April 17, 2015

Bioconductors:

We are pleased to announce Bioconductor 3.1, consisting of 1024 software packages, 241 experiment data packages, and 917 up-to-date annotation packages.

There are 95 new software packages, and many updates and improvements to existing packages; Bioconductor 3.1 is compatible with R 3.2, and is supported on Linux, 32- and 64-bit Windows, and Mac OS X. This release includes an updated Bioconductor Amazon Machine Image and Docker containers.

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

Contents

Getting Started with Bioconductor 3.1

To update to or install Bioconductor 3.1:

  1. Install R 3.2. Bioconductor 3.1 has been designed expressly for this version of R.

  2. Follow the instructions at http://bioconductor.org/install/.

New Software Packages

There are 95 new packages in this release of Bioconductor.

AIMS - This package contains the AIMS implementation. It contains necessary functions to assign the five intrinsic molecular subtypes (Luminal A, Luminal B, Her2-enriched, Basal-like, Normal-like). Assignments could be done on individual samples as well as on dataset of gene expression data.

AnalysisPageServer - AnalysisPageServer is a modular system that enables sharing of customizable R analyses via the web.

bamsignals - This package allows to efficiently obtain count vectors from indexed bam files. It counts the number of reads in given genomic ranges and it computes reads profiles and coverage profiles. It also handles paired-end data.

BEclear - Provides some functions to detect and correct for batch effects in DNA methylation data. The core function “BEclear” is based on latent factor models and can also be used to predict missing values in any other matrix containing real numbers.

birte - Expression levels of mRNA molecules are regulated by different processes, comprising inhibition or activation by transcription factors and post-transcriptional degradation by microRNAs. biRte uses regulatory networks of TFs, miRNAs and possibly other factors, together with mRNA, miRNA and other available expression data to predict the relative influence of a regulator on the expression of its target genes. Inference is done in a Bayesian modeling framework using Markov-Chain-Monte-Carlo. A special feature is the possibility for follow-up network reverse engineering between active regulators.

BrowserViz - Interactvive graphics in a web browser from R, using websockets and JSON

BrowserVizDemo - A BrowserViz subclassing example, xy plotting in the browser using d3

BubbleTree - BubbleTree utilizes homogenous pertinent somatic copy number alterations (SCNAs) as markers of tumor clones to extract estimates of tumor ploidy, purity and clonality.

canceR - The package is user friendly interface based on the cgdsr and other modeling packages to explore, compare, and analyse all available Cancer Data (Clinical data, Gene Mutation, Gene Methylation, Gene Expression, Protein Phosphorylation, Copy Number Alteration) hosted by the Computational Biology Center at Memorial-Sloan-Kettering Cancer Center (MSKCC).

CAnD - Functions to perform the non-parametric and parametric CAnD tests on a set of ancestry proportions. For a particular ancestral subpopulation, a user will supply the estimated ancestry proportion for each sample, and each chromosome or chromosomal segment of interest. A p-value for each chromosome as well as an overall CAnD p-value will be returned for each test. Plotting functions are also available.

Cardinal - Implements statistical & computational tools for analyzing mass spectrometry imaging datasets, including methods for efficient pre-processing, spatial segmentation, and classification.

chromDraw - Package chromDraw is a simple package for linear and circular type of karyotype visualization. The linear type of visualization is usually used for demonstrating chromosomes structures in karyotype and the circular type of visualization is used for comparing of karyotypes between each other. This tool has own input data format or genomicRanges structure can be used as input. Each chromosome containing definition of blocks and centromere position. Output file formats are *.eps and *.svg.

Clonality - Statistical tests for clonality versus independence of tumors from the same patient based on their LOH or genomewide copy number profiles

CODEX - A normalization and copy number variation calling procedure for whole exome DNA sequencing data. CODEX relies on the availability of multiple samples processed using the same sequencing pipeline for normalization, and does not require matched controls. The normalization model in CODEX includes terms that specifically remove biases due to GC content, exon length and targeting and amplification efficiency, and latent systemic artifacts. CODEX also includes a Poisson likelihood-based recursive segmentation procedure that explicitly models the count-based exome sequencing data.

