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    "License": "GPL",
    "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.",
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      "OneChannel",
      "Preprocessing",
      "Software"
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    "Author": "Yongming Andrew Sun",
    "Maintainer": "Yongming Andrew Sun <sunya@appliedbiosystems.com>",
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      "stats"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "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.",
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      "Software"
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    "Author": "Sean Davis <sdavis2@mail.nih.gov>",
    "Maintainer": "Sean Davis <sdavis2@mail.nih.gov>",
    "URL": "http://watson.nci.nih.gov/~sdavis",
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    "win.binary.ver": "bin/windows/contrib/2.14/ACME_2.10.0.zip",
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      "ff"
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    "Enhances": [
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    "Archs": "i386, x64",
    "Title": "Analysis of 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.",
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      "Software"
    ],
    "Author": "Ramon Diaz-Uriarte <rdiaz02@gmail.com>, Oscar M. Rueda <rueda.om@gmail.com>. Wavelet-based aCGH smoothing code from Li Hsu <lih@fhcrc.org> and Douglas Grove <dgrove@fhcrc.org>. Imagemap code from Barry Rowlingson <B.Rowlingson@lancaster.ac.uk>. HaarSeg code from Erez Ben-Yaacov; downloaded from <http://www.ee.technion.ac.il/people/YoninaEldar/Info/software/HaarSeg.htm>",
    "Maintainer": "Ramon Diaz-Uriarte <rdiaz02@gmail.com>",
    "URL": "http://launchpad.net/adacgh, http://wavicgh.bioinfo.cnio.es",
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    "Imports": [
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    "License": "GPL Version 2 or later",
    "Title": "Agreement of Differential Expression Analysis",
    "Description": "A tool to evaluate agreement of differential expression for cross-species genomics",
    "biocViews": [
      "Bioinformatics",
      "GeneExpression",
      "Genetics",
      "Microarray",
      "Software"
    ],
    "Author": "Stan Pounds <stanley.pounds@stjude.org>; Cuilan Lani Gao <cuilan.gao@stjude.org>",
    "Maintainer": "Cuilan lani Gao <cuilan.gao@stjude.org>",
    "source.ver": "src/contrib/AGDEX_1.2.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/AGDEX_1.2.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/AGDEX_1.2.0.zip",
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    "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.",
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    "Maintainer": "Martin Morgan <mtmorgan@fhcrc.org>",
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    "Version": "1.20.0",
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      "test3cdf",
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      "estrogen",
      "annaffy",
      "gcrma"
    ],
    "License": "LGPL",
    "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": [
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      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "OneChannel",
      "Preprocessing",
      "QualityControl",
      "ReportWriting",
      "Software",
      "Visualization"
    ],
    "Author": "Xiwei Wu <xwu@coh.org>, Xuejun Arthur Li <xueli@coh.org>",
    "Maintainer": "Xuejun Arthur Li <xueli@coh.org>",
    "source.ver": "src/contrib/AffyExpress_1.20.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/AffyExpress_1.20.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/AffyExpress_1.20.0.zip",
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    "vignettes": [
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    "Depends": [
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      "utils",
      "preprocessCore"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "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",
      "Software"
    ],
    "Author": "Charles G. Danko <dankoc@gmail.com>",
    "Maintainer": "Charles G. Danko <dankoc@gmail.com>",
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    "win.binary.ver": "bin/windows/contrib/2.14/AffyTiling_1.12.1.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/AffyTiling_1.12.1.zip",
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    "vignettes": [
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    "Version": "1.14.0",
    "Depends": [
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      "Biobase",
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      "gtools",
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    "License": "GPL-3",
    "Title": "PreProcessing of Agilent 4x44 array data",
    "Description": "Preprocessing of Agilent 4x44 array data",
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      "OneChannel",
      "Preprocessing",
      "Software"
    ],
    "Author": "Pedro Lopez-Romero <plopez@cnic.es>",
    "Maintainer": "Pedro Lopez-Romero <plopez@cnic.es>",
    "source.ver": "src/contrib/Agi4x44PreProcess_1.14.0.tar.gz",
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    "vignettes": [
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    "Version": "2.4.0",
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      "methods",
      "Biobase",
      "limma",
      "affy (>= 1.22)",
      "preprocessCore",
      "affycoretools"
    ],
    "Imports": [
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    ],
    "Suggests": [
      "geneplotter",
      "marray",
      "gplots",
      "gtools",
      "gdata",
      "codelink"
    ],
    "License": "GPL-3",
    "Title": "Processing and Differential Expression Analysis of Agilent microRNA chips",
    "Description": "Processing and Analysis of Agilent microRNA data",
    "biocViews": [
      "AgilentChip",
      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "OneChannel",
      "Preprocessing",
      "Software"
    ],
    "Author": "Pedro Lopez-Romero <plopez@cnic.es>",
    "Maintainer": "Pedro Lopez-Romero <plopez@cnic.es>",
    "source.ver": "src/contrib/AgiMicroRna_2.4.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/AgiMicroRna_2.4.0.zip",
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      "Infrastructure",
      "Software"
    ],
    "Author": "Herve Pages, Marc Carlson, Seth Falcon, Nianhua Li",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
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    "Title": "Normalization of array CGH data with imbalanced aberrations.",
    "Description": "Normalization 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).",
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    "Description": "Dimension Reduction for Array CGH Data with Minimal Information Loss",
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    "License": "GPL (>= 2)",
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    "Author": "Martin Slawski <ms@cs.uni-sb.de>, Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>, Christoph Bernau <bernau@ibe.med.uni-muenchen.de>.",
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    "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.",
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      "Software"
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    "Author": "Stefano Berri <s.berri@leeds.ac.uk> or <s.berri@gmail.com> and Arief Gusnanto <a.gusnanto@leeds.ac.uk>.",
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    "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",
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    "License": "GPL-3",
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    "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.",
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      "Software"
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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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    "Author": "Dongxiao Zhu and Youjuan Li",
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    "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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    "Author": "Henrik Failmezger <failmezger@cip.ifi.lmu.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@cip.ifi.lmu.de>, Yinyin Yuan <Yinyin.Yuan@cancer.org.uk>",
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    "License": "Artistic-2.0",
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    "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": [
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    "Title": "R version of CellNOpt, boolean features only",
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    "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",
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    "Title": "Different test statistics based on co-citation.",
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    "Description": "Coordinated Gene Activity in Pattern Sets (CoGAPS) infers biological processes which are active in individual gene sets from corresponding microarray measurements.  CoGAPS achieves this inference by combining a MCMC matrix decomposition algorithm (GAPS) with a novel statistic inferring activity on gene sets.",
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    "Imports": [
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    "Description": "A toolset that assist in the design of hybridization probes.",
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    "Title": "Differential Expression via Distance Summary for Microarray Data",
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    "Title": "Two-sample tests on a graph",
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    "Author": "Laurent Jacob, Pierre Neuvial and Sandrine Dudoit",
    "Maintainer": "Laurent Jacob <laurent.jacob@gmail.com>",
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    "Title": "Identify Differentially Expressed Genes from RNA-seq data",
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    "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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    "Title": "Disease Ontology Semantic and Enrichment analysis",
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    "License": "Artistic-2.0",
    "Title": "Linear Model Toolset for Gene Set Enrichment Analysis",
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    "Author": "Assaf Oron, Robert Gentleman (with contributions from S. Falcon and Z. Jiang)",
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    "URL": "http://julian-gehring.github.com/GSRI/",
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    "Enhances": [
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      "multicore"
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    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "Title": "Gene Set Variation Analysis",
    "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. Users on all platforms must install the GNU Scientific Library; see the README file, available in the source distribution of this file, for details.",
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    "Author": "Justin Guinney <justin.guinney@sagebase.org> (with contributions from Robert Castelo <robert.castelo@upf.edu> and Sonja Haenzelmann <shanzelmann@imim.es)",
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    "License": "Artistic-2.0",
    "Title": "GWASTools: 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.",
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      "SNP",
      "Software"
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    "Author": "Stephanie Gogarten, Cathy Laurie, Tushar Bhangale, Matt Conomos, Cecilia Laurie, Caitlin McHugh, Ian Painter, Xiuwen Zheng, Rohit Swarnkar",
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    "Author": "L. Cottret, A. Lucas <antoinelucas@gmail.com>, E. Marrakchi, O. Rogier, V. Lefort, P. Durosay, A. Viari, C. Thermes & Y. d'Aubenton-Carafa.",
    "Maintainer": "Y. d'Aubenton-Carafa <daubenton@cgm.cnrs-gif.fr>",
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      "affy",
      "AnnotationDbi"
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    "License": "GPL (>= 2)",
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    "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.",
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      "SNP",
      "Software",
      "Visualization"
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    "Author": "Lasse Folkersen, Diego Diez",
    "Maintainer": "Lasse Folkersen <lasse.folkersen@ki.se>",
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    "Version": "1.12.0",
    "Depends": [
      "R (>= 2.6.0)",
      "GeneR"
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    "License": "CeCILL-2.0",
    "Title": "R for genes and sequences, using viennaRNA package (fold)",
    "Description": "Package manipulating sequences with fold routines",
    "biocViews": [
      "Annotation",
      "Genetics",
      "Software"
    ],
    "Author": "Y. d'Aubenton-Carafa, A. Lucas & C. Thermes",
    "Maintainer": "Antoine Lucas <lucas@cgm.cnrs-gif.fr>",
    "URL": "http://www.tbi.univie.ac.at/~ivo/RNA/",
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      "vignettes/GeneRfold/inst/doc/rnafold.pdf"
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    "dependsOnMe": [
      "GeneGA"
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      "Biobase"
    ],
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      "MASS",
      "graphics",
      "stats",
      "survival",
      "limma"
    ],
    "Suggests": [
      "ALL"
    ],
    "License": "GPL (>= 2)",
    "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": [
      "Bioinformatics",
      "DifferentialExpression",
      "Software"
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    "Author": "Weiliang Qiu <stwxq@channing.harvard.edu>, Wenqing He <whe@stats.uwo.ca>, Xiaogang Wang <stevenw@mathstat.yorku.ca>, Ross Lazarus <ross.lazarus@channing.harvard.edu>.",
