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    "NeedsCompilation": "no",
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    "Description": "provides a pipeline for the low-level analysis of gene expression microarray data, primarily Agilent data",
    "Author": "Benny Chain <b.chain@ucl.ac.uk>",
    "Maintainer": "Benny Chain <b.chain@ucl.ac.uk>",
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    "Archs": "i386, x64",
    "MD5sum": "6c1285c05ba5bb7623dd0e0fec3de086",
    "NeedsCompilation": "yes",
    "Title": "Basic graphic utilities for visualization of genomic data.",
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    "Author": "Tengfei Yin, Michael Lawrence, Dianne Cook",
    "Maintainer": "Tengfei Yin <yintengfei@gmail.com>",
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    "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.",
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      "GeneExpression",
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    "Author": "Benedikt Zacher, Khalid Abnaof, Stephan Gade, Erfan Younesi, Achim Tresch, Holger Froehlich",
    "Maintainer": "Benedikt Zacher <zacher@lmb.uni-muenchen.de>, Holger Froehlich <frohlich@bit.uni-bonn.de>",
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    "Title": "Processing and analyzing bisulfite sequencing data",
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      "HighThroughputSequencing",
      "Methylseq",
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    "Author": "Katja Hebestreit, Hans-Ulrich Klein",
    "Maintainer": "Katja Hebestreit <katja.hebestreit@uni-muenster.de>",
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    "MD5sum": "94834584a36d29fdb224879a21a8ef6e",
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    "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.",
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    "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.",
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    "MD5sum": "8f9fcacd00d60a1299a4fa6737c38aa1",
    "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.",
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    "Title": "Visualization and manage of the protein-protein interaction networks.",
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    "MD5sum": "6b3225359adbd96cdba31965be595cf7",
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    "Title": "Tools for performing taxonomic assignment.",
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    "Title": "statistical analysis and visulization of functional profiles for genes and gene clusters",
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    "Title": "Compute cluster stability scores for microarray data",
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    "Title": "cancer outlier Gene Profile Sets",
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    "MD5sum": "e2f3498425e5a340ac8163eed534b9b1",
    "NeedsCompilation": "no",
    "Title": "ConsensusClusterPlus",
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    "Archs": "i386, x64",
    "MD5sum": "fcbd2f783aaaa9a9bfd912bc63382a15",
    "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.",
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    "MD5sum": "177b8e824665c6a46cb95075b7b8b4bf",
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    "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.",
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      "Software",
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    "Maintainer": "Gro Nilsen <gronilse@ifi.uio.no>",
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    "Title": "Correlation Motif Fit",
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    "biocViews": [
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    "Author": "Zhenpeng Li, Yang Huang, Yabo Ouyang, Yiming Shao, Liying Ma",
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    "source.ver": "src/contrib/CorMut_1.2.0.tar.gz",
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    "Title": "Analysis of co-knock-down RNAi data",
    "Description": "Analysis of combinatorial cell-based RNAi screens",
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    "Author": "Elin Axelsson",
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    "Title": "Multivariate Correlation Estimator and Statistical Inference Procedures.",
    "Description": "Multivariate correlation estimation and statistical inference. See package vignette.",
    "biocViews": [
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    "Title": "Conditional quantile normalization",
    "Description": "A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method.",
    "biocViews": [
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      "Preprocessing",
      "RNAseq",
      "Software"
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    "Author": "Jean (Zhijin) Wu, Kasper Daniel Hansen",
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    "vignettes": [
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    "MD5sum": "53a8150bac2d84b9be707a663bd7b5bb",
    "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": [
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      "Software"
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    "Author": "Henrik Failmezger <failmezger@mpipz.mpg.de>, Yinyin Yuan <Yinyin.Yuan@cancer.org.uk>, Oscar Rueda <oscar.rueda@cancer.org.uk>, Florian Markowetz <Florian.Markowetz@cancer.org.uk>",
    "Maintainer": "Henrik Failmezger <failmezger@mpipz.mpg.de>, Yinyin Yuan <Yinyin.Yuan@cancer.org.uk>",
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    "vignettes": [
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    "Archs": "i386, x64",
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    "Title": "Differential gene expression analysis based on the negative binomial distribution",
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    "Title": "Inference of differential exon usage in RNA-Seq",
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    "MD5sum": "64e22a3c11f6890831141805d8786e38",
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    "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.",
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      "RNAseqData",
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      "Zea_Mays"
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    "Author": "Guenter Klambauer",
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    "MD5sum": "0b5feaaea66dcdaa2ccb601962d9d650",
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    "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.",
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    "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)",
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    "URL": "http://www.holstegelab.nl/publications/margaritis_lijnzaad",
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      "vignettes/dyebias/inst/doc/gassco.pdf"
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    "License": "Artistic-2.0",
    "MD5sum": "57cb979e7f4a3ffa6efe0c8844ae2d8e",
    "NeedsCompilation": "no",