cogena - Description: Gene set enrichment analysis is a valuable tool for the study of molecular mechanisms that underpin complex biological traits. As the method is conventionally used on entire omic datasets, such as transcriptomes, it may be dominated by pathways and processes that are substantially represented in a dataset, however the approach may overlook smaller scale, but highly correlated cellular events that may be of great biological relevance. In order to detect these discrete molecular triggers, we developed a tool, co-expressed gene-set enrichment analysis (cogena), for clustering differentially expressed genes and identification of highly correlated molecular expression clusters. Cogena offers the user a range of clustering methods, including hierarchical clustering, model based clustering and self-organised mapping, based on different distance metrics like correlation and mutual information. After obtaining and visualising clusters, cogena performs gene set enrichment. These gene sets can be sourced from the Molecular Signatures Database (MSigDB) or user-defined gene sets. By performing gene set enrichment across expression clusters, we find considerable enhancement in the resolution of molecular signatures in omic data at the cluster level compared to the whole.

coMET - Visualisation of EWAS results in a genomic region. In addition to phenotype-association P-values, coMET also generates plots of co-methylation patterns and provides a series of annotation tracks. It can be used to other omic-wide association scans as long as the data can be translated to genomic level and for any species.

ComplexHeatmap - Complex heatmaps are efficient to visualize associations between different sources of data sets and reveal potential features. Here the ComplexHeatmap package provides a highly flexible way to arrange multiple heatmaps and supports self-defined annotation graphics.

conumee - This package contains a set of processing and plotting methods for performing copy-number variation (CNV) analysis using Illumina 450k methylation arrays.

CopywriteR - CopywriteR generate DNA copy number profiles from whole exome sequencing by analysing the offtarget sequence reads. By exploiting the offtarget sequence reads, it allows for creation of robust copy number profiles from WES with a uniform read depth and evenly distributed data points over the genome.

cpvSNP - Gene set analysis methods exist to combine SNP-level association p-values into gene sets, calculating a single association p-value for each gene set. This package implements two such methods that require only the calculated SNP p-values, the gene set(s) of interest, and a correlation matrix (if desired). One method (GLOSSI) requires independent SNPs and the other (VEGAS) can take into account correlation (LD) among the SNPs. Built-in plotting functions are available to help users visualize results.

cytofkit - An integrated mass cytometry data analysis pipeline that enables simultaneous illustration of cellular diversity and progression.

diffHic - Detects differential interactions across biological conditions in a Hi-C experiment. Methods are provided for read alignment and data pre-processing into interaction counts. Statistical analysis is based on edgeR and supports normalization and filtering. Several visualization options are also available.

diggit - Inference of Genetic Variants Driving Cellullar Phenotypes by the DIGGIT algorithm

DMRcaller - Uses Bisulfite sequencing data in two conditions and identifies differentially methylated regions between the conditions in CG and non-CG context. The input is the CX report files produced by Bismark and the output is a list of DMRs stored as GRanges objects.

edge - The edge package implements methods for carrying out differential expression analyses of genome-wide gene expression studies. Significance testing using the optimal discovery procedure and generalized likelihood ratio tests (equivalent to F-tests and t-tests) are implemented for general study designs. Special functions are available to facilitate the analysis of common study designs, including time course experiments. Other packages such as snm, sva, and qvalue are integrated in edge to provide a wide range of tools for gene expression analysis.

EMDomics - The EMDomics algorithm is used to perform a supervised two-class analysis to measure the magnitude and statistical significance of observed continuous genomics data between two groups. Usually the data will be gene expression values from array-based or sequence-based experiments, but data from other types of experiments can also be analyzed (e.g. copy number variation). Traditional methods like Significance Analysis of Microarrays (SAM) and Linear Models for Microarray Data (LIMMA) use significance tests based on summary statistics (mean and standard deviation) of the two distributions. This approach lacks power to identify expression differences between groups that show high levels of intra-group heterogeneity. The Earth Mover’s Distance (EMD) algorithm instead computes the “work” needed to transform one distribution into the other, thus providing a metric of the overall difference in shape between two distributions. Permutation of sample labels is used to generate q-values for the observed EMD scores.

ENCODExplorer - This package allows user to quickly access ENCODE project files metadata and give access to helper functions to query the ENCODE rest api, download ENCODE datasets and save the database in SQLite format.