    "Maintainer": "Weiliang Qiu <stwxq@channing.harvard.edu>",
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      "R (>= 2.5.1)",
      "methods",
      "stats",
      "Biobase"
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      "siggenes",
      "samr",
      "limma"
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      "multtest",
      "siggenes",
      "samr",
      "limma"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "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": [
      "DifferentialExpression",
      "Software",
      "Statistics"
    ],
    "Author": "Martin Slawski <ms@cs.uni-sb.de>, Anne-Laure Boulesteix <boulesteix@ibe.med.uni-muenchen.de>.",
    "Maintainer": "Martin Slawski <ms@cs.uni-sb.de>",
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    "win.binary.ver": "bin/windows/contrib/2.14/GeneSelector_2.4.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/GeneSelector_2.4.0.zip",
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    "Version": "1.22.0",
    "Imports": [
      "gmodels",
      "graphics",
      "gtools (>= 2.4.0)",
      "mvtnorm",
      "stats"
    ],
    "License": "GPL-2",
    "Title": "Functions for designing genetics studies",
    "Description": "This package contains functions useful for designing genetics studies, including power and sample-size calculations.",
    "biocViews": [
      "Genetics",
      "Software"
    ],
    "Author": "Gregory Warnes <warnes@bst.rochester.edu> David Duffy <davidD@qumr.edu.au>, Michael Man <michael.man@pfizer.com> Weiliang Qiu <stwxq@channing.harvard.edu> Ross Lazarus <ross.lazarus@channing.harvard.edu>",
    "Maintainer": "The R Genetics Project <r-genetics-talk@lists.sourceforge.net>",
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    "win.binary.ver": "bin/windows/contrib/2.14/GeneticsDesign_1.22.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/GeneticsDesign_1.22.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/GeneticsDesign_1.22.0.tgz",
    "vignettes": [
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    ]
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    "Version": "1.16.0",
    "Depends": [
      "R (>= 2.4.0)",
      "gdata (>= 2.3.0)",
      "genetics (>= 1.3.0)",
      "MASS"
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    "License": "LGPL (>= 2.1)",
    "Archs": "i386, x64",
    "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",
      "Software"
    ],
    "Author": "Gregor Gorjanc and David A. Henderson <dnadave@comcast.net>, with code contributions by Brian Kinghorn and Andrew Percy (see file COPYING)",
    "Maintainer": "David Henderson <DNADavenator@GMail.Com>",
    "URL": "http://rgenetics.org",
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    "win.binary.ver": "bin/windows/contrib/2.14/GeneticsPed_1.16.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/GeneticsPed_1.16.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/GeneticsPed_1.16.0.tgz",
    "vignettes": [
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      "vignettes/GeneticsPed/inst/doc/pedigreeHandling.pdf",
      "vignettes/GeneticsPed/inst/doc/quanGenAnimalModel.pdf"
    ]
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    "Version": "1.14.0",
    "Depends": [
      "methods",
      "biomaRt",
      "grid"
    ],
    "License": "Artistic-2.0",
    "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": [
      "Microarray",
      "Software",
      "Visualization"
    ],
    "Author": "Steffen Durinck <sdurinck@gmail.com>, James Bullard <bullard@berkeley.edu>",
    "Maintainer": "Steffen Durinck <sdurinck@gmail.com>",
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      "oneChannelGUI",
      "rMAT"
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    "dependsOnMe": [
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      "GenomicRanges (>= 1.5.42)",
      "AnnotationDbi (>= 1.15.39)"
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      "DBI (>= 0.2-5)",
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    "License": "Artistic-2.0",
    "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": [
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      "Genetics",
      "HighThroughputSequencing",
      "Infrastructure",
      "Software"
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    "Author": "M. Carlson, H. Pages, P. Aboyoun, S. Falcon, M. Morgan, D. Sarkar, M. Lawrence",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
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      "chipseq",
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      "TxDb.Hsapiens.UCSC.hg18.knownGene",
      "TxDb.Hsapiens.UCSC.hg19.knownGene",
      "TxDb.Mmusculus.UCSC.mm9.knownGene",
      "TxDb.Rnorvegicus.UCSC.rn4.ensGene",
      "TxDb.Scerevisiae.UCSC.sacCer2.ensGene",
      "TxDb.Scerevisiae.UCSC.sacCer2.sgdGene"
    ],
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      "ggbio",
      "TxDb.Athaliana.BioMart.plantsmart10",
      "TxDb.Celegans.UCSC.ce6.ensGene",
      "TxDb.Dmelanogaster.UCSC.dm3.ensGene",
      "TxDb.Hsapiens.UCSC.hg18.knownGene",
      "TxDb.Hsapiens.UCSC.hg19.knownGene",
      "TxDb.Mmusculus.UCSC.mm9.knownGene",
      "TxDb.Rnorvegicus.UCSC.rn4.ensGene",
      "TxDb.Scerevisiae.UCSC.sacCer2.ensGene",
      "TxDb.Scerevisiae.UCSC.sacCer2.sgdGene",
      "VariantAnnotation"
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    "Version": "1.6.7",
    "Depends": [
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      "methods",
      "IRanges (>= 1.11.26)"
    ],
    "Imports": [
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      "IRanges"
    ],
    "LinkingTo": [
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    "Suggests": [
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      "DESeq",
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      "org.Sc.sgd.db",
      "BSgenome.Scerevisiae.UCSC.sacCer2",
      "DEXSeq",
      "pasilla"
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    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "Title": "Representation and manipulation of genomic intervals",
    "Description": "The ability to efficiently store genomic annotations and alignments is playing a central role when it comes to analyze high-throughput sequencing data (a.k.a. NGS data). The package defines general purpose containers for storing genomic intervals as well as more specialized containers for storing alignments against a reference genome.",
    "biocViews": [
      "Annotation",
      "Genetics",
      "HighThroughputSequencing",
      "Sequencing",
      "Software"
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    "Author": "P. Aboyoun, H. Pages and M. Lawrence",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
    "source.ver": "src/contrib/GenomicRanges_1.6.7.tar.gz",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/GenomicRanges_1.6.7.zip",
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    "vignettes": [
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      "vignettes/GenomicRanges/inst/doc/GenomicRangesUseCases.pdf",
      "vignettes/GenomicRanges/inst/doc/summarizeOverlaps-modes.pdf",
      "vignettes/GenomicRanges/inst/doc/summarizeOverlaps.pdf"
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      "cheung2010",
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      "EatonEtAlChIPseq",
      "exomeCopy",
      "fastseg",
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      "GGtools",
      "ggtut",
      "htSeqTools",
      "PICS",
      "Rsamtools",
      "segmentSeq",
      "seqbias",
      "ShortRead",
      "SNPlocs.Hsapiens.dbSNP.20090506",
      "SNPlocs.Hsapiens.dbSNP.20100427",
      "SNPlocs.Hsapiens.dbSNP.20101109",
      "SNPlocs.Hsapiens.dbSNP.20110815",
      "VariantAnnotation"
    ],
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      "biovizBase",
      "chipseq",
      "ChIPseqR",
      "GenomicFeatures",
      "genoset",
      "ggbio",
      "leeBamViews",
      "nucleR",
      "PICS",
      "Repitools",
      "rnaSeqMap",
      "Rsamtools",
      "rtracklayer",
      "segmentSeq",
      "ShortRead",
      "SNPlocs.Hsapiens.dbSNP.20090506",
      "SNPlocs.Hsapiens.dbSNP.20100427",
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      "SNPlocs.Hsapiens.dbSNP.20110815"
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    "Version": "1.8.0",
    "Depends": [
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      "RSQLite",
      "DBI (>= 0.2-5)",
      "IRanges",
      "GenomeGraphs"
    ],
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      "graphics",
      "stats",
      "utils"
    ],
    "Suggests": [
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      "ShortRead",
      "yeastRNASeq"
    ],
    "License": "Artistic-2.0",
    "Title": "Analyze, manage and store genomic data",
    "Description": "Tools for storing, accessing, analyzing and visualizing genomic data.",
    "biocViews": [
      "Infrastructure",
      "Software"
    ],
    "Author": "James Bullard, Kasper Daniel Hansen",
    "Maintainer": "James Bullard <bullard@stat.berkeley.edu>",
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    "vignettes": [
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    "suggestsMe": [
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    "Version": "3.22.0",
    "Depends": [
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      "corpcor",
      "globaltest"
    ],
    "Imports": [
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      "AnnotationDbi"
    ],
    "Suggests": [
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      "annotate",
      "GO.db",
      "KEGG.db",
      "golubEsets",
      "hu6800.db",
      "vsn",
      "GSEABase",
      "Rgraphviz"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "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": [
      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "OneChannel",
      "Pathways",
      "Software"
    ],
    "Author": "U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with contributions from S. Knueppel",
    "Maintainer": "Manuela Hummel <manuela.hummel@crg.eu>",
    "source.ver": "src/contrib/GlobalAncova_3.22.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/GlobalAncova_3.22.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/GlobalAncova_3.22.0.zip",
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    "vignettes": [
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  "GraphAT": {
    "Package": "GraphAT",
    "Version": "1.26.0",
    "Depends": [
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      "methods"
    ],
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      "MCMCpack",
      "methods",
      "stats"
    ],
    "License": "LGPL",
    "Title": "Graph Theoretic Association Tests",
    "Description": "Functions and data used in Balasubramanian, et al. (2004)",
    "biocViews": [
      "Bioinformatics",
      "GraphsAndNetworks",
      "Software"
    ],
    "Author": "R. Balasubramanian, T. LaFramboise, D. Scholtens",
    "Maintainer": "Thomas LaFramboise <tlaframb@hsph.harvard.edu>",
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    "win.binary.ver": "bin/windows/contrib/2.14/GraphAT_1.26.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/GraphAT_1.26.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/GraphAT_1.26.0.tgz"
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  "GraphAlignment": {
    "Package": "GraphAlignment",
    "Version": "1.16.0",
    "License": "file LICENSE",
    "Archs": "i386, x64",
    "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": [
      "GraphsAndNetworks",
      "Software"
    ],
    "Author": "Joern P. Meier <mail@ionflux.org>, Michal Kolar, Ville Mustonen, Michael Laessig, and Johannes Berg.",
    "Maintainer": "Joern P. Meier <mail@ionflux.org>",
    "URL": "http://www.thp.uni-koeln.de/~berg/GraphAlignment/",
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    "vignettes": [
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      "vignettes/GraphAlignment/inst/doc/a.pdf",
      "vignettes/GraphAlignment/inst/doc/align_principle2b1.pdf",
      "vignettes/GraphAlignment/inst/doc/align_principle2c1.pdf",
      "vignettes/GraphAlignment/inst/doc/align_principle_short1.pdf",
      "vignettes/GraphAlignment/inst/doc/binning-01a.pdf"
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    "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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    "License": "Artistic-2.0",
    "Title": "NPMLE for Censored and Truncated Data",
    "Description": "Many functions for computing the NPMLE for censored and truncated data.",
    "biocViews": [
      "Bioinformatics",
      "Infrastructure",
      "Software"
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    "Author": "R. Gentleman and Alain Vandal",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
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      "XML",
      "RCurl",
      "rChoiceDialogs",
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      "DAVIDQuery",
      "AffyCompatible",
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      "R.oo",
      "utils"
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    "License": "GPL-2",
    "Title": "ID Mapping Data Retrieval",
    "Description": "Data retrieval for identifier mapping performance analysis",
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      "Bioinformatics",
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      "Software"
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    "Maintainer": "Alex Lisovich <all67@pitt.edu>, Roger Day <day01@pitt.edu>",
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    "License": "GPL-2",
    "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 using 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 excat distribution and permutation. The other four test statistics are obtained using permutation . 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) (both permutations (Ge et al., 2003) and the Significance Analysis of Microarrays (SAM), Tusher et al., 2001).",
    "biocViews": [