    "Title": "Dynamic document tools",
    "Description": "A set of functions to create and interact with dynamic documents and vignettes.",
    "biocViews": [
      "Infrastructure",
      "ReportWriting",
      "Software"
    ],
    "Author": "R. Gentleman, Jeff Gentry",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/DynDoc_1.38.0.tgz",
    "hasREADME": false,
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    "Version": "1.2.0",
    "Imports": [
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      "matrixStats",
      "plotrix",
      "gWidgetsRGtk2"
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    "Suggests": [
      "SLqPCR",
      "qpcrNorm",
      "qpcR",
      "knitr"
    ],
    "License": "GPL (>=2)",
    "MD5sum": "bf824722cb5430b722fc9be3390a0902",
    "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": [
      "GeneExpression",
      "Software",
      "qPCR"
    ],
    "Author": "Le Pape Sylvain",
    "Maintainer": "Le Pape Sylvain <sylvain.le.pape@univ-poitiers.fr>",
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    "win64.binary.ver": "bin/windows64/contrib/2.16/EasyqpcR_1.2.0.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/EasyqpcR_1.2.0.tgz",
    "vignettes": [
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    ],
    "vignetteTitles": [
      "EasyqpcR"
    ],
    "hasREADME": false,
    "hasNEWS": true,
    "hasINSTALL": false,
    "hasLICENSE": false,
    "Rfiles": [
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      "methods",
      "parallel",
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      "biomaRt (>= 2.16.0)",
      "edgeR (>= 3.2.4)",
      "Biostrings (>= 2.28.0)",
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    "Enhances": [
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      "DESeq",
      "ShortRead"
    ],
    "License": "Artistic-2.0",
    "MD5sum": "610122343542e581218286c2d0782a50",
    "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",
      "Genetics",
      "Preprocessing",
      "RNAseq",
      "Software"
    ],
    "Author": "Nicolas Delhomme, Ismael Padioleau",
    "Maintainer": "Nicolas Delhomme <delhomme@embl.de>",
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    "win64.binary.ver": "bin/windows64/contrib/2.16/easyRNASeq_1.6.4.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/easyRNASeq_1.6.4.tgz",
    "vignettes": [
      "vignettes/easyRNASeq/inst/doc/easyRNASeq.pdf"
    ],
    "vignetteTitles": [
      "RNA-Seq"
    ],
    "hasREADME": false,
    "hasNEWS": true,
    "hasINSTALL": false,
    "hasLICENSE": false,
    "Rfiles": [
      "vignettes/easyRNASeq/inst/doc/easyRNASeq.R"
    ],
    "dependsOnMe": [
      "RnaSeqTutorial"
    ]
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  "EBarrays": {
    "Package": "EBarrays",
    "Version": "2.24.0",
    "Depends": [
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      "Biobase",
      "lattice",
      "methods"
    ],
    "Imports": [
      "Biobase",
      "cluster",
      "graphics",
      "grDevices",
      "lattice",
      "methods",
      "stats"
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    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "MD5sum": "93ffaa72f59fe3013146279b4730d7e2",
    "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",
      "Software"
    ],
    "Author": "Ming Yuan, Michael Newton, Deepayan Sarkar and Christina Kendziorski",
    "Maintainer": "Ming Yuan <myuan@isye.gatech.edu>",
    "source.ver": "src/contrib/EBarrays_2.24.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.16/EBarrays_2.24.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.16/EBarrays_2.24.0.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/EBarrays_2.24.0.tgz",
    "vignettes": [
      "vignettes/EBarrays/inst/doc/vignette.pdf"
    ],
    "vignetteTitles": [
      "Introduction to EBarrays"
    ],
    "hasREADME": false,
    "hasNEWS": false,
    "hasINSTALL": false,
    "hasLICENSE": false,
    "Rfiles": [
      "vignettes/EBarrays/inst/doc/vignette.R"
    ],
    "dependsOnMe": [
      "EBcoexpress",
      "gaga",
      "geNetClassifier"
    ],
    "suggestsMe": [
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  "EBcoexpress": {
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    "Version": "1.4.0",
    "Depends": [
      "EBarrays",
      "mclust",
      "minqa"
    ],
    "Suggests": [
      "graph",
      "igraph",
      "colorspace"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "MD5sum": "8aa07723163fec6861ff69fff5177869",
    "NeedsCompilation": "yes",
    "Title": "EBcoexpress for Differential Co-Expression Analysis",
    "Description": "An Empirical Bayesian Approach to Differential Co-Expression Analysis at the Gene-Pair Level",
    "Author": "John A. Dawson",
    "Maintainer": "John A. Dawson <jadawson@wisc.edu>",
    "source.ver": "src/contrib/EBcoexpress_1.4.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.16/EBcoexpress_1.4.0.zip",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/EBcoexpress_1.4.0.tgz",
    "vignettes": [
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    ],
    "vignetteTitles": [
      "EBcoexpress Demo"
    ],
    "hasREADME": false,
    "hasNEWS": false,
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    "hasLICENSE": false,
    "Rfiles": [
      "vignettes/EBcoexpress/inst/doc/EBcoexpressVignette.R"
    ],
    "biocViews": [
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  "EBImage": {
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    "Imports": [
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      "graphics",
      "stats",
      "abind",
      "utils",
      "tiff",
      "jpeg",
      "png",
      "locfit"
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    "License": "LGPL",
    "Archs": "i386, x64",
    "MD5sum": "72cf5f7aa0092a5e35d700f0ee071329",
    "NeedsCompilation": "yes",
    "Title": "Image processing toolbox for R",
    "Description": "EBImage is an R package which provides general purpose functionality for the reading, writing, processing and analysis of images. Furthermore, in the context of microscopy based cellular assays, EBImage offers tools to transform the images, segment cells and extract quantitative cellular descriptors.",
    "biocViews": [
      "Software",
      "Visualization"
    ],
    "Author": "Gregoire Pau, Andrzej Oles, Mike Smith, Oleg Sklyar, Wolfgang Huber",
    "Maintainer": "Andrzej Oles <andrzej.oles@embl.de>",
    "source.ver": "src/contrib/EBImage_4.2.1.tar.gz",
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    "win64.binary.ver": "bin/windows64/contrib/2.16/EBImage_4.2.1.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/EBImage_4.2.1.tgz",
    "vignettes": [
      "vignettes/EBImage/inst/doc/EBImage-introduction.pdf"
    ],
    "vignetteTitles": [
      "Introduction to EBImage"
    ],
    "hasREADME": false,
    "hasNEWS": true,
    "hasINSTALL": false,
    "hasLICENSE": false,
    "Rfiles": [
      "vignettes/EBImage/inst/doc/EBImage-introduction.R"
    ],
    "dependsOnMe": [
      "CRImage",
      "HD2013SGI",
      "imageHTS"
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    "suggestsMe": [
