd2/inst/doc/CNVrd2.pdf vignetteTitles: A Markdown Vignette with knitr hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CNVtools Version: 1.64.0 Depends: R (>= 2.10), survival License: GPL-3 Archs: i386, x64 MD5sum: 0e3550cec5cebcdd436492bdabafd9fa 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_1.64.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CNVtools_1.64.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CNVtools_1.64.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CNVtools_1.64.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CNVtools_1.64.0.tgz vignettes: vignettes/CNVtools/inst/doc/CNVtools-vignette.pdf vignetteTitles: Copy Number Variation Tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cobindR Version: 1.8.0 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: 704ca695a4c34f9a4a73cfad5af1a4f1 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cobindR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cobindR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cobindR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cobindR_1.8.0.tgz vignettes: vignettes/cobindR/inst/doc/cobindR.pdf vignetteTitles: Using cobindR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CoCiteStats Version: 1.42.0 Depends: R (>= 2.0), org.Hs.eg.db Imports: AnnotationDbi License: CPL MD5sum: 2154b994f84931868acfd21854a021ad 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CoCiteStats_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CoCiteStats_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CoCiteStats_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoCiteStats_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: codelink Version: 1.38.0 Depends: R (>= 2.10), BiocGenerics (>= 0.3.2), methods, Biobase (>= 2.17.8), limma Imports: annotate Suggests: genefilter, parallel, knitr License: GPL-2 MD5sum: 099a6d9e3838c961a3c47d605328bfd1 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/codelink_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/codelink_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/codelink_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/codelink_1.38.0.tgz vignettes: vignettes/codelink/inst/doc/Codelink_Introduction.pdf, vignettes/codelink/inst/doc/Codelink_Legacy.pdf vignetteTitles: Codelink Intruction, Codelink Legacy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CODEX Version: 1.2.0 Depends: R (>= 3.1.2), Rsamtools, GenomeInfoDb, BSgenome.Hsapiens.UCSC.hg19 Suggests: WES.1KG.WUGSC License: GPL-2 MD5sum: 04c1c0f5c030da53a0a353665fc3d4fc 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CODEX_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CODEX_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CODEX_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CODEX_1.2.0.tgz vignettes: vignettes/CODEX/inst/doc/CODEX_vignettes.pdf vignetteTitles: Using CODEX hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CoGAPS Version: 2.4.0 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: 1eb9d7f93db543a0dc279954ee1b98d1 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CoGAPS_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CoGAPS_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CoGAPS_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoGAPS_2.4.0.tgz vignettes: vignettes/CoGAPS/inst/doc/CoGAPSUsersManual.pdf vignetteTitles: GAPS/CoGAPS Users Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: cogena Version: 1.4.0 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 License: LGPL-3 MD5sum: e8bf5300275eaacc3107a37cd188b5e1 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cogena_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cogena_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cogena_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cogena_1.4.0.tgz vignettes: vignettes/cogena/inst/doc/cogena-vignette.pdf vignetteTitles: cogena-vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: coGPS Version: 1.14.0 Depends: R (>= 2.13.0) Imports: graphics, grDevices Suggests: limma License: GPL-2 MD5sum: 8a02936bb627a63345dcba22bfbd8a32 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/coGPS_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/coGPS_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/coGPS_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/coGPS_1.14.0.tgz vignettes: vignettes/coGPS/inst/doc/coGPS.pdf vignetteTitles: coGPS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: COHCAP Version: 1.8.0 Depends: WriteXLS, COHCAPanno License: GPL-3 MD5sum: c524e03cb7417c96bb0d5b26e490182a 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/COHCAP_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/COHCAP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/COHCAP_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/COHCAP_1.8.0.tgz vignettes: vignettes/COHCAP/inst/doc/COHCAP.pdf vignetteTitles: COHCAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: coMET Version: 1.2.1 Depends: R (>= 3.1.0), grid, utils, biomaRt, Gviz (>= 1.10.9), psych Imports: colortools, hash, grDevices, gridExtra, rtracklayer, IRanges, S4Vectors, GenomicRanges, ggbio, ggplot2, trackViewer, stats, corrplot Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: 71f7c741ee16b4a1cb5fdb675bc64347 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/coMET_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/coMET_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/coMET_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/coMET_1.2.1.tgz vignettes: vignettes/coMET/inst/doc/coMET_manual.pdf, vignettes/coMET/inst/doc/coMET.pdf vignetteTitles: coMET_manual.pdf, coMET users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: COMPASS Version: 1.8.1 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: 1ef9bc3a7cf909572838445bcd49b439 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/COMPASS_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/COMPASS_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/COMPASS_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/COMPASS_1.8.1.tgz vignettes: vignettes/COMPASS/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/COMPASS/inst/doc/COMPASS.html htmlTitles: "COMPASS" Package: compcodeR Version: 1.6.0 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: c3cfc657c544cec076973305965a3c6b 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/compcodeR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/compcodeR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/compcodeR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/compcodeR_1.6.0.tgz vignettes: vignettes/compcodeR/inst/doc/compcodeR.pdf vignetteTitles: compcodeR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: compEpiTools Version: 1.4.0 Depends: R (>= 3.1.1), methods, topGO, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, Rsamtools, parallel, grDevices, gplots, IRanges, GenomicFeatures, XVector, methylPipe, GO.db, S4Vectors, GenomeInfoDb Suggests: BSgenome.Mmusculus.UCSC.mm9, TxDb.Mmusculus.UCSC.mm9.knownGene, org.Mm.eg.db, knitr, rtracklayer License: GPL MD5sum: 70e1fe5cc0d99c41e192a9e5e296935c 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: http://genomics.iit.it/groups/computational-epigenomics.html Maintainer: Kamal Kishore VignetteBuilder: knitr source.ver: src/contrib/compEpiTools_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/compEpiTools_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/compEpiTools_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/compEpiTools_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/compEpiTools_1.4.0.tgz vignettes: vignettes/compEpiTools/inst/doc/compEpiTools.pdf vignetteTitles: compEpiTools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CompGO Version: 1.6.0 Depends: RDAVIDWebService Imports: rtracklayer, Rgraphviz, ggplot2, GenomicFeatures, TxDb.Mmusculus.UCSC.mm9.knownGene, pcaMethods, reshape2, pathview License: GPL-2 MD5sum: 3c4ec6c813617dc4982a7417ec578fb2 NeedsCompilation: no Title: An R pipeline for .bed file annotation, comparing GO term enrichment between gene sets and data visualisation Description: This package contains functions to accomplish several tasks. It is able to download full genome databases from UCSC, import .bed files easily, annotate these .bed file regions with genes (plus distance) from aforementioned database dumps, interface with DAVID to create functional annotation and gene ontology enrichment charts based on gene lists (such as those generated from input .bed files) and finally visualise and compare these enrichments using either directed acyclic graphs or scatterplots. biocViews: GeneSetEnrichment, MultipleComparison, GO, Visualization Author: Sam D. Bassett [aut], Ashley J. Waardenberg [aut, cre] Maintainer: Ashley J. Waardenberg source.ver: src/contrib/CompGO_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CompGO_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CompGO_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CompGO_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CompGO_1.6.0.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 Package: ComplexHeatmap Version: 1.6.0 Depends: R (>= 3.1.2), grid, graphics, stats, grDevices Imports: methods, circlize (>= 0.3.1), GetoptLong, colorspace, RColorBrewer, dendextend (>= 1.0.1), GlobalOptions (>= 0.0.6) Suggests: testthat (>= 0.3), knitr, markdown, cluster, MASS, pvclust, dendsort, HilbertCurve License: GPL (>= 2) MD5sum: 29d9b1c2d132d58d9b9e57415a5ee4dc 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ComplexHeatmap_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ComplexHeatmap_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ComplexHeatmap_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ComplexHeatmap_1.6.0.tgz vignettes: vignettes/ComplexHeatmap/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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 importsMe: EnrichmentBrowser suggestsMe: HilbertCurve Package: ConsensusClusterPlus Version: 1.24.0 Imports: Biobase, ALL, graphics, stats, utils, cluster License: GPL version 2 MD5sum: a1ef1629446dc0f75360493a77fdf6cc 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ConsensusClusterPlus_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ConsensusClusterPlus_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ConsensusClusterPlus_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ConsensusClusterPlus_1.24.0.tgz vignettes: vignettes/ConsensusClusterPlus/inst/doc/ConsensusClusterPlus.pdf vignetteTitles: ConsensusClusterPlus Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: FlowSOM importsMe: TCGAbiolinks Package: conumee Version: 1.2.0 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: 56fe572051b0a19bf1b9e857ae86b9ae 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/conumee_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/conumee_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/conumee_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/conumee_1.2.0.tgz vignettes: vignettes/conumee/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/conumee/inst/doc/conumee.html htmlTitles: "The conumee vignette" Package: convert Version: 1.46.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.33), limma (>= 1.7.0), marray, utils, methods License: LGPL MD5sum: e0c745f50f5dd5e12928026991f55040 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/convert_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/convert_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/convert_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/convert_1.46.0.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: 1.38.0 Depends: Biobase, methods Suggests: colonCA License: Artistic-2.0 Archs: i386, x64 MD5sum: 9b9e8aa7020ed45f45cffbd9785351ab 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/copa_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/copa_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/copa_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/copa_1.38.0.tgz vignettes: vignettes/copa/inst/doc/copa.pdf vignetteTitles: copa Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: copynumber Version: 1.10.0 Depends: R (>= 2.10), BiocGenerics Imports: S4Vectors, IRanges, GenomicRanges License: Artistic-2.0 MD5sum: 995e6fc71be83bea8a2c053a5d03ea1f 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/copynumber_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/copynumber_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/copynumber_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/copynumber_1.10.0.tgz vignettes: vignettes/copynumber/inst/doc/copynumber.pdf vignetteTitles: copynumber.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CopyNumber450k Version: 1.6.0 Depends: Biobase, minfi, DNAcopy, preprocessCore, BiocGenerics Imports: methods Suggests: CopyNumber450kData, minfiData License: Artistic-2.0 MD5sum: 87a17dd108f1cb350584d20c3dfe7ab4 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CopyNumber450k_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CopyNumber450k_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CopyNumber450k_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CopyNumber450k_1.6.0.tgz vignettes: vignettes/CopyNumber450k/inst/doc/CopyNumber450k.pdf vignetteTitles: CopyNumber450k User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CopywriteR Version: 2.2.0 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: 099d09f01ef42607e48b7c0bd56c505a 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CopywriteR_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CopywriteR_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CopywriteR_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CopywriteR_2.2.0.tgz vignettes: vignettes/CopywriteR/inst/doc/CopywriteR.pdf vignetteTitles: CopywriteR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: CoRegNet Version: 1.6.0 Depends: R (>= 2.14), igraph, shiny, arules, methods Suggests: RColorBrewer, gplots, BiocStyle, knitr License: GPL-3 Archs: i386, x64 MD5sum: 4e6b39ce29afe915559a0c381a8cbc82 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CoRegNet_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CoRegNet_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CoRegNet_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoRegNet_1.6.0.tgz vignettes: vignettes/CoRegNet/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/CoRegNet/inst/doc/CoRegNet.html htmlTitles: "CoRegNet: Reconstruction and integrated analysis of Co-Regulatory Networks" Package: Cormotif Version: 1.16.0 Depends: R (>= 2.12.0), affy, limma Imports: affy, graphics, grDevices License: GPL-2 MD5sum: 9d2593454ad0534270c1c19653da7122 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Cormotif_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Cormotif_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Cormotif_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Cormotif_1.16.0.tgz vignettes: vignettes/Cormotif/inst/doc/CormotifVignette.pdf vignetteTitles: Cormotif Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CorMut Version: 1.12.0 Depends: seqinr,igraph License: GPL-2 MD5sum: afe8b22e1264d6c69206735dd19884c3 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CorMut_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CorMut_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CorMut_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CorMut_1.12.0.tgz vignettes: vignettes/CorMut/inst/doc/CorMut.pdf vignetteTitles: CorMut hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: coRNAi Version: 1.20.0 Depends: R (>= 2.10), cellHTS2, limma, locfit Imports: MASS, gplots, lattice, grDevices, graphics, stats License: Artistic-2.0 MD5sum: 8e96bdee1cc11ea0c342444286ffbaea 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/coRNAi_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/coRNAi_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/coRNAi_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/coRNAi_1.20.0.tgz vignettes: vignettes/coRNAi/inst/doc/coRNAi.pdf vignetteTitles: coRNAi hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CORREP Version: 1.36.0 Imports: e1071, stats Suggests: cluster, MASS License: GPL (>= 2) MD5sum: 8756727fa950a87d36ab61c3f9cb8865 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CORREP_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CORREP_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CORREP_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CORREP_1.36.0.tgz vignettes: vignettes/CORREP/inst/doc/CORREP.pdf vignetteTitles: Multivariate Correlation Estimator hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cosmiq Version: 1.4.0 Depends: R (>= 3.0.2), Rcpp Imports: pracma, xcms, MassSpecWavelet, faahKO Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 Archs: i386, x64 MD5sum: a7815daca8544eb5d47e9619354ab68d 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cosmiq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cosmiq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cosmiq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cosmiq_1.4.0.tgz vignettes: vignettes/cosmiq/inst/doc/cosmiq.pdf vignetteTitles: cosmiq primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: COSNet Version: 1.4.1 Suggests: bionetdata, PerfMeas, RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 4d8d595cfb4a0d728e8fb0aa45086b7a 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/COSNet_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/COSNet_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/COSNet_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/COSNet_1.4.1.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 Package: CoverageView Version: 1.6.0 Depends: R (>= 2.10), methods, Rsamtools (>= 1.19.17), rtracklayer Imports: BiocGenerics, S4Vectors (>= 0.7.21), IRanges(>= 2.3.23), GenomicRanges, GenomicAlignments, parallel, tools License: Artistic-2.0 MD5sum: 08a8506ec89af1cde6b3943e59d8ddd7 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_1.6.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/CoverageView_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CoverageView_1.6.0.tgz vignettes: vignettes/CoverageView/inst/doc/CoverageView.pdf vignetteTitles: Easy visualization of the read coverage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cpvSNP Version: 1.2.0 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: 3c0150a0198fe4051af3f07ee3aa0fb8 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cpvSNP_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cpvSNP_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cpvSNP_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cpvSNP_1.2.0.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 Package: cqn Version: 1.16.0 Depends: R (>= 2.10.0), mclust, nor1mix, stats, preprocessCore, splines, quantreg Imports: splines Suggests: scales, edgeR License: Artistic-2.0 MD5sum: ef14223eee5b6e3e82d1186e34e82cc3 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cqn_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cqn_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cqn_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cqn_1.16.0.tgz vignettes: vignettes/cqn/inst/doc/cqn.pdf vignetteTitles: CQN (Conditional Quantile Normalization) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: tweeDEseq Package: CRImage Version: 1.18.0 Depends: EBImage, DNAcopy, aCGH Imports: MASS, e1071, foreach, sgeostat License: Artistic-2.0 MD5sum: 15db8cd6565f54169e50f945557ee5be 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CRImage_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CRImage_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CRImage_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CRImage_1.18.0.tgz vignettes: vignettes/CRImage/inst/doc/CRImage.pdf vignetteTitles: CRImage Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: CRISPRseek Version: 1.10.3 Depends: R (>= 3.0.1), BiocGenerics, Biostrings Imports: parallel, data.table, seqinr, S4Vectors, IRanges, BSgenome, BiocParallel Suggests: RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db License: GPL (>= 2) MD5sum: df42eee8a931e9edeb9a1c67ae2b8c55 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, Isana Veksler-Lublinsky, Victor Ambros, Neil Aronin and Michael Brodsky Maintainer: Lihua Julie Zhu source.ver: src/contrib/CRISPRseek_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/CRISPRseek_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.2/CRISPRseek_1.10.3.zip mac.binary.ver: bin/macosx/contrib/3.2/CRISPRseek_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CRISPRseek_1.10.3.tgz vignettes: vignettes/CRISPRseek/inst/doc/CRISPRseek.pdf vignetteTitles: CRISPRseek Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: GUIDEseq Package: crlmm Version: 1.28.2 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: 2871fedfcf5da3929604c6ef09de2da0 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_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/crlmm_1.28.2.zip win64.binary.ver: bin/windows64/contrib/3.2/crlmm_1.28.2.zip mac.binary.ver: bin/macosx/contrib/3.2/crlmm_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/crlmm_1.28.2.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 importsMe: VanillaICE suggestsMe: ArrayTV, oligoClasses, SNPchip Package: CSAR Version: 1.22.0 Depends: R (>= 2.15.0), S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Imports: stats, utils Suggests: ShortRead, Biostrings License: Artistic-2.0 Archs: i386, x64 MD5sum: 197f823c67770bce6d13d11863ea6229 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CSAR_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CSAR_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CSAR_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CSAR_1.22.0.tgz vignettes: vignettes/CSAR/inst/doc/CSAR.pdf vignetteTitles: CSAR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: NarrowPeaks suggestsMe: NarrowPeaks Package: csaw Version: 1.4.1 Depends: R (>= 3.2.0), GenomicRanges, SummarizedExperiment Imports: Rsamtools, edgeR, limma, GenomicFeatures, AnnotationDbi, methods, GenomicAlignments, S4Vectors, IRanges, GenomeInfoDb Suggests: org.Mm.eg.db, TxDb.Mmusculus.UCSC.mm10.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 7ee43d43873dfa4f89d16d38563bd4b0 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 Author: Aaron Lun , Gordon Smyth Maintainer: Aaron Lun source.ver: src/contrib/csaw_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/csaw_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/csaw_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/csaw_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/csaw_1.4.1.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: 1.8.0 Imports: methods, splines, stats, utils Suggests: testthat License: GPL-2 Archs: i386, x64 MD5sum: 3d49cb0b86c3ac2d79f9751ff7fab2cd 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/CSSP_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/CSSP_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/CSSP_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/CSSP_1.8.0.tgz vignettes: vignettes/CSSP/inst/doc/cssp.pdf vignetteTitles: cssp.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ctc Version: 1.44.0 Depends: amap License: GPL-2 MD5sum: effe2690457a66d76c1406ebe1949787 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ctc_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ctc_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ctc_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ctc_1.44.0.tgz vignettes: vignettes/ctc/inst/doc/ctc.pdf vignetteTitles: Introduction to ctc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cummeRbund Version: 2.12.1 Depends: R (>= 2.7.0), BiocGenerics (>= 0.3.2), RSQLite, ggplot2, reshape2, fastcluster, rtracklayer, Gviz Imports: methods, plyr, BiocGenerics, Biobase Suggests: cluster, plyr, NMFN, stringr, GenomicFeatures, GenomicRanges, rjson License: Artistic-2.0 MD5sum: e0a65840fd34185b8710e50b377227da 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_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/cummeRbund_2.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/cummeRbund_2.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/cummeRbund_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cummeRbund_2.12.1.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 dependsOnMe: meshr, spliceR suggestsMe: oneChannelGUI Package: customProDB Version: 1.10.0 Depends: R (>= 3.0.1), IRanges, AnnotationDbi, biomaRt Imports: 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: e4b306b2308cff8a9d7e44ccb98e0639 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/customProDB_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/customProDB_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/customProDB_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/customProDB_1.10.0.tgz vignettes: vignettes/customProDB/inst/doc/customProDB.pdf vignetteTitles: Introduction to customProDB hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: PGA Package: cycle Version: 1.24.0 Depends: R (>= 2.10.0), Mfuzz Imports: Biobase, stats License: GPL-2 MD5sum: a764f7c07b821f243d4266b58b7a421a 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://itb.biologie.hu-berlin.de/~futschik/software/R/cycle/index.html source.ver: src/contrib/cycle_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/cycle_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/cycle_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/cycle_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cycle_1.24.0.tgz vignettes: vignettes/cycle/inst/doc/cycle.pdf vignetteTitles: Introduction to cycle hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: cytofkit Version: 1.2.4 Depends: R (>= 2.10.0), ggplot2(>= 0.9.3.1), plyr Imports: tcltk, stats, Rtsne, e1071, flowCore, gplots, VGAM, reshape, reshape2, shiny, vegan, Biobase, doParallel, pdist, 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: 595964f9d483db5c03e646d65fced1ef NeedsCompilation: yes Title: cytofkit: an integrated analysis pipeline for mass cytometry data 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_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/cytofkit_1.2.4.zip win64.binary.ver: bin/windows64/contrib/3.2/cytofkit_1.2.4.zip mac.binary.ver: bin/macosx/contrib/3.2/cytofkit_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/cytofkit_1.2.4.tgz vignettes: vignettes/cytofkit/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/cytofkit/inst/doc/cytofkit_example.html, vignettes/cytofkit/inst/doc/cytofkit_workflow.html htmlTitles: "Cytofkit: Quick Start", "Cytofkit: Integrated Mass Cytometry Data Analysis Pipeline" Package: dagLogo Version: 1.8.0 Depends: R (>= 3.0.1), methods, biomaRt, grImport, grid, motifStack Imports: pheatmap, Biostrings Suggests: XML, UniProt.ws, RUnit, BiocGenerics, BiocStyle, knitr, rmarkdown License: GPL (>=2) MD5sum: b0af49a6578d2cbae61229975d0e20b4 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dagLogo_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dagLogo_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dagLogo_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dagLogo_1.8.0.tgz vignettes: vignettes/dagLogo/inst/doc/dagLogo.pdf vignetteTitles: dagLogo Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/dagLogo/inst/doc/dagLogoHTML.html htmlTitles: "dagLogo Vignette" Package: daMA Version: 1.42.0 Imports: MASS, stats License: GPL (>= 2) MD5sum: e4f4dcf5c43a79289b3363cd8748d7e4 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/daMA_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/daMA_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/daMA_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/daMA_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DAPAR Version: 1.0.0 Depends: R (>= 3.2), MSnbase Imports: RColorBrewer,stats,preprocessCore,Cairo,png, lattice,reshape2,gplots,pcaMethods,ggplot2, limma,knitr,tmvtnorm,norm,impute, imputeLCMD, XLConnect Suggests: BiocGenerics, testthat, BiocStyle, Prostar License: Artistic-2.0 MD5sum: 0e06368814c7c361d3092c3a51962aff 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 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DAPAR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DAPAR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DAPAR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DAPAR_1.0.0.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 dependsOnMe: Prostar Package: DART Version: 1.18.0 Depends: R (>= 2.10.0), igraph (>= 0.6.0) Suggests: breastCancerVDX, breastCancerMAINZ, Biobase License: GPL-2 MD5sum: 37bb4e3e33954b3af26c121b9e63d12f 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DART_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DART_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DART_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DART_1.18.0.tgz vignettes: vignettes/DART/inst/doc/DART.pdf vignetteTitles: DART Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DASiR Version: 1.10.0 Depends: Biostrings,IRanges,GenomicRanges Imports: XML License: LGPL (>= 3) MD5sum: 2fd2d472f8fea83160b4a7e58fadf2a2 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DASiR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DASiR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DASiR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DASiR_1.10.0.tgz vignettes: vignettes/DASiR/inst/doc/DASiR.pdf vignetteTitles: Programmatic retrieval of information from DAS servers hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DAVIDQuery Version: 1.29.0 Depends: RCurl (>= 1.4.0), utils License: GPL-2 MD5sum: 8f51816c13ea4299e7b212f863a4c12b NeedsCompilation: no Title: Retrieval from the DAVID bioinformatics data resource into R Description: Tools to retrieve data from DAVID, the Database for Annotation, Visualization and Integrated Discovery biocViews: Annotation Author: Roger Day, Alex Lisovich Maintainer: Roger Day source.ver: src/contrib/DAVIDQuery_1.29.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DAVIDQuery_1.29.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DAVIDQuery_1.29.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DAVIDQuery_1.29.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DAVIDQuery_1.29.0.tgz vignettes: vignettes/DAVIDQuery/inst/doc/DAVIDQuery.pdf vignetteTitles: An R Package for retrieving data from DAVID into R objects. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DBChIP Version: 1.14.0 Depends: R (>= 2.15.0), edgeR, DESeq Suggests: ShortRead, BiocGenerics License: GPL (>= 2) MD5sum: 1fd654d9d85cc43250a18688fa50a40c 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DBChIP_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DBChIP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DBChIP_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DBChIP_1.14.0.tgz vignettes: vignettes/DBChIP/inst/doc/DBChIP.pdf vignetteTitles: DBChIP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: metagene Package: DChIPRep Version: 1.0.4 Depends: R (>= 3.2.0) Imports: methods, ggplot2, DESeq2, fdrtool, reshape2, GenomicRanges, smoothmest, plyr, tidyr, assertthat, S4Vectors Suggests: mgcv, testthat, BiocStyle, knitr, rmarkdown License: MIT + file LICENCE MD5sum: 65c5d5ecf732343eca1c04bf4fce5dc9 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. biocViews: Sequencing, ChIPSeq Author: Bernd Klaus [aut, cre], Christophe Chabbert [aut] Maintainer: Bernd Klaus SystemRequirements: Python 2.7, HTSeq (>= 0.6.1), numpy, argparse, sys VignetteBuilder: knitr source.ver: src/contrib/DChIPRep_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/DChIPRep_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.2/DChIPRep_1.0.4.zip mac.binary.ver: bin/macosx/contrib/3.2/DChIPRep_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DChIPRep_1.0.4.tgz vignettes: vignettes/DChIPRep/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/DChIPRep/inst/doc/DChIPRepVignette.html htmlTitles: "Introduction to the DChIPRep package" Package: ddCt Version: 1.26.0 Depends: R (>= 2.3.0), methods Imports: Biobase (>= 1.10.0), RColorBrewer (>= 0.1-3), xtable, lattice, BiocGenerics Suggests: RUnit License: LGPL-3 MD5sum: fba15405899ed29207653625f2add74c 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ddCt_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ddCt_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ddCt_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ddCt_1.26.0.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 Package: ddgraph Version: 1.14.0 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: 1417ca69de88450fb04d3b38dd120a2e 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ddgraph_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ddgraph_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ddgraph_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ddgraph_1.14.0.tgz vignettes: vignettes/ddgraph/inst/doc/ddgraph.pdf vignetteTitles: Overview of the 'ddgraph' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DECIPHER Version: 1.16.1 Depends: R (>= 2.13.0), 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: ce10c78b2906a5a10c98f290eb5d7e9f NeedsCompilation: yes Title: Database Enabled Code for Ideal Probe Hybridization Employing R 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_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/DECIPHER_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/DECIPHER_1.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/DECIPHER_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DECIPHER_1.16.1.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 Package: DeconRNASeq Version: 1.12.0 Depends: R (>= 2.14.0), limSolve, pcaMethods, ggplot2, grid License: GPL-2 MD5sum: d9ae7ce20e5f8fd7c33058292ea882c0 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DeconRNASeq_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DeconRNASeq_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DeconRNASeq_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DeconRNASeq_1.12.0.tgz vignettes: vignettes/DeconRNASeq/inst/doc/DeconRNASeq.pdf vignetteTitles: DeconRNASeq Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DEDS Version: 1.44.0 Depends: R (>= 1.7.0) License: LGPL Archs: i386, x64 MD5sum: 74070ad7f14f2833dd390228fb45141e 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEDS_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DEDS_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DEDS_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEDS_1.44.0.tgz vignettes: vignettes/DEDS/inst/doc/DEDS.pdf vignetteTitles: DEDS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: deepSNV Version: 1.16.0 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: 7802e7d552a131641e11656ccdf06904 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/deepSNV_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/deepSNV_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/deepSNV_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/deepSNV_1.16.0.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: DEGraph Version: 1.22.0 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: 7daf6b5ca0563f73fc644a16b5b33628 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEGraph_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DEGraph_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DEGraph_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEGraph_1.22.0.tgz vignettes: vignettes/DEGraph/inst/doc/DEGraph.pdf vignetteTitles: DEGraph: differential expression testing for gene networks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: graphite, ToPASeq Package: DEGreport Version: 1.6.1 Depends: R (>= 3.2.0), quantreg Imports: plyr, utils, ggplot2, Nozzle.R1, edgeR Suggests: knitr, biomaRt, RUnit, BiocStyle, BiocGenerics, BiocParallel License: GPL (>=2) MD5sum: f714d5c4e7e249ee6b03803033fa2570 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEGreport_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/DEGreport_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/DEGreport_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEGreport_1.6.1.tgz vignettes: vignettes/DEGreport/inst/doc/DEGreport.pdf vignetteTitles: DEGreport hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DEGseq Version: 1.24.0 Depends: R (>= 2.8.0), qvalue, samr, methods Imports: graphics, grDevices, methods, stats, utils License: LGPL (>=2) Archs: i386, x64 MD5sum: ac586d5f6ecd13d374c48b2d84b437eb 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEGseq_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DEGseq_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DEGseq_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEGseq_1.24.0.tgz vignettes: vignettes/DEGseq/inst/doc/DEGseq.pdf vignetteTitles: DEGseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: deltaGseg Version: 1.10.1 Depends: R (>= 2.15.1), methods, ggplot2, changepoint, wavethresh, tseries, pvclust, fBasics, grid, reshape, scales Suggests: knitr License: GPL-2 MD5sum: 7b62c1383608240f9972d9ac5d34d7b6 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/deltaGseg_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/deltaGseg_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/deltaGseg_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/deltaGseg_1.10.1.tgz vignettes: vignettes/deltaGseg/inst/doc/deltaGseg.pdf vignetteTitles: deltaGseg hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DeMAND Version: 1.0.0 Depends: R (>= 2.14.0), KernSmooth, methods License: CC BY-NC 2.0 MD5sum: 867aa9942b61062885a4ad6baecbb1bd 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 source.ver: src/contrib/DeMAND_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DeMAND_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DeMAND_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DeMAND_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DeMAND_1.0.0.tgz vignettes: vignettes/DeMAND/inst/doc/DeMAND.pdf vignetteTitles: Using DeMAND hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: derfinder Version: 1.4.4 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 (>= 0.7.20) Suggests: BiocStyle, biovizBase, devtools (>= 1.6), derfinderData (>= 0.99.0), ggplot2, knitcitations (>= 1.0.1), knitr (>= 1.6), rmarkdown (>= 0.3.3), testthat, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: c6bfa232a0b7443bf338d72a43dcddd9 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) identification of expressed-regions. biocViews: DifferentialExpression, Sequencing, RNASeq, 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_1.4.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/derfinder_1.4.4.zip win64.binary.ver: bin/windows64/contrib/3.2/derfinder_1.4.4.zip mac.binary.ver: bin/macosx/contrib/3.2/derfinder_1.4.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/derfinder_1.4.4.tgz vignettes: vignettes/derfinder/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/derfinder/inst/doc/derfinder.html, vignettes/derfinder/inst/doc/derfinderAdvanced.html htmlTitles: "Introduction to derfinder", "derfinder advanced details and usage" importsMe: derfinderPlot, regionReport Package: derfinderHelper Version: 1.4.1 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: 42c6d462b9c8d4172ecfd05dd9b0a1a4 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/derfinderHelper_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/derfinderHelper_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/derfinderHelper_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/derfinderHelper_1.4.1.tgz vignettes: vignettes/derfinderHelper/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/derfinderHelper/inst/doc/derfinderHelper.html htmlTitles: "Introduction to derfinderHelper" importsMe: derfinder Package: derfinderPlot Version: 1.4.1 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, 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: d248c82f52d0d0657db1323b57de6c53 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/derfinderPlot_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/derfinderPlot_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/derfinderPlot_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/derfinderPlot_1.4.1.tgz vignettes: vignettes/derfinderPlot/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/derfinderPlot/inst/doc/derfinderPlot.html htmlTitles: "Introduction to derfinderPlot" importsMe: regionReport Package: DESeq Version: 1.22.1 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: c33cdeda38e731efd8f424502da1eaa6 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_1.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/DESeq_1.22.1.zip win64.binary.ver: bin/windows64/contrib/3.2/DESeq_1.22.1.zip mac.binary.ver: bin/macosx/contrib/3.2/DESeq_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DESeq_1.22.1.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 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, GenomicAlignments, GenomicRanges, oneChannelGUI, RUVSeq, SSPA, XBSeq Package: DESeq2 Version: 1.10.1 Depends: S4Vectors, IRanges, GenomicRanges, SummarizedExperiment (>= 0.2.0), Rcpp (>= 0.10.1), RcppArmadillo (>= 0.3.4.4) Imports: BiocGenerics (>= 0.7.5), Biobase, BiocParallel, genefilter, methods, locfit, geneplotter, ggplot2, Hmisc LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, knitr, BiocStyle, vsn, pheatmap, RColorBrewer, airway, pasilla (>= 0.2.10), DESeq License: LGPL (>= 3) Archs: i386, x64 MD5sum: 2a92d5c7f9e38d26c629aa6a558b76de 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 Author: Michael Love (HSPH Boston), Simon Anders, Wolfgang Huber (EMBL Heidelberg) Maintainer: Michael Love VignetteBuilder: knitr source.ver: src/contrib/DESeq2_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/DESeq2_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/DESeq2_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/DESeq2_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DESeq2_1.10.1.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 dependsOnMe: DEXSeq, FourCSeq, MLSeq, rgsepd, TCC, XBSeq importsMe: DChIPRep, EnrichmentBrowser, FourCSeq, HTSFilter, ReportingTools, systemPipeR, ToPASeq suggestsMe: biobroom, BiocGenerics, compcodeR, DiffBind, gage, oneChannelGUI, phyloseq, subSeq Package: destiny Version: 1.0.0 Depends: R (>= 3.2.0), methods, Biobase Imports: graphics, Rcpp (>= 0.10.3), RcppEigen, BiocGenerics, Matrix, FNN, VIM, proxy, igraph, scatterplot3d LinkingTo: Rcpp, RcppEigen Suggests: RColorBrewer, ggplot2, nbconvertR Enhances: rgl License: GPL Archs: i386, x64 MD5sum: 1443d4ca25c7c5d01b0d748435ff922e 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/destiny_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/destiny_1.0.0.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/destiny_1.0.0.tgz vignettes: vignettes/destiny/inst/doc/destiny.pdf vignetteTitles: destiny.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DEXSeq Version: 1.16.10 Depends: BiocParallel, Biobase, IRanges (>= 2.1.10), GenomicRanges (>= 1.19.6), DESeq2 (>= 1.9.11) Imports: BiocGenerics, biomaRt, hwriter, methods, stringr, Rsamtools, statmod, geneplotter, genefilter, RColorBrewer Suggests: GenomicFeatures (>= 1.13.29), pasilla (>= 0.2.22), parathyroidSE, BiocStyle, knitr Enhances: parallel License: GPL (>= 3) MD5sum: 1babe3b809d1fe2f04ddf103b9824896 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 , both at EMBL Heidelberg Maintainer: Alejandro Reyes VignetteBuilder: knitr source.ver: src/contrib/DEXSeq_1.16.10.tar.gz win.binary.ver: bin/windows/contrib/3.2/DEXSeq_1.16.10.zip win64.binary.ver: bin/windows64/contrib/3.2/DEXSeq_1.16.10.zip mac.binary.ver: bin/macosx/contrib/3.2/DEXSeq_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DEXSeq_1.16.10.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 suggestsMe: GenomicRanges, oneChannelGUI, subSeq Package: dexus Version: 1.10.0 Depends: R (>= 2.15), methods, BiocGenerics Suggests: parallel, statmod, stats, DESeq, RColorBrewer License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 1abe7f42b656bfbd6e76c0ebd3330edf 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dexus_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dexus_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dexus_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dexus_1.10.0.tgz vignettes: vignettes/dexus/inst/doc/dexus.pdf vignetteTitles: dexus: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DFP Version: 1.28.0 Depends: methods, Biobase (>= 2.5.5) License: GPL-2 MD5sum: 9f576754e59fe3a0dfbb90cb70cf2eb4 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DFP_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DFP_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DFP_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DFP_1.28.0.tgz vignettes: vignettes/DFP/inst/doc/DFP.pdf vignetteTitles: Howto: Discriminat Fuzzy Pattern hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DiffBind Version: 1.16.3 Depends: R (>= 3.2.2), GenomicRanges, SummarizedExperiment, limma, GenomicAlignments, locfit Imports: RColorBrewer, amap, edgeR, gplots, grDevices, stats, utils, IRanges, zlibbioc, lattice, systemPipeR, tools LinkingTo: Rsamtools (>= 1.19.38) Suggests: DESeq, Rsamtools, DESeq2, BiocStyle Enhances: rgl, parallel, BiocParallel, XLConnect License: Artistic-2.0 Archs: i386, x64 MD5sum: 0a565fcb40c62f6c1a4ae21a289916a6 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_1.16.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/DiffBind_1.16.3.zip win64.binary.ver: bin/windows64/contrib/3.2/DiffBind_1.16.3.zip mac.binary.ver: bin/macosx/contrib/3.2/DiffBind_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DiffBind_1.16.3.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 dependsOnMe: ChIPQC Package: diffGeneAnalysis Version: 1.52.0 Imports: graphics, grDevices, minpack.lm (>= 1.0-4), stats, utils License: GPL MD5sum: ae673296bbca0f771ec6e5a6fde03be0 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_1.52.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/diffGeneAnalysis_1.52.0.zip win64.binary.ver: bin/windows64/contrib/3.2/diffGeneAnalysis_1.52.0.zip mac.binary.ver: bin/macosx/contrib/3.2/diffGeneAnalysis_1.52.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/diffGeneAnalysis_1.52.0.tgz vignettes: vignettes/diffGeneAnalysis/inst/doc/diffGeneAnalysis.pdf vignetteTitles: Documentation on diffGeneAnalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: diffHic Version: 1.2.2 Depends: R (>= 3.2.0), GenomicRanges Imports: Rsamtools, Biostrings, BSgenome, rhdf5, edgeR, limma, csaw, locfit, methods, IRanges, S4Vectors, GenomeInfoDb, BiocGenerics Suggests: BSgenome.Ecoli.NCBI.20080805 License: GPL-3 Archs: i386, x64 MD5sum: 52ad3fa67a0f2dd4497a9fe58b68c4c4 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/diffHic_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/diffHic_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/diffHic_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/diffHic_1.2.2.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 Package: DiffLogo Version: 1.0.0 Depends: R (>= 1.8.0), stats, cba, Suggests: knitr, testthat, seqLogo, MotifDb License: GPL (>= 2) MD5sum: dbdf950ad3045fb53f39fb716d99e1e3 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DiffLogo_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DiffLogo_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DiffLogo_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DiffLogo_1.0.0.tgz vignettes: vignettes/DiffLogo/inst/doc/DiffLogoBasics.pdf vignetteTitles: Basics of the DiffLogo package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: diggit Version: 1.2.0 Depends: R (>= 3.0.2), Biobase, methods Imports: ks, viper(>= 1.3.1), parallel Suggests: diggitdata License: GPL (>=2) MD5sum: 9c2cc7e62fb8cd8e3aa7a65b0560810e 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/diggit_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/diggit_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/diggit_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/diggit_1.2.0.tgz vignettes: vignettes/diggit/inst/doc/diggit.pdf vignetteTitles: Using DIGGIT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DirichletMultinomial Version: 1.12.1 Depends: S4Vectors, IRanges Imports: stats4, methods Suggests: lattice, parallel, MASS, RColorBrewer, xtable License: LGPL-3 Archs: i386, x64 MD5sum: 740650c1d048ee472e8fa4827ec05551 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/DirichletMultinomial_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/DirichletMultinomial_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/DirichletMultinomial_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DirichletMultinomial_1.12.1.tgz vignettes: vignettes/DirichletMultinomial/inst/doc/DirichletMultinomial.pdf vignetteTitles: An introduction to DirichletMultinomial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: TFBSTools Package: dks Version: 1.16.0 Depends: R (>= 2.8) Imports: cubature License: GPL MD5sum: 49b03b387efd4a237e279f927c4bdf38 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dks_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dks_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dks_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dks_1.16.0.tgz vignettes: vignettes/dks/inst/doc/dks.pdf vignetteTitles: dksTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DMRcaller Version: 1.2.0 Depends: R (>= 3.2), GenomicRanges, IRanges, S4Vectors Imports: parallel, Rcpp, RcppRoll Suggests: knitr, RUnit, BiocGenerics License: GPL-3 MD5sum: c8e3940a24df7eee064bc47cd839a554 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DMRcaller_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DMRcaller_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DMRcaller_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DMRcaller_1.2.0.tgz vignettes: vignettes/DMRcaller/inst/doc/DMRcaller.pdf vignetteTitles: DMRcaller hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DMRcate Version: 1.6.53 Depends: R (>= 3.2.2), minfi, DSS, DMRcatedata Imports: limma, GenomicRanges, parallel, methods, graphics, plyr, Gviz, IRanges Suggests: knitr, RUnit, BiocGenerics, IlluminaHumanMethylation450kanno.ilmn12.hg19 License: file LICENSE MD5sum: c3fc6de0ac3225b213b992d739fd8c3b 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 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_1.6.53.tar.gz win.binary.ver: bin/windows/contrib/3.2/DMRcate_1.6.53.zip win64.binary.ver: bin/windows64/contrib/3.2/DMRcate_1.6.53.zip mac.binary.ver: bin/macosx/contrib/3.2/DMRcate_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DMRcate_1.6.53.tgz vignettes: vignettes/DMRcate/inst/doc/DMRcate.pdf vignetteTitles: The DMRcate package user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: MEAL Package: DMRforPairs Version: 1.6.0 Depends: R (>= 2.15.2), Gviz (>= 1.2.1), R2HTML (>= 2.2.1), GenomicRanges (>= 1.10.7), parallel License: GPL (>= 2) MD5sum: 20ea0c098c9d3e08326e6603f59d0ce4 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DMRforPairs_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DMRforPairs_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DMRforPairs_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DMRforPairs_1.6.0.tgz vignettes: vignettes/DMRforPairs/inst/doc/DMRforPairs_vignette.pdf vignetteTitles: DMRforPairs_vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: DNABarcodes Version: 1.0.0 Depends: Matrix, parallel Imports: Rcpp (>= 0.11.2), BH LinkingTo: Rcpp, BH Suggests: knitr, BiocStyle, rmarkdown License: GPL-2 Archs: i386, x64 MD5sum: 5faad3f9c664ca9e4c85f0cf353c35cb 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DNABarcodes_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DNABarcodes_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DNABarcodes_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DNABarcodes_1.0.0.tgz vignettes: vignettes/DNABarcodes/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/DNABarcodes/inst/doc/DNABarcodes.html htmlTitles: "DNABarcodes" Package: DNAcopy Version: 1.44.0 License: GPL (>= 2) Archs: i386, x64 MD5sum: 7a453e5d891d61bec98988480511c78d 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DNAcopy_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DNAcopy_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DNAcopy_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DNAcopy_1.44.0.tgz vignettes: vignettes/DNAcopy/inst/doc/DNAcopy.pdf vignetteTitles: DNAcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, cghMCR, Clonality, CopyNumber450k, CRImage, snapCGH, SomatiCA importsMe: ADaCGH2, ArrayTV, ChAMP, Clonality, cn.farms, CNAnorm, CNVrd2, conumee, CopywriteR, GWASTools, MEDIPS, MinimumDistance, QDNAseq, Repitools, snapCGH, SomatiCA suggestsMe: beadarraySNP, Clonality, cn.mops, fastseg, genoset Package: domainsignatures Version: 1.30.0 Depends: R (>= 2.4.0), KEGG.db, prada, biomaRt, methods Imports: AnnotationDbi License: Artistic-2.0 MD5sum: fcdc5389becf2b8a60764d5ab8b75039 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/domainsignatures_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/domainsignatures_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/domainsignatures_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/domainsignatures_1.30.0.tgz vignettes: vignettes/domainsignatures/inst/doc/domainenrichment.pdf vignetteTitles: Gene set enrichment using InterPro domain signatures hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DOQTL Version: 1.6.0 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: 24fe3eb298b73127c3b9d1ad85c691b0 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 Maintainer: Daniel Gatti URL: http://do.jax.org source.ver: src/contrib/DOQTL_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DOQTL_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DOQTL_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DOQTL_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DOQTL_1.6.0.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 Package: DOSE Version: 2.8.3 Depends: R (>= 3.1.0) Imports: methods, plyr, qvalue, stats4, AnnotationDbi, DO.db, igraph, scales, reshape2, graphics, GOSemSim, grid, ggplot2 Suggests: org.Hs.eg.db, clusterProfiler, knitr, BiocStyle License: Artistic-2.0 MD5sum: 9bd036a04898f4a2beb786acb207eea8 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: https://github.com/GuangchuangYu/DOSE VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/DOSE/issues source.ver: src/contrib/DOSE_2.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/DOSE_2.8.3.zip win64.binary.ver: bin/windows64/contrib/3.2/DOSE_2.8.3.zip mac.binary.ver: bin/macosx/contrib/3.2/DOSE_2.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DOSE_2.8.3.tgz vignettes: vignettes/DOSE/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/DOSE/inst/doc/DOSE.html htmlTitles: "Disease Ontology Semantic and Enrichment analysis" importsMe: clusterProfiler, facopy, ReactomePA suggestsMe: ChIPseeker, GOSemSim Package: DriverNet Version: 1.10.0 Depends: R (>= 2.10), methods License: GPL-3 MD5sum: 418080bc5638ba2845d764e1068da5ea 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DriverNet_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DriverNet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DriverNet_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DriverNet_1.10.0.tgz vignettes: vignettes/DriverNet/inst/doc/DriverNet-Overview.pdf vignetteTitles: An introduction to DriverNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DrugVsDisease Version: 2.10.2 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: b3c0c42d9061ad2a8676826d25d18b49 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_2.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/DrugVsDisease_2.10.2.zip win64.binary.ver: bin/windows64/contrib/3.2/DrugVsDisease_2.10.2.zip mac.binary.ver: bin/macosx/contrib/3.2/DrugVsDisease_2.9.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DrugVsDisease_2.10.2.tgz vignettes: vignettes/DrugVsDisease/inst/doc/DrugVsDisease.pdf vignetteTitles: DrugVsDisease hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DSS Version: 2.10.0 Depends: Biobase, bsseq, splines, methods Suggests: BiocStyle License: GPL Archs: i386, x64 MD5sum: 72ecc536b9032114823bb2211eef1e72 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DSS_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DSS_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DSS_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DSS_2.10.0.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 dependsOnMe: DMRcate Package: DTA Version: 2.16.0 Depends: R (>= 2.10), LSD Imports: scatterplot3d License: Artistic-2.0 MD5sum: e2f9c20003a78c6117d01df4f831f92c 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DTA_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DTA_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DTA_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DTA_2.16.0.tgz vignettes: vignettes/DTA/inst/doc/DTA.pdf vignetteTitles: A guide to Dynamic Transcriptome Analysis (DTA) hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: dualKS Version: 1.30.0 Depends: R (>= 2.6.0), Biobase (>= 1.15.0), affy, methods Imports: graphics License: LGPL (>= 2.0) MD5sum: 7781abd78e5df00f927f0f63a9d65403 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dualKS_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dualKS_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dualKS_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dualKS_1.30.0.tgz vignettes: vignettes/dualKS/inst/doc/dualKS.pdf vignetteTitles: dualKS.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: DupChecker Version: 1.8.0 Imports: tools, R.utils, RCurl Suggests: knitr License: GPL (>= 2) MD5sum: 5e32a08b50b7a24155cb340aa52c912e 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DupChecker_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DupChecker_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DupChecker_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DupChecker_1.8.0.tgz vignettes: vignettes/DupChecker/inst/doc/DupChecker.pdf vignetteTitles: Validate genomic data with "DupChecker" package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: dupRadar Version: 1.0.0 Depends: R (>= 3.2.0) Imports: Rsubread (>= 1.14.1) Suggests: BiocStyle, knitr, rmarkdown, AnnotationHub License: GPL-3 MD5sum: b8070ecce14b62b0d95ed8b12aa6080e 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_1.0.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/dupRadar_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dupRadar_1.0.0.tgz vignettes: vignettes/dupRadar/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/dupRadar/inst/doc/dupRadar.html htmlTitles: "Using the dupRadar package" Package: dyebias Version: 1.28.0 Depends: R (>= 1.4.1), marray, Biobase Suggests: limma, convert, GEOquery, dyebiasexamples, methods License: GPL-3 MD5sum: 7f685967494ca4e167bf0099e3a96988 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/dyebias_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/dyebias_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/dyebias_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/dyebias_1.28.0.tgz vignettes: vignettes/dyebias/inst/doc/dyebias-vignette.pdf vignetteTitles: dye bias correction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: DynDoc Version: 1.48.0 Depends: methods, utils Imports: methods License: Artistic-2.0 MD5sum: 8614d1fa5832515779c30e89dc9d3938 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/DynDoc_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/DynDoc_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/DynDoc_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/DynDoc_1.48.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets Package: EasyqpcR Version: 1.12.0 Imports: plyr, matrixStats, plotrix, gWidgetsRGtk2 Suggests: SLqPCR, qpcrNorm, qpcR, knitr License: GPL (>=2) MD5sum: 6b62fb9a1dc75be912d43e931a14d34e 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EasyqpcR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EasyqpcR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EasyqpcR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EasyqpcR_1.12.0.tgz vignettes: vignettes/EasyqpcR/inst/doc/vignette_EasyqpcR.pdf vignetteTitles: EasyqpcR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: easyRNASeq Version: 2.6.3 Imports: Biobase (>= 2.30.0), BiocGenerics (>= 0.16.1), BiocParallel (>= 1.4.0), biomaRt (>= 2.26.1), Biostrings (>= 2.38.2), DESeq (>= 1.22.0), edgeR (>= 3.12.0), GenomeInfoDb (>= 1.6.1), genomeIntervals (>= 1.26.0), GenomicAlignments (>= 1.6.1), GenomicRanges (>= 1.22.1), SummarizedExperiment (>= 1.0.1), graphics, IRanges (>= 2.4.4), LSD (>= 3.0), locfit, methods, parallel, Rsamtools (>= 1.22.0), S4Vectors (>= 0.8.3), ShortRead (>= 1.28.0), utils Suggests: BiocStyle (>= 1.8.0), BSgenome (>= 1.38.0), BSgenome.Dmelanogaster.UCSC.dm3 (>= 1.4.0), curl, GenomicFeatures (>= 1.22.5), knitr, rmarkdown, RnaSeqTutorial (>= 0.7.0), RUnit (>= 0.4.31) License: Artistic-2.0 MD5sum: 0e03e83a354c989db5716704ed920cf4 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_2.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/easyRNASeq_2.6.3.zip win64.binary.ver: bin/windows64/contrib/3.2/easyRNASeq_2.6.3.zip mac.binary.ver: bin/macosx/contrib/3.2/easyRNASeq_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/easyRNASeq_2.6.3.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: "A walkthrough the easyRNASeq package functionalities" suggestsMe: SeqGSEA Package: EBarrays Version: 2.34.0 Depends: R (>= 1.8.0), Biobase, lattice, methods Imports: Biobase, cluster, graphics, grDevices, lattice, methods, stats License: GPL (>= 2) Archs: i386, x64 MD5sum: b3dd663b4739fce6320b62473eb6ad0e 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBarrays_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EBarrays_2.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EBarrays_2.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBarrays_2.34.0.tgz vignettes: vignettes/EBarrays/inst/doc/vignette.pdf vignetteTitles: Introduction to EBarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: EBcoexpress, gaga, geNetClassifier importsMe: casper suggestsMe: Category Package: EBcoexpress Version: 1.14.1 Depends: EBarrays, mclust, minqa Suggests: graph, igraph, colorspace License: GPL (>= 2) Archs: i386, x64 MD5sum: f9c6f319d4298b79f92360fcf3e334e3 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_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBcoexpress_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.2/EBcoexpress_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.2/EBcoexpress_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBcoexpress_1.14.1.tgz vignettes: vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.pdf vignetteTitles: EBcoexpress Demo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: EBImage Version: 4.12.2 Imports: BiocGenerics (>= 0.7.1), methods, graphics, grDevices, stats, abind, tiff, jpeg, png, locfit, fftwtools (>= 0.9-7) Suggests: BiocStyle, digest, knitr, rmarkdown License: LGPL Archs: i386, x64 MD5sum: cf708eb0300a6fc2323789fffc533ea7 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 VignetteBuilder: knitr source.ver: src/contrib/EBImage_4.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBImage_4.12.2.zip win64.binary.ver: bin/windows64/contrib/3.2/EBImage_4.12.2.zip mac.binary.ver: bin/macosx/contrib/3.2/EBImage_4.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBImage_4.12.2.tgz vignettes: vignettes/EBImage/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/EBImage/inst/doc/EBImage-introduction.html htmlTitles: "Introduction to EBImage" dependsOnMe: CRImage, flowcatchR, imageHTS importsMe: flowCHIC suggestsMe: ggtree, HilbertVis Package: EBSeq Version: 1.10.0 Depends: blockmodeling, gplots, testthat, R (>= 3.0.0) License: Artistic-2.0 MD5sum: 2a7d3dbcf63cfa88fcf3f291f2bbd4ad 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBSeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EBSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EBSeq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBSeq_1.10.0.tgz vignettes: vignettes/EBSeq/inst/doc/EBSeq_Vignette.pdf vignetteTitles: EBSeq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: EBSeqHMM, Oscope suggestsMe: compcodeR Package: EBSeqHMM Version: 1.4.0 Depends: EBSeq License: Artistic-2.0 MD5sum: 0cbd7d6d436ebf06dd6aee83bafbf6f1 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EBSeqHMM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EBSeqHMM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EBSeqHMM_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EBSeqHMM_1.4.0.tgz vignettes: vignettes/EBSeqHMM/inst/doc/EBSeqHMM_vignette.pdf vignetteTitles: HMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ecolitk Version: 1.42.0 Depends: R (>= 2.10) Imports: Biobase, graphics, methods Suggests: ecoliLeucine, ecolicdf, graph, multtest, affy License: GPL (>= 2) MD5sum: 1c45aaf8e20a359fc2f85db8cab7753c 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ecolitk_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ecolitk_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ecolitk_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ecolitk_1.42.0.tgz vignettes: vignettes/ecolitk/inst/doc/ecolitk.pdf vignetteTitles: ecolitk hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: EDASeq Version: 2.4.1 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: af0012e383c205cb9766d7ead4842983 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_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/EDASeq_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/EDASeq_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/EDASeq_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EDASeq_2.4.1.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 dependsOnMe: metaseqR, RUVSeq importsMe: EnrichmentBrowser, TCGAbiolinks suggestsMe: HTSFilter, oneChannelGUI Package: EDDA Version: 1.8.0 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: 9eddcbe09e83e750392877237321a2da 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EDDA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EDDA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EDDA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EDDA_1.8.0.tgz vignettes: vignettes/EDDA/inst/doc/EDDA.pdf vignetteTitles: EDDA Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: edge Version: 2.2.1 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: 10f52969e7a2423ba5eab49f1e9aeec9 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_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/edge_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/edge_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/edge_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/edge_2.2.1.tgz vignettes: vignettes/edge/inst/doc/edge.pdf vignetteTitles: edge Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: edgeR Version: 3.12.1 Depends: R (>= 2.15.0), limma Imports: methods Suggests: MASS, statmod, splines, locfit, KernSmooth License: GPL (>=2) Archs: i386, x64 MD5sum: ad2d932e30c33214f22321f2d3b21b9b 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_3.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/edgeR_3.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/edgeR_3.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/edgeR_3.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/edgeR_3.12.1.tgz vignettes: vignettes/edgeR/inst/doc/edgeR.pdf, vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf vignetteTitles: edgeR Vignette, edgeRUsersGuide.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: DBChIP, manta, methylMnM, MLSeq, RUVSeq, TCC, tRanslatome importsMe: ampliQueso, ArrayExpressHTS, compcodeR, csaw, DEGreport, DiffBind, diffHic, easyRNASeq, EDDA, EnrichmentBrowser, erccdashboard, HTSFilter, MEDIPS, metaseqR, msmsTests, PROPER, Repitools, rnaSeqMap, STATegRa, systemPipeR, TCGAbiolinks, ToPASeq, tweeDEseq suggestsMe: baySeq, biobroom, BitSeq, ClassifyR, clonotypeR, cqn, EDASeq, gage, GenomicAlignments, GenomicRanges, goseq, groHMM, GSAR, GSVA, missMethyl, oneChannelGUI, SSPA, subSeq, variancePartition Package: eiR Version: 1.10.1 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: 986e8b174cb22348e133f210fa24d28b 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_1.10.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/eiR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/eiR_1.10.1.tgz vignettes: vignettes/eiR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/eiR/inst/doc/eiR.html htmlTitles: "eiR" Package: eisa Version: 1.22.0 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: f539f95169a94a8a2ec3c9afd1149d01 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/eisa_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/eisa_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/eisa_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/eisa_1.22.0.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 dependsOnMe: ExpressionView importsMe: ExpressionView Package: ELBOW Version: 1.6.0 Depends: R (>= 2.15.0) Suggests: DESeq, GEOquery, limma, simpleaffy, affyPLM, RColorBrewer License: file LICENSE License_is_FOSS: yes License_restricts_use: no MD5sum: 60161a655aa9a26a4b7c7a66f9e61ccb 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ELBOW_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ELBOW_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ELBOW_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ELBOW_1.6.0.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 Package: ELMER Version: 1.2.1 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 License: GPL-3 MD5sum: 2e4d84f200988865a0098dbbb527e22f 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ELMER_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ELMER_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ELMER_1.1.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ELMER_1.2.1.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 Package: EMDomics Version: 2.0.0 Depends: R (>= 3.2.1) Imports: emdist, BiocParallel, matrixStats, ggplot2, CDFt, preprocessCore Suggests: knitr License: MIT + file LICENSE MD5sum: 3349f6306f54b43c21072d964a33c2f0 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_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EMDomics_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EMDomics_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EMDomics_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EMDomics_2.0.0.tgz vignettes: vignettes/EMDomics/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/EMDomics/inst/doc/EMDomics.html htmlTitles: "EMDomics Vignette" Package: ENCODExplorer Version: 1.2.4 Depends: R (>= 3.2) Imports: tools, jsonlite, RSQLite Suggests: RUnit,BiocGenerics,knitr, curl, httr License: Artistic-2.0 MD5sum: 4ada42d61e7ebc08b21097a8579904ca 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_1.2.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/ENCODExplorer_1.2.4.zip win64.binary.ver: bin/windows64/contrib/3.2/ENCODExplorer_1.2.4.zip mac.binary.ver: bin/macosx/contrib/3.2/ENCODExplorer_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ENCODExplorer_1.2.4.tgz vignettes: vignettes/ENCODExplorer/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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: 1.4.1 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: f68c65eec90ef5e4e63815dbcc8ddfdb NeedsCompilation: no Title: Data preprocessing and quality control for Illumina HumanMethylation450 BeadChip Description: Illumina HumanMethylation450 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. The package utilizes a novel model-based background correction method, ENmix, that significantly improve accuracy and reproducibility of methylation 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 commonly used data preprocessing methods, including BMIQ probe design type bias correction and ComBat batch effect correction. 