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      "GUI",
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      "Software"
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    "Author": "Setia Pramana, Dan Lin, Philippe Haldermans, Tobias Verbeke",
    "Maintainer": "Setia Pramana <setia.pramana@uhasselt.be>",
    "URL": "http://www.ibiostat.be/software/IsoGeneGUI/index.html",
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      "siggenes",
      "multtest",
      "KernSmooth"
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      "Biobase",
      "CGHbase"
    ],
    "License": "GPL-3",
    "Title": "Multi sample aCGH analysis package using kernel convolution",
    "Description": "Multi sample aCGH analysis package using kernel convolution",
    "biocViews": [
      "DNACopyNumber",
      "Microarray",
      "Software",
      "Visualization",
      "aCGH"
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    "Author": "Jorma de Ronde, Christiaan Klijn, Arno Velds",
    "Maintainer": "Jorma de Ronde <j.d.ronde@nki.nl>",
    "source.ver": "src/contrib/KCsmart_2.12.0.tar.gz",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/KCsmart_2.12.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/KCsmart_2.12.0.tgz",
    "vignettes": [
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    "Depends": [
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    "Imports": [
      "XML",
      "RCurl",
      "SSOAP (>= 0.2-2)",
      "XMLSchema"
    ],
    "License": "BSD",
    "Title": "Client-side SOAP access KEGG",
    "Description": "A package that provides a client interface to the KEGG SOAP server",
    "biocViews": [
      "Annotation",
      "Pathways",
      "Software"
    ],
    "Author": "J. Zhang and R. Gentleman",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
    "source.ver": "src/contrib/KEGGSOAP_1.28.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/KEGGSOAP_1.28.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/KEGGSOAP_1.28.0.zip",
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      "RBGL",
      "KEGG.db",
      "org.Hs.eg.db",
      "hgu133plus2.db",
      "SPIA"
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    "License": "GPL (>= 2)",
    "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": [
      "GraphsAndNetworks",
      "Pathways",
      "Software"
    ],
    "Author": "Jitao David Zhang",
    "Maintainer": "Jitao David Zhang <jitao_david.zhang@roche.com>",
    "URL": "http://www.nextbiomotif.com",
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      "grDevices",
      "methods",
      "stats",
      "utils"
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    "Suggests": [
      "qvalue"
    ],
    "License": "GPL-2",
    "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": [
      "Bioinformatics",
      "MultipleComparisons",
      "Software"
    ],
    "Author": "Cyril Dalmasso",
    "Maintainer": "Cyril Dalmasso <dalmasso@vjf.inserm.fr>",
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    "win.binary.ver": "bin/windows/contrib/2.14/LBE_1.22.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/LBE_1.22.0.zip",
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    "Package": "LMGene",
    "Version": "2.10.0",
    "Depends": [
      "R (>= 2.10.0)",
      "Biobase (>= 2.5.5)",
      "multtest",
      "survival",
      "affy"
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    "Suggests": [
      "affydata"
    ],
    "License": "LGPL",
    "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": [
      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "Preprocessing",
      "Software"
    ],
    "Author": "David Rocke, Geun Cheol Lee, John Tillinghast, Blythe Durbin-Johnson, and Shiquan Wu",
    "Maintainer": "Blythe Durbin-Johnson <bpdurbin@ucdavis.edu>",
    "URL": "http://dmrocke.ucdavis.edu/software.html",
    "source.ver": "src/contrib/LMGene_2.10.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/LMGene_2.10.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/LMGene_2.10.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/LMGene_2.10.0.tgz",
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    "Version": "1.28.0",
    "Imports": [
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    ],
    "License": "LGPL",
    "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": [
      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "Software"
    ],
    "Author": "Nitin Jain <emailnitinjain@gmail.com>, Michael O'Connell <michaelo@warath.com>, Jae K. Lee <jaeklee@virginia.edu>. Includes R source code contributed by HyungJun Cho <hcho@virginia.edu>",
    "Maintainer": "Nitin Jain <emailnitinjain@gmail.com>",
    "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_1.28.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/LPE_1.28.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/LPE_1.28.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/LPE_1.28.0.tgz",
    "vignettes": [
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    ],
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    "Version": "1.14.0",
    "Depends": [
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    ],
    "Imports": [
      "LPE",
      "stats"
    ],
    "License": "LGPL",
    "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": [
      "Bioinformatics",
      "Microarray",
      "Proteomics",
      "Software"
    ],
    "Author": "Carl Murie <carl.murie@mcgill.ca>, Robert Nadon <robert.nadon@mcgill.ca>",
    "Maintainer": "Carl Murie <carl.murie@mcgill.ca>",
    "source.ver": "src/contrib/LPEadj_1.14.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/LPEadj_1.14.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/LPEadj_1.14.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/LPEadj_1.14.0.tgz",
    "vignettes": [
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      "quantreg",
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      "vsn"
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    "Enhances": [
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      "snow",
      "Rmpi"
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    "License": "GPL2",
    "Archs": "i386, x64",
    "Title": "LVS normalization for Agilent miRNA data",
    "Description": "Normalization of Agilent miRNA arrays.",
    "biocViews": [
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      "Microarray",
      "OneChannel",
      "Preprocessing",
      "Software"
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    "Author": "Stefano Calza, Suo Chen, Yudi Pawitam",
    "Maintainer": "Stefano Calza <stefano.calza@biostatistics.it>",
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    "win.binary.ver": "bin/windows/contrib/2.14/LVSmiRNA_1.4.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/LVSmiRNA_1.4.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/LVSmiRNA_1.4.0.tgz",
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      "methods",
      "yeastCC",
      "org.Sc.sgd.db"
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      "graphics",
      "grDevices",
      "methods",
      "stats"
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    "License": "GPL (>=3)",
    "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": [
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      "CellBiology",
      "GeneExpression",
      "Genetics",
      "Pathways",
      "Software",
      "TimeCourse"
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    "Author": "Yen-Yi Ho <yho@jhsph.edu>",
    "Maintainer": "Yen-Yi Ho <yho@jhsph.edu>",
    "source.ver": "src/contrib/LiquidAssociation_1.8.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/LiquidAssociation_1.8.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/LiquidAssociation_1.8.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/LiquidAssociation_1.8.0.tgz",
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      "GLAD"
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      "graphics",
      "grDevices",
      "stats",
      "utils"
    ],
    "License": "GPL-2",
    "Archs": "i386, x64",
    "Title": "CGH Micro-Array NORmalization",
    "Description": "Importation, normalization, visualization, and quality control functions to correct identified sources of variability in array-CGH experiments.",
    "biocViews": [
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      "DataImport",
      "Microarray",
      "Preprocessing",
      "QualityControl",
      "Software",
      "TwoChannel"
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    "Maintainer": "Pierre Neuvial <pierre.neuvial@gmail.com>",
    "URL": "http://bioinfo.curie.fr/projects/manor/index.html",
    "source.ver": "src/contrib/MANOR_1.26.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/MANOR_1.26.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/MANOR_1.26.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/MANOR_1.26.0.tgz",
    "vignettes": [
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    ]
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    "Version": "1.8.0",
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      "tcltk",
      "tcltk2"
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    "Imports": [
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      "stats",
      "utils"
    ],
    "License": "GPL (>= 2)",
    "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",
      "Software"
    ],
    "Author": "Yang Xie <Yang.Xie@UTSouthwestern.edu>",
    "Maintainer": "Jeff Allen <Jeffrey.Allen@UTSouthwestern.edu>",
    "URL": "http://www.utsouthwestern.edu",
    "source.ver": "src/contrib/MBCB_1.8.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/MBCB_1.8.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/MBCB_1.8.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/MBCB_1.8.0.tgz",
    "vignettes": [
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    "Version": "2.10.0",
    "Depends": [
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      "golubEsets (>= 1.4.6)"
    ],
    "Imports": [
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      "pamr (>= 1.22)",
      "randomForest (>= 3.9-6)",
      "RColorBrewer (>= 0.1-3)",
      "Biobase (>= 2.5.5)",
      "graphics",
      "grDevices",
      "stats",
      "utils"
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      "ROC (>= 1.8.0)",
      "genefilter (>= 1.12.0)",
      "gpls (>= 1.6.0)"
    ],
    "License": "GPL (>= 2)",
    "Title": "Misclassification error estimation with cross-validation",
    "Description": "This package includes a function for combining preprocessing and classification methods to calculate misclassification errors",
    "biocViews": [
      "Bioinformatics",
      "Classification",
      "Software"
    ],
    "Author": "Marc Johannes, Markus Ruschhaupt, Holger Froehlich, Ulrich Mansmann, Andreas Buness, Patrick Warnat, Wolfgang Huber, Axel Benner, Tim Beissbarth",
    "Maintainer": "Marc Johannes <m.johannes@dkfz.de>",
    "source.ver": "src/contrib/MCRestimate_2.10.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/MCRestimate_2.10.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/MCRestimate_2.10.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/MCRestimate_2.10.0.tgz",
    "vignettes": [
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    "Version": "1.4.0",
    "Depends": [
      "R (>= 2.12.0)",
      "BSgenome"
    ],
    "Imports": [
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      "BSgenome",
      "graphics",
      "gtools",
      "IRanges",
      "methods",
      "stats",
      "utils"
    ],
    "Suggests": [
      "BSgenome",
      "BSgenome.Hsapiens.UCSC.hg19"
    ],
    "License": "GPL (>=2)",
    "Archs": "i386, x64",
    "Title": "MeDIP-Seq data analysis",
    "Description": "MEDIPS was developed for analyzing data derived from methylated DNA immunoprecipitation (MeDIP) experiments followed by sequencing (MeDIP-Seq). Nevertheless, functionalities like the quality controls may be applied to other types of sequencing data (e.g. ChIP-Seq). MEDIPS adresses several aspects in the context of MeDIP-Seq data analysis.",
    "biocViews": [
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      "CpGIsland",
      "DNAMethylation",
      "DifferentialExpression",
      "HighThroughputSequencing",
      "Methylseq",
      "Preprocessing",
      "QualityControl",
      "Sequencing",
      "Software",
      "Visualization"
    ],
    "Author": "Lukas Chavez, Joern Dietrich",
    "Maintainer": "Lukas Chavez <chavez@molgen.mpg.de>",
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    "win.binary.ver": "bin/windows/contrib/2.14/MEDIPS_1.4.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/MEDIPS_1.4.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/MEDIPS_1.4.0.tgz",
    "vignettes": [
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    "Version": "1.14.0",
    "Depends": [
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    "License": "GPL-2",
    "Title": "Position RElated Data Anlysis",
    "Description": "Package for the position related analysis of quantitative functional genomics data.",
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      "GeneExpression",
      "Genetics",
      "Software"
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    "Author": "Francesco Ferrari <francesco.ferrari@unimore.it>",
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      "R (>= 2.11.0)",
      "Biobase",
      "GSEABase"
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      "Biobase",
      "GSEABase",
      "stats"
    ],
    "License": "GPL (>= 2)",
    "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": [
      "Bioinformatics",
      "GeneExpression",
      "Microarray",
      "MultipleComparisons",
      "OneChannel",
      "Software"
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    "Author": "Stan Pounds <stanley.pounds@stjude.org>, Xueyuan Cao <xueyuan.cao@stjude.org>",
    "Maintainer": "Xueyuan Cao <xueyuan.cao@stjude.org>",
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      "grDevices",
      "Icens",
      "stats",
      "utils"
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    "License": "Artistic-2.0",