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  "ecolitk": {
    "Package": "ecolitk",
    "Version": "1.32.0",
    "Depends": [
      "R (>= 2.10)"
    ],
    "Imports": [
      "Biobase",
      "graphics",
      "methods"
    ],
    "Suggests": [
      "ecoliLeucine",
      "ecolicdf",
      "graph",
      "multtest",
      "affy"
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    "License": "GPL (>= 2)",
    "MD5sum": "ff13606dce31aeb0b7c404a3f8390589",
    "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": [
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      "Software",
      "Visualization"
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    "Author": "Laurent Gautier",
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    "hasREADME": false,
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    "Rfiles": [
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    "Package": "EDASeq",
    "Version": "1.6.0",
    "Depends": [
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      "ShortRead (>= 1.11.42)",
      "Rsamtools (>= 1.5.75)",
      "aroma.light"
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      "graphics",
      "BiocGenerics",
      "IRanges (>= 1.13.9)",
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    "Suggests": [
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      "leeBamViews",
      "edgeR",
      "DESeq"
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    "License": "Artistic-2.0",
    "MD5sum": "7b4485e03361b7b92453ffb992e1a730",
    "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": [
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      "HighThroughputSequencing",
      "Preprocessing",
      "QualityControl",
      "RNAseq",
      "Software"
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    "Author": "Davide Risso and Sandrine Dudoit",
    "Maintainer": "Davide Risso <risso.davide@gmail.com>",
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    "vignettes": [
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    ],
    "vignetteTitles": [
      "EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq data"
    ],
    "hasREADME": false,
    "hasNEWS": true,
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    "hasLICENSE": false,
    "Rfiles": [
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    ],
    "Suggests": [
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    "License": "GPL (>=2)",
    "Archs": "i386, x64",
    "MD5sum": "f3781187ab1b5974ed2400eb542cf127",
    "NeedsCompilation": "yes",
    "Title": "Empirical analysis of digital gene expression data in R",
    "Description": "Differential expression analysis of RNA-seq and digital gene expression profiles with biological replication.  Uses empirical Bayes estimation and exact tests based on the negative binomial distribution.  Also useful for differential signal analysis with other types of genome-scale count data.",
    "biocViews": [
      "Bioinformatics",
      "ChIPseq",
      "DifferentialExpression",
      "HighThroughputSequencing",
      "RNAseq",
      "SAGE",
      "Software"
    ],
    "Author": "Mark Robinson <mrobinson@wehi.edu.au>, Davis McCarthy <dmccarthy@wehi.edu.au>, Yunshun Chen <yuchen@wehi.edu.au>, Aaron Lun <alun@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>",
    "Maintainer": "Mark Robinson <mrobinson@wehi.edu.au>, Davis McCarthy <dmccarthy@wehi.edu.au>, Yunshun Chen <yuchen@wehi.edu.au>, Gordon Smyth <smyth@wehi.edu.au>",
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    "vignettes": [
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      "vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf"
    ],
    "vignetteTitles": [
      "edgeR Vignette",
      "edgeRUsersGuide.pdf"
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    "dependsOnMe": [
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      "easyRNASeq",
      "manta"
    ],
    "importsMe": [
      "ArrayExpressHTS",
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      "MEDIPS",
      "Repitools",
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    "suggestsMe": [
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      "pasilla",
      "Repitools",
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    "Version": "1.0.3",
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      "ChemmineR (>= 2.11.18)",
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    "Imports": [
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      "snowfall",
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      "methods",
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      "RCurl",
      "digest",
      "BiocGenerics"
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    "License": "Artistic-2.0",
    "MD5sum": "745a6c6f905db94e135c5c3ae9b86711",
    "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": [
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      "Clustering",
      "DataImport",
      "Infrastructure",
      "Proteomics",
      "Software"
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    "Author": "Kevin Horan",
    "Maintainer": "Kevin Horan <khoran@cs.ucr.edu>",
    "source.ver": "src/contrib/eiR_1.0.3.tar.gz",
    "mac.binary.ver": "bin/macosx/contrib/2.16/eiR_1.0.3.tgz",
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    "hasREADME": true,
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  "eisa": {
    "Package": "eisa",
    "Version": "1.12.2",
    "Depends": [
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      "Biobase (>= 2.17.8)",
      "AnnotationDbi",
      "methods"
    ],
    "Imports": [
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      "Category",
      "genefilter",
      "DBI"
    ],
    "Suggests": [
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      "Matrix",
      "GOstats",
      "GO.db",
      "KEGG.db",
      "biclust",
      "MASS",
      "xtable",
      "ALL",
      "hgu95av2.db",
      "targetscan.Hs.eg.db",
      "org.Hs.eg.db"
    ],
    "License": "GPL (>= 2)",
    "MD5sum": "cf72f15b8f6755974ae9165c865bd69c",
    "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",
      "GeneExpression",
      "Microarray",
      "Software",
      "Visualization"
    ],
    "Author": "Gabor Csardi <csardi.gabor@gmail.com>",
    "Maintainer": "Gabor Csardi <csardi.gabor@gmail.com>",
    "source.ver": "src/contrib/eisa_1.12.2.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.16/eisa_1.12.2.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.16/eisa_1.12.2.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/eisa_1.12.2.tgz",
    "vignettes": [
      "vignettes/eisa/inst/doc/EISA_biclust.pdf",
      "vignettes/eisa/inst/doc/EISA_tutorial.pdf",
      "vignettes/eisa/inst/doc/ISA_internals.pdf",
      "vignettes/eisa/inst/doc/tissues.pdf"
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      "Software"
    ],
    "Author": "Simon G. Coetzee <simon@simoncoetzee.com> and Houtan Noushmehr, PhD <houtan@usp.br>",
    "Maintainer": "Simon G. Coetzee <simon@simoncoetzee.com>",
    "URL": "http://coetzeeseq.usc.edu/publication/Coetzee_SG_et_al_2012/",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/FunciSNP_1.2.0.tgz",