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 Author: Zongli Xu [cre, aut], Liang Niu [aut], Leping Li [ctb], Jack Taylor [ctb] Maintainer: Zongli Xu source.ver: src/contrib/ENmix_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ENmix_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ENmix_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ENmix_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ENmix_1.4.1.tgz vignettes: vignettes/ENmix/inst/doc/ENmix.pdf vignetteTitles: ENmix User's Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: EnrichedHeatmap Version: 1.0.0 Depends: R (>= 3.1.2), grid, ComplexHeatmap (>= 1.4.0), GenomicRanges, IRanges, locfit Imports: methods, matrixStats, stats Suggests: testthat (>= 0.3), knitr, markdown, circlize (>= 0.3.1) License: GPL (>= 2) MD5sum: 4c30776706399a359d98388535302aec 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/EnrichedHeatmap_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/EnrichedHeatmap_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/EnrichedHeatmap_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EnrichedHeatmap_1.0.0.tgz vignettes: vignettes/EnrichedHeatmap/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/EnrichedHeatmap/inst/doc/EnrichedHeatmap.html htmlTitles: "Make Enriched Heatmaps" Package: EnrichmentBrowser Version: 2.0.15 Depends: R(>= 3.0.0), Biobase, GSEABase, pathview Imports: AnnotationDbi, ComplexHeatmap, DESeq2, EDASeq, GO.db, KEGGREST, KEGGgraph, MASS, PathNet, ReportingTools, Rgraphviz, S4Vectors, SparseM, SPIA, SummarizedExperiment, biocGraph, biomaRt, edgeR, geneplotter, graph, hwriter, limma, mixtools, neaGUI, npGSEA, safe, stringr, topGO Suggests: ALL, BiocStyle, airway, hgu95av2.db, testthat License: Artistic-2.0 MD5sum: b7bac0618089b5fe5b2675d622020b6d 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_2.0.15.tar.gz win.binary.ver: bin/windows/contrib/3.2/EnrichmentBrowser_2.0.15.zip win64.binary.ver: bin/windows64/contrib/3.2/EnrichmentBrowser_2.0.15.zip mac.binary.ver: bin/macosx/contrib/3.2/EnrichmentBrowser_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/EnrichmentBrowser_2.0.15.tgz vignettes: vignettes/EnrichmentBrowser/inst/doc/EnrichmentBrowser.pdf vignetteTitles: EnrichmentBrowser Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ensembldb Version: 1.2.2 Depends: BiocGenerics (>= 0.15.10), GenomicRanges, GenomicFeatures Imports: methods, RSQLite, DBI, Biobase, GenomeInfoDb, AnnotationDbi (>= 1.31.19), rtracklayer, S4Vectors, AnnotationHub, Rsamtools, IRanges Suggests: knitr, BiocStyle, EnsDb.Hsapiens.v75 (>= 0.99.7), RUnit, shiny License: LGPL MD5sum: c85a05b49a67d8b74419b110a70b45d1 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/ensembldb_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/ensembldb_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/ensembldb_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ensembldb_1.2.2.tgz vignettes: vignettes/ensembldb/inst/doc/ensembldb.pdf vignetteTitles: Generating an using Ensembl based annotation packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: ChIPpeakAnno Package: ensemblVEP Version: 1.10.3 Depends: methods, BiocGenerics, GenomicRanges, VariantAnnotation Imports: Biostrings Suggests: RUnit, S4Vectors License: Artistic-2.0 MD5sum: 129751f4859d42ac0a2cf43f28974347 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_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/ensemblVEP_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ensemblVEP_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ensemblVEP_1.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ensemblVEP_1.10.3.tgz vignettes: vignettes/ensemblVEP/inst/doc/ensemblVEP.pdf vignetteTitles: ensemblVEP hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ENVISIONQuery Version: 1.18.0 Depends: rJava, XML, utils License: GPL-2 MD5sum: 0b029b2a3f9593a4e0f430c0b3837116 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ENVISIONQuery_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ENVISIONQuery_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ENVISIONQuery_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ENVISIONQuery_1.18.0.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 importsMe: IdMappingRetrieval Package: epigenomix Version: 1.10.0 Depends: R (>= 2.12.0), methods, Biobase, S4Vectors, IRanges, GenomicRanges, SummarizedExperiment (>= 0.2.0) Imports: BiocGenerics, Rsamtools, beadarray License: LGPL-3 MD5sum: fa74d7670f2b6a5bd7d1401daa4bb382 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/epigenomix_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/epigenomix_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/epigenomix_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/epigenomix_1.10.0.tgz vignettes: vignettes/epigenomix/inst/doc/epigenomix.pdf vignetteTitles: epigenomix package vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: epivizr Version: 1.8.1 Depends: R (>= 3.0.1), methods, Biobase, SummarizedExperiment (>= 0.2.0), rtracklayer Imports: S4Vectors, httpuv (>= 1.3.0), rjson, OrganismDbi, R6 (>= 2.0.0), mime (>= 0.2), GenomeInfoDb, GenomicRanges, GenomicFeatures Suggests: testthat, roxygen2, knitr, antiProfilesData, hgu133plus2.db, knitrBootstrap, Mus.musculus License: Artistic-2.0 MD5sum: 2618db53749a68db9fc8d9736283204d NeedsCompilation: no Title: R Interface to epiviz web app Description: This package provides Websocket communication 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. 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 Maintainer: Hector Corrada Bravo VignetteBuilder: knitr Video: https://www.youtube.com/watch?v=099c4wUxozA source.ver: src/contrib/epivizr_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/epivizr_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/epivizr_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/epivizr_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/epivizr_1.8.1.tgz vignettes: vignettes/epivizr/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/epivizr/inst/doc/IntroToEpivizr.html htmlTitles: "Introduction to epivizr" Package: erccdashboard Version: 1.4.0 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: 375f67a563a8ff82e230e3dd8c43b134 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/erccdashboard_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/erccdashboard_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/erccdashboard_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/erccdashboard_1.4.0.tgz vignettes: vignettes/erccdashboard/inst/doc/erccdashboard.pdf vignetteTitles: erccdashboard examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: erma Version: 0.2.0 Depends: R (>= 3.1), methods, Homo.sapiens Imports: GenomicFiles (>= 1.5.2), rtracklayer, S4Vectors, BiocGenerics, GenomicRanges, ggplot2, Biobase, shiny, foreach Suggests: rmarkdown, BiocStyle, knitr, GO.db, BiocParallel, png, DT, doParallel License: Artistic-2.0 MD5sum: bf378d5fdbb8b77ba52292b4439b2de8 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_0.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/erma_0.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/erma_0.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/erma_0.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/erma_0.2.0.tgz vignettes: vignettes/erma/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/erma/inst/doc/erma.html htmlTitles: "ermaInteractive" suggestsMe: gQTLBase Package: eudysbiome Version: 1.0.0 Depends: R (>= 3.2.1) Imports: plyr(>= 1.8.1) License: GPL-2 MD5sum: 45c0a61a8eb100ccd774a1991899ac41 NeedsCompilation: no Title: pseudo-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 as 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. biocViews: Microbiome, Metagenomics,DifferentialExpression, Annotation, Visualization, MultipleComparison, SystemsBiology, Classification, Sequencing, Software Author: Xiaoyuan Zhou, Christine Nardini Maintainer: Xiaoyuan Zhou source.ver: src/contrib/eudysbiome_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/eudysbiome_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/eudysbiome_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/eudysbiome_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/eudysbiome_1.0.0.tgz vignettes: vignettes/eudysbiome/inst/doc/eudysbiome.pdf vignetteTitles: eudysbiome User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ExiMiR Version: 2.12.0 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: 6147f50c7d3215e824a4f530ade04ebf 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_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ExiMiR_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ExiMiR_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ExiMiR_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ExiMiR_2.12.0.tgz vignettes: vignettes/ExiMiR/inst/doc/ExiMiR-vignette.pdf vignetteTitles: Description of ExiMiR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: exomeCopy Version: 1.16.0 Depends: IRanges, GenomicRanges, Rsamtools Imports: stats4, methods, GenomeInfoDb Suggests: Biostrings License: GPL (>= 2) Archs: i386, x64 MD5sum: d0f03cdf9744904d5c1672fcca8efe79 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/exomeCopy_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/exomeCopy_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/exomeCopy_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/exomeCopy_1.16.0.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 importsMe: CNVPanelizer, Rariant Package: exomePeak Version: 2.2.2 Depends: Rsamtools, GenomicFeatures (>= 1.14.5), rtracklayer, GenomicAlignments License: GPL-2 MD5sum: 08315bedfd32c5aac66d4fe5ccdd3b6d 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_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/exomePeak_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/exomePeak_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/exomePeak_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/exomePeak_2.2.2.tgz vignettes: vignettes/exomePeak/inst/doc/exomePeak-Overview.pdf vignetteTitles: An introduction to exomePeak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: explorase Version: 1.34.0 Depends: R (>= 2.6.2) Imports: limma, rggobi, RGtk2 Suggests: cairoDevice License: GPL-2 MD5sum: 2312ffe8a9a7af970d9f10fc2fc9b9e2 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/explorase_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/explorase_1.34.0.zip vignettes: vignettes/explorase/inst/doc/explorase.pdf vignetteTitles: Introduction to exploRase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ExpressionView Version: 1.22.0 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: 5359ae65222360dcc2a22a7ab03d963e 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ExpressionView_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ExpressionView_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ExpressionView_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ExpressionView_1.22.0.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 Package: fabia Version: 2.16.0 Depends: R (>= 2.8.0), Biobase Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 05eefa8e126fda4039e16d1bfb75039a 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/fabia_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/fabia_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/fabia_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fabia_2.16.0.tgz vignettes: vignettes/fabia/inst/doc/fabia.pdf vignetteTitles: FABIA: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: hapFabia Package: facopy Version: 1.4.1 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, IRanges, MASS, nnet, reshape2, Rgraphviz, scales License: CC BY-NC 4.0 MD5sum: 77501f8c0e026c3cbc05d0b7064129e8 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/facopy_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/facopy_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/facopy_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/facopy_1.4.1.tgz vignettes: vignettes/facopy/inst/doc/facopy.pdf vignetteTitles: facopy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: factDesign Version: 1.46.0 Depends: Biobase (>= 2.5.5) Imports: stats Suggests: affy, genefilter, multtest License: LGPL MD5sum: db58cd9ecf5c1514570bfbdad9ae8cfe 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/factDesign_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/factDesign_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/factDesign_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/factDesign_1.46.0.tgz vignettes: vignettes/factDesign/inst/doc/factDesign.pdf vignetteTitles: factDesign hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: farms Version: 1.22.0 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: b3c4d1f67240fd9e6c5af359da3b3c3f 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/farms_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/farms_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/farms_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/farms_1.22.0.tgz vignettes: vignettes/farms/inst/doc/farms.pdf vignetteTitles: Using farms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: fastLiquidAssociation Version: 1.6.1 Depends: methods, LiquidAssociation, parallel, stats, Hmisc Imports: WGCNA Suggests: GOstats, yeastCC, org.Sc.sgd.db License: GPL-2 MD5sum: f1574069798a0963110abe255c085739 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/fastLiquidAssociation_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/fastLiquidAssociation_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/fastLiquidAssociation_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fastLiquidAssociation_1.6.1.tgz vignettes: vignettes/fastLiquidAssociation/inst/doc/fastLiquidAssociation.pdf vignetteTitles: fastLiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: fastseg Version: 1.16.0 Depends: R (>= 2.13), GenomicRanges, Biobase Imports: graphics, stats, IRanges, BiocGenerics Suggests: DNAcopy, oligo License: LGPL (>= 2.0) Archs: i386, x64 MD5sum: 62f6efe8300367365a05a3d63db16a90 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/fastseg_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/fastseg_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/fastseg_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fastseg_1.16.0.tgz vignettes: vignettes/fastseg/inst/doc/fastseg.pdf vignetteTitles: fastseg: Manual for the R package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: fCI Version: 1.0.0 Depends: R (>= 3.1),FNN, psych, gtools, zoo, rgl, grid, VennDiagram Suggests: knitr, rmarkdown, BiocStyle License: GPL (>= 2) MD5sum: daaa0f5e6d4c19a9d5db5aacbbbcbd7f NeedsCompilation: no Title: f-divergence Cutoff Index 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/fCI_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/fCI_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/fCI_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fCI_1.0.0.tgz vignettes: vignettes/fCI/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/fCI/inst/doc/fCI.html htmlTitles: "fCI" Package: fdrame Version: 1.42.0 Imports: tcltk, graphics, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: c4a76322f0cdce55ded00e65bdbaec2a 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/fdrame_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/fdrame_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/fdrame_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fdrame_1.42.0.tgz vignettes: vignettes/fdrame/inst/doc/fdrame.pdf vignetteTitles: Annotation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: FEM Version: 2.6.0 Depends: AnnotationDbi,Matrix,marray,corrplot,igraph,impute,limma,org.Hs.eg.db,graph,BiocGenerics Imports: graph License: GPL (>=2) MD5sum: 5f3ed18bb3fe4036ef964ed7c97797c7 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FEM_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FEM_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FEM_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FEM_2.6.0.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 Package: ffpe Version: 1.14.0 Depends: R (>= 2.10.0), TTR, methods Imports: Biobase, BiocGenerics, affy, lumi, methylumi, sfsmisc Suggests: genefilter, ffpeExampleData License: GPL (>2) MD5sum: 4bc93dc5f7848971d78adbc083477fa5 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ffpe_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ffpe_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ffpe_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ffpe_1.14.0.tgz vignettes: vignettes/ffpe/inst/doc/ffpe.pdf vignetteTitles: ffpe package user guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: FGNet Version: 3.4.0 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: 7ba03d5ca8574433f93e99dfd17ebf20 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_3.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FGNet_3.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FGNet_3.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FGNet_3.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FGNet_3.4.0.tgz vignettes: vignettes/FGNet/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/FGNet/inst/doc/FGNet.html htmlTitles: "FGNet" Package: FindMyFriends Version: 1.0.7 Imports: methods, Biobase, tools, dplyr, IRanges, Biostrings, S4Vectors, kebabs, igraph, Matrix, digest, filehash, Rcpp, ggplot2, gtable, grid, reshape2, ggdendro, BiocParallel LinkingTo: Rcpp Suggests: testthat, knitr, rmarkdown, reutils License: GPL (>=2) Archs: i386, x64 MD5sum: 537358acee239f2156948af27d112d19 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_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/FindMyFriends_1.0.7.zip win64.binary.ver: bin/windows64/contrib/3.2/FindMyFriends_1.0.7.zip mac.binary.ver: bin/macosx/contrib/3.2/FindMyFriends_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FindMyFriends_1.0.7.tgz vignettes: vignettes/FindMyFriends/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/FindMyFriends/inst/doc/FindMyFriends_intro.html htmlTitles: "Creating pangenomes using FindMyFriends" Package: FISHalyseR Version: 1.4.0 Depends: EBImage,abind Suggests: knitr License: Artistic-2.0 MD5sum: d70b111292c130f560243562f8e89ca5 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FISHalyseR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FISHalyseR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FISHalyseR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FISHalyseR_1.4.0.tgz vignettes: vignettes/FISHalyseR/inst/doc/FISHalyseR.pdf vignetteTitles: FISHAlyseR Automated fluorescence in situ hybridisation quantification in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: flagme Version: 1.26.0 Depends: gcspikelite, xcms, CAMERA Imports: gplots, graphics, MASS, methods, SparseM, stats, utils License: LGPL (>= 2) Archs: i386, x64 MD5sum: a5b142a80ba4347ade8c496c0c31cd61 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flagme_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flagme_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flagme_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flagme_1.26.0.tgz vignettes: vignettes/flagme/inst/doc/flagme.pdf vignetteTitles: Using flagme -- Fragment-level analysis of GCMS-based metabolomics data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: flipflop Version: 1.8.2 Depends: R (>= 2.10.0) Imports: methods, Matrix, IRanges, GenomicRanges, parallel Suggests: GenomicFeatures License: GPL-3 Archs: i386, x64 MD5sum: 662fab536784eda4cefdb1c586af2212 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_1.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/flipflop_1.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/flipflop_1.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/flipflop_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flipflop_1.8.2.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 Package: flowBeads Version: 1.8.0 Depends: R (>= 2.15.0), methods, Biobase, rrcov, flowCore Imports: flowCore, rrcov, knitr, xtable Suggests: flowViz License: Artistic-2.0 MD5sum: c8046e86bde3aa0d207dce8f4b7285ac 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowBeads_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowBeads_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowBeads_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowBeads_1.8.0.tgz vignettes: vignettes/flowBeads/inst/doc/HowTo-flowBeads.pdf vignetteTitles: Analysis of Flow Cytometry Bead Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowBin Version: 1.6.0 Depends: methods, flowCore, flowFP, R (>= 2.10) Imports: class, limma, snow, BiocGenerics Suggests: parallel License: Artistic-2.0 MD5sum: d101d7271512d01945a7482bd71403eb 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowBin_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowBin_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowBin_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowBin_1.6.0.tgz vignettes: vignettes/flowBin/inst/doc/flowBin.pdf vignetteTitles: flowBin hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowcatchR Version: 1.4.0 Depends: R (>= 2.10), methods, EBImage Imports: rgl, colorRamps, abind, BiocParallel Suggests: BiocStyle, knitr, shiny License: BSD_3_clause + file LICENSE MD5sum: 3b6340cba52bd2dab9c11e775ddc6eba 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowcatchR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowcatchR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowcatchR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowcatchR_1.4.0.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 Package: flowCHIC Version: 1.4.0 Depends: R (>= 3.1.0) Imports: methods, flowCore, EBImage, vegan, hexbin, ggplot2, grid License: GPL-2 MD5sum: 2a10b03ede2036fde204e4bbba2e7479 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCHIC_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCHIC_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCHIC_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCHIC_1.4.0.tgz vignettes: vignettes/flowCHIC/inst/doc/flowCHICmanual.pdf vignetteTitles: Analyze flow cytometric data using histogram information hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowCL Version: 1.8.0 Depends: R (>= 3.0.2), Rgraphviz, SPARQL Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: de3d2626548dbc93f872625f659544f8 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCL_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCL_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCL_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCL_1.8.0.tgz vignettes: vignettes/flowCL/inst/doc/flowCL.pdf vignetteTitles: flowCL package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowClean Version: 1.6.0 Depends: R (>= 2.15.0), flowCore Imports: bit, changepoint, sfsmisc Suggests: flowViz, grid, gridExtra License: Artistic-2.0 MD5sum: d86dfc917f1afad219ba8727b5bd25a8 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowClean_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowClean_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowClean_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowClean_1.6.0.tgz vignettes: vignettes/flowClean/inst/doc/flowClean.pdf vignetteTitles: flowClean hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowClust Version: 3.8.0 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: 64fd0ae6a0f0860eba200b0b457d0a7a 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_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowClust_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowClust_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowClust_3.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowClust_3.8.0.tgz vignettes: vignettes/flowClust/inst/doc/flowClust.pdf vignetteTitles: flowClust package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: flowTrans, flowType suggestsMe: BiocGenerics Package: flowCore Version: 1.36.9 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: 5a15ddedcc5e3d544069ccfc3ecd9c6c 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_1.36.9.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCore_1.36.9.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCore_1.36.9.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCore_1.36.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCore_1.36.9.tgz vignettes: vignettes/flowCore/inst/doc/HowTo-flowCore.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: flowBeads, flowBin, flowClean, flowClust, flowFP, flowMatch, FlowSOM, flowStats, flowTrans, flowUtils, flowViz, flowVS, immunoClust, ncdfFlow, plateCore importsMe: cytofkit, flowBeads, flowCHIC, flowDensity, flowFit, flowMeans, flowQ, flowStats, flowTrans, flowType, flowViz, plateCore, spade suggestsMe: COMPASS, flowQB, FlowRepositoryR, RchyOptimyx Package: flowCyBar Version: 1.6.0 Depends: R (>= 3.0.0) Imports: gplots, vegan, methods License: GPL-2 MD5sum: f86d8a14e56b2b777fe4a499fc563b03 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowCyBar_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowCyBar_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowCyBar_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowCyBar_1.6.0.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 Package: flowDensity Version: 1.4.0 Depends: R (>= 2.10.0), methods Imports: flowCore, graphics, car, gplots, RFOC, GEOmap, methods, grDevices License: Artistic-2.0 MD5sum: 5c8156fab13638a0e21ca6a0951fe478 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowDensity_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowDensity_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowDensity_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowDensity_1.4.0.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 Package: flowFit Version: 1.8.0 Depends: R (>= 2.12.2) Imports: flowCore, flowViz, graphics, kza, methods, minpack.lm, gplots Suggests: flowFitExampleData License: Artistic-2.0 MD5sum: 53e0c03ee0917cd38b4979099bbf6746 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowFit_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowFit_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowFit_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowFit_1.8.0.tgz vignettes: vignettes/flowFit/inst/doc/HowTo-flowFit.pdf vignetteTitles: Fitting Functions for Flow Cytometry Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowFP Version: 1.28.0 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: 6fb345abf5c64289b910867e9484eeaa 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowFP_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowFP_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowFP_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowFP_1.28.0.tgz vignettes: vignettes/flowFP/inst/doc/flowFP_HowTo.pdf vignetteTitles: Fingerprinting for Flow Cytometry hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: flowBin Package: flowMap Version: 1.8.0 Depends: R (>= 3.0.1), ade4(>= 1.5-2), doParallel(>= 1.0.3), abind(>= 1.4.0), reshape2(>= 1.2.2), scales(>= 0.2.3), Matrix(>= 1.1-4), methods (>= 2.14) Suggests: BiocStyle, knitr License: GPL (>=2) MD5sum: aa15e7c1f1e74acf22fc5173d32268c6 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMap_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMap_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMap_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMap_1.8.0.tgz vignettes: vignettes/flowMap/inst/doc/flowMap.pdf vignetteTitles: Mapping cell populations in flow cytometry data flowMap-FR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowMatch Version: 1.6.0 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: 09f22f288cb3c4e956ecb5cbc9206c87 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMatch_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMatch_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMatch_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMatch_1.6.0.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 Package: flowMeans Version: 1.30.0 Depends: R (>= 2.10.0) Imports: Biobase, graphics, grDevices, methods, rrcov, stats, feature, flowCore License: Artistic-2.0 MD5sum: 3742d85b3c0f435fde1d2fc95226e332 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMeans_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMeans_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMeans_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMeans_1.30.0.tgz vignettes: vignettes/flowMeans/inst/doc/flowMeans.pdf vignetteTitles: flowMeans: Non-parametric Flow Cytometry Data Gating hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: flowType Package: flowMerge Version: 2.18.0 Depends: graph,feature,flowClust,Rgraphviz,foreach,snow Imports: rrcov,flowCore, graphics, methods, stats, utils Enhances: doMC, multicore License: Artistic-2.0 MD5sum: 5e9c466ed4540b796c38cb89da512067 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowMerge_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowMerge_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowMerge_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowMerge_2.18.0.tgz vignettes: vignettes/flowMerge/inst/doc/flowMerge.pdf vignetteTitles: flowMerge package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: flowType Package: flowPeaks Version: 1.12.0 Depends: R (>= 2.12.0) Enhances: flowCore License: Artistic-1.0 Archs: i386, x64 MD5sum: bb2ed76b2f47aa7bc2f0fadabb511e63 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowPeaks_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowPeaks_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowPeaks_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowPeaks_1.12.0.tgz vignettes: vignettes/flowPeaks/inst/doc/flowPeaks-guide.pdf vignetteTitles: Tutorial of flowPeaks package hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowPlots Version: 1.18.0 Depends: R (>= 2.13.0), methods Suggests: vcd License: Artistic-2.0 MD5sum: 23c007432aee57778d801f229fbed67c 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowPlots_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowPlots_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowPlots_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowPlots_1.18.0.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 Package: flowQ Version: 1.30.0 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: d1420b4fecaa9f4a64db00399fa39ec9 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_1.30.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/flowQ_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowQ_1.30.0.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 Package: flowQB Version: 1.14.0 Imports: Biobase, graphics,methods, flowCore,stats,MASS Suggests: MASS, flowCore License: Artistic-2.0 MD5sum: 51fe810020a5aa7cf57287487b3f8961 NeedsCompilation: no Title: Automated Quadratic Characterization of Flow Cytometer Instrument Sensitivity: Q, B and CVinstrinsic 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 Author: Faysal El Khettabi Maintainer: Faysal El Khettabi source.ver: src/contrib/flowQB_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowQB_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowQB_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowQB_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowQB_1.14.0.tgz vignettes: vignettes/flowQB/inst/doc/flowQBVignettes.pdf vignetteTitles: flowQB package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: FlowRepositoryR Version: 1.2.0 Depends: R (>= 3.2) Imports: XML, RCurl, tools, utils Suggests: RUnit, BiocGenerics, flowCore, methods License: Artistic-2.0 MD5sum: db17c44b6ece236c1626d923fb9594f4 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FlowRepositoryR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FlowRepositoryR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FlowRepositoryR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FlowRepositoryR_1.2.0.tgz vignettes: vignettes/FlowRepositoryR/inst/doc/HowTo-FlowRepositoryR.pdf vignetteTitles: FlowRepository R Interface hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: FlowSOM Version: 1.2.0 Depends: R (>= 2.11), flowCore, igraph, ConsensusClusterPlus, BiocGenerics, tsne Suggests: flowUtils, BiocStyle License: GPL (>= 2) Archs: i386, x64 MD5sum: aa74cfd800923da31225fe35fed95953 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FlowSOM_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FlowSOM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FlowSOM_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FlowSOM_1.2.0.tgz vignettes: vignettes/FlowSOM/inst/doc/FlowSOM.pdf vignetteTitles: Using SOMs for visualization of cytometry data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowStats Version: 3.28.1 Depends: R (>= 2.10), flowCore, fda (>= 2.2.6), mvoutlier, 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: 81e964396f38132e24794e7092552148 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_3.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowStats_3.28.1.zip win64.binary.ver: bin/windows64/contrib/3.2/flowStats_3.28.1.zip mac.binary.ver: bin/macosx/contrib/3.2/flowStats_3.28.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowStats_3.28.1.tgz vignettes: vignettes/flowStats/inst/doc/GettingStartedWithFlowStats.pdf vignetteTitles: flowStats Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: flowVS importsMe: plateCore suggestsMe: flowCore, flowQ Package: flowTrans Version: 1.22.0 Depends: R (>= 2.11.0), flowCore, flowViz,flowClust Imports: flowCore, methods, flowViz, stats, flowClust License: Artistic-2.0 MD5sum: 81da336fc497963967c92ef5eb71f339 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowTrans_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowTrans_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowTrans_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowTrans_1.22.0.tgz vignettes: vignettes/flowTrans/inst/doc/flowTrans.pdf vignetteTitles: flowTrans package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: flowType Version: 2.8.2 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: f5a48578c60dbac8e8b3798fa1641852 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 PackageStatus: Deprecated source.ver: src/contrib/flowType_2.8.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowType_2.8.2.zip win64.binary.ver: bin/windows64/contrib/3.2/flowType_2.8.2.zip mac.binary.ver: bin/macosx/contrib/3.2/flowType_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowType_2.8.2.tgz vignettes: vignettes/flowType/inst/doc/flowType.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: RchyOptimyx Package: flowUtils Version: 1.34.0 Depends: R (>= 2.2.0), flowCore (>= 1.32.0) Imports: Biobase, graph, methods, stats, utils, flowViz, corpcor, RUnit, XML Suggests: gatingMLData License: Artistic-2.0 MD5sum: 87c5c4593d2e2f0807dbb28843e96a7a 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 Maintainer: Josef Spidlen source.ver: src/contrib/flowUtils_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowUtils_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowUtils_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowUtils_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowUtils_1.34.0.tgz vignettes: vignettes/flowUtils/inst/doc/HowTo-flowUtils.pdf vignetteTitles: Gating-ML support in R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: FlowSOM Package: flowViz Version: 1.34.1 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: a3d66a2116ab3a481754cf44ac4d6040 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_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowViz_1.34.1.zip win64.binary.ver: bin/windows64/contrib/3.2/flowViz_1.34.1.zip mac.binary.ver: bin/macosx/contrib/3.2/flowViz_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowViz_1.34.1.tgz vignettes: vignettes/flowViz/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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, flowUtils suggestsMe: flowBeads, flowClean, flowCore, spade Package: flowVS Version: 1.2.0 Depends: R (>= 3.2), methods, flowCore, flowViz, flowStats Suggests: knitr, vsn, License: Artistic-2.0 MD5sum: 549c381d19b5bb21ceb7f8e127dad28f 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowVS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/flowVS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/flowVS_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowVS_1.2.0.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 Package: flowWorkspace Version: 3.16.16 Depends: R (>= 2.16.0),flowCore(>= 1.36.4),flowViz(>= 1.29.27),ncdfFlow(>= 2.16.1),gridExtra Imports: Biobase, BiocGenerics, graph, graphics, lattice, methods, stats, stats4, utils, RBGL, XML, tools,gridExtra,Rgraphviz ,data.table ,dplyr ,latticeExtra ,Rcpp ,RColorBrewer ,stringr ,flowUtils ,jsonlite LinkingTo: Rcpp, BH(>= 1.60.0-1) Suggests: testthat ,flowWorkspaceData ,RSVGTipsDevice ,knitr License: Artistic-2.0 Archs: i386, x64 MD5sum: f2295c4d1b718f92f8197b5b2fd600eb NeedsCompilation: yes Title: Import flowJo Workspaces into BioConductor and replicate flowJo gating with flowCore 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_3.16.16.tar.gz win.binary.ver: bin/windows/contrib/3.2/flowWorkspace_3.16.16.zip win64.binary.ver: bin/windows64/contrib/3.2/flowWorkspace_3.16.16.zip mac.binary.ver: bin/macosx/contrib/3.2/flowWorkspace_3.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/flowWorkspace_3.16.16.tgz vignettes: vignettes/flowWorkspace/inst/doc/flowWorkspace.pdf vignetteTitles: Importing flowJo Workspaces into R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: TRUE hasLICENSE: FALSE Rfiles: vignettes/flowWorkspace/inst/doc/flowWorkspace.R htmlDocs: vignettes/flowWorkspace/inst/doc/HowToMergeGatingSet.html, vignettes/flowWorkspace/inst/doc/HowToParseGatingML.html, vignettes/flowWorkspace/inst/doc/plotGate.html htmlTitles: "How to merge GatingSets", "How to parse gatingML into a GatingSet", "How to plot gated data" dependsOnMe: flowStats, openCyto suggestsMe: COMPASS Package: fmcsR Version: 1.12.2 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: 72f86afa5f4cb1e178c2671bce80e7bb 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_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/fmcsR_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.2/fmcsR_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.2/fmcsR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/fmcsR_1.12.2.tgz vignettes: vignettes/fmcsR/inst/doc/ hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/fmcsR/inst/doc/fmcsR.html htmlTitles: "fmcsR" importsMe: Rcpi suggestsMe: ChemmineR Package: focalCall Version: 1.4.0 Depends: R(>= 2.10.0), CGHcall Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: f7419da59ca6cbc791e745fcca619f4e 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/focalCall_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/focalCall_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/focalCall_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/focalCall_1.4.0.tgz vignettes: vignettes/focalCall/inst/doc/focalCall.pdf vignetteTitles: focalCall hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: FourCSeq Version: 1.4.0 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: 5ed5150732c539dc2acac94dd4a62126 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FourCSeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FourCSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FourCSeq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FourCSeq_1.4.0.tgz vignettes: vignettes/FourCSeq/inst/doc/FourCSeq.pdf vignetteTitles: FourCSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: FRGEpistasis Version: 1.6.0 Depends: R (>= 2.15), MASS, fda, methods, stats Imports: utils License: GPL-2 MD5sum: cde0b2ba4ca325c1053b7bd561338f0d 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FRGEpistasis_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FRGEpistasis_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FRGEpistasis_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FRGEpistasis_1.6.0.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 Package: frma Version: 1.22.0 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: 653bbd57c83204d4957e409a8a77b345 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/frma_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/frma_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/frma_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/frma_1.22.0.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 importsMe: ChIPXpress suggestsMe: frmaTools Package: frmaTools Version: 1.22.0 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: f19039aa322415bc5f685acc1f9273d2 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/frmaTools_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/frmaTools_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/frmaTools_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/frmaTools_1.22.0.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 Package: FunciSNP Version: 1.12.0 Depends: R (>= 2.14.0), ggplot2, TxDb.Hsapiens.UCSC.hg19.knownGene, FunciSNP.data Imports: AnnotationDbi, IRanges, Rsamtools (>= 1.6.1), rtracklayer(>= 1.14.1), methods, ChIPpeakAnno (>= 2.2.0), GenomicRanges, VariantAnnotation, plyr, org.Hs.eg.db, snpStats, ggplot2 (>= 0.9.0), reshape (>= 0.8.4), scales Enhances: parallel License: GPL-3 MD5sum: 31f898f8911ab1154ce4a4611736a210 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/FunciSNP_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/FunciSNP_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/FunciSNP_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/FunciSNP_1.12.0.tgz vignettes: vignettes/FunciSNP/inst/doc/FunciSNP_vignette.pdf vignetteTitles: FunciSNP Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: gaga Version: 2.16.0 Depends: R (>= 2.8.0), Biobase, coda, EBarrays, mgcv Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 008b1af7a2fe0a37fa9a4abc675f8b41 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaga_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaga_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaga_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaga_2.16.0.tgz vignettes: vignettes/gaga/inst/doc/gagamanual.pdf vignetteTitles: Manual for the gaga library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: casper Package: gage Version: 2.20.1 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: 184c6118ea7b4ca12d748f8f065c70e9 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_2.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/gage_2.20.1.zip win64.binary.ver: bin/windows64/contrib/3.2/gage_2.20.1.zip mac.binary.ver: bin/macosx/contrib/3.2/gage_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gage_2.20.1.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 suggestsMe: FGNet, pathview Package: gaggle Version: 1.38.0 Depends: R (>= 2.3.0), rJava (>= 0.4), graph (>= 1.10.2), RUnit (>= 0.4.17) License: GPL version 2 or newer MD5sum: c22aa6df9a7af51131e9cffdd264cf80 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaggle_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaggle_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaggle_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaggle_1.38.0.tgz vignettes: vignettes/gaggle/inst/doc/gaggle.pdf vignetteTitles: Gaggle Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gaia Version: 2.14.0 Depends: R (>= 2.10) License: GPL-2 MD5sum: a3150894c2cf31ce9d97c70b24dc6d9b 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaia_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaia_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaia_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaia_2.14.0.tgz vignettes: vignettes/gaia/inst/doc/gaia.pdf vignetteTitles: gaia hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gaucho Version: 1.6.0 Depends: R (>= 3.0.0), compiler, GA, graph, heatmap.plus, png, Rgraphviz Suggests: knitr License: GPL-3 MD5sum: dbdc4c831cde47f6ee1a65713a5fca6b 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gaucho_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gaucho_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gaucho_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gaucho_1.6.0.tgz vignettes: vignettes/gaucho/inst/doc/gaucho_vignette.pdf vignetteTitles: An introduction to gaucho hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: gcatest Version: 1.0.0 Depends: R (>= 3.2) Imports: lfa Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: 758626fa2306ef5281b61bb3ec2db136 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gcatest_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gcatest_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gcatest_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gcatest_1.0.0.tgz vignettes: vignettes/gcatest/inst/doc/gcatest.pdf vignetteTitles: gcat Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gCMAP Version: 1.14.0 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: 906afc512ae5614b34e141a28ccae3d1 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gCMAP_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gCMAP_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gCMAP_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gCMAP_1.14.0.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 dependsOnMe: gCMAPWeb Package: gCMAPWeb Version: 1.10.0 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: 6a7432c60f1f3c0905c310c8d40a2768 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gCMAPWeb_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gCMAPWeb_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gCMAPWeb_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gCMAPWeb_1.10.0.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 Package: gcrma Version: 2.42.0 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: 67b204ae584d83f69e76a1e756e145da 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_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gcrma_2.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gcrma_2.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gcrma_2.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gcrma_2.42.0.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: 1.6.2 Depends: R (>= 2.14.0) Imports: methods Suggests: parallel, crayon, RUnit, knitr, BiocGenerics License: LGPL-3 Archs: i386, x64 MD5sum: d2fb57c22baacb36539154191b05ccaf 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 also supported with relatively efficient random access. It is 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) 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_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/gdsfmt_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/gdsfmt_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/gdsfmt_1.6.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gdsfmt_1.6.2.tgz vignettes: vignettes/gdsfmt/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/gdsfmt/inst/doc/gdsfmt_vignette.html htmlTitles: "R Interface to CoreArray Genomic Data Structure (GDS) Files" dependsOnMe: SeqArray, SNPRelate importsMe: GENESIS, GWASTools, SeqVarTools suggestsMe: HIBAG Package: geecc Version: 1.4.0 Depends: R (>= 3.0.0), methods Imports: MASS, hypergea (>= 1.2.3), gplots Suggests: hgu133plus2.db, GO.db, AnnotationDbi License: GPL (>= 2) MD5sum: 7df50ae5a5d176060a14ebbb2dc5cf2d 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geecc_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geecc_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geecc_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geecc_1.4.0.tgz vignettes: vignettes/geecc/inst/doc/geecc.pdf vignetteTitles: geecc User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: genArise Version: 1.46.0 Depends: R (>= 1.7.1), locfit, tkrplot, methods Imports: graphics, grDevices, methods, stats, tcltk, utils, xtable License: file LICENSE License_restricts_use: yes MD5sum: 2cd41428534cc2e9be03d628c47d2637 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genArise_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genArise_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genArise_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genArise_1.46.0.tgz vignettes: vignettes/genArise/inst/doc/genArise.pdf vignetteTitles: genAriseGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: GENE.E Version: 1.10.0 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: d0784a96f3d14c2e82e9a9e915b2aaa5 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GENE.E_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GENE.E_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GENE.E_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GENE.E_1.10.0.tgz vignettes: vignettes/GENE.E/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/GENE.E/inst/doc/GENE.E-vignette.html htmlTitles: "GENE.E Overview" Package: GeneAnswers Version: 2.12.0 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: 01ac31b938311d4b5caf6896c67ca8a7 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_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneAnswers_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneAnswers_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneAnswers_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneAnswers_2.12.0.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 Package: GeneBreak Version: 1.0.0 Depends: R(>= 3.2), QDNAseq, CGHcall, CGHbase, GenomicRanges Imports: graphics, methods License: GPL-2 MD5sum: 91a3f1f33dfa7ba245354a19e8b49414 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneBreak_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneBreak_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneBreak_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneBreak_1.0.0.tgz vignettes: vignettes/GeneBreak/inst/doc/GeneBreak.pdf vignetteTitles: GeneBreak hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneExpressionSignature Version: 1.16.0 Depends: R (>= 2.13), Biobase, PGSEA Suggests: apcluster,GEOquery License: GPL-2 MD5sum: 93872b34cd1021548cc90c9e2e031bb0 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneExpressionSignature_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneExpressionSignature_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneExpressionSignature_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneExpressionSignature_1.16.0.tgz vignettes: vignettes/GeneExpressionSignature/inst/doc/GeneExpressionSignature.pdf vignetteTitles: GeneExpressionSignature hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: genefilter Version: 1.52.1 Imports: 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: f0eed7c67eed467cccd0e1bac8c9764b 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_1.52.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/genefilter_1.52.1.zip win64.binary.ver: bin/windows64/contrib/3.2/genefilter_1.52.1.zip mac.binary.ver: bin/macosx/contrib/3.2/genefilter_1.52.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genefilter_1.52.1.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: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: a4Base, cellHTS2, charm, CNTools, GeneMeta, simpleaffy, sva importsMe: affyQCReport, annmap, arrayQualityMetrics, Category, cellHTS, DESeq, DESeq2, DEXSeq, eisa, gCMAP, GGBase, GSRI, methyAnalysis, methylumi, minfi, MLInterfaces, mogsa, PECA, phenoTest, Ringo, simpleaffy, TCGAbiolinks, tilingArray, XDE suggestsMe: AffyExpress, annotate, ArrayTools, BiocCaseStudies, BioNet, Category, categoryCompare, clusterStab, codelink, compcodeR, factDesign, ffpe, GenomicFiles, GOstats, GSAR, GSEAlm, GSVA, logicFS, lumi, MCRestimate, npGSEA, oligo, oneChannelGUI, phyloseq, pvac, qpgraph, rtracklayer, siggenes, SSPA, topGO, XDE Package: genefu Version: 2.2.0 Depends: survcomp, mclust, 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: a74dc20daee774edc07b039a8b4b3900 NeedsCompilation: no Title: Computation of Gene Expression-Based Signatures in Breast Cancer Description: Description: This package contains functions implementing various tasks usually required by gene expression analysis, especially in breast cancer studies: gene mapping between different microarray platforms, identification of molecular subtypes, implementation of published gene signatures, gene selection, and survival analysis. biocViews: DifferentialExpression, GeneExpression, Visualization, Clustering, Classification Author: Deena M.A. Gendoo, Natchar Ratanasirigulchai, Markus S. Schroeder, Benjamin Haibe-Kains Maintainer: Benjamin Haibe-Kains , Markus Schroeder URL: http://www.pmgenomics.ca/bhklab/software/genefu VignetteBuilder: knitr source.ver: src/contrib/genefu_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genefu_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genefu_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genefu_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genefu_2.2.0.tgz vignettes: vignettes/genefu/inst/doc/genefu.pdf vignetteTitles: genefu An Introduction (HowTo) hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneGA Version: 1.20.0 Depends: seqinr, hash, methods License: GPL version 2 MD5sum: b2ac3afdde91b3fbaad4395e9566e843 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_1.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/GeneGA_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneGA_1.20.0.tgz vignettes: vignettes/GeneGA/inst/doc/GeneGA.pdf vignetteTitles: GeneGA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneMeta Version: 1.42.0 Depends: R (>= 2.10), methods, Biobase (>= 2.5.5), genefilter Imports: methods, Biobase (>= 2.5.5) Suggests: RColorBrewer License: Artistic-2.0 MD5sum: bd9c18651a0dbf84fdf0bdcc131fe561 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneMeta_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneMeta_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneMeta_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneMeta_1.42.0.tgz vignettes: vignettes/GeneMeta/inst/doc/GeneMeta.pdf vignetteTitles: GeneMeta Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: genefu, XDE Package: GeneNetworkBuilder Version: 1.12.0 Depends: R (>= 2.15.1), Rcpp (>= 0.9.13), graph Imports: plyr, graph LinkingTo: Rcpp Suggests: RUnit, BiocGenerics, Rgraphviz, XML, RCytoscape, RBGL, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: 26dc25d2127ceda7718982eb822d9fb6 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneNetworkBuilder_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneNetworkBuilder_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneNetworkBuilder_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneNetworkBuilder_1.12.0.tgz vignettes: vignettes/GeneNetworkBuilder/inst/doc/GeneNetworkBuilder.pdf vignetteTitles: GeneNetworkBuilder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneOverlap Version: 1.6.0 Imports: stats, RColorBrewer, gplots, methods Suggests: RUnit, BiocGenerics, BiocStyle License: GPL-3 MD5sum: 193d69c076913bb77cb28a032fc141e5 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneOverlap_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneOverlap_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneOverlap_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneOverlap_1.6.0.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 Package: geneplotter Version: 1.48.0 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: 9f74bd0d9d500f158300ef7b0374df20 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geneplotter_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geneplotter_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geneplotter_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geneplotter_1.48.0.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 dependsOnMe: HMMcopy importsMe: biocGraph, DESeq, DESeq2, DEXSeq, EnrichmentBrowser, flowQ, IsoGeneGUI, MethylSeekR, RNAinteract, RNAither suggestsMe: BiocCaseStudies, biocGraph, Category, chimera, GOstats Package: geneRecommender Version: 1.42.0 Depends: R (>= 1.8.0), Biobase (>= 1.4.22), methods Imports: Biobase, methods, stats License: GPL (>= 2) MD5sum: b1ec5f58c841aabd30588d7ff9a78073 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geneRecommender_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geneRecommender_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geneRecommender_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geneRecommender_1.42.0.tgz vignettes: vignettes/geneRecommender/inst/doc/geneRecommender.pdf vignetteTitles: Using the geneRecommender Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneRegionScan Version: 1.26.0 Depends: methods, Biobase (>= 2.5.5), Biostrings Imports: Biobase (>= 2.5.5), affxparser, RColorBrewer, Biostrings Suggests: BSgenome, affy, AnnotationDbi License: GPL (>= 2) MD5sum: 935623289edc4476a02b3a503db0a93b 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneRegionScan_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneRegionScan_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneRegionScan_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneRegionScan_1.26.0.tgz vignettes: vignettes/GeneRegionScan/inst/doc/GeneRegionScan.pdf vignetteTitles: GeneRegionScan hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: geneRxCluster Version: 1.6.0 Depends: GenomicRanges,IRanges Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: aaf5284f0afefb3bb7bf7d03e63fcb80 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geneRxCluster_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geneRxCluster_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geneRxCluster_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geneRxCluster_1.6.0.tgz vignettes: vignettes/geneRxCluster/inst/doc/tutorial.pdf vignetteTitles: Using geneRxCluster hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneSelectMMD Version: 2.14.0 Depends: R (>= 2.13.2), Biobase Imports: Biobase, MASS, graphics, stats, survival, limma Suggests: ALL License: GPL (>= 2) Archs: i386, x64 MD5sum: df172db8923ad5c8200bb5efc378a1fb 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneSelectMMD_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneSelectMMD_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneSelectMMD_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneSelectMMD_2.14.0.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 importsMe: iCheck Package: GeneSelector Version: 2.20.0 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: b9894286b4196446fc1b402409ba122b 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_2.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneSelector_2.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneSelector_2.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneSelector_2.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneSelector_2.20.0.tgz vignettes: vignettes/GeneSelector/inst/doc/GeneSelector.pdf vignetteTitles: GeneSelector.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GENESIS Version: 2.0.1 Depends: GWASTools Imports: gdsfmt Suggests: SNPRelate, RUnit, BiocGenerics, knitr License: GPL-3 MD5sum: c8e4640d9f30f07b7dd22aefdb096451 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) and PC-Relate (Conomos et al., In Review). 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. 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_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GENESIS_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GENESIS_2.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GENESIS_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GENESIS_2.0.1.tgz vignettes: vignettes/GENESIS/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/GENESIS/inst/doc/pcair.html htmlTitles: "Population Structure and Relatedness Inference using the GENESIS Package" Package: geNetClassifier Version: 1.10.0 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: 932181ed031704f4a2cfc7708ea40d0f 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/geNetClassifier_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/geNetClassifier_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/geNetClassifier_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/geNetClassifier_1.10.0.tgz vignettes: vignettes/geNetClassifier/inst/doc/geNetClassifier-vignette.pdf vignetteTitles: geNetClassifier-vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneticsDesign Version: 1.38.0 Imports: gmodels, graphics, gtools (>= 2.4.0), mvtnorm, stats License: GPL-2 MD5sum: acf3eae6be0fc42eb748c216b5258193 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneticsDesign_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneticsDesign_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneticsDesign_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneticsDesign_1.38.0.tgz vignettes: vignettes/GeneticsDesign/inst/doc/GPC.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GeneticsPed Version: 1.32.0 Depends: R (>= 2.4.0), MASS Imports: gdata, genetics Suggests: RUnit, gtools License: LGPL (>= 2.1) | file LICENSE Archs: i386, x64 MD5sum: 38a58f17a769df95642195577f2141be 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GeneticsPed_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GeneticsPed_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GeneticsPed_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GeneticsPed_1.32.0.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 Package: genoCN Version: 1.22.0 Imports: graphics, stats, utils License: GPL (>=2) Archs: i386, x64 MD5sum: d661095584c28ddee2d74f445f258230 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genoCN_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genoCN_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genoCN_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genoCN_1.22.0.tgz vignettes: vignettes/genoCN/inst/doc/genoCN.pdf vignetteTitles: add stuff hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: genomation Version: 1.2.2 Depends: R (>= 3.0.0),grid Imports: Biostrings, BSgenome, data.table, GenomeInfoDb, GenomicRanges, GenomicAlignments, 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: 05b9776d71d7d653835487787ee896d7 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/genomation_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/genomation_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/genomation_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genomation_1.2.2.tgz vignettes: vignettes/genomation/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/genomation/inst/doc/GenomationManual-knitr.html htmlTitles: "genomation" importsMe: CexoR Package: GenomeGraphs Version: 1.30.0 Depends: R (>= 2.10), methods, biomaRt, grid License: Artistic-2.0 MD5sum: 716eee87ed4bb90b337e0d5eb19b2b03 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomeGraphs_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomeGraphs_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomeGraphs_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomeGraphs_1.30.0.tgz vignettes: vignettes/GenomeGraphs/inst/doc/GenomeGraphs.pdf vignetteTitles: The GenomeGraphs users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Genominator, waveTiling suggestsMe: oligo, rMAT, triplex Package: GenomeInfoDb Version: 1.6.3 Depends: R (>= 3.1), methods, stats4, BiocGenerics (>= 0.13.8), S4Vectors (>= 0.7.11), 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: aaec18652bbbe80c76bb0c2cde51eff6 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. Pages Maintainer: Bioconductor Package Maintainer VignetteBuilder: knitr Video: http://youtu.be/wdEjCYSXa7w source.ver: src/contrib/GenomeInfoDb_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomeInfoDb_1.6.3.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomeInfoDb_1.6.3.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomeInfoDb_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomeInfoDb_1.6.3.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 dependsOnMe: BSgenome, bsseq, bumphunter, CODEX, CSAR, GenomicAlignments, GenomicFeatures, GenomicRanges, GenomicTuples, gmapR, groHMM, htSeqTools, methyAnalysis, Rsamtools, TitanCNA, VariantAnnotation importsMe: AllelicImbalance, AnnotationHubData, ballgown, biovizBase, BiSeq, BSgenome, casper, CexoR, ChIPpeakAnno, ChIPseeker, CNEr, CNPBayes, compEpiTools, conumee, CopywriteR, csaw, customProDB, derfinder, derfinderPlot, diffHic, easyRNASeq, ensembldb, epivizr, exomeCopy, genomation, genomeIntervals, GenomicInteractions, genoset, genotypeeval, ggbio, GGtools, GoogleGenomics, gQTLstats, GreyListChIP, Gviz, gwascat, h5vc, HiTC, InPAS, IVAS, metagene, methylPipe, methylumi, minfi, MinimumDistance, motifbreakR, myvariant, NarrowPeaks, podkat, prebs, ProteomicsAnnotationHubData, qpgraph, QuasR, r3Cseq, RareVariantVis, Rariant, regionReport, Repitools, RiboProfiling, rtracklayer, seqplots, SGSeq, ShortRead, SNPchip, soGGi, SomaticSignatures, SplicingGraphs, SummarizedExperiment, TarSeqQC, TFBSTools, VanillaICE, VariantFiltering, VariantTools suggestsMe: AnnotationHub, gQTLBase, QDNAseq Package: genomeIntervals Version: 1.26.0 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: 2fd95301e6e2ac8f1f1aafd068cbf567 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genomeIntervals_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genomeIntervals_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genomeIntervals_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genomeIntervals_1.26.0.tgz vignettes: vignettes/genomeIntervals/inst/doc/genomeIntervals.pdf vignetteTitles: Overview of the genomeIntervals package. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: girafe importsMe: easyRNASeq Package: genomes Version: 2.16.1 Depends: R (>= 2.11), XML, RCurl, GenomicRanges, IRanges, Biostrings License: Artistic-2.0 MD5sum: c1e8babe8779f5737aebd5742d056e77 NeedsCompilation: no Title: Genome sequencing project metadata Description: Collects genome sequencing project data from NCBI biocViews: Annotation, Genetics Author: Chris Stubben Maintainer: Chris Stubben source.ver: src/contrib/genomes_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/genomes_2.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/genomes_2.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/genomes_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genomes_2.16.1.tgz vignettes: vignettes/genomes/inst/doc/genome-tables.pdf vignetteTitles: Introduction to genome projects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GenomicAlignments Version: 1.6.3 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.15.3), S4Vectors (>= 0.8.6), IRanges (>= 2.3.21), GenomeInfoDb (>= 1.1.20), GenomicRanges (>= 1.21.6), SummarizedExperiment (>= 0.3.1), Biostrings (>= 2.37.1), Rsamtools (>= 1.21.4) Imports: methods, 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, DESeq, edgeR, RUnit, BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 72db6a4c7212025323ef04f95806c4ba 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: Herv\'e Pag\`es, 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_1.6.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicAlignments_1.6.3.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicAlignments_1.6.3.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicAlignments_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicAlignments_1.6.3.tgz vignettes: vignettes/GenomicAlignments/inst/doc/OverlapEncodings.pdf, vignettes/GenomicAlignments/inst/doc/summarizeOverlaps.pdf, vignettes/GenomicAlignments/inst/doc/WorkingWithAlignedNucleotides.pdf vignetteTitles: Overlap encodings, Counting reads with summarizeOverlaps, Working with aligned nucleotides hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AllelicImbalance, Basic4Cseq, chimera, DiffBind, exomePeak, GoogleGenomics, groHMM, Guitar, hiReadsProcessor, prebs, RIPSeeker, rnaSeqMap, ShortRead, SplicingGraphs importsMe: biovizBase, ChIPQC, CNEr, CopywriteR, CoverageView, csaw, customProDB, derfinder, easyRNASeq, FourCSeq, genomation, GenomicFiles, ggbio, gmapR, GreyListChIP, Gviz, HTSeqGenie, INSPEcT, metagene, methylPipe, PICS, QuasR, Repitools, RiboProfiling, RNAprobR, roar, Rqc, rtracklayer, SGSeq, similaRpeak, soGGi, SplicingGraphs, trackViewer suggestsMe: BiocParallel, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, oneChannelGUI, Rsamtools, Streamer Package: GenomicFeatures Version: 1.22.13 Depends: BiocGenerics (>= 0.1.0), S4Vectors (>= 0.7.17), IRanges (>= 2.3.21), GenomeInfoDb (>= 1.5.16), GenomicRanges (>= 1.21.32), AnnotationDbi (>= 1.27.9) Imports: methods, utils, tools, DBI (>= 0.2-5), RSQLite (>= 0.8-1), 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: ca5e00304857c03a753d7acb376ccdcf 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_1.22.13.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicFeatures_1.22.13.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicFeatures_1.22.13.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicFeatures_1.22.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicFeatures_1.22.13.tgz vignettes: vignettes/GenomicFeatures/inst/doc/GenomicFeatures.pdf vignetteTitles: Making and Utilizing TxDb Objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cpvSNP, ensembldb, exomePeak, Guitar, InPAS, OrganismDbi, RNAprobR, SplicingGraphs importsMe: AllelicImbalance, AnnotationHubData, biovizBase, bumphunter, casper, ChIPpeakAnno, ChIPseeker, compEpiTools, CompGO, csaw, customProDB, derfinder, derfinderPlot, EDASeq, epivizr, ggbio, gmapR, gQTLstats, Gviz, gwascat, HTSeqGenie, INSPEcT, lumi, metagene, methyAnalysis, PGA, proBAMr, qpgraph, QuasR, RiboProfiling, SGSeq, SplicingGraphs, systemPipeR, TCGAbiolinks, trackViewer, VariantAnnotation, VariantFiltering, VariantTools, wavClusteR suggestsMe: AnnotationHub, biomvRCNS, Biostrings, chipseq, cummeRbund, DEXSeq, easyRNASeq, flipflop, GenomeInfoDb, GenomicAlignments, GenomicRanges, groHMM, MiRaGE, RIPSeeker, Rsamtools, ShortRead, SummarizedExperiment Package: GenomicFiles Version: 1.6.2 Depends: R (>= 3.1.0), methods, BiocGenerics (>= 0.11.2), GenomicRanges, SummarizedExperiment, BiocParallel (>= 1.1.0), Rsamtools (>= 1.17.29), rtracklayer (>= 1.25.3) Imports: GenomicAlignments, IRanges, S4Vectors Suggests: BiocStyle, RUnit, genefilter, deepSNV, RNAseqData.HNRNPC.bam.chr14, Biostrings License: Artistic-2.0 MD5sum: 5d925d04d0efe7b0b649ca3268809427 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_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicFiles_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicFiles_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicFiles_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicFiles_1.6.2.tgz vignettes: vignettes/GenomicFiles/inst/doc/GenomicFiles.pdf vignetteTitles: Introduction to GenomicFiles hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: derfinder, erma, gQTLBase, QuasR, Rqc Package: GenomicInteractions Version: 1.4.2 Depends: R (>= 2.10) Imports: Rsamtools, GenomicRanges, IRanges, BiocGenerics (>= 0.15.3), data.table, stringr, GenomeInfoDb, ggplot2, grid, gridExtra, methods, igraph, S4Vectors, dplyr, Gviz Suggests: knitr, BiocStyle, testthat License: GPL-3 MD5sum: 31ec093f9e450582aa4f43e60dd1ca21 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_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicInteractions_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicInteractions_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicInteractions_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicInteractions_1.4.2.tgz vignettes: vignettes/GenomicInteractions/inst/doc/chiapet_vignette.pdf vignetteTitles: chiapet_vignette.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/GenomicInteractions/inst/doc/hic_vignette.html htmlTitles: "GenomicInteractions-HiC" Package: GenomicRanges Version: 1.22.4 Depends: R (>= 2.10), methods, BiocGenerics (>= 0.16.1), S4Vectors (>= 0.8.6), IRanges (>= 2.4.6), 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), GenomicAlignments, rtracklayer, BSgenome, GenomicFeatures, Gviz, VariantAnnotation, AnnotationHub, DESeq, 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: 65c45d79e2350166519087f8a62b561f 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_1.22.