    "Title": "Ciphergen SELDI-TOF Processing",
    "Description": "A package for processing protein mass spectrometry data.",
    "biocViews": [
      "MassSpectrometry",
      "Proteomics",
      "Software"
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    "Author": "Xiaochun Li",
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      "limma",
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      "stats"
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    "Suggests": [
      "KEGG.db"
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    "License": "GPL (>= 2.0)",
    "Archs": "i386, x64",
    "Title": "Patient-oriented gene-set analysis",
    "Description": "Patient-oriented analysis of mutations from cancer genome studies.",
    "biocViews": [
      "Bioinformatics",
      "Software"
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    "Author": "Simina M. Boca, Giovanni Parmigiani",
    "Maintainer": "Simina M. Boca <sboca@jhsph.edu>",
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      "ShortRead"
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      "rtracklayer",
      "ShortRead",
      "Rsamtools",
      "BSgenome.Hsapiens.UCSC.hg19"
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    "License": "LGPL-3",
    "Archs": "i386, x64",
    "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": [
      "DataImport",
      "DataRepresentation",
      "HighThroughputSequencing",
      "Infrastructure",
      "QualityControl",
      "ReportWriting",
      "Software",
      "Visualization"
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    "Author": "Hans-Ulrich Klein, Christoph Bartenhagen, Christian Ruckert",
    "Maintainer": "Hans-Ulrich Klein <h.klein@uni-muenster.de>",
    "source.ver": "src/contrib/R453Plus1Toolbox_1.4.0.tar.gz",
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    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "Title": "An interface to the BOOST graph library",
    "Description": "A fairly extensive and comprehensive interface to the graph algorithms contained in the BOOST library.",
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    "Author": "Vince Carey <stvjc@channing.harvard.edu>, Li Long <li.long@isb-sib.ch>, R. Gentleman",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
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      "graph",
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      "keggorthology",
      "yeastExpData"
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      "BioNet",
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      "CAMERA",
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    "Package": "RBioinf",
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    "Depends": [
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    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "Title": "RBioinf",
    "Description": "Functions and datasets and examples to accompany the monograph R For Bioinformatics.",
    "biocViews": [
      "Annotation",
      "Classification",
      "Clustering",
      "GeneExpression",
      "Microarray",
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      "Preprocessing",
      "QualityControl",
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    "Author": "Robert Gentleman",
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    "Version": "1.0.0",
    "License": "GPL (>=3)",
    "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",
    "Maintainer": "Douaa Mugahid <douaa.mugahid@gmail.com>, Lars Kaderali <lars.kaderali@bioquant.uni-heidelberg.de>",
    "source.ver": "src/contrib/RCASPAR_1.0.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/RCASPAR_1.0.0.zip",
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    "Version": "1.4.4",
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    "Suggests": [
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    "License": "GPL-2",
    "Title": "Display and manipulate graphs in Cytoscape",
    "Description": "Interactvive viewing and exploration of graphs, connecting R to Cytoscape.",
    "biocViews": [
      "GraphsAndNetworks",
      "Software"
    ],
    "Author": "Paul Shannon",
    "Maintainer": "Paul Shannon <pshannon@systemsbiology.org>",
    "URL": "http://rcytoscape.systemsbiology.net",
    "source.ver": "src/contrib/RCytoscape_1.4.4.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/RCytoscape_1.4.4.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/RCytoscape_1.4.4.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/RCytoscape_1.4.4.tgz",
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    "Version": "1.4.0",
    "Depends": [
      "R (>= 2.9.0)",
      "rgl"
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      "grDevices",
      "methods",
      "stats",
      "MASS",
      "rgl"
    ],
    "Suggests": [
      "golubEsets"
    ],
    "License": "GPL (>= 2)",
    "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": [
      "ClusterValidation",
      "Dimension",
      "DimensionReduction",
      "FeatureExtraction",
      "Microarray",
      "Software",
      "Visualization"
    ],
    "Author": "Christoph Bartenhagen",
    "Maintainer": "Christoph Bartenhagen <christoph.bartenhagen@ukmuenster.de>",
    "source.ver": "src/contrib/RDRToolbox_1.4.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/RDRToolbox_1.4.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/RDRToolbox_1.4.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/RDRToolbox_1.4.0.tgz",
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      "vignettes/RDRToolbox/inst/doc/SwissRoll.pdf",
      "vignettes/RDRToolbox/inst/doc/plot3D.pdf",
      "vignettes/RDRToolbox/inst/doc/vignette.pdf"
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    "Package": "REDseq",
    "Version": "1.0.0",
    "Depends": [
      "R (>= 2.14.0)",
      "BSgenome.Celegans.UCSC.ce2",
      "multtest",
      "Biostrings",
      "BSgenome",
      "ChIPpeakAnno"
    ],
    "Imports": [
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      "Biostrings",
      "ChIPpeakAnno",
      "graphics",
      "IRanges",
      "multtest",
      "stats",
      "utils"
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    "License": "GPL (>=2)",
    "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": [
      "Preprocessing",
      "SequenceMatching",
      "Sequencing",
      "Software"
    ],
    "Author": "Lihua Julie Zhu and Thomas Fazzio",
    "Maintainer": "Lihua Julie Zhu <julie.zhu@umassmed.edu>",
    "source.ver": "src/contrib/REDseq_1.0.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/REDseq_1.0.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/REDseq_1.0.0.zip",
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    "Version": "1.16.0",
    "Depends": [
      "R (>= 2.1.0)"
    ],
    "Imports": [
      "graphics",
      "grDevices",
      "MASS",
      "stats",
      "utils"
    ],
    "License": "LGPL (>= 2)",
    "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": [
      "GeneticVariability",
      "Microarray",
      "OneChannel",
      "SNP",
      "Software"
    ],
    "Author": "Nusrat Rabbee <nrabbee@post.harvard.edu>, Gary Wong <wongg62@berkeley.edu>",
    "Maintainer": "Nusrat Rabbee <nrabbee@post.harvard.edu>",
    "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_1.16.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/RLMM_1.16.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/RLMM_1.16.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/RLMM_1.16.0.tgz",
    "vignettes": [
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  "RMAGEML": {
    "Package": "RMAGEML",
    "Version": "2.28.0",
    "Depends": [
      "R (>= 2.6.0)",
      "methods",
      "marray",
      "limma",
      "Biobase"
    ],
    "License": "LGPL",
    "Title": "Handling MAGEML documents",
    "Description": "This package can be used to handle MAGEML documents in Bioconductor",
    "biocViews": [
      "Microarray",
      "Software"
    ],
    "Author": "Steffen Durinck <steffen.durinck@esat.kuleuven.ac.be>, Joke Allemeersch <joke.allemeersch@esat.kuleuven.ac.be> and Vincent Carey <stvjc@channing.harvard.edu>",
    "Maintainer": "Steffen Durinck <steffen@stat.berkeley.edu>",
    "URL": "http://www.esat.kuleuven.ac.be/~jallemee,http://www.esat.kuleuven.ac.be/~sdurinck",
    "source.ver": "src/contrib/RMAGEML_2.28.0.tar.gz",
    "vignettes": [
      "vignettes/RMAGEML/inst/doc/RMAGEML.pdf"
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  "RMAPPER": {
    "Package": "RMAPPER",
    "Version": "1.4.0",
    "Depends": [
      "methods"
    ],
    "Suggests": [
      "RCurl"
    ],
    "License": "Artistic 2.0",
    "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": [
      "Annotation",
      "Genetics",
      "Software"
    ],
    "Author": "VJ Carey <stvjc@channing.harvard.edu>",
    "Maintainer": "Heike Sichtig <hsichtig@ufl.edu>, Alberto Riva <ariva@ufl.edu>",
    "source.ver": "src/contrib/RMAPPER_1.4.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/RMAPPER_1.4.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/RMAPPER_1.4.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/RMAPPER_1.4.0.tgz",
    "vignettes": [
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  "RNAinteract": {
    "Package": "RNAinteract",
    "Version": "1.2.0",
    "Depends": [
      "R (>= 2.12.0)",
      "abind",
      "locfit",
      "Biobase"
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    "Imports": [
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      "ICS",
      "ICSNP",
      "cellHTS2",
      "geneplotter",
      "gplots",
      "grid",
      "hwriter",
      "lattice",
      "latticeExtra",
      "limma",
      "methods",
      "splots (>= 1.13.12)"
    ],
    "License": "Artistic-2.0",
    "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",
      "Preprocessing",
      "QualityControl",
      "Software",
      "Visualization"
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    "Author": "Bernd Fischer <bernd.fischer@embl.de>",
    "Maintainer": "Bernd Fischer <bernd.fischer@embl.de>",
    "source.ver": "src/contrib/RNAinteract_1.2.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/RNAinteract_1.2.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/RNAinteract_1.2.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/RNAinteract_1.2.0.tgz",
    "vignettes": [
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    "dependsOnMe": [
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  "RNAither": {
    "Package": "RNAither",
    "Version": "2.2.0",
    "Depends": [
      "R (>= 2.7.0)",
      "topGO",
      "RankProd",
      "prada"
    ],
    "Imports": [
      "geneplotter",
      "limma",
      "biomaRt",
      "car",
      "splots",
      "methods"
    ],
    "License": "Artistic-2.0",
    "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": [
      "Annotation",
      "Bioinformatics",
      "CellBasedAssays",
      "GO",
      "Preprocessing",
      "QualityControl",
      "Software",
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    "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",
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    "Title": "Open-source toolkit to analyse data from xCELLigence System (RTCA)",
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    "Title": "Data mining of public microarray data through connections to the TranscriptomeBrowser database.",
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    "Description": "the RTopper package is designed to perform and integrate gene set enrichment results across multiple genomic platforms.",
    "biocViews": [
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    "Author": "Nianhua Li, MT Morgan",
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      "graph"
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    "License": "Artistic-2.0",
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    "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": [
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    "Author": "Markus Schroeder, Daniel Gusenleitner, Aedin Culhane, Benjamin Haibe-Kains, John Quackenbush",
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    "Title": "Rank Product method for identifying differentially expressed genes with application in meta-analysis",
    "Description": "Non-paramteric 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": [
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    "Author": "Fangxin Hong <fxhong@jimmy.harvard.edu> and Ben Wittner <Wittner.Ben@mgh.harvard.edu> with contribution from Rainer Breitling <r.breitling@bio.gla.ac.uk> and Colin Smith <colin@colinsmith.org>",
    "Maintainer": "Fangxin Hong <fxhong@jimmy.harvard.edu>",
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    "License": "Artistic-2.0",
    "Title": "Support for Springer monograph on Bioconductor",
    "Description": "tools for building book",
    "biocViews": [
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    "Author": "Vince Carey <stvjc@channing.harvard.edu> and Wolfgang Huber <huber@ebi.ac.uk>",
    "Maintainer": "Vince Carey <stvjc@channing.harvard.edu>",
    "URL": "http://www.biostat.harvard.edu/~carey",
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    "License": "GPL-2",
    "Archs": "i386, x64",
    "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": [
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      "Software"
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    "Author": "Anton Pervukhin <apervukh@minet.uni-jena.de>, Steffen Neumann <sneumann@ipb-halle.de>",
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    "URL": "http://msbi.ipb-halle.de/",
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    "License": "GPL version 2",
    "Title": "Recalibrating Quality Of Nucleotides",
    "Description": "Algorithm for recalibrating the base quality scores for aligned sequencing data in BAM format.",
    "biocViews": [
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    "Author": "Christopher Cabanski, Keary Cavin, Chris Bizon",
    "Maintainer": "Christopher Cabanski <cabanski@email.unc.edu>",
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    "License": "LGPL-3",