ENmix - Illumina HumanMethylation450 BeadChip has a complex array design, and the measurement is subject to experimental variations. The ENmix R package provides tools for low level data preprocessing to improve data quality. It incorporates a model based background correction method ENmix, and functions for inter-array quantile normalization, data quality checking, exploration of multimodally distributed CpGs and source of data variation. To support large scale data analysis, the package also provides multi-processor parallel computing wrappers for some commonly used data preprocessing methods, such as BMIQ probe design type bias correction and ComBat batch effect correction.

ensembldb - The package provides functions to create and use transcript centric annotation databases/packages. The annotation for the databases are directly fetched from Ensembl using their Perl API. The functionality and data is similar to that of the TxDb packages from the GenomicFeatures package, but, in addition to retrieve all gene/transcript models and annotations from the database, the ensembldb package provides also a filter framework allowing to retrieve annotations for specific entries like genes encoded on a chromosome region or transcript models of lincRNA genes.

FISHalyseR - FISHalyseR provides functionality to process and analyse digital cell culture images, in particular to quantify FISH probes within nuclei. Furthermore, it extract the spatial location of each nucleus as well as each probe enabling spatial co-localisation analysis.

FlowRepositoryR - This package provides an interface to search and download data and annotations from FlowRepository (flowrepository.org). It uses the FlowRepository programming interface to communicate with a FlowRepository server.

FlowSOM - FlowSOM offers visualization options for cytometry data, by using Self-Organizing Map clustering and Minimal Spanning Trees

flowVS - Per-channel variance stabilization from a collection of flow cytometry samples by Bertlett test for homogeneity of variances. The approach is applicable to microarrays data as well.

gdsfmt - This package provides a high-level R interface to CoreArray Genomic Data Structure (GDS) data files, which are portable across platforms and include hierarchical structure to store multiple scalable array-oriented data sets with metadata information. It is suited for large-scale datasets, especially for data which are much larger than the available random-access memory. The gdsfmt package offers the efficient operations specifically designed for integers with less than 8 bits, since a single genetic/genomic variant, like single-nucleotide polymorphism (SNP), usually occupies fewer bits than a byte. Data compression and decompression are also supported with relatively efficient random access. It is allowed to read a GDS file in parallel with multiple R processes supported by the package parallel.

GENESIS - The GENESIS package provides methodology for estimating, inferring, and accounting for population and pedigree structure in genetic analyses. The current implementation provides functions to perform PC-AiR (Conomos et al., 2015): a Principal Components Analysis with genome-wide SNP genotype data for robust population structure inference in samples with related individuals (known or cryptic).

genomation - A package for summary and annotation of genomic intervals. Users can visualize and quantify genomic intervals over pre-defined functional regions, such as promoters, exons, introns, etc. The genomic intervals represent regions with a defined chromosome position, which may be associated with a score, such as aligned reads from HT-seq experiments, TF binding sites, methylation scores, etc. The package can use any tabular genomic feature data as long as it has minimal information on the locations of genomic intervals. In addition, It can use BAM or BigWig files as input.

gespeR - Estimates gene-specific phenotypes from off-target confounded RNAi screens. The phenotype of each siRNA is modeled based on on-targeted and off-targeted genes, using a regularized linear regression model.

ggtree - ggtree extends the ggplot2 plotting system which implemented the grammar of graphics. ggtree is designed for visualizing phylogenetic tree and different types of associated annotation data.

GoogleGenomics - Provides an R package to interact with the Google Genomics API.

gQTLBase - Infrastructure for eQTL, mQTL and similar studies.

gQTLstats - computationally efficient analysis of eQTL, mQTL, dsQTL, etc.

GreyListChIP - Identify regions of ChIP experiments with high signal in the input, that lead to spurious peaks during peak calling. Remove reads aligning to these regions prior to peak calling, for cleaner ChIP analysis.

gtrellis - Genome level Trellis graph visualizes genomic data conditioned by genomic categories (e.g. chromosomes). For each genomic category, multiple dimensional data which are represented as tracks describe different features from different aspects. This package provides high flexibility to arrange genomic categories and add self-defined graphics in the plot.