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicRanges_1.22.4.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicRanges_1.22.4.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicRanges_1.22.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicRanges_1.22.4.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 GenomicRanges, A quick introduction to GRanges and GRangesList objects hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AllelicImbalance, annmap, AnnotationHubData, Basic4Cseq, baySeq, biomvRCNS, BiSeq, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, casper, chimera, ChIPpeakAnno, ChIPQC, chipseq, cleanUpdTSeq, cn.mops, CNPBayes, cnvGSA, CNVPanelizer, compEpiTools, CSAR, csaw, deepSNV, DESeq2, DEXSeq, DiffBind, diffHic, DMRcaller, DMRforPairs, DOQTL, EnrichedHeatmap, ensembldb, ensemblVEP, epigenomix, exomeCopy, fastseg, FourCSeq, GeneBreak, genomes, GenomicAlignments, GenomicFeatures, GenomicFiles, GenomicTuples, genoset, GenoView, gmapR, GOTHiC, GreyListChIP, groHMM, Guitar, Gviz, hiAnnotator, HilbertCurve, HiTC, htSeqTools, IdeoViz, InPAS, intansv, MBASED, metagene, methyAnalysis, methylPipe, minfi, PGA, PING, podkat, QuasR, r3Cseq, Rariant, Rcade, regioneR, rfPred, rGREAT, riboSeqR, RIPSeeker, RnBeads, Rsamtools, RSVSim, rtracklayer, segmentSeq, seqbias, SGSeq, SigFuge, SomatiCA, SomaticSignatures, SummarizedExperiment, TarSeqQC, trackViewer, VanillaICE, VariantAnnotation, VariantTools, vtpnet, wavClusteR importsMe: ALDEx2, ArrayExpressHTS, ballgown, bamsignals, beadarray, BEAT, biovizBase, BiSeq, BSgenome, CAGEr, CexoR, ChAMP, chipenrich, ChIPseeker, chipseq, ChIPseqR, chromDraw, CNEr, coMET, conumee, copynumber, CopywriteR, CoverageView, customProDB, DChIPRep, derfinder, derfinderPlot, DMRcate, easyRNASeq, EDASeq, epivizr, erma, flipflop, FourCSeq, FunciSNP, genomation, genomeIntervals, GenomicAlignments, GenomicInteractions, genotypeeval, GGBase, ggbio, GGtools, GoogleGenomics, gQTLBase, gQTLstats, GUIDEseq, gwascat, h5vc, hiReadsProcessor, HTSeqGenie, INSPEcT, IVAS, LedPred, LOLA, lumi, M3D, MEAL, MEDIPS, methyAnalysis, MethylSeekR, methylumi, MinimumDistance, motifbreakR, NarrowPeaks, nucleR, oligoClasses, OrganismDbi, Pbase, pepStat, PICS, prebs, proBAMr, Pviz, pwOmics, qpgraph, R453Plus1Toolbox, RareVariantVis, regioneR, regionReport, Repitools, rgsepd, RiboProfiling, RNAprobR, rnaSeqMap, roar, seq2pathway, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, SICtools, simulatorZ, SNPchip, soGGi, SomatiCA, spliceR, SplicingGraphs, SVM2CRM, systemPipeR, TCGAbiolinks, TFBSTools, ToPASeq, tracktables, triplex, VariantFiltering, waveTiling suggestsMe: AnnotationHub, biobroom, BiocGenerics, BiocParallel, cummeRbund, GenomeInfoDb, gtrellis, interactiveDisplay, IRanges, metaseqR, MiRaGE, NarrowPeaks, NGScopy, SeqGSEA, STAN Package: GenomicTuples Version: 1.4.5 Depends: R (>= 3.2.0), GenomicRanges (>= 1.19.47), GenomeInfoDb, BiocGenerics, methods Imports: Rcpp (>= 0.11.2), S4Vectors, Biobase, IRanges, data.table LinkingTo: Rcpp Suggests: testthat, knitr, BiocStyle, rmarkdown License: Artistic-2.0 Archs: i386, x64 MD5sum: 8848f21fb754cc54b9f342abaaf4934e 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_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenomicTuples_1.4.5.zip win64.binary.ver: bin/windows64/contrib/3.2/GenomicTuples_1.4.5.zip mac.binary.ver: bin/macosx/contrib/3.2/GenomicTuples_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenomicTuples_1.4.5.tgz vignettes: vignettes/GenomicTuples/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/GenomicTuples/inst/doc/GenomicTuplesIntroduction.html htmlTitles: "GenomicTuples: Classes and Methods" Package: Genominator Version: 1.24.0 Depends: R (>= 2.10), methods, RSQLite, DBI (>= 0.2-5), BiocGenerics (>= 0.1.0), IRanges, GenomeGraphs Imports: graphics, stats, utils Suggests: biomaRt, ShortRead, yeastRNASeq License: Artistic-2.0 MD5sum: b34bfacac12b8750dd113e9e41802cd3 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Genominator_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Genominator_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Genominator_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Genominator_1.24.0.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 suggestsMe: oneChannelGUI Package: genoset Version: 1.24.0 Depends: R (>= 2.10), BiocGenerics (>= 0.11.3), Biobase (>= 2.15.1), GenomicRanges (>= 1.17.19), SummarizedExperiment Imports: S4Vectors (>= 0.2.3), GenomeInfoDb (>= 1.1.3), IRanges, methods, graphics Suggests: RUnit, knitr, BiocStyle, DNAcopy, stats, BSgenome, Biostrings Enhances: parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: a5353638ec51e17e65d833ff4ab340dd NeedsCompilation: yes Title: Provides classes similar to ExpressionSet for copy number analysis Description: Load, manipulate, and plot copynumber and BAF data. GenoSet class extends eSet by adding a "locData" slot for a GRanges object. This object contains feature genome location data and provides for efficient subsetting on genome location. Provides convenience functions for processing of copy number and B-Allele Frequency data. Provides the class RleDataFrame to store runs of data along the genome for multiple samples. 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genoset_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genoset_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genoset_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genoset_1.24.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: VegaMC importsMe: methyAnalysis Package: genotypeeval Version: 1.0.0 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: 135aa4ee854706c2b749e42e4d35febc 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/genotypeeval_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/genotypeeval_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/genotypeeval_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/genotypeeval_1.0.0.tgz vignettes: vignettes/genotypeeval/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/genotypeeval/inst/doc/genotypeeval_vignette.html htmlTitles: "genotypeeval_vignette" Package: GenoView Version: 1.3.0 Depends: R (>= 2.10), gridExtra, GenomicRanges Imports: ggbio, ggplot2, grid, biovizBase Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, PFAM.db, AnnotationDbi, gtable, gWidgets, gWidgetsRGtk2, RGtk2, RColorBrewer License: GPL-3 MD5sum: c8be1452ff4a3957fda6cf3c35080db9 NeedsCompilation: no Title: Condensed, overlapped plotting of genomic data tracks Description: Superimposing input data over existing genomic references allows for fast, accurate visual comparisons. The GenoView package is a novel bioinformatics package which condenses genomic data tracks to offer a comprehensive view of genetic variants. Its main function is to display mutation data over exons and protein domains, which easily identifies potential genomic locations of interest. biocViews: Visualization Author: Sharon Lee, Dennis Wang Maintainer: Sharon Lee source.ver: src/contrib/GenoView_1.3.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GenoView_1.3.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GenoView_1.3.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GenoView_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GenoView_1.3.0.tgz vignettes: vignettes/GenoView/inst/doc/GenoView.pdf vignetteTitles: GenoView hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GEOmetadb Version: 1.30.2 Depends: GEOquery,RSQLite Suggests: knitr, rmarkdown, dplyr, tm, wordcloud License: Artistic-2.0 MD5sum: cd59dfdca91761c590540211a8ab4795 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_1.30.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEOmetadb_1.30.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GEOmetadb_1.30.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GEOmetadb_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEOmetadb_1.30.2.tgz vignettes: vignettes/GEOmetadb/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/GEOmetadb/inst/doc/GEOmetadb.html htmlTitles: "GEOmetadb" Package: GEOquery Version: 2.36.0 Depends: methods, Biobase Imports: XML, RCurl Suggests: limma, knitr, rmarkdown, RUnit, BiocGenerics License: GPL-2 MD5sum: 42e87ca823f075b3adbe34181971ac7d 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_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEOquery_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GEOquery_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GEOquery_2.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEOquery_2.36.0.tgz vignettes: vignettes/GEOquery/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/GEOquery/inst/doc/GEOquery.html htmlTitles: "Using GEOquery" dependsOnMe: DrugVsDisease, SCAN.UPC importsMe: AnnotationHubData, ChIPXpress, minfi, SRAdb suggestsMe: dyebias, ELBOW, PGSEA, RGSEA, RnBeads, Rnits, skewr, TargetScore Package: GEOsearch Version: 1.0.1 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: ced70a00683fa8b08b7c24d42de97685 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEOsearch_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GEOsearch_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GEOsearch_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEOsearch_1.0.1.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 Package: GEOsubmission Version: 1.22.0 Imports: affy, Biobase, utils License: GPL (>= 2) MD5sum: eaec8d561c31921733748c8bd8747bde 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEOsubmission_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GEOsubmission_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GEOsubmission_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEOsubmission_1.22.0.tgz vignettes: vignettes/GEOsubmission/inst/doc/GEOsubmission.pdf vignetteTitles: GEOsubmission Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: gespeR Version: 1.2.0 Depends: methods, graphics, ggplot2, R(>= 2.10) Imports: Matrix, glmnet, cellHTS2, Biobase, biomaRt, doParallel, parallel, foreach, reshape2, dplyr Suggests: knitr License: GPL-3 MD5sum: bcee4317e7c85dfcc9521ecfaa0ee179 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gespeR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gespeR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gespeR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gespeR_1.2.0.tgz vignettes: vignettes/gespeR/inst/doc/gespeR.pdf vignetteTitles: An R package for deconvoluting off-target confounded RNAi screens hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GEWIST Version: 1.14.0 Depends: R (>= 2.10), car License: GPL-2 MD5sum: d91491ce78b63b5b8f53103716d3b236 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GEWIST_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GEWIST_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GEWIST_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GEWIST_1.14.0.tgz vignettes: vignettes/GEWIST/inst/doc/GEWIST.pdf vignetteTitles: GEWIST.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GGBase Version: 3.32.0 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: 25f12abb0a8df17ec12a95f59fb73451 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_3.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GGBase_3.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GGBase_3.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GGBase_3.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GGBase_3.32.0.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 dependsOnMe: GGtools Package: ggbio Version: 1.18.5 Depends: methods, BiocGenerics, ggplot2 (>= 1.0.0) Imports: grid, grDevices, graphics, stats, utils, gridExtra, scales, reshape2, gtable, Hmisc, biovizBase (>= 1.17.1), Biobase, S4Vectors, 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 Suggests: vsn, BSgenome.Hsapiens.UCSC.hg19, Homo.sapiens, TxDb.Hsapiens.UCSC.hg19.knownGene, chipseq, TxDb.Mmusculus.UCSC.mm9.knownGene, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 284795ec42c026c1a4b3f154be290b33 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, cre], Michael Lawrence [aut, ths], Dianne Cook [aut, ths] Maintainer: Tengfei Yin URL: http://tengfei.github.com/ggbio/ VignetteBuilder: knitr BugReports: https://github.com/tengfei/ggbio/issues source.ver: src/contrib/ggbio_1.18.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/ggbio_1.18.5.zip win64.binary.ver: bin/windows64/contrib/3.2/ggbio_1.18.5.zip mac.binary.ver: bin/macosx/contrib/3.2/ggbio_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ggbio_1.18.5.tgz vignettes: vignettes/ggbio/inst/doc/ggbio.pdf vignetteTitles: ggbio: visualize genomic data with grammar of graphics. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CAFE, intansv importsMe: coMET, derfinderPlot, FourCSeq, GenoView, gwascat, Rariant, regionReport, ReportingTools, RiboProfiling, SomaticSignatures suggestsMe: beadarray, GoogleGenomics, gQTLstats, interactiveDisplay, RnBeads Package: GGtools Version: 5.6.0 Depends: R (>= 2.14), GGBase (>= 3.19.7), data.table, parallel Imports: methods, utils, stats, BiocGenerics, snpStats, ff, Rsamtools, AnnotationDbi, Biobase, bit, VariantAnnotation, hexbin, rtracklayer, Gviz, stats4, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, iterators, Biostrings, ROCR, biglm, ggplot2, reshape2 Suggests: GGdata, illuminaHumanv1.db, SNPlocs.Hsapiens.dbSNP.20120608, multtest, aod, rmeta Enhances: MatrixEQTL, Homo.sapiens, foreach, doParallel, gwascat License: Artistic-2.0 MD5sum: c9467ca7cba4fe87a7ec98efb8f9eb52 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_5.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GGtools_5.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GGtools_5.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GGtools_5.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GGtools_5.6.0.tgz vignettes: vignettes/GGtools/inst/doc/GGtools.pdf vignetteTitles: GGtools: software for eQTL identification hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: GGBase, gQTLBase, gwascat Package: ggtree Version: 1.2.17 Depends: R (>= 3.2.0), ggplot2 (>= 2.0.0) Imports: ape, Biostrings, grid, jsonlite, magrittr, methods, stats4, tidyr Suggests: colorspace, EBImage, gridExtra, knitr, phylobase, phytools, phangorn, rmarkdown, scales, testthat License: Artistic-2.0 MD5sum: f598c5e8559245c19d78f93294a1f4f2 NeedsCompilation: no Title: a phylogenetic tree viewer for different types of tree annotations 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: https://github.com/GuangchuangYu/ggtree VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/ggtree/issues source.ver: src/contrib/ggtree_1.2.17.tar.gz win.binary.ver: bin/windows/contrib/3.2/ggtree_1.2.17.zip win64.binary.ver: bin/windows64/contrib/3.2/ggtree_1.2.17.zip mac.binary.ver: bin/macosx/contrib/3.2/ggtree_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ggtree_1.2.17.tgz vignettes: vignettes/ggtree/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ggtree/inst/doc/advanceTreeAnnotation.html, vignettes/ggtree/inst/doc/ggtree.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: "Advance Tree Annotation", "ggtree: a phylogenetic tree viewer for different types of tree annotations", "Tree Annotation", "Tree Data Import", "Tree Manipulation", "Tree Visualization" Package: girafe Version: 1.22.0 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: 81b126276a7f7ddee81d2181d8c5e32e 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/girafe_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/girafe_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/girafe_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/girafe_1.22.0.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 Package: GLAD Version: 2.34.0 Depends: R (>= 2.10) Suggests: aws, tcltk License: GPL-2 Archs: i386, x64 MD5sum: 234f8ea8a1c9573de85726732f19c2e9 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GLAD_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GLAD_2.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GLAD_2.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GLAD_2.34.0.tgz vignettes: vignettes/GLAD/inst/doc/GLAD.pdf vignetteTitles: GLAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ITALICS, MANOR, seqCNA importsMe: ADaCGH2, ITALICS, MANOR, snapCGH Package: GlobalAncova Version: 3.38.0 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: b93db0831c564bae2fed2ac3032ce9e2 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_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GlobalAncova_3.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GlobalAncova_3.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GlobalAncova_3.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GlobalAncova_3.38.0.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 Package: globaltest Version: 5.24.0 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: a6576f5edbb2de1f1af3589a8edeb7b6 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_5.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/globaltest_5.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/globaltest_5.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/globaltest_5.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/globaltest_5.24.0.tgz vignettes: vignettes/globaltest/inst/doc/GlobalTest.pdf vignetteTitles: Global Test hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: GlobalAncova importsMe: BiSeq, SIM suggestsMe: topGO Package: gmapR Version: 1.12.0 Depends: R (>= 2.15.0), methods, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.12) Imports: S4Vectors, IRanges, Rsamtools (>= 1.17.8), rtracklayer (>= 1.25.5), 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: 2c12bebb954d48717c3849a5f6a12e1c 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_1.12.0.tar.gz vignettes: vignettes/gmapR/inst/doc/gmapR.pdf vignetteTitles: gmapR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: HTSeqGenie importsMe: VariantTools Package: GOexpress Version: 1.4.1 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: e271013ecd5c5617c39d92220f95bd3e 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 enriched in genes with expression levels best clustering 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 ability 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOexpress_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GOexpress_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GOexpress_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOexpress_1.4.1.tgz vignettes: vignettes/GOexpress/inst/doc/GOexpress-UsersGuide.pdf vignetteTitles: UsersGuide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GOFunction Version: 1.18.0 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, SparseM License: GPL (>= 2) MD5sum: 5b449d0b52276a12f5940c4d740afdef 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOFunction_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOFunction_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOFunction_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOFunction_1.18.0.tgz vignettes: vignettes/GOFunction/inst/doc/GOFunction.pdf vignetteTitles: GO-function hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GoogleGenomics Version: 1.2.0 Depends: R (>= 3.1.0), GenomicAlignments (>= 1.0.1), VariantAnnotation Imports: Biostrings, GenomeInfoDb, GenomicRanges, IRanges, httr, rjson, Rsamtools, S4Vectors 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: d63c23df90aea349432c2b5a89a4f6cb 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GoogleGenomics_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GoogleGenomics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GoogleGenomics_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GoogleGenomics_1.2.0.tgz vignettes: vignettes/GoogleGenomics/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE 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: 1.32.0 Depends: Biobase, AnnotationDbi, GO.db Suggests: org.Hs.eg.db License: GPL-2 MD5sum: d0d0f73951b7e2892b9b04ab64445ddc 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: Microarray, GO Author: Alex Sanchez, Jordi Ocana and Miquel Salicru Maintainer: Alex Sanchez source.ver: src/contrib/goProfiles_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/goProfiles_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/goProfiles_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/goProfiles_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/goProfiles_1.32.0.tgz vignettes: vignettes/goProfiles/inst/doc/goProfiles.pdf vignetteTitles: goProfiles Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GOSemSim Version: 1.28.2 Depends: R (>= 3.1.0) Imports: Rcpp, AnnotationDbi, GO.db LinkingTo: Rcpp Suggests: DOSE, clusterProfiler, org.Hs.eg.db, knitr, BiocStyle, BiocInstaller License: Artistic-2.0 Archs: i386, x64 MD5sum: 96a6a406487d4229ebeff5a5b3590d9e 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. Maintainer: Guangchuang Yu URL: https://github.com/GuangchuangYu/GOSemSim VignetteBuilder: knitr BugReports: https://github.com/GuangchuangYu/GOSemSim/issues source.ver: src/contrib/GOSemSim_1.28.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOSemSim_1.28.2.zip win64.binary.ver: bin/windows64/contrib/3.2/GOSemSim_1.28.2.zip mac.binary.ver: bin/macosx/contrib/3.2/GOSemSim_1.28.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOSemSim_1.28.2.tgz vignettes: vignettes/GOSemSim/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/GOSemSim/inst/doc/GOSemSim.html htmlTitles: "GO-terms Semantic Similarity Measures" dependsOnMe: tRanslatome importsMe: clusterProfiler, DOSE, Rcpi suggestsMe: SemDist Package: goseq Version: 1.22.0 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: 89f3a296005f71e49cbba2772997b6bc 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 source.ver: src/contrib/goseq_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/goseq_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/goseq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/goseq_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/goseq_1.22.0.tgz vignettes: vignettes/goseq/inst/doc/goseq.pdf vignetteTitles: goseq User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: rgsepd suggestsMe: oneChannelGUI Package: GOSim Version: 1.8.0 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: 225943e101636c8f04ea72fd2e6c04e5 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOSim_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOSim_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOSim_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOSim_1.8.0.tgz vignettes: vignettes/GOSim/inst/doc/GOSim.pdf vignetteTitles: GOsim hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GOstats Version: 2.36.0 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: 55afa051acac22a4acccba24c1c6c806 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_2.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOstats_2.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOstats_2.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOstats_2.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOstats_2.36.0.tgz vignettes: vignettes/GOstats/inst/doc/GOstatsForUnsupportedOrganisms.pdf, vignettes/GOstats/inst/doc/GOstatsHyperG.pdf, vignettes/GOstats/inst/doc/GOvis.pdf vignetteTitles: Hypergeometric tests for less common model organisms, Hypergeometric Tests Using GOstats, Visualizing Data Using GOstats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: attract, MineICA, RDAVIDWebService importsMe: affycoretools, attract, categoryCompare, facopy, mvGST, ProCoNA, systemPipeR suggestsMe: BiocCaseStudies, Category, eisa, fastLiquidAssociation, GSEAlm, HTSanalyzeR, interactiveDisplay, MineICA, miRLAB, MLP, MmPalateMiRNA, oneChannelGUI, phenoDist, qpgraph, RnBeads, safe Package: GOsummaries Version: 2.4.7 Depends: R (>= 2.15), Rcpp Imports: plyr, grid, gProfileR, reshape2, limma, ggplot2, gtable LinkingTo: Rcpp Suggests: vegan License: GPL (>= 2) Archs: i386, x64 MD5sum: fb08ff45252ded7a4d9c15ff96282910 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_2.4.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOsummaries_2.4.7.zip win64.binary.ver: bin/windows64/contrib/3.2/GOsummaries_2.4.7.zip mac.binary.ver: bin/macosx/contrib/3.2/GOsummaries_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOsummaries_2.4.7.tgz vignettes: vignettes/GOsummaries/inst/doc/GOsummaries-basics.pdf vignetteTitles: GOsummaries basics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GOTHiC Version: 1.6.0 Depends: R (>= 2.15.1), methods, utils, GenomicRanges, Biostrings, BSgenome, data.table Imports: BiocGenerics, S4Vectors, IRanges, ShortRead, rtracklayer, ggplot2 Suggests: HiCDataLymphoblast Enhances: parallel License: GPL-3 MD5sum: 7868e1643638bb04494a726248adac33 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GOTHiC_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GOTHiC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GOTHiC_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GOTHiC_1.6.0.tgz vignettes: vignettes/GOTHiC/inst/doc/package_vignettes.pdf vignetteTitles: package_vignettes.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: goTools Version: 1.44.0 Depends: GO.db Imports: AnnotationDbi, GO.db, graphics, grDevices Suggests: hgu133a.db License: GPL-2 MD5sum: 8994670d7a2e3f89780328d1aef44d48 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/goTools_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/goTools_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/goTools_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/goTools_1.44.0.tgz vignettes: vignettes/goTools/inst/doc/goTools.pdf vignetteTitles: goTools overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: gpls Version: 1.42.0 Imports: stats Suggests: MASS License: Artistic-2.0 MD5sum: 67658d21ee4663cd5bbfea03c78d15aa 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gpls_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gpls_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gpls_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gpls_1.42.0.tgz vignettes: vignettes/gpls/inst/doc/gpls.pdf vignetteTitles: gpls Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MCRestimate, MLInterfaces Package: gprege Version: 1.14.0 Depends: R (>= 2.8.0), gptk Suggests: spam License: AGPL-3 MD5sum: 4b1114341dd1fcd4ef7b17f77664dac9 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gprege_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gprege_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gprege_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gprege_1.14.0.tgz vignettes: vignettes/gprege/inst/doc/gprege_quick.pdf vignetteTitles: gprege Quick Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: gQTLBase Version: 1.2.1 Imports: GenomicRanges, methods, BatchJobs, BBmisc, S4Vectors, BiocGenerics, foreach, doParallel, bit, ff, rtracklayer, ffbase, GenomicFiles Suggests: geuvStore, knitr, rmarkdown, BiocStyle, RUnit, GGtools, Homo.sapiens, IRanges, erma, GenomeInfoDb, gwascat, geuvPack License: Artistic-2.0 MD5sum: 02a998eb0f808b4265d8d933bc109f9a 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/gQTLBase_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/gQTLBase_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/gQTLBase_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gQTLBase_1.2.1.tgz vignettes: vignettes/gQTLBase/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/gQTLBase/inst/doc/gQTLBase.html htmlTitles: "gQTLBase infrastructure for eQTL archives" importsMe: gQTLstats Package: gQTLstats Version: 1.2.0 Depends: R (>= 3.1.0) Imports: methods, snpStats, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, VariantAnnotation, Biobase, BatchJobs, gQTLBase, limma, gam, dplyr, AnnotationDbi, GenomicFeatures, ggplot2, reshape2, doParallel, foreach, ffbase Suggests: geuvPack, geuvStore, Rsamtools, knitr, rmarkdown, ggbio, BiocStyle, Homo.sapiens License: Artistic-2.0 MD5sum: 5701b47f75159f679f2fea00a4f6d8c0 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gQTLstats_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gQTLstats_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gQTLstats_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gQTLstats_1.2.0.tgz vignettes: vignettes/gQTLstats/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/gQTLstats/inst/doc/gQTLstats.html htmlTitles: "gQTLstats: statistics for genetics of genomic features" importsMe: gwascat Package: graph Version: 1.48.0 Depends: R (>= 2.10), methods Imports: stats, stats4, tools, utils, BiocGenerics (>= 0.13.11) Suggests: SparseM (>= 0.36), XML, RBGL, RUnit, cluster Enhances: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: a5d88958573c7c8389b652aae8d7424f 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/graph_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/graph_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/graph_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/graph_1.48.0.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 dependsOnMe: apComplex, biocGraph, BioMVCClass, BioNet, CellNOptR, clipper, CNORfeeder, ddgraph, flowClust, gaggle, gaucho, GeneNetworkBuilder, 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, BiocCheck, biocGraph, biocViews, CAMERA, Category, categoryCompare, ChIPpeakAnno, DEGraph, EnrichmentBrowser, FEM, flowCore, flowUtils, flowWorkspace, gage, GeneNetworkBuilder, GOFunction, GOSim, GOstats, GraphAT, graphite, gwascat, HTSanalyzeR, hyperdraw, KEGGgraph, keggorthology, mvGST, NCIgraph, nem, netresponse, OncoSimulR, OrganismDbi, pathview, PCpheno, pkgDepTools, ppiStats, qpgraph, RchyOptimyx, rsbml, Rtreemix, SplicingGraphs, Streamer, topGO, VariantFiltering suggestsMe: AnnotationDbi, BiocCaseStudies, Category, DEGraph, EBcoexpress, ecolitk, GeneAnswers, mmnet, MmPalateMiRNA, netbenchmark, NetPathMiner, rBiopaxParser, rTRM, SPIA, VariantTools Package: GraphAlignment Version: 1.34.0 License: file LICENSE License_restricts_use: yes Archs: i386, x64 MD5sum: 7d7424911f7e7e0c140e875133976e82 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GraphAlignment_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GraphAlignment_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GraphAlignment_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GraphAlignment_1.34.0.tgz vignettes: vignettes/GraphAlignment/inst/doc/GraphAlignment.pdf vignetteTitles: GraphAlignment hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: GraphAT Version: 1.42.0 Depends: R (>= 2.10), graph, methods Imports: graph, MCMCpack, methods, stats License: LGPL MD5sum: 16061e7f689e2e6b4bbf5f00e376f711 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GraphAT_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GraphAT_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GraphAT_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GraphAT_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: graphite Version: 1.16.0 Depends: R (>= 2.10), BiocGenerics, methods Imports: AnnotationDbi, graph, stats, utils, rappdirs Suggests: BiocStyle, DEGraph (>= 1.4), hgu133plus2.db, RCytoscape (>= 1.6), SPIA (>= 2.2), topologyGSA (>= 1.4.0), clipper, ALL License: AGPL-3 MD5sum: 7ecb6332bd14b90fd70990eefb614645 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/graphite_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/graphite_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/graphite_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/graphite_1.16.0.tgz vignettes: vignettes/graphite/inst/doc/graphite.pdf vignetteTitles: GRAPH Interaction from pathway Topological Environment hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ToPASeq importsMe: facopy, mogsa, ReactomePA suggestsMe: clipper Package: GraphPAC Version: 1.12.1 Depends: R(>= 2.15),iPAC, igraph, TSP, RMallow Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 0239f79d7ab53f75f61c3b954be2fe76 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GraphPAC_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GraphPAC_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GraphPAC_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GraphPAC_1.12.1.tgz vignettes: vignettes/GraphPAC/inst/doc/GraphPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: QuartPAC Package: GRENITS Version: 1.22.0 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: b902caad47e97d947b513ed1197222e6 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GRENITS_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GRENITS_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GRENITS_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GRENITS_1.22.0.tgz vignettes: vignettes/GRENITS/inst/doc/GRENITS_package.pdf vignetteTitles: GRENITS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GreyListChIP Version: 1.2.0 Depends: R (>= 3.1), methods, GenomicRanges Imports: GenomicAlignments, BSgenome, Rsamtools, rtracklayer, MASS, parallel, GenomeInfoDb Suggests: BiocStyle, BiocGenerics, RUnit Enhances: BSgenome.Hsapiens.UCSC.hg19 License: Artistic-2.0 MD5sum: f768186ca86c1e646724c1398d1af65b 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GreyListChIP_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GreyListChIP_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GreyListChIP_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GreyListChIP_1.2.0.tgz vignettes: vignettes/GreyListChIP/inst/doc/GreyList-demo.pdf vignetteTitles: Generating Grey Lists from Input Libraries hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: groHMM Version: 1.4.1 Depends: R (>= 3.0.2), MASS, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, GenomicAlignments, rtracklayer, parallel Suggests: BiocStyle, GenomicFeatures, edgeR, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene License: GPL-3 Archs: i386, x64 MD5sum: 9be4cce9ccd7190c94da5189b9b64636 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, W. Lee Kraus source.ver: src/contrib/groHMM_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/groHMM_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/groHMM_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/groHMM_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/groHMM_1.4.1.tgz vignettes: vignettes/groHMM/inst/doc/groHMM.pdf vignetteTitles: groHMM tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GSAR Version: 1.4.0 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: 2fa2f7790fe98f48fa0fa4fcb9a3de5b 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSAR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSAR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSAR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSAR_1.4.0.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 Package: GSCA Version: 1.8.0 Depends: shiny, sp, gplots, ggplot2, reshape2, RColorBrewer, rhdf5, R(>= 2.10.0) Imports: graphics Suggests: Affyhgu133aExpr, Affymoe4302Expr, Affyhgu133A2Expr, Affyhgu133Plus2Expr License: GPL(>=2) MD5sum: 69cd8e8194b8811294f26962aeea1fa3 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSCA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSCA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSCA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSCA_1.8.0.tgz vignettes: vignettes/GSCA/inst/doc/GSCA.pdf vignetteTitles: GSCA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: GSEABase Version: 1.32.0 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: c7ac9ec574470b100988a82df842562c 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSEABase_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSEABase_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSEABase_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSEABase_1.32.0.tgz vignettes: vignettes/GSEABase/inst/doc/GSEABase.pdf vignetteTitles: An introduction to GSEABase hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AGDEX, BicARE, cpvSNP, EnrichmentBrowser, gCMAP, npGSEA, PROMISE importsMe: Category, categoryCompare, cellHTS2, gCMAPWeb, GSRI, GSVA, HTSanalyzeR, mogsa, PCpheno, phenoTest, PROMISE, ReportingTools suggestsMe: BiocCaseStudies, gage, GlobalAncova, globaltest, GOstats, GSAR, PGSEA, phenoTest Package: GSEAlm Version: 1.30.0 Depends: Biobase Suggests: GSEABase,Category, multtest, ALL, annotate, hgu95av2.db, genefilter, GOstats, RColorBrewer License: Artistic-2.0 MD5sum: d50e79e67f394cd9556ad6e04ce30708 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSEAlm_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSEAlm_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSEAlm_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSEAlm_1.30.0.tgz vignettes: vignettes/GSEAlm/inst/doc/GSEAlm.pdf vignetteTitles: Linear models in GSEA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: gCMAP Package: GSReg Version: 1.4.0 Depends: R (>= 2.13.1) Suggests: GSBenchMark License: GPL-2 Archs: i386, x64 MD5sum: 5820bd7aed78002ff9f2ffb6fd326c68 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSReg_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSReg_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSReg_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSReg_1.4.0.tgz vignettes: vignettes/GSReg/inst/doc/GSReg.pdf vignetteTitles: Working with the GSReg package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: GSRI Version: 2.18.1 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: 9b1516ffb7b756c3cc9caab028288ea4 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_2.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSRI_2.18.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GSRI_2.18.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GSRI_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSRI_2.18.1.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 Package: GSVA Version: 1.18.0 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: d6271be667752d25986a61d7ba91ceaa 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/GSVA_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/GSVA_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/GSVA_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GSVA_1.18.0.tgz vignettes: vignettes/GSVA/inst/doc/GSVA.pdf vignetteTitles: Gene Set Variation Analysis hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: gtrellis Version: 1.2.0 Depends: R (>= 3.1.2), grid Imports: circlize (>= 0.2.3), GetoptLong Suggests: testthat (>= 0.3), knitr, GenomicRanges, RColorBrewer, markdown License: GPL (>= 2) MD5sum: 953cd4a3ce5e00b8926ca74e904d9678 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 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/gtrellis_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/gtrellis_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/gtrellis_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gtrellis_1.2.0.tgz vignettes: vignettes/gtrellis/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/gtrellis/inst/doc/gtrellis.html htmlTitles: "Make Genome Level Trellis Graph" Package: GUIDEseq Version: 1.0.4 Depends: R (>= 3.2.0), IRanges, BiocGenerics, S4Vectors Imports: BiocParallel, Biostrings, CRISPRseek, ChIPpeakAnno, GenomicRanges, data.table, matrixStats, BSgenome, parallel Suggests: knitr, RUnit, BiocStyle, BSgenome.Hsapiens.UCSC.hg19 License: GPL (>= 2) MD5sum: c0b3b0ad428476c4e3759c2c3f7c199a 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 events), estimating the locations of the cleavage sites, aka, peaks, merging estimated cleavage sites from plus and minus strand, and performing off target search of the extended regions around cleavage 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_1.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/GUIDEseq_1.0.4.zip win64.binary.ver: bin/windows64/contrib/3.2/GUIDEseq_1.0.4.zip mac.binary.ver: bin/macosx/contrib/3.2/GUIDEseq_0.99.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GUIDEseq_1.0.4.tgz vignettes: vignettes/GUIDEseq/inst/doc/GUIDEseq.pdf vignetteTitles: GUIDEseq Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Guitar Version: 1.7.0 Depends: Rsamtools, GenomicFeatures, rtracklayer, GenomicAlignments, GenomicRanges, ggplot2, grid, IRanges License: GPL-2 MD5sum: 61a001120fec24ecb196ba9d65ccd028 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_1.7.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Guitar_1.7.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Guitar_1.7.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Guitar_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Guitar_1.7.0.tgz vignettes: vignettes/Guitar/inst/doc/Guitar-Overview.pdf vignetteTitles: Sample Guitar workflow hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Gviz Version: 1.14.7 Depends: R (>= 2.10.0), methods, S4Vectors (>= 0.1.0), 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: de4a10c43b807cf070aaf4b592619342 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_1.14.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/Gviz_1.14.7.zip win64.binary.ver: bin/windows64/contrib/3.2/Gviz_1.14.7.zip mac.binary.ver: bin/macosx/contrib/3.2/Gviz_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Gviz_1.14.7.tgz vignettes: vignettes/Gviz/inst/doc/Gviz.pdf vignetteTitles: Gviz users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: biomvRCNS, coMET, cummeRbund, DMRforPairs, Pbase, Pviz importsMe: AllelicImbalance, DMRcate, GenomicInteractions, GGtools, gwascat, InPAS, methyAnalysis, methylPipe, motifbreakR, PING, trackViewer, VariantFiltering suggestsMe: annmap, CNEr, GenomicRanges, interactiveDisplay, QuasR, RnBeads, SplicingGraphs, STAN Package: gwascat Version: 2.2.1 Depends: R (>= 3.0.0), Homo.sapiens Imports: methods, BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, snpStats, Biostrings, Rsamtools, rtracklayer, gQTLstats, Gviz, VariantAnnotation, AnnotationHub, AnnotationDbi, GenomicFeatures, graph, ggbio, ggplot2 Suggests: DO.db, DT, utils, knitr, RBGL, RUnit, GGtools Enhances: SNPlocs.Hsapiens.dbSNP.20120608 License: Artistic-2.0 MD5sum: 3ec3ca73e7f6fa53a7ff7e5cc6282c27 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_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/gwascat_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/gwascat_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/gwascat_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/gwascat_2.2.1.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 htmlDocs: vignettes/gwascat/inst/doc/gwascatOnt.html htmlTitles: "gwascat: exploring GWAS results using the experimental factor ontology" dependsOnMe: vtpnet suggestsMe: gQTLBase Package: GWASTools Version: 1.16.1 Depends: Biobase Imports: methods, ncdf, gdsfmt, DBI, RSQLite, GWASExactHW, DNAcopy, survival, sandwich, lmtest, logistf, quantsmooth Suggests: GWASdata, BiocGenerics, RUnit, SNPRelate, snpStats, VariantAnnotation License: Artistic-2.0 MD5sum: 6069efa51255c9fde732ad5042db71f9 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_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/GWASTools_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/GWASTools_1.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/GWASTools_1.16.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/GWASTools_1.16.1.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 dependsOnMe: GENESIS suggestsMe: podkat Package: h5vc Version: 2.4.1 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: 537b5e968ff8b9a06c7e6df61b6e6a67 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_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/h5vc_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/h5vc_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/h5vc_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/h5vc_2.4.1.tgz vignettes: vignettes/h5vc/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE 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: 1.12.0 Depends: R (>= 2.12.0), Biobase, fabia (>= 2.3.1) Imports: methods, graphics, grDevices, stats, utils License: LGPL (>= 2.1) Archs: i386, x64 MD5sum: 8db62b65ba193878d6cf3a480600333e 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hapFabia_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hapFabia_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hapFabia_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hapFabia_1.12.0.tgz vignettes: vignettes/hapFabia/inst/doc/hapfabia.pdf vignetteTitles: hapFabia: Manual for the R package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Harshlight Version: 1.42.0 Depends: R (>= 2.10) Imports: affy, altcdfenvs, Biobase, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: c919e8734534540d6cf9ce3a01e98f65 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Harshlight_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Harshlight_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Harshlight_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Harshlight_1.42.0.tgz vignettes: vignettes/Harshlight/inst/doc/Harshlight.pdf vignetteTitles: Harshlight hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: HCsnip Version: 1.10.0 Depends: R(>= 2.10.0), survival, coin, fpc, clusterRepro, impute, randomForestSRC, sm, sigaR, Biobase License: GPL (>= 2) MD5sum: ccc4afcee5749bfedcef39af08838b27 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HCsnip_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HCsnip_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HCsnip_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HCsnip_1.10.0.tgz vignettes: vignettes/HCsnip/inst/doc/HCsnip.pdf vignetteTitles: HCsnip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: HDTD Version: 1.4.0 Imports: stats License: GPL-3 MD5sum: d3b738c68fc74ff9327ee9c37faf76ed 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HDTD_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HDTD_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HDTD_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HDTD_1.4.0.tgz vignettes: vignettes/HDTD/inst/doc/Manual.pdf vignetteTitles: HDTD to Analyze High-Dimensional Transposable Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Heatplus Version: 2.16.0 Imports: graphics, grDevices, stats, RColorBrewer Suggests: Biobase, hgu95av2.db, limma License: GPL (>= 2) MD5sum: a51b39d57fdf9cc538109d03915b10ea 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 source.ver: src/contrib/Heatplus_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Heatplus_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Heatplus_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Heatplus_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Heatplus_2.16.0.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 dependsOnMe: GeneAnswers, phenoTest, tRanslatome Package: HELP Version: 1.28.0 Depends: R (>= 2.8.0), stats, graphics, grDevices, Biobase, methods License: GPL (>= 2) MD5sum: bfc3315b978aefb2011e808c32a5f544 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HELP_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HELP_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HELP_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HELP_1.28.0.tgz vignettes: vignettes/HELP/inst/doc/HELP.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: HEM Version: 1.42.0 Depends: R (>= 2.1.0) Imports: Biobase, grDevices, stats, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: a273843209d95f2181f2d190dd3d10ff 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HEM_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HEM_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HEM_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HEM_1.42.0.tgz vignettes: vignettes/HEM/inst/doc/HEM.pdf vignetteTitles: HEM Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hiAnnotator Version: 1.4.0 Depends: GenomicRanges, R (>= 2.10) Imports: foreach, iterators, rtracklayer, dplyr, BSgenome, ggplot2, scales Suggests: knitr, doParallel, testthat, BiocGenerics License: GPL (>= 2) MD5sum: 75a96fbac3698e6e4cfa90161a323f31 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hiAnnotator_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hiAnnotator_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hiAnnotator_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hiAnnotator_1.4.0.tgz vignettes: vignettes/hiAnnotator/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/hiAnnotator/inst/doc/Intro.html htmlTitles: "Introduction" dependsOnMe: hiReadsProcessor Package: HIBAG Version: 1.6.0 Depends: R (>= 2.14.0) Imports: methods Suggests: parallel, BiocStyle, knitr, gdsfmt (>= 1.2.2), SNPRelate (>= 1.1.6) License: GPL-3 Archs: i386, x64 MD5sum: 355c645fcb27c2313cf1640377d0bc61 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HIBAG_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HIBAG_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HIBAG_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HIBAG_1.6.0.tgz vignettes: vignettes/HIBAG/inst/doc/HIBAG_Tutorial.pdf vignetteTitles: HIBAG vignette pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/HIBAG/inst/doc/HIBAG_Tutorial_html.html htmlTitles: "HIBAG – an R Package for HLA Genotype Imputation with Attribute Bagging" Package: hierGWAS Version: 1.0.0 Depends: R (>= 3.2.0) Imports: fastcluster,glmnet, fmsb Suggests: BiocGenerics, RUnit, MASS License: GPL-3 MD5sum: 95a9b399c9aedaf6c54ee7d420be1241 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hierGWAS_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hierGWAS_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hierGWAS_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hierGWAS_1.0.0.tgz vignettes: vignettes/hierGWAS/inst/doc/hierGWAS.pdf vignetteTitles: User manual for R-Package hierGWAS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: HilbertCurve Version: 1.0.0 Depends: R (>= 3.1.2), grid, IRanges, GenomicRanges Imports: methods, HilbertVis, png, grDevices Suggests: knitr, testthat (>= 0.3), circlize (>= 0.3.1), ComplexHeatmap (>= 1.3.0), markdown License: GPL (>= 2) MD5sum: 1b8d151c1431deeb848ffae17368fd5a 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 keep the locality. This package aims to provide a 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HilbertCurve_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HilbertCurve_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HilbertCurve_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HilbertCurve_1.0.0.tgz vignettes: vignettes/HilbertCurve/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/HilbertCurve/inst/doc/HilbertCurve.html htmlTitles: "Making Hilbert Curve" suggestsMe: ComplexHeatmap Package: HilbertVis Version: 1.28.0 Depends: R (>= 2.6.0), grid, lattice Suggests: IRanges, EBImage License: GPL (>= 3) Archs: i386, x64 MD5sum: 71868d71cf5bc9906a8e32be6410ff71 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HilbertVis_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HilbertVis_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HilbertVis_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HilbertVis_1.28.0.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 dependsOnMe: HilbertVisGUI importsMe: ChIPseqR, HilbertCurve Package: HilbertVisGUI Version: 1.28.1 Depends: R (>= 2.6.0), HilbertVis (>= 1.1.6) Suggests: lattice, IRanges License: GPL (>= 3) Archs: i386, x64 MD5sum: 3c7f9870be4bcc71cd249df9c6a7681a 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_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/HilbertVisGUI_1.28.1.zip win64.binary.ver: bin/windows64/contrib/3.2/HilbertVisGUI_1.28.1.zip mac.binary.ver: bin/macosx/contrib/3.2/HilbertVisGUI_1.28.0.tgz vignettes: vignettes/HilbertVisGUI/inst/doc/HilbertVisGUI.pdf vignetteTitles: See vignette in package HilbertVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: TRUE hasLICENSE: FALSE Package: hiReadsProcessor Version: 1.6.0 Depends: Biostrings, GenomicAlignments, xlsx, BiocParallel, hiAnnotator, R (>= 3.0) Imports: sonicLength, dplyr, GenomicRanges, BiocGenerics, rSFFreader Suggests: knitr, testthat License: GPL-3 MD5sum: 20af433af03abc80b978c89dd12382fb 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hiReadsProcessor_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hiReadsProcessor_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hiReadsProcessor_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hiReadsProcessor_1.6.0.tgz vignettes: vignettes/hiReadsProcessor/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/hiReadsProcessor/inst/doc/Tutorial.html htmlTitles: "Introduction" Package: HiTC Version: 1.14.0 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: a3062474d348fc39ed9373e615799b94 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HiTC_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HiTC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HiTC_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HiTC_1.14.0.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 Package: HMMcopy Version: 1.12.0 Depends: R (>= 2.10.0), IRanges (>= 1.4.16), geneplotter (>= 1.24.0) License: GPL-3 Archs: i386, x64 MD5sum: a65e5f0f4037b4c3b319a2fd9d5dde61 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HMMcopy_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HMMcopy_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HMMcopy_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HMMcopy_1.12.0.tgz vignettes: vignettes/HMMcopy/inst/doc/HMMcopy.pdf vignetteTitles: HMMcopy hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hopach Version: 2.30.0 Depends: R (>= 2.11.0), cluster, Biobase, methods Imports: graphics, grDevices, stats, utils, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: ab88b7dc74b541f6bfa92082cc68283c 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hopach_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hopach_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hopach_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hopach_2.30.0.tgz vignettes: vignettes/hopach/inst/doc/hopach.pdf vignetteTitles: hopach hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: phenoTest suggestsMe: BiocCaseStudies Package: hpar Version: 1.12.0 Depends: R (>= 2.15) Suggests: org.Hs.eg.db, GO.db, knitr, BiocStyle, testthat License: Artistic-2.0 MD5sum: 15aa81af33345c5d2ddaef4dde83f5b6 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hpar_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hpar_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hpar_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hpar_1.12.0.tgz vignettes: vignettes/hpar/inst/doc/hpar.pdf vignetteTitles: Human Protein Atlas in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: pRoloc Package: HTqPCR Version: 1.24.0 Depends: Biobase, RColorBrewer, limma Imports: affy, Biobase, gplots, graphics, grDevices, limma, methods, RColorBrewer, stats, stats4, utils Suggests: statmod License: Artistic-2.0 MD5sum: 0d091aff8fbf7fc4ec08050564379add 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HTqPCR_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HTqPCR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HTqPCR_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HTqPCR_1.24.0.tgz vignettes: vignettes/HTqPCR/inst/doc/HTqPCR.pdf vignetteTitles: qPCR analysis in R hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: nondetects, unifiedWMWqPCR Package: HTSanalyzeR Version: 2.22.0 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: 310c143a0f623098f8f471e194516aa0 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HTSanalyzeR_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HTSanalyzeR_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HTSanalyzeR_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HTSanalyzeR_2.22.0.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 importsMe: phenoTest suggestsMe: RTN Package: HTSeqGenie Version: 3.20.1 Depends: R (>= 3.0.0), gmapR (>= 1.6.4), ShortRead (>= 1.19.13), VariantAnnotation (>= 1.8.3) Imports: BiocGenerics (>= 0.2.0), S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), GenomicRanges (>= 1.7.12), 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 (>= 1.6.1) Suggests: TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, LungCancerLines, org.Hs.eg.db, RUnit License: Artistic-2.0 MD5sum: 27f25bb922bcf88a6f67b0f48c65573c 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_3.20.1.tar.gz vignettes: vignettes/HTSeqGenie/inst/doc/HTSeqGenie.pdf vignetteTitles: HTSeqGenie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: htSeqTools Version: 1.16.0 Depends: R (>= 2.12.2), methods, BiocGenerics (>= 0.1.0), Biobase, IRanges, methods, MASS, BSgenome, GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.17.11) Enhances: parallel,multicore License: GPL (>=2) MD5sum: 39209a1a98c73dd07033dbb068a3491f 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/htSeqTools_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/htSeqTools_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/htSeqTools_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/htSeqTools_1.16.0.tgz vignettes: vignettes/htSeqTools/inst/doc/htSeqTools.pdf vignetteTitles: Manual for the htSeqTools library hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: HTSFilter Version: 1.10.0 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: 08d49e10efa1888422948051f9770942 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HTSFilter_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HTSFilter_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HTSFilter_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HTSFilter_1.10.0.tgz vignettes: vignettes/HTSFilter/inst/doc/HTSFilter.pdf vignetteTitles: HTSFilter Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: HybridMTest Version: 1.14.0 Depends: R (>= 2.9.0), Biobase, fdrtool, MASS, survival Imports: stats License: GPL Version 2 or later MD5sum: 4cd733e1b1a8828f73d735936b7fc550 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/HybridMTest_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/HybridMTest_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/HybridMTest_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/HybridMTest_1.14.0.tgz vignettes: vignettes/HybridMTest/inst/doc/HybridMTest.pdf vignetteTitles: Hybrid Multiple Testing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: hyperdraw Version: 1.22.0 Depends: R (>= 2.9.0) Imports: methods, grid, graph, hypergraph, Rgraphviz, stats4 License: GPL (>= 2) MD5sum: 75bb9eb2e46127bbd26e475bb216dcf7 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hyperdraw_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hyperdraw_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hyperdraw_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hyperdraw_1.22.0.tgz vignettes: vignettes/hyperdraw/inst/doc/hyperdraw.pdf vignetteTitles: Hyperdraw hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BiGGR Package: hypergraph Version: 1.42.0 Depends: R (>= 2.1.0), methods, utils, graph Suggests: BiocGenerics, RUnit License: Artistic-2.0 MD5sum: df429d916bec13cb2309b9221c3ef6f2 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/hypergraph_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/hypergraph_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/hypergraph_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/hypergraph_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs, RpsiXML importsMe: BiGGR, hyperdraw Package: iASeq Version: 1.14.0 Depends: R (>= 2.14.1) Imports: graphics, grDevices License: GPL-2 MD5sum: 4d49bb19771b01ceddbe47e7f30ba9ed 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iASeq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iASeq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iASeq_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iASeq_1.14.0.tgz vignettes: vignettes/iASeq/inst/doc/iASeqVignette.pdf vignetteTitles: iASeq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: iBBiG Version: 1.14.0 Depends: biclust Imports: stats4,xtable,ade4 Suggests: methods License: Artistic-2.0 Archs: i386, x64 MD5sum: 7996b3129518b66d9bf2761bf55ad710 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iBBiG_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iBBiG_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iBBiG_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iBBiG_1.14.0.tgz vignettes: vignettes/iBBiG/inst/doc/tutorial.pdf vignetteTitles: iBBiG User Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ibh Version: 1.18.0 Depends: simpIntLists Suggests: yeastCC, stats License: GPL (>= 2) MD5sum: fa372730385b4e6f28f0df23f4dff7cc 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ibh_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ibh_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ibh_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ibh_1.18.0.tgz vignettes: vignettes/ibh/inst/doc/ibh.pdf vignetteTitles: ibh hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: iBMQ Version: 1.10.0 Depends: R(>= 2.15.0),Biobase (>= 2.16.0), ggplot2 (>= 0.9.2) License: Artistic-2.0 Archs: i386, x64 MD5sum: f947234ed20fd8e0d47ff6e685883b03 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iBMQ_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iBMQ_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iBMQ_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iBMQ_1.10.0.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 Package: Icens Version: 1.42.0 Depends: survival Imports: graphics License: Artistic-2.0 MD5sum: ff0371078b3cbd2fe7684efac0aac667 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Icens_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Icens_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Icens_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Icens_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PROcess importsMe: PROcess Package: iCheck Version: 1.0.0 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: 4c590d5887e6b29fefb66d446802fb8c 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iCheck_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iCheck_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iCheck_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iCheck_1.0.0.tgz vignettes: vignettes/iCheck/inst/doc/iCheck.pdf vignetteTitles: iCheck hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: iChip Version: 1.24.0 Depends: R (>= 2.10.0) Imports: limma License: GPL (>= 2) Archs: i386, x64 MD5sum: 7a15603c4bd2f389af7fd60601a9adf4 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iChip_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iChip_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iChip_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iChip_1.24.0.tgz vignettes: vignettes/iChip/inst/doc/iChip.pdf vignetteTitles: iChip hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: iClusterPlus Version: 1.6.0 Depends: R (>= 2.15.0), parallel Suggests: RUnit, BiocGenerics License: GPL (>= 2) Archs: i386, x64 MD5sum: 4f3ed0a3a996f9b41a5bd2e337303d06 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iClusterPlus_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iClusterPlus_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iClusterPlus_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iClusterPlus_1.6.0.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: IdeoViz Version: 1.4.0 Depends: Biobase, IRanges, GenomicRanges, RColorBrewer, rtracklayer,graphics,GenomeInfoDb License: GPL-2 MD5sum: 7f55fcf1482cc19ffbf9b52d0fdb2ed1 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IdeoViz_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IdeoViz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IdeoViz_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IdeoViz_1.4.0.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 Package: idiogram Version: 1.46.0 Depends: R (>= 2.10), methods, Biobase, annotate, plotrix Suggests: hu6800.db, hgu95av2.db, golubEsets License: GPL-2 MD5sum: 4a388c7d4b1ebb270ae98f01a4f6baf9 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/idiogram_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/idiogram_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/idiogram_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/idiogram_1.46.0.tgz vignettes: vignettes/idiogram/inst/doc/idiogram.pdf vignetteTitles: HOWTO: idiogram hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: reb Package: IdMappingAnalysis Version: 1.14.0 Depends: R (>= 2.14), R.oo (>= 1.13.0), rChoiceDialogs Imports: boot, mclust, RColorBrewer, Biobase License: GPL-2 MD5sum: 2667ccf1158c5b09e29c22331f0b2a1b 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IdMappingAnalysis_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IdMappingAnalysis_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IdMappingAnalysis_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IdMappingAnalysis_1.14.0.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 Package: IdMappingRetrieval Version: 1.18.2 Depends: R.oo, XML, RCurl, rChoiceDialogs Imports: biomaRt, ENVISIONQuery, AffyCompatible, R.methodsS3, utils License: GPL-2 MD5sum: 4d8994ef6cbefb4a72d8346b1ef4c71a 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_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/IdMappingRetrieval_1.18.2.zip win64.binary.ver: bin/windows64/contrib/3.2/IdMappingRetrieval_1.18.2.zip mac.binary.ver: bin/macosx/contrib/3.2/IdMappingRetrieval_1.17.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IdMappingRetrieval_1.18.2.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 Package: iGC Version: 1.0.0 Depends: R (>= 3.2.0) Imports: plyr, data.table Suggests: BiocStyle, knitr, rmarkdown Enhances: doMC License: GPL-2 MD5sum: fa9fe10fa9e2bf2cf3bf0c8bfbdd8e2c 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iGC_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iGC_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iGC_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iGC_1.0.0.tgz vignettes: vignettes/iGC/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/iGC/inst/doc/Introduction.html htmlTitles: "Introduction to iGC" Package: illuminaio Version: 0.12.0 Imports: base64 Suggests: RUnit, BiocGenerics, IlluminaDataTestFiles (>= 1.0.2), BiocStyle License: GPL-2 Archs: i386, x64 MD5sum: e4d9412117dbdf60c8ed293026316b19 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_0.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/illuminaio_0.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/illuminaio_0.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/illuminaio_0.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/illuminaio_0.12.0.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 dependsOnMe: RnBeads importsMe: beadarray, crlmm, methylumi, minfi suggestsMe: limma Package: imageHTS Version: 1.20.0 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: f0208fa91ba5632e9c308c8097889088 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/imageHTS_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/imageHTS_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/imageHTS_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/imageHTS_1.20.0.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 dependsOnMe: phenoDist Package: Imetagene Version: 1.0.0 Depends: R (>= 3.2.0), metagene, shiny Imports: d3heatmap, shinyBS, shinyFiles, shinythemes, ggplot2 Suggests: knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 5c965faa6e27ca087b2da967fea6af86 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Imetagene_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Imetagene_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Imetagene_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Imetagene_1.0.0.tgz vignettes: vignettes/Imetagene/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/Imetagene/inst/doc/imetagene.html htmlTitles: "Imetagene: the iteractive interface for metagene" Package: immunoClust Version: 1.2.0 Depends: R(>= 2.13.0), methods, stats, graphics, grid, lattice, flowCore Suggests: BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 3ce9092f9746c5c928b4c01c0fe45275 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 Author: Till Soerensen Maintainer: Till Soerensen source.ver: src/contrib/immunoClust_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/immunoClust_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/immunoClust_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/immunoClust_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/immunoClust_1.2.0.tgz vignettes: vignettes/immunoClust/inst/doc/immunoClust.pdf vignetteTitles: immunoClust package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: IMPCdata Version: 1.4.0 Depends: R (>= 2.3.0) Imports: rjson License: file LICENSE MD5sum: 6fd68bd26bfe5bd20446b243c433bfda 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IMPCdata_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IMPCdata_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IMPCdata_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IMPCdata_1.4.0.tgz vignettes: vignettes/IMPCdata/inst/doc/IMPCdata.pdf vignetteTitles: IMPCdata Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: impute Version: 1.44.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: 1c7a0689511953d80b3255db37bb469c 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/impute_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/impute_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/impute_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/impute_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: CGHcall, HCsnip, TIN importsMe: ChAMP, genomation, metaX, miRLAB, MSnbase, Rnits suggestsMe: BioNet Package: InPAS Version: 1.2.1 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: 72e790b412c23e11ad48d2b0bf12668c 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/InPAS_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/InPAS_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/InPAS_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/InPAS_1.2.1.tgz vignettes: vignettes/InPAS/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/InPAS/inst/doc/InPAS.html htmlTitles: "InPAS Vignette" Package: INPower Version: 1.6.0 Depends: R (>= 3.1.0), mvtnorm Suggests: RUnit, BiocGenerics License: GPL-2 + file LICENSE MD5sum: 3f490e43d4f4cfd94f69b21d4dc9afaa 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/INPower_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/INPower_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/INPower_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/INPower_1.6.0.tgz vignettes: vignettes/INPower/inst/doc/vignette.pdf vignetteTitles: INPower Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: inSilicoDb Version: 2.6.0 Depends: R (>= 3.0.0), rjson, Biobase, RCurl Suggests: limma License: GPL-2 MD5sum: 5b8c8e27fb4be9068728cd2106eec7b4 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/inSilicoDb_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/inSilicoDb_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/inSilicoDb_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/inSilicoDb_2.6.0.