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    "Description": "The package provides functions to read raw RT-qPCR data of different platforms.",
    "Author": "James Perkins, Matthias Kohl",
    "Maintainer": "James Perkins <jperkins@biochem.ucl.ac.uk>",
    "URL": "http://qpcr.r-forge.r-project.org/",
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    "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, etc.",
    "biocViews": [
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    "Description": "This package performs read mapping, exon junction detection and other tasks for next-generation sequencing data. The read mapping function implements a novel mapping paradigm, which is entirely different from the \"seed-and-extent\" paradigm. It can be used to map both short reads and long reads (>200bp) and reads of variable lengths. It also provides functions to summarize read counts to genes or exons and gives detection p values for each gene in the RNA-seq data.",
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    "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.",
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    "License": "Artistic-2.0",
    "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.  sra or sra-lite files can be downloaded for doing alignment locally. 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.",
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    "Author": "Jack Zhu and Sean Davis",
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    "URL": "http://watson.nci.nih.gov/",
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      "stats"
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    "License": "GPL (>= 2)",
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    "Description": "Sample size and power analysis for microarray data, where two groups are analysed.",
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    "Author": "Maarten van Iterson",
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    "Title": "In Silico Interactome",
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    "Title": "Classes and methods for high-throughput short-read sequencing data.",
    "Description": "Base classes, functions, and methods for representation of high-throughput, short-read sequencing data.",
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    "License": "LGPL (>=2)",
    "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.",
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    "Archs": "i386, x64",
    "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.",
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      "OneChannel",
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    "Enhances": [
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    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "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.",
    "Author": "Martin Morgan, Nishant Gopalakrishnan",
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    "Depends": [
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      "Biobase"
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    "License": "GPL-2",
    "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.",
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      "Software",
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    "Author": "Zoppoli P.,Morganella S., Ceccarelli M.",
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    "License": "Artistic-2.0",
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    "Author": "R. Gentleman",
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    "Version": "1.28.0",
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      "stats"
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    "License": "GPL",
    "Title": "Annotate microarrays and perform cross-species gene expression analyses using flat file databases.",
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    "License": "GPL-3",
    "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.",
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      "GeneExpression",
      "HighThroughputSequencing",
      "Microarray",
      "Software"
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    "Author": "Ola Larsson <ola.larsson@ki.se>, Nahum Sonenberg <nahum.sonenberg@mcgill.ca>, Robert Nadon <robert.nadon@mcgill.ca>",
    "Maintainer": "Ola Larsson <ola.larsson@ki.se>",
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      "stats",
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    "License": "LGPL",
    "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": [
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      "MassSpectrometry",
      "Software",
      "Visualization"
    ],
    "Author": "Denise Scholtens <dscholtens@northwestern.edu>",
    "Maintainer": "Denise Scholtens <dscholtens@northwestern.edu>",
    "source.ver": "src/contrib/apComplex_2.20.0.tar.gz",
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    "Title": "Light-weight methods for normalization and visualization of microarray data using only basic R data types",
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    "Title": "multivariate outlier detection for expression array QA",
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    "License": "GPL (>=2)",
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    "License": "Artistic-2.0",
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    "Description": "The package implements methods to analyze and visualize functional profiles (GO and KEGG) of gene and gene clusters.",
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    "Title": "Compute cluster stability scores for microarray data",
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    "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.",
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    "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++.",
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    "Title": "Analysis of co-knock-down RNAi data",
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    "Title": "Functions to perform cancer outlier profile analysis.",
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      "cosmo"
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    "License": "LGPL (>= 2)",
    "Title": "GUI for constructing constraint sets used by the cosmo package",
    "Description": "cosmoGUI allows the user to interactively define constraint sets that can be used by the cosmo package to supervise the search for shared motifs in a set of DNA sequences. The constraints can be either adapted from a set of commonly used templates or defined from scratch.",
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      "SequenceMatching",
      "Software"
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    "Author": "Fabian Gallusser, Oliver Bembom, and Sandrine Dudoit",
    "Maintainer": "Oliver Bembom <oliver.bembom@gmail.com>",
    "URL": "http://cosmoweb.berkeley.edu/intro.html, http://www.bepress.com/ucbbiostat/paper209/",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/cosmoGUI_1.20.0.zip",
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      "methods",
      "utils",
      "seqLogo"
    ],
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      "cosmoGUI"
    ],
    "License": "file LICENSE",
    "Archs": "i386, x64",
    "Title": "Supervised detection of conserved motifs in DNA sequences",
    "Description": "cosmo searches a set of unaligned DNA sequences for a shared motif that may, for example, represent a common transcription factor binding site. The algorithm is similar to MEME, but also allows the user to specify a set of constraints that the position weight matrix of the unknown motif must satisfy. Such constraints may include bounds on the information content across certain regions of the unknown motif, for example, and can often be formulated on the basis of prior knowledge about the structure of the transcription factor in question. The unknown motif width, the distribution of motif occurrences (OOPS, ZOOPS, or TCM), as well as the appropriate constraint set can be selected data-adaptively.",
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      "Software"
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    "Author": "Oliver Bembom, Fabian Gallusser, and Sandrine Dudoit",
    "Maintainer": "Oliver Bembom <oliver.bembom@gmail.com>",
    "URL": "http://cosmoweb.berkeley.edu/intro.html, http://www.bepress.com/sagmb/vol6/iss1/art8",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/cosmo_1.20.0.zip",
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      "preprocessCore",
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      "quantreg"
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      "splines"
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    "Suggests": [
      "scales"
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    "License": "Artistic-2.0",
    "Title": "Conditional quantile normalization",
    "Description": "A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method.",
    "biocViews": [
      "DifferentialExpression",
      "Preprocessing",
      "RNAseq",
      "Software"
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    "Author": "Jean (Zhijin) Wu, Kasper Daniel Hansen",
    "Maintainer": "Kasper Daniel Hansen <khansen@jhsph.edu>",
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    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "Title": "Genotype Calling (CRLMM) and Copy Number Analysis tool for Affymetrix SNP 5.0 and 6.0 and Illumina arrays.",
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    "biocViews": [
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      "SNP",
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    "Author": "Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.edu.au>, Ingo Ruczinski <iruczins@jhsph.edu>, Rafael A Irizarry",
    "Maintainer": "Benilton S Carvalho <Benilton.Carvalho@cancer.org.uk>, Robert Scharpf <rscharpf@jhsph.edu>, Matt Ritchie <mritchie@wehi.EDU.AU>",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/crlmm_1.12.1.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/crlmm_1.12.1.tgz",
    "vignettes": [
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      "vignettes/crlmm/inst/doc/CopyNumberOverview.pdf",
      "vignettes/crlmm/inst/doc/IlluminaPreprocessCN.pdf",
      "vignettes/crlmm/inst/doc/Infrastructure.pdf",
      "vignettes/crlmm/inst/doc/copynumber.pdf",
      "vignettes/crlmm/inst/doc/crlmmDownstream.pdf",
      "vignettes/crlmm/inst/doc/crlmmIllumina.pdf",
      "vignettes/crlmm/inst/doc/genotyping.pdf",
      "vignettes/crlmm/inst/doc/illumina_copynumber.pdf"
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    "Package": "ctc",
    "Version": "1.28.0",
    "Depends": [
      "amap"
    ],
    "License": "GPL-2",
    "Title": "Cluster and Tree Conversion.",
    "Description": "Tools for export and import classification trees and clusters to other programs",
    "biocViews": [
      "Classification",
      "Clustering",
      "DataImport",
      "Microarray",
      "Software",
      "Visualization"
    ],
    "Author": "Antoine Lucas <antoinelucas@gmail.com>, Laurent Gautier <laurent@cbs.dtu.dk>",
    "Maintainer": "Antoine Lucas <antoinelucas@gmail.com>",
    "URL": "http://antoinelucas.free.fr/ctc",
    "source.ver": "src/contrib/ctc_1.28.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/ctc_1.28.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/ctc_1.28.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/ctc_1.28.0.tgz",
    "vignettes": [
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  "cummeRbund": {
    "Package": "cummeRbund",
    "Version": "1.0.0",
    "Depends": [
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      "RSQLite",
      "reshape",
      "ggplot2"
    ],
    "Imports": [
      "methods"
    ],
    "License": "Artistic-2.0",
    "Title": "cummeRbund: The finishing touch on your Tuxedo workflow. Analysis, exploration, manipulation, and visualization of Cufflinks HTS 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": [
      "Bioinformatics",
      "Clustering",
      "DataImport",
      "DataRepresentation",
      "DifferentialExpression",
      "GeneExpression",
      "HighThroughputSequencing",
      "HighThroughputSequencingData",
      "Infrastructure",
      "MultipleComparisons",
      "QualityControl",
      "RNAseq",
      "RNAseqData",
      "Software",
      "Visualization"
    ],
    "Author": "L. Goff, C. Trapnell",
    "Maintainer": "Loyal A. Goff <lgoff@csail.mit.edu>",
    "source.ver": "src/contrib/cummeRbund_1.0.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/cummeRbund_1.0.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/cummeRbund_1.0.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/cummeRbund_1.0.0.tgz",
    "vignettes": [
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      "vignettes/cummeRbund/inst/doc/cummeRbund-manual.pdf"
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  "cycle": {
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    "Version": "1.8.0",
    "Depends": [
      "R (>= 2.10.0)",
      "Mfuzz"
    ],
    "Imports": [
      "Biobase",
      "stats"
    ],
    "License": "GPL-2",
    "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": [
      "Bioinformatics",
      "Microarray",
      "Software",
      "TimeCourse"
    ],
    "Author": "Matthias Futschik <mfutschik@ualg.pt>",
    "Maintainer": "Matthias Futschik <mfutschik@ualg.pt>",
    "URL": "http://itb.biologie.hu-berlin.de/~futschik/software/R/cycle/index.html",
    "source.ver": "src/contrib/cycle_1.8.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/cycle_1.8.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/cycle_1.8.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/cycle_1.8.0.tgz",
    "vignettes": [
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    ]
  },
  "daMA": {
    "Package": "daMA",
    "Version": "1.26.0",
    "Imports": [
      "MASS",
      "stats"
    ],
    "License": "GPL (>= 2)",
    "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": [
      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "Software",