    "vignettes": [
      "vignettes/FunciSNP/inst/doc/FunciSNP_vignette.pdf",
      "vignettes/FunciSNP/inst/doc/UCSC_genomeviewer_glioma.pdf"
    ],
    "vignetteTitles": [
      "FunciSNP Vignette",
      "UCSC_genomeviewer_glioma.pdf"
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    "hasNEWS": true,
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      "R (>= 2.8.0)",
      "Biobase",
      "coda",
      "EBarrays",
      "mgcv"
    ],
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    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "MD5sum": "2b97d12d19da58784e42f56bac976f8d",
    "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": [
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      "MassSpectrometry",
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    "Author": "David Rossell <rosselldavid@gmail.com>.",
    "Maintainer": "David Rossell <rosselldavid@gmail.com>",
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      "gageData",
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      "KEGG.db",
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    "License": "GPL (>=2.0)",
    "MD5sum": "911fa0f4549ab068940c9ab39a4f9ee8",
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    "Title": "Generally Applicable Gene-set Enrichment for Pathway Analysis",
    "Description": "GAGE is a published method for gene set 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.",
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      "Software",
      "TwoChannel"
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    "Author": "Weijun Luo",
    "Maintainer": "Weijun Luo <luo_weijun@yahoo.com>",
    "URL": "http://www.biomedcentral.com/1471-2105/10/161",
    "source.ver": "src/contrib/gage_2.10.0.tar.gz",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/gage_2.10.0.tgz",
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      "vignettes/gage/inst/doc/gage.pdf"
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    "vignetteTitles": [
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    ],
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    "hasNEWS": true,
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    "hasLICENSE": false,
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      "rJava (>= 0.4)",
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    "License": "GPL version 2 or newer",
    "MD5sum": "513895c1b084e9ab0b145c33ac51bddd",
    "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": [
      "Annotation",
      "ConnectTools",
      "DataImport",
      "GraphsAndNetworks",
      "NetworkVisualization",
      "Software"
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    "Author": "Paul Shannon <pshannon@systemsbiology.org>",
    "Maintainer": "Christopher Bare <cbare@systemsbiology.org>",
    "URL": "http://gaggle.systemsbiology.net/docs/geese/r/",
    "source.ver": "src/contrib/gaggle_1.28.0.tar.gz",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/gaggle_1.28.0.tgz",
    "vignettes": [
      "vignettes/gaggle/inst/doc/gaggle.pdf"
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    "hasNEWS": false,
    "hasINSTALL": false,
    "hasLICENSE": false,
    "Rfiles": [
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    ],
    "License": "GPL-2",
    "MD5sum": "57cd8ad1ec3b6f45466eab2095e34c78",
    "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": [
      "CopyNumberVariants",
      "Software",
      "aCGH"
    ],
    "Author": "Sandro Morganella et al.",
    "Maintainer": "S. Morganella <morganellaalx@gmail.com>",
    "source.ver": "src/contrib/gaia_2.4.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.16/gaia_2.4.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.16/gaia_2.4.0.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/gaia_2.4.0.tgz",
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      "methods",
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      "Category",
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      "bigmemoryExtras (>= 1.1.2)",
      "Matrix (>= 1.0.9)",
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      "genefilter",
      "AnnotationDbi"
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      "KEGG.db",
      "reactome.db",
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    "License": "Artistic-2.0",
    "OS_type": "unix",
    "MD5sum": "15c10d41abe337acfec4532966bc3b42",
    "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": [
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      "Bioinformatics",
      "Microarray",
      "Pathways",
      "Software"
    ],
    "Author": "Thomas Sandmann <sandmann.thomas@gene.com>, Richard Bourgon <bourgon.richard@gene.com> and Sarah Kummerfeld <kummerfeld.sarah@gene.com>",
    "Maintainer": "Thomas Sandmann <sandmann.thomas@gene.com>",
    "source.ver": "src/contrib/gCMAP_1.4.1.tar.gz",
    "mac.binary.ver": "bin/macosx/contrib/2.16/gCMAP_1.4.1.tgz",
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    "hasNEWS": true,
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    "hasLICENSE": false,
    "Rfiles": [
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      "vignettes/gCMAP/inst/doc/gCMAP.R",
      "vignettes/gCMAP/inst/doc/keggReactome.R"
    ],
    "dependsOnMe": [
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  },
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    "Package": "gCMAPWeb",
    "Version": "1.0.3",
    "Depends": [
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      "gCMAP (>= 1.3.0)",
      "R (>= 2.15.0)",
      "yaml"
    ],
    "Imports": [
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      "annotate",
      "AnnotationDbi",
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      "graphics",
      "grDevices",
      "GSEABase",
      "hwriter",
      "IRanges",
      "methods",
      "parallel",
      "Rook",
      "stats",
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    ],
    "Suggests": [
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      "org.Mm.eg.db",
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      "hgfocus.db",
      "ArrayExpress",
      "hgu133a.db",
      "mgug4104a.db",
      "RUnit"
    ],
    "License": "Artistic-2.0",
    "MD5sum": "8ff5dc953a20d199d24958ce4c618292",
    "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": [
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      "GUI",
      "GeneSetEnrichment",
      "Software",
      "Visualization"
    ],
    "Author": "Thomas Sandmann",
    "Maintainer": "Thomas Sandmann <sandmann.thomas@gene.com>",
    "source.ver": "src/contrib/gCMAPWeb_1.0.3.tar.gz",
    "mac.binary.ver": "bin/macosx/contrib/2.16/gCMAPWeb_1.0.3.tgz",
    "vignettes": [
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      "vignettes/gCMAPWeb/inst/doc/referenceDatasets.pdf",
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    ],
    "vignetteTitles": [
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      "Recreating the Broad Connectivity Map v1",
      "tutorial.pdf"
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    "hasREADME": false,
    "hasNEWS": true,
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    "hasLICENSE": false,