HIBAG - It is a software package for imputing HLA types using SNP data, and relies on a training set of HLA and SNP genotypes. HIBAG can be used by researchers with published parameter estimates instead of requiring access to large training sample datasets. It combines the concepts of attribute bagging, an ensemble classifier method, with haplotype inference for SNPs and HLA types. Attribute bagging is a technique which improves the accuracy and stability of classifier ensembles using bootstrap aggregating and random variable selection.

immunoClust - Model based clustering and meta-clustering of Flow Cytometry Data

InPAS - Alternative polyadenylation (APA) is one of the important post-transcriptional regulation mechanism which occurs in most human genes. InPAS, developed form DaPars algorithm, predicts and estimates APA and cleavage sites for mRNA-seq data. It uses the power of cleanUpdTSeq to adjust cleavage sites.

IVAS - Identification of genetic variants affecting alternative splicing.

LEA - LEA is an R package dedicated to landscape genomics and ecological association tests. LEA can run analyses of population structure and genome scans for local adaptation. It includes statistical methods for estimating ancestry coefficients from large genotypic matrices and evaluating the number of ancestral populations (snmf, pca); and identifying genetic polymorphisms that exhibit high correlation with some environmental gradient or with the variables used as proxies for ecological pressures (lfmm), and controlling the false discovery rate. LEA is mainly based on optimized C programs that can scale with the dimension of very large data sets.

LowMACA - The LowMACA package is a simple suite of tools to investigate and analyze the mutation profile of several proteins or pfam domains via consensus alignment. You can conduct an hypothesis driven exploratory analysis using our package simply providing a set of genes or pfam domains of your interest.

mAPKL - We propose a hybrid FS method (mAP-KL), which combines multiple hypothesis testing and affinity propagation (AP)-clustering algorithm along with the Krzanowski & Lai cluster quality index, to select a small yet informative subset of genes.

MatrixRider - Calculates a single number for a whole sequence that reflects the propensity of a DNA binding protein to interact with it. The DNA binding protein has to be described with a PFM matrix, for example gotten from Jaspar.

mdgsa - Functions to preform a Gene Set Analysis in several genomic dimensions. Including methods for miRNAs.

MeSHSim - Provide for measuring semantic similarity over MeSH headings and MEDLINE documents

MethTargetedNGS - Perform step by step methylation analysis of Next Generation Sequencing data.

mogsa - This package provide a method for doing gene set analysis based on multiple omics data.

msa - This package provides a unified R/Bioconductor interface to the multiple sequence alignment algorithms ClustalW, ClustalOmega, and Muscle. All three algorithms are integrated in the package, therefore, they do not depend on any external software tools and are available for all major platforms. The multiple sequence alignment algorithms are complemented by a function for pretty-printing multiple sequence alignments using the LaTeX package TeXshade.

muscle - MUSCLE performs multiple sequence alignments of nucleotide or amino acid sequences.

NanoStringQCPro - NanoStringQCPro provides a set of quality metrics that can be used to assess the quality of NanoString mRNA gene expression data – i.e. to identify outlier probes and outlier samples. It also provides different background subtraction and normalization approaches for this data. It outputs suggestions for flagging samples/probes and an easily sharable html quality control output.

netbenchmark - This package implements a benchmarking of several gene network inference algorithms from gene expression data.

nethet - Package nethet is an implementation of statistical solid methodology enabling the analysis of network heterogeneity from high-dimensional data. It combines several implementations of recent statistical innovations useful for estimation and comparison of networks in a heterogeneous, high-dimensional setting. In particular, we provide code for formal two-sample testing in Gaussian graphical models (differential network and GGM-GSA; Stadler and Mukherjee, 2013, 2014) and make a novel network-based clustering algorithm available (mixed graphical lasso, Stadler and Mukherjee, 2013).

OmicsMarkeR - Tools for classification and feature selection for ‘omics’ level datasets. It is a tool to provide multiple multivariate classification and feature selection techniques complete with multiple stability metrics and aggregation techniques. It is primarily designed for analysis of metabolomics datasets but potentially extendable to proteomics and transcriptomics applications.

pandaR - Runs PANDA, an algorithm for discovering novel network structure by combining information from multiple complimentary data sources.

parglms - support for parallelized estimation of GLMs/GEEs, catering for dispersed data

pmm - The Parallel Mixed Model (PMM) approach is suitable for hit selection and cross-comparison of RNAi screens generated in experiments that are performed in parallel under several conditions. For example, we could think of the measurements or readouts from cells under RNAi knock-down, which are infected with several pathogens or which are grown from different cell lines.

podkat - This package provides an association test that is capable of dealing with very rare and even private variants. This is accomplished by a kernel-based approach that takes the positions of the variants into account. The test can be used for pre-processed matrix data, but also directly for variant data stored in VCF files. Association testing can be performed whole-genome, whole-exome, or restricted to pre-defined regions of interest. The test is complemented by tools for analyzing and visualizing the results.