      "TwoChannel"
    ],
    "Author": "Jobst Landgrebe <jlandgr1@gwdg.de> and Frank Bretz <bretz@bioinf.uni-hannover.de>",
    "Maintainer": "Jobst Landgrebe <jlandgr1@gwdg.de>",
    "URL": "http://www.microarrays.med.uni-goettingen.de",
    "source.ver": "src/contrib/daMA_1.26.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/daMA_1.26.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/daMA_1.26.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/daMA_1.26.0.tgz"
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  "ddCt": {
    "Package": "ddCt",
    "Version": "1.8.0",
    "Depends": [
      "R (>= 2.3.0)",
      "Biobase (>= 1.10.0)",
      "RColorBrewer (>= 0.1-3)",
      "xtable",
      "lattice",
      "methods"
    ],
    "License": "LGPL-3",
    "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": [
      "DifferentialExpression",
      "GeneExpression",
      "MicrotitrePlateAssay",
      "Software",
      "qPCR"
    ],
    "Author": "Jitao David Zhang, Rudolf Biczok and Markus Ruschhaupt",
    "Maintainer": "Jitao David Zhang <jitao_david.zhang@roche.com>",
    "source.ver": "src/contrib/ddCt_1.8.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/ddCt_1.8.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/ddCt_1.8.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/ddCt_1.8.0.tgz",
    "vignettes": [
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      "vignettes/ddCt/inst/doc/rtPCR-usage.pdf",
      "vignettes/ddCt/inst/doc/rtPCR.pdf"
    ]
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  "diffGeneAnalysis": {
    "Package": "diffGeneAnalysis",
    "Version": "1.36.0",
    "Imports": [
      "graphics",
      "grDevices",
      "minpack.lm (>= 1.0-4)",
      "stats",
      "utils"
    ],
    "License": "GPL",
    "Title": "Performs differential gene expression Analysis",
    "Description": "Analyze microarray data",
    "biocViews": [
      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "Software"
    ],
    "Author": "Choudary Jagarlamudi",
    "Maintainer": "Choudary Jagarlamudi <choudary.jagar@swosu.edu>",
    "source.ver": "src/contrib/diffGeneAnalysis_1.36.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/diffGeneAnalysis_1.36.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/diffGeneAnalysis_1.36.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/diffGeneAnalysis_1.36.0.tgz",
    "vignettes": [
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  "dks": {
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    "Version": "1.0.0",
    "Depends": [
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    ],
    "Imports": [
      "cubature"
    ],
    "License": "GPL",
    "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": [
      "Bioinformatics",
      "MultipleComparisons",
      "QualityControl",
      "Software"
    ],
    "Author": "Jeffrey T. Leek <jleek@jhsph.edu>",
    "Maintainer": "Jeffrey T. Leek <jleek@jhsph.edu>",
    "source.ver": "src/contrib/dks_1.0.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/dks_1.0.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/dks_1.0.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/dks_1.0.0.tgz",
    "vignettes": [
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      "vignettes/dks/inst/doc/dks.pdf"
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  "domainsignatures": {
    "Package": "domainsignatures",
    "Version": "1.14.0",
    "Depends": [
      "R (>= 2.4.0)",
      "KEGG.db",
      "prada",
      "biomaRt",
      "methods"
    ],
    "Imports": [
      "AnnotationDbi"
    ],
    "License": "Artistic-2.0",
    "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",
      "Bioinformatics",
      "Pathways",
      "Software"
    ],
    "Author": "Florian Hahne, Tim Beissbarth",
    "Maintainer": "Florian Hahne <florian.hahne@novartis.com>",
    "source.ver": "src/contrib/domainsignatures_1.14.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/domainsignatures_1.14.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/domainsignatures_1.14.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/domainsignatures_1.14.0.tgz",
    "vignettes": [
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  "dualKS": {
    "Package": "dualKS",
    "Version": "1.14.0",
    "Depends": [
      "R (>= 2.6.0)",
      "Biobase (>= 1.15.0)",
      "affy",
      "methods"
    ],
    "License": "LGPL (>= 2.0)",
    "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": [
      "Bioinformatics",
      "Microarray",
      "Software"
    ],
    "Author": "Eric J. Kort, Yarong Yang",
    "Maintainer": "Eric J. Kort <eric.kort@vai.org>",
    "source.ver": "src/contrib/dualKS_1.14.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/dualKS_1.14.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/dualKS_1.14.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/dualKS_1.14.0.tgz",
    "vignettes": [
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  "dyebias": {
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    "Version": "1.12.0",
    "Depends": [
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      "marray",
      "Biobase"
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    "Suggests": [
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      "convert",
      "GEOquery",
      "dyebiasexamples",
      "methods"
    ],
    "License": "GPL-3",
    "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",
      "Preprocessing",
      "QualityControl",
      "Software",
      "TwoChannel"
    ],
    "Author": "Philip Lijnzaad and Thanasis Margaritis",
    "Maintainer": "Philip Lijnzaad <p.lijnzaad@umcutrecht.nl>",
    "URL": "http://www.holstegelab.nl/publications/margaritis_lijnzaad",
    "source.ver": "src/contrib/dyebias_1.12.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/dyebias_1.12.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/dyebias_1.12.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/dyebias_1.12.0.tgz",
    "vignettes": [
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      "vignettes/dyebias/inst/doc/dyebiasCompleteVignette.pdf",
      "vignettes/dyebias/inst/doc/gassco.pdf"
    ],
    "suggestsMe": [
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    ]
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  "ecolitk": {
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    "Version": "1.26.0",
    "Depends": [
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    ],
    "Imports": [
      "Biobase",
      "graphics",
      "methods"
    ],
    "Suggests": [
      "ecoliLeucine",
      "ecolicdf",
      "graph",
      "multtest"
    ],
    "License": "GPL (>= 2)",
    "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",
      "Software",
      "Visualization"
    ],
    "Author": "Laurent Gautier",
    "Maintainer": "Laurent Gautier <lgautier@gmail.com>",
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    "win.binary.ver": "bin/windows/contrib/2.14/ecolitk_1.26.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/ecolitk_1.26.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/ecolitk_1.26.0.tgz",
    "vignettes": [
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  "edd": {
    "Package": "edd",
    "Version": "1.32.0",
    "Depends": [
      "methods",
      "golubEsets",
      "Biobase",
      "class",
      "nnet",
      "xtable"
    ],
    "License": "LGPL",
    "Title": "expression density diagnostics",
    "Description": "tools for evaluating cohort distributions of gene expression levels",
    "biocViews": [
      "Bioinformatics",
      "DifferentialExpression",
      "Software"
    ],
    "Author": "Vince Carey <stvjc@channing.harvard.edu>",
    "Maintainer": "Vince Carey <stvjc@channing.harvard.edu>",
    "source.ver": "src/contrib/edd_1.32.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/edd_1.32.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/edd_1.32.0.zip",
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    "vignettes": [
      "vignettes/edd/inst/doc/HOWTO-edd.pdf",
      "vignettes/edd/inst/doc/eddDetails.pdf"
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    "Package": "edgeR",
    "Version": "2.4.6",
    "Depends": [
      "R (>= 2.3.0)",
      "methods",
      "limma"
    ],
    "Suggests": [
      "MASS",
      "statmod",
      "splines"
    ],
    "License": "LGPL (>= 2)",
    "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",
      "DifferentialExpression",
      "HighThroughputSequencing",
      "RNAseq",
      "SAGE",
      "Software"
    ],
    "Author": "Mark Robinson <mrobinson@wehi.edu.au>, Davis McCarthy <dmccarthy@wehi.edu.au>, Yunshun Chen <yuchen@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>",
    "source.ver": "src/contrib/edgeR_2.4.6.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/edgeR_2.4.6.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/edgeR_2.4.6.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/edgeR_2.4.6.tgz",
    "vignettes": [
      "vignettes/edgeR/inst/doc/edgeR.pdf",
      "vignettes/edgeR/inst/doc/edgeRUsersGuide.pdf"
    ],
    "suggestsMe": [
      "baySeq",
      "EDASeq",
      "GenomicRanges",
      "goseq",
      "leeBamViews",
      "oneChannelGUI",
      "pasilla",
      "Repitools"
    ],
    "importsMe": [
      "ArrayExpressHTS",
      "DiffBind",
      "Repitools",
      "rnaSeqMap",
      "tweeDEseq"
    ]
  },
  "eisa": {
    "Package": "eisa",
    "Version": "1.6.0",
    "Depends": [
      "methods",
      "isa2",
      "Biobase",
      "AnnotationDbi",
      "Category",
      "genefilter",
      "DBI"
    ],
    "Suggests": [
      "igraph (>= 0.5.5)",
      "Matrix",
      "GOstats",
      "GO.db",
      "KEGG.db",
      "biclust",
      "MASS",
      "xtable",
      "ALL",
      "hgu95av2.db"
    ],
    "License": "GPL (>= 2)",
    "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_1.6.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/eisa_1.6.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/eisa_1.6.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/eisa_1.6.0.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"
    ],
    "importsMe": [
      "ExpressionView"
    ]
  },
  "exomeCopy": {
    "Package": "exomeCopy",
    "Version": "1.0.3",
    "Depends": [
      "methods",
      "graphics",
      "IRanges",
      "GenomicRanges",
      "Rsamtools (>= 1.4.3)"
    ],
    "Suggests": [
      "Biostrings"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "Title": "Detection of CNV in exome/targeted sequencing data",
    "Description": "A hidden Markov model for the detection of copy number variants (CNV) in exome/targeted sequencing read depth data. The package 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": [
      "CopyNumberVariants",
      "Genetics",
      "HighThroughputSequencing",
      "Sequencing",
      "Software"
    ],
    "Author": "Michael Love",
    "Maintainer": "Michael Love <love@molgen.mpg.de>",
    "source.ver": "src/contrib/exomeCopy_1.0.3.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/exomeCopy_1.0.3.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/exomeCopy_1.0.3.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/exomeCopy_1.0.3.tgz",
    "vignettes": [
      "vignettes/exomeCopy/inst/doc/exomeCopy.pdf"
    ]
  },
  "explorase": {
    "Package": "explorase",
    "Version": "1.18.0",
    "Depends": [
      "R (>= 2.6.2)"
    ],
    "Imports": [
      "limma",
      "rggobi",
      "RGtk2"
    ],
    "Suggests": [
      "cairoDevice"
    ],
    "License": "GPL-2",
    "Title": "GUI for exploratory data analysis of systems biology data",
    "Description": "explore and analyze *omics data with R and GGobi",
    "biocViews": [
      "GUI",
      "Microarray",
      "Software",
      "Visualization"
    ],
    "Author": "Michael Lawrence, Eun-kyung Lee, Dianne Cook, Jihong Kim, Hogeun An, and Dongshin Kim",
    "Maintainer": "Michael Lawrence <mflawren@fhcrc.org>",
    "URL": "http://www.metnetdb.org/MetNet_exploRase.htm",
    "source.ver": "src/contrib/explorase_1.18.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/explorase_1.18.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/explorase_1.18.0.zip",
    "vignettes": [
      "vignettes/explorase/inst/doc/explorase.pdf"
    ]
  },
  "externalVector": {
    "Package": "externalVector",
    "Version": "1.20.0",
    "Depends": [
      "R (>= 1.8.0)",
      "methods",
      "stats"
    ],
    "License": "LGPL",
    "Archs": "i386, x64",
    "Title": "Vector objects for R with external storage",
    "Description": "Basic class definitions and generics for external pointer based vector objects for R.  A simple in-memory implementation is also provided.",
    "biocViews": [
      "Infrastructure",
      "Software"
    ],
    "Author": "Saikat DebRoy",
    "Maintainer": "Bioconductor Package Maintainer <maintainer@bioconductor.org>",
    "source.ver": "src/contrib/externalVector_1.20.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/externalVector_1.20.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/externalVector_1.20.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/externalVector_1.20.0.tgz"
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  "fabia": {
    "Package": "fabia",
    "Version": "2.0.0",
    "Depends": [
      "R (>= 2.8.0)"
    ],
    "Imports": [
      "methods",
      "graphics",
      "grDevices",
      "stats",
      "utils"
    ],
    "License": "LGPL (>= 2.1)",
    "Archs": "i386, x64",
    "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": [
      "Bioinformatics",
      "Clustering",
      "DifferentialExpression",
      "Microarray",
      "MultipleComparisons",
      "Software",
      "Statistics",
      "Visualization"
    ],
    "Author": "Sepp Hochreiter <hochreit@bioinf.jku.at>",
    "Maintainer": "Sepp Hochreiter <hochreit@bioinf.jku.at>",
    "URL": "http://www.bioinf.jku.at/software/fabia/fabia.html",
    "source.ver": "src/contrib/fabia_2.0.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/fabia_2.0.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/fabia_2.0.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/fabia_2.0.0.tgz",
    "vignettes": [
      "vignettes/fabia/inst/doc/fabia.pdf"