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 suggestsMe: inSilicoMerging Package: inSilicoMerging Version: 1.14.0 Depends: R (>= 2.11.1), Biobase Suggests: BiocGenerics, inSilicoDb License: GPL-2 MD5sum: 47d8bdd0c2dc57dfee08009c81fc0487 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/inSilicoMerging_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/inSilicoMerging_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/inSilicoMerging_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/inSilicoMerging_1.14.0.tgz vignettes: vignettes/inSilicoMerging/inst/doc/inSilicoMerging.pdf vignetteTitles: Using the inSilicoMerging package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: INSPEcT Version: 1.0.2 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: 413f932813ac7efec351b1016692432a 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/INSPEcT_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/INSPEcT_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/INSPEcT_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/INSPEcT_1.0.2.tgz vignettes: vignettes/INSPEcT/inst/doc/INSPEcT.pdf vignetteTitles: INSPEcT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: intansv Version: 1.10.0 Depends: R (>= 2.14.0), plyr, ggbio, GenomicRanges Imports: BiocGenerics, IRanges License: Artistic-2.0 MD5sum: 03d39242904f4d7927bdef9ff794c80e 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/3.2/intansv_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/intansv_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/intansv_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/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 Package: interactiveDisplay Version: 1.8.0 Depends: R (>= 2.10), methods, BiocGenerics, grid Imports: interactiveDisplayBase (>= 1.7.3), shiny, RColorBrewer, ggplot2, reshape2, plyr, gridSVG, XML, Category, AnnotationDbi Suggests: RUnit, hgu95av2.db, knitr, GenomicRanges, SummarizedExperiment, GOstats, ggbio, GO.db, Gviz, rtracklayer, metagenomeSeq, gplots, vegan, Biobase Enhances: rstudio License: Artistic-2.0 MD5sum: 90e05d80c7b9f6c7205b6c67ea2aa18f 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/interactiveDisplay_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/interactiveDisplay_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/interactiveDisplay_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/interactiveDisplay_1.8.0.tgz vignettes: vignettes/interactiveDisplay/inst/doc/interactiveDisplay.pdf vignetteTitles: interactiveDisplay: A package for enabling interactive visualization of Bioconductor objects hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: metagenomeSeq Package: interactiveDisplayBase Version: 1.8.0 Depends: R (>= 2.10), methods, BiocGenerics Imports: shiny Suggests: knitr Enhances: rstudioapi License: Artistic-2.0 MD5sum: 8c5f7ef6716d74f57ed1150ffbb05c93 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/interactiveDisplayBase_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/interactiveDisplayBase_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/interactiveDisplayBase_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/interactiveDisplayBase_1.8.0.tgz vignettes: vignettes/interactiveDisplayBase/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/interactiveDisplayBase/inst/doc/interactiveDisplayBase.html htmlTitles: "Using interactiveDisplayBase for Bioconductor object visualization and modification" importsMe: AnnotationHub, interactiveDisplay Package: inveRsion Version: 1.18.0 Depends: methods, haplo.stats Imports: graphics, methods, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 430a8132fe581523eee73fa0d269ff2e 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/inveRsion_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/inveRsion_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/inveRsion_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/inveRsion_1.18.0.tgz vignettes: vignettes/inveRsion/inst/doc/inveRsion.pdf vignetteTitles: Quick start guide for inveRsion package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: IONiseR Version: 1.0.1 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: 98c4cb1b87f3ac554aec5a26350d9483 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/IONiseR_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/IONiseR_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/IONiseR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IONiseR_1.0.1.tgz vignettes: vignettes/IONiseR/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/IONiseR/inst/doc/IONiseR.html htmlTitles: "IONiseR" Package: iontree Version: 1.16.0 Depends: methods, rJava, RSQLite, XML Suggests: iontreeData License: GPL-2 MD5sum: 19a05aef9e31f798b6c942f713d3750d 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iontree_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iontree_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iontree_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iontree_1.16.0.tgz vignettes: vignettes/iontree/inst/doc/iontree_doc.pdf vignetteTitles: MSn hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: iPAC Version: 1.14.0 Depends: R(>= 2.15),gdata, scatterplot3d, Biostrings, multtest License: GPL-2 MD5sum: 1becac0abe614a479197f283020b1ced 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iPAC_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iPAC_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iPAC_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iPAC_1.14.0.tgz vignettes: vignettes/iPAC/inst/doc/iPAC.pdf vignetteTitles: iPAC: identification of Protein Amino acid Mutations hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: QuartPAC Package: IPPD Version: 1.18.0 Depends: R (>= 2.12.0), MASS, Matrix, XML, digest, bitops Imports: methods, stats, graphics License: GPL (version 2 or later) Archs: i386, x64 MD5sum: 3723cd9a60e9b65f1f61558ea6e1ce49 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IPPD_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IPPD_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IPPD_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IPPD_1.18.0.tgz vignettes: vignettes/IPPD/inst/doc/IPPD.pdf vignetteTitles: IPPD Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: IRanges Version: 2.4.8 Depends: R (>= 3.1.0), methods, utils, stats, BiocGenerics (>= 0.15.10), S4Vectors (>= 0.8.4) Imports: stats4 LinkingTo: S4Vectors Suggests: XVector, GenomicRanges, BSgenome.Celegans.UCSC.ce2, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 1bd76ca551e1430d559307bc2daef492 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. Pages, P. Aboyoun and M. Lawrence Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/IRanges_2.4.8.tar.gz win.binary.ver: bin/windows/contrib/3.2/IRanges_2.4.8.zip win64.binary.ver: bin/windows64/contrib/3.2/IRanges_2.4.8.zip mac.binary.ver: bin/macosx/contrib/3.2/IRanges_2.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IRanges_2.4.8.tgz vignettes: vignettes/IRanges/inst/doc/IRangesOverview.pdf vignetteTitles: An Introduction to IRanges hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AnnotationDbi, AnnotationHubData, BayesPeak, biomvRCNS, Biostrings, BiSeq, BSgenome, bsseq, BubbleTree, bumphunter, CAFE, casper, CexoR, ChIPpeakAnno, chipseq, chroGPS, cn.mops, CSAR, customProDB, deepSNV, DESeq2, DEXSeq, DirichletMultinomial, DMRcaller, EnrichedHeatmap, epigenomix, exomeCopy, GenomeInfoDb, genomes, GenomicAlignments, GenomicFeatures, GenomicRanges, Genominator, groHMM, GUIDEseq, Guitar, Gviz, HilbertCurve, HiTC, HMMcopy, htSeqTools, IdeoViz, methyAnalysis, MotifDb, motifRG, oneChannelGUI, OTUbase, pepStat, PGA, PING, proBAMr, PSICQUIC, RefNet, rfPred, rGADEM, rGREAT, RIPSeeker, rMAT, Rsamtools, scsR, segmentSeq, SGSeq, SomatiCA, TEQC, TitanCNA, triform, triplex, VariantTools, XVector importsMe: ALDEx2, AllelicImbalance, annmap, AnnotationDbi, ArrayExpressHTS, ballgown, bamsignals, BayesPeak, beadarray, Biostrings, biovizBase, BiSeq, BitSeq, BSgenome, CAGEr, ChAMP, charm, chipenrich, ChIPQC, ChIPseeker, chipseq, ChIPseqR, ChIPsim, ChromHeatMap, cleaver, CNEr, CNPBayes, CNVPanelizer, CNVrd2, cobindR, coMET, compEpiTools, conumee, copynumber, CopywriteR, CoverageView, CRISPRseek, csaw, customProDB, DECIPHER, derfinder, derfinderHelper, derfinderPlot, DiffBind, diffHic, DMRcate, DOQTL, easyRNASeq, EDASeq, ensembldb, facopy, fastseg, FindMyFriends, flipflop, flowQ, FunciSNP, genomation, genomeIntervals, GenomicAlignments, GenomicFiles, GenomicInteractions, GenomicTuples, genoset, genotypeeval, GGBase, ggbio, GGtools, girafe, gmapR, GoogleGenomics, GOTHiC, gQTLstats, gwascat, h5vc, HTSeqGenie, InPAS, INSPEcT, intansv, IVAS, LOLA, M3D, MatrixRider, MEAL, MEDIPS, metagene, methVisual, methyAnalysis, methylPipe, MethylSeekR, methylumi, minfi, MinimumDistance, mosaics, motifbreakR, MotIV, msa, MSnbase, NarrowPeaks, nucleR, oligoClasses, OrganismDbi, Pbase, pdInfoBuilder, PICS, PING, plethy, podkat, polyester, prebs, Pviz, qpgraph, QuasR, R3CPET, r3Cseq, R453Plus1Toolbox, RareVariantVis, Rariant, REDseq, regionReport, Repitools, ReportingTools, rGADEM, RiboProfiling, rMAT, rnaSeqMap, RnBeads, Rolexa, Rqc, rSFFreader, RSVSim, RTN, rtracklayer, SCAN.UPC, SeqArray, seqPattern, seqplots, SeqVarTools, ShortRead, SICtools, simulatorZ, skewr, SNPchip, soGGi, SomatiCA, SomaticSignatures, spliceR, SplicingGraphs, SummarizedExperiment, SVM2CRM, TarSeqQC, TCGAbiolinks, TFBSTools, tracktables, trackViewer, TransView, triform, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, wavClusteR, waveTiling, XVector suggestsMe: AnnotationHub, BaseSpaceR, BiocGenerics, ClassifyR, gQTLBase, HilbertVis, HilbertVisGUI, MiRaGE, S4Vectors, STAN Package: iSeq Version: 1.22.0 Depends: R (>= 2.10.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: e3e60ca8e8321b128deeac23f43542d1 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iSeq_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iSeq_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iSeq_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iSeq_1.22.0.tgz vignettes: vignettes/iSeq/inst/doc/iSeq.pdf vignetteTitles: iSeq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: isobar Version: 1.16.1 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: 43539513c2439b926393be7a7e53cc3e 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: http://www.ms-isobar.org source.ver: src/contrib/isobar_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/isobar_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/isobar_1.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/isobar_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/isobar_1.16.1.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: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: IsoGeneGUI Version: 2.6.0 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: 439a2b93ae8b4e1fbc1a14011d35f1af 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_2.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IsoGeneGUI_2.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IsoGeneGUI_2.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IsoGeneGUI_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IsoGeneGUI_2.6.0.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: ITALICS Version: 2.30.0 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: b2486f3bed44312b54d6b51f59cb1e50 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ITALICS_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ITALICS_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ITALICS_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ITALICS_2.30.0.tgz vignettes: vignettes/ITALICS/inst/doc/ITALICS.pdf vignetteTitles: ITALICS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: iterativeBMA Version: 1.28.0 Depends: BMA, leaps, Biobase (>= 2.5.5) License: GPL (>= 2) MD5sum: 33227214d874580fcb4f8a0ce3838f66 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iterativeBMA_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iterativeBMA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iterativeBMA_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iterativeBMA_1.28.0.tgz vignettes: vignettes/iterativeBMA/inst/doc/iterativeBMA.pdf vignetteTitles: The Iterative Bayesian Model Averaging Algorithm hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: iterativeBMAsurv Version: 1.28.0 Depends: BMA, leaps, survival, splines Imports: graphics, grDevices, stats, survival, utils License: GPL (>= 2) MD5sum: d48835dd3a58ed7ef9c2b177a8ccdb84 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/iterativeBMAsurv_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/iterativeBMAsurv_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/iterativeBMAsurv_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/iterativeBMAsurv_1.28.0.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 Package: IVAS Version: 1.2.0 Depends: R (> 3.0.0),GenomicFeatures Imports: doParallel, lme4, Matrix, BiocGenerics, GenomicRanges, IRanges, foreach, AnnotationDbi, S4Vectors, GenomeInfoDb Suggests: BiocStyle License: GPL-2 MD5sum: 1ce59b5ca60e0fa8acd2cce6a77b0e66 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/IVAS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/IVAS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/IVAS_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/IVAS_1.2.0.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 Package: jmosaics Version: 1.10.0 Depends: R (>= 2.15.2), mosaics License: GPL (>= 2) MD5sum: 15b73becd7634a962e53d8b2c7af5668 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/jmosaics_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/jmosaics_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/jmosaics_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/jmosaics_1.10.0.tgz vignettes: vignettes/jmosaics/inst/doc/jmosaics.pdf vignetteTitles: jMOSAiCS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: joda Version: 1.18.0 Depends: R (>= 2.0), bgmm, RBGL License: GPL (>= 2) MD5sum: c4f14ef379c4add693048c149ff8a0fa 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/joda_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/joda_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/joda_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/joda_1.18.0.tgz vignettes: vignettes/joda/inst/doc/JodaVignette.pdf vignetteTitles: Introduction to joda hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: KCsmart Version: 2.28.0 Depends: siggenes, multtest, KernSmooth Imports: methods, BiocGenerics Enhances: Biobase, CGHbase License: GPL-3 MD5sum: 45056e54b7b56b409ca23dacd7031dd5 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_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/KCsmart_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/KCsmart_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/KCsmart_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KCsmart_2.28.0.tgz vignettes: vignettes/KCsmart/inst/doc/KCS.pdf vignetteTitles: KCsmart example session hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: kebabs Version: 1.4.1 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 LinkingTo: IRanges, XVector, Biostrings, Rcpp, S4Vectors Suggests: SparseM, apcluster, Biobase, BiocGenerics License: GPL (>= 2.1) Archs: i386, x64 MD5sum: bb2304f1c83aacfdaf09460d3367efd5 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/ source.ver: src/contrib/kebabs_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/kebabs_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/kebabs_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/kebabs_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/kebabs_1.4.1.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 importsMe: FindMyFriends Package: KEGGgraph Version: 1.28.0 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: 188adb9b3d94bd62637d9ba57233d344 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/KEGGgraph_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/KEGGgraph_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/KEGGgraph_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KEGGgraph_1.28.0.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 dependsOnMe: ROntoTools, SPIA importsMe: clipper, DEGraph, EnrichmentBrowser, NCIgraph, pathview, ToPASeq suggestsMe: DEGraph, GenomicRanges Package: keggorthology Version: 2.22.0 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: 1f9168e63fd3970e37b74d5c9dd4cc3d 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/keggorthology_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/keggorthology_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/keggorthology_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/keggorthology_2.22.0.tgz vignettes: vignettes/keggorthology/inst/doc/keggorth.pdf vignetteTitles: keggorthology overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MLInterfaces Package: KEGGprofile Version: 1.12.0 Imports: AnnotationDbi,png,TeachingDemos,XML,KEGG.db,KEGGREST,biomaRt License: GPL (>= 2) MD5sum: ed4ffa05364c496a65976876f435bc03 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/KEGGprofile_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/KEGGprofile_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/KEGGprofile_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KEGGprofile_1.12.0.tgz vignettes: vignettes/KEGGprofile/inst/doc/KEGGprofile.pdf vignetteTitles: KEGGprofile: Application Examples hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: FGNet Package: KEGGREST Version: 1.10.1 Imports: methods, httr, png, Biostrings Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: def415ebc6d6756f7f1462b68a5c3f5d 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/KEGGREST_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/KEGGREST_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/KEGGREST_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/KEGGREST_1.10.1.tgz vignettes: vignettes/KEGGREST/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/KEGGREST/inst/doc/KEGGREST-vignette.html htmlTitles: "Accessing the KEGG REST API" dependsOnMe: PAPi, ROntoTools importsMe: clusterProfiler, EnrichmentBrowser, gage, mmnet, pathview Package: lapmix Version: 1.36.0 Depends: R (>= 2.6.0),stats Imports: Biobase, graphics, grDevices, methods, stats, tools, utils License: GPL (>= 2) MD5sum: aaacb9887e6203d11ee0a44e857c9b12 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lapmix_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lapmix_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lapmix_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lapmix_1.36.0.tgz vignettes: vignettes/lapmix/inst/doc/lapmix-example.pdf vignetteTitles: lapmix example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: LBE Version: 1.38.0 Depends: stats Imports: graphics, grDevices, methods, stats, utils Suggests: qvalue License: GPL-2 MD5sum: 0594a94857bda88cdd590b8695a4600e 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LBE_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LBE_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LBE_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LBE_1.38.0.tgz vignettes: vignettes/LBE/inst/doc/LBE.pdf vignetteTitles: LBE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ldblock Version: 1.0.0 Depends: R (>= 3.1), methods Imports: Matrix, snpStats Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 MD5sum: 13ce8a7a9ef5df58332355e35cb106d8 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ldblock_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ldblock_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ldblock_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ldblock_1.0.0.tgz vignettes: vignettes/ldblock/inst/doc/ldblock.pdf vignetteTitles: ldblock.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: LEA Version: 1.2.0 Depends: R (>= 3.1.0), methods License: GPL-3 Archs: i386, x64 MD5sum: da8cc905df176c9bded5a01373835730 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LEA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LEA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LEA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LEA_1.2.0.tgz vignettes: vignettes/LEA/inst/doc/LEA.pdf vignetteTitles: LEA: An R Package for Landscape and Ecological Association Studies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: LedPred Version: 1.2.1 Depends: R (>= 3.2.0), e1071 (>= 1.6) Imports: akima, GenomicRanges (>= 1.18.4), irr, jsonlite, parallel, plot3D, plyr, RCurl, ROCR, testthat License: MIT | file LICENSE MD5sum: 86461ef7f5a97d2ade6b1074847be10b 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 Author: Elodie Darbo, Denis Seyres, Aitor Gonzalez Maintainer: Aitor Gonzalez BugReports: https://github.com/aitgon/LedPred/issues source.ver: src/contrib/LedPred_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/LedPred_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/LedPred_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/LedPred_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LedPred_1.2.1.tgz vignettes: vignettes/LedPred/inst/doc/LedPred.pdf vignetteTitles: LedPred Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: les Version: 1.20.1 Depends: R (>= 2.13.2), methods, graphics, fdrtool Imports: boot, gplots, RColorBrewer Suggests: Biobase, limma Enhances: parallel License: GPL-3 MD5sum: 7a6e3105e4421252b2f8ca58781386ce 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_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/les_1.20.1.zip win64.binary.ver: bin/windows64/contrib/3.2/les_1.20.1.zip mac.binary.ver: bin/macosx/contrib/3.2/les_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/les_1.20.1.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 importsMe: GSRI Package: lfa Version: 1.0.0 Depends: R (>= 3.2) Imports: corpcor Suggests: knitr, ggplot2 License: GPL-3 Archs: i386, x64 MD5sum: b7ecd15a5d36c4f4ce74c6ac4bd71331 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lfa_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lfa_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lfa_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lfa_1.0.0.tgz vignettes: vignettes/lfa/inst/doc/lfa.pdf vignetteTitles: lfa Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: gcatest Package: limma Version: 3.26.9 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: 335aa265eea1418f949a8de906d14b8f 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], Matthew Ritchie [ctb], Jeremy Silver [ctb], James Wettenhall [ctb], Natalie Thorne [ctb], Davis McCarthy [ctb], Di Wu [ctb], Yifang Hu [ctb], Wei Shi [ctb], Belinda Phipson [ctb], Alicia Oshlack [ctb], Carolyn de Graaf [ctb], Mette Langaas [ctb], Egil Ferkingstad [ctb], Marcus Davy [ctb], Francois Pepin [ctb], Dongseok Choi [ctb], Aaron Lun [ctb] Maintainer: Gordon Smyth URL: http://bioinf.wehi.edu.au/limma source.ver: src/contrib/limma_3.26.9.tar.gz win.binary.ver: bin/windows/contrib/3.2/limma_3.26.9.zip win64.binary.ver: bin/windows64/contrib/3.2/limma_3.26.9.zip mac.binary.ver: bin/macosx/contrib/3.2/limma_3.26.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/limma_3.26.9.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, attract, birta, CALIB, cghMCR, codelink, convert, Cormotif, coRNAi, DiffBind, DrugVsDisease, edgeR, ExiMiR, gCMAP, HTqPCR, maigesPack, marray, metagenomeSeq, metaseqR, MLSeq, MmPalateMiRNA, qpcrNorm, qusage, RBM, Ringo, RnBeads, Rnits, snapCGH, SSPA, tRanslatome, TurboNorm, wateRmelon importsMe: ABSSeq, affycoretools, affylmGUI, ArrayExpress, arrayQuality, arrayQualityMetrics, ArrayTools, attract, ballgown, beadarray, betr, birte, BubbleTree, bumphunter, CALIB, CancerMutationAnalysis, casper, ChAMP, charm, ChIPpeakAnno, compcodeR, csaw, derfinderPlot, diffHic, DMRcate, EnrichmentBrowser, erccdashboard, explorase, flowBin, GeneSelectMMD, GeneSelector, GGBase, GOsummaries, gQTLstats, HTqPCR, iCheck, iChip, InPAS, limmaGUI, lmdme, LVSmiRNA, mAPKL, maSigPro, MEAL, minfi, miRLAB, missMethyl, MmPalateMiRNA, monocle, MSstats, nem, nethet, nondetects, OGSA, OLIN, PAA, PADOG, PECA, pepStat, phenoTest, polyester, Ringo, RNAinteract, RNAither, RTN, RTopper, SimBindProfiles, snapCGH, STATegRa, systemPipeR, TCGAbiolinks, timecourse, ToPASeq, tweeDEseq, variancePartition, vsn suggestsMe: ABarray, ADaCGH2, beadarraySNP, biobroom, BiocCaseStudies, BioNet, Category, categoryCompare, ClassifyR, CMA, coGPS, dyebias, ELBOW, gage, GeneSelector, GEOquery, GSRI, GSVA, Heatplus, inSilicoDb, isobar, les, lumi, mdgsa, methylumi, MLP, npGSEA, oligo, oneChannelGUI, paxtoolsr, PGSEA, piano, plw, PREDA, puma, Rcade, RTopper, rtracklayer, subSeq, sva Package: limmaGUI Version: 1.46.0 Imports: limma, tcltk, BiocInstaller, tkrplot, R2HTML, xtable, gcrma, AnnotationDbi License: GPL (>=2) MD5sum: 2ebaab837078e3106c8d433c10d9a0ba 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 , Keith Satterley URL: http://bioinf.wehi.edu.au/limmaGUI/ source.ver: src/contrib/limmaGUI_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/limmaGUI_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/limmaGUI_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/limmaGUI_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/limmaGUI_1.46.0.tgz vignettes: vignettes/limmaGUI/inst/doc/extract.pdf, vignettes/limmaGUI/inst/doc/limmaGUI.pdf vignetteTitles: Extracting limma objects from limmaGUI files, limmaGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: LiquidAssociation Version: 1.24.0 Depends: geepack, methods, yeastCC, org.Sc.sgd.db Imports: Biobase, graphics, grDevices, methods, stats License: GPL (>=3) MD5sum: 4c54f10ad0fd92fc1b81214099626203 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LiquidAssociation_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LiquidAssociation_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LiquidAssociation_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LiquidAssociation_1.24.0.tgz vignettes: vignettes/LiquidAssociation/inst/doc/LiquidAssociation.pdf vignetteTitles: LiquidAssociation Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: fastLiquidAssociation Package: lmdme Version: 1.12.0 Depends: R (>= 2.14.1), pls, stemHypoxia Imports: stats, methods, limma Enhances: parallel License: GPL (>=2) MD5sum: 9a23639022a66dac1d5d434bc5c781be 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lmdme_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lmdme_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lmdme_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lmdme_1.12.0.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 Package: LMGene Version: 2.26.0 Depends: R (>= 2.10.0), Biobase (>= 2.5.5), multtest, survival, affy Suggests: affydata License: LGPL MD5sum: 688dd86b10459719c88ca5ff915c7850 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LMGene_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LMGene_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LMGene_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LMGene_2.26.0.tgz vignettes: vignettes/LMGene/inst/doc/LMGene.pdf vignetteTitles: LMGene User's Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: logicFS Version: 1.40.0 Depends: LogicReg, mcbiopi Suggests: genefilter, siggenes License: LGPL (>= 2) MD5sum: 62af481adf484f5737ff434d7c9d0475 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/logicFS_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/logicFS_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/logicFS_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/logicFS_1.40.0.tgz vignettes: vignettes/logicFS/inst/doc/logicFS.pdf vignetteTitles: logicFS Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: trio Package: logitT Version: 1.28.0 Depends: affy Suggests: SpikeInSubset License: GPL (>= 2) Archs: i386, x64 MD5sum: 2ec19e0e358cac91adc9b177a5ca6fe1 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/logitT_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/logitT_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/logitT_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/logitT_1.28.0.tgz vignettes: vignettes/logitT/inst/doc/logitT.pdf vignetteTitles: logitT primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: lol Version: 1.18.0 Depends: penalized, Matrix Imports: Matrix, penalized, graphics, grDevices, stats License: GPL-2 MD5sum: c64195f2dd87d60e3279dc2db89c34f7 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lol_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lol_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lol_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lol_1.18.0.tgz vignettes: vignettes/lol/inst/doc/lol.pdf vignetteTitles: An introduction to the lol package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: LOLA Version: 1.0.0 Imports: GenomicRanges, IRanges, data.table Suggests: knitr, parallel, BiocGenerics, testthat Enhances: simpleCache, qvalue License: GPL-3 MD5sum: 044eea38dfc6ae4acc22049a6d358105 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LOLA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LOLA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LOLA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LOLA_1.0.0.tgz vignettes: vignettes/LOLA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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: 1.2.0 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: af47fbb06a59014b115c1ac8a0bb5c60 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LowMACA_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LowMACA_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LowMACA_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LowMACA_1.2.0.tgz vignettes: vignettes/LowMACA/inst/doc/LowMACA.pdf vignetteTitles: LowMACA hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: LPE Version: 1.44.0 Depends: R (>= 2.10) Imports: stats License: LGPL MD5sum: fdb8f41c69e750dae7cedc6789b06ee4 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LPE_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LPE_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LPE_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LPE_1.44.0.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 dependsOnMe: LPEadj, PLPE importsMe: LPEadj suggestsMe: ABarray Package: LPEadj Version: 1.30.0 Depends: LPE Imports: LPE, stats License: LGPL MD5sum: ebcf42fd79f358507624ef3bb55a28f9 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LPEadj_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LPEadj_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LPEadj_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LPEadj_1.30.0.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 Package: lpNet Version: 2.2.0 Depends: lpSolve, nem License: Artistic License 2.0 MD5sum: 99a95eb3a0409c0488b8020e6875ca4f 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/lpNet_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/lpNet_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/lpNet_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lpNet_2.2.0.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 Package: lumi Version: 2.22.1 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: 9a50369a75d03cb68361700fb92ae0e4 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_2.22.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/lumi_2.22.1.zip win64.binary.ver: bin/windows64/contrib/3.2/lumi_2.22.1.zip mac.binary.ver: bin/macosx/contrib/3.2/lumi_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/lumi_2.22.1.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 dependsOnMe: arrayMvout, iCheck, wateRmelon importsMe: ffpe, methyAnalysis, MineICA suggestsMe: beadarray, blima, methylumi, tigre Package: LVSmiRNA Version: 1.20.0 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: 821f80ddef6d423538dea1b77b295437 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/LVSmiRNA_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/LVSmiRNA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/LVSmiRNA_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/LVSmiRNA_1.20.0.tgz vignettes: vignettes/LVSmiRNA/inst/doc/LVSmiRNA.pdf vignetteTitles: LVSmiRNA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: M3D Version: 1.4.0 Depends: R (>= 3.0.0) Imports: GenomicRanges, IRanges, BiSeq, parallel Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: Artistic License 2.0 MD5sum: 8a9df75220136bc48adce8d9f1d4431f 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/M3D_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/M3D_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/M3D_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/M3D_1.4.0.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 Package: maanova Version: 1.40.0 Depends: R (>= 2.10) Imports: Biobase, graphics, grDevices, methods, stats, utils Suggests: qvalue, snow Enhances: Rmpi License: GPL (>= 2) Archs: i386, x64 MD5sum: 967cd4d713dd956db331555b5b371752 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maanova_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maanova_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maanova_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maanova_1.40.0.tgz vignettes: vignettes/maanova/inst/doc/maanova.pdf vignetteTitles: R/maanova HowTo hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: macat Version: 1.44.1 Depends: Biobase, annotate Suggests: hgu95av2.db, stjudem License: Artistic-2.0 MD5sum: b5fb24e794f0536df86c48c7fcfc1785 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_1.44.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/macat_1.44.1.zip win64.binary.ver: bin/windows64/contrib/3.2/macat_1.44.1.zip mac.binary.ver: bin/macosx/contrib/3.2/macat_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/macat_1.44.1.tgz vignettes: vignettes/macat/inst/doc/macat.pdf vignetteTitles: MicroArray Chromosome Analysis Tool hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: maCorrPlot Version: 1.40.0 Depends: lattice Imports: graphics, grDevices, lattice, stats License: GPL (>= 2) MD5sum: b3e44517c563c3699ce573050c7a520d 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maCorrPlot_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maCorrPlot_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maCorrPlot_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maCorrPlot_1.40.0.tgz vignettes: vignettes/maCorrPlot/inst/doc/maCorrPlot.pdf vignetteTitles: maCorrPlot Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: made4 Version: 1.44.0 Depends: ade4, RColorBrewer,gplots,scatterplot3d Suggests: affy License: Artistic-2.0 MD5sum: 92371a1d89506e49fb02e2349facf424 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/made4_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/made4_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/made4_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/made4_1.44.0.tgz vignettes: vignettes/made4/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: bgafun importsMe: omicade4 Package: maigesPack Version: 1.34.0 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: 179134c8dd3f4d97f6e836938e0bb86d 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maigesPack_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maigesPack_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maigesPack_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maigesPack_1.34.0.tgz vignettes: vignettes/maigesPack/inst/doc/maigesPack_tutorial.pdf vignetteTitles: maigesPack Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MAIT Version: 1.4.0 Depends: R (>= 2.10), CAMERA, Rcpp, pls Imports: gplots,e1071,class,MASS,plsgenomics,agricolae,xcms,methods,caret Enhances: rgl License: GPL-2 MD5sum: da453bfef088dc317390e87f830239a4 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MAIT_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MAIT_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MAIT_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MAIT_1.4.0.tgz vignettes: vignettes/MAIT/inst/doc/MAIT_Vignette.pdf vignetteTitles: MAIT Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: makecdfenv Version: 1.46.0 Depends: R (>= 2.6.0), affyio Imports: Biobase, affy, methods, stats, utils, zlibbioc License: GPL (>= 2) Archs: i386, x64 MD5sum: dee6b653b758170c685994ee4b70a779 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/makecdfenv_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/makecdfenv_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/makecdfenv_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/makecdfenv_1.46.0.tgz vignettes: vignettes/makecdfenv/inst/doc/makecdfenv.pdf vignetteTitles: makecdfenv primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: altcdfenvs Package: MANOR Version: 1.42.0 Depends: R (>= 2.10), GLAD Imports: GLAD, graphics, grDevices, stats, utils License: GPL-2 Archs: i386, x64 MD5sum: 161f2af9acaf3e44f1a9ea1b94575f66 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MANOR_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MANOR_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MANOR_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MANOR_1.42.0.tgz vignettes: vignettes/MANOR/inst/doc/MANOR.pdf vignetteTitles: MANOR overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: manta Version: 1.16.0 Depends: R (>= 1.8.0), methods, edgeR (>= 2.5.13) Imports: Hmisc, caroline(>= 0.6.6) Suggests: RSQLite, plotrix License: Artistic-2.0 MD5sum: 55d002c3c32e18badcadccdc9f62e92e 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/manta_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/manta_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/manta_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/manta_1.16.0.tgz vignettes: vignettes/manta/inst/doc/manta.pdf vignetteTitles: manta hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: MantelCorr Version: 1.40.0 Depends: R (>= 2.10) Imports: stats License: GPL (>= 2) MD5sum: 913a2a0ce2e03b958fa4106eb5983c76 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MantelCorr_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MantelCorr_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MantelCorr_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MantelCorr_1.40.0.tgz vignettes: vignettes/MantelCorr/inst/doc/MantelCorrVignette.pdf vignetteTitles: MantelCorrVignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mAPKL Version: 1.2.0 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: 0a19b70ee1b01f5f2f4f7e5c6841cd4e 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mAPKL_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mAPKL_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mAPKL_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mAPKL_1.2.0.tgz vignettes: vignettes/mAPKL/inst/doc/mAPKL.pdf vignetteTitles: mAPKL Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: maPredictDSC Version: 1.8.0 Depends: R (>= 2.15.0), MASS,affy,limma,gcrma,ROC,class,e1071,caret,hgu133plus2.db,ROCR,AnnotationDbi,LungCancerACvsSCCGEO Suggests: parallel License: GPL-2 MD5sum: c237dfa50aa714a8c4bf2f954dfcf9c7 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maPredictDSC_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maPredictDSC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maPredictDSC_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maPredictDSC_1.8.0.tgz vignettes: vignettes/maPredictDSC/inst/doc/maPredictDSC.pdf vignetteTitles: maPredictDSC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: marray Version: 1.48.0 Depends: R (>= 2.10.0), limma, methods Suggests: tkWidgets License: LGPL MD5sum: a3bfd98810b966619e8637372180498f 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/marray_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/marray_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/marray_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/marray_1.48.0.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 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: 1.42.0 Depends: R (>= 2.3.1), stats, Biobase, MASS Imports: Biobase, graphics, grDevices, limma, Mfuzz, stats, utils, MASS License: GPL (>= 2) MD5sum: d7d31f2ffef0705de85aa54104764451 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maSigPro_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maSigPro_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maSigPro_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maSigPro_1.42.0.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: 1.14.0 Depends: R (>= 2.10), gcrma (>= 2.27.1), affy Suggests: hgu95av2probe License: GPL version 2 or newer MD5sum: 1bb011e6ebf0626bf9f75a51e6e54700 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/maskBAD_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/maskBAD_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/maskBAD_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/maskBAD_1.14.0.tgz vignettes: vignettes/maskBAD/inst/doc/maskBAD.pdf vignetteTitles: Package maskBAD hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MassArray Version: 1.22.0 Depends: R (>= 2.10.0), methods Imports: graphics, grDevices, methods, stats, utils License: GPL (>=2) MD5sum: 31c08b0b5b0a3467892a4914bd54d431 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MassArray_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MassArray_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MassArray_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MassArray_1.22.0.tgz vignettes: vignettes/MassArray/inst/doc/MassArray.pdf vignetteTitles: 1. Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: massiR Version: 1.6.0 Depends: cluster, gplots, diptest, Biobase, R (>= 3.0.2) Suggests: biomaRt, RUnit, BiocGenerics License: GPL-3 MD5sum: 801fd8668605265040423b32470371c9 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/massiR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/massiR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/massiR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/massiR_1.6.0.tgz vignettes: vignettes/massiR/inst/doc/massiR_Vignette.pdf vignetteTitles: massiR_Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MassSpecWavelet Version: 1.36.0 Depends: waveslim Suggests: xcms, caTools License: LGPL (>= 2) Archs: i386, x64 MD5sum: ff65255af9fa7ab828507639f02ecc52 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MassSpecWavelet_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MassSpecWavelet_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MassSpecWavelet_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MassSpecWavelet_1.36.0.tgz vignettes: vignettes/MassSpecWavelet/inst/doc/MassSpecWavelet.pdf vignetteTitles: MassSpecWavelet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: cosmiq suggestsMe: xcms Package: matchBox Version: 1.12.0 Depends: R (>= 2.8.0) License: Artistic-2.0 MD5sum: 070538254bfdc4be7771cbbee1567f88 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/matchBox_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/matchBox_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/matchBox_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/matchBox_1.12.0.tgz vignettes: vignettes/matchBox/inst/doc/matchBox.pdf vignetteTitles: Working with the matchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MatrixRider Version: 1.2.0 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: 291d05d4c25b4ebc23de637856fe194f 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MatrixRider_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MatrixRider_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MatrixRider_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MatrixRider_1.2.0.tgz vignettes: vignettes/MatrixRider/inst/doc/MatrixRider.pdf vignetteTitles: Total affinity and occupancies hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MBAmethyl Version: 1.4.0 Depends: R (>= 2.15) License: Artistic-2.0 MD5sum: c406f20c73b35cdfa7ff06735df41686 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MBAmethyl_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MBAmethyl_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MBAmethyl_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MBAmethyl_1.4.0.tgz vignettes: vignettes/MBAmethyl/inst/doc/MBAmethyl.pdf vignetteTitles: MBAmethyl Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MBASED Version: 1.4.0 Depends: RUnit, BiocGenerics, BiocParallel, GenomicRanges, SummarizedExperiment Suggests: BiocStyle License: Artistic-2.0 MD5sum: 6fe069e0b345de0efda16f8faf803811 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MBASED_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MBASED_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MBASED_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MBASED_1.4.0.tgz vignettes: vignettes/MBASED/inst/doc/MBASED.pdf vignetteTitles: MBASED hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MBCB Version: 1.24.0 Depends: R (>= 2.9.0), tcltk, tcltk2 Imports: preprocessCore, stats, utils License: GPL (>= 2) MD5sum: 948bec56cb8998b9eb122918a38ae909 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MBCB_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MBCB_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MBCB_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MBCB_1.24.0.tgz vignettes: vignettes/MBCB/inst/doc/MBCB.pdf vignetteTitles: MBCB hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mBPCR Version: 1.24.0 Depends: oligoClasses, SNPchip Imports: Biobase Suggests: xtable License: GPL (>= 2) MD5sum: 7d2a79654ca029709c6b687f89775b66 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mBPCR_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mBPCR_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mBPCR_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mBPCR_1.24.0.tgz vignettes: vignettes/mBPCR/inst/doc/mBPCR.pdf vignetteTitles: mBPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mcaGUI Version: 1.18.0 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: e8f5e5b521575363356c446a5c834678 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mcaGUI_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mcaGUI_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mcaGUI_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mcaGUI_1.18.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MCRestimate Version: 2.26.0 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: 988082987cf3de165655422771ba0a8f 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MCRestimate_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MCRestimate_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MCRestimate_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MCRestimate_2.26.0.tgz vignettes: vignettes/MCRestimate/inst/doc/UsingMCRestimate.pdf vignetteTitles: HOW TO use MCRestimate hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mdgsa Version: 1.2.0 Depends: R (>= 2.14) Imports: AnnotationDbi, DBI, GO.db, KEGG.db, cluster, Matrix Suggests: BiocStyle, knitr, rmarkdown, limma, ALL, hgu95av2.db, RUnit, BiocGenerics License: GPL MD5sum: dbec61e72e52e254dedcb7dddbdf378a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mdgsa_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mdgsa_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mdgsa_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mdgsa_1.2.0.tgz vignettes: vignettes/mdgsa/inst/doc/mdgsa_vignette.pdf vignetteTitles: mdgsa_vignette.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mdqc Version: 1.32.0 Depends: R (>= 2.2.1), cluster, MASS License: LGPL (>= 2) MD5sum: 57e7a5bb26168ecdffe269394b3f1393 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mdqc_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mdqc_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mdqc_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mdqc_1.32.0.tgz vignettes: vignettes/mdqc/inst/doc/mdqcvignette.pdf vignetteTitles: Introduction to MDQC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: arrayMvout Package: MEAL Version: 1.0.3 Depends: R (>= 3.2.0), Biobase Imports: GenomicRanges, SNPassoc, limma, DMRcate, snpStats, vegan, BiocGenerics, minfi, IRanges, S4Vectors, methods, doParallel, parallel, ggplot2, sva Suggests: testthat, IlluminaHumanMethylation450kanno.ilmn12.hg19, knitr, minfiData, MEALData, BiocStyle License: Artistic-2.0 MD5sum: 80e6d9b7c11d33525805facaae3ef150 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_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/MEAL_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/MEAL_1.0.3.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MEAL_1.0.3.tgz vignettes: vignettes/MEAL/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/MEAL/inst/doc/caseExample.html, vignettes/MEAL/inst/doc/MEAL.html htmlTitles: "MEAL case example", "Introduction to MEAL" Package: MeasurementError.cor Version: 1.42.0 License: LGPL MD5sum: aba847ee54d1a263c7ffff77bd7428cb 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MeasurementError.cor_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MeasurementError.cor_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MeasurementError.cor_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MeasurementError.cor_1.42.0.tgz vignettes: vignettes/MeasurementError.cor/inst/doc/MeasurementError.cor.pdf vignetteTitles: MeasurementError.cor Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MEDIPS Version: 1.20.1 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: 2345cf67c9b60b627598d4b6a7a9817c 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_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MEDIPS_1.20.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MEDIPS_1.20.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MEDIPS_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MEDIPS_1.20.1.tgz vignettes: vignettes/MEDIPS/inst/doc/MEDIPS.pdf vignetteTitles: MEDIPS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MEDME Version: 1.30.0 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: 38bd111ed4924e29211d2205fbc25cd5 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MEDME_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MEDME_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MEDME_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MEDME_1.30.0.tgz vignettes: vignettes/MEDME/inst/doc/MEDME.pdf vignetteTitles: MEDME.pdf hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MEIGOR Version: 1.4.0 Depends: Rsolnp, snowfall, CNORode, deSolve Suggests: CellNOptR License: GPL-3 MD5sum: 47910122e062fcbe932d90b079759c3c 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MEIGOR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MEIGOR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MEIGOR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MEIGOR_1.4.0.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 Package: MergeMaid Version: 2.42.0 Depends: R (>= 2.10.0), survival, Biobase, MASS, methods License: GPL (>= 2) MD5sum: 1c5f33130e04da698179b0384a3af78e 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_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MergeMaid_2.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MergeMaid_2.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MergeMaid_2.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MergeMaid_2.42.0.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: MeSHDbi Version: 1.6.0 Depends: R (>= 3.0.1), BiocGenerics (>= 0.15.10) Imports: methods, AnnotationDbi (>= 1.31.19), RSQLite, Biobase Suggests: RUnit License: Artistic-2.0 MD5sum: b8b86a72ca617463ed36cf549fea3248 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MeSHDbi_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MeSHDbi_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MeSHDbi_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MeSHDbi_1.6.0.tgz vignettes: vignettes/MeSHDbi/inst/doc/MeSHDbi.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: meshr Package: meshr Version: 1.6.2 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: 5c085c0509e69a1cf94e162ad196a4b7 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_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/meshr_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/meshr_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/meshr_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/meshr_1.6.2.tgz vignettes: vignettes/meshr/inst/doc/MeSH.pdf vignetteTitles: MeSH.db hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MeSHSim Version: 1.2.0 Depends: R(>= 3.0.0) Imports: XML, RCurl License: GPL-2 MD5sum: 1177e55c16f34ec898a3324791c0e8b0 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MeSHSim_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MeSHSim_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MeSHSim_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MeSHSim_1.2.0.tgz vignettes: vignettes/MeSHSim/inst/doc/MeSHSim.pdf vignetteTitles: MeSHSim Quick Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: messina Version: 1.6.0 Depends: R (>= 3.1.0), survival (>= 2.37-4), methods Imports: Rcpp (>= 0.11.1), plyr (>= 1.8), ggplot2 (>= 0.9.3.1), grid (>= 3.1.0), foreach (>= 1.4.1), graphics LinkingTo: Rcpp Suggests: knitr (>= 1.5), antiProfilesData (>= 0.99.2), Biobase (>= 2.22.0), BiocStyle Enhances: doMC (>= 1.3.3) License: EPL (>= 1.0) Archs: i386, x64 MD5sum: f200d844741da6d3ff8d15783cc32088 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/messina_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/messina_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/messina_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/messina_1.6.0.tgz vignettes: vignettes/messina/inst/doc/messina.pdf vignetteTitles: Using Messina hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: metaArray Version: 1.48.0 Imports: Biobase, MergeMaid, graphics, stats License: LGPL-2 Archs: i386, x64 MD5sum: 49aefa83b7c0dc104d73b2ccc3c2f89a 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaArray_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metaArray_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metaArray_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaArray_1.48.0.tgz vignettes: vignettes/metaArray/inst/doc/metaArray.pdf vignetteTitles: metaArray Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oneChannelGUI Package: Metab Version: 1.4.1 Depends: xcms, R (>= 3.0.1), svDialogs Imports: pander Suggests: RUnit, BiocGenerics License: GPL (>=2) MD5sum: 5b109431cc7c0c93118d9210660bc764 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/Metab_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/Metab_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/Metab_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Metab_1.4.1.tgz vignettes: vignettes/Metab/inst/doc/MetabPackage.pdf vignetteTitles: Applying Metab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: metabomxtr Version: 1.4.0 Depends: methods,Biobase Imports: optimx, Formula, plyr, multtest Suggests: xtable, ggplot2, reshape2 License: GPL-2 MD5sum: e4571d3d6d15f1ab51c9b4d5d2014acf 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metabomxtr_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metabomxtr_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metabomxtr_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metabomxtr_1.4.0.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 Package: metagene Version: 2.2.1 Depends: R (>= 3.2.0), R6 (>= 2.0), GenomicRanges, BiocParallel Imports: rtracklayer, gplots, tools, GenomicAlignments, GenomeInfoDb, GenomicFeatures, IRanges, ggplot2, muStat, Rsamtools, DBChIP Suggests: RUnit, BiocGenerics, knitr, BiocStyle, rmarkdown License: Artistic-2.0 | file LICENSE MD5sum: 6825ad085f253b45d9c65e96f279aca2 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_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/metagene_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/metagene_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/metagene_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metagene_2.2.1.tgz vignettes: vignettes/metagene/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/metagene/inst/doc/metagene.html htmlTitles: "metagene: a package to produce metagene plots" dependsOnMe: Imetagene Package: metagenomeFeatures Version: 1.0.0 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: d3395b0936a6e19f22d306ee3969832e 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metagenomeFeatures_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metagenomeFeatures_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metagenomeFeatures_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metagenomeFeatures_1.0.0.tgz vignettes: vignettes/metagenomeFeatures/inst/doc/ 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: "Example 16S Annotation Workflow", "Exploring a MgDb Object" Package: metagenomeSeq Version: 1.12.1 Depends: R(>= 3.0), Biobase, limma, glmnet, methods, RColorBrewer Imports: parallel, matrixStats, foreach, Matrix, gplots Suggests: annotate, BiocGenerics, biom, knitr, gss, RUnit, vegan, interactiveDisplay License: Artistic-2.0 MD5sum: b241a40ccb88bc45b5c91ab2cdcca089 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/metagenomeSeq_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/metagenomeSeq_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/metagenomeSeq_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metagenomeSeq_1.12.1.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 suggestsMe: interactiveDisplay, phyloseq Package: metahdep Version: 1.28.0 Depends: R (>= 2.10), methods Suggests: affyPLM License: GPL-3 Archs: i386, x64 MD5sum: 66f8f2f623e8273871cc162486a0b7d8 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metahdep_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metahdep_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metahdep_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metahdep_1.28.0.tgz vignettes: vignettes/metahdep/inst/doc/metahdep.pdf vignetteTitles: metahdep Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: metaMS Version: 1.6.0 Depends: R (>= 2.10), methods, CAMERA, xcms (>= 1.35) Imports: Matrix, tools, robustbase, BiocGenerics Suggests: metaMSdata, RUnit License: GPL (>= 2) MD5sum: a89f9dbfcae3ec3d4c46df6e9077c6dd 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaMS_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metaMS_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metaMS_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaMS_1.6.0.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 Package: metaSeq Version: 1.10.0 Depends: R (>= 2.13.0), NOISeq, snow, Rcpp License: Artistic-2.0 MD5sum: 16d76bc99c13ebe9c91c3301086f52b7 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaSeq_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metaSeq_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metaSeq_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaSeq_1.10.0.tgz vignettes: vignettes/metaSeq/inst/doc/metaSeq.pdf vignetteTitles: metaSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: metaseqR Version: 1.10.0 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: 94ab3d8f090ebbd59dd6c4f3890b7cbe 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaseqR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/metaseqR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/metaseqR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaseqR_1.10.0.tgz vignettes: vignettes/metaseqR/inst/doc/metaseqr-pdf.pdf vignetteTitles: RNA-Seq data analysis using mulitple statistical algorithms with metaseqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: metaX Version: 1.0.3 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 Suggests: knitr, BiocStyle, R.utils, RUnit,BiocGenerics License: LGPL-2 MD5sum: e3b77e79a19261026940d55b73d87183 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 VignetteBuilder: knitr PackageStatus: Deprecated source.ver: src/contrib/metaX_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/metaX_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/metaX_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/metaX_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/metaX_1.0.3.tgz vignettes: vignettes/metaX/inst/doc/metaX.pdf vignetteTitles: metaX tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MethTargetedNGS Version: 1.2.0 Depends: R (>= 3.1.2), stringr, seqinr, gplots, Biostrings License: Artistic-2.0 MD5sum: e256dfd58b38fae7e3d088beb2f3562a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethTargetedNGS_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MethTargetedNGS_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MethTargetedNGS_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethTargetedNGS_1.2.0.tgz vignettes: vignettes/MethTargetedNGS/inst/doc/MethTargetedNGS.pdf vignetteTitles: Introduction to MethTargetedNGS hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: methVisual Version: 1.22.0 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: b3226993cc7202a942de063cd6e77084 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/methVisual_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/methVisual_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/methVisual_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methVisual_1.22.0.tgz vignettes: vignettes/methVisual/inst/doc/methVisual.pdf vignetteTitles: Introduction to methVisual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: methyAnalysis Version: 1.12.2 Depends: R (>= 2.10), grid, BiocGenerics, IRanges, GenomeInfoDb, GenomicRanges, Biobase (>= 2.5.5), org.Hs.eg.db Imports: lumi, methylumi, Gviz, genoset, 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: f93fcd020212ac044f53db1cf358cc03 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_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/methyAnalysis_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.2/methyAnalysis_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.2/methyAnalysis_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methyAnalysis_1.12.2.tgz vignettes: vignettes/methyAnalysis/inst/doc/methyAnalysis.pdf vignetteTitles: An Introduction to the methyAnalysis package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: methylumi Package: MethylAid Version: 1.4.0 Depends: R (>= 3.0) Imports: Biobase, BiocParallel, BiocGenerics, FDb.InfiniumMethylation.hg19, ggplot2, grid, gridBase, hexbin, IlluminaHumanMethylation450kmanifest, matrixStats, minfi, methods, RColorBrewer, shiny Suggests: BiocStyle, knitr, MethylAidData, minfiData, RUnit License: GPL (>= 2) MD5sum: 846b17930d5e1bc07a77cbb1123ade27 NeedsCompilation: no Title: Visual and interactive quality control of large Illumina 450k 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, Elmar. Tobi, Roderick Slieker, Wouter den Hollander, Rene Luijk and Bas Heijmans Maintainer: M. van Iterson URL: http://shiny.bioexp.nl/MethylAid VignetteBuilder: knitr source.ver: src/contrib/MethylAid_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethylAid_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MethylAid_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MethylAid_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethylAid_1.4.0.tgz vignettes: vignettes/MethylAid/inst/doc/MethylAid.pdf vignetteTitles: MethylAid: Visual and interactive quality control of large Illumina 450k data sets hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MethylMix Version: 1.4.0 Depends: R (>= 3.1.1) Imports: foreach,parallel,doParallel,RColorBrewer,optimx,RPMM Suggests: BiocStyle License: GPL-2 MD5sum: 2a5d2fe5cc110f8abcb3f307abac095a 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethylMix_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MethylMix_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MethylMix_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethylMix_1.4.0.tgz vignettes: vignettes/MethylMix/inst/doc/MethylMix.pdf vignetteTitles: MethylMix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: methylMnM Version: 1.8.0 Depends: R (>= 2.12.1), edgeR, statmod License: GPL-3 Archs: i386, x64 MD5sum: dfe524013c1ec5e4ff2babf66ba84ecc 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/methylMnM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/methylMnM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/methylMnM_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methylMnM_1.8.0.tgz vignettes: vignettes/methylMnM/inst/doc/methylMnM.pdf vignetteTitles: methylMnM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: methylPipe Version: 1.4.5 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: 5713bb15eaf0a7b19f63d5c9a14863b0 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_1.4.5.tar.gz win.binary.ver: bin/windows/contrib/3.2/methylPipe_1.4.5.zip win64.binary.ver: bin/windows64/contrib/3.2/methylPipe_1.4.5.zip mac.binary.ver: bin/macosx/contrib/3.2/methylPipe_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methylPipe_1.4.5.tgz vignettes: vignettes/methylPipe/inst/doc/methylPipe.pdf vignetteTitles: methylPipe.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: compEpiTools Package: MethylSeekR Version: 1.10.0 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: ced6a6ec4f17f0ac73639687c6c64044 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MethylSeekR_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MethylSeekR_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MethylSeekR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MethylSeekR_1.10.0.tgz vignettes: vignettes/MethylSeekR/inst/doc/MethylSeekR.pdf vignetteTitles: MethylSeekR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: methylPipe Package: methylumi Version: 2.16.0 Depends: Biobase, methods, R (>= 2.13), scales, reshape2, ggplot2, matrixStats, FDb.InfiniumMethylation.hg19 (>= 2.2.0), minfi Imports: BiocGenerics, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment, Biobase, graphics, lattice, annotate, genefilter, AnnotationDbi, minfi, stats4, illuminaio Suggests: lumi, lattice, limma, xtable, SQN, MASS, matrixStats, parallel, rtracklayer, Biostrings, methyAnalysis, TCGAMethylation450k, IlluminaHumanMethylation450kanno.ilmn12.hg19, FDb.InfiniumMethylation.hg18 (>= 2.2.0), Homo.sapiens, knitr License: GPL-2 MD5sum: ebe96a09329d513552e0c2ccf2ee367e 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/methylumi_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/methylumi_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/methylumi_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/methylumi_2.16.0.tgz vignettes: vignettes/methylumi/inst/doc/methylumi.pdf, vignettes/methylumi/inst/doc/methylumi450k.pdf vignetteTitles: An Introduction to the methylumi package, Working with Illumina 450k Arrays using methylumi hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RnBeads, skewr, wateRmelon importsMe: ffpe, lumi, methyAnalysis, missMethyl Package: Mfuzz Version: 2.30.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), e1071 Imports: tcltk, tkWidgets Suggests: marray License: GPL-2 MD5sum: 318092170fd87bd009d462511374bdc6 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://www.sysbiolab.eu/software/R/Mfuzz/index.html source.ver: src/contrib/Mfuzz_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Mfuzz_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Mfuzz_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Mfuzz_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Mfuzz_2.30.0.tgz vignettes: vignettes/Mfuzz/inst/doc/Mfuzz.pdf vignetteTitles: Introduction to Mfuzz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cycle importsMe: maSigPro suggestsMe: pwOmics Package: MGFM Version: 1.4.0 Depends: AnnotationDbi,annotate Suggests: hgu133a.db License: GPL-3 MD5sum: 547e4fef46c60a426b9888ebc252c86a 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MGFM_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MGFM_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MGFM_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MGFM_1.4.0.tgz vignettes: vignettes/MGFM/inst/doc/MGFM.pdf vignetteTitles: Using MGFM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: mgsa Version: 1.18.0 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: 3fdc0863631aee059022181674905822 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mgsa_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mgsa_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mgsa_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mgsa_1.18.0.tgz vignettes: vignettes/mgsa/inst/doc/mgsa.pdf vignetteTitles: Overview of the mgsa package. hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: gCMAP Package: MiChip Version: 1.24.0 Depends: R (>= 2.3.0), Biobase Imports: Biobase License: GPL (>= 2) MD5sum: 25358f80d316b0d480c9ecbd26a6ae55 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MiChip_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MiChip_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MiChip_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MiChip_1.24.0.tgz vignettes: vignettes/MiChip/inst/doc/MiChip.pdf vignetteTitles: MiChip miRNA Microarray Processing hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: microRNA Version: 1.28.0 Depends: R (>= 2.10) Imports: Biostrings (>= 2.11.32) Suggests: Biostrings (>= 2.11.32) Enhances: Rlibstree License: Artistic-2.0 MD5sum: 16e2ae870fcd58769dc05f5db4149859 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/microRNA_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/microRNA_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/microRNA_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/microRNA_1.28.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Roleswitch suggestsMe: MmPalateMiRNA, rtracklayer Package: MIMOSA Version: 1.8.1 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: 0e4e5dc1d79a6422024989e022132675 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MIMOSA_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MIMOSA_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MIMOSA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MIMOSA_1.8.1.tgz vignettes: vignettes/MIMOSA/inst/doc/MIMOSA.pdf vignetteTitles: MIMOSA: Mixture Models For Single Cell Assays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MineICA Version: 1.10.0 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: 7139383e14d26335b85a236980fe7c88 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MineICA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MineICA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MineICA_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MineICA_1.10.0.tgz vignettes: vignettes/MineICA/inst/doc/MineICA.pdf vignetteTitles: MineICA: Independent component analysis of genomic data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: minet Version: 3.28.0 Imports: infotheo License: file LICENSE Archs: i386, x64 MD5sum: 1a1d7fa23b2556762b9373679425ad27 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_3.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/minet_3.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/minet_3.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/minet_3.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/minet_3.28.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BUS, geNetClassifier, netresponse importsMe: netbenchmark, RTN suggestsMe: CNORfeeder, predictionet Package: minfi Version: 1.16.1 Depends: methods, BiocGenerics (>= 0.15.3), Biobase (>= 2.17.8), lattice, GenomicRanges, SummarizedExperiment (>= 0.3.1), Biostrings, utils, bumphunter (>= 1.1.9) Imports: S4Vectors, GenomeInfoDb, IRanges, beanplot, RColorBrewer, nor1mix, siggenes, limma, preprocessCore, illuminaio, matrixStats, mclust, genefilter, nlme, reshape, MASS, quadprog, GEOquery, mixOmics Suggests: IlluminaHumanMethylation450kmanifest (>= 0.2.0), IlluminaHumanMethylation450kanno.ilmn12.hg19 (>= 0.2.1), minfiData (>= 0.4.1), FlowSorted.Blood.450k (>= 1.0.1), RUnit, digest, data.table License: Artistic-2.0 MD5sum: 30335d8ee3fcea0ece6a40cddfdb8558 NeedsCompilation: no Title: Analyze Illumina's 450k methylation arrays Description: Tools for analyzing and visualizing Illumina's 450k 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 Andrews [ctb] Maintainer: Kasper Daniel Hansen URL: https://github.com/kasperdanielhansen/minfi BugReports: https://github.com/kasperdanielhansen/minfi/issues source.ver: src/contrib/minfi_1.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/minfi_1.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/minfi_1.