PROPER - This package provide simulation based methods for evaluating the statistical power in differential expression analysis from RNA-seq data.

ProtGenerics - S4 generic functions needed by Bioconductor proteomics packages.

pwOmics - pwOmics performs pathway-based level-specific data comparison of matching omics data sets based on pre-analysed user-specified lists of differential genes/transcripts and proteins. A separate downstream analysis of proteomic data including pathway identification and enrichment analysis, transcription factor identification and target gene identification is opposed to the upstream analysis starting with gene or transcript information as basis for identification of upstream transcription factors and regulators. The cross-platform comparative analysis allows for comprehensive analysis of single time point experiments and time-series experiments by providing static and dynamic analysis tools for data integration.

QuartPAC - Identifies clustering of somatic mutations in proteins over the entire quaternary structure.

R3CPET - The package provides a method to infer the set of proteins that are more probably to work together to maintain chormatin interaction given a ChIA-PET experiment results.

RBM - Use A Resampling-Based Empirical Bayes Approach to Assess Differential Expression in Two-Color Microarrays and RNA-Seq data sets.

rcellminer - The NCI-60 cancer cell line panel has been used over the course of several decades as an anti-cancer drug screen. This panel was developed as part of the Developmental Therapeutics Program (DTP, http://dtp.nci.nih.gov/) of the U.S. National Cancer Institute (NCI). Thousands of compounds have been tested on the NCI-60, which have been extensively characterized by many platforms for gene and protein expression, copy number, mutation, and others (Reinhold, et al., 2012). The purpose of the CellMiner project (http://discover.nci.nih.gov/cellminer) has been to integrate data from multiple platforms used to analyze the NCI-60 and to provide a powerful suite of tools for exploration of NCI-60 data.

RCyjs - Interactvive viewing and exploration of graphs, connecting R to Cytoscape.js

regioneR - regioneR offers a statistical framework based on customizable permutation tests to assess the association between genomic region sets and other genomic features.

rGREAT - This package makes GREAT (Genomic Regions Enrichment of Annotations Tool) analysis automatic by constructing a HTTP POST request according to user’s input and automatically retrieving results from GREAT web server.

rgsepd - R/GSEPD is a bioinformatics package for R to help disambiguate transcriptome samples (a matrix of RNA-Seq counts at RefSeq IDs) by automating differential expression (with DESeq2), then gene set enrichment (with GOSeq), and finally a N-dimensional projection to quantify in which ways each sample is like either treatment group.

Rhtslib - This package provides version 1.1 of the ‘HTSlib’ C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package=”Rhtslib”, “Rhtslib”).

RNAprobR - This package facilitates analysis of Next Generation Sequencing data for which positional information with a single nucleotide resolution is a key. It allows for applying different types of relevant normalizations, data visualization and export in a table or UCSC compatible bedgraph file.

RnaSeqSampleSize - RnaSeqSampleSize package provides a sample size calculation method based on negative binomial model and the exact test for assessing differential expression analysis of RNA-seq data

RnBeads - RnBeads facilitates comprehensive analysis of various types of DNA methylation data at the genome scale.

RUVcorr - RUVcorr allows to apply global removal of unwanted variation (ridged version) to real and simulated gene expression data.

saps - Functions implementing the Significance Analysis of Prognostic Signatures method (SAPS). SAPS provides a robust method for identifying biologically significant gene sets associated with patient survival. Three basic statistics are computed. First, patients are clustered into two survival groups based on differential expression of a candidate gene set. P_pure is calculated as the probability of no survival difference between the two groups. Next, the same procedure is applied to randomly generated gene sets, and P_random is calculated as the proportion achieving a P_pure as significant as the candidate gene set. Finally, a pre-ranked Gene Set Enrichment Analysis (GSEA) is performed by ranking all genes by concordance index, and P_enrich is computed to indicate the degree to which the candidate gene set is enriched for genes with univariate prognostic significance. A SAPS_score is calculated to summarize the three statistics, and optionally a Q-value is computed to estimate the significance of the SAPS_score by calculating SAPS_scores for random gene sets.