    ]
  },
  "factDesign": {
    "Package": "factDesign",
    "Version": "1.30.0",
    "Depends": [
      "Biobase (>= 2.5.5)"
    ],
    "Imports": [
      "stats"
    ],
    "Suggests": [
      "affy",
      "genefilter"
    ],
    "License": "LGPL",
    "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": [
      "Bioinformatics",
      "DifferentialExpression",
      "Microarray",
      "Software"
    ],
    "Author": "Denise Scholtens",
    "Maintainer": "Denise Scholtens <dscholtens@northwestern.edu>",
    "source.ver": "src/contrib/factDesign_1.30.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/factDesign_1.30.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/factDesign_1.30.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/factDesign_1.30.0.tgz",
    "vignettes": [
      "vignettes/factDesign/inst/doc/factDesign.pdf"
    ]
  },
  "farms": {
    "Package": "farms",
    "Version": "1.6.0",
    "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)",
    "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",
      "Software"
    ],
    "Author": "Djork-Arne Clevert <okko@clevert.de>",
    "Maintainer": "Djork-Arne Clevert <okko@clevert.de>",
    "URL": "http://www.bioinf.jku.at/software/farms/farms.html",
    "source.ver": "src/contrib/farms_1.6.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/farms_1.6.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/farms_1.6.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/farms_1.6.0.tgz",
    "vignettes": [
      "vignettes/farms/inst/doc/farms.pdf"
    ]
  },
  "fastseg": {
    "Package": "fastseg",
    "Version": "1.0.4",
    "Depends": [
      "R (>= 2.13)",
      "GenomicRanges"
    ],
    "Imports": [
      "graphics",
      "stats",
      "IRanges"
    ],
    "Suggests": [
      "DNAcopy",
      "oligo"
    ],
    "License": "LGPL (>= 2.0)",
    "Archs": "i386, x64",
    "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",
      "CopyNumberVariants",
      "Software"
    ],
    "Author": "Guenter Klambauer",
    "Maintainer": "Guenter Klambauer <fastseg@bioinf.jku.at>",
    "URL": "http://www.bioinf.jku.at/software/fastseg/fastseg.html",
    "source.ver": "src/contrib/fastseg_1.0.4.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/fastseg_1.0.4.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/fastseg_1.0.4.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/fastseg_1.0.4.tgz",
    "vignettes": [
      "vignettes/fastseg/inst/doc/fastseg.pdf"
    ]
  },
  "fdrame": {
    "Package": "fdrame",
    "Version": "1.26.0",
    "Imports": [
      "tcltk",
      "graphics",
      "grDevices",
      "stats",
      "utils"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "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": [
      "DifferentialExpression",
      "Microarray",
      "MultipleComparisons",
      "Software"
    ],
    "Author": "Yoav Benjamini, Effi Kenigsberg, Anat Reiner, Daniel Yekutieli",
    "Maintainer": "Effi Kenigsberg <effiken.fdrame@gmail.com>",
    "source.ver": "src/contrib/fdrame_1.26.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/fdrame_1.26.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/fdrame_1.26.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/fdrame_1.26.0.tgz",
    "vignettes": [
      "vignettes/fdrame/inst/doc/fdrame.pdf"
    ]
  },
  "flagme": {
    "Package": "flagme",
    "Version": "1.10.0",
    "Depends": [
      "gcspikelite"
    ],
    "Imports": [
      "gplots",
      "graphics",
      "MASS",
      "methods",
      "SparseM",
      "stats",
      "utils",
      "xcms"
    ],
    "License": "LGPL (>= 2)",
    "Archs": "i386, x64",
    "Title": "Analysis of Metabolomics GC/MS Data",
    "Description": "Fragment-level analysis of gas chromatography - mass spectrometry metabolomics data",
    "biocViews": [
      "Bioinformatics",
      "DifferentialExpression",
      "MassSpectrometry",
      "Software"
    ],
    "Author": "Mark Robinson <mrobinson@wehi.edu.au>",
    "Maintainer": "Mark Robinson <mrobinson@wehi.edu.au>",
    "source.ver": "src/contrib/flagme_1.10.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/flagme_1.10.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/flagme_1.10.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/flagme_1.10.0.tgz",
    "vignettes": [
      "vignettes/flagme/inst/doc/flagme.pdf"
    ]
  },
  "flowClust": {
    "Package": "flowClust",
    "Version": "2.12.1",
    "Depends": [
      "R(>= 2.5.0)",
      "methods",
      "mnormt",
      "ellipse",
      "Biobase",
      "flowCore(>= 1.11.23)"
    ],
    "Suggests": [
      "snowfall"
    ],
    "Enhances": [
      "multicore"
    ],
    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "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": [
      "Bioinformatics",
      "Clustering",
      "FlowCytometry",
      "Software",
      "Visualization"
    ],
    "Author": "Raphael Gottardo <raph@stat.ubc.ca>, Kenneth Lo <c.lo@stat.ubc.ca>",
    "Maintainer": "Raphael Gottardo <raph@stat.ubc.ca>",
    "source.ver": "src/contrib/flowClust_2.12.1.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/flowClust_2.12.1.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/flowClust_2.12.1.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/flowClust_2.12.1.tgz",
    "vignettes": [
      "vignettes/flowClust/inst/doc/flowClust.pdf"
    ],
    "dependsOnMe": [
      "flowMerge",
      "flowTrans"
    ],
    "importsMe": [
      "flowMerge",
      "flowPhyto",
      "flowTrans",
      "flowType"
    ]
  },
  "flowCore": {
    "Package": "flowCore",
    "Version": "1.20.0",
    "Depends": [
      "R (>= 2.10.0)",
      "Biobase",
      "rrcov"
    ],
    "Imports": [
      "Biobase",
      "feature",
      "graph",
      "graphics",
      "grDevices",
      "MASS",
      "methods",
      "rrcov",
      "stats",
      "utils"
    ],
    "Suggests": [
      "Rgraphviz",
      "flowViz",
      "ncdf"
    ],
    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "Title": "flowCore: Basic structures for flow cytometry data",
    "Description": "Provides S4 data structures and basic functions to deal with flow cytometry data.",
    "biocViews": [
      "CellBasedAssays",
      "Flowcytometry",
      "Infrastructure",
      "Software"
    ],
    "Author": "B. Ellis, P. Haaland, F. Hahne, N. Le Meur, N. Gopalakrishnan",
    "Maintainer": "F. Hahne <florian.hahne@novartis.com>",
    "source.ver": "src/contrib/flowCore_1.20.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/flowCore_1.20.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/flowCore_1.20.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/flowCore_1.20.0.tgz",
    "vignettes": [
      "vignettes/flowCore/inst/doc/HowTo-flowCore.pdf"
    ],
    "dependsOnMe": [
      "flowClust",
      "flowFP",
      "flowMerge",
      "flowStats",
      "flowTrans",
      "flowUtils",
      "flowViz",
      "flowWorkspace",
      "iFlow",
      "ncdfFlow",
      "plateCore"
    ],
    "importsMe": [
      "flowFlowJo",
      "flowFP",
      "flowMeans",
      "flowMerge",
      "flowPhyto",
      "flowQ",
      "flowStats",
      "flowTrans",
      "flowType",
      "flowUtils",
      "flowViz",
      "flowWorkspace",
      "iFlow",
      "ncdfFlow",
      "plateCore"
    ]
  },
  "flowFP": {
    "Package": "flowFP",
    "Version": "1.12.1",
    "Depends": [
      "R(>= 2.5.0)",
      "flowCore",
      "flowViz"
    ],
    "Imports": [
      "Biobase",
      "flowCore",
      "flowViz",
      "graphics",
      "grDevices",
      "methods",
      "stats"
    ],
    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "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": [
      "Bioinformatics",
      "CellBasedAssays",
      "Clustering",
      "Flowcytometry",
      "Software",
      "Visualization"
    ],
    "Author": "Herb Holyst <holyst@mail.med.upenn.edu>, Wade Rogers <rogersw@mail.med.upenn.edu>",
    "Maintainer": "Herb Holyst <holyst@mail.med.upenn.edu>",
    "source.ver": "src/contrib/flowFP_1.12.1.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/flowFP_1.12.1.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/flowFP_1.12.1.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/flowFP_1.12.1.tgz",
    "vignettes": [
      "vignettes/flowFP/inst/doc/flowFP_HowTo.pdf"
    ]
  },
  "flowFlowJo": {
    "Package": "flowFlowJo",
    "Version": "1.12.0",
    "Depends": [
      "R (>= 2.5.0)",
      "MASS"
    ],
    "Imports": [
      "flowCore",
      "XML (>= 1.96)",
      "methods",
      "Biobase"
    ],
    "License": "GPL (>=3)",
    "Title": "Tools for extracting information from a FlowJo workspace and working with the data in the flowCore paradigm.",
    "Description": "FlowJo is a commercial GUI based software package from TreeStar Inc. for the visualization and analysis of flow cytometry data.  One of the FlowJo standard export file types is the \"FlowJo Workspace\".  This is an XML document that describes files and manipulations that have been performed in the FlowJo GUI environment.  This package can take apart the FlowJo workspace and deliver the data into R in the flowCore paradigm.",
    "biocViews": [
      "FlowCytometry",
      "Software"
    ],
    "Author": "John J. Gosink <gosinkj@amgen.com>",
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    "Title": "Provides classes similar to ExpressionSet for copy number analysis",
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    "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.",
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    "Author": "Mingshu Cao",
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    "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.",
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    "Author": "Florian P Breitwieser <fbreitwieser@cemm.oeaw.ac.at> and Jacques Colinge <jcolinge@cemm.oeaw.ac.at>, with contributions from Xavier Robin <xavier.robin@unige.ch>",
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      "Software"
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    "Author": "Ka Yee Yeung, University of Washington, Seattle, WA, with contributions from Adrian Raftery and Ian Painter",
    "Maintainer": "Ka Yee Yeung <kayee@u.washington.edu>",
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    "biocViews": [
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      "Software"
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    "Author": "Amalia Annest, University of Washington, Tacoma, WA Ka Yee Yeung, University of Washington, Seattle, WA",
    "Maintainer": "Ka Yee Yeung <kayee@u.washington.edu>",
    "URL": "http://expression.washington.edu/ibmasurv/protected",
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    "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).",
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    "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.",
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    "Author": "Gordon Smyth with contributions from Matthew Ritchie, Jeremy Silver, James Wettenhall, Natalie Thorne, Mette Langaas, Egil Ferkingstad, Marcus Davy, Francois Pepin, Dongseok Choi, Davis McCarthy, Di Wu, Alicia Oshlack, Carolyn de Graaf, Yifang Hu, Wei Shi and Belinda Phipson.",
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    "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.",
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    "Maintainer": "Johannes Rainer <johannes.rainer@i-med.ac.at>",
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    "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.",
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    "Maintainer": "Maria Jose Nueda <mj.nueda@ua.es>",
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    "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.",
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    "URL": "http://research.jax.org/faculty/churchill",
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    "Archs": "i386, x64",
    "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",
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    "Description": "Class definitions for two-color spotted microarray data. Fuctions for data input, diagnostic plots, normalization and quality checking.",
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    "Maintainer": "Yee Hwa (Jean) Yang <jean@biostat.ucsf.edu>",
    "URL": "http://www.maths.usyd.edu.au/u/jeany/",
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    "License": "The Artistic License, Version 2.0",
    "Title": "A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops.",
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      "utils"
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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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      "tools",
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    "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",
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      "Bioinformatics",
      "MultipleComparisons",
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    "Description": "build graphs from pathway databases, render them by Rgraphviz",
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      "Software"
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    "Author": "Li Long <lilong@isb-sib.ch>",
    "Maintainer": "Li Long <lilong@isb-sib.ch>",
    "URL": "http://www.bioconductor.org",
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    "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 unique interface to the PCA results. Initiated at the Max-Planck Institute for Molecular Plant Physiology, Golm, Germany. Now developed at CAS-MPG Partner Institute for Computational Biology (PICB) Shanghai, P.R. China and RIKEN Plant Science Center, Yokohama Japan.",
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    "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.",
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    "Author": "Sarah Song, Mik Black",
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    "Author": "Seth Falcon, Benilton Carvalho with contributions by Vince Carey and Matt Settles",