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/minfi_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/minfi_1.16.1.tgz vignettes: vignettes/minfi/inst/doc/minfi.pdf vignetteTitles: Minfi Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ChAMP, conumee, CopyNumber450k, DMRcate, methylumi, shinyMethyl importsMe: MEAL, MethylAid, methylumi, missMethyl, quantro, skewr suggestsMe: RnBeads Package: MinimumDistance Version: 1.14.0 Depends: R (>= 3.01), VanillaICE (>= 1.31.3) Imports: methods, oligoClasses, S4Vectors, Biobase, DNAcopy, BiocGenerics, ff, foreach, matrixStats, IRanges, lattice, GenomicRanges (>= 1.17.16), SummarizedExperiment (>= 0.2.0), GenomeInfoDb, data.table, grid, stats Suggests: human610quadv1bCrlmm (>= 1.0.3), BSgenome.Hsapiens.UCSC.hg18, BSgenome.Hsapiens.UCSC.hg19, SNPchip, RUnit Enhances: snow, doSNOW License: Artistic-2.0 MD5sum: b03e48de9903c18842c0d8b598d4970a 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MinimumDistance_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MinimumDistance_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MinimumDistance_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MinimumDistance_1.14.0.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 Package: MiPP Version: 1.42.0 Depends: R (>= 2.4) Imports: Biobase, e1071, MASS, stats License: GPL (>= 2) MD5sum: f321cf9ac56df46f90e9a47df8e88932 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MiPP_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MiPP_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MiPP_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MiPP_1.42.0.tgz vignettes: vignettes/MiPP/inst/doc/MiPP.pdf vignetteTitles: MiPP Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MiRaGE Version: 1.12.0 Depends: R (>= 3.1.0), Biobase(>= 2.23.3) Imports: AnnotationDbi, BiocGenerics 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: 90c4db80bee517a41113ea14d070c21f 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MiRaGE_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MiRaGE_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MiRaGE_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MiRaGE_1.12.0.tgz vignettes: vignettes/MiRaGE/inst/doc/MiRaGE.pdf vignetteTitles: How to use MiRaGE Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: miRcomp Version: 1.0.0 Depends: R (>= 3.2), Biobase (>= 2.22.0), miRcompData Imports: utils, methods Suggests: BiocStyle, knitr, rmarkdown, RUnit, BiocGenerics License: GPL-3 | file LICENSE MD5sum: 7e2471d072389b0ed047ff7d8d6b9a35 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/miRcomp_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/miRcomp_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/miRcomp_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/miRcomp_1.0.0.tgz vignettes: vignettes/miRcomp/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/miRcomp/inst/doc/miRcomp.html htmlTitles: "Assessment and comparison of miRNA expression estimation methods (miRcomp)" Package: mirIntegrator Version: 1.0.0 Depends: R (>= 3.2) Imports: graph,ROntoTools, ggplot2, org.Hs.eg.db, AnnotationDbi, Rgraphviz Suggests: RUnit, BiocGenerics License: GPL (>=3) MD5sum: 7fbbdadf0420867389163091cfa83e89 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mirIntegrator_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mirIntegrator_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mirIntegrator_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mirIntegrator_1.0.0.tgz vignettes: vignettes/mirIntegrator/inst/doc/mirIntegrator.pdf vignetteTitles: mirIntegrator Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: miRLAB Version: 1.0.1 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: 4f3341167585a2304d03619fad629ca5 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/miRLAB_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/miRLAB_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/miRLAB_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/miRLAB_1.0.1.tgz vignettes: vignettes/miRLAB/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/miRLAB/inst/doc/miRLAB-vignette.html htmlTitles: "miRLAB" Package: miRNApath Version: 1.30.0 Depends: methods, R(>= 2.7.0) License: LGPL-2.1 MD5sum: f1e3c554fee9d0e555dcd9a730b60d88 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/miRNApath_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/miRNApath_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/miRNApath_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/miRNApath_1.30.0.tgz vignettes: vignettes/miRNApath/inst/doc/miRNApath.pdf vignetteTitles: miRNApath: Pathway Enrichment for miRNA Expression Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oneChannelGUI Package: miRNAtap Version: 1.4.0 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: 6c7ac988d6789901db2378f9731c9dc6 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/miRNAtap_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/miRNAtap_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/miRNAtap_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/miRNAtap_1.4.0.tgz vignettes: vignettes/miRNAtap/inst/doc/miRNAtap.pdf vignetteTitles: miRNAtap hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oneChannelGUI Package: Mirsynergy Version: 1.6.0 Depends: R (>= 3.0.2), igraph, ggplot2 Imports: graphics, grDevices, gridExtra, Matrix, parallel, RColorBrewer, reshape, scales, utils Suggests: glmnet, RUnit, BiocGenerics, knitr License: GPL-2 MD5sum: 801f1ad554a9bc98670626be3a50b79b 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Mirsynergy_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Mirsynergy_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Mirsynergy_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Mirsynergy_1.6.0.tgz vignettes: vignettes/Mirsynergy/inst/doc/Mirsynergy.pdf vignetteTitles: Mirsynergy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: missMethyl Version: 1.4.0 Depends: R (>= 2.3.0) Imports: limma, minfi, methylumi, IlluminaHumanMethylation450kmanifest, statmod, ruv, stringr, IlluminaHumanMethylation450kanno.ilmn12.hg19, org.Hs.eg.db Suggests: minfiData, BiocStyle, knitr, edgeR, tweeDEseqCountData License: GPL-2 MD5sum: 545a66feb17512a2ff86c0a8043c1a6b 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/missMethyl_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/missMethyl_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/missMethyl_1.3.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/missMethyl_1.4.0.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 Package: mitoODE Version: 1.8.0 Depends: R (>= 2.14.0), minpack.lm, MASS, parallel, mitoODEdata, KernSmooth License: LGPL Archs: i386, x64 MD5sum: 8c359660e33d80683625cff11ecc2c1e 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mitoODE_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mitoODE_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mitoODE_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mitoODE_1.8.0.tgz vignettes: vignettes/mitoODE/inst/doc/mitoODE-introduction.pdf vignetteTitles: mitoODE hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MLInterfaces Version: 1.50.0 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, mlbench 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: 30a786a32fbb5966a539ea5adb6b19a7 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_1.50.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MLInterfaces_1.50.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MLInterfaces_1.50.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MLInterfaces_1.50.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MLInterfaces_1.50.0.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 dependsOnMe: a4Classif, pRoloc, SigCheck suggestsMe: BiocCaseStudies Package: MLP Version: 1.18.1 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: f62abe3968986bad60ce9a2308b37d7d 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_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MLP_1.18.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MLP_1.18.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MLP_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MLP_1.18.1.tgz vignettes: vignettes/MLP/inst/doc/UsingMLP.pdf vignetteTitles: UsingMLP hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: a4 Package: MLSeq Version: 1.8.1 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: 7d9babd86ce5ec9aae507ae7d81c12cb 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MLSeq_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MLSeq_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MLSeq_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MLSeq_1.8.1.tgz vignettes: vignettes/MLSeq/inst/doc/MLSeq.pdf vignetteTitles: MLSeq hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MMDiff Version: 1.10.0 Depends: R (>= 2.14.0),GenomicRanges,parallel,DiffBind,GMD,Rsamtools Imports: GenomicRanges,IRanges,Biobase Suggests: MMDiffBamSubset License: Artistic-2.0 MD5sum: 79a1f6646081d5c09621a80a26945516 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). biocViews: ChIPSeq, MultipleComparison Author: Gabriele Schweikert Maintainer: Gabriele Schweikert source.ver: src/contrib/MMDiff_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MMDiff_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MMDiff_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MMDiff_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MMDiff_1.10.0.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 Package: mmnet Version: 1.8.1 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: c3384a8d37e89d91ba2497d652ca5c6a 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/mmnet_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/mmnet_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/mmnet_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mmnet_1.8.1.tgz vignettes: vignettes/mmnet/inst/doc/mmnet.pdf vignetteTitles: mmnet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: MmPalateMiRNA Version: 1.20.0 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: 080b906b0906917ca83707a7efe8f8ec 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MmPalateMiRNA_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MmPalateMiRNA_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MmPalateMiRNA_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MmPalateMiRNA_1.20.0.tgz vignettes: vignettes/MmPalateMiRNA/inst/doc/MmPalateMiRNA.pdf vignetteTitles: Palate miRNA Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mogsa Version: 1.2.1 Depends: R (>= 3.2.0) Imports: methods, graphite, genefilter, BiocGenerics, gplots, GSEABase, Biobase, parallel, corpcor, svd, cluster Suggests: BiocStyle, knitr License: GPL-2 MD5sum: ae05ad80a0e2ce7a081343428e0b43a1 NeedsCompilation: no Title: Multiple omics data integration and gene set analysis Description: This package provide a method for doing gene set analysis based on multiple omics data. biocViews: GeneExpression, PrincipalComponent, StatisticalMethod, Software Author: Chen Meng Maintainer: Chen Meng VignetteBuilder: knitr source.ver: src/contrib/mogsa_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/mogsa_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/mogsa_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/mogsa_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mogsa_1.2.1.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 Package: monocle Version: 1.4.0 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: 1fe7b2059534b28b60bc2c2d10661d79 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/monocle_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/monocle_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/monocle_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/monocle_1.4.0.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 suggestsMe: sincell Package: MoPS Version: 1.4.0 Imports: Biobase License: GPL-3 MD5sum: 19420b0651f79a6cd128ca8787135973 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MoPS_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MoPS_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MoPS_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MoPS_1.4.0.tgz vignettes: vignettes/MoPS/inst/doc/MoPS.pdf vignetteTitles: Model-based Periodicity Screening hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mosaics Version: 2.4.1 Depends: R (>= 3.0.0), methods, graphics, Rcpp Imports: MASS, splines, lattice, IRanges LinkingTo: Rcpp Suggests: mosaicsExample Enhances: parallel License: GPL (>= 2) Archs: i386, x64 MD5sum: 7f8c230dcd8283ed1264f4ced11a25a7 NeedsCompilation: yes Title: MOSAiCS (MOdel-based one and two Sample Analysis and Inference for ChIP-Seq) Description: This package provides functions for fitting MOSAiCS, a statistical framework to analyze one-sample or two-sample ChIP-seq data. biocViews: ChIPseq, Sequencing, Transcription, Genetics, Bioinformatics Author: Dongjun Chung, Pei Fen Kuan, Sunduz Keles Maintainer: Dongjun Chung URL: http://groups.google.com/group/mosaics_user_group SystemRequirements: Perl source.ver: src/contrib/mosaics_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/mosaics_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/mosaics_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/mosaics_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mosaics_2.4.1.tgz vignettes: vignettes/mosaics/inst/doc/mosaics-example.pdf vignetteTitles: MOSAiCS hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: jmosaics Package: motifbreakR Version: 1.0.8 Depends: R (>= 3.2), grid, MotifDb Imports: BiocParallel, compiler, methods, motifStack, BSgenome, BiocGenerics, Biostrings, GenomeInfoDb, GenomicRanges, Gviz, S4Vectors, grDevices, grImport, rtracklayer, stringr, IRanges, VariantAnnotation, 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: 29d70b33c2fdca7727626d8a6c1a4a46 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_1.0.8.tar.gz win.binary.ver: bin/windows/contrib/3.2/motifbreakR_1.0.8.zip win64.binary.ver: bin/windows64/contrib/3.2/motifbreakR_1.0.8.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/motifbreakR_1.0.8.tgz vignettes: vignettes/motifbreakR/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/motifbreakR/inst/doc/motifbreakR-vignette.html htmlTitles: "motifbreakR: an Introduction" Package: MotifDb Version: 1.12.1 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: 25830881c502e41c87194c63f5165021 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MotifDb_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MotifDb_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MotifDb_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MotifDb_1.12.1.tgz vignettes: vignettes/MotifDb/inst/doc/MotifDb.pdf vignetteTitles: MotifDb Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: motifbreakR importsMe: rTRMui suggestsMe: DiffLogo, motifStack, PWMEnrich, rTRM, vtpnet Package: motifRG Version: 1.14.0 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: 93dad828ef10c03507e478f546514482 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/motifRG_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/motifRG_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/motifRG_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/motifRG_1.14.0.tgz vignettes: vignettes/motifRG/inst/doc/motifRG.pdf vignetteTitles: motifRG: regression-based discriminative motif discovery hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: motifStack Version: 1.14.0 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: f983b78b9fedc8eb6a31114b9dbfb31b 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/motifStack_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/motifStack_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/motifStack_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/motifStack_1.14.0.tgz vignettes: vignettes/motifStack/inst/doc/motifStack.pdf vignetteTitles: motifStack Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/motifStack/inst/doc/motifStack_HTML.html htmlTitles: "motifStack Vignette" dependsOnMe: dagLogo importsMe: LowMACA, motifbreakR Package: MotIV Version: 1.26.0 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: c55f483cfec0d17581a69f0ab80eb51e 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MotIV_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MotIV_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MotIV_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MotIV_1.26.0.tgz vignettes: vignettes/MotIV/inst/doc/MotIV.pdf vignetteTitles: The MotIV users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: motifStack suggestsMe: MotifDb Package: MPFE Version: 1.6.0 License: GPL (>= 3) MD5sum: 2ade111e267f94654a85d14ddfd1ca52 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MPFE_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MPFE_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MPFE_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MPFE_1.6.0.tgz vignettes: vignettes/MPFE/inst/doc/MPFE.pdf vignetteTitles: MPFE hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mQTL.NMR Version: 1.4.0 Depends: R (>= 2.15.0) Imports: qtl, GenABEL, MASS, outliers, graphics, stats, utils Suggests: BiocStyle License: Artistic-2.0 MD5sum: 4666cb7b972509cd5694f62708fb5e4d 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mQTL.NMR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mQTL.NMR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mQTL.NMR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mQTL.NMR_1.4.0.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 Package: msa Version: 1.2.1 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 License: GPL (>= 2) Archs: i386, x64 MD5sum: 2c6b73f3445d1fcbb0830013ca5eae08 NeedsCompilation: yes Title: Multiple Sequence Alignment Description: This 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/msa_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/msa_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/msa_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/msa_1.2.1.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 Package: MSGFgui Version: 1.4.0 Depends: mzR, xlsx Imports: shiny, mzID (>= 1.2), MSGFplus, shinyFiles (>= 0.4.0), tools Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 836868e6b10a9d3386c2f7b1c1de403a 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSGFgui_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MSGFgui_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MSGFgui_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSGFgui_1.4.0.tgz vignettes: vignettes/MSGFgui/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/MSGFgui/inst/doc/Using_MSGFgui.html htmlTitles: "Using MSGFgui" Package: MSGFplus Version: 1.4.0 Depends: methods Imports: mzID Suggests: gWidgets, knitr, testthat License: GPL (>= 2) MD5sum: 9665d9b594a6981693ae93abd90e5d7f 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSGFplus_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MSGFplus_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MSGFplus_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSGFplus_1.4.0.tgz vignettes: vignettes/MSGFplus/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/MSGFplus/inst/doc/Using_MSGFplus.html htmlTitles: "Using MSGFplus" importsMe: MSGFgui Package: msmsEDA Version: 1.8.0 Depends: R (>= 3.0.1), MSnbase Imports: MASS, gplots, RColorBrewer License: GPL-2 MD5sum: 529657cc93b562aa52e91b228709df51 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/msmsEDA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/msmsEDA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/msmsEDA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/msmsEDA_1.8.0.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 dependsOnMe: msmsTests Package: msmsTests Version: 1.8.0 Depends: R (>= 3.0.1), MSnbase, msmsEDA Imports: edgeR, qvalue License: GPL-2 MD5sum: 7be3d148eb53f7a0603247197c2c736e 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/msmsTests_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/msmsTests_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/msmsTests_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/msmsTests_1.8.0.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 suggestsMe: MSnID Package: MSnbase Version: 1.18.1 Depends: R (>= 3.1), methods, BiocGenerics (>= 0.7.1), Biobase (>= 2.15.2), mzR, BiocParallel, ProtGenerics 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 License: Artistic-2.0 Archs: i386, x64 MD5sum: f6e2511063467b9e5394c2b2b5e93281 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 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_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSnbase_1.18.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MSnbase_1.18.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MSnbase_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSnbase_1.18.1.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 dependsOnMe: DAPAR, msmsEDA, msmsTests, ProCoNA, pRoloc, pRolocGUI, proteoQC, synapter importsMe: MSnID, MSstats, Pbase, ProteomicsAnnotationHubData suggestsMe: AnnotationHub, biobroom, BiocGenerics, isobar, qcmetrics, rpx Package: MSnID Version: 1.4.0 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: 5993c7232590055c1b8e534970ae9bd1 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSnID_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MSnID_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MSnID_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSnID_1.4.0.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 Package: MSstats Version: 3.2.1 Depends: R (>= 3.2), ggplot2 (>= 2.0.0), Rcpp, grid, reshape2 Imports: lme4, marray, limma, gplots, ggrepel, preprocessCore, data.table, MSnbase, reshape, survival License: Artistic-2.0 MD5sum: 31c9adf7f5bcfe38eacec2e3f26f29a6 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 , Olga Vitek Maintainer: Meena Choi URL: http://msstats.org BugReports: https://groups.google.com/forum/#!forum/msstats source.ver: src/contrib/MSstats_3.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/MSstats_3.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/MSstats_3.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/MSstats_3.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MSstats_3.2.1.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: 1.20.0 Depends: R (>= 2.10), fields, Biobase Imports: graphics, grDevices, stats, methods License: GPL-2 Archs: i386, x64 MD5sum: bff3b7e9125c68825dd038df9e6adfc8 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Mulcom_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Mulcom_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Mulcom_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Mulcom_1.20.0.tgz vignettes: vignettes/Mulcom/inst/doc/MulcomVignette.pdf vignetteTitles: Mulcom Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MultiMed Version: 1.4.0 Depends: R (>= 3.1.0) Suggests: RUnit, BiocGenerics License: GPL (>= 2) + file LICENSE MD5sum: a2438edfea8eaea824b1b6f720f4acc5 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MultiMed_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MultiMed_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MultiMed_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MultiMed_1.4.0.tgz vignettes: vignettes/MultiMed/inst/doc/MultiMed.pdf vignetteTitles: MultiMedTutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: multiscan Version: 1.30.0 Depends: R (>= 2.3.0) Imports: Biobase, utils License: GPL (>= 2) Archs: i386, x64 MD5sum: 7d9bebee4d87c3748d647fa3761c7205 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/multiscan_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/multiscan_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/multiscan_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/multiscan_1.30.0.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 Package: multtest Version: 2.26.0 Depends: R (>= 2.10), methods, BiocGenerics, Biobase Imports: survival, MASS, stats4 Suggests: snow License: LGPL Archs: i386, x64 MD5sum: 77592f4a4f61628d00bb356da06b6179 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_2.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/multtest_2.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/multtest_2.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/multtest_2.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/multtest_2.26.0.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, GSEAlm, maigesPack, MmPalateMiRNA, oneChannelGUI, pcot2, topGO, xcms Package: muscle Version: 3.12.0 Depends: Biostrings License: Unlimited Archs: i386, x64 MD5sum: 8924361dc68edad9ddbe1b1be72c090f 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_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/muscle_3.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/muscle_3.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/muscle_3.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/muscle_3.12.0.tgz vignettes: vignettes/muscle/inst/doc/muscle-vignette.pdf vignetteTitles: A guide to using muscle hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: MVCClass Version: 1.44.0 Depends: R (>= 2.1.0), methods License: LGPL MD5sum: 7fa7b7e553adf63c82de96e4652fdfb8 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/MVCClass_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/MVCClass_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/MVCClass_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/MVCClass_1.44.0.tgz vignettes: vignettes/MVCClass/inst/doc/MVCClass.pdf vignetteTitles: MVCClass hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BioMVCClass Package: mvGST Version: 1.4.0 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: a7f7b27d752786da3c88d699f453cd84 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mvGST_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mvGST_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mvGST_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mvGST_1.4.0.tgz vignettes: vignettes/mvGST/inst/doc/mvGST.pdf vignetteTitles: mvGST Tutorial Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: mygene Version: 1.6.0 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: 12f3a9b12d7f92e6409298d5c745748d 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mygene_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mygene_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mygene_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mygene_1.6.0.tgz vignettes: vignettes/mygene/inst/doc/mygene.pdf vignetteTitles: Using mygene.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: myvariant Version: 1.0.1 Depends: R (>= 3.2.1), VariantAnnotation Imports: httr, jsonlite, S4Vectors, Hmisc, plyr, magrittr, GenomeInfoDb Suggests: BiocStyle License: Artistic-2.0 MD5sum: adf4de806e7e9064019dfca368341a52 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/myvariant_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/myvariant_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/myvariant_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/myvariant_1.0.1.tgz vignettes: vignettes/myvariant/inst/doc/myvariant.pdf vignetteTitles: Using MyVariant.R hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: mzID Version: 1.8.0 Depends: methods Imports: XML, plyr, parallel, doParallel, foreach, iterators, ProtGenerics Suggests: knitr, testthat License: GPL (>= 2) MD5sum: 07fc9852773d174a67dcdd6b1980dce9 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/mzID_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/mzID_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/mzID_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mzID_1.8.0.tgz vignettes: vignettes/mzID/inst/doc/HOWTO_mzID.pdf vignetteTitles: Using mzID hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: MSGFgui, MSGFplus, MSnbase, MSnID, Pbase suggestsMe: mzR Package: mzR Version: 2.4.1 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: 23f43c3c651b0d032f7819054e79de59 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: GNU make, NetCDF VignetteBuilder: knitr BugReports: https://github.com/sneumann/mzR/issues/new source.ver: src/contrib/mzR_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/mzR_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/mzR_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/mzR_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/mzR_2.4.1.tgz vignettes: vignettes/mzR/inst/doc/mzR.pdf vignetteTitles: Accessin raw mass spectrometry and identification data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: MSGFgui, MSnbase, xcms importsMe: Pbase, ProteomicsAnnotationHubData, SIMAT suggestsMe: AnnotationHub, qcmetrics Package: NanoStringDiff Version: 1.0.0 Depends: Biobase Imports: matrixStats, methods Suggests: testthat, BiocStyle License: GPL MD5sum: 486a8a830d5ed0e5d070aed683a48d27 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NanoStringDiff_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NanoStringDiff_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NanoStringDiff_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NanoStringDiff_1.0.0.tgz vignettes: vignettes/NanoStringDiff/inst/doc/NanoStringDiff.pdf vignetteTitles: NanoStringDiff Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NanoStringQCPro Version: 1.2.0 Depends: R (>= 3.2), methods Imports: AnnotationDbi (>= 1.26.0), org.Hs.eg.db (>= 2.14.0), Biobase (>= 2.24.0), knitr (>= 1.6), NMF (>= 0.20.5), RColorBrewer (>= 1.0-5), png (>= 0.1-7) Suggests: roxygen2 (>= 4.0.1), testthat, BiocStyle License: Artistic-2.0 MD5sum: e1d7a4b065eedbc406d0df93e0ca3dd9 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NanoStringQCPro_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NanoStringQCPro_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NanoStringQCPro_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NanoStringQCPro_1.2.0.tgz vignettes: vignettes/NanoStringQCPro/inst/doc/vignetteNanoStringQCPro.pdf vignetteTitles: NanoStringQCPro overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NarrowPeaks Version: 1.14.0 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: 786d66ccc8fdb729e562f61603c91467 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NarrowPeaks_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NarrowPeaks_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NarrowPeaks_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NarrowPeaks_1.14.0.tgz vignettes: vignettes/NarrowPeaks/inst/doc/NarrowPeaks.pdf vignetteTitles: NarrowPeaks Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ncdfFlow Version: 2.16.1 Depends: R (>= 2.14.0), flowCore, flowViz, RcppArmadillo, BH Imports: Biobase,flowCore,flowViz,methods,zlibbioc LinkingTo: Rcpp,RcppArmadillo,BH Suggests: testthat,parallel License: Artistic-2.0 Archs: i386, x64 MD5sum: 671eb40de120730907a7445167ad6420 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_2.16.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ncdfFlow_2.16.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ncdfFlow_2.16.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ncdfFlow_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ncdfFlow_2.16.1.tgz vignettes: vignettes/ncdfFlow/inst/doc/ncdfFlow.pdf vignetteTitles: Basic Functions for Flow Cytometry Data hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: COMPASS Package: NCIgraph Version: 1.18.0 Depends: graph, R (>= 2.10.0) Imports: graph, KEGGgraph, methods, RBGL, RCytoscape, R.methodsS3 Suggests: Rgraphviz Enhances: DEGraph License: GPL-3 MD5sum: 59536c9f56eabd3797445895400866a5 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NCIgraph_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NCIgraph_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NCIgraph_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NCIgraph_1.18.0.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 importsMe: DEGraph suggestsMe: DEGraph Package: neaGUI Version: 1.8.0 Depends: tcltk Imports: hwriter Suggests: AnnotationDbi, org.Hs.eg.db, KEGG.db, GO.db, reactome.db, RUnit, GOstats,hwriter License: GPL-2 MD5sum: 95c5162b69115cf303fea8cc4188eb02 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/neaGUI_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/neaGUI_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/neaGUI_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/neaGUI_1.8.0.tgz vignettes: vignettes/neaGUI/inst/doc/neaGUI_vignette.pdf vignetteTitles: neaGUI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EnrichmentBrowser Package: nem Version: 2.44.0 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: 618223c0a019f26905ceeb6ac519ba1f 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_2.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nem_2.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nem_2.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nem_2.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nem_2.44.0.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 dependsOnMe: lpNet importsMe: birte suggestsMe: rBiopaxParser Package: netbenchmark Version: 1.2.0 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: 4bcede2e5a0959d8657b9875b913da09 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/netbenchmark_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/netbenchmark_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/netbenchmark_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/netbenchmark_1.2.0.tgz vignettes: vignettes/netbenchmark/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/netbenchmark/inst/doc/netbenchmark.html htmlTitles: "Netbenchmark" Package: netbiov Version: 1.4.0 Depends: R (>= 3.1.0), igraph (>= 0.7.1) Suggests: BiocStyle,RUnit,BiocGenerics,Matrix License: GPL (>= 2) MD5sum: baf319855e1eeca64963b0d4856db06b 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/netbiov_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/netbiov_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/netbiov_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/netbiov_1.4.0.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 Package: nethet Version: 1.2.0 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: 85929e0d108dc2e75d82f68c182839b8 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nethet_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nethet_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nethet_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nethet_1.2.0.tgz vignettes: vignettes/nethet/inst/doc/nethet.pdf vignetteTitles: nethet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NetPathMiner Version: 1.6.0 Depends: R (>= 3.0.2), igraph (>= 1.0) Suggests: rBiopaxParser (>= 2.1), RCurl, RCytoscape, graph License: GPL (>= 2) Archs: i386, x64 MD5sum: 2e0c2d92acca0285c063a1a2069a64b9 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NetPathMiner_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NetPathMiner_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NetPathMiner_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NetPathMiner_1.6.0.tgz vignettes: vignettes/NetPathMiner/inst/doc/NPMVignette.pdf vignetteTitles: NetPathMiner Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: netresponse Version: 1.20.15 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: 754d4c12a379a3bf9c2e055f9fcb240b 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_1.20.15.tar.gz win.binary.ver: bin/windows/contrib/3.2/netresponse_1.20.15.zip win64.binary.ver: bin/windows64/contrib/3.2/netresponse_1.20.15.zip mac.binary.ver: bin/macosx/contrib/3.2/netresponse_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/netresponse_1.20.15.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NetSAM Version: 1.10.0 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: 383d7096bb36c3b0033d2298b052b89a 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NetSAM_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NetSAM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NetSAM_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NetSAM_1.10.0.tgz vignettes: vignettes/NetSAM/inst/doc/NetSAM.pdf vignetteTitles: NetSAM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: networkBMA Version: 1.12.0 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: 1f033390aa3f65fd8205afa58b6e4e85 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/networkBMA_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/networkBMA_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/networkBMA_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/networkBMA_1.12.0.tgz vignettes: vignettes/networkBMA/inst/doc/networkBMA.pdf vignetteTitles: networkBMA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NGScopy Version: 1.4.0 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: 7059ffb2fd89f1cc42f609acc849a261 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NGScopy_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NGScopy_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NGScopy_1.3.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NGScopy_1.4.0.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 Package: nnNorm Version: 2.34.0 Depends: R(>= 2.2.0), marray Imports: graphics, grDevices, marray, methods, nnet, stats License: LGPL MD5sum: 235f21504a319d394391814eccd46f0f 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_2.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nnNorm_2.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nnNorm_2.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nnNorm_2.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nnNorm_2.34.0.tgz vignettes: vignettes/nnNorm/inst/doc/nnNorm.pdf vignetteTitles: nnNorm Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: NOISeq Version: 2.14.1 Depends: R (>= 2.13.0), methods, Biobase (>= 2.13.11), splines (>= 3.0.1), Matrix (>= 1.2) License: Artistic-2.0 MD5sum: dfdf2b241fc0e703d8f58d30addedb83 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_2.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/NOISeq_2.14.1.zip win64.binary.ver: bin/windows64/contrib/3.2/NOISeq_2.14.1.zip mac.binary.ver: bin/macosx/contrib/3.2/NOISeq_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NOISeq_2.14.1.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 dependsOnMe: metaSeq importsMe: CNVPanelizer, metaseqR suggestsMe: compcodeR Package: nondetects Version: 2.0.0 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 | file LICENSE MD5sum: dee31e79c5188074a159b5479fbb8ee8 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_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nondetects_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nondetects_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nondetects_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nondetects_2.0.0.tgz vignettes: vignettes/nondetects/inst/doc/nondetects.pdf vignetteTitles: nondetects - vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: NormqPCR Version: 1.16.0 Depends: R(>= 2.14.0), stats, RColorBrewer, Biobase, methods, ReadqPCR, qpcR License: LGPL-3 MD5sum: bcc2cace5dede9864f7092d0f5f7f48d 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NormqPCR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NormqPCR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NormqPCR_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NormqPCR_1.16.0.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 Package: npGSEA Version: 1.6.0 Depends: GSEABase (>= 1.24.0) Imports: Biobase, methods, BiocGenerics, graphics, stats Suggests: ALL, genefilter, limma, hgu95av2.db, ReportingTools, BiocStyle License: Artistic-2.0 MD5sum: 82a968f9ef5ad7e055673e0c09eed278 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/npGSEA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/npGSEA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/npGSEA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/npGSEA_1.6.0.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 importsMe: EnrichmentBrowser Package: NTW Version: 1.20.0 Depends: R (>= 2.3.0) Imports: mvtnorm, stats, utils License: GPL-2 MD5sum: 7dfff03dedb982cce35d3efa0882a333 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NTW_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NTW_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NTW_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NTW_1.20.0.tgz vignettes: vignettes/NTW/inst/doc/NTW.pdf vignetteTitles: NTW vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: nucleR Version: 2.2.0 Depends: ShortRead Imports: methods, BiocGenerics, IRanges, Biobase, GenomicRanges, Rsamtools, stats, graphics, parallel, S4Vectors Suggests: Starr License: LGPL (>= 3) MD5sum: 2a9a0d790a903bbcceb51f8f2cb587ac 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nucleR_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nucleR_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nucleR_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nucleR_2.2.0.tgz vignettes: vignettes/nucleR/inst/doc/nucleR.pdf vignetteTitles: nucleR hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: nudge Version: 1.36.0 Imports: stats License: GPL-2 MD5sum: 8f5be4b648d415c8ffbce2b025679a47 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/nudge_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/nudge_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/nudge_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/nudge_1.36.0.tgz vignettes: vignettes/nudge/inst/doc/nudge.vignette.pdf vignetteTitles: nudge Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: NuPoP Version: 1.20.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: ff0edddfe80c9348d986c87088380783 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/NuPoP_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/NuPoP_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/NuPoP_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/NuPoP_1.20.0.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 Package: occugene Version: 1.30.0 Depends: R (>= 2.0.0) License: GPL (>= 2) MD5sum: a72425c3d4ae99e353c6241d21ab1057 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/occugene_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/occugene_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/occugene_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/occugene_1.30.0.tgz vignettes: vignettes/occugene/inst/doc/occugene.pdf vignetteTitles: occugene hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: OCplus Version: 1.44.0 Depends: R (>= 2.1.0), akima Imports: multtest (>= 1.7.3), graphics, grDevices, stats License: LGPL MD5sum: 479f78e3a47366ae716b910de05cfc19 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OCplus_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OCplus_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OCplus_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OCplus_1.44.0.tgz vignettes: vignettes/OCplus/inst/doc/OCplus.pdf vignetteTitles: OCplus Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: OGSA Version: 1.0.0 Depends: R (>= 3.2.0) Imports: gplots(>= 2.8.0), limma(>= 3.18.13), Biobase License: GPL (== 2) MD5sum: a0507e37a48b5dc82b36b4136d78b8ff 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OGSA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OGSA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OGSA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OGSA_1.0.0.tgz vignettes: vignettes/OGSA/inst/doc/OGSAUsersManual.pdf vignetteTitles: OGSA Users Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: oligo Version: 1.34.2 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: e40fd7e241ff641303528fc90ece6287 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_1.34.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/oligo_1.34.2.zip win64.binary.ver: bin/windows64/contrib/3.2/oligo_1.34.2.zip mac.binary.ver: bin/macosx/contrib/3.2/oligo_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oligo_1.34.2.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: 1.32.0 Depends: R (>= 2.14) Imports: BiocGenerics (>= 0.3.2), Biobase (>= 2.17.8), methods, graphics, IRanges (>= 2.1.10), GenomicRanges (>= 1.19.6), SummarizedExperiment, Biostrings (>= 2.23.6), affyio (>= 1.23.2), ff, foreach, BiocInstaller, utils, S4Vectors, 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: 302c4ff6b419cb3d8efae390dedeed67 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/oligoClasses_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/oligoClasses_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/oligoClasses_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oligoClasses_1.32.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: cn.farms, crlmm, mBPCR, oligo, puma, waveTiling importsMe: affycoretools, ArrayTV, charm, CNPBayes, frma, ITALICS, MinimumDistance, pdInfoBuilder, puma, SNPchip, VanillaICE suggestsMe: BiocGenerics Package: OLIN Version: 1.48.0 Depends: R (>= 2.10), methods, locfit, marray Imports: graphics, grDevices, limma, marray, methods, stats Suggests: convert License: GPL-2 MD5sum: 7b79e1ebba4a88eecdb80e6b8e473713 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://w3.ualg.pt/~mfutschik/software/R/OLIN/index.html source.ver: src/contrib/OLIN_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OLIN_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OLIN_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OLIN_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OLIN_1.48.0.tgz vignettes: vignettes/OLIN/inst/doc/OLIN.pdf vignetteTitles: Introduction to OLIN hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: OLINgui importsMe: OLINgui suggestsMe: maigesPack Package: OLINgui Version: 1.44.0 Depends: R (>= 2.0.0), OLIN (>= 1.4.0) Imports: graphics, marray, OLIN, tcltk, tkWidgets, widgetTools License: GPL-2 MD5sum: f6632a61aabf3d6e862982185180411c 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://w3.ualg.pt/~mfutschik/software/R/OLIN/index.html source.ver: src/contrib/OLINgui_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OLINgui_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OLINgui_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OLINgui_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OLINgui_1.44.0.tgz vignettes: vignettes/OLINgui/inst/doc/OLINgui.pdf vignetteTitles: Introduction to OLINgui hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: omicade4 Version: 1.10.0 Depends: R (>= 3.0.0), ade4 Imports: made4 Suggests: BiocStyle License: GPL-2 MD5sum: 3a2e1711d8fb40efc52e85cfce2a28f1 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/omicade4_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/omicade4_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/omicade4_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/omicade4_1.10.0.tgz vignettes: vignettes/omicade4/inst/doc/omicade4.pdf vignetteTitles: Using omicade4 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: OmicCircos Version: 1.8.1 Depends: R (>= 2.14.0), methods,GenomicRanges License: GPL-2 MD5sum: 45d12f0bbbe733e699b83aa57a7ada13 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/OmicCircos_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/OmicCircos_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/OmicCircos_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OmicCircos_1.8.1.tgz vignettes: vignettes/OmicCircos/inst/doc/OmicCircos_vignette.pdf vignetteTitles: OmicCircos vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: OmicsMarkeR Version: 1.2.0 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: e5626fbb2aed4c9362d75cdedcb6d95a 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OmicsMarkeR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OmicsMarkeR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OmicsMarkeR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OmicsMarkeR_1.2.0.tgz vignettes: vignettes/OmicsMarkeR/inst/doc/OmicsMarkeR.pdf vignetteTitles: A Short Introduction to the OmicMarkeR Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: OncoSimulR Version: 2.0.1 Depends: R (>= 3.1.0) Imports: Rcpp (>= 0.11.1), parallel, data.table, graph, Rgraphviz, gtools, igraph, methods LinkingTo: Rcpp Suggests: BiocStyle, knitr, Oncotree, testthat License: GPL (>= 3) Archs: i386, x64 MD5sum: e51bb7c486c190f76d3e295700e91e3f 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. 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_2.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/OncoSimulR_2.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/OncoSimulR_2.0.1.zip mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OncoSimulR_2.0.1.tgz vignettes: vignettes/OncoSimulR/inst/doc/OncoSimulR.pdf vignetteTitles: OncoSimulR Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: oneChannelGUI Version: 1.36.0 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: d1ce92f7943454a7041fe8016cde22bc 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/oneChannelGUI_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/oneChannelGUI_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/oneChannelGUI_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oneChannelGUI_1.36.0.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 Package: ontoCAT Version: 1.22.0 Depends: rJava, methods License: Apache License 2.0 MD5sum: f42b7f6a7a132e4fb7922e7400efebea 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ontoCAT_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ontoCAT_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ontoCAT_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ontoCAT_1.22.0.tgz vignettes: vignettes/ontoCAT/inst/doc/ontoCAT.pdf vignetteTitles: ontoCAT package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE suggestsMe: RMassBank Package: openCyto Version: 1.8.4 Depends: flowWorkspace(>= 3.16.5) Imports: methods,Biobase,gtools,flowCore(>= 1.31.17),flowViz,ncdfFlow(>= 2.11.34),flowWorkspace,flowStats(>= 3.28.1),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: ea12c3570ea406ec0e98bdd16de2c5a8 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_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/openCyto_1.8.4.zip win64.binary.ver: bin/windows64/contrib/3.2/openCyto_1.8.4.zip mac.binary.ver: bin/macosx/contrib/3.2/openCyto_1.8.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/openCyto_1.8.4.tgz vignettes: vignettes/openCyto/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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" Package: OperaMate Version: 1.2.3 Depends: R (>= 3.2.0),stats,methods,grDevices Imports: pheatmap,grid,ggplot2,fBasics,gProfileR,gridExtra,reshape2,stabledist Suggests: BiocStyle License: GPL (>= 3) MD5sum: 6e30f0e7c14d4b54f00d77bd80b83d06 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_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/OperaMate_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/OperaMate_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/OperaMate_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OperaMate_1.2.3.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 Package: oposSOM Version: 1.6.0 Depends: R (>= 3.0) Imports: som, fastICA, scatterplot3d, pixmap, fdrtool, ape, igraph, KernSmooth, biomaRt, Biobase License: GPL (>=2) MD5sum: de065019a1e10609b9f08b69e39aeace 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/oposSOM_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/oposSOM_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/oposSOM_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/oposSOM_1.6.0.tgz vignettes: vignettes/oposSOM/inst/doc/Vignette.pdf vignetteTitles: The oposSOM users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: OrderedList Version: 1.42.0 Depends: R (>= 2.1.0), Biobase (>= 1.5.12), twilight (>= 1.9.2), methods Imports: Biobase, graphics, methods, stats, twilight License: GPL (>= 2) MD5sum: 4dac32948cbc5dcfb128cdc5d3383640 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OrderedList_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OrderedList_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OrderedList_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OrderedList_1.42.0.tgz vignettes: vignettes/OrderedList/inst/doc/tr_2006_01.pdf vignetteTitles: Similarities of Ordered Gene Lists hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: OrganismDbi Version: 1.12.1 Depends: R (>= 2.14.0), methods, BiocGenerics (>= 0.15.10), AnnotationDbi (>= 1.31.19), GenomicFeatures (>= 1.21.12) Imports: Biobase, BiocInstaller, GenomicRanges, graph, IRanges, RBGL, RSQLite, S4Vectors, stats Suggests: Homo.sapiens, Rattus.norvegicus, BSgenome.Hsapiens.UCSC.hg19, biomaRt, rtracklayer, RUnit License: Artistic-2.0 MD5sum: 5c2116ba4d42d03527ad43db1552d807 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/OrganismDbi_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/OrganismDbi_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/OrganismDbi_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OrganismDbi_1.12.1.tgz vignettes: vignettes/OrganismDbi/inst/doc/OrganismDbi.pdf vignetteTitles: OrganismDbi: A meta framework for Annotation Packages hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHubData, epivizr, ggbio Package: OSAT Version: 1.18.0 Depends: methods,stats Suggests: xtable, Biobase License: Artistic-2.0 MD5sum: 74b68c537db80b8207a0a5a8f08c7f01 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OSAT_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OSAT_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OSAT_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OSAT_1.18.0.tgz vignettes: vignettes/OSAT/inst/doc/OSAT.pdf vignetteTitles: An introduction to OSAT hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Oscope Version: 1.0.0 Depends: EBSeq, cluster, testthat, BiocParallel Suggests: BiocStyle License: Artistic-2.0 MD5sum: 6df4c3e9833ffcad9653fa679ae2abe5 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Oscope_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Oscope_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Oscope_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Oscope_1.0.0.tgz vignettes: vignettes/Oscope/inst/doc/Oscope_vignette.pdf vignetteTitles: Oscope_vigette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: OTUbase Version: 1.20.0 Depends: R (>= 2.9.0), methods, S4Vectors, IRanges, ShortRead (>= 1.23.15), Biobase, vegan Imports: Biostrings License: Artistic-2.0 MD5sum: dc1c1978e9f0cf6b7ed86538a6a445dc 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OTUbase_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OTUbase_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OTUbase_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OTUbase_1.20.0.tgz vignettes: vignettes/OTUbase/inst/doc/Introduction_to_OTUbase.pdf vignetteTitles: An introduction to OTUbase hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: mcaGUI Package: OutlierD Version: 1.34.0 Depends: R (>= 2.3.0), Biobase, quantreg License: GPL (>= 2) MD5sum: ce12303bc448b38e0c925c7fcb4a496c 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/OutlierD_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/OutlierD_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/OutlierD_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/OutlierD_1.34.0.tgz vignettes: vignettes/OutlierD/inst/doc/OutlierD.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PAA Version: 1.4.1 Depends: R (>= 3.2.0), Rcpp (>= 0.11.6) Imports: e1071, gplots, limma, MASS, mRMRe, randomForest, ROCR, sva LinkingTo: Rcpp Suggests: BiocStyle, RUnit, BiocGenerics, vsn License: BSD_3_clause + file LICENSE Archs: i386, x64 MD5sum: edaccb3effd8bc99272e82a0440060dc 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 pre-processing (background correction, batch filtering, normalization) univariate feature pre-selection 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.medizinisches-proteom-center.de/PAA SystemRequirements: C++ software package Random Jungle source.ver: src/contrib/PAA_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/PAA_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/PAA_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/PAA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PAA_1.4.1.tgz vignettes: vignettes/PAA/inst/doc/PAA_vignette.pdf vignetteTitles: PAA tutorial hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: PADOG Version: 1.12.0 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: f9ea64f6843862a9bb1795d526a0c8b0 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PADOG_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PADOG_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PADOG_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PADOG_1.12.0.tgz vignettes: vignettes/PADOG/inst/doc/PADOG.pdf vignetteTitles: PADOG hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: paircompviz Version: 1.8.0 Depends: R (>= 2.10), Rgraphviz Imports: Rgraphviz Suggests: multcomp, reshape, rpart, plyr, xtable License: GPL (>=3.0) MD5sum: a4e7645e277cabcaa66d05be5df9cb17 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/paircompviz_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/paircompviz_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/paircompviz_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/paircompviz_1.8.0.tgz vignettes: vignettes/paircompviz/inst/doc/vignette.pdf vignetteTitles: Using paircompviz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: pandaR Version: 1.2.0 Depends: R (>= 3.0.0), methods Imports: matrixStats, igraph Suggests: knitr License: GPL-2 MD5sum: 31060a5f34706624f86d13ae4111ef1e NeedsCompilation: no Title: PANDA algorithm Description: Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complimentary data sources. biocViews: StatisticalMethod, GraphAndNetwork, Microarray, GeneRegulation, NetworkInference, GeneExpression, Transcription, Network Author: "Dan Schlauch " Maintainer: Dan Schlauch VignetteBuilder: knitr source.ver: src/contrib/pandaR_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pandaR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pandaR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pandaR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pandaR_1.2.0.tgz vignettes: vignettes/pandaR/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/pandaR/inst/doc/pandaR.html htmlTitles: "Introduction" Package: PAnnBuilder Version: 1.34.0 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: 6fec3b3257f38184bc5db10fe13ef2a8 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PAnnBuilder_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PAnnBuilder_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PAnnBuilder_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PAnnBuilder_1.34.0.tgz vignettes: vignettes/PAnnBuilder/inst/doc/PAnnBuilder.pdf vignetteTitles: Using the PAnnBuilder Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: panp Version: 1.40.0 Depends: R (>= 2.10), affy (>= 1.23.4), Biobase (>= 2.5.5) Imports: Biobase, methods, stats, utils Suggests: gcrma License: GPL (>= 2) MD5sum: 7f51b10fdda3e5f390fd973c4245d831 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/panp_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/panp_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/panp_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/panp_1.40.0.tgz vignettes: vignettes/panp/inst/doc/panp.pdf vignetteTitles: gene presence/absence calls hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PANR Version: 1.16.0 Depends: R (>= 2.14), igraph Imports: graphics, grDevices, MASS, methods, pvclust, stats, utils, RedeR Suggests: snow License: Artistic-2.0 MD5sum: 397746c8c5b1413365d0b3f5346c9b52 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PANR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PANR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PANR_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PANR_1.16.0.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 suggestsMe: RedeR Package: PAPi Version: 1.10.0 Depends: R (>= 2.15.2), svDialogs, KEGGREST License: GPL(>= 2) MD5sum: 3d9f91c5f87e4ea63d0e594d3af42b0a 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PAPi_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PAPi_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PAPi_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PAPi_1.10.0.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 Package: parglms Version: 1.2.0 Depends: methods Imports: BiocGenerics, BatchJobs, foreach, doParallel Suggests: RUnit, sandwich, MASS License: Artistic-2.0 MD5sum: 96325c614b3aa4b095b8a8d31b72bf81 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/parglms_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/parglms_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/parglms_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/parglms_1.2.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: parody Version: 1.28.0 Depends: R (>= 2.5.0), methods, tools, utils License: Artistic-2.0 MD5sum: b99b2e761bf3da809d47e0b70e3e1589 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/parody_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/parody_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/parody_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/parody_1.28.0.tgz vignettes: vignettes/parody/inst/doc/parody.pdf vignetteTitles: parody: parametric and resistant outlier detection hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: arrayMvout, flowQ Package: Path2PPI Version: 1.0.0 Depends: R (>= 3.2.1), igraph (>= 1.0.1), methods Suggests: knitr, rmarkdown, RUnit, BiocGenerics, BiocStyle License: GPL (>= 2) MD5sum: e6d722bc216526e642d28a6f1a63c6be NeedsCompilation: no Title: Prediction of pathway-specific protein-protein interaction networks Description: Package to predict pathway specific protein-protein interaction (PPI) networks in target organisms for which only a view information about PPIs is available. Path2PPI uses PPIs of the pathway of interest from other well established model organisms to predict a certain pathway in the 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Path2PPI_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Path2PPI_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Path2PPI_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Path2PPI_1.0.0.tgz vignettes: vignettes/Path2PPI/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/Path2PPI/inst/doc/Path2PPI-tutorial.html htmlTitles: "The Path2PPI package" Package: pathifier Version: 1.8.0 Imports: R.oo, princurve License: Artistic-1.0 MD5sum: 839a98ad0ff3a155bc3294951adcaa1c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pathifier_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pathifier_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pathifier_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pathifier_1.8.0.tgz vignettes: vignettes/pathifier/inst/doc/Overview.pdf vignetteTitles: Quantify deregulation of pathways in cancer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PathNet Version: 1.10.0 Depends: R (>= 1.14.0) Suggests: PathNetData, RUnit, BiocGenerics License: GPL-3 MD5sum: cce59a7e614d8ff37fb817b25bf1ab53 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PathNet_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PathNet_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PathNet_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PathNet_1.10.0.tgz vignettes: vignettes/PathNet/inst/doc/PathNet.pdf vignetteTitles: PathNet hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EnrichmentBrowser Package: pathRender Version: 1.38.0 Depends: graph, Rgraphviz, RColorBrewer, cMAP, AnnotationDbi, methods, stats4 Suggests: ALL, hgu95av2.db License: LGPL MD5sum: 31741a62071d85a832e92c77d027e00b 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pathRender_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pathRender_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pathRender_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pathRender_1.38.0.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 Package: pathVar Version: 1.0.1 Depends: R (>= 3.2.2), methods, ggplot2, gridExtra Imports: EMT, mclust, Matching, data.table License: LGPL (>= 2.0) MD5sum: 890f5b5746b66240a6d3fd2a4ebccb26 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/pathVar_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/pathVar_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/pathVar_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pathVar_1.0.1.tgz vignettes: vignettes/pathVar/inst/doc/pathVar.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: pathview Version: 1.10.1 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: 27782ae43a3ba4c01fe416435b743604 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.r-forge.r-project.org/ source.ver: src/contrib/pathview_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/pathview_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/pathview_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/pathview_1.10.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pathview_1.10.1.tgz vignettes: vignettes/pathview/inst/doc/pathview.pdf vignetteTitles: Pathview: pathway based data integration and visualization hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: EnrichmentBrowser importsMe: CompGO suggestsMe: clusterProfiler, gage Package: paxtoolsr Version: 1.4.6 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: 288ca6220d5e465df28ec7914dac558a 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://bitbucket.org/cbio_mskcc/paxtoolsr SystemRequirements: Java (>= 1.5) VignetteBuilder: knitr source.ver: src/contrib/paxtoolsr_1.4.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/paxtoolsr_1.4.6.zip win64.binary.ver: bin/windows64/contrib/3.2/paxtoolsr_1.4.6.zip mac.binary.ver: bin/macosx/contrib/3.2/paxtoolsr_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/paxtoolsr_1.4.6.tgz vignettes: vignettes/paxtoolsr/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/paxtoolsr/inst/doc/using_paxtoolsr.html htmlTitles: "Using PaxtoolsR" Package: Pbase Version: 0.10.0 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: 76dc121d96774d4158b9f87ffa0bcd9e 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_0.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Pbase_0.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Pbase_0.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Pbase_0.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Pbase_0.10.0.tgz vignettes: vignettes/Pbase/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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: pcaGoPromoter Version: 1.14.0 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: 88b38c5f3be77a8cf4affd20f1b5ead1 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pcaGoPromoter_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pcaGoPromoter_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pcaGoPromoter_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pcaGoPromoter_1.14.0.tgz vignettes: vignettes/pcaGoPromoter/inst/doc/pcaGoPromoter.pdf vignetteTitles: pcaGoPromoter hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: pcaMethods Version: 1.60.0 Depends: Biobase, methods Imports: BiocGenerics, Rcpp (>= 0.11.3), MASS LinkingTo: Rcpp Suggests: matrixStats, lattice License: GPL (>= 3) Archs: i386, x64 MD5sum: ac7c534ab58b59f2c86f9a3aa26c4175 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 SystemRequirements: Rcpp source.ver: src/contrib/pcaMethods_1.60.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pcaMethods_1.60.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pcaMethods_1.60.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pcaMethods_1.60.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pcaMethods_1.60.0.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 dependsOnMe: DeconRNASeq importsMe: CompGO, metaX, MSnbase, SomaticSignatures Package: pcot2 Version: 1.38.0 Depends: R (>= 2.0.0), grDevices, Biobase, amap Suggests: multtest, hu6800.db, KEGG.db, mvtnorm License: GPL (>= 2) MD5sum: 0c96c2a1cf36e4fea9939e68329d72c8 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pcot2_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pcot2_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pcot2_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pcot2_1.38.0.tgz vignettes: vignettes/pcot2/inst/doc/pcot2.pdf vignetteTitles: PCOT2 Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PCpheno Version: 1.32.0 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: 0e090f72de5aa46fc03d256988561d7c 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PCpheno_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PCpheno_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PCpheno_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PCpheno_1.32.0.tgz vignettes: vignettes/PCpheno/inst/doc/PCpheno.pdf vignetteTitles: PCpheno Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: pdInfoBuilder Version: 1.34.1 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: 0cdf1aa919399d186b39421140d524a6 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_1.34.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/pdInfoBuilder_1.34.1.zip win64.binary.ver: bin/windows64/contrib/3.2/pdInfoBuilder_1.34.1.zip mac.binary.ver: bin/macosx/contrib/3.2/pdInfoBuilder_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pdInfoBuilder_1.34.1.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 Package: pdmclass Version: 1.42.0 Depends: Biobase (>= 1.4.22), R (>= 1.9.0), fibroEset, mda License: Artistic-2.0 MD5sum: 68bcfd7e2791e72f68ba816ea9760413 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pdmclass_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pdmclass_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pdmclass_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pdmclass_1.42.0.tgz vignettes: vignettes/pdmclass/inst/doc/pdmclass.pdf vignetteTitles: pdmclass Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oneChannelGUI Package: PECA Version: 1.6.0 Imports: limma, affy, genefilter, preprocessCore, aroma.affymetrix, aroma.core Suggests: SpikeIn License: GPL (>= 2) MD5sum: 371cab6cbe3f0140ee959fdacf333d57 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PECA_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PECA_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PECA_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PECA_1.6.0.tgz vignettes: vignettes/PECA/inst/doc/PECA.pdf vignetteTitles: PECA: Probe-level Expression Change Averaging hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: pepStat Version: 1.4.0 Depends: R (>= 3.0.0), Biobase, IRanges Imports: limma, fields, GenomicRanges, ggplot2, plyr, tools, methods, data.table Suggests: pepDat, Pviz, knitr, shiny License: Artistic-2.0 MD5sum: 9016ab2528390784a0604ecf435d2b62 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pepStat_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pepStat_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pepStat_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pepStat_1.4.0.tgz vignettes: vignettes/pepStat/inst/doc/pepStat.pdf vignetteTitles: Full peptide microarray analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: pepXMLTab Version: 1.4.0 Depends: R (>= 3.0.1) Imports: XML(>= 3.98-1.1) Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d1200a33f4db9f6bd7d25a955eccdac7 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pepXMLTab_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pepXMLTab_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pepXMLTab_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pepXMLTab_1.4.0.tgz vignettes: vignettes/pepXMLTab/inst/doc/pepXMLTab.pdf vignetteTitles: Introduction to pepXMLTab hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: PGA Version: 1.0.0 Depends: R (>= 3.0.1), IRanges, GenomicRanges, Biostrings (>= 2.26.3), data.table, rTANDEM Imports: 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: 5f63213834b67553f1d01c812ff14f18 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, 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PGA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PGA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PGA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PGA_1.0.0.tgz vignettes: vignettes/PGA/inst/doc/PGA.pdf vignetteTitles: PGA tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: PGSEA Version: 1.44.0 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: ac1101e36d42d5bee7f2015cf2464129 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PGSEA_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PGSEA_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PGSEA_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PGSEA_1.44.0.