    "Rfiles": [
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  "gcrma": {
    "Package": "gcrma",
    "Version": "2.32.0",
    "Depends": [
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      "affy (>= 1.23.2)",
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    "Imports": [
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    "Suggests": [
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    "License": "LGPL",
    "Archs": "i386, x64",
    "MD5sum": "5295580f9b5ddc10ee1d40e17c383dcd",
    "NeedsCompilation": "yes",
    "Title": "Background Adjustment Using Sequence Information",
    "Description": "Background adjustment using sequence information",
    "biocViews": [
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    "Author": "Jean(ZHIJIN) Wu, Rafael Irizarry with contributions from James MacDonald <jmacdon@med.umich.edu> Jeff Gentry",
    "Maintainer": "Z. Wu <zwu@stat.brown.edu>",
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    "win64.binary.ver": "bin/windows64/contrib/2.16/gcrma_2.32.0.zip",
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    "vignettes": [
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    "vignetteTitles": [
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    "hasREADME": false,
    "hasNEWS": true,
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    "hasLICENSE": false,
    "Rfiles": [
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    "dependsOnMe": [
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      "affylmGUI",
      "affyPLM",
      "bgx",
      "maskBAD",
      "simpleaffy",
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    ],
    "importsMe": [
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      "simpleaffy",
      "virtualArray"
    ],
    "suggestsMe": [
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      "ArrayTools",
      "BiocCaseStudies",
      "panp"
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  "genArise": {
    "Package": "genArise",
    "Version": "1.36.0",
    "Depends": [
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      "locfit",
      "tkrplot",
      "methods"
    ],
    "Imports": [
      "graphics",
      "grDevices",
      "methods",
      "stats",
      "tcltk",
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      "xtable"
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    "License": "file LICENSE",
    "License_restricts_use": "yes",
    "MD5sum": "d7c8300cd31eeaf2269a6c479a4968ca",
    "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": [
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      "Preprocessing",
      "Software",
      "TwoChannel"
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    "Author": "Ana Patricia Gomez Mayen <pgomez@ifc.unam.mx>,\\\\ Gustavo Corral Guille <gcorral@ifc.unam.mx>, \\\\ Lina Riego Ruiz <lriego@ifc.unam.mx>,\\\\ Gerardo Coello Coutino <gcoello@ifc.unam.mx>",
    "Maintainer": "IFC Development Team <info-genarise@ifc.unam.mx>",
    "URL": "http://www.ifc.unam.mx/genarise",
    "source.ver": "src/contrib/genArise_1.36.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.16/genArise_1.36.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.16/genArise_1.36.0.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/genArise_1.36.0.tgz",
    "vignettes": [
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    "vignetteTitles": [
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    "hasREADME": false,
    "hasNEWS": false,
    "hasINSTALL": false,
    "hasLICENSE": true,
    "Rfiles": [
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  "GeneAnswers": {
    "Package": "GeneAnswers",
    "Version": "2.2.1",
    "Depends": [
      "R (>= 2.10.0)",
      "igraph",
      "RCurl",
      "annotate",
      "Biobase (>= 1.12.0)",
      "methods",
      "XML",
      "RSQLite",
      "MASS",
      "Heatplus",
      "RColorBrewer"
    ],
    "Imports": [
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      "annotate"
    ],
    "Suggests": [
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      "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": "b760038b10171c3af03bcc8eb377163a",
    "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": [
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      "GraphsAndNetworks",
      "Infrastructure",
      "Software",
      "Visualization"
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    "Author": "Gang Feng, Pan Du, Tian Xia, Warren Kibbe and Simon Lin",
    "Maintainer": "Gang Feng <g-feng@northwestern.edu> , Pan Du <du4pan1@gmail.com> and Tian Xia <isutian@gmail.com>",
    "source.ver": "src/contrib/GeneAnswers_2.2.1.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.16/GeneAnswers_2.2.1.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.16/GeneAnswers_2.2.1.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/GeneAnswers_2.2.1.tgz",
    "vignettes": [
      "vignettes/GeneAnswers/inst/doc/geneAnswers.pdf"
    ],
    "vignetteTitles": [
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    ],
    "hasREADME": false,
    "hasNEWS": true,
    "hasINSTALL": false,
    "hasLICENSE": false,
    "Rfiles": [
      "vignettes/GeneAnswers/inst/doc/GeneAnswersCWAnnotation.R",
      "vignettes/GeneAnswers/inst/doc/geneAnswers.R",
      "vignettes/GeneAnswers/inst/doc/geneFunctionSummarize.R"
    ]
  },
  "GENE.E": {
    "Package": "GENE.E",
    "Version": "1.1.0",
    "Depends": [
      "R (>= 2.7.0)",
      "h5r (>= 1.4.1)",
      "RCurl (>= 1.6-6)"
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    "Imports": [
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      "RCurl"
    ],
    "Suggests": [
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      "knitr",
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    "License": "GPL-2",
    "MD5sum": "f87b72ac1859fc62e5a14bf43c654472",
    "NeedsCompilation": "no",
    "Title": "Interact with GENE-E from R",
    "Description": "Interactive exploration of matrices in GENE-E.",
    "biocViews": [
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      "Software"
    ],
    "Author": "Joshua Gould",
    "Maintainer": "Joshua Gould <jgould@broadinstitute.org>",
    "URL": "http://www.broadinstitute.org/cancer/software/GENE-E",
    "SystemRequirements": "GENE-E software.",
    "VignetteBuilder": "knitr",
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    "win64.binary.ver": "bin/windows64/contrib/2.16/GENE.E_1.1.0.zip",
    "mac.binary.ver": "bin/macosx/contrib/2.16/GENE.E_1.1.0.tgz",
    "vignettes": [
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    "hasREADME": false,
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    "MD5sum": "0a1d8652c7a0348de45b1fef1e6a3978",
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    "Author": "VJ Carey <stvjc@channing.harvard.edu>",
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    "Description": "Classes for storing very large GWAS data sets and annotation, and functions for GWAS data cleaning and analysis.",
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    "Author": "Stephanie M. Gogarten, Cathy Laurie, Tushar Bhangale, Matthew P. Conomos, Cecelia Laurie, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Jess Shen, Rohit Swarnkar",