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    "URL": "http://www.dkfz.de/signaling, http://www.embl.de/research/units/genome_biology/huber/",
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    "License": "GPL (>=2)",
    "Archs": "i386, x64",
    "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": [
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    "Description": "Functions to Analyze Microarray (Gene Expression) Data.",
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      "Software"
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    "URL": "http://www.stat.wisc.edu/~yandell/statgen",
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      "dmt"
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    "License": "FreeBSD",
    "Title": "Pairwise INTegration of functional genomics data",
    "Description": "Pairwise data integration for functional genomics. In particular, tools to screen functionally active chromosomal aberrations from paired copy number, gene expression and miRNA measurements from the same patients.",
    "biocViews": [
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      "grid"
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    "License": "GPL-2",
    "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.",
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    "Maintainer": "Jan Oosting <j.oosting@lumc.nl>",
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    "Imports": [
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      "grDevices",
      "stats",
      "tcltk"
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    "License": "LGPL",
    "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. 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. 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.",
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    "Author": "Alan Dabney <adabney@u.washington.edu> and John D. Storey <jstorey@u.washington.edu>, with assistance from Gregory R. Warnes <gregory_r_warnes@groton.pfizer.com>",
    "Maintainer": "John D. Storey <jstorey@u.washington.edu>",
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    "Title": "Analysis of Chromosome Conformation Capture and Next-generation Sequencing (3C-seq)",
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    "Maintainer": "Supat Thongjuea <supat.thongjuea@bccs.uib.no>",
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      "methods",
      "graphics",
      "seqLogo"
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    "License": "Artistic-2.0",
    "Archs": "i386, x64",
    "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": [
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      "GenomicSequence",
      "Microarray",
      "Sequencing",
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    "Author": "Arnaud Droit, Raphael Gottardo, Gordon Robertson and Leiping Li",
    "Maintainer": "Arnaud Droit <arnaud.droit@crchuq.ulaval.ca>",
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    "License": "GPL-2",
    "Title": "Hidden Variable Dynamic Modeling",
    "Description": "A R implementation of HVDM (Genome Biol 2006, V7(3) R25)",
    "biocViews": [
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      "GraphsAndNetworks",
      "Microarray",
      "Software",
      "Transcription"
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    "Author": "Martino Barenco",
    "Maintainer": "Martino Barenco<m.barenco@ucl.ac.uk>",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/rHVDM_1.20.0.zip",
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    "Suggests": [
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      "rtracklayer"
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    "License": "Artistic-2.0",
    "Archs": "i386",
    "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 anf 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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      "Preprocessing",
      "Software"
    ],
    "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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    "win.binary.ver": "bin/windows/contrib/2.14/rMAT_3.4.1.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/rMAT_3.4.1.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/rMAT_3.4.1.tgz",
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    "Version": "1.28.0",
    "Depends": [
      "R(>= 2.5.0)"
    ],
    "License": "GPL (>= 2)",
    "Archs": "i386, x64",
    "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",
      "Preprocessing",
      "QualityControl",
      "Software",
      "TwoChannel"
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    "Author": "Raphael Gottardo",
    "Maintainer": "Raphael Gottardo <raph@stat.ubc.ca>",
    "source.ver": "src/contrib/rama_1.28.0.tar.gz",
    "win.binary.ver": "bin/windows/contrib/2.14/rama_1.28.0.zip",
    "win64.binary.ver": "bin/windows64/contrib/2.14/rama_1.28.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/rama_1.28.0.tgz",
    "vignettes": [
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    "dependsOnMe": [
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    "Version": "1.0.0",
    "Depends": [
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    "Imports": [
      "Biobase"
    ],
    "License": "Artistic 2.0",
    "Title": "Randomization routines for Clinical Trials",
    "Description": "A suite of classes and functions for randomizing patients in clinical trials.",
    "biocViews": [
      "Software",
      "Statistics"
    ],
    "Author": "Vincent Carey <stvjc@channing.harvard.edu> and Robert Gentleman",
    "Maintainer": "Robert Gentleman <rgentlem@gmail.com>",
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    "Depends": [
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      "Biobase (>= 2.5.5)",
      "survival"
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    "License": "GPL (>= 2)",
    "Title": "Robust likelihood-based survival modeling with microarray data",
    "Description": "This package selects genes associated with survival.",
    "biocViews": [
      "Bioinformatics",
      "Microarray",
      "Software"
    ],
    "Author": "HyungJun Cho <hj4cho@korea.ac.kr>, Sukwoo Kim <s4kim@korea.ac.kr>, Soo-heang Eo <hanansh@korea.ac.kr>, Jaewoo Kang <kangj@korea.ac.kr>",
    "Maintainer": "Soo-heang Eo <hanansh@korea.ac.kr>",
    "URL": "http://www.korea.ac.kr/~stat2242/",
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    "win.binary.ver": "bin/windows/contrib/2.14/rbsurv_2.12.0.zip",
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      "Biobase",
      "idiogram (>= 1.5.3)"
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    "License": "GPL-2",
    "Archs": "i386, x64",
    "Title": "Regional Expression Biases",
    "Description": "A set of functions to dentify regional expression biases",
    "biocViews": [
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      "Microarray",
      "Software",
      "Visualization"
    ],
    "Author": "Kyle A. Furge <kyle.furge@vai.org> and Karl Dykema <karl.dykema@vai.org>",
    "Maintainer": "Karl J. Dykema <karl.dykema@vai.org>",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/reb_1.30.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/reb_1.30.0.tgz",
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  "rnaSeqMap": {
    "Package": "rnaSeqMap",
    "Version": "2.8.0",
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      "methods",
      "xmapcore",
      "Biobase",
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    "Imports": [
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      "IRanges",
      "edgeR",
      "DESeq",
      "DBI",
      "RMySQL (>= 0.6-0)"
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    "License": "GPL-2",
    "Title": "rnaSeq secondary analyses",
    "Description": "Provides means of analysis for RNAseq data, used together with genomic annotation. Requires a set of BAM files on the input or alternatively, an xmapcore database in MySQL as a back-end, which is also a storage for sequencing reads. Front-end analyses include transformations of the coverage function, splicing analysis, finding irreducible regions with the two-sliding-windows algorithm and genomic region visualizations.",
    "biocViews": [
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      "Bioinformatics",
      "DifferentialExpression",
      "GeneExpression",
      "HighThroughputSequencing",
      "RNAseq",
      "ReportWriting",
      "SAGE",
      "Software",
      "Transcription",
      "Visualization"
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    "Author": "Anna Lesniewska <alesniewska@cs.put.poznan.pl>; Michal Okoniewski <michal@fgcz.ethz.ch>",
    "Maintainer": "Michal Okoniewski <michal@fgcz.ethz.ch>",
    "source.ver": "src/contrib/rnaSeqMap_2.8.0.tar.gz",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/rnaSeqMap_2.8.0.tgz",
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    "Version": "1.0.0",
    "Depends": [
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      "Biobase",
      "biclust"
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      "biclust"
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    "Suggests": [
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    "License": "GPL-2",
    "Archs": "i386, x64",
    "Title": "Qualitative biclustering algorithm for expression data analysis in R",
    "Description": "This package implements the QUBIC algorithm introduced by Li et al. for the qualitative biclustering with gene expression data.",
    "biocViews": [
      "Clustering",
      "Microarray",
      "Software"
    ],
    "Author": "Jitao David Zhang, with inputs from Laura Badi and Martin Ebeling",
    "Maintainer": "Jitao David Zhang <jitao_david.zhang@roche.com>",
    "source.ver": "src/contrib/rqubic_1.0.0.tar.gz",
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    "win64.binary.ver": "bin/windows64/contrib/2.14/rqubic_1.0.0.zip",
    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/rqubic_1.0.0.tgz",
    "vignettes": [
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    "Version": "2.12.0",
    "Depends": [
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      "methods",
      "utils"
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      "utils"
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    "License": "Artistic-2.0",
    "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": [
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      "Software"
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    "Author": "Michael Lawrence <michafla@gene.com>",
    "Maintainer": "Michael Lawrence <michafla@gene.com>",
    "URL": "http://www.sbml.org",
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    "mac.binary.leopard.ver": "bin/macosx/leopard/contrib/2.14/rsbml_2.12.0.tgz",
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      "zlibbioc"
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      "genefilter",
      "limma",
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      "BSgenome.Hsapiens.UCSC.hg19",
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      "hgu133plus2.db",
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    "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, and BigWig 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.",
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      "DataImport",
      "Software",
      "Visualization"
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    "Author": "Michael Lawrence, Vince Carey, Robert Gentleman",
    "Maintainer": "Michael Lawrence <michafla@gene.com>",
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      "ind1KG",
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      "oneChannelGUI",
      "PICS",
      "R453Plus1Toolbox",
      "Ringo",
      "rMAT",
      "TSSi"
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    "dependsOnMe": [
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      "r3Cseq"
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      "GenomicFeatures",
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      "Rsamtools"
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      "Biobase",
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      "methods"
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      "GO.db",
      "annotate",
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      "survival",
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      "Rgraphviz",
      "multtest",
      "hu6800.db",
      "survival"
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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.",
    "biocViews": [
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      "GeneExpression",
      "Software"
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    "Author": "William T. Barry",
    "Maintainer": "William T. Barry <bill.barry@duke.edu>",
    "URL": "http://www.duke.edu/~dinbarry/SAFE/",
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    "win.binary.ver": "bin/windows/contrib/2.14/safe_2.14.0.zip",
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      "methods",
      "SparseM",
      "stats",
      "utils"
    ],
    "License": "GPL (>= 2)",
    "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": [
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      "Software"
    ],
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    "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.",
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    "Title": "Transcript mapping with high-density oligonucleotide tiling arrays",
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    "Title": "Transcriptional Regulatory Inference from Genetics of Gene ExpRession",
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    ],
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    "Archs": "i386, x64",
    "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.",
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    "Archs": "i386, x64",
    "Title": "RNA-seq data analysis using the Poisson-Tweedie family of distributions",
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