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 dependsOnMe: GeneExpressionSignature Package: phenoDist Version: 1.18.0 Depends: R (>= 2.9.0), imageHTS, e1071 Suggests: GOstats, MASS License: LGPL-2.1 MD5sum: 855a289805c2a6093787d13168155ffc 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/phenoDist_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/phenoDist_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/phenoDist_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/phenoDist_1.18.0.tgz vignettes: vignettes/phenoDist/inst/doc/phenoDist.pdf vignetteTitles: Introduction to phenoDist hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: phenoTest Version: 1.18.0 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: 8aeda3076a0a5e0cae423a65a7654ee4 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/phenoTest_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/phenoTest_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/phenoTest_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/phenoTest_1.18.0.tgz vignettes: vignettes/phenoTest/inst/doc/phenoTest.pdf vignetteTitles: Manual for the phenoTest library hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: canceR Package: PhenStat Version: 2.4.0 Depends: R (>= 2.3.0) Imports: methods, car, nlme, nortest, MASS, logistf Suggests: RUnit, BiocGenerics License: file LICENSE MD5sum: 479b26351d37dac2723f6b5e29e5ba18 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PhenStat_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PhenStat_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PhenStat_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PhenStat_2.4.0.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 Package: phyloseq Version: 1.14.0 Depends: R (>= 3.1.0) Imports: BiocGenerics (>= 0.14.0), ade4 (>= 1.7.2), ape (>= 3.1.1), biom (>= 0.3.9), Biostrings (>= 2.28.0), cluster (>= 1.14.4), data.table (>= 1.9.4), foreach (>= 1.4.2), ggplot2 (>= 1.0.0), igraph (>= 0.7.0), methods (>= 3.1.0), multtest (>= 2.16.0), plyr (>= 1.8), reshape2 (>= 1.2.2), scales (>= 0.2.3), vegan (>= 2.0.10), Biobase Suggests: DESeq2 (>= 1.8), genefilter (>= 1.50), testthat (>= 0.10), knitr (>= 1.10), metagenomeSeq (>= 1.10) Enhances: doParallel (>= 1.0.1) License: AGPL-3 MD5sum: ef118832d7a14e2e43db10e687afc871 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/phyloseq_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/phyloseq_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/phyloseq_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/phyloseq_1.14.0.tgz vignettes: vignettes/phyloseq/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/phyloseq/inst/doc/phyloseq-analysis.html, vignettes/phyloseq/inst/doc/phyloseq-basics.html, vignettes/phyloseq/inst/doc/phyloseq-mixture-models.html htmlTitles: "phyloseq analysis vignette", "phyloseq basics vignette", "phyloseq and DESeq2 on Colorectal Cancer Data" Package: piano Version: 1.10.2 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: 5019f65b2e924823218ff0109150f115 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_1.10.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/piano_1.10.2.zip win64.binary.ver: bin/windows64/contrib/3.2/piano_1.10.2.zip mac.binary.ver: bin/macosx/contrib/3.2/piano_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/piano_1.10.2.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 importsMe: saps Package: pickgene Version: 1.42.0 Imports: graphics, grDevices, MASS, stats, utils License: GPL (>= 2) MD5sum: 2bb6e3dda8526e426df84b56aaf98451 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pickgene_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pickgene_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pickgene_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pickgene_1.42.0.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: 2.14.0 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: d73b2e6f20903de31a0ee7a0c6ddb0e0 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PICS_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PICS_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PICS_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PICS_2.14.0.tgz vignettes: vignettes/PICS/inst/doc/PICS.pdf vignetteTitles: The PICS users guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: PING Package: PING Version: 2.14.0 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: 7534398e33a6ef45e9b20c848a415425 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PING_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PING_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PING_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PING_2.14.0.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 Package: pint Version: 1.20.0 Depends: mvtnorm, methods, graphics, Matrix, dmt License: BSD_2_clause + file LICENSE MD5sum: 27065e4a1560e5dde7bbc2c93790cffc 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pint_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pint_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pint_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pint_1.20.0.tgz vignettes: vignettes/pint/inst/doc/depsearch.pdf vignetteTitles: pint hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: pkgDepTools Version: 1.36.0 Depends: methods, graph, RBGL Imports: graph, RBGL Suggests: Biobase, Rgraphviz, RCurl, BiocInstaller License: GPL-2 MD5sum: 1139ea61bfe181d9243f7396cf8dedde 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pkgDepTools_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pkgDepTools_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pkgDepTools_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pkgDepTools_1.36.0.tgz vignettes: vignettes/pkgDepTools/inst/doc/pkgDepTools.pdf vignetteTitles: How to Use pkgDepTools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: plateCore Version: 1.28.0 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: e94fbca40b26e6f14ccf28a990d1cc76 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plateCore_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plateCore_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plateCore_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plateCore_1.28.0.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 Package: plethy Version: 1.8.0 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: 57756c00e887f04c0aa5385cc1d874e8 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plethy_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plethy_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plethy_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plethy_1.8.0.tgz vignettes: vignettes/plethy/inst/doc/plethy.pdf vignetteTitles: plethy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: plgem Version: 1.42.0 Depends: R (>= 2.10) Imports: utils, Biobase (>= 2.5.5), MASS License: GPL-2 MD5sum: 37657063af8b512815e784ec2c6e9633 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plgem_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plgem_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plgem_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plgem_1.42.0.tgz vignettes: vignettes/plgem/inst/doc/plgem.pdf vignetteTitles: An introduction to PLGEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: plier Version: 1.40.0 Depends: R (>= 2.0), methods Imports: affy, Biobase, methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 82a32a066b2cb1a7081622a7ea129e44 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plier_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plier_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plier_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plier_1.40.0.tgz hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: piano Package: PLPE Version: 1.30.0 Depends: R (>= 2.6.2), Biobase (>= 2.5.5), LPE, MASS, methods License: GPL (>= 2) MD5sum: fd8a0474c487fb4683d69baa42b06e61 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PLPE_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PLPE_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PLPE_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PLPE_1.30.0.tgz vignettes: vignettes/PLPE/inst/doc/PLPE.pdf vignetteTitles: PLPE Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: plrs Version: 1.10.0 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: 30e2c5d87cc36b07d9facc28d8170359 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plrs_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plrs_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plrs_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plrs_1.10.0.tgz vignettes: vignettes/plrs/inst/doc/plrs_vignette.pdf vignetteTitles: plrs hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: plw Version: 1.30.0 Depends: R (>= 2.10), affy (>= 1.23.4) Imports: MASS, affy, graphics, splines, stats Suggests: limma License: GPL-2 Archs: i386, x64 MD5sum: 1d1042f54e98c03a102525c931ed4ab7 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/plw_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/plw_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/plw_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/plw_1.30.0.tgz vignettes: vignettes/plw/inst/doc/HowToPLW.pdf vignetteTitles: HowTo plw hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: pmm Version: 1.2.0 Depends: R (>= 2.10) Imports: lme4, splines License: GPL-3 MD5sum: c75bb35659328aebb8d15b9d8a4439eb 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pmm_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pmm_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pmm_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pmm_1.2.0.tgz vignettes: vignettes/pmm/inst/doc/pmm-package.pdf vignetteTitles: User manual for R-Package PMM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: podkat Version: 1.2.0 Depends: R (>= 3.2.0), methods, Rsamtools, GenomicRanges Imports: Rcpp (>= 0.11.1), parallel, stats, graphics, 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: 6d5cba82f8a67eafa8cfc9bdaf3074ca 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/podkat_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/podkat_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/podkat_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/podkat_1.2.0.tgz vignettes: vignettes/podkat/inst/doc/podkat.pdf vignetteTitles: PODKAT - An R Package for Association Testing Involving Rare and Private Variants hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: polyester Version: 1.6.0 Depends: R (>= 3.0.0) Imports: Biostrings (>= 2.32.0), IRanges, S4Vectors, logspline, limma Suggests: knitr, ballgown License: Artistic-2.0 MD5sum: 6fa9290d6e8d47fd7990adc219c62225 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/polyester_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/polyester_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/polyester_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/polyester_1.6.0.tgz vignettes: vignettes/polyester/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/polyester/inst/doc/polyester.html htmlTitles: "The Polyester package for simulating RNA-seq reads" Package: Polyfit Version: 1.4.0 Depends: DESeq Suggests: BiocStyle License: GPL (>= 3) MD5sum: 806ac13483a6079d9eefba28d4ffe34e 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Polyfit_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Polyfit_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Polyfit_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Polyfit_1.4.0.tgz vignettes: vignettes/Polyfit/inst/doc/polyfit.pdf vignetteTitles: Polyfit hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ppiStats Version: 1.36.0 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: ee25ad39e79e5b06ddd013cc49092437 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ppiStats_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ppiStats_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ppiStats_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ppiStats_1.36.0.tgz vignettes: vignettes/ppiStats/inst/doc/ppiStats.pdf vignetteTitles: ppiStats hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PCpheno suggestsMe: BiocCaseStudies, RpsiXML Package: prada Version: 1.46.0 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: a26cdcd3a67d406b02a2fdf3d072765a 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/prada_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/prada_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/prada_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/prada_1.46.0.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 dependsOnMe: domainsignatures, RNAither importsMe: cellHTS, cellHTS2 Package: prebs Version: 1.10.0 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: e61a2d58b14025bd51b64bff0b856602 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/prebs_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/prebs_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/prebs_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/prebs_1.10.0.tgz vignettes: vignettes/prebs/inst/doc/prebs.pdf vignetteTitles: prebs User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: PREDA Version: 1.16.0 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: eb5eb79f453ea2fcc836bc7e3065aa68 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PREDA_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PREDA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PREDA_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PREDA_1.16.0.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 Package: predictionet Version: 1.16.0 Depends: igraph, catnet Imports: penalized, RBGL, MASS Suggests: network, minet, knitr License: Artistic-2.0 MD5sum: 5af05ea9e962f5bcbdbb92c00aed1bc5 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_1.16.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/predictionet_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/predictionet_1.16.0.tgz vignettes: vignettes/predictionet/inst/doc/predictionet.pdf vignetteTitles: predictionet hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: preprocessCore Version: 1.32.0 Imports: stats License: LGPL (>= 2) Archs: i386, x64 MD5sum: 6372f96b0aff5b4487ff459eae9175c8 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 source.ver: src/contrib/preprocessCore_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/preprocessCore_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/preprocessCore_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/preprocessCore_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/preprocessCore_1.32.0.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: oneChannelGUI Package: Prize Version: 1.0.0 Imports: diagram, stringr, ggplot2, reshape2, grDevices, matrixcalc, stats, gplots, methods, utils, graphics Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 2906bec1a0ae4301fb4d762c4f56ea00 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Prize_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Prize_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Prize_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Prize_1.0.0.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 Package: proBAMr Version: 1.4.3 Depends: R (>= 3.0.1), IRanges, AnnotationDbi Imports: GenomicRanges, Biostrings, GenomicFeatures, rtracklayer Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: d1dabeb694e2a2d88f5b4df8ffdf9a33 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_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/proBAMr_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.2/proBAMr_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.2/proBAMr_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/proBAMr_1.4.3.tgz vignettes: vignettes/proBAMr/inst/doc/proBAMr.pdf vignetteTitles: Introduction to proBAMr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: PROcess Version: 1.46.0 Depends: Icens Imports: graphics, grDevices, Icens, stats, utils License: Artistic-2.0 MD5sum: fd35cde723a597089406e6940b1ae123 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PROcess_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PROcess_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PROcess_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PROcess_1.46.0.tgz vignettes: vignettes/PROcess/inst/doc/howtoprocess.pdf vignetteTitles: HOWTO PROcess hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: procoil Version: 1.20.0 Depends: R (>= 2.12.0) Imports: methods, stats, graphics Suggests: Biostrings License: GPL (>= 2) MD5sum: 1e1bdc376f7538e0f98f21e80d9c90ca NeedsCompilation: no Title: Prediction of Oligomerization of Coiled Coil Proteins Description: The procoil package allows to predict 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. The predict function not only computes the prediction itself, but also a profile which allows to determine the strengths to which the individual residues are indicative for either class. Profiles can also be plotted and exported to files. biocViews: Proteomics, Classification Author: Ulrich Bodenhofer Maintainer: Ulrich Bodenhofer URL: http://www.bioinf.jku.at/software/procoil/ https://github.com/UBod/procoil source.ver: src/contrib/procoil_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/procoil_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/procoil_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/procoil_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/procoil_1.20.0.tgz vignettes: vignettes/procoil/inst/doc/procoil.pdf vignetteTitles: PrOCoil - A Web Service and an R Package for Predicting the Oligomerization of Coiled-Coil Proteins hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ProCoNA Version: 1.8.0 Depends: R (>= 2.10), methods, WGCNA, MSnbase, flashClust Imports: BiocGenerics, GOstats Suggests: RUnit License: GPL (>= 2) MD5sum: f99c4ebaed318c6159acae5ff0279531 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ProCoNA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ProCoNA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ProCoNA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ProCoNA_1.8.0.tgz vignettes: vignettes/ProCoNA/inst/doc/ProCoNA_Vignette.pdf vignetteTitles: De Novo Peptide Network Example hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: pRoloc Version: 1.10.3 Depends: R (>= 2.15), MSnbase (>= 1.17.1), 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 LinkingTo: Rcpp, RcppArmadillo Suggests: testthat, pRolocdata (>= 1.5.8), roxygen2, synapter, xtable, tsne, BiocStyle, hpar, dplyr, GO.db, AnnotationDbi License: GPL-2 Archs: i386, x64 MD5sum: 494eda596488b8f23d3ecfb6dd9ca557 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_1.10.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/pRoloc_1.10.3.zip win64.binary.ver: bin/windows64/contrib/3.2/pRoloc_1.10.3.zip mac.binary.ver: bin/macosx/contrib/3.2/pRoloc_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pRoloc_1.10.3.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 dependsOnMe: pRolocGUI suggestsMe: MSnbase Package: pRolocGUI Version: 1.4.1 Depends: R (>= 3.1.0), pRoloc (>= 1.5.12), MSnbase (>= 1.13.11), methods Imports: pRolocdata, shiny (>= 0.9.1), tools (>= 3.1.0), scales, DT Suggests: RUnit, BiocGenerics, knitr, knitrBootstrap, bibtex, knitcitations (>= 1.0-1) License: GPL-2 MD5sum: a3d0d246f3e96b755c61f1a6b24c8075 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: Thomas Naake and Laurent Gatto, with contributions from Lisa M Breckels Maintainer: Laurent Gatto , Thomas Naake 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/pRolocGUI_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/pRolocGUI_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/pRolocGUI_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pRolocGUI_1.4.1.tgz vignettes: vignettes/pRolocGUI/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/pRolocGUI/inst/doc/pRolocGUI.html htmlTitles: "pRolocVis and pRolocComp application" Package: PROMISE Version: 1.22.0 Depends: R (>= 3.1.0), Biobase, GSEABase Imports: Biobase, GSEABase, stats License: GPL (>= 2) MD5sum: f69113fb1aaf25101ecb30a8d722b7f0 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PROMISE_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PROMISE_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PROMISE_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PROMISE_1.22.0.tgz vignettes: vignettes/PROMISE/inst/doc/PROMISE.pdf vignetteTitles: An introduction to PROMISE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: PROPER Version: 1.2.0 Depends: R (>= 2.10) Imports: edgeR Suggests: BiocStyle,DESeq,DSS License: GPL MD5sum: afe752b8108c258978a54f1da30b3f52 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 source.ver: src/contrib/PROPER_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PROPER_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PROPER_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PROPER_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PROPER_1.2.0.tgz vignettes: vignettes/PROPER/inst/doc/PROPER.pdf vignetteTitles: Power and Sample size analysis for gene expression from RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Prostar Version: 1.0.1 Depends: DAPAR, shiny Imports: shinyTree,rhandsontable, quantmod, data.table Suggests: BiocStyle License: Artistic-2.0 MD5sum: 21674dc780d5d35c9745e1257f30c7d6 NeedsCompilation: no Title: Provides a GUI for DAPAR Description: This package provides a GUI interface for DAPAR. biocViews: MassSpectrometry, Proteomics, GUI Author: Samuel Wieczorek [cre,aut], Florence Combes [aut], Thomas Burger [aut] Maintainer: Samuel Wieczorek source.ver: src/contrib/Prostar_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/Prostar_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/Prostar_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/Prostar_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Prostar_1.0.1.tgz vignettes: vignettes/Prostar/inst/doc/Prostar_UserManual.pdf vignetteTitles: Prostar user manual hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: DAPAR Package: prot2D Version: 1.8.0 Depends: R (>= 2.15),fdrtool,st,samr,Biobase,limma,Mulcom,impute,MASS,qvalue Suggests: made4,affy License: GPL (>= 2) MD5sum: 6413db68907ff9ca5e282a7af1536890 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/prot2D_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/prot2D_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/prot2D_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/prot2D_1.8.0.tgz vignettes: vignettes/prot2D/inst/doc/prot2D.pdf vignetteTitles: prot2D hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: proteinProfiles Version: 1.10.1 Depends: R (>= 2.15.2) Imports: graphics, stats Suggests: testthat License: GPL-3 MD5sum: 209a0ba474edf2f6453d2f5ab5fb4672 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/proteinProfiles_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/proteinProfiles_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/proteinProfiles_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/proteinProfiles_1.10.1.tgz vignettes: vignettes/proteinProfiles/inst/doc/proteinProfiles.pdf vignetteTitles: The proteinProfiles package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ProteomicsAnnotationHubData Version: 1.0.1 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: 0a5cef24215acce5b858b7b3527d07da 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_1.0.1.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/ProteomicsAnnotationHubData_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ProteomicsAnnotationHubData_1.0.1.tgz vignettes: vignettes/ProteomicsAnnotationHubData/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ProteomicsAnnotationHubData/inst/doc/ProteomicsAnnotationHubData.html htmlTitles: "Proteomics Data in Annotation Hub" Package: proteoQC Version: 1.6.0 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: c78444ec7b70d6306143bae9ea709793 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/proteoQC_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/proteoQC_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/proteoQC_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/proteoQC_1.6.0.tgz vignettes: vignettes/proteoQC/inst/doc/proteoQC.pdf vignetteTitles: proteoQC tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ProtGenerics Version: 1.2.1 Depends: methods, BiocGenerics License: Artistic-2.0 MD5sum: 131a926cbfbfdd25b99abdd4bb848163 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/ProtGenerics_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/ProtGenerics_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/ProtGenerics_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ProtGenerics_1.2.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Cardinal, MSnbase, xcms importsMe: MSnID, mzID, mzR Package: PSEA Version: 1.4.0 Imports: Biobase, MASS Suggests: BiocStyle License: Artistic-2.0 MD5sum: e7ce79f2fbd648c4b6304de4782b7d93 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PSEA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PSEA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PSEA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PSEA_1.4.0.tgz vignettes: vignettes/PSEA/inst/doc/PSEA.pdf vignetteTitles: PSEA: Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: PSICQUIC Version: 1.8.3 Depends: R (>= 3.2), methods, IRanges, biomaRt, BiocGenerics, httr, plyr Imports: RCurl Suggests: org.Hs.eg.db License: Apache License 2.0 MD5sum: 68c9dce6e794396f8595faa4a38a28df 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_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/PSICQUIC_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.2/PSICQUIC_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.2/PSICQUIC_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PSICQUIC_1.8.3.tgz vignettes: vignettes/PSICQUIC/inst/doc/PSICQUIC.pdf vignetteTitles: PSICQUIC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RefNet Package: puma Version: 3.12.0 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: fee8cbd2381f889594c3b541d4394878 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_3.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/puma_3.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/puma_3.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/puma_3.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/puma_3.12.0.tgz vignettes: vignettes/puma/inst/doc/puma.pdf vignetteTitles: puma User Guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: tigre Package: pvac Version: 1.18.0 Depends: R (>= 2.8.0) Imports: affy (>= 1.20.0), stats, Biobase Suggests: pbapply, affydata, ALLMLL, genefilter License: LGPL (>= 2.0) MD5sum: fbe0cbf10d430521f8e62e3246d1de40 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pvac_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pvac_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pvac_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pvac_1.18.0.tgz vignettes: vignettes/pvac/inst/doc/pvac.pdf vignetteTitles: PCA-based gene filtering for Affymetrix GeneChips hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: pvca Version: 1.10.0 Depends: R (>= 2.15.1) Imports: Matrix, Biobase, vsn, lme4 Suggests: golubEsets License: LGPL (>= 2.0) MD5sum: 9fca146da78971f85ca5420c9b60bd13 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pvca_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pvca_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pvca_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pvca_1.10.0.tgz vignettes: vignettes/pvca/inst/doc/pvca.pdf vignetteTitles: Batch effect estimation in Microarray data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Pviz Version: 1.4.0 Depends: R(>= 3.0.0), Gviz(>= 1.7.10) Imports: biovizBase, Biostrings, GenomicRanges, IRanges, data.table, methods Suggests: knitr, pepDat License: Artistic-2.0 MD5sum: 6affd1fa7991f2704cf669675de968b3 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Pviz_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Pviz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Pviz_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Pviz_1.4.0.tgz vignettes: vignettes/Pviz/inst/doc/Pviz.pdf vignetteTitles: The Pviz users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: Pbase suggestsMe: pepStat Package: PWMEnrich Version: 4.6.0 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: 35de7dfcd05191a4747ee277ea597b64 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_4.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/PWMEnrich_4.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/PWMEnrich_4.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/PWMEnrich_4.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/PWMEnrich_4.6.0.tgz vignettes: vignettes/PWMEnrich/inst/doc/PWMEnrich.pdf vignetteTitles: Overview of the 'PWMEnrich' package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: rTRM Package: pwOmics Version: 1.2.0 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: 9fdb5a5d9e5b0361ddea707628946ab4 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/pwOmics_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/pwOmics_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/pwOmics_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/pwOmics_1.2.0.tgz vignettes: vignettes/pwOmics/inst/doc/pwOmics.pdf vignetteTitles: pwOmics hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: qcmetrics Version: 1.8.0 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: 9c8ec2fde1edc29f50104a8d4d9dc6f1 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qcmetrics_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qcmetrics_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qcmetrics_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qcmetrics_1.8.0.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 Package: QDNAseq Version: 1.6.1 Depends: R (>= 2.15.0) Imports: graphics, methods, stats, utils, matrixStats (>= 0.13.1), R.utils (>= 1.28.4), Biobase (>= 2.18.0), CGHbase (>= 1.18.0), CGHcall (>= 2.18.0), DNAcopy (>= 1.32.0), Rsamtools (>= 1.19.17) Suggests: R.cache (>= 0.9.0), digest, snowfall, BSgenome, GenomeInfoDb License: GPL MD5sum: c2a793698a60ddbbaaabd450f35649b7 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/QDNAseq_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/QDNAseq_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/QDNAseq_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QDNAseq_1.6.1.tgz vignettes: vignettes/QDNAseq/inst/doc/QDNAseq.pdf vignetteTitles: Introduction to QDNAseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: GeneBreak Package: qpcrNorm Version: 1.28.0 Depends: methods, Biobase, limma, affy License: LGPL (>= 2) MD5sum: b715adab3cc2263b2345e239d6d6ba5c 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qpcrNorm_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qpcrNorm_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qpcrNorm_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qpcrNorm_1.28.0.tgz vignettes: vignettes/qpcrNorm/inst/doc/qpcrNorm.pdf vignetteTitles: qPCR Normalization Example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: EasyqpcR Package: qpgraph Version: 2.4.2 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: 82e70e7b201067671834a99220e25bec NeedsCompilation: yes Title: Estimation of genetic and molecular regulatory networks from high-throughput genomics data Description: Procedures to 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: R. Castelo and A. Roverato Maintainer: Robert Castelo URL: http://functionalgenomics.upf.edu/qpgraph source.ver: src/contrib/qpgraph_2.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/qpgraph_2.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/qpgraph_2.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/qpgraph_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qpgraph_2.4.2.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 importsMe: clipper, ToPASeq Package: qrqc Version: 1.24.0 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: 53ad27356a77e1c81c9a47b928373b5c 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/qrqc_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/qrqc_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/qrqc_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qrqc_1.24.0.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 Package: QUALIFIER Version: 1.14.1 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: e008afe8f08961d63622b69a0f699e31 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_1.14.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/QUALIFIER_1.14.1.zip win64.binary.ver: bin/windows64/contrib/3.2/QUALIFIER_1.14.1.zip mac.binary.ver: bin/macosx/contrib/3.2/QUALIFIER_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QUALIFIER_1.14.1.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: quantro Version: 1.4.0 Depends: R (>= 3.1.3) Imports: Biobase, minfi, doParallel, foreach, iterators, ggplot2, methods, RColorBrewer Suggests: knitr, RUnit, BiocGenerics, BiocStyle License: GPL (>=3) MD5sum: 270e68ca417b2d2868cb738804e48c5d 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/quantro_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/quantro_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/quantro_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/quantro_1.4.0.tgz vignettes: vignettes/quantro/inst/doc/quantro-vignette.pdf vignetteTitles: The quantro user's guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: quantsmooth Version: 1.36.0 Depends: R(>= 2.10.0), quantreg, grid License: GPL-2 MD5sum: 2018fd4a4c864ad22a4e659303ab3f1b 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/quantsmooth_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/quantsmooth_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/quantsmooth_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/quantsmooth_1.36.0.tgz vignettes: vignettes/quantsmooth/inst/doc/quantsmooth.pdf vignetteTitles: quantsmooth hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: beadarraySNP importsMe: GWASTools, SIM suggestsMe: PREDA Package: QuartPAC Version: 1.2.0 Depends: iPAC, GraphPAC, SpacePAC, data.table Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 1943e30c8c23bb3199e3245396978682 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/QuartPAC_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/QuartPAC_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/QuartPAC_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QuartPAC_1.2.0.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 Package: QuasR Version: 1.10.1 Depends: parallel, GenomicRanges (>= 1.13.3), Rbowtie Imports: methods, zlibbioc, BiocGenerics, S4Vectors, 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: 0397e1388e194af31eec01e24cfc7422 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_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/QuasR_1.10.1.zip win64.binary.ver: bin/windows64/contrib/3.2/QuasR_1.10.1.zip mac.binary.ver: bin/macosx/contrib/3.2/QuasR_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/QuasR_1.10.1.tgz vignettes: vignettes/QuasR/inst/doc/QuasR.pdf vignetteTitles: An introduction to QuasR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: qusage Version: 2.2.2 Depends: R (>= 2.10), limma (>= 3.14), methods Imports: utils, Biobase, nlme, lsmeans License: GPL (>= 2) MD5sum: 9d918be7408e15de0758d998a189884a 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_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/qusage_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/qusage_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/qusage_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qusage_2.2.2.tgz vignettes: vignettes/qusage/inst/doc/qusage.pdf vignetteTitles: Running qusage hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: SigCheck Package: qvalue Version: 2.2.2 Depends: R(>= 2.10) Imports: splines, ggplot2, grid, reshape2 Suggests: knitr License: LGPL MD5sum: c960b85d7faf35b22a1cfdf81e15ce33 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_2.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/qvalue_2.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/qvalue_2.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/qvalue_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/qvalue_2.2.2.tgz vignettes: vignettes/qvalue/inst/doc/qvalue.pdf vignetteTitles: qvalue Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE 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: 1.2.0 Depends: R (>= 3.2), Rcpp (>= 0.10.4), methods Imports: parallel, clues, ggplot2, pheatmap, IRanges, clValid, igraph,data.table,GenomicRanges,S4Vectors, ggbio,reshape2,Hmisc, RCurl 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: 446c2fc9f0ea0d2936eeada612e86550 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: M. N. Djekidel, Yang Chen et al. Maintainer: Mohamed Nadhir Djekidel VignetteBuilder: knitr BugReports: https://github.com/sirusb/R3CPET/issues source.ver: src/contrib/R3CPET_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/R3CPET_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/R3CPET_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/R3CPET_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/R3CPET_1.2.0.tgz vignettes: vignettes/R3CPET/inst/doc/R3CPET.pdf vignetteTitles: 3CPET: Finding Co-factor Complexes maintaining Chia-PET interactions hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: r3Cseq Version: 1.16.0 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: 9ab6cf8ecb7fc3c21fad43fd5e1be89d 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/r3Cseq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/r3Cseq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/r3Cseq_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/r3Cseq_1.16.0.tgz vignettes: vignettes/r3Cseq/inst/doc/r3Cseq.pdf vignetteTitles: r3Cseq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: R453Plus1Toolbox Version: 1.20.0 Depends: R (>= 2.12.0), methods, VariantAnnotation, Biostrings, Biobase Imports: utils, grDevices, graphics, stats, tools, xtable, R2HTML, TeachingDemos, BiocGenerics, S4Vectors, 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: 87a7449f1d7ee66ad502bbf791ad6f4e 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/R453Plus1Toolbox_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/R453Plus1Toolbox_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/R453Plus1Toolbox_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/R453Plus1Toolbox_1.20.0.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 Package: rain Version: 1.4.0 Depends: R (>= 2.10), gmp, multtest Suggests: lattice, BiocStyle License: GPL-2 MD5sum: e0373aa9979f0ea118a8c2348d19560a 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rain_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rain_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rain_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rain_1.4.0.tgz vignettes: vignettes/rain/inst/doc/rain.pdf vignetteTitles: Rain Usage hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: rama Version: 1.44.0 Depends: R(>= 2.5.0) License: GPL (>= 2) Archs: i386, x64 MD5sum: 1ccd4daf3d5569eb0737c5fef4491dd4 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rama_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rama_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rama_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rama_1.44.0.tgz vignettes: vignettes/rama/inst/doc/rama.pdf vignetteTitles: rama Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: bridge Package: RamiGO Version: 1.16.0 Depends: gsubfn,methods Imports: igraph,RCurl,png,RCytoscape,graph License: Artistic-2.0 MD5sum: 83af5c6ae4a7d651e4c712b48c49d946 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RamiGO_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RamiGO_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RamiGO_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RamiGO_1.16.0.tgz vignettes: vignettes/RamiGO/inst/doc/RamiGO.pdf vignetteTitles: RamiGO: An Introduction (HowTo) hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: randPack Version: 1.16.0 Depends: methods Imports: Biobase License: Artistic 2.0 MD5sum: 1c4ce1b8c909b094293df48e2c729d37 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/randPack_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/randPack_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/randPack_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/randPack_1.16.0.tgz vignettes: vignettes/randPack/inst/doc/randPack.pdf vignetteTitles: Clinical trial randomization infrastructure hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RankProd Version: 2.42.0 Depends: R (>= 1.9.0) Imports: graphics License: file LICENSE License_restricts_use: yes MD5sum: 0d12c444a60d93eb674a785fe3734141 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_2.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RankProd_2.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RankProd_2.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RankProd_2.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RankProd_2.42.0.tgz vignettes: vignettes/RankProd/inst/doc/RankProd.pdf vignetteTitles: RankProd Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: RNAither, tRanslatome importsMe: HTSanalyzeR, synlet suggestsMe: oneChannelGUI Package: RareVariantVis Version: 1.2.0 Depends: BiocGenerics, VariantAnnotation, googleVis Imports: S4Vectors, IRanges, GenomeInfoDb, GenomicRanges Suggests: knitr, AshkenazimSonChr21 License: Artistic-2.0 MD5sum: d224332eccd9b040af3804563a69a050 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RareVariantVis_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RareVariantVis_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RareVariantVis_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RareVariantVis_1.2.0.tgz vignettes: vignettes/RareVariantVis/inst/doc/RareVariantsVis.pdf vignetteTitles: RareVariantVis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rariant Version: 1.6.2 Depends: R (>= 3.0.2), GenomicRanges, VariantAnnotation Imports: IRanges, ggbio, ggplot2, exomeCopy, SomaticSignatures, Rsamtools, shiny, methods, VGAM, dplyr, reshape2, GenomeInfoDb, S4Vectors Suggests: h5vcData, testthat, knitr, optparse, BSgenome.Hsapiens.UCSC.hg19 License: GPL-3 MD5sum: 2225f038ea3a9ba7aa69d8d54fdfa7e0 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 (EMBL Heidelberg) Maintainer: Julian Gehring VignetteBuilder: knitr source.ver: src/contrib/Rariant_1.6.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rariant_1.6.2.zip win64.binary.ver: bin/windows64/contrib/3.2/Rariant_1.6.2.zip mac.binary.ver: bin/macosx/contrib/3.2/Rariant_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rariant_1.6.2.tgz vignettes: vignettes/Rariant/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/Rariant/inst/doc/Rariant-vignette.html htmlTitles: "Rariant" Package: RbcBook1 Version: 1.38.0 Depends: R (>= 2.10), Biobase, graph, rpart License: Artistic-2.0 MD5sum: c6083da7a74cc115c718570f7ac06d55 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RbcBook1_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RbcBook1_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RbcBook1_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RbcBook1_1.38.0.tgz vignettes: vignettes/RbcBook1/inst/doc/RbcBook1.pdf vignetteTitles: RbcBook1 Primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RBGL Version: 1.46.0 Depends: graph, methods Imports: methods Suggests: Rgraphviz, XML, RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 382d3a0c6f33f9b07a63f4f20b631fca 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RBGL_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RBGL_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RBGL_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RBGL_1.46.0.tgz vignettes: vignettes/RBGL/inst/doc/RBGL.pdf vignetteTitles: RBGL Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: apComplex, BioNet, CellNOptR, joda, pkgDepTools, RpsiXML importsMe: biocViews, CAMERA, Category, ChIPpeakAnno, 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: 1.30.0 Depends: graph, methods Suggests: Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 1731da63ef77350c116c09eb8a0e9603 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RBioinf_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RBioinf_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RBioinf_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RBioinf_1.30.0.tgz vignettes: vignettes/RBioinf/inst/doc/RBioinf.pdf vignetteTitles: RBioinf Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: rBiopaxParser Version: 2.8.0 Depends: R (>= 3.0.0), data.table Imports: XML Suggests: Rgraphviz, RCurl, graph, RUnit, BiocGenerics, nem, RBGL License: GPL (>= 2) MD5sum: c21ae1e7b7f33d20834d788860e8499c 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rBiopaxParser_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rBiopaxParser_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rBiopaxParser_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rBiopaxParser_2.8.0.tgz vignettes: vignettes/rBiopaxParser/inst/doc/rBiopaxParserVignette.pdf vignetteTitles: rBiopaxParser Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: AnnotationHubData, pwOmics suggestsMe: AnnotationHub, NetPathMiner Package: RBM Version: 1.2.0 Depends: R (>= 3.2.0), limma, marray License: GPL (>= 2) MD5sum: cf1871ce1e53ba22b214d24dc9ab51ee 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RBM_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RBM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RBM_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RBM_1.2.0.tgz vignettes: vignettes/RBM/inst/doc/RBM.pdf vignetteTitles: RBM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rbowtie Version: 1.10.0 Suggests: parallel License: Artistic-1.0 | file LICENSE Archs: x64 MD5sum: 4e1ab034cf5280f6bf14fc0b67a01633 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rbowtie_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rbowtie_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rbowtie_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rbowtie_1.10.0.tgz vignettes: vignettes/Rbowtie/inst/doc/Rbowtie-Overview.pdf vignetteTitles: An introduction to Rbowtie hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: QuasR Package: rbsurv Version: 2.28.0 Depends: R (>= 2.5.0), Biobase (>= 2.5.5), survival License: GPL (>= 2) MD5sum: 189627a73582c033e1b0f4dfc7a17e74 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_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rbsurv_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rbsurv_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rbsurv_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rbsurv_2.28.0.tgz vignettes: vignettes/rbsurv/inst/doc/rbsurv.pdf vignetteTitles: Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rcade Version: 1.12.0 Depends: R (>= 2.14.0), methods, GenomicRanges, baySeq, Rsamtools Imports: graphics, S4Vectors, rgl, plotrix Suggests: limma, biomaRt, RUnit, BiocGenerics, BiocStyle License: GPL-2 MD5sum: a87c16fcfd6924f9b66e44ac93d7ae33 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rcade_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rcade_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rcade_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rcade_1.12.0.tgz vignettes: vignettes/Rcade/inst/doc/Rcade.pdf vignetteTitles: Rcade Vignette hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RCASPAR Version: 1.16.0 License: GPL (>=3) MD5sum: e129da248b66256ad12fa99b54f854c7 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RCASPAR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RCASPAR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RCASPAR_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RCASPAR_1.16.0.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 Package: rcellminer Version: 1.2.3 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: 9d59e0fab4e50293dc36b920aeb3f72c 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_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/rcellminer_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/rcellminer_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/rcellminer_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rcellminer_1.2.3.tgz vignettes: vignettes/rcellminer/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/rcellminer/inst/doc/rcellminerUsage.html htmlTitles: "Using rcellminer" Package: rCGH Version: 1.0.2 Depends: R (>= 3.2.1),methods Imports: plyr,DNAcopy,lattice,aCGH,ggplot2,grid,shiny (>= 0.11.1), limma,affy,mclust,TxDb.Hsapiens.UCSC.hg19.knownGene, org.Hs.eg.db,GenomicFeatures,GenomeInfoDb,GenomicRanges,AnnotationDbi, parallel,stats,utils,graphics,IRanges,grDevices Suggests: BiocStyle, knitr, BiocGenerics, RUnit License: Artistic-2.0 MD5sum: 9781bf82949ce2b65a2e3a1ccf2e144a NeedsCompilation: no Title: Comprehensive Pipeline for Analyzing and Visualizing Agilent and Affymetrix Array-Based CGH Data Description: A comprehensive pipeline for analyzing and interactively visualizing genomic profiles generated through Agilent and Affymetrix microarrays. As inputs, rCGH supports Agilent dual-color Feature Extraction files (.txt), from 44 to 400K, and Affymetrix SNP6.0 and cytoScan probeset.txt, cychp.txt, and cnchp.txt files, exported from ChAS or Affymetrix Power Tools. This package takes over all the steps required for a genomic profile analysis, from reading the files to the segmentation and genes annotations, and provides several visualization functions (static or interactive) which facilitate 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/rCGH_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/rCGH_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/rCGH_1.0.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rCGH_1.0.2.tgz vignettes: vignettes/rCGH/inst/doc/rCGH.pdf vignetteTitles: using rCGH package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rchemcpp Version: 2.8.0 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: 1d6c135965222f3a4da91fd1477e250b 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rchemcpp_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rchemcpp_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rchemcpp_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rchemcpp_2.8.0.tgz vignettes: vignettes/Rchemcpp/inst/doc/Rchemcpp.pdf vignetteTitles: Rchemcpp hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RchyOptimyx Version: 2.10.0 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: bd9337ae4b651761f530f84ca43caa20 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RchyOptimyx_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RchyOptimyx_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RchyOptimyx_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RchyOptimyx_2.10.0.tgz vignettes: vignettes/RchyOptimyx/inst/doc/RchyOptimyx.pdf vignetteTitles: flowType package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rcpi Version: 1.6.0 Imports: RCurl, rjson, rcdk, foreach, doParallel, Biostrings, GOSemSim, ChemmineR, fmcsR Suggests: RUnit, BiocGenerics Enhances: ChemmineOB License: Artistic-2.0 MD5sum: 120e215822ee2af9d88f31fd828fd37c 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rcpi_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rcpi_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rcpi_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rcpi_1.6.0.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 Package: RCy3 Version: 1.0.1 Depends: R (>= 3.2), graph (>= 1.31.0), httr, RJSONIO, RCurl Imports: methods Suggests: RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 0996535533726ebe0dbf909912d0ec1f NeedsCompilation: no Title: Display and manipulate graphs in Cytoscape >= 3.2.1 Description: Vizualize, analyze and explore graphs, connecting R to Cytoscape >= 3.2.1. biocViews: Visualization, GraphAndNetwork, ThirdPartyClient, Network Author: Tanja Muetze, Georgi Kolishovski, Paul Shannon Maintainer: Tanja Muetze , Paul Shannon , Georgi Kolishovski source.ver: src/contrib/RCy3_1.0.1.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RCy3_1.0.0.tgz vignettes: vignettes/RCy3/inst/doc/RCy3.pdf vignetteTitles: RCy3 Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RCyjs Version: 1.2.3 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), igraph, BiocGenerics Suggests: RUnit, BiocStyle, RefNet License: GPL-2 MD5sum: 7ea77d43c0d44a6b9a59067345649c75 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_1.2.3.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/RCyjs_1.1.6.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RCyjs_1.2.0.tgz vignettes: vignettes/RCyjs/inst/doc/RCyjs.pdf vignetteTitles: RCyjs hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RCytoscape Version: 1.20.1 Depends: R (>= 2.14.0), graph (>= 1.31.0), XMLRPC (>= 0.2.4) Imports: methods, XMLRPC, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: d8e264e72cf9ddb3441d555cf4352509 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_1.20.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/RCytoscape_1.20.1.zip win64.binary.ver: bin/windows64/contrib/3.2/RCytoscape_1.20.1.zip mac.binary.ver: bin/macosx/contrib/3.2/RCytoscape_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RCytoscape_1.20.1.tgz vignettes: vignettes/RCytoscape/inst/doc/RCytoscape.pdf vignetteTitles: RCytoscape Overview hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: categoryCompare, NCIgraph suggestsMe: clipper, GeneNetworkBuilder, graphite, mmnet, NetPathMiner Package: RDAVIDWebService Version: 1.8.0 Depends: R (>= 2.14.1), methods, graph, GOstats, ggplot2 Imports: Category, GO.db, RBGL, rJava Suggests: Rgraphviz License: GPL (>=2) MD5sum: 9d9fc971c78a97225ef6ebae36adb989 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RDAVIDWebService_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RDAVIDWebService_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RDAVIDWebService_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RDAVIDWebService_1.8.0.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 dependsOnMe: CompGO suggestsMe: clusterProfiler, FGNet Package: Rdisop Version: 1.30.0 Depends: R (>= 2.0.0), RcppClassic LinkingTo: RcppClassic, Rcpp Suggests: RUnit License: GPL-2 Archs: i386, x64 MD5sum: be4e7be8a3525e5d40cee95f78babb03 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rdisop_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rdisop_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rdisop_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rdisop_1.30.0.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: 1.20.0 Depends: R (>= 2.9.0) Imports: graphics, grDevices, methods, stats, MASS, rgl Suggests: golubEsets License: GPL (>= 2) MD5sum: 1d0755853daf2d604948e2db4a0c46d9 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RDRToolbox_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RDRToolbox_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RDRToolbox_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RDRToolbox_1.20.0.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 Package: ReactomePA Version: 1.14.4 Imports: DOSE, AnnotationDbi, reactome.db, igraph, graphite Suggests: BiocStyle, clusterProfiler, knitr License: GPL-2 MD5sum: cd53ff7ebe10b4f4c1b2c306ef7438b6 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 VignetteBuilder: knitr source.ver: src/contrib/ReactomePA_1.14.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReactomePA_1.14.4.zip win64.binary.ver: bin/windows64/contrib/3.2/ReactomePA_1.14.4.zip mac.binary.ver: bin/macosx/contrib/3.2/ReactomePA_1.14.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReactomePA_1.14.4.tgz vignettes: vignettes/ReactomePA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/ReactomePA/inst/doc/ReactomePA.html htmlTitles: "An R package for Reactome Pathway Analysis" suggestsMe: ChIPseeker, clusterProfiler Package: ReadqPCR Version: 1.16.0 Depends: R(>= 2.14.0), Biobase, methods, affy Imports: Biobase Suggests: qpcR License: LGPL-3 MD5sum: 8070f24f6c84f95f407566436297b8f4 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReadqPCR_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ReadqPCR_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ReadqPCR_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReadqPCR_1.16.0.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 dependsOnMe: NormqPCR Package: reb Version: 1.48.0 Depends: R (>= 2.0), Biobase, idiogram (>= 1.5.3) License: GPL-2 Archs: i386, x64 MD5sum: 11eef83f9178ea905e55c211a905812b 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/reb_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/reb_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/reb_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/reb_1.48.0.tgz vignettes: vignettes/reb/inst/doc/reb.pdf vignetteTitles: Smoothing of Microarray Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RedeR Version: 1.18.1 Depends: R (>= 2.15), methods, igraph Imports: RCurl, XML, pvclust Suggests: PANR License: GPL (>= 2) MD5sum: c163725db6c54e8db925242989ec5b7a 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_1.18.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/RedeR_1.18.1.zip win64.binary.ver: bin/windows64/contrib/3.2/RedeR_1.18.1.zip mac.binary.ver: bin/macosx/contrib/3.2/RedeR_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RedeR_1.18.1.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 importsMe: PANR, RTN Package: REDseq Version: 1.16.0 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: f1fbce403b511190aa5966dfb5fffb63 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/REDseq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/REDseq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/REDseq_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/REDseq_1.16.0.tgz vignettes: vignettes/REDseq/inst/doc/REDseq.pdf vignetteTitles: REDseq Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RefNet Version: 1.6.1 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: d04aa9fa8059a3f5525a6bb7ce46d6da 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/RefNet_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/RefNet_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/RefNet_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RefNet_1.6.1.tgz vignettes: vignettes/RefNet/inst/doc/RefNet.pdf vignetteTitles: RefNet hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: RCyjs Package: RefPlus Version: 1.40.0 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: 8217344703c24712116671b2962f7cc2 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RefPlus_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RefPlus_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RefPlus_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RefPlus_1.40.0.tgz vignettes: vignettes/RefPlus/inst/doc/RefPlus.pdf vignetteTitles: RefPlus Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: regioneR Version: 1.2.3 Depends: memoise, GenomicRanges, BSgenome, rtracklayer, parallel Imports: memoise, GenomicRanges, BSgenome, rtracklayer, parallel Suggests: BiocStyle, knitr, BSgenome.Hsapiens.UCSC.hg19.masked, testthat License: Artistic-2.0 MD5sum: 534bde18f7ebfb0cab3a3e4c0d19376e 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_1.2.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/regioneR_1.2.3.zip win64.binary.ver: bin/windows64/contrib/3.2/regioneR_1.2.3.zip mac.binary.ver: bin/macosx/contrib/3.2/regioneR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/regioneR_1.2.3.tgz vignettes: vignettes/regioneR/inst/doc/regioneR.pdf vignetteTitles: regioneR vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: ChIPpeakAnno Package: regionReport Version: 1.4.1 Depends: R(>= 3.2) Imports: bumphunter (>= 1.7.6), derfinder (>= 1.1.0), derfinderPlot (>= 1.3.2), devtools (>= 1.6), GenomeInfoDb, GenomicRanges, ggbio (>= 1.13.13), ggplot2, grid, gridExtra, IRanges, knitcitations (>= 1.0.1), knitr (>= 1.6), knitrBootstrap (>= 0.9.0), mgcv, RColorBrewer, rmarkdown (>= 0.3.3), whisker Suggests: BiocStyle, biovizBase, Cairo, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: 2603a3e65f1cf804613bc38eecc4e485 NeedsCompilation: no Title: Generate HTML reports for exploring a set of regions Description: Generate HTML 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. biocViews: DifferentialExpression, Sequencing, RNASeq, Software, Visualization, Transcription, Coverage 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/regionReport_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/regionReport_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/regionReport_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/regionReport_1.4.1.tgz vignettes: vignettes/regionReport/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/regionReport/inst/doc/bumphunterExample.html, vignettes/regionReport/inst/doc/regionReport.html htmlTitles: "Example with data from bumphunter", "Introduction to regionReport" Package: Repitools Version: 1.16.0 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.8.0) Imports: S4Vectors, IRanges (>= 1.20.0), GenomeInfoDb, GenomicRanges, GenomicAlignments, BSgenome, gplots, grid, MASS, gsmoothr, edgeR (>= 3.4.0), DNAcopy, Ringo, aroma.affymetrix, Rsolnp, parallel, Biostrings, Rsamtools, cluster, rtracklayer Suggests: ShortRead, BSgenome.Hsapiens.UCSC.hg18 License: LGPL (>= 2) Archs: i386, x64 MD5sum: 4dc407e5bd2b675761b369c02bc0074d 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Repitools_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Repitools_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Repitools_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Repitools_1.16.0.tgz vignettes: vignettes/Repitools/inst/doc/Repitools_vignette.pdf vignetteTitles: Using Repitools for Epigenomic Sequencing Data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ReportingTools Version: 2.10.0 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: 6586a64b1eb45cfc548f60ab6380cf95 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReportingTools_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ReportingTools_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ReportingTools_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReportingTools_2.10.0.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/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: 1.16.0 Depends: R (>= 3.0.2), Rsamtools, seqbias Imports: rJava, graphics, stats, utils, grDevices Suggests: BiocStyle License: GPL-2 MD5sum: 8ab7ec18313b6bac88b0ade10fcc1487 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ReQON_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ReQON_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ReQON_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ReQON_1.16.0.tgz vignettes: vignettes/ReQON/inst/doc/ReQON.pdf vignetteTitles: ReQON Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: rfPred Version: 1.8.0 Depends: Rsamtools, GenomicRanges, IRanges, data.table, methods, parallel Suggests: BiocStyle License: GPL (>=2 ) Archs: i386, x64 MD5sum: cd9fffa548b36d060a2531f2abebf40e 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rfPred_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rfPred_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rfPred_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rfPred_1.8.0.tgz vignettes: vignettes/rfPred/inst/doc/vignette.pdf vignetteTitles: CalculatingrfPredscoreswithpackagerfPred hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: rGADEM Version: 2.18.0 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: f1223a3fc0b3e326fc0d3afe7ed563d1 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_2.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rGADEM_2.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rGADEM_2.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rGADEM_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rGADEM_2.18.0.tgz vignettes: vignettes/rGADEM/inst/doc/rGADEM.pdf vignetteTitles: The rGADEM users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: MotIV Package: RGalaxy Version: 1.14.0 Depends: XML, methods, tools, optparse, digest, Imports: BiocGenerics, Biobase, roxygen2 Suggests: RUnit, hgu95av2.db, knitr, formatR, Rserve Enhances: RSclient License: Artistic-2.0 MD5sum: e7cd5d52bd49bf070e51f9059b3f3c87 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RGalaxy_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RGalaxy_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RGalaxy_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RGalaxy_1.14.0.tgz vignettes: vignettes/RGalaxy/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/RGalaxy/inst/doc/RGalaxy-vignette.html htmlTitles: "Introduction to RGalaxy" Package: Rgraphviz Version: 2.14.0 Depends: R (>= 2.6.0), methods, utils, graph, grid Imports: stats4, graphics, grDevices Suggests: RUnit, BiocGenerics, XML License: EPL Archs: i386, x64 MD5sum: d11e12a3028c2abe61127f8d4d2e3dc9 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rgraphviz_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rgraphviz_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rgraphviz_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rgraphviz_2.14.0.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 dependsOnMe: biocGraph, BioMVCClass, CellNOptR, flowCL, gaucho, GOFunction, MineICA, mvGST, netresponse, paircompviz, pathRender, ROntoTools, SplicingGraphs, TDARACNE, ToPASeq importsMe: apComplex, biocGraph, CompGO, DEGraph, EnrichmentBrowser, facopy, 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: 1.2.0 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: 6a92c9c9711a16a83b0b5987314e15a8 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rGREAT_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rGREAT_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rGREAT_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rGREAT_1.2.0.tgz vignettes: vignettes/rGREAT/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/rGREAT/inst/doc/rGREAT.html htmlTitles: "Analyze with GREAT" Package: RGSEA Version: 1.4.0 Depends: R(>= 2.10.0) Imports: BiocGenerics Suggests: BiocStyle, GEOquery, knitr, RUnit License: GPL(>=3) MD5sum: 1344e9bbb8369a47750c3ace238fbb3b 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RGSEA_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RGSEA_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RGSEA_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RGSEA_1.4.0.tgz vignettes: vignettes/RGSEA/inst/doc/RGSEA.pdf vignetteTitles: Introduction to RGSEA hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: rgsepd Version: 1.2.0 Depends: R (>= 3.0.0), DESeq2, goseq (>= 1.17) Imports: gplots, biomaRt, org.Hs.eg.db, GO.db, GenomicRanges, hash, AnnotationDbi Suggests: boot, tools, RUnit, BiocGenerics, knitr, xtable License: GPL-3 MD5sum: 14dd729222a27603b7850c14c8eda7dd 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rgsepd_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rgsepd_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rgsepd_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rgsepd_1.2.0.tgz vignettes: vignettes/rgsepd/inst/doc/rgsepd.pdf vignetteTitles: An Introduction to the rgsepd package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: rhdf5 Version: 2.14.0 Depends: methods Imports: zlibbioc Suggests: bit64,BiocStyle License: Artistic-2.0 Archs: i386, x64 MD5sum: 69f51f64d0f4112dbb3ca62b4b081e41 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_2.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rhdf5_2.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rhdf5_2.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rhdf5_2.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rhdf5_2.14.0.tgz vignettes: vignettes/rhdf5/inst/doc/rhdf5.pdf vignetteTitles: rhdf5 hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: GENE.E, GSCA importsMe: diffHic, DOQTL, GENE.E, h5vc, IONiseR suggestsMe: SummarizedExperiment Package: Rhtslib Version: 1.2.1 Imports: zlibbioc LinkingTo: zlibbioc Suggests: BiocStyle, knitr License: LGPL (>= 2) Archs: i386, x64 MD5sum: 9b9355d06e714963fcc6344dae84c5ce 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rhtslib_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/Rhtslib_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/Rhtslib_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rhtslib_1.2.1.tgz vignettes: vignettes/Rhtslib/inst/doc/ hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/Rhtslib/inst/doc/Rhtslib.html htmlTitles: "Motivation and Use of Rhtslib" dependsOnMe: deepSNV importsMe: deepSNV Package: rHVDM Version: 1.36.0 Depends: R (>= 2.10), R2HTML (>= 1.5), affy (>= 1.23.4), minpack.lm (>= 1.0-5), Biobase (>= 2.5.5) License: GPL-2 MD5sum: a68074c1190766fbdfac0ad29966d8f9 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rHVDM_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rHVDM_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rHVDM_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rHVDM_1.36.0.tgz vignettes: vignettes/rHVDM/inst/doc/rHVDM.pdf vignetteTitles: rHVDM primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RiboProfiling Version: 1.0.3 Depends: R (>= 3.2.2), Biostrings Imports: BiocGenerics, GenomeInfoDb, GenomicRanges, IRanges, reshape, GenomicFeatures, grid, plyr, S4Vectors, GenomicAlignments, ggplot2, ggbio, Rsamtools, rtracklayer Suggests: knitr, BiocStyle, TxDb.Hsapiens.UCSC.hg19.knownGene, BSgenome.Hsapiens.UCSC.hg19, testthat License: GPL-3 MD5sum: 850c046687fc04bdb9445653e5ddfeae 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: Sequencing, Coverage, Alignment, QualityControl, Software, PrincipalComponent Author: Alexandra Popa Maintainer: A. Popa VignetteBuilder: knitr source.ver: src/contrib/RiboProfiling_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/RiboProfiling_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/RiboProfiling_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/RiboProfiling_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RiboProfiling_1.0.3.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: 1.4.0 Depends: R (>= 3.0.2), methods, GenomicRanges, abind Suggests: baySeq, BiocStyle, RUnit, BiocGenerics License: GPL-3 MD5sum: 306661851b5de832075ad32cd4885aaa 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 Author: Thomas J. Hardcastle Maintainer: Thomas J. Hardcastle source.ver: src/contrib/riboSeqR_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/riboSeqR_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/riboSeqR_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/riboSeqR_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/riboSeqR_1.4.0.tgz vignettes: vignettes/riboSeqR/inst/doc/riboSeqR.pdf vignetteTitles: riboSeqR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Ringo Version: 1.34.0 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: 36f8dbd2b63737ec6511799baff18875 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Ringo_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Ringo_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Ringo_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Ringo_1.34.0.tgz vignettes: vignettes/Ringo/inst/doc/Ringo.pdf vignetteTitles: R Investigation of NimbleGen Oligoarrays hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: SimBindProfiles, Starr importsMe: Repitools Package: RIPSeeker Version: 1.10.0 Depends: R (>= 2.15), methods, IRanges, GenomicRanges, SummarizedExperiment, Rsamtools, GenomicAlignments, rtracklayer Suggests: biomaRt, ChIPpeakAnno, parallel, GenomicFeatures License: GPL-2 MD5sum: 2b6eb145647980ede3d2b37df699a5bf 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RIPSeeker_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RIPSeeker_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RIPSeeker_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RIPSeeker_1.10.0.