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    "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.",
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    "MD5sum": "114a0f5619258046fb232504a6d19232",
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    "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.",
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    "Title": "Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree",
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    "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.",
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    "Author": "Alexander Ploner <Alexander.Ploner@ki.se>",
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    "License": "GPL (>= 2)",
    "MD5sum": "ddc86bb62b0e38de1af054e25750d4b0",
    "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": [
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      "Software",
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      "Visualization"
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    "Author": "Reid F. Thompson <rthompso@aecom.yu.edu>, John M. Greally <jgreally@aecom.yu.edu>, with contributions from Mark Reimers <mreimers@vcu.edu>",
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    "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",
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    "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).",
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    "Title": "Tools for analyzing Micro Array experiments",
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    "Title": "Visualize artificial correlation in microarray data",
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    "Title": "Integration of Microarray Data for Meta-analysis",
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    "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.",
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    "Title": "Methods for visualization and statistics on DNA methylation data",
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    "Title": "Similarities of Ordered Gene Lists",
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    "Title": "Outlier detection using quantile regression on the M-A scatterplots of high-throughput data",
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    "URL": "http://www.korea.ac.kr/~stat2242/",
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    "Title": "Protein annotation data package builder",
    "Description": "Processing annotation data from public data repositories and building protein-centric annotation data packages.",
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    "URL": "http://www.biosino.org/PAnnBuilder",
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    "NeedsCompilation": "no",
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    "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.",
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    "License": "Artistic-2.0",
    "MD5sum": "3552674a91e5a6aa51457f0801c9063b",
    "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.",
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    "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.",
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    "NeedsCompilation": "no",
    "Title": "Parametric And Resistant Outlier DYtection",
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    "License": "GPL-3",
    "MD5sum": "786ad718d12f9dfb63c77f7d2e23b2a3",
    "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.",
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      "Software"
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    "Author": "Bhaskar Dutta <bhaskar.dutta@gmail.com>, Anders Wallqvist <awallqvist@bhsai.org>, and Jaques Reifman <jreifman@bhsai.org>",
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    "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.",
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    "MD5sum": "3bc8f4c63b35939e4f6774a408972a4b",
    "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.",
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    "Author": "Yue Li",
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    "URL": "http://www.cs.utoronto.ca/~yueli/software.html",
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    "MD5sum": "af62b47b59859ae0c94e6654a4b3ab38",
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    "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.",
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    "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.",
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    "Author": "Camille Maumet <Rmagpie@gmail.com>, with contributions from C. Ambroise J. Zhu",
    "Maintainer": "Camille Maumet <Rmagpie@gmail.com>",
    "URL": "http://www.bioconductor.org/",
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    "MD5sum": "9d9505bf642eabdc23b07ff78e81a365",
    "NeedsCompilation": "no",
    "Title": "R interface to the MAPPER database of transcription factor binding sites",
    "Description": "The RMAPPER package allows you to retrieve a set of predicted transcription factor binding sites from the MAPPER database (http://genome.ufl.edu/mapper/) through a simple HTTP request.",
    "biocViews": [
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      "Software"
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    "Author": "VJ Carey <stvjc@channing.harvard.edu>",
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    "License": "Artistic-2.0",
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    "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.",
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    "Author": "Michael Stravs, Emma Schymanski",
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    "License": "Artistic-2.0",
    "MD5sum": "bf1e198fb0e98f4a41c45acafae2a5e4",
    "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": [
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    "Author": "Charles Cheung and Arnaud Droit and Raphael Gottardo",
    "Maintainer": "Arnaud Droit <arnaud.droit@crchuq.ulaval.ca> and Raphael Gottardo <rgottard@fhcrc.org>",
    "URL": "http://www.rglab.org",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/rMAT_3.10.0.tgz",
    "vignettes": [
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    "License": "Artistic-2.0",
    "MD5sum": "59e66158ee7dd513c0dc744102e3671b",
    "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": [
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    "Author": "Francesco Favero",
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    "License": "Artistic-2.0",
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    "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": [
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    "Author": "Bernd Fischer <bernd.fischer@embl.de>",
    "Maintainer": "Bernd Fischer <bernd.fischer@embl.de>",
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    "Rfiles": [
