[Bioc-sig-seq] Bioconductor 2.8 is released

[Dan Tenenbaum]

Bioconductors:

We are pleased to announce Bioconductor 2.8, consisting of 466
software packages and more than 500 up-to-date annotation packages.
There are 48 new software packages, and many updates and improvements
to existing packages. Two software packages that were in the previous
version have been removed. Bioconductor 2.8 is compatible with
R 2.13.0, and is supported on Linux, 32- and 64-bit Windows, and Mac
OS.  Visit

http://bioconductor.org

for details and downloads.

Contents
========

* Getting Started with Bioconductor 2.8
* New Software Packages
* Using Bioconductor in the cloud

Getting Started with Bioconductor 2.8
=====================================

To install Bioconductor 2.8:

1. Install R 2.13.0.  Bioconductor 2.8 has been designed expressly for
this version of R.

2. Follow the instructions here:

http://bioconductor.org/install/

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

New Software Packages
=====================

There are 48 new packages in this release of Bioconductor.

a4

 Automated Affymetrix Array Analysis Umbrella Package

a4Base

 Automated Affymetrix Array Analysis Base Package

a4Classif

 Automated Affymetrix Array Analysis Classification Package

a4Core

 Automated Affymetrix Array Analysis Core Package

a4Preproc

 Automated Affymetrix Array Analysis Preprocessing Package

a4Reporting

 Automated Affymetrix Array Analysis Reporting Package

AnnotationFuncs

 Annotation translation functions

anota

 ANalysis Of Translational Activity

chopsticks

 The snp.matrix and X.snp.matrix classes

Clonality

 Clonality testing

clst

 Classification by local similarity threshold

clstutils

 Tools for performing taxonomic assignment

clusterProfiler

 statistical analysis and visulization of
 functional profiles for genes and gene clusters

cn.farms

 Factor Analysis for copy number estimation

ENVISIONQuery

 Retrieval from the ENVISION bioinformatics data portal into R

ExiMiR

 R functions for the normalization of Exiqon miRNA array data

flowPhyto

 Methods for Continuous Flow Cytometry

flowPlots

 analysis plots and data class for gated flow cytometry data

gaia

 An R package for genomic analysis of significant
 chromosomal aberrations

genefu

 Relevant Functions for Gene Expression Analysis,
 Especially in Breast Cancer

genoset

 Provides classes similar to ExpressionSet for copy number analysis

GSVA

 Gene Set Variation Analysis

ibh

 Interaction Based Homogeneity for Evaluating Gene Lists

inveRsion

 Inversions in genotype data

IPPD

 Isotopic peak pattern deconvolution for Protein Mass
 Spectrometry by template matching

joda

 JODA algorithm for quantifying gene deregulation using knowledge

lol

 Lots Of Lasso

mcaGUI

 Microbial Community Analysis GUI

mgsa

 Model-based gene set analysis

MLP

 Mean Log P Analysis

mosaics

 MOdel-based one and two Sample Analysis and Inference for ChIP-Seq

MSnbase

 Base Functions and Classes for MS-based Proteomics

NCIgraph

 Pathways from the NCI Pathways Database

phenoDist

 Phenotypic distance measures

phenoTest

 Tools to test correlation between gene expression and phenotype

procoil

 Prediction of Oligomerization of Coiled Coil Proteins

pvac

 PCA-based gene filtering for Affymetrix arrays

qrqc

 Quick Read Quality Control

RNAinteract

 Estimate Pairwise Interactions from multidimensional features

Rsubread

 a super fast, sensitive and accurate read aligner for mapping
 next-generation sequencing reads

seqbias

 Estimation of per-position bias in high-throughput sequencing data

snm

 Supervised Normalization of Microarrays

snpStats

 SnpMatrix and XSnpMatrix classes and methods

survcomp

 Performance Assessment and Comparison for Survival Analysis

TDARACNE

 Network reverse engineering from time course data

TEQC

 Quality control for target capture experiments

TurboNorm

 A fast scatterplot smoother suitable for microarray normalization

Vega

 An R package for copy number data segmentation


Using Bioconductor in the cloud
===============================

This release features the Bioconductor Amazon Machine
Image (AMI), which allows easy access to R and Bioconductor
within the Elastic Compute Cloud (EC2). It's easy to run
parallelizable tasks on MPI clusters, run R from within
your web browser using RStudio Server, and more. No
installation required. Information available at:

http://bioconductor.org/help/bioconductor-cloud-ami/