[Bioc-devel] Bioconductor 2.10 is released

Dan Tenenbaum dtenenba at fhcrc.org
Tue Apr 3 01:02:16 CEST 2012


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
http://bioconductor.org for details and downloads.


* 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


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 http://bioconductor.org/install/.

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

- 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

- 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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