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  "RNAither": {
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    "Version": "2.8.0",
    "Depends": [
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      "topGO",
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      "prada"
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    "Imports": [
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    "License": "Artistic-2.0",
    "MD5sum": "1a9819b5ae13bc435b24e3a3557e5878",
    "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": [
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      "Visualization"
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    "Author": "Nora Rieber and Lars Kaderali, University of Heidelberg, Viroquant Research Group Modeling, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany",
    "Maintainer": "Nora Rieber <RNAither@gmx.de>",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/RNAither_2.8.0.tgz",
    "vignettes": [
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    "vignetteTitles": [
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    "Version": "2.14.0",
    "Depends": [
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      "methods",
      "xmapcore",
      "Biobase",
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    "Imports": [
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      "edgeR",
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    ],
    "License": "GPL-2",
    "MD5sum": "4f2274328c2c31e318894a59f16f7d69",
    "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",
      "Bioinformatics",
      "DifferentialExpression",
      "GeneExpression",
      "HighThroughputSequencing",
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      "ReportWriting",
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    ],
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    "Title": "Rsubread: an R package for the alignment, summarization and analyses of next-generation sequencing data",
    "Description": "This R package provides facilities for processing the read data generated by the next-gen sequencing technologies. These facilities include quality assessment, read alignment, read summarization, exon-exon junction detection, absolute expression calling and SNP discovery. This package can be used to process both short and long reads. It supports major sequencing platforms such as Illumina GA/HiSeq, Roche 454, ABI SOLiD and Ion Torrent.",
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    "Archs": "i386, x64",
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    "Author": "Frederic Fournier <frederic.fournier@crchuq.ulaval.ca>, Charles Joly Beauparlant <charles.joly-beauparlant@crchul.ulaval.ca>, Rene Paradis <rene.paradis@genome.ulaval.ca>, Arnaud Droit <arnaud.droit@crchuq.ulaval.ca>",
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    "License": "LGPL-3",
    "MD5sum": "5d183c02cba8610897cacd4b1c9db76f",
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    "Title": "Open-source toolkit to analyse data from xCELLigence System (RTCA)",
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    "NeedsCompilation": "no",
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    "Description": "the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms.",
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      "geneLenDataBase",
      "GenomicFeatures",
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      "gmapR",
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    "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.",
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    "Author": "Nianhua Li, MT Morgan",
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    "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.",
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    "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.",
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    "Title": "Statistical Analysis of the GeneChip",
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    "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.",
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    "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.",
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    "Title": "Gene Set Enrichment Analysis (GSEA) of RNA-Seq Data: integrating differential expression and splicing",
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    "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.",
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      "Microarray",
      "Software",
      "Visualization"
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    "Author": "Adrian Alexa, Jorg Rahnenfuhrer",
    "Maintainer": "Adrian Alexa <alexa@mpi-inf.mpg.de>",
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      "topGO"
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    "Package": "TransView",
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    "Depends": [
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    "Archs": "i386, x64",
    "MD5sum": "9c35ee122edb77be19eb6fdd23ea64d0",
    "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": [
      "Bioinformatics",
      "ChIPseq",
      "Clustering",
      "DNAMethylation",
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      "HighThroughputSequencing",
      "Methylseq",
      "Microarray",
      "MultipleComparisons",
      "RNAseq",
      "Sequencing",
      "Software",
      "Transcription",
      "Visualization"
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    "Author": "Julius Muller",
    "Maintainer": "Julius Muller <ju-mu@alumni.ethz.ch>",
    "URL": "http://bioconductor.org/packages/release/bioc/html/TransView.html",
    "source.ver": "src/contrib/TransView_1.4.5.tar.gz",
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    "vignettes": [
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    "hasNEWS": true,
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    "Package": "triform",
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      "BiocGenerics"
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    "License": "GPL-2",
    "MD5sum": "8dcf4b38e7b77f68bb6c4707de601a47",
    "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": [
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      "Sequencing",
      "Software"
    ],
    "Author": "Karl Kornacker Developer [aut], Tony Håndstad Developer [aut, cre]",
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    "vignettes": [
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  "trigger": {
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    "Archs": "i386, x64",
    "MD5sum": "77eef48eef1c5a6337b9f58f4f38096d",
    "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": [
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      "GeneticVariability",
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      "Microarray",
      "SNP",
      "Software"
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    "Author": "Lin S. Chen <lchen@health.bsd.uchicago.edu>, Dipen P. Sangurdekar <dps@genomics.princeton.edu> and John D. Storey <jstorey@princeton.edu>",
