Question: Bioconductor 2.10 is released
gravatar for Dan Tenenbaum
7.1 years ago by
Dan Tenenbaum8.2k
United States
Dan Tenenbaum8.2k 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
Please log in to add an answer.


Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 16.09
Traffic: 303 users visited in the last hour