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 Package: Risa Version: 1.12.0 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: adbac4225f1b55cd5e4b6a289f55ea27 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Risa_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Risa_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Risa_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Risa_1.12.0.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 Package: RLMM Version: 1.32.0 Depends: R (>= 2.1.0) Imports: graphics, grDevices, MASS, stats, utils License: LGPL (>= 2) MD5sum: 6985e4dd0f2769e42123aa73421bb827 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RLMM_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RLMM_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RLMM_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RLMM_1.32.0.tgz vignettes: vignettes/RLMM/inst/doc/RLMM.pdf vignetteTitles: RLMM Doc hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rmagpie Version: 1.26.0 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: 85e1989ced3c087de36f56b1f5eb314a 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rmagpie_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rmagpie_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rmagpie_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rmagpie_1.26.0.tgz vignettes: vignettes/Rmagpie/inst/doc/Magpie_examples.pdf vignetteTitles: Rmagpie Examples hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RMassBank Version: 1.12.1 Depends: Rcpp Imports: XML,RCurl,rjson, rcdk,yaml,mzR,methods Suggests: gplots,RMassBankData, xcms (>= 1.37.1), CAMERA, ontoCAT, RUnit License: Artistic-2.0 MD5sum: fdf2013af9e0af43341712af6b2ca49b 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_1.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/RMassBank_1.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/RMassBank_1.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/RMassBank_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RMassBank_1.12.1.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 Package: rMAT Version: 3.20.0 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: ef706cc907e1db58f0a5c9145d722019 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_3.20.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/rMAT_3.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rMAT_3.20.0.tgz vignettes: vignettes/rMAT/inst/doc/rMAT.pdf vignetteTitles: The rMAT users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RmiR Version: 1.26.0 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: 7c1542e8d4b56c7b86fe4478ff180c97 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RmiR_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RmiR_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RmiR_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RmiR_1.26.0.tgz vignettes: vignettes/RmiR/inst/doc/RmiR.pdf vignetteTitles: RmiR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oneChannelGUI Package: RNAinteract Version: 1.18.0 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: f030dd94a85404312270fc6196688a21 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNAinteract_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RNAinteract_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RNAinteract_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNAinteract_1.18.0.tgz vignettes: vignettes/RNAinteract/inst/doc/RNAinteract.pdf vignetteTitles: RNAinteract hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: RNAither Version: 2.18.7 Depends: R (>= 2.10), topGO, RankProd, prada Imports: geneplotter, limma, biomaRt, car, splots, methods License: Artistic-2.0 MD5sum: 86cd3473f0a0b159f4fabc84ed802bcc 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_2.18.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNAither_2.18.7.zip win64.binary.ver: bin/windows64/contrib/3.2/RNAither_2.18.7.zip mac.binary.ver: bin/macosx/contrib/3.2/RNAither_2.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNAither_2.18.7.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 Package: RNAprobR Version: 1.2.0 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: 06cfa586d5eea2a882b9749ba995ab4b 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNAprobR_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RNAprobR_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RNAprobR_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNAprobR_1.2.0.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 Package: rnaseqcomp Version: 1.0.2 Depends: R (>= 3.2.0) Imports: RColorBrewer, methods Suggests: BiocStyle, knitr, rmarkdown License: GPL-3 MD5sum: 3ac97cd1bab5b6b9e183f9f18be20eb7 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/rnaseqcomp_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/rnaseqcomp_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/rnaseqcomp_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rnaseqcomp_1.0.2.tgz vignettes: vignettes/rnaseqcomp/inst/doc/rnaseqcomp.pdf vignetteTitles: rnaseqcomp.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: rnaSeqMap Version: 2.28.0 Depends: R (>= 2.11.0), methods, Biobase, Rsamtools, GenomicAlignments Imports: GenomicRanges , IRanges, edgeR, DESeq, DBI License: GPL-2 Archs: i386, x64 MD5sum: d073e445f2e88925a7f12c86f75baed7 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_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rnaSeqMap_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rnaSeqMap_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rnaSeqMap_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rnaSeqMap_2.28.0.tgz vignettes: vignettes/rnaSeqMap/inst/doc/rnaSeqMap.pdf vignetteTitles: rnaSeqMap primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: ampliQueso Package: RNASeqPower Version: 1.10.0 License: LGPL (>=2) MD5sum: 2ffbb2ebd6ce6a9a232afbed59732fba 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RNASeqPower_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RNASeqPower_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RNASeqPower_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RNASeqPower_1.10.0.tgz vignettes: vignettes/RNASeqPower/inst/doc/samplesize.pdf vignetteTitles: RNAseq samplesize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RnaSeqSampleSize Version: 1.2.0 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: 91c821943dc6a5c81705f8efd9094b2f 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RnaSeqSampleSize_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RnaSeqSampleSize_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RnaSeqSampleSize_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RnaSeqSampleSize_1.2.0.tgz vignettes: vignettes/RnaSeqSampleSize/inst/doc/RnaSeqSampleSize.pdf vignetteTitles: RnaSeqSampleSize: Sample size estimation by real data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RnBeads Version: 1.2.2 Depends: R (>= 3.0.0), BiocGenerics, 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 License: GPL-3 MD5sum: c5b4238973950fd3b5ca8eff355aa457 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_1.2.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/RnBeads_1.2.2.zip win64.binary.ver: bin/windows64/contrib/3.2/RnBeads_1.2.2.zip mac.binary.ver: bin/macosx/contrib/3.2/RnBeads_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RnBeads_1.2.2.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 Package: Rnits Version: 1.4.0 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: 4038e3e6545498a0596382ded7b1e567 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rnits_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rnits_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rnits_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rnits_1.4.0.tgz vignettes: vignettes/Rnits/inst/doc/Rnits-vignette.pdf vignetteTitles: R/Bioconductor package for normalization and differential expression inference in time series gene expression microarray data. hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: roar Version: 1.6.1 Depends: R (>= 3.0.1) Imports: GenomicRanges, SummarizedExperiment, GenomicAlignments(>= 0.99.4), methods, rtracklayer, S4Vectors Suggests: RUnit, BiocGenerics, RNAseqData.HNRNPC.bam.chr14 License: GPL-3 MD5sum: e2259650ffe34c2709ff215634933ac7 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/roar_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/roar_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/roar_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/roar_1.6.1.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 Package: ROC Version: 1.46.0 Depends: R (>= 1.9.0), utils, methods Suggests: Biobase License: Artistic-2.0 Archs: i386, x64 MD5sum: 456703594755f79fb9f9acd55b69fcd8 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ROC_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ROC_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ROC_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ROC_1.46.0.tgz vignettes: vignettes/ROC/inst/doc/ROCnotes.pdf vignetteTitles: ROC notes hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: TCC, wateRmelon importsMe: clst suggestsMe: genefilter, MCRestimate Package: Roleswitch Version: 1.8.0 Depends: R (>= 2.10), pracma, reshape, plotrix, microRNA, biomaRt, Biostrings, Biobase, DBI Suggests: ggplot2 License: GPL-2 MD5sum: 6a37a19019c222bce7d0197a43a69c3e 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Roleswitch_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Roleswitch_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Roleswitch_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Roleswitch_1.8.0.tgz vignettes: vignettes/Roleswitch/inst/doc/Roleswitch.pdf vignetteTitles: Roleswitch hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: miRLAB Package: Rolexa Version: 1.26.0 Depends: R (>= 2.9.0), graphics, grDevices, methods, ShortRead Imports: mclust, Biostrings, graphics, grDevices, IRanges, methods, ShortRead, stats Enhances: fork License: GPL-2 MD5sum: 6e15e2baa3f5889ca0d716347ccc12ea 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rolexa_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rolexa_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rolexa_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rolexa_1.26.0.tgz vignettes: vignettes/Rolexa/inst/doc/Rolexa-vignette.pdf vignetteTitles: Rolexa hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: rols Version: 1.12.2 Depends: methods Imports: XML, XMLSchema (>= 0.6.0), SSOAP (>= 0.8.0), Biobase, utils Suggests: xtable, GO.db, knitr (>= 1.1.0), BiocStyle, testthat License: GPL-2 MD5sum: 42f803771d94f17c531ec673be6d9472 NeedsCompilation: no Title: An R interface to the Ontology Lookup Service Description: This package allows to query EBI's Ontology Lookup Service (OLS) using Simple Object Access Protocol (SOAP). biocViews: Software, Annotation, MassSpectrometry, GO Author: Laurent Gatto Maintainer: Laurent Gatto URL: http://lgatto.github.com/rols/ VignetteBuilder: knitr source.ver: src/contrib/rols_1.12.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/rols_1.12.2.zip win64.binary.ver: bin/windows64/contrib/3.2/rols_1.12.2.zip mac.binary.ver: bin/macosx/contrib/3.2/rols_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rols_1.12.2.tgz vignettes: vignettes/rols/inst/doc/rols.pdf vignetteTitles: The rols interface to the Ontology Lookup Service hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: MSnbase Package: ROntoTools Version: 1.10.0 Depends: methods, graph, boot, KEGGREST, KEGGgraph, Rgraphviz Suggests: RUnit, BiocGenerics License: CC BY-NC-ND 4.0 + file LICENSE MD5sum: 935f1bba52b28569af798aeb50425a62 NeedsCompilation: no Title: R Onto-Tools suite Description: Suite of tools for functional analysis biocViews: NetworkAnalysis, Microarray, GraphsAndNetworks Author: Calin Voichita and Sorin Draghici Maintainer: Calin Voichita source.ver: src/contrib/ROntoTools_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ROntoTools_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ROntoTools_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ROntoTools_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ROntoTools_1.10.0.tgz vignettes: vignettes/ROntoTools/inst/doc/rontotools.pdf vignetteTitles: ROntoTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: ropls Version: 1.2.14 Suggests: RUnit, BiocGenerics, BiocStyle License: CeCILL MD5sum: 0d0cfa9b7458da283ce343eff9211a81 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_1.2.14.tar.gz win.binary.ver: bin/windows/contrib/3.2/ropls_1.2.14.zip win64.binary.ver: bin/windows64/contrib/3.2/ropls_1.2.14.zip mac.binary.ver: bin/macosx/contrib/3.2/ropls_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ropls_1.2.14.tgz vignettes: vignettes/ropls/inst/doc/ropls.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RPA Version: 1.26.0 Depends: R (>= 3.1.1), parallel, affy, methods Suggests: affydata License: BSD_2_clause + file LICENSE MD5sum: 81b5b370bccd1c5ee2ad3a0e8e772309 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RPA_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RPA_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RPA_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RPA_1.26.0.tgz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: prebs Package: RpsiXML Version: 2.12.0 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: 2725f67fbaa8be31b7a7d9406ee22897 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_2.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RpsiXML_2.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RpsiXML_2.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RpsiXML_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RpsiXML_2.12.0.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 dependsOnMe: ScISI importsMe: ScISI Package: rpx Version: 1.6.0 Depends: methods Imports: XML, RCurl, utils Suggests: MSnbase, Biostrings, BiocStyle, testthat, knitr License: GPL-2 MD5sum: 114e51874f93bb440b84aaadfd8c7967 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 VignetteBuilder: knitr BugReports: https://github.com/lgatto/rpx/issues source.ver: src/contrib/rpx_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rpx_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rpx_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rpx_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rpx_1.6.0.tgz vignettes: vignettes/rpx/inst/doc/rpx.pdf vignetteTitles: An interface to proteomics data repositories hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: proteoQC Package: Rqc Version: 1.4.2 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: 626a7c93faf47168c82c1d84a018d1a0 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_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rqc_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/Rqc_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/Rqc_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rqc_1.4.2.tgz vignettes: vignettes/Rqc/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/Rqc/inst/doc/Rqc.html htmlTitles: "Rqc - Quality Control Tool for High-Throughput Sequencing Data" Package: rqubic Version: 1.16.0 Imports: methods, Biobase, BiocGenerics, biclust Suggests: RColorBrewer License: GPL-2 Archs: i386, x64 MD5sum: 69a24206ddd03e448feadb83f0dda875 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rqubic_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rqubic_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rqubic_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rqubic_1.16.0.tgz vignettes: vignettes/rqubic/inst/doc/rqubic.pdf vignetteTitles: Qualitative Biclustering with Bioconductor Package rqubic hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: rRDP Version: 1.4.0 Depends: Biostrings (>= 2.26.2) Suggests: rRDPData License: GPL-2 | file LICENSE MD5sum: cb86fd484d4fa7020de100000e1495e1 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rRDP_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rRDP_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rRDP_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rRDP_1.4.0.tgz vignettes: vignettes/rRDP/inst/doc/rRDP.pdf vignetteTitles: rRDP: Interface to the RDP Classifier hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: RRHO Version: 1.10.0 Depends: R (>= 2.10), grid Imports: VennDiagram Suggests: lattice License: GPL-2 MD5sum: 9ca55d9c7eeaaa1e43582c760dd462f5 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RRHO_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RRHO_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RRHO_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RRHO_1.10.0.tgz vignettes: vignettes/RRHO/inst/doc/RRHO.pdf vignetteTitles: RRHO hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: Rsamtools Version: 1.22.0 Depends: methods, S4Vectors (>= 0.7.11), IRanges (>= 2.3.7), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.21.6), XVector (>= 0.9.1), Biostrings (>= 2.37.1) Imports: utils, BiocGenerics (>= 0.1.3), 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: bbac8423ae011a8ec937d06adfba64c5 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rsamtools_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rsamtools_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rsamtools_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rsamtools_1.22.0.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 dependsOnMe: ArrayExpressHTS, BitSeq, chimera, CODEX, CoverageView, exomeCopy, exomePeak, GenomicAlignments, GenomicFiles, girafe, Guitar, MEDIPS, methylPipe, oneChannelGUI, podkat, qrqc, r3Cseq, Rcade, ReQON, rfPred, RIPSeeker, rnaSeqMap, ShortRead, SICtools, SNPhood, systemPipeR, TarSeqQC, TEQC, TitanCNA, VariantAnnotation, wavClusteR importsMe: AllelicImbalance, annmap, AnnotationHubData, ArrayExpressHTS, BBCAnalyzer, biovizBase, BSgenome, CAGEr, casper, CexoR, ChIPQC, cn.mops, CNVPanelizer, CNVrd2, compEpiTools, CopywriteR, csaw, customProDB, derfinder, DEXSeq, diffHic, DOQTL, easyRNASeq, EDASeq, ensembldb, epigenomix, FourCSeq, FunciSNP, genomation, GenomicAlignments, GenomicInteractions, ggbio, GGtools, gmapR, GoogleGenomics, GreyListChIP, Gviz, gwascat, h5vc, HTSeqGenie, INSPEcT, metagene, nucleR, PGA, PICS, QDNAseq, QuasR, R453Plus1Toolbox, Rariant, Repitools, RiboProfiling, RNAprobR, Rqc, rtracklayer, SGSeq, similaRpeak, soGGi, SplicingGraphs, tracktables, trackViewer, TransView, VariantFiltering, VariantTools suggestsMe: AnnotationHub, bamsignals, BaseSpaceR, BiocParallel, biomvRCNS, DiffBind, gage, GenomeInfoDb, GenomicFeatures, GenomicRanges, gQTLstats, metaseqR, seqbias, SigFuge, Streamer Package: rsbml Version: 2.28.0 Depends: R (>= 2.6.0), BiocGenerics (>= 0.3.2), methods, utils Imports: BiocGenerics, graph, utils License: Artistic-2.0 Archs: i386, x64 MD5sum: 3d6fb149afc6000bf8b3619e178d8225 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_2.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rsbml_2.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rsbml_2.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rsbml_2.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rsbml_2.28.0.tgz vignettes: vignettes/rsbml/inst/doc/quick-start.pdf vignetteTitles: Quick start for rsbml hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: BiGGR suggestsMe: piano, SBMLR Package: rSFFreader Version: 0.18.0 Depends: ShortRead (>= 1.23.17) Imports: methods, Biostrings, IRanges LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: xtable License: Artistic-2.0 MD5sum: 33b798ad01eff8329b2b537d022bc167 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_0.18.0.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/rSFFreader_0.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rSFFreader_0.18.0.tgz vignettes: vignettes/rSFFreader/inst/doc/rSFFreader.pdf vignetteTitles: An introduction to rSFFreader hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: hiReadsProcessor Package: Rsubread Version: 1.20.6 License: GPL-3 MD5sum: 1f7bb1f1cf479e00c512f7e28eecab65 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_1.20.6.tar.gz mac.binary.ver: bin/macosx/contrib/3.2/Rsubread_1.20.2.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rsubread_1.20.6.tgz vignettes: vignettes/Rsubread/inst/doc/Rsubread.pdf vignetteTitles: Rsubread Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: dupRadar Package: RSVSim Version: 1.10.0 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: 29b1a2157fb118d244541e99872eda87 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RSVSim_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RSVSim_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RSVSim_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RSVSim_1.10.0.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 Package: rTANDEM Version: 1.10.0 Depends: XML, Rcpp, data.table (>= 1.8.8) Imports: methods LinkingTo: Rcpp Suggests: biomaRt License: Artistic-1.0 | file LICENSE Archs: i386, x64 MD5sum: 682384a7f67f941171ae9ed532bddbe6 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rTANDEM_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rTANDEM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rTANDEM_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rTANDEM_1.10.0.tgz vignettes: vignettes/rTANDEM/inst/doc/rTANDEM.pdf vignetteTitles: The rTANDEM users guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: PGA, shinyTANDEM importsMe: proteoQC Package: RTCA Version: 1.22.0 Depends: methods,stats,graphics,Biobase,RColorBrewer, gtools Suggests: xtable License: LGPL-3 MD5sum: 78da346f31a34e6c8c8718f6629b3cee 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_1.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTCA_1.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RTCA_1.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RTCA_1.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTCA_1.22.0.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 Package: RTCGA Version: 1.0.2 Depends: R (>= 3.2.0), knitr Imports: XML, assertthat, stringi, rvest, data.table, magrittr, xml2 Suggests: testthat, pander, RTCGA.rnaseq, RTCGA.clinical, RTCGA.mutations License: GPL-2 MD5sum: 3091d231d147017572fc3e27dd53e7cb 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 Author: Marcin Kosinski , Przemyslaw Biecek Maintainer: Marcin Kosinski VignetteBuilder: knitr BugReports: https://github.com/RTCGA/RTCGA/issues source.ver: src/contrib/RTCGA_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTCGA_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/RTCGA_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/RTCGA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTCGA_1.0.2.tgz vignettes: vignettes/RTCGA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/RTCGA/inst/doc/RTCGA_Family.html, vignettes/RTCGA/inst/doc/RTCGA_Tutotial.html htmlTitles: "RTCGA.data - The Family of R Packages with Data from The Cancer Genome Atlas Study", "RTCGA package tutorial" Package: RTCGAToolbox Version: 2.0.0 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: 40db09ea8cd8080c9bdd574a50993599 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_2.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTCGAToolbox_2.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RTCGAToolbox_2.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RTCGAToolbox_2.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTCGAToolbox_2.0.0.tgz vignettes: vignettes/RTCGAToolbox/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/RTCGAToolbox/inst/doc/RTCGAToolbox-vignette.html htmlTitles: "RTCGAToolbox" Package: RTN Version: 1.8.4 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: bd94add62802891a65a697d0aa1280a9 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_1.8.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTN_1.8.4.zip win64.binary.ver: bin/windows64/contrib/3.2/RTN_1.8.4.zip mac.binary.ver: bin/macosx/contrib/3.2/RTN_1.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTN_1.8.4.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 Package: RTopper Version: 1.16.0 Depends: R (>= 2.11.0), Biobase Imports: limma, multtest Suggests: limma, org.Hs.eg.db, KEGG.db, GO.db License: GPL (>= 3) MD5sum: d2d1777d146164d3af60ac8b1964fef2 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RTopper_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RTopper_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RTopper_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RTopper_1.16.0.tgz vignettes: vignettes/RTopper/inst/doc/RTopper.pdf vignetteTitles: RTopper user's manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: rtracklayer Version: 1.30.4 Depends: R (>= 2.10), methods, GenomicRanges (>= 1.21.20) Imports: XML (>= 1.98-0), BiocGenerics (>= 0.13.8), S4Vectors (>= 0.7.11), 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: 657f9e579ac955db8d132d463079b5b1 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_1.30.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/rtracklayer_1.30.4.zip win64.binary.ver: bin/windows64/contrib/3.2/rtracklayer_1.30.4.zip mac.binary.ver: bin/macosx/contrib/3.2/rtracklayer_1.30.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rtracklayer_1.30.4.tgz vignettes: vignettes/rtracklayer/inst/doc/rtracklayer.pdf vignetteTitles: rtracklayer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE dependsOnMe: BSgenome, CoverageView, cummeRbund, epivizr, exomePeak, GenomicFiles, groHMM, Guitar, MethylSeekR, r3Cseq, regioneR, RIPSeeker, spliceR importsMe: AnnotationHubData, ballgown, BiSeq, BSgenome, CAGEr, casper, CexoR, ChIPseeker, ChromHeatMap, CNEr, coMET, CompGO, conumee, customProDB, derfinder, ensembldb, erma, FourCSeq, FunciSNP, GenomicFeatures, genotypeeval, ggbio, GGtools, gmapR, GOTHiC, gQTLBase, GreyListChIP, Gviz, gwascat, hiAnnotator, HiTC, HTSeqGenie, MEDIPS, metagene, methyAnalysis, motifbreakR, MotifDb, Pbase, PGA, proBAMr, QuasR, regioneR, Repitools, RiboProfiling, RNAprobR, roar, seqplots, SGSeq, similaRpeak, soGGi, TFBSTools, trackViewer, VariantAnnotation, VariantTools, wavClusteR suggestsMe: AnnotationHub, biovizBase, ChIPpeakAnno, compEpiTools, GenomicAlignments, GenomicRanges, goseq, InPAS, interactiveDisplay, metaseqR, methylumi, MotIV, NarrowPeaks, oneChannelGUI, OrganismDbi, PICS, PING, R453Plus1Toolbox, Ringo, rMAT, RnBeads, RSVSim, triplex, TSSi Package: Rtreemix Version: 1.32.0 Depends: R (>= 2.5.0) Imports: methods, graph, Biobase, Hmisc Suggests: Rgraphviz License: LGPL Archs: i386, x64 MD5sum: 966798acd57db3faf1321d6c52fead8f 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_1.32.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Rtreemix_1.32.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Rtreemix_1.32.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Rtreemix_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Rtreemix_1.32.0.tgz vignettes: vignettes/Rtreemix/inst/doc/Rtreemix.pdf vignetteTitles: Rtreemix hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: rTRM Version: 1.8.1 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: 753e01a2662dce6ebf8dd7d5bfc7f440 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/rTRM_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/rTRM_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/rTRM_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rTRM_1.8.1.tgz vignettes: vignettes/rTRM/inst/doc/rTRM_Introduction.pdf vignetteTitles: Introduction to rTRM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: rTRMui Package: rTRMui Version: 1.8.0 Imports: shiny (>= 0.9), rTRM, MotifDb, org.Hs.eg.db, org.Mm.eg.db License: GPL-3 MD5sum: 8a6ff7cf14f6e9d530fbda7d49f52ffc 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/rTRMui_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/rTRMui_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/rTRMui_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/rTRMui_1.8.0.tgz vignettes: vignettes/rTRMui/inst/doc/rTRMui.pdf vignetteTitles: Introduction to rTRMui hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RUVcorr Version: 1.2.0 Imports: corrplot, MASS, stats, lattice, grDevices, gridExtra, snowfall, psych, BiocParallel, grid, bladderbatch, reshape2 Suggests: knitr, BiocStyle, hgu133a2.db License: GPL-2 MD5sum: dc78131acf34f9ee29eed58643ba27dc 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RUVcorr_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RUVcorr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RUVcorr_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RUVcorr_1.2.0.tgz vignettes: vignettes/RUVcorr/inst/doc/RUVcorrVignetteNew.pdf vignetteTitles: RUVcorr hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RUVnormalize Version: 1.4.1 Depends: R (>= 2.10.0) Imports: RUVnormalizeData, Biobase Enhances: spams License: GPL-3 MD5sum: fe0fe66814ee9cc5ad05ed74ee4b79ef 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/RUVnormalize_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/RUVnormalize_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/RUVnormalize_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RUVnormalize_1.4.1.tgz vignettes: vignettes/RUVnormalize/inst/doc/RUVnormalize.pdf vignetteTitles: RUVnormalize hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RUVSeq Version: 1.4.0 Depends: Biobase, EDASeq (>= 1.99.1), edgeR Imports: methods, MASS Suggests: BiocStyle, knitr, RColorBrewer, zebrafishRNASeq, DESeq License: Artistic-2.0 MD5sum: 08c9d524b86bbfb79de7ea4b727d6cc7 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/RUVSeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/RUVSeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/RUVSeq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/RUVSeq_1.4.0.tgz vignettes: vignettes/RUVSeq/inst/doc/RUVSeq.pdf vignetteTitles: RUVSeq: Remove Unwanted Variation from RNA-Seq Data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: RWebServices Version: 1.34.0 Depends: SJava (>= 0.85), TypeInfo, methods, tools (>= 2.10.0), R (>= 2.5.0) Imports: RCurl License: file LICENSE License_restricts_use: no MD5sum: 261f104a147cf2fd7dc6c2ba7d61ca4c 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 source.ver: src/contrib/RWebServices_1.34.0.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 Package: S4Vectors Version: 0.8.11 Depends: R (>= 3.1.0), methods, utils, stats, stats4, BiocGenerics (>= 0.15.10) Imports: methods, utils, stats, stats4, BiocGenerics Suggests: IRanges, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 02b20a992bf46bb2cbdef8c90d76b421 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. Pages, M. Lawrence and P. Aboyoun Maintainer: Bioconductor Package Maintainer source.ver: src/contrib/S4Vectors_0.8.11.tar.gz win.binary.ver: bin/windows/contrib/3.2/S4Vectors_0.8.11.zip win64.binary.ver: bin/windows64/contrib/3.2/S4Vectors_0.8.11.zip mac.binary.ver: bin/macosx/contrib/3.2/S4Vectors_0.8.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/S4Vectors_0.8.11.tgz vignettes: vignettes/S4Vectors/inst/doc/RleTricks.pdf vignetteTitles: Rle Tips and Tricks hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: AnnotationHubData, Biostrings, BiSeq, BSgenome, bumphunter, CexoR, ChIPpeakAnno, chipseq, ChIPseqR, CSAR, DESeq2, DirichletMultinomial, DMRcaller, epigenomix, GenomeInfoDb, GenomicAlignments, GenomicFeatures, GenomicRanges, girafe, groHMM, GUIDEseq, Gviz, InPAS, IRanges, meshr, MotifDb, OTUbase, plethy, Rsamtools, segmentSeq, triplex, VariantTools, XVector importsMe: ALDEx2, AllelicImbalance, AnnotationDbi, AnnotationForge, AnnotationHub, ballgown, biovizBase, BiSeq, BitSeq, BSgenome, bsseq, casper, ChIPQC, ChIPseeker, cleaver, CNEr, CNPBayes, coMET, compEpiTools, copynumber, CopywriteR, CoverageView, CRISPRseek, csaw, DChIPRep, DECIPHER, derfinder, derfinderHelper, diffHic, easyRNASeq, EnrichmentBrowser, ensembldb, epivizr, erma, FindMyFriends, GenomeInfoDb, genomeIntervals, GenomicAlignments, GenomicFiles, GenomicInteractions, GenomicTuples, genoset, GGBase, ggbio, GGtools, gmapR, GoogleGenomics, GOTHiC, gQTLBase, gQTLstats, gwascat, h5vc, HTSeqGenie, INSPEcT, IVAS, kebabs, MEAL, methylPipe, methylumi, minfi, MinimumDistance, motifbreakR, MotIV, msa, MSnbase, mygene, myvariant, NarrowPeaks, nucleR, oligoClasses, OrganismDbi, Pbase, pdInfoBuilder, PICS, PING, polyester, prebs, qcmetrics, qpgraph, QuasR, R453Plus1Toolbox, RareVariantVis, Rariant, Rcade, Repitools, RiboProfiling, roar, Rqc, rtracklayer, SeqArray, seqplots, SeqVarTools, SGSeq, ShortRead, simulatorZ, soGGi, SomaticSignatures, SplicingGraphs, SummarizedExperiment, TarSeqQC, TCGAbiolinks, TFBSTools, TSSi, VanillaICE, VariantAnnotation, VariantFiltering, XVector suggestsMe: BiocGenerics, ensemblVEP Package: safe Version: 3.10.0 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: 4111b0ef349ea0873781aeed7c11b2df 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_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/safe_3.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/safe_3.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/safe_3.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/safe_3.10.0.tgz vignettes: vignettes/safe/inst/doc/SAFEmanual3.pdf vignetteTitles: SAFE manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: EnrichmentBrowser Package: sagenhaft Version: 1.40.0 Depends: R (>= 2.10), SparseM (>= 0.73), methods Imports: graphics, methods, SparseM, stats, utils License: GPL (>= 2) MD5sum: ecb7681d7137a1083f82b8f6cd23bfc5 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sagenhaft_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sagenhaft_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sagenhaft_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sagenhaft_1.40.0.tgz vignettes: vignettes/sagenhaft/inst/doc/SAGEnhaft.pdf vignetteTitles: SAGEnhaft hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SAGx Version: 1.44.0 Depends: R (>= 2.5.0), stats, multtest, methods Imports: Biobase, stats4 Suggests: KEGG.db, hu6800.db, MASS License: GPL-3 Archs: i386, x64 MD5sum: 64d31ab17584196959ecb35d58deac21 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SAGx_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SAGx_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SAGx_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SAGx_1.44.0.tgz vignettes: vignettes/SAGx/inst/doc/samroc-ex.pdf vignetteTitles: samroc - example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SamSPECTRAL Version: 1.24.0 Depends: R (>= 2.10) Imports: methods License: GPL (>= 2) Archs: i386, x64 MD5sum: 9e2c01758f2bd582c5c148189aa46627 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SamSPECTRAL_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SamSPECTRAL_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SamSPECTRAL_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SamSPECTRAL_1.24.0.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 Package: sangerseqR Version: 1.6.0 Depends: R (>= 3.0.2), Biostrings Imports: methods, shiny Suggests: BiocStyle, knitr, RUnit, BiocGenerics License: GPL-2 MD5sum: 86fb0d23b6af5644a2d7b058a3e37f6b 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sangerseqR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sangerseqR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sangerseqR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sangerseqR_1.6.0.tgz vignettes: vignettes/sangerseqR/inst/doc/sangerseq_walkthrough.pdf vignetteTitles: sangerseqR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SANTA Version: 2.8.0 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: f691f6f11a2ce57afcc4c3fec02b79ee 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_2.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SANTA_2.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SANTA_2.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SANTA_2.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SANTA_2.8.0.tgz vignettes: vignettes/SANTA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SANTA/inst/doc/SANTA-vignette.html htmlTitles: "Introduction to SANTA" Package: sapFinder Version: 1.8.0 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: ce873be945e66a5e8d19a743d16cde77 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sapFinder_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sapFinder_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sapFinder_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sapFinder_1.8.0.tgz vignettes: vignettes/sapFinder/inst/doc/sapFinder.pdf vignetteTitles: sapFinder Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: saps Version: 2.2.0 Depends: R (>= 2.14.0), survival Imports: piano, survcomp, reshape2 Suggests: snowfall, knitr License: MIT + file LICENSE MD5sum: fa31debe792e7e302217359b1bf028e6 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_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/saps_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/saps_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/saps_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/saps_2.2.0.tgz vignettes: vignettes/saps/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/saps/inst/doc/saps.html htmlTitles: "SAPS Vignette" Package: savR Version: 1.8.0 Depends: ggplot2 Imports: methods, reshape2, scales, gridExtra, XML Suggests: Cairo, testthat License: AGPL-3 MD5sum: a58713c08bf92db1fb760b81c4b8db9c 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/savR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/savR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/savR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/savR_1.8.0.tgz vignettes: vignettes/savR/inst/doc/savR.pdf vignetteTitles: Using savR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: sbgr Version: 1.0.0 Depends: methods, utils, stats Imports: httr, jsonlite, objectProperties, Suggests: BiocStyle, knitr, rmarkdown, testthat License: MIT + file LICENSE MD5sum: 29e8a0bcdc2accdcb3169be9bb7f31b3 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sbgr_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sbgr_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sbgr_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sbgr_1.0.0.tgz vignettes: vignettes/sbgr/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/sbgr/inst/doc/easy_api.html, vignettes/sbgr/inst/doc/sbgr.html htmlTitles: "Easy API: A user-friendly cascading API", "Running the FASTQC Pipeline with sbgr" Package: SBMLR Version: 1.66.0 Depends: XML, deSolve Suggests: rsbml License: GPL-2 MD5sum: 5222a196518ee52b1bd0cd9e61159e99 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_1.66.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SBMLR_1.66.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SBMLR_1.66.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SBMLR_1.66.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SBMLR_1.66.0.tgz vignettes: vignettes/SBMLR/inst/doc/quick-start.pdf vignetteTitles: Quick intro to SBMLR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SCAN.UPC Version: 2.12.1 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: bc90a2ca8e5430e4e03c5ba1acc49112 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_2.12.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/SCAN.UPC_2.12.1.zip win64.binary.ver: bin/windows64/contrib/3.2/SCAN.UPC_2.12.1.zip mac.binary.ver: bin/macosx/contrib/3.2/SCAN.UPC_2.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SCAN.UPC_2.12.1.tgz vignettes: vignettes/SCAN.UPC/inst/doc/SCAN.vignette.pdf vignetteTitles: Primer hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ScISI Version: 1.42.0 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: 55e6a34c665a9ddfffa71121e0430ebc 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ScISI_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ScISI_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ScISI_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ScISI_1.42.0.tgz vignettes: vignettes/ScISI/inst/doc/vignette.pdf vignetteTitles: ScISI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PCpheno, ppiStats, SLGI importsMe: PCpheno, SLGI suggestsMe: RpsiXML Package: scsR Version: 1.6.0 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: 21c19745539009fd1fc526a87945a413 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/scsR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/scsR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/scsR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/scsR_1.6.0.tgz vignettes: vignettes/scsR/inst/doc/scsR.pdf vignetteTitles: scsR Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: segmentSeq Version: 2.4.0 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: eb6647a111664f60c3d17ec339dd7f64 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_2.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/segmentSeq_2.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/segmentSeq_2.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/segmentSeq_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/segmentSeq_2.4.0.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 Package: SELEX Version: 1.2.0 Depends: R (>= 2.7.0), rJava (>= 0.5-0), Biostrings (>= 2.26.0) License: GPL (>=2) MD5sum: be71ca876118f77505ed8aea9168c2d8 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SELEX_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SELEX_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SELEX_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SELEX_1.2.0.tgz vignettes: vignettes/SELEX/inst/doc/SELEX.pdf vignetteTitles: Motif Discovery with SELEX-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SemDist Version: 1.4.0 Depends: R (>= 3.1), AnnotationDbi, GO.db, annotate Suggests: GOSemSim License: GPL (>= 2) MD5sum: 985525aefae1ea57e775e50f8e35bba1 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SemDist_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SemDist_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SemDist_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SemDist_1.4.0.tgz vignettes: vignettes/SemDist/inst/doc/introduction.pdf vignetteTitles: introduction.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SEPA Version: 1.0.0 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: f4848e06d7453ad2291b86effa7c2328 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SEPA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SEPA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SEPA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SEPA_1.0.0.tgz vignettes: vignettes/SEPA/inst/doc/SEPA.pdf vignetteTitles: SEPA: Single-Cell Gene Expression Pattern Analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: seq2pathway Version: 1.2.0 Depends: R (>= 2.10.0) Imports: nnet, WGCNA, GSA, biomaRt, GenomicRanges, seq2pathway.data License: GPL-2 MD5sum: e618327d44a5845942bc694eb37a5229 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seq2pathway_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seq2pathway_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seq2pathway_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seq2pathway_1.2.0.tgz vignettes: vignettes/seq2pathway/inst/doc/seq2pathwaypackage.pdf vignetteTitles: An R package for sequence hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SeqArray Version: 1.10.6 Depends: gdsfmt (>= 1.6.2) Imports: methods, Biostrings, GenomicRanges, IRanges, S4Vectors, VariantAnnotation, SummarizedExperiment LinkingTo: gdsfmt Suggests: parallel, RUnit, BiocGenerics, knitr, Rcpp, SNPRelate License: GPL-3 Archs: i386, x64 MD5sum: a16d4bec33d9fb5c9b795dbc260a9fcf NeedsCompilation: yes Title: Big Data Management of Genome-Wide Sequence Variants Description: Big data management of genome-wide sequence variants with thousands of individuals: genotypic data (e.g., SNPs, indels and structural variation calls) and annotations in GDS files are stored in an array-oriented and compressed manner, with efficient data access using the R programming language. biocViews: Infrastructure, Sequencing, Genetics Author: Xiuwen Zheng [aut, cre], Stephanie Gogarten [aut], 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_1.10.6.tar.gz win.binary.ver: bin/windows/contrib/3.2/SeqArray_1.10.6.zip win64.binary.ver: bin/windows64/contrib/3.2/SeqArray_1.10.6.zip mac.binary.ver: bin/macosx/contrib/3.2/SeqArray_1.10.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SeqArray_1.10.6.tgz vignettes: vignettes/SeqArray/inst/doc/SeqArray-JSM2013.pdf vignetteTitles: SeqArray-JSM2013.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SeqArray/inst/doc/AnalysisTutorial.html, vignettes/SeqArray/inst/doc/SeqArrayTutorial.html htmlTitles: "A Brief Introduction to Data Analytics on SeqArray GDS Files", "SeqArray – Big Data Management of Genome-Wide Sequence Variants" dependsOnMe: SeqVarTools Package: seqbias Version: 1.18.0 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: 54fdf0a43b44a4061275fb836483077e 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqbias_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqbias_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqbias_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqbias_1.18.0.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 dependsOnMe: ReQON Package: seqCNA Version: 1.16.0 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: e47cc59ef9fa8dc8ef13e62cbe78deca 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqCNA_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqCNA_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqCNA_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqCNA_1.16.0.tgz vignettes: vignettes/seqCNA/inst/doc/seqCNA.pdf vignetteTitles: seqCNA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SeqGSEA Version: 1.10.0 Depends: Biobase, doParallel, DESeq Imports: methods, biomaRt Suggests: easyRNASeq, GenomicRanges License: GPL (>= 3) MD5sum: fa735615b1f5ebe188c6af70bcdc3cb6 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SeqGSEA_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SeqGSEA_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SeqGSEA_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SeqGSEA_1.10.0.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 Package: seqLogo Version: 1.36.0 Depends: methods, grid Imports: stats4 License: LGPL (>= 2) MD5sum: f841c7c49c10890c4fbc0a749f397f35 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqLogo_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqLogo_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqLogo_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqLogo_1.36.0.tgz vignettes: vignettes/seqLogo/inst/doc/seqLogo.pdf vignetteTitles: Sequence logos for DNA sequence alignments hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: motifRG, rGADEM importsMe: PWMEnrich, rGADEM, TFBSTools suggestsMe: BCRANK, DiffLogo, MotifDb Package: seqPattern Version: 1.2.0 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: 89c32074acf477e397280eac868944db 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqPattern_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/seqPattern_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/seqPattern_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqPattern_1.2.0.tgz vignettes: vignettes/seqPattern/inst/doc/seqPattern.pdf vignetteTitles: seqPattern hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: genomation Package: seqplots Version: 1.8.1 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: 387e077b212ca2345eae5c01ea8ebd2a 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_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqplots_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/seqplots_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/seqplots_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqplots_1.8.1.tgz vignettes: vignettes/seqplots/inst/doc/ 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: "SeqPlots R workflow", "SeqPlots R workflow" Package: seqTools Version: 1.4.1 Depends: methods,utils,zlibbioc LinkingTo: zlibbioc Suggests: RUnit, BiocGenerics License: Artistic-2.0 Archs: i386, x64 MD5sum: 01e122bde0c61ce75e85c5613c3484ac 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_1.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/seqTools_1.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/seqTools_1.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/seqTools_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/seqTools_1.4.1.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 Package: SeqVarTools Version: 1.8.1 Depends: SeqArray (>= 1.1.1) Imports: methods, stringr, gdsfmt, GenomicRanges, IRanges, S4Vectors, GWASExactHW, VariantAnnotation, Biobase Suggests: BiocGenerics, BiocStyle, RUnit License: GPL-3 MD5sum: 560b23b6469995c48e2b19056031db72 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 Maintainer: Stephanie M. Gogarten , Xiuwen Zheng , Adrienne Stilp source.ver: src/contrib/SeqVarTools_1.8.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/SeqVarTools_1.8.1.zip win64.binary.ver: bin/windows64/contrib/3.2/SeqVarTools_1.8.1.zip mac.binary.ver: bin/macosx/contrib/3.2/SeqVarTools_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SeqVarTools_1.8.1.tgz vignettes: vignettes/SeqVarTools/inst/doc/SeqVarTools.pdf vignetteTitles: Introduction to SeqVarTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SGSeq Version: 1.4.3 Depends: methods, IRanges, GenomicRanges Imports: AnnotationDbi, BiocGenerics, Biostrings, GenomicAlignments, GenomicFeatures, GenomeInfoDb, igraph, parallel, Rsamtools, rtracklayer, RUnit, S4Vectors, SummarizedExperiment (>= 0.2.0) Suggests: BiocStyle, BSgenome.Hsapiens.UCSC.hg19, knitr, rmarkdown, TxDb.Hsapiens.UCSC.hg19.knownGene License: Artistic-2.0 MD5sum: b7c192871cdb09b74f2e61b4230febab NeedsCompilation: no Title: Splice event prediction and quantification from RNA-seq data Description: Predict splice junctions and exons from BAM files and obtain compatible read counts and FPKMs. Identify splice events and estimate relative usage of splice variants based on compatible read counts at event boundaries. biocViews: AlternativeSplicing, RNASeq, Transcription Author: Leonard Goldstein Maintainer: Leonard Goldstein VignetteBuilder: knitr source.ver: src/contrib/SGSeq_1.4.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/SGSeq_1.4.3.zip win64.binary.ver: bin/windows64/contrib/3.2/SGSeq_1.4.3.zip mac.binary.ver: bin/macosx/contrib/3.2/SGSeq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SGSeq_1.4.3.tgz vignettes: vignettes/SGSeq/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SGSeq/inst/doc/SGSeq.html htmlTitles: "Splice event prediction and quantification from RNA-seq data" Package: shinyMethyl Version: 1.4.0 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: 644aadb8415922d35819a6dfd1af7b1d 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/shinyMethyl_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/shinyMethyl_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/shinyMethyl_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/shinyMethyl_1.4.0.tgz vignettes: vignettes/shinyMethyl/inst/doc/shinyMethyl.pdf vignetteTitles: shinyMethyl: interactive visualization of Illumina 450K methylation arrays hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: shinyTANDEM Version: 1.8.0 Depends: rTANDEM (>= 1.3.5), shiny, mixtools, methods, xtable License: GPL-3 MD5sum: ba055ccd1f2c839a8526831b8824fdd5 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/shinyTANDEM_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/shinyTANDEM_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/shinyTANDEM_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/shinyTANDEM_1.8.0.tgz vignettes: vignettes/shinyTANDEM/inst/doc/shinyTANDEM.pdf vignetteTitles: shinyTANDEM user guide hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ShortRead Version: 1.28.0 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: 93fbee93e271dc82ea663729c1817b5f 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ShortRead_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ShortRead_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ShortRead_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ShortRead_1.28.0.tgz vignettes: vignettes/ShortRead/inst/doc/Overview.pdf vignetteTitles: An introduction to ShortRead hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: chipseq, EDASeq, girafe, HTSeqGenie, nucleR, OTUbase, Rolexa, Rqc, rSFFreader, segmentSeq, systemPipeR importsMe: ArrayExpressHTS, BEAT, chipseq, ChIPseqR, ChIPsim, easyRNASeq, GOTHiC, IONiseR, metagenomeFeatures, QuasR, R453Plus1Toolbox, Rolexa, RSVSim suggestsMe: BiocParallel, CSAR, DBChIP, GenomicAlignments, Genominator, PICS, PING, Repitools, Rsamtools Package: SICtools Version: 1.0.1 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) Imports: foreach, IRanges, GenomicRanges Suggests: knitr, RUnit, BiocGenerics License: GPL (>=2) MD5sum: 5831d65b5d0d74e26c34f867c06704c3 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_1.0.1.tar.gz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SICtools_1.0.1.tgz vignettes: vignettes/SICtools/inst/doc/SICtools.pdf vignetteTitles: SICtools.pdf hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sigaR Version: 1.14.0 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: 538b3e18c1cc7e005b8de62cf9b0baec 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sigaR_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sigaR_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sigaR_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sigaR_1.14.0.tgz vignettes: vignettes/sigaR/inst/doc/sigaR.pdf vignetteTitles: sigaR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: HCsnip Package: SigCheck Version: 2.2.1 Depends: R (>= 3.2.1), MLInterfaces, Biobase, e1071, BiocParallel, survival Imports: graphics, stats, utils, methods Suggests: BiocStyle, breastCancerNKI, qusage License: Artistic-2.0 MD5sum: b7013d3a01de04b106744c117a9ed185 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_2.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/SigCheck_2.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/SigCheck_2.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/SigCheck_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SigCheck_2.2.1.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 Package: SigFuge Version: 1.8.0 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: c5ff9736bbcebdd6bf04195cf8ab62df 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SigFuge_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SigFuge_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SigFuge_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SigFuge_1.8.0.tgz vignettes: vignettes/SigFuge/inst/doc/SigFuge.pdf vignetteTitles: SigFuge Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: siggenes Version: 1.44.0 Depends: methods, Biobase, multtest, splines, graphics Imports: stats4 Suggests: affy, annotate, genefilter, KernSmooth, scrime (>= 1.2.5) License: LGPL (>= 2) MD5sum: 6a43af5926936c5c7fbef88f280b4968 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/siggenes_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/siggenes_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/siggenes_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/siggenes_1.44.0.tgz vignettes: vignettes/siggenes/inst/doc/siggenes.pdf vignetteTitles: siggenes Manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: KCsmart, oneChannelGUI importsMe: charm, GeneSelector, minfi suggestsMe: GeneSelector, logicFS, trio, XDE Package: sigPathway Version: 1.38.0 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: b3bfafdcc4f8bb1a6bae1e368b2b96d6 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sigPathway_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sigPathway_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sigPathway_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sigPathway_1.38.0.tgz vignettes: vignettes/sigPathway/inst/doc/sigPathway-vignette.pdf vignetteTitles: sigPathway hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tRanslatome Package: sigsquared Version: 1.2.0 Depends: R (>= 3.2.0), methods Imports: Biobase, survival Suggests: RUnit, BiocGenerics License: GPL version 3 MD5sum: 18c5b5827417a97317fb6cfb9eb49466 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sigsquared_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sigsquared_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sigsquared_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sigsquared_1.2.0.tgz vignettes: vignettes/sigsquared/inst/doc/sigsquared.pdf vignetteTitles: SigSquared hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SIM Version: 1.40.0 Depends: R (>= 2.4), quantreg Imports: graphics, stats, globaltest, quantsmooth Suggests: biomaRt, RColorBrewer License: GPL (>= 2) Archs: i386, x64 MD5sum: f1c1c8d40d16250679a5b7ba67882114 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SIM_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SIM_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SIM_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SIM_1.40.0.tgz vignettes: vignettes/SIM/inst/doc/SIM.pdf vignetteTitles: SIM vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SIMAT Version: 1.2.0 Depends: R (>= 3.0.0), Rcpp (>= 0.11.3) Imports: mzR, ggplot2, grid, reshape2 Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: 16ffd44dec5b672ee4e315cae3f04acb 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SIMAT_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SIMAT_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SIMAT_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SIMAT_1.2.0.tgz vignettes: vignettes/SIMAT/inst/doc/SIMAT-vignette.pdf vignetteTitles: UNDO Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SimBindProfiles Version: 1.8.0 Depends: R (>= 2.10), methods, Ringo Imports: limma, mclust, Biobase License: GPL-3 MD5sum: 7f81e0e12ac47ee102c454b7445231fb 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SimBindProfiles_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SimBindProfiles_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SimBindProfiles_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SimBindProfiles_1.8.0.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 Package: similaRpeak Version: 1.2.0 Depends: R6 (>= 2.0) Imports: rtracklayer, GenomicAlignments, Rsamtools Suggests: RUnit, BiocGenerics, knitr License: Artistic-2.0 | file LICENSE MD5sum: b5b15e243dd7809446f63c35f303fb5c NeedsCompilation: no Title: similaRpeak: 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/similaRpeak_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/similaRpeak_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/similaRpeak_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/similaRpeak_1.2.0.tgz vignettes: vignettes/similaRpeak/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE htmlDocs: vignettes/similaRpeak/inst/doc/similaRpeak.html htmlTitles: "similaRpeak: similarity between two ChIP-Seq profiles" Package: simpleaffy Version: 2.46.0 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: eec0c7f6fe2f96bbc9470f8534842e80 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_2.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/simpleaffy_2.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/simpleaffy_2.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/simpleaffy_2.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/simpleaffy_2.46.0.tgz vignettes: vignettes/simpleaffy/inst/doc/simpleAffy.pdf vignetteTitles: simpleaffy primer hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: yaqcaffy importsMe: affyQCReport, arrayMvout suggestsMe: AffyExpress, ArrayTools, ELBOW Package: simulatorZ Version: 1.4.0 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: e80fdda38342944b896315f398d53755 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/simulatorZ_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/simulatorZ_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/simulatorZ_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/simulatorZ_1.4.0.tgz vignettes: vignettes/simulatorZ/inst/doc/simulatorZ-vignette.pdf vignetteTitles: SimulatorZ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sincell Version: 1.2.0 Depends: R (>= 3.0.2), igraph Imports: Rcpp (>= 0.11.2), entropy, scatterplot3d, MASS, TSP, ggplot2, reshape2, fields, proxy, parallel, Rtsne, fastICA, cluster, statmod LinkingTo: Rcpp Suggests: BiocStyle, knitr, biomaRt, stringr, monocle License: GPL (>= 2) Archs: i386, x64 MD5sum: f4b32abbf2e2267f6915586f710c9f3c 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sincell_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sincell_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sincell_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sincell_1.2.0.tgz vignettes: vignettes/sincell/inst/doc/sincell-vignette.pdf vignetteTitles: Sincell: Analysis of cell state hierarchies from single-cell RNA-seq hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SISPA Version: 1.0.0 Depends: R (>= 3.2),GSVA,changepoint,data.table,ggplot2,plyr Suggests: knitr License: GPL-2 MD5sum: b9c3968283dc7d783608d0aa07cc7abf 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 Ph.D. & Jeanne Kowalski Ph.D. Maintainer: Bhakti Dwivedi VignetteBuilder: knitr source.ver: src/contrib/SISPA_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SISPA_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SISPA_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SISPA_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SISPA_1.0.0.tgz vignettes: vignettes/SISPA/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SISPA/inst/doc/SISPA.html htmlTitles: "replace me with the vignette title" Package: sizepower Version: 1.40.0 Depends: stats License: LGPL MD5sum: aa67ea8874dff0cfac5ce2f93fbcaf75 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sizepower_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sizepower_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sizepower_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sizepower_1.40.0.tgz vignettes: vignettes/sizepower/inst/doc/sizepower.pdf vignetteTitles: package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oneChannelGUI Package: SJava Version: 0.96.0 Depends: R (>= 2.10.0), methods License: GPL (>= 2) MD5sum: fcdc75d8ef7d892ee5e071f757e3260c 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 source.ver: src/contrib/SJava_0.96.0.tar.gz hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RWebServices Package: skewr Version: 1.2.0 Depends: R (>= 3.1.1), methylumi, wateRmelon, mixsmsn, IlluminaHumanMethylation450kmanifest Imports: minfi, IRanges, RColorBrewer Suggests: GEOquery, knitr, minfiData License: GPL-2 MD5sum: 49b2b09718202585935084a80d133598 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/skewr_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/skewr_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/skewr_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/skewr_1.2.0.tgz vignettes: vignettes/skewr/inst/doc/skewr.pdf vignetteTitles: An Introduction to the skewr Package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SLGI Version: 1.30.0 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: 72ca8004a811fb7df6d76f8329f1148d 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SLGI_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SLGI_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SLGI_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SLGI_1.30.0.tgz vignettes: vignettes/SLGI/inst/doc/SLGI.pdf vignetteTitles: SLGI Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: PCpheno Package: SLqPCR Version: 1.36.0 Depends: R(>= 2.4.0) Imports: stats Suggests: RColorBrewer License: GPL (>= 2) MD5sum: f8a2565a244cf1d692d1f8688c392527 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SLqPCR_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SLqPCR_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SLqPCR_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SLqPCR_1.36.0.tgz vignettes: vignettes/SLqPCR/inst/doc/SLqPCR.pdf vignetteTitles: SLqPCR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: EasyqpcR Package: SMAP Version: 1.34.0 Depends: R (>= 2.10), methods License: GPL-2 Archs: i386, x64 MD5sum: 57c57d7ccc8b724ac28780f8cb62af1f 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_1.34.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SMAP_1.34.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SMAP_1.34.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SMAP_1.34.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SMAP_1.34.0.tgz vignettes: vignettes/SMAP/inst/doc/SMAP.pdf vignetteTitles: SMAP hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SNAGEE Version: 1.10.0 Depends: R (>= 2.6.0), SNAGEEdata Suggests: ALL, hgu95av2.db Enhances: parallel License: Artistic-2.0 MD5sum: 0233b8716843d0a5a35f213ef23b041d 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SNAGEE_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SNAGEE_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SNAGEE_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SNAGEE_1.10.0.tgz vignettes: vignettes/SNAGEE/inst/doc/SNAGEE.pdf vignetteTitles: SNAGEE Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: snapCGH Version: 1.40.0 Depends: limma, DNAcopy, methods Imports: aCGH, cluster, DNAcopy, GLAD, graphics, grDevices, limma, methods, stats, tilingArray, utils License: GPL Archs: i386, x64 MD5sum: cefef5de1631053e7c43dd97baec6bc8 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_1.40.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/snapCGH_1.40.0.zip win64.binary.ver: bin/windows64/contrib/3.2/snapCGH_1.40.0.zip mac.binary.ver: bin/macosx/contrib/3.2/snapCGH_1.40.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/snapCGH_1.40.0.tgz vignettes: vignettes/snapCGH/inst/doc/snapCGHguide.pdf vignetteTitles: Segmentation Overview hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: ADaCGH2 suggestsMe: beadarraySNP Package: snm Version: 1.18.0 Depends: R (>= 2.12.0) Imports: corpcor, lme4 (>= 1.0), splines License: LGPL MD5sum: 37235cbbd3059b95f3bedc8aae273337 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/snm_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/snm_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/snm_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/snm_1.18.0.tgz vignettes: vignettes/snm/inst/doc/snm.pdf vignetteTitles: snm Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: edge Package: SNPchip Version: 2.16.0 Depends: R (>= 2.14.0) Imports: methods, graphics, lattice, grid, foreach, utils, Biobase, 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: 77e8e46063084a3302e9e6c9af447562 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SNPchip_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SNPchip_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SNPchip_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SNPchip_2.16.0.tgz vignettes: vignettes/SNPchip/inst/doc/PlottingIdiograms.pdf vignetteTitles: Plotting Idiograms hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: mBPCR importsMe: crlmm, phenoTest suggestsMe: Category, MinimumDistance, oligoClasses, VanillaICE Package: SNPhood Version: 1.0.7 Depends: R (>= 3.2),GenomicRanges, Rsamtools, data.table, checkmate Imports: DESeq2,cluster,ggplot2,lattice,GenomeInfoDb,BiocParallel,VariantAnnotation,BiocGenerics,IRanges,methods, RColorBrewer, Biostrings, grDevices,gridExtra,stats,grid,utils, graphics, reshape2 Suggests: BiocStyle, knitr, rmarkdown, SNPhoodData, corrplot License: LGPL (>= 3) MD5sum: 2a4936f87ca937e4a518786d807d9799 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_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/SNPhood_1.0.7.zip win64.binary.ver: bin/windows64/contrib/3.2/SNPhood_1.0.7.zip mac.binary.ver: bin/macosx/contrib/3.2/SNPhood_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SNPhood_1.0.7.tgz vignettes: vignettes/SNPhood/inst/doc/ 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: 1.4.2 Depends: R (>= 2.14), gdsfmt (>= 1.5.9) LinkingTo: gdsfmt Suggests: parallel, RUnit, knitr, MASS, BiocGenerics Enhances: SeqArray License: GPL-3 Archs: i386, x64 MD5sum: a16b565756e142cb8dabcdd8fda18d9a 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 in this package 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 variation (SNV), 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_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/SNPRelate_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/SNPRelate_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/SNPRelate_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SNPRelate_1.4.2.tgz vignettes: vignettes/SNPRelate/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SNPRelate/inst/doc/SNPRelateTutorial.html htmlTitles: "Tutorials for the R/Bioconductor Package SNPRelate" suggestsMe: GENESIS, GWASTools, HIBAG, SeqArray Package: snpStats Version: 1.20.0 Depends: R(>= 2.10.0), survival, Matrix, methods Imports: graphics, grDevices, stats, utils, BiocGenerics, zlibbioc Suggests: hexbin License: GPL-3 Archs: i386, x64 MD5sum: e68151b12c4b88f6afa4411fd87b8e92 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/snpStats_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/snpStats_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/snpStats_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/snpStats_1.20.0.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 dependsOnMe: GGBase importsMe: FunciSNP, GGtools, gQTLstats, gwascat, ldblock, MEAL suggestsMe: crlmm, GWASTools, VariantAnnotation Package: soGGi Version: 1.2.1 Depends: R (>= 3.2.0), BiocGenerics Imports: methods, reshape2, ggplot2, S4Vectors, IRanges, GenomeInfoDb, GenomicRanges, SummarizedExperiment (>= 0.2.0), Biostrings, Rsamtools, GenomicAlignments, rtracklayer, preprocessCore, chipseq, BiocParallel Suggests: testthat, BiocStyle, knitr License: GPL (>= 3) MD5sum: 99876f27dae5ede2ebbd170c9aebfb8b 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_1.2.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/soGGi_1.2.1.zip win64.binary.ver: bin/windows64/contrib/3.2/soGGi_1.2.1.zip mac.binary.ver: bin/macosx/contrib/3.2/soGGi_1.2.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/soGGi_1.2.1.tgz vignettes: vignettes/soGGi/inst/doc/soggi.pdf vignetteTitles: soggi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SomatiCA Version: 2.1.0 Depends: R (>= 2.14.0), lars, DNAcopy, foreach, methods, rebmix, GenomicRanges, IRanges, doParallel, mvtnorm Imports: foreach, lars, sn, DNAcopy, methods, rebmix, GenomicRanges, IRanges Enhances: sn, SomatiCAData License: GPL (>=2) MD5sum: ff9b5c5887c511615a83afd0077266b7 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 source.ver: src/contrib/SomatiCA_2.1.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SomatiCA_2.1.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SomatiCA_2.1.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SomatiCA_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SomatiCA_2.1.0.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 Package: SomaticSignatures Version: 2.6.1 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.UCSC.hg19, SomaticCancerAlterations, ggdendro, fastICA, sva License: GPL-3 MD5sum: 76ee9e4809ed1bad2c8e591c9ad044bb 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 (EMBL Heidelberg) Maintainer: Julian Gehring URL: http://bioconductor.org/packages/release/bioc/html/SomaticSignatures.html, https://github.com/julian-gehring/SomaticSignatures-release VignetteBuilder: knitr source.ver: src/contrib/SomaticSignatures_2.