Question: Bioconductor 2.10 is released
gravatar for Dan Tenenbaum
5.6 years ago by
Dan Tenenbaum ♦♦ 8.1k
United States
Dan Tenenbaum ♦♦ 8.1k wrote:
Bioconductors: We are pleased to announce Bioconductor 2.10, consisting of 554 software packages and more than 600 up-to-date annotation packages. There are 45 new software packages, and many updates and improvements to existing packages; 5 packages have been removed from this release. Bioconductor 2.10 is compatible with R 2.15.0, and is supported on Linux, 32- and 64-bit Windows, and Mac OS. This release includes an updated Bioconductor Amazon Machine Image. Visit for details and downloads. Contents -------- * Getting Started with Bioconductor 2.10 * New Software Packages For the full release announcement, including package NEWS and a list of packages removed from the release, please visit the BioC 2.10 release page: Getting Started with Bioconductor 2.10 ===================================== To install Bioconductor 2.10: 1. Install R 2.15.0. Bioconductor 2.10 has been designed expressly for this version of R. 2. Follow the instructions at New Software Packages ===================== There are 45 new packages in this release of Bioconductor. - AffyRNADegradation: Analyze and correct probe positional bias in microarray data due to RNA degradation - ASEB: Predict Acetylated Lysine Sites - BiocGenerics: Generic functions for Bioconductor - birta: Bayesian Inference of Regulation of Transcriptional Activity - BitSeq: Transcript expression inference and differential expression - BRAIN: Baffling Recursive Algorithm for Isotope distributioN calculations - BrainStars: query gene expression data and plots from BrainStars (B*) - CancerMutationAnalysis: Cancer mutation analysis - categoryCompare: Meta-analysis of high-throughput experiments using feature annotations - cellGrowth: Fitting cell population growth models - cnvGSA: Gene Set Analysis of (Rare) Copy Number Variants - coGPS: cancer outlier Gene Profile Sets - DART: Denoising Algorithm based on Relevance network Topology - deepSNV: Test for subclonal SNVs in deep sequencing experiments. - easyRNASeq: Count summarization and normalization for RNA-Seq data. - EBcoexpress: EBcoexpress for Differential Co-Expression Analysis - ffpe: Quality assessment and control for FFPE microarray expression - GeneGroupAnalysis: Gene Functional Class Analysis - GEWIST: Gene Environment Wide Interaction Search Threshold - gprege: Gaussian Process Ranking and Estimation of Gene Expression time-series - Gviz: Plotting data and annotation information along genomic coordinates - gwascat: representing and modeling data in the NHGRI GWAS catalog - HiTC: High Throughput Chromosome Conformation Capture analysis - HybridMTest: Hybrid Multiple Testing - iASeq: iASeq: integrating multiple sequencing datasets for detecting allele-specific events - iBBiG: Iterative Binary Biclustering of Genesets - IdMappingAnalysis: ID Mapping Analysis - inSilicoMerging: Collection of Merging Techniques for Gene Expression Data - manta: Microbial Assemblage Normalized Transcript Analysis - maskBAD: Masking probes with binding affinity differences - MinimumDistance: A package for de novo CNV detection in case-parent trios - motifRG: A package for discriminative motif discovery, designed for high throughput sequencing dataset - NarrowPeaks: Functional Principal Component Analysis to Narrow Down Transcription Factor Binding Site Candidates - pcaGoPromoter: pcaGoPromoter is used to analyze DNA micro array data - phyloseq: Handling and analysis of high-throughput phylogenetic sequence data. - PING: Probabilistic inference for Nucleosome Positioning with MNase-based or Sonicated Short-read Data - QUALIFIER: Qualitiy Control of Gated Flow Cytometry Experiments - RchyOptimyx: Optimyzed Cellular Hierarchies for Flow Cytometry - ReactomePA: Reactome Pathway Analysis - rhdf5: HDF5 interface to R - sigaR: statistics for integrative genomics analyses in R - spade: SPADE -- An analysis and visualization tool for Flow Cytometry - ternarynet: Ternary Network Estimation - VegaMC: VegaMC: A Package Implementing a Variational Piecewise Smooth Model for Identification of Driver Chromosomal Imbalances in Cancer - virtualArray: Build virtual array from different microarray platforms
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