    "Maintainer": "John D. Storey <jstorey@princeton.edu>",
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    "Archs": "i386, x64",
    "MD5sum": "37419b7889d32eb75b70a19284bc9fb1",
    "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": [
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      "SequenceMatching",
      "Software"
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    "Author": "Jiri Hon, Matej Lexa, Tomas Martinek and Kamil Rajdl with contributions from Daniel Kopecek",
    "Maintainer": "Jiri Hon <jiri.hon@gmail.com>",
    "URL": "http://www.fi.muni.cz/~lexa/triplex/",
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    "MD5sum": "ca79d67753f17b38b6067c4dd5f11b2c",
    "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": [
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      "Microarray",
      "Software"
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    "Author": "Jeffrey T. Leek <jtleek@jhu.edu>",
    "Maintainer": "Jeffrey T. Leek <jtleek@jhu.edu>",
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    "Enhances": [
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    "Archs": "i386, x64",
    "MD5sum": "2c95d68c821ed1b1ec0df2f6b11f235b",
    "NeedsCompilation": "yes",
    "Title": "Transcription Start Site Identification",
    "Description": "Identify and normalize transcription start sites in high-throughput sequencing data.",
    "biocViews": [
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      "HighThroughputSequencing",
      "Preprocessing",
      "RNAseq",
      "Sequencing",
      "Software"
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    "Author": "Clemens Kreutz, Julian Gehring",
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    "URL": "http://julian-gehring.github.com/TSSi/",
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    "vignetteTitles": [
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      "limma (>= 1.7.0)",
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    "Imports": [
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      "affy",
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    "Suggests": [
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    "License": "LGPL",
    "Archs": "i386, x64",
    "MD5sum": "9229b3eb7731c613c9610bf50f2b66e7",
    "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": [
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      "DNAMethylation",
      "Microarray",
      "OneChannel",
      "Preprocessing",
      "Software",
      "TwoChannel"
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    "Author": "Maarten van Iterson and Chantal van Leeuwen",
    "Maintainer": "Maarten van Iterson <mviterson@gmail.com>",
    "URL": "http://www.humgen.nl/MicroarrayAnalysisGroup.html",
    "source.ver": "src/contrib/TurboNorm_1.8.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.16/TurboNorm_1.8.0.zip",
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    "mac.binary.ver": "bin/macosx/contrib/2.16/TurboNorm_1.8.0.tgz",
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    "hasREADME": false,
    "hasNEWS": true,
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    "Rfiles": [
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    "Package": "tweeDEseq",
    "Version": "1.6.2",
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    "Imports": [
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      "limma",
      "edgeR",
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      "cqn"
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    "Suggests": [
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      "xtable"
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    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "MD5sum": "bdb529c7c0cd4bd3cb985139a905a9ae",
    "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": [
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      "HighThroughputSequencing",
      "RNAseq",
      "Software",
      "Statistics"
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    "Author": "Juan R Gonzalez <jrgonzalez@creal.cat> and Mikel Esnaola <mesnaola@creal.cat> (with contributions from Robert Castelo <robert.castelo@upf.edu>)",
    "Maintainer": "Juan R Gonzalez <jrgonzalez@creal.cat>",
    "URL": "http://www.creal.cat/jrgonzalez/software.htm",
    "source.ver": "src/contrib/tweeDEseq_1.6.2.tar.gz",
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    "vignettes": [
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    ],
    "vignetteTitles": [
      "tweeDEseq: analysis of RNA-seq data using the Poisson-Tweedie family of distributions"
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    "Rfiles": [
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    "Package": "twilight",
    "Version": "1.36.0",
    "Depends": [
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      "splines (>= 2.2.0)",
      "stats (>= 2.2.0)",
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    "Imports": [
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      "grDevices",
      "stats"
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    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "MD5sum": "ed3f40dcf66bb5338322c5f4338b6478",
    "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": [
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      "DifferentialExpression",
      "Microarray",
      "MultipleComparisons",
      "Software"
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    "Author": "Stefanie Scheid <stefanie.scheid@gmx.de>",
    "Maintainer": "Stefanie Scheid <stefanie.scheid@gmx.de>",
    "URL": "http://compdiag.molgen.mpg.de/software/twilight.shtml",
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      "Estimation of Local False Discovery Rates"
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    "Rfiles": [
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    "License": "BSD",
    "MD5sum": "5de8bb833d2a0d14e1321fbd3bc7c789",
    "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": [
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      "Software"
    ],
    "Author": "Duncan Temple Lang Robert Gentleman (<rgentlem@fhcrc.org>)",
    "Maintainer": "Duncan Temple Lang <duncan@wald.ucdavis.edu>",
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    "Rfiles": [
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    ],
    "htmlTitles": [
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    "dependsOnMe": [
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  "UniProt.ws": {
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    "Version": "2.0.1",
    "Depends": [
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      "RCurl",
      "methods",
      "utils"
    ],
    "Imports": [
      "BiocGenerics",
      "AnnotationDbi"
    ],
    "Suggests": [
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    "License": "Artistic License 2.0",
    "MD5sum": "01dbd574a7d5bf1279a9f93880e0484b",
    "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",
      "Software"
    ],
    "Author": "Marc Carlson",
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