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/SomaticSignatures_2.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/SomaticSignatures_2.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/SomaticSignatures_2.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SomaticSignatures_2.6.1.tgz vignettes: vignettes/SomaticSignatures/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SomaticSignatures/inst/doc/SomaticSignatures-vignette.html htmlTitles: "SomaticSignatures" importsMe: Rariant Package: SpacePAC Version: 1.8.0 Depends: R(>= 2.15),iPAC Suggests: RUnit, BiocGenerics, rgl License: GPL-2 MD5sum: 40c5810049418057b94e1895359668f9 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SpacePAC_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SpacePAC_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SpacePAC_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SpacePAC_1.8.0.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 dependsOnMe: QuartPAC Package: spade Version: 1.18.2 Depends: R (>= 2.11), igraph, Rclusterpp Imports: Biobase, flowCore Suggests: flowViz License: GPL-2 Archs: i386, x64 MD5sum: fd0e5de2813adba947791fbad77bf443 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_1.18.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/spade_1.18.2.zip win64.binary.ver: bin/windows64/contrib/3.2/spade_1.18.2.zip mac.binary.ver: bin/macosx/contrib/3.2/spade_1.18.0.tgz vignettes: vignettes/spade/inst/doc/SPADE.pdf vignetteTitles: spade package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: specL Version: 1.4.0 Depends: R (>= 3.0.2), methods, DBI, RSQLite, seqinr, protViz (>= 0.2.5), LinkingTo: Rcpp (>= 0.9.9) Suggests: RUnit, BiocGenerics, BiocStyle, BiocParallel, plotrix License: GPL-3 Archs: i386, x64 MD5sum: b444ab1a9ba61c5453855320cbd803aa 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 BugReports: https://github.com/fgcz/specL/issues source.ver: src/contrib/specL_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/specL_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/specL_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/specL_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/specL_1.4.0.tgz vignettes: vignettes/specL/inst/doc/specL.pdf vignetteTitles: Introduction to specL hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SpeCond Version: 1.24.0 Depends: R (>= 2.10.0), mclust (>= 3.3.1), Biobase (>= 1.15.13), fields, hwriter (>= 1.1), RColorBrewer, methods License: LGPL (>=2) MD5sum: bd758e7ff9ad1571e50f2391e5a18b47 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_1.24.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SpeCond_1.24.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SpeCond_1.24.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SpeCond_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SpeCond_1.24.0.tgz vignettes: vignettes/SpeCond/inst/doc/SpeCond.pdf vignetteTitles: SpeCond hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SPEM Version: 1.10.0 Depends: R (>= 2.15.1), Rsolnp, Biobase, methods License: GPL-2 MD5sum: 796e3df761cfa75ecfa8f1ed0acaa174 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SPEM_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SPEM_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SPEM_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SPEM_1.10.0.tgz vignettes: vignettes/SPEM/inst/doc/SPEM-package.pdf vignetteTitles: Vignette for SPEM hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SPIA Version: 2.22.0 Depends: R (>= 2.14.0), graphics, KEGGgraph Imports: graphics Suggests: graph, Rgraphviz, hgu133plus2.db License: file LICENSE License_restricts_use: yes MD5sum: b9fa72d9297037f6e08505a43457135a 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SPIA_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SPIA_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SPIA_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SPIA_2.22.0.tgz vignettes: vignettes/SPIA/inst/doc/SPIA.pdf vignetteTitles: SPIA hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE importsMe: EnrichmentBrowser suggestsMe: graphite, KEGGgraph Package: spikeLI Version: 2.30.0 Imports: graphics, grDevices, stats, utils License: GPL-2 MD5sum: 8fa275e2d69930df5236f0b852181bb1 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_2.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spikeLI_2.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spikeLI_2.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spikeLI_2.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spikeLI_2.30.0.tgz vignettes: vignettes/spikeLI/inst/doc/spikeLI.pdf vignetteTitles: spikeLI hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: spkTools Version: 1.26.0 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: deeeb4ac2bbc958a96f9dacfcd264c3d 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spkTools_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spkTools_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spkTools_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spkTools_1.26.0.tgz vignettes: vignettes/spkTools/inst/doc/spkDoc.pdf vignetteTitles: spkTools: Spike-in Data Analysis and Visualization hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: splicegear Version: 1.42.0 Depends: R (>= 2.6.0), methods, Biobase(>= 2.5.5) Imports: annotate, Biobase, graphics, grDevices, grid, methods, utils, XML License: LGPL MD5sum: 5084c013638b5964c80c86edcaccfeac 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/splicegear_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/splicegear_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/splicegear_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/splicegear_1.42.0.tgz vignettes: vignettes/splicegear/inst/doc/splicegear.pdf vignetteTitles: splicegear Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: spliceR Version: 1.12.0 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: af469436f9a7ff2106ded58a8e220859 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spliceR_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spliceR_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spliceR_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spliceR_1.12.0.tgz vignettes: vignettes/spliceR/inst/doc/spliceR.pdf vignetteTitles: spliceR Vignette hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: spliceSites Version: 1.8.3 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: 7d8b06476346190bf02346d5a51ec995 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_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/spliceSites_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.2/spliceSites_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.2/spliceSites_1.7.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spliceSites_1.8.3.tgz vignettes: vignettes/spliceSites/inst/doc/spliceSites.pdf vignetteTitles: RNA-seq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SplicingGraphs Version: 1.10.0 Depends: GenomicFeatures (>= 1.17.13), GenomicAlignments (>= 1.1.22), Rgraphviz (>= 2.3.7) Imports: methods, utils, igraph, BiocGenerics, S4Vectors, IRanges (>= 2.3.21), GenomeInfoDb, GenomicRanges, GenomicFeatures, Rsamtools, GenomicAlignments, graph, Rgraphviz Suggests: igraph, Gviz, TxDb.Hsapiens.UCSC.hg19.knownGene, RNAseqData.HNRNPC.bam.chr14, RUnit License: Artistic-2.0 MD5sum: 9bd5e7ee18fbd8cc4822fede4227be8b 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SplicingGraphs_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SplicingGraphs_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SplicingGraphs_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SplicingGraphs_1.10.0.tgz vignettes: vignettes/SplicingGraphs/inst/doc/SplicingGraphs.pdf vignetteTitles: Splicing graphs and RNA-seq data hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: splots Version: 1.36.0 Imports: grid, RColorBrewer License: LGPL MD5sum: 52866e7ca71a324d57f6198ddf1b1c7f 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/splots_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/splots_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/splots_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/splots_1.36.0.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 dependsOnMe: cellHTS2 importsMe: RNAinteract, RNAither Package: spotSegmentation Version: 1.44.0 Depends: R (>= 2.10), mclust License: GPL (>= 2) MD5sum: 5f448b49c2541629843b13d16767b3b9 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/spotSegmentation_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/spotSegmentation_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/spotSegmentation_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/spotSegmentation_1.44.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: SQUADD Version: 1.20.0 Depends: R (>= 2.11.0) Imports: graphics, grDevices, methods, RColorBrewer, stats, utils License: GPL (>=2) MD5sum: 8db7992e71e3e78f456d02311852e5ad 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SQUADD_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SQUADD_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SQUADD_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SQUADD_1.20.0.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 Package: SRAdb Version: 1.28.1 Depends: RSQLite, graph, RCurl Imports: GEOquery Suggests: Rgraphviz License: Artistic-2.0 MD5sum: baa5b8d2b2808662c304b412199dc995 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_1.28.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/SRAdb_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SRAdb_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SRAdb_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SRAdb_1.28.1.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 Package: sRAP Version: 1.10.0 Depends: WriteXLS Imports: gplots, pls, ROCR, qvalue License: GPL-3 MD5sum: b019cb642f7d9aa8be48d7aa85ea4d22 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sRAP_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sRAP_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sRAP_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sRAP_1.10.0.tgz vignettes: vignettes/sRAP/inst/doc/sRAP.pdf vignetteTitles: sRAP Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: sscore Version: 1.42.0 Depends: R (>= 1.8.0), affy, affyio Suggests: affydata License: GPL (>= 2) MD5sum: ccdab17c3406714131d2bfed2689f692 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sscore_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sscore_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sscore_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sscore_1.42.0.tgz vignettes: vignettes/sscore/inst/doc/sscore.pdf vignetteTitles: SScore primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: sSeq Version: 1.8.0 Depends: R (>= 3.0), caTools, RColorBrewer License: GPL (>= 3) MD5sum: 2479484e197e92243b15d6c6af1138b2 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sSeq_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sSeq_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sSeq_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sSeq_1.8.0.tgz vignettes: vignettes/sSeq/inst/doc/sSeq.pdf vignetteTitles: sSeq hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: ssize Version: 1.44.0 Depends: gdata, xtable License: LGPL MD5sum: 7b480cf7988ab11216afd5066b2f8ad9 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_1.44.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ssize_1.44.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ssize_1.44.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ssize_1.44.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ssize_1.44.0.tgz vignettes: vignettes/ssize/inst/doc/ssize.pdf vignetteTitles: package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: oneChannelGUI Package: SSPA Version: 2.10.0 Depends: R (>= 2.12), methods, qvalue, lattice, limma Imports: graphics, stats Suggests: BiocStyle, genefilter, edgeR, DESeq License: GPL (>= 2) Archs: i386, x64 MD5sum: 396be7fe0f99035c3d024326afa011f8 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_2.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SSPA_2.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SSPA_2.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SSPA_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SSPA_2.10.0.tgz vignettes: vignettes/SSPA/inst/doc/SSPA.pdf vignetteTitles: SSPA Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ssviz Version: 1.4.0 Depends: R (>= 2.15.1),methods,Rsamtools,Biostrings,reshape,ggplot2,RColorBrewer Suggests: knitr License: GPL-2 MD5sum: c02a117d422473b6b6be1626fa63e311 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ssviz_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ssviz_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ssviz_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ssviz_1.4.0.tgz vignettes: vignettes/ssviz/inst/doc/ssviz.pdf vignetteTitles: ssviz hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: STAN Version: 1.4.0 Depends: Rsolnp, methods Suggests: BiocStyle, Gviz, GenomicRanges, IRanges, gplots, knitr License: GPL (>= 2) Archs: i386, x64 MD5sum: c714f6076ee344ee7a06a4c84f2124ed NeedsCompilation: yes Title: STrand-specific ANnotation of genomic data Description: STAN (STrand-specic ANnotation of genomic data) implements bidirectional Hidden Markov Models (bdHMM), which are designed for studying directed genomic processes, such as gene transcription, DNA replication, recombination or DNA repair by integrating genomic data. bdHMMs model a sequence of successive observations (e.g. ChIP or RNA measurements along the genome) by a discrete number of 'directed genomic states', which e.g. reflect distinct genome-associated complexes. Unlike standard HMM approaches, bdHMMs allow the integration of strand-specific (e.g. RNA) and non strand-specific data (e.g. ChIP). biocViews: HiddenMarkovModel, GenomeAnnotation, Microarray, Sequencing Author: Benedikt Zacher, Julien Gagneur, Achim Tresch Maintainer: Benedikt Zacher VignetteBuilder: knitr source.ver: src/contrib/STAN_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/STAN_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/STAN_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/STAN_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/STAN_1.4.0.tgz vignettes: vignettes/STAN/inst/doc/STAN.pdf vignetteTitles: STrand-specific ANnotation of genomic data hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: staRank Version: 1.12.0 Depends: methods, cellHTS2, R (>= 2.10) License: GPL MD5sum: 6c80dd83295641e6c5e1fe9436eb17b2 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/staRank_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/staRank_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/staRank_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/staRank_1.12.0.tgz vignettes: vignettes/staRank/inst/doc/staRank.pdf vignetteTitles: Using staRank hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Starr Version: 1.26.0 Depends: Ringo, affy, affxparser Imports: pspline, MASS, zlibbioc License: Artistic-2.0 Archs: i386, x64 MD5sum: 452cf0f6544cdd7e990bf6aac06d75bd 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Starr_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Starr_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Starr_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Starr_1.26.0.tgz vignettes: vignettes/Starr/inst/doc/Starr.pdf vignetteTitles: Simple tiling array analysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: nucleR Package: STATegRa Version: 1.4.0 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: 643f04f643535ab6863f9c445917b497 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/STATegRa_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/STATegRa_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/STATegRa_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/STATegRa_1.4.0.tgz vignettes: vignettes/STATegRa/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/STATegRa/inst/doc/STATegRa.html htmlTitles: "STATegRa User’s Guide" Package: stepNorm Version: 1.42.0 Depends: R (>= 1.8.0), marray, methods Imports: marray, MASS, methods, stats License: LGPL MD5sum: f7dc4b8b92f08bce94eeeb7fbbba857d 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/stepNorm_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/stepNorm_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/stepNorm_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/stepNorm_1.42.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: stepwiseCM Version: 1.16.0 Depends: R (>= 2.14), randomForest, MAclinical, tspair, pamr, snowfall, glmpath, penalized, e1071, Biobase License: GPL (>2) MD5sum: fe1ba882ee4c9fd38b7273e6a9a17171 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/stepwiseCM_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/stepwiseCM_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/stepwiseCM_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/stepwiseCM_1.16.0.tgz vignettes: vignettes/stepwiseCM/inst/doc/stepwiseCM.pdf vignetteTitles: stepwiseCM hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Streamer Version: 1.16.0 Imports: methods, graph, RBGL, parallel, BiocGenerics Suggests: RUnit, Rsamtools (>= 1.5.53), GenomicAlignments, Rgraphviz License: Artistic-2.0 Archs: i386, x64 MD5sum: 5dc9685415b17621439e570cdcb81f10 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Streamer_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Streamer_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Streamer_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Streamer_1.16.0.tgz vignettes: vignettes/Streamer/inst/doc/Streamer.pdf vignetteTitles: Streamer: A simple example hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: plethy Package: STRINGdb Version: 1.10.1 Depends: R (>= 2.14.0), png, sqldf, plyr, igraph, RCurl, plotrix, methods, RColorBrewer, gplots, hash Suggests: RUnit, BiocGenerics License: GPL-2 MD5sum: f762b3b1aae3c85995c5ccb3bbb55860 NeedsCompilation: no Title: STRINGdb (Search Tool for the Retrieval of Interacting proteins database) Description: The STRINGdb package provides a user-friendly interface to the STRING protein-protein interactions database ( http://www.string-db.org ). biocViews: Network Author: Andrea Franceschini Maintainer: Andrea Franceschini PackageStatus: Deprecated source.ver: src/contrib/STRINGdb_1.10.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/STRINGdb_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/STRINGdb_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/STRINGdb_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/STRINGdb_1.10.1.tgz vignettes: vignettes/STRINGdb/inst/doc/STRINGdb.pdf vignetteTitles: STRINGdb Vignette hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: scsR importsMe: pwOmics Package: subSeq Version: 1.0.1 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: 3bb5ba39116f753e46af34cf751a6aa5 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_1.0.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/subSeq_1.0.1.zip win64.binary.ver: bin/windows64/contrib/3.2/subSeq_1.0.1.zip mac.binary.ver: bin/macosx/contrib/3.2/subSeq_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/subSeq_1.0.1.tgz vignettes: vignettes/subSeq/inst/doc/subSeq.pdf vignetteTitles: subSeq Example hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: SummarizedExperiment Version: 1.0.2 Depends: R (>= 3.2), methods, GenomicRanges (>= 1.22.1), Biobase Imports: BiocGenerics (>= 0.15.3), S4Vectors (>= 0.7.11), IRanges, GenomeInfoDb Suggests: annotate, AnnotationDbi, GenomicFeatures, BiocStyle, knitr, rmarkdown, jsonlite, rhdf5 License: Artistic-2.0 MD5sum: ce5b5d0aa2868f754bc8d136ebd2e6cd 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/SummarizedExperiment_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/SummarizedExperiment_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/SummarizedExperiment_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SummarizedExperiment_1.0.2.tgz vignettes: vignettes/SummarizedExperiment/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/SummarizedExperiment/inst/doc/SummarizedExperiment.html htmlTitles: "SummarizedExperiment for Coordinating Experimental Assays,, Samples,, and Regions of Interest" dependsOnMe: ALDEx2, AllelicImbalance, BiSeq, bsseq, csaw, deepSNV, DESeq2, DiffBind, epigenomix, epivizr, GenomicAlignments, GenomicFiles, genoset, MBASED, methylPipe, minfi, RIPSeeker, simulatorZ, VanillaICE, VariantAnnotation importsMe: biovizBase, BiSeq, easyRNASeq, EnrichmentBrowser, FourCSeq, GGBase, ggbio, gQTLstats, methylumi, MinimumDistance, oligoClasses, R453Plus1Toolbox, roar, SeqArray, SGSeq, SNPchip, soGGi, systemPipeR, TCGAbiolinks suggestsMe: AnnotationHub, biobroom, GenomicRanges, interactiveDisplay Package: supraHex Version: 1.8.0 Depends: R (>= 3.0.2), hexbin Imports: ape, MASS License: GPL-2 MD5sum: 49bb30f77e389ba5f961c0cd0b018d83 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/supraHex_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/supraHex_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/supraHex_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/supraHex_1.8.0.tgz vignettes: vignettes/supraHex/inst/doc/supraHex_vignettes.pdf vignetteTitles: supraHex User Manual hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: TCGAbiolinks Package: survcomp Version: 1.20.0 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: 032d35f6abde061147ab1d630859d20e 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/survcomp_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/survcomp_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/survcomp_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/survcomp_1.20.0.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 dependsOnMe: genefu importsMe: saps suggestsMe: metaseqR Package: Sushi Version: 1.8.0 Depends: R (>= 2.10), zoo,biomaRt Imports: graphics, grDevices License: GPL (>= 2) MD5sum: f53daeb08098636b5adfa20210e2a6ff 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Sushi_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Sushi_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Sushi_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Sushi_1.8.0.tgz vignettes: vignettes/Sushi/inst/doc/Sushi.pdf vignetteTitles: Sushi hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: sva Version: 3.18.0 Depends: R (>= 2.8), mgcv, genefilter Suggests: limma, pamr, bladderbatch, BiocStyle, zebrafishRNASeq, testthat License: Artistic-2.0 Archs: i386, x64 MD5sum: 9560d608d9e0d8f538420850272f3ff3 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_3.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/sva_3.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/sva_3.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/sva_3.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/sva_3.18.0.tgz vignettes: vignettes/sva/inst/doc/sva.pdf vignetteTitles: sva tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: SCAN.UPC importsMe: ballgown, ChAMP, charm, edge, MEAL, PAA, trigger suggestsMe: RnBeads, SomaticSignatures Package: SVM2CRM Version: 1.2.0 Depends: R (>= 3.2.0), LiblineaR, SVM2CRMdata Imports: AnnotationDbi, mclust, GenomicRanges, IRanges, zoo, squash, pls,rtracklayer,ROCR,verification License: GPL-3 MD5sum: c2945f5dcb19018ffbbb305dcc58ea40 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SVM2CRM_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SVM2CRM_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SVM2CRM_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SVM2CRM_1.2.0.tgz vignettes: vignettes/SVM2CRM/inst/doc/SVM2CRM.pdf vignetteTitles: Package hasREADME: TRUE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SWATH2stats Version: 1.0.3 Imports: data.table, reshape2, grid Suggests: testthat, MSstats, aLFQ Enhances: imsbInfer License: GPL-3 MD5sum: 7b53b363dbbfd0cac35e1436682f8543 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 source.ver: src/contrib/SWATH2stats_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/SWATH2stats_1.0.3.zip win64.binary.ver: bin/windows64/contrib/3.2/SWATH2stats_1.0.3.zip mac.binary.ver: bin/macosx/contrib/3.2/SWATH2stats_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SWATH2stats_1.0.3.tgz vignettes: vignettes/SWATH2stats/inst/doc/SWATH2stats_vignette.pdf vignetteTitles: Using the SWATH2stats package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: SwimR Version: 1.8.0 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: 10aaf07903c940de177b0d135ea44b79 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/SwimR_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/SwimR_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/SwimR_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/SwimR_1.8.0.tgz vignettes: vignettes/SwimR/inst/doc/SwimR.pdf vignetteTitles: SwimR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: switchBox Version: 1.4.0 Depends: R (>= 2.13.1) License: GPL-2 Archs: i386, x64 MD5sum: d90675487590796e46f21ab300ee26a5 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/switchBox_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/switchBox_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/switchBox_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/switchBox_1.4.0.tgz vignettes: vignettes/switchBox/inst/doc/switchBox.pdf vignetteTitles: Working with the switchBox package hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: synapter Version: 1.12.0 Depends: R (>= 2.15), methods, MSnbase Imports: hwriter, RColorBrewer, lattice, qvalue, multtest, utils, Biobase, knitr, Biostrings, cleaver, BiocParallel Suggests: synapterdata, xtable, tcltk, tcltk2, BiocStyle License: GPL-2 MD5sum: 051dbd280d10f957841bdb0c3070540d 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/synapter_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/synapter_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/synapter_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/synapter_1.12.0.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 suggestsMe: pRoloc Package: synlet Version: 1.0.0 Depends: R (>= 3.2.0), ggplot2 Imports: doBy, dplyr, grid, magrittr, RColorBrewer, RankProd, reshape2 Suggests: knitr, testthat License: GPL-3 MD5sum: 93ad3d8b3f25e393f4e43f8070fc94cf 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/synlet_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/synlet_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/synlet_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/synlet_1.0.0.tgz vignettes: vignettes/synlet/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/synlet/inst/doc/synlet-vignette.html htmlTitles: "A working Demo for synlet" Package: systemPipeR Version: 1.4.8 Depends: Rsamtools, Biostrings, ShortRead, methods Imports: BiocGenerics, GenomicRanges, GenomicFeatures, SummarizedExperiment, VariantAnnotation, rjson, grid, ggplot2, limma, edgeR, DESeq2, GOstats, GO.db, annotate, pheatmap, BatchJobs Suggests: ape, RUnit, BiocStyle, knitr, rmarkdown, biomaRt, BiocParallel License: Artistic-2.0 MD5sum: fcd79fe5c93f73219d7b25148c9b4948 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_1.4.8.tar.gz win.binary.ver: bin/windows/contrib/3.2/systemPipeR_1.4.8.zip win64.binary.ver: bin/windows64/contrib/3.2/systemPipeR_1.4.8.zip mac.binary.ver: bin/macosx/contrib/3.2/systemPipeR_1.4.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/systemPipeR_1.4.8.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 htmlDocs: vignettes/systemPipeR/inst/doc/systemPipeR.html htmlTitles: "Overview Vignette" importsMe: DiffBind Package: TargetScore Version: 1.8.0 Depends: pracma, Matrix Suggests: TargetScoreData, gplots, Biobase, GEOquery License: GPL-2 MD5sum: b0e31803010b08a10773a33f9037a2a1 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TargetScore_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TargetScore_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TargetScore_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TargetScore_1.8.0.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 Package: TargetSearch Version: 1.26.0 Depends: ncdf Imports: graphics, grDevices, methods, stats, tcltk, utils Suggests: TargetSearchData License: GPL (>= 2) Archs: i386, x64 MD5sum: 5c4e9da6b07c09412466dc6f2477a156 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_1.26.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TargetSearch_1.26.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TargetSearch_1.26.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TargetSearch_1.26.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TargetSearch_1.26.0.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 Package: TarSeqQC Version: 1.0.3 Depends: R (>= 3.2.1), methods, GenomicRanges, Rsamtools (>= 1.20.4), ggplot2, plyr, openxlsx Imports: S4Vectors, IRanges, BiocGenerics, reshape2, GenomeInfoDb, BiocParallel, cowplot Suggests: RUnit License: GPL (>=2) MD5sum: caa6079a68e03cf5e1e8c1e254728318 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_1.0.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/TarSeqQC_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/TarSeqQC_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/TarSeqQC_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TarSeqQC_1.0.3.tgz vignettes: vignettes/TarSeqQC/inst/doc/TarSeqQC-vignette.pdf vignetteTitles: TarSeqQC: Targeted Sequencing Experiment Quality Control hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: TCC Version: 1.10.0 Depends: R (>= 2.15), methods, DESeq, DESeq2, edgeR, baySeq, ROC Imports: samr Suggests: RUnit, BiocGenerics Enhances: snow License: GPL-2 MD5sum: a0ee9073c511c439d97f8ab0b745463b 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TCC_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TCC_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TCC_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TCC_1.10.0.tgz vignettes: vignettes/TCC/inst/doc/TCC.pdf vignetteTitles: TCC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: compcodeR Package: TCGAbiolinks Version: 1.0.10 Depends: R (>= 3.2) Imports: downloader (>= 0.4), GGally, grDevices, graphics, GenomicRanges, XML, Biobase, affy, heatmap.plus, xtable, data.table, EDASeq (>= 2.0.0), RCurl, edgeR (>= 3.0.0), rjson, plyr, CNTools, cghMCR, biomaRt, coin, gplots, ggplot2, survival, stringr (>= 1.0.0), IRanges, scales, rvest, stats, utils, dnet, igraph, supraHex, S4Vectors, SummarizedExperiment, BiocGenerics, GenomicFeatures, TxDb.Hsapiens.UCSC.hg19.knownGene, limma, knitr, devtools, genefilter, ConsensusClusterPlus, RColorBrewer, doParallel, dplyr, parallel, xml2 Suggests: testthat, png, BiocStyle, rmarkdown License: GPL (>= 3) MD5sum: 327a24e248780f6bd5245f2b2daed69e 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 M. Malta, Stefano M. Pagnotta, Isabella Castiglioni, Michele Ceccarelli, Gianluca Bontempi, Houtan Noushmehr Maintainer: Antonio Colaprico , Tiago Chedraoui Silva VignetteBuilder: knitr source.ver: src/contrib/TCGAbiolinks_1.0.10.tar.gz win.binary.ver: bin/windows/contrib/3.2/TCGAbiolinks_1.0.10.zip win64.binary.ver: bin/windows64/contrib/3.2/TCGAbiolinks_1.0.10.zip mac.binary.ver: bin/macosx/contrib/3.2/TCGAbiolinks_1.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TCGAbiolinks_1.0.10.tgz vignettes: vignettes/TCGAbiolinks/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/TCGAbiolinks/inst/doc/tcgaBiolinks.html htmlTitles: "Working with TCGAbiolinks package" Package: TDARACNE Version: 1.20.0 Depends: GenKern, Rgraphviz, Biobase License: GPL-2 MD5sum: db0217d2e25edfb739587fbdbb2cd551 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_1.20.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TDARACNE_1.20.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TDARACNE_1.20.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TDARACNE_1.20.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TDARACNE_1.20.0.tgz vignettes: vignettes/TDARACNE/inst/doc/TDARACNE.pdf vignetteTitles: TDARACNE hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: TEQC Version: 3.10.0 Depends: methods, BiocGenerics (>= 0.1.0), IRanges (>= 1.13.5), Rsamtools, hwriter Imports: Biobase (>= 2.15.1) License: GPL (>= 2) MD5sum: 262be9209b424b711e2e5e3131fbb8a8 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_3.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TEQC_3.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TEQC_3.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TEQC_3.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TEQC_3.10.0.tgz vignettes: vignettes/TEQC/inst/doc/TEQC.pdf vignetteTitles: TEQC hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: ternarynet Version: 1.14.0 Depends: R (>= 2.10.0), methods Imports: utils, igraph License: GPL (>= 2) Archs: i386, x64 MD5sum: 336746c7d78606e0c73c67cdaaaddf59 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ternarynet_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ternarynet_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ternarynet_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ternarynet_1.14.0.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 Package: TFBSTools Version: 1.8.3 Depends: R (>= 3.2.2) Imports: Biostrings(>= 2.36.4), RSQLite(>= 1.0.0), seqLogo(>= 1.34.0), GenomicRanges(>= 1.20.6), caTools(>= 1.17.1), XVector(>= 0.8.0), rtracklayer(>= 1.28.10), BSgenome(>= 1.36.3), S4Vectors(>= 0.6.5), IRanges(>= 2.2.7), methods, gtools(>= 3.5.0), CNEr(>= 1.4.0), BiocParallel(>= 1.2.21), DirichletMultinomial(>= 1.10.0), TFMPvalue(>= 0.0.5), BiocGenerics(>= 0.14.0), XML(>= 3.98-1.3), grid, Biobase(>= 2.28), GenomeInfoDb(>= 1.6.1) Suggests: JASPAR2014(>= 1.4.0), RUnit(>= 0.4.29), BiocStyle(>= 1.7.7), knitr(>= 1.11) License: GPL-2 Archs: i386, x64 MD5sum: 7cc8c7dbfc680ea03700a00f81ad920c 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 and transcription factor profile matrices. biocViews: MotifAnnotation, GeneRegulation, MotifDiscovery, Transcription, Alignment Author: Ge Tan Maintainer: Ge Tan URL: https://bitbucket.org/ge11232002/tfbstools VignetteBuilder: knitr BugReports: https://bitbucket.org/ge11232002/tfbstools/issues source.ver: src/contrib/TFBSTools_1.8.3.tar.gz win.binary.ver: bin/windows/contrib/3.2/TFBSTools_1.8.3.zip win64.binary.ver: bin/windows64/contrib/3.2/TFBSTools_1.8.3.zip mac.binary.ver: bin/macosx/contrib/3.2/TFBSTools_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TFBSTools_1.8.3.tgz vignettes: vignettes/TFBSTools/inst/doc/TFBSTools.pdf vignetteTitles: Transcription factor binding site (TFBS) analysis with the "TFBSTools" package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: MatrixRider Package: tigre Version: 1.24.2 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: 925e4a87631059208b52abba98f716ab 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_1.24.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/tigre_1.24.2.zip win64.binary.ver: bin/windows64/contrib/3.2/tigre_1.24.2.zip mac.binary.ver: bin/macosx/contrib/3.2/tigre_1.24.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tigre_1.24.2.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 Package: tilingArray Version: 1.48.0 Depends: R (>= 2.11.0), Biobase, methods, pixmap Imports: strucchange, affy, vsn, genefilter, RColorBrewer, grid, stats4 License: Artistic-2.0 Archs: i386, x64 MD5sum: 394e55715ed6739268bdbb34a7ab8f99 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tilingArray_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tilingArray_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tilingArray_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tilingArray_1.48.0.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 importsMe: ADaCGH2, snapCGH Package: timecourse Version: 1.42.0 Depends: R (>= 2.1.1), MASS, methods Imports: Biobase, graphics, limma (>= 1.8.6), MASS, marray, methods, stats License: LGPL MD5sum: 2d520cd92ad5756a9b55de2d80ca56cb 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/timecourse_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/timecourse_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/timecourse_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/timecourse_1.42.0.tgz vignettes: vignettes/timecourse/inst/doc/timecourse.pdf vignetteTitles: timecourse manual hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: TIN Version: 1.2.0 Depends: R (>= 2.12.0), data.table, impute, aroma.affymetrix Imports: WGCNA, squash, stringr Suggests: knitr, aroma.light, affxparser, RUnit, BiocGenerics License: Artistic-2.0 MD5sum: 91a80bc678404f310c9b75b875e62c6f 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_1.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TIN_1.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TIN_1.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TIN_1.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TIN_1.2.0.tgz vignettes: vignettes/TIN/inst/doc/TIN.pdf vignetteTitles: Introduction to the TIN package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: TitanCNA Version: 1.8.0 Depends: R (>= 3.1.0), foreach (>= 1.4.0), IRanges (>= 1.99.1), Rsamtools (>= 1.17.11), GenomeInfoDb (>= 1.2.4) License: file LICENSE Archs: i386, x64 MD5sum: 35c57b450e5242def68e0a49c81fb3db 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TitanCNA_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TitanCNA_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TitanCNA_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TitanCNA_1.8.0.tgz vignettes: vignettes/TitanCNA/inst/doc/TitanCNA.pdf vignetteTitles: TitanCNA hasREADME: TRUE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: TRUE Package: tkWidgets Version: 1.48.0 Depends: R (>= 2.0.0), methods, widgetTools (>= 1.1.7), DynDoc (>= 1.3.0), tools Suggests: Biobase, hgu95av2 License: Artistic-2.0 MD5sum: f280e819af22824b0ec59b181fdab275 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tkWidgets_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tkWidgets_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tkWidgets_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tkWidgets_1.48.0.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 dependsOnMe: oneChannelGUI importsMe: Mfuzz, OLINgui suggestsMe: affy, affyQCReport, annotate, Biobase, genefilter, marray Package: ToPASeq Version: 1.4.0 Depends: graphite (>= 1.14), gRbase, graph, locfit, Rgraphviz Imports: R.utils, methods, Biobase, parallel, edgeR, DESeq2, GenomicRanges, 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: b488ca4f890bf82ce76f4e4d89c52577 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_1.4.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/ToPASeq_1.4.0.zip win64.binary.ver: bin/windows64/contrib/3.2/ToPASeq_1.4.0.zip mac.binary.ver: bin/macosx/contrib/3.2/ToPASeq_1.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/ToPASeq_1.4.0.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 Package: topGO Version: 2.22.0 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: methods, graph, Biobase, SparseM, AnnotationDbi, lattice Suggests: ALL, hgu95av2.db, hgu133a.db, genefilter, xtable, multtest, Rgraphviz, globaltest License: LGPL MD5sum: 53cdd3a7b3e7d6432a0021ea67f943b2 NeedsCompilation: no Title: topGO: 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_2.22.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/topGO_2.22.0.zip win64.binary.ver: bin/windows64/contrib/3.2/topGO_2.22.0.zip mac.binary.ver: bin/macosx/contrib/3.2/topGO_2.22.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/topGO_2.22.0.tgz vignettes: vignettes/topGO/inst/doc/topGO.pdf vignetteTitles: topGO hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: compEpiTools, RNAither, tRanslatome importsMe: clusterProfiler, EnrichmentBrowser, GOSim, mvGST, SEPA suggestsMe: FGNet, miRNAtap, Ringo Package: TPP Version: 2.0.4 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: 213fbd63bb57a156f89eda3867b35af0 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_2.0.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/TPP_2.0.4.zip win64.binary.ver: bin/windows64/contrib/3.2/TPP_2.0.4.zip mac.binary.ver: bin/macosx/contrib/3.2/TPP_2.0.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TPP_2.0.4.tgz vignettes: vignettes/TPP/inst/doc/TPP_introduction.pdf vignetteTitles: TPP_introduction hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: tracktables Version: 1.4.2 Depends: R (>= 3.0.0) Imports: IRanges, GenomicRanges, XVector, Rsamtools, XML, ore, stringr, RColorBrewer, methods Suggests: knitr, BiocStyle License: GPL (>= 3) MD5sum: e9e2c2c2830e2de51625e9fa1e59150f 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, QualityControl Author: Tom Carroll, Sanjay Khadayate, Anne Pajon, Ziwei Liang Maintainer: Tom Carroll VignetteBuilder: knitr source.ver: src/contrib/tracktables_1.4.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/tracktables_1.4.2.zip win64.binary.ver: bin/windows64/contrib/3.2/tracktables_1.4.2.zip mac.binary.ver: bin/macosx/contrib/3.2/tracktables_1.4.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tracktables_1.4.2.tgz vignettes: vignettes/tracktables/inst/doc/tracktables.pdf vignetteTitles: Creating IGV HTML reports with tracktables hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: trackViewer Version: 1.6.1 Depends: R (>= 3.1.0), methods, GenomicRanges, grid Imports: GenomicAlignments, GenomicFeatures, Gviz, pbapply, Rsamtools, rtracklayer, scales, tools, IRanges Suggests: biomaRt, TxDb.Hsapiens.UCSC.hg19.knownGene, RUnit, BiocGenerics, BiocStyle, knitr License: GPL (>= 2) MD5sum: e41389d88406a4982ee55f9755ec6a95 NeedsCompilation: no Title: A bioconductor package with minimalist design for plotting elegant track layers Description: visualize mapped reads along with annotation as track layers for NGS dataset such as ChIP-seq, RNA-seq, miRNA-seq, DNA-seq. biocViews: Visualization Author: Jianhong Ou, Yong-Xu Wang, Lihua Julie Zhu Maintainer: Jianhong Ou VignetteBuilder: knitr source.ver: src/contrib/trackViewer_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/trackViewer_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/trackViewer_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/trackViewer_1.6.1.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/trackViewer_1.6.1.tgz vignettes: vignettes/trackViewer/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/trackViewer/inst/doc/trackViewer.html htmlTitles: "trackViewer Vignette" importsMe: coMET Package: tRanslatome Version: 1.8.0 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: 69c28e5a30a537c281509c9ee87e07fb 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tRanslatome_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tRanslatome_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tRanslatome_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tRanslatome_1.8.0.tgz vignettes: vignettes/tRanslatome/inst/doc/tRanslatome_package.pdf vignetteTitles: tRanslatome hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: TransView Version: 1.14.0 Depends: methods,GenomicRanges Imports: Rsamtools (>= 1.19.38), zlibbioc, gplots, IRanges LinkingTo: Rsamtools Suggests: RUnit, pasillaBamSubset License: GPL-3 Archs: i386, x64 MD5sum: e95d79111506e047e6018145ee4c9009 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_1.14.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TransView_1.14.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TransView_1.14.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TransView_1.14.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TransView_1.14.0.tgz vignettes: vignettes/TransView/inst/doc/TransView.pdf vignetteTitles: An introduction to TransView hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: traseR Version: 1.0.0 Depends: R (>= 3.2.0),GenomicRanges,IRanges,BSgenome.Hsapiens.UCSC.hg19 Suggests: BiocStyle,RUnit, BiocGenerics License: GPL MD5sum: e920032c53ace85ed707f116a774a2c0 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_1.0.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/traseR_1.0.0.zip win64.binary.ver: bin/windows64/contrib/3.2/traseR_1.0.0.zip mac.binary.ver: bin/macosx/contrib/3.2/traseR_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/traseR_1.0.0.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 Package: triform Version: 1.12.0 Depends: R (>= 2.11.0), IRanges, yaml Imports: IRanges, yaml, BiocGenerics Suggests: RUnit License: GPL-2 MD5sum: ece8627a0d29077b0f3072e26ad64967 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/triform_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/triform_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/triform_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/triform_1.12.0.tgz vignettes: vignettes/triform/inst/doc/triform.pdf vignetteTitles: Triform users guide hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: trigger Version: 1.16.0 Depends: R (>= 2.14.0), corpcor, qtl Imports: qvalue, methods, graphics, sva License: GPL-3 Archs: i386, x64 MD5sum: db211b4317e82a62d2ba95886f086fb5 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/trigger_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/trigger_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/trigger_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/trigger_1.16.0.tgz vignettes: vignettes/trigger/inst/doc/trigger.pdf vignetteTitles: Trigger Tutorial hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: trio Version: 3.8.0 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: 470edd27d548c47622acf244ab33a7b9 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_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/trio_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/trio_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/trio_3.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/trio_3.8.0.tgz vignettes: vignettes/trio/inst/doc/trio.pdf vignetteTitles: Trio Logic Regression and genotypic TDT hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: triplex Version: 1.10.0 Depends: R (>= 2.15.0), S4Vectors (>= 0.5.14), IRanges (>= 1.99.1), XVector (>= 0.7.3), Biostrings (>= 2.33.3) 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: 722b18ccdf582aeabc51c00d1479d7a9 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_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/triplex_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/triplex_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/triplex_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/triplex_1.10.0.tgz vignettes: vignettes/triplex/inst/doc/triplex.pdf vignetteTitles: Triplex User Guide hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: TRUE Package: TRONCO Version: 2.2.0 Depends: R (>= 3.1), doParallel, bnlearn, Imports: Rgraphviz, ggplot2, RColorBrewer, reshape2, cgdsr, igraph, grid, gridExtra, xtable, gtable, scales Suggests: BiocGenerics, BiocStyle, testthat, R.matlab License: GPL (>= 3.0) MD5sum: bc0f430d2a9a4182dbb058f04f94f16d NeedsCompilation: no Title: TRONCO, an R package for TRanslational ONCOlogy Description: TRONCO is an R package for the inference of cancer progression models from heterogeneous genomic data. biocViews: Cancer Author: Marco Antoniotti [cph], Giulio Caravagna [aut, cre], Luca De Sano [aut], Alex Graudenzi [aut], Ilya Korsunsky [cph], Mattia Longoni [ctb], Loes Olde Loohuis [cph], Giancarlo Mauri [cph], Bud Mishra [cph], Daniele Ramazzotti [aut] Maintainer: BIMIB Group URL: https://sites.google.com/site/troncopackage/ BugReports: https://github.com/BIMIB-DISCo/TRONCO source.ver: src/contrib/TRONCO_2.2.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TRONCO_2.2.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TRONCO_2.2.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TRONCO_2.2.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TRONCO_2.2.0.tgz vignettes: vignettes/TRONCO/inst/doc/vignette.pdf vignetteTitles: An R Package for TRanslational ONCOlogy hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: TSCAN Version: 1.8.0 Depends: R(>= 2.10.0) Imports: ggplot2, shiny, plyr, grid, fastICA, igraph, combinat, mgcv, mclust, gplots Suggests: knitr License: GPL(>=2) MD5sum: 3ff6b9eab88dfa42d641e8391f3f0de6 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_1.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TSCAN_1.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TSCAN_1.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TSCAN_1.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TSCAN_1.8.0.tgz vignettes: vignettes/TSCAN/inst/doc/TSCAN.pdf vignetteTitles: TSCAN: Tools for Single-Cell ANalysis hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: tspair Version: 1.28.0 Depends: R (>= 2.10), Biobase (>= 2.4.0) License: GPL-2 Archs: i386, x64 MD5sum: 12b66416f283b8f4c283338d60594131 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tspair_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tspair_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tspair_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tspair_1.28.0.tgz vignettes: vignettes/tspair/inst/doc/tsp.pdf vignetteTitles: tspTutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: stepwiseCM Package: TSSi Version: 1.16.2 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: e3d25a0e24e6c716a45a2042ace20a28 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_1.16.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/TSSi_1.16.2.zip win64.binary.ver: bin/windows64/contrib/3.2/TSSi_1.16.2.zip mac.binary.ver: bin/macosx/contrib/3.2/TSSi_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TSSi_1.16.2.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 Package: TurboNorm Version: 1.18.0 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: 4bdff1ceb543f1480f7665e72d5953a0 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TurboNorm_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TurboNorm_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TurboNorm_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TurboNorm_1.18.0.tgz vignettes: vignettes/TurboNorm/inst/doc/turbonorm.pdf vignetteTitles: TurboNorm Overview hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: tweeDEseq Version: 1.16.0 Depends: R (>= 2.12.0) Imports: MASS, limma, edgeR, parallel, cqn Suggests: tweeDEseqCountData, xtable License: GPL (>= 2) Archs: i386, x64 MD5sum: a019c049cdac4ece5a75a4c2ae3cdf37 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/tweeDEseq_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/tweeDEseq_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/tweeDEseq_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/tweeDEseq_1.16.0.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 Package: twilight Version: 1.46.0 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: 9db1f9c541826ee01056661912c618fc 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/twilight_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/twilight_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/twilight_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/twilight_1.46.0.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 dependsOnMe: OrderedList importsMe: OrderedList Package: TypeInfo Version: 1.36.0 Depends: methods Suggests: Biobase License: BSD MD5sum: 98743766f4b00956e119bcceaccfedec 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/TypeInfo_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/TypeInfo_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/TypeInfo_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/TypeInfo_1.36.0.tgz vignettes: vignettes/TypeInfo/inst/doc/TypeInfoNews.pdf vignetteTitles: TypeInfo R News hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: RWebServices Package: UNDO Version: 1.12.0 Depends: R (>= 2.15.2), methods, BiocGenerics, Biobase Imports: MASS, boot, nnls, stats, utils License: GPL-2 MD5sum: 56cfb7536f23174f23a91793441ca8bf 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/UNDO_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/UNDO_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/UNDO_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/UNDO_1.12.0.tgz vignettes: vignettes/UNDO/inst/doc/UNDO-vignette.pdf vignetteTitles: UNDO Demo hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: unifiedWMWqPCR Version: 1.6.0 Depends: methods Imports: BiocGenerics, stats, graphics, HTqPCR License: GPL (>=2) MD5sum: c5edfb2dec289e0e5ea0f97cdab979ff 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_1.6.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/unifiedWMWqPCR_1.6.0.zip win64.binary.ver: bin/windows64/contrib/3.2/unifiedWMWqPCR_1.6.0.zip mac.binary.ver: bin/macosx/contrib/3.2/unifiedWMWqPCR_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/unifiedWMWqPCR_1.6.0.tgz vignettes: vignettes/unifiedWMWqPCR/inst/doc/unifiedWMWqPCR.pdf vignetteTitles: unifiedWMWqPCR hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: UniProt.ws Version: 2.10.4 Depends: methods, utils, RSQLite, RCurl, BiocGenerics (>= 0.13.8) Imports: AnnotationDbi Suggests: RUnit License: Artistic License 2.0 MD5sum: 0285833e4697a0f57010ec8ad782de71 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_2.10.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/UniProt.ws_2.10.4.zip win64.binary.ver: bin/windows64/contrib/3.2/UniProt.ws_2.10.4.zip mac.binary.ver: bin/macosx/contrib/3.2/UniProt.ws_2.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/UniProt.ws_2.10.4.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 suggestsMe: cleaver, dagLogo Package: VanillaICE Version: 1.32.2 Depends: R (>= 3.0.0), BiocGenerics (>= 0.13.6), GenomicRanges (>= 1.19.47), SummarizedExperiment (>= 0.2.0) Imports: Biobase, oligoClasses (>= 1.31.1), IRanges (>= 1.14.0), S4Vectors, foreach, matrixStats, data.table, grid, lattice, methods, GenomeInfoDb, crlmm, tools Suggests: RUnit, SNPchip, human610quadv1bCrlmm, BSgenome.Hsapiens.UCSC.hg18, ArrayTV Enhances: doMC, doMPI, doSNOW, doParallel, doRedis License: LGPL-2 Archs: i386, x64 MD5sum: 0915a9f3c1a5407b8907df663a661fc0 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_1.32.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/VanillaICE_1.32.2.zip win64.binary.ver: bin/windows64/contrib/3.2/VanillaICE_1.32.2.zip mac.binary.ver: bin/macosx/contrib/3.2/VanillaICE_1.32.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VanillaICE_1.32.2.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 dependsOnMe: MinimumDistance suggestsMe: CNPBayes, oligoClasses Package: variancePartition Version: 1.0.7 Depends: ggplot2, foreach, Biobase, methods Imports: lme4, iterators, reshape2, doParallel, limma, dendextend Suggests: edgeR, knitr, BiocStyle License: GPL (>= 2) MD5sum: e57797114ce16a2236b7f67acd1013e5 NeedsCompilation: no Title: Quantifying and interpreting drivers of variation in complex gene expression studies 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_1.0.7.tar.gz win.binary.ver: bin/windows/contrib/3.2/variancePartition_1.0.7.zip win64.binary.ver: bin/windows64/contrib/3.2/variancePartition_1.0.7.zip mac.binary.ver: bin/macosx/contrib/3.2/variancePartition_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/variancePartition_1.0.7.tgz vignettes: vignettes/variancePartition/inst/doc/variancePartition.pdf vignetteTitles: variancePartition hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: VariantAnnotation Version: 1.16.4 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.15.3), GenomeInfoDb (>= 1.1.3), GenomicRanges (>= 1.19.47), SummarizedExperiment (>= 0.3.1), Rsamtools (>= 1.19.52) Imports: utils, DBI, zlibbioc, Biobase, S4Vectors (>= 0.7.11), 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, 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: 03f51aaa15fa2824154a5e328b2b209c 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_1.16.4.tar.gz win.binary.ver: bin/windows/contrib/3.2/VariantAnnotation_1.16.4.zip win64.binary.ver: bin/windows64/contrib/3.2/VariantAnnotation_1.16.4.zip mac.binary.ver: bin/macosx/contrib/3.2/VariantAnnotation_1.16.3.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VariantAnnotation_1.16.4.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 dependsOnMe: CNVrd2, deepSNV, DOQTL, ensemblVEP, genotypeeval, GoogleGenomics, HTSeqGenie, myvariant, R453Plus1Toolbox, RareVariantVis, Rariant, SomaticSignatures, VariantFiltering, VariantTools importsMe: AllelicImbalance, BBCAnalyzer, biovizBase, customProDB, FunciSNP, ggbio, GGtools, gmapR, gQTLstats, gwascat, methyAnalysis, motifbreakR, PGA, SeqArray, SeqVarTools, systemPipeR suggestsMe: AnnotationHub, GenomicRanges, GWASTools, podkat, trio, vtpnet Package: VariantFiltering Version: 1.6.21 Depends: R (>= 3.0.0), methods, BiocGenerics (>= 0.13.8), VariantAnnotation (>= 1.13.29) Imports: DBI, RSQLite (>= 1.0.0), Biobase, S4Vectors (>= 0.7.21), IRanges (>= 2.3.23), RBGL, graph, AnnotationDbi, BiocParallel, Biostrings (>= 2.33.11), GenomeInfoDb (>= 1.3.6), GenomicRanges (>= 1.19.13), GenomicFeatures, Rsamtools (>= 1.17.8), BSgenome, Gviz, shiny LinkingTo: S4Vectors, IRanges, XVector, Biostrings Suggests: BiocStyle, org.Hs.eg.db, BSgenome.Hsapiens.UCSC.hg19, TxDb.Hsapiens.UCSC.hg19.knownGene, SNPlocs.Hsapiens.dbSNP.20120608, MafDb.ALL.wgs.phase1.release.v3.20101123, MafDb.ESP6500SI.V2.SSA137, MafDb.ExAC.r0.3.sites, phastCons100way.UCSC.hg19, PolyPhen.Hsapiens.dbSNP131, SIFT.Hsapiens.dbSNP137 License: Artistic-2.0 Archs: i386, x64 MD5sum: 8c186f2377813565ec1293b93441f623 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, minimum 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_1.6.21.tar.gz win.binary.ver: bin/windows/contrib/3.2/VariantFiltering_1.6.20.zip win64.binary.ver: bin/windows64/contrib/3.2/VariantFiltering_1.6.20.zip mac.binary.ver: bin/macosx/contrib/3.2/VariantFiltering_1.6.4.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VariantFiltering_1.6.21.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 Package: VariantTools Version: 1.12.0 Depends: S4Vectors (>= 0.0.2), IRanges (>= 1.99.2), GenomicRanges (>= 1.17.7), VariantAnnotation (>= 1.11.16), methods Imports: Rsamtools (>= 1.17.6), BiocGenerics, Biostrings, parallel, gmapR (>= 1.7.2), 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: bb5227a4e2a58d2f6263a58ea382bf9f 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_1.12.0.tar.gz vignettes: vignettes/VariantTools/inst/doc/VariantTools.pdf vignetteTitles: Introduction to VariantTools hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: HTSeqGenie Package: vbmp Version: 1.38.0 Depends: R (>= 2.10) Suggests: Biobase (>= 2.5.5), statmod License: GPL (>= 2) MD5sum: fbab211ade8a4b3701456a2779f02826 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_1.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/vbmp_1.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/vbmp_1.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/vbmp_1.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/vbmp_1.38.0.tgz vignettes: vignettes/vbmp/inst/doc/vbmp.pdf vignetteTitles: vbmp Tutorial hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: Vega Version: 1.18.0 Depends: R (>= 2.10) License: GPL-2 Archs: i386, x64 MD5sum: db5e7fbde23807ff26793699896e6606 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_1.18.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/Vega_1.18.0.zip win64.binary.ver: bin/windows64/contrib/3.2/Vega_1.18.0.zip mac.binary.ver: bin/macosx/contrib/3.2/Vega_1.18.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/Vega_1.18.0.tgz vignettes: vignettes/Vega/inst/doc/Vega.pdf vignetteTitles: Vega hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: VegaMC Version: 3.8.0 Depends: R (>= 2.10.0), biomaRt, Biobase, genoset Imports: methods License: GPL-2 Archs: i386, x64 MD5sum: e0e91792af3270e6d9f5896cd30b87e1 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_3.8.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/VegaMC_3.8.0.zip win64.binary.ver: bin/windows64/contrib/3.2/VegaMC_3.8.0.zip mac.binary.ver: bin/macosx/contrib/3.2/VegaMC_3.8.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/VegaMC_3.8.0.tgz vignettes: vignettes/VegaMC/inst/doc/VegaMC.pdf vignetteTitles: VegaMC hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: viper Version: 1.6.1 Depends: R (>= 2.14.0), Biobase, methods Imports: mixtools, stats, parallel, e1071, KernSmooth Suggests: bcellViper License: GPL (>=2) MD5sum: 326c6623d31cb496e9d6f4dfe7298197 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_1.6.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/viper_1.6.1.zip win64.binary.ver: bin/windows64/contrib/3.2/viper_1.6.1.zip mac.binary.ver: bin/macosx/contrib/3.2/viper_1.6.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/viper_1.6.1.tgz vignettes: vignettes/viper/inst/doc/viper.pdf vignetteTitles: Using VIPER hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE importsMe: diggit Package: vsn Version: 3.38.0 Depends: R (>= 2.10), Biobase Imports: methods, affy, limma, lattice, ggplot2, hexbin Suggests: affydata, hgu95av2cdf License: Artistic-2.0 Archs: i386, x64 MD5sum: 2b9b7aca04419363de7118f4980a6fb3 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_3.38.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/vsn_3.38.0.zip win64.binary.ver: bin/windows64/contrib/3.2/vsn_3.38.0.zip mac.binary.ver: bin/macosx/contrib/3.2/vsn_3.38.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/vsn_3.38.0.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 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: 0.10.0 Depends: R (>= 3.0.0), graph, GenomicRanges, gwascat, doParallel, foreach Suggests: MotifDb, VariantAnnotation, Rgraphviz License: Artistic-2.0 MD5sum: 1f1ab53374a15e3fd3307b06a4684d11 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_0.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/vtpnet_0.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/vtpnet_0.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/vtpnet_0.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/vtpnet_0.10.0.tgz vignettes: vignettes/vtpnet/inst/doc/vtpnet.pdf vignetteTitles: vtpnet: variant-transcription factor-network tools hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: wateRmelon Version: 1.10.0 Depends: R (>= 2.10), limma, methods, matrixStats, methylumi, lumi, ROC, IlluminaHumanMethylation450kanno.ilmn12.hg19 Suggests: RPMM Enhances: minfi, methylumi, IMA License: GPL-3 MD5sum: c856147836ccf12d6edd3a525a6e5099 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, minfi and IMA 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 Maintainer: Leo source.ver: src/contrib/wateRmelon_1.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/wateRmelon_1.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/wateRmelon_1.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/wateRmelon_1.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/wateRmelon_1.10.0.tgz vignettes: vignettes/wateRmelon/inst/doc/wateRmelon.pdf vignetteTitles: Package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: skewr importsMe: ChAMP suggestsMe: RnBeads Package: wavClusteR Version: 2.4.1 Depends: R (>= 3.0.0), GenomicRanges, Rsamtools Imports: Biostrings, foreach, GenomicFeatures, ggplot2, Hmisc, IRanges, mclust, rtracklayer, seqinr, stringr, wmtsa Suggests: BSgenome.Hsapiens.UCSC.hg19 Enhances: doMC License: GPL-2 MD5sum: b5331e4a6f4f1c785bf1e64efc79e745 NeedsCompilation: no Title: Sensitive and highly resolved identification of RNA-protein interaction sites in PAR-CLIP data Description: A comprehensive 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: HighThroughputSequencing, Software Author: Federico Comoglio and Cem Sievers Maintainer: Federico Comoglio source.ver: src/contrib/wavClusteR_2.4.1.tar.gz win.binary.ver: bin/windows/contrib/3.2/wavClusteR_2.4.1.zip win64.binary.ver: bin/windows64/contrib/3.2/wavClusteR_2.4.1.zip mac.binary.ver: bin/macosx/contrib/3.2/wavClusteR_2.4.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/wavClusteR_2.4.1.tgz vignettes: vignettes/wavClusteR/inst/doc/wavCluster_vignette.pdf vignetteTitles: wavClusteR hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: waveTiling Version: 1.12.0 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: 6e0d0073977267cf90245566ee73be83 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_1.12.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/waveTiling_1.12.0.zip win64.binary.ver: bin/windows64/contrib/3.2/waveTiling_1.12.0.zip mac.binary.ver: bin/macosx/contrib/3.2/waveTiling_1.12.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/waveTiling_1.12.0.tgz vignettes: vignettes/waveTiling/inst/doc/waveTiling-vignette.pdf vignetteTitles: The waveTiling package hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE Package: weaver Version: 1.36.0 Depends: R (>= 2.5.0), digest, tools, utils, codetools Suggests: codetools License: GPL-2 MD5sum: e233981be56f1f6d0f85cfe6c4e15c8d 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_1.36.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/weaver_1.36.0.zip win64.binary.ver: bin/windows64/contrib/3.2/weaver_1.36.0.zip mac.binary.ver: bin/macosx/contrib/3.2/weaver_1.36.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/weaver_1.36.0.tgz vignettes: vignettes/weaver/inst/doc/weaver_howTo.pdf vignetteTitles: Using weaver to process Sweave documents hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: BiocCaseStudies Package: webbioc Version: 1.42.0 Depends: R (>= 1.8.0), Biobase, affy, multtest, annaffy, vsn, gcrma, qvalue Imports: multtest, qvalue, stats, utils, BiocInstaller License: GPL (>= 2) MD5sum: 1a25b9bce55263d9fc775ecdcaa5c4fb 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_1.42.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/webbioc_1.42.0.zip win64.binary.ver: bin/windows64/contrib/3.2/webbioc_1.42.0.zip mac.binary.ver: bin/macosx/contrib/3.2/webbioc_1.42.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/webbioc_1.42.0.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: 1.48.0 Depends: R (>= 2.4.0), methods, utils, tcltk Suggests: Biobase License: LGPL MD5sum: 004a9296df9aa462decab7ef743bba22 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_1.48.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/widgetTools_1.48.0.zip win64.binary.ver: bin/windows64/contrib/3.2/widgetTools_1.48.0.zip mac.binary.ver: bin/macosx/contrib/3.2/widgetTools_1.48.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/widgetTools_1.48.0.tgz vignettes: vignettes/widgetTools/inst/doc/widgetTools.pdf vignetteTitles: widgetTools Introduction hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: tkWidgets importsMe: OLINgui suggestsMe: affy Package: XBSeq Version: 1.0.2 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: d8b6b1f9fc28a4e126a43bbc500685e9 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_1.0.2.tar.gz win.binary.ver: bin/windows/contrib/3.2/XBSeq_1.0.2.zip win64.binary.ver: bin/windows64/contrib/3.2/XBSeq_1.0.2.zip mac.binary.ver: bin/macosx/contrib/3.2/XBSeq_1.0.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/XBSeq_1.0.2.tgz vignettes: vignettes/XBSeq/inst/doc/ hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE htmlDocs: vignettes/XBSeq/inst/doc/XBSeq.html htmlTitles: "Differential expression analysis of count data using XBSeq package" Package: xcms Version: 1.46.0 Depends: R (>= 2.14.0), methods, mzR (>= 1.1.6), BiocGenerics, ProtGenerics, Biobase Imports: lattice, RColorBrewer Suggests: faahKO, msdata, ncdf, multtest, rgl, MassSpecWavelet (>= 1.5.2), RANN, RUnit, parallel Enhances: Rgraphviz, Rmpi, XML License: GPL (>= 2) + file LICENSE Archs: i386, x64 MD5sum: 4f0a2e79db56d7fd71fcb0ad2829d4a1 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_1.46.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/xcms_1.46.0.zip win64.binary.ver: bin/windows64/contrib/3.2/xcms_1.46.0.zip mac.binary.ver: bin/macosx/contrib/3.2/xcms_1.46.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/xcms_1.46.0.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 dependsOnMe: CAMERA, flagme, Metab, metaMS importsMe: CAMERA, cosmiq, metaX, Risa suggestsMe: MassSpecWavelet, RMassBank Package: XDE Version: 2.16.0 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: 7051b812428f6cf026d729fa09ddadc8 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_2.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/XDE_2.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/XDE_2.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/XDE_2.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/XDE_2.16.0.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 Package: xmapbridge Version: 1.28.0 Depends: R (>= 2.0), methods Suggests: RUnit, RColorBrewer License: LGPL-3 MD5sum: e34b151c46e419960da9f105a6a6f078 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_1.28.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/xmapbridge_1.28.0.zip win64.binary.ver: bin/windows64/contrib/3.2/xmapbridge_1.28.0.zip mac.binary.ver: bin/macosx/contrib/3.2/xmapbridge_1.28.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/xmapbridge_1.28.0.tgz vignettes: vignettes/xmapbridge/inst/doc/xmapbridge.pdf vignetteTitles: xmapbridge primer hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE Package: xps Version: 1.30.0 Depends: R (>= 2.6.0), methods, utils Suggests: tools License: GPL (>= 2.0) Archs: i386 MD5sum: 329d5201759a39b023c18cf1cefd6333 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/xps_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/xps_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/xps_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/xps_1.30.0.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 Package: XVector Version: 0.10.0 Depends: R (>= 2.8.0), methods, BiocGenerics (>= 0.11.3), S4Vectors (>= 0.7.1), IRanges (>= 2.3.7) Imports: methods, zlibbioc, BiocGenerics, S4Vectors, IRanges LinkingTo: S4Vectors, IRanges Suggests: Biostrings, drosophila2probe, RUnit License: Artistic-2.0 Archs: i386, x64 MD5sum: 11d9f41215d0f0a96de9c8a6afb8a684 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: H. Pages and P. Aboyoun Maintainer: H. Pages source.ver: src/contrib/XVector_0.10.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/XVector_0.10.0.zip win64.binary.ver: bin/windows64/contrib/3.2/XVector_0.10.0.zip mac.binary.ver: bin/macosx/contrib/3.2/XVector_0.10.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/XVector_0.10.0.tgz hasREADME: FALSE hasNEWS: FALSE hasINSTALL: FALSE hasLICENSE: FALSE dependsOnMe: Biostrings, motifRG, Rsamtools, triplex importsMe: Biostrings, BSgenome, ChIPsim, CNEr, compEpiTools, DECIPHER, gcrma, GenomicFeatures, GenomicRanges, Gviz, IONiseR, kebabs, MatrixRider, R453Plus1Toolbox, rtracklayer, TFBSTools, tracktables, VariantAnnotation suggestsMe: IRanges Package: yaqcaffy Version: 1.30.0 Depends: simpleaffy (>= 2.19.3), methods Imports: stats4 Suggests: MAQCsubsetAFX, affydata, xtable, tcltk2, tcltk License: Artistic-2.0 MD5sum: fbb4cce836d915e8af8edb79a8ad98b0 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_1.30.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/yaqcaffy_1.30.0.zip win64.binary.ver: bin/windows64/contrib/3.2/yaqcaffy_1.30.0.zip mac.binary.ver: bin/macosx/contrib/3.2/yaqcaffy_1.30.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/yaqcaffy_1.30.0.tgz vignettes: vignettes/yaqcaffy/inst/doc/yaqcaffy.pdf vignetteTitles: yaqcaffy: Affymetrix quality control and MAQC reproducibility hasREADME: FALSE hasNEWS: TRUE hasINSTALL: FALSE hasLICENSE: FALSE suggestsMe: qcmetrics Package: zlibbioc Version: 1.16.0 License: Artistic-2.0 + file LICENSE Archs: i386, x64 MD5sum: 4fe30cfd3e9eacf7864f9b2e4ee8f511 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_1.16.0.tar.gz win.binary.ver: bin/windows/contrib/3.2/zlibbioc_1.16.0.zip win64.binary.ver: bin/windows64/contrib/3.2/zlibbioc_1.16.0.zip mac.binary.ver: bin/macosx/contrib/3.2/zlibbioc_1.16.0.tgz mac.binary.mavericks.ver: bin/macosx/mavericks/contrib/3.2/zlibbioc_1.16.0.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