SELEX - Tools for quantifying DNA binding specificities based on SELEX-seq data

seq2pathway - Seq2pathway is a novel tool for functional gene-set (or termed as pathway) analysis of next-generation sequencing data, consisting of “seq2gene” and “gene2path” components. The seq2gene links sequence-level measurements of genomic regions (including SNPs or point mutation coordinates) to gene-level scores, and the gene2pathway summarizes gene scores to pathway-scores for each sample. The seq2gene has the feasibility to assign both coding and non-exon regions to a broader range of neighboring genes than only the nearest one, thus facilitating the study of functional non-coding regions. The gene2pathway takes into account the quantity of significance for gene members within a pathway compared those outside a pathway. The output of seq2pathway is a general structure of quantitative pathway-level scores, thus allowing one to functional interpret such datasets as RNA-seq, ChIP-seq, GWAS, and derived from other next generational sequencing experiments.

seqPattern - Visualising oligonucleotide patterns and sequence motifs occurrences across a large set of sequences centred at a common reference point and sorted by a user defined feature.

sigsquared - By leveraging statistical properties (log-rank test for survival) of patient cohorts defined by binary thresholds, poor-prognosis patients are identified by the sigsquared package via optimization over a cost function reducing type I and II error.

SIMAT - This package provides a pipeline for analysis of GC-MS data acquired in selected ion monitoring (SIM) mode. The tool also provides a guidance in choosing appropriate fragments for the targets of interest by using an optimization algorithm. This is done by considering overlapping peaks from a provided library by the user.

similaRpeak - This package calculates metrics which assign a level of similarity between ChIP-Seq profiles.

sincell - Cell differentiation processes are achieved through a continuum of hierarchical intermediate cell-states that might be captured by single-cell RNA seq. Existing computational approaches for the assessment of cell-state hierarchies from single-cell data might be formalized under a general workflow composed of i) a metric to assess cell-to-cell similarities (combined or not with a dimensionality reduction step), and ii) a graph-building algorithm (optionally making use of a cells-clustering step). Sincell R package implements a methodological toolbox allowing flexible workflows under such framework. Furthermore, Sincell contributes new algorithms to provide cell-state hierarchies with statistical support while accounting for stochastic factors in single-cell RNA seq. Graphical representations and functional association tests are provided to interpret hierarchies.

skewr - The skewr package is a tool for visualizing the output of the Illumina Human Methylation 450k BeadChip to aid in quality control. It creates a panel of nine plots. Six of the plots represent the density of either the methylated intensity or the unmethylated intensity given by one of three subsets of the 485,577 total probes. These subsets include Type I-red, Type I-green, and Type II.The remaining three distributions give the density of the Beta-values for these same three subsets. Each of the nine plots optionally displays the distributions of the “rs” SNP probes and the probes associated with imprinted genes as series of ‘tick’ marks located above the x-axis.

soGGi - The soGGi package provides a toolset to create genomic interval aggregate/summary plots of signal or motif occurence from BAM and bigWig files as well as PWM, rlelist, GRanges and GAlignments Bioconductor objects. soGGi allows for normalisation, transformation and arithmetic operation on and between summary plot objects as well as grouping and subsetting of plots by GRanges objects and user supplied metadata. Plots are created using the GGplot2 libary to allow user defined manipulation of the returned plot object. Coupled together, soGGi features a broad set of methods to visualise genomics data in the context of groups of genomic intervals such as genes, superenhancers and transcription factor binding events.

SVM2CRM - Detection of cis-regulatory elements using svm implemented in LiblineaR.

TIN - The TIN package implements a set of tools for transcriptome instability analysis based on exon expression profiles. Deviating exon usage is studied in the context of splicing factors to analyse to what degree transcriptome instability is correlated to splicing factor expression. In the transcriptome instability correlation analysis, the data is compared to both random permutations of alternative splicing scores and expression of random gene sets.

TPP - Analyze thermal proteome profiling (TPP) experiments with varying temperatures (TR) or compound concentrations (CCR).

TRONCO - Genotype-level cancer progression models describe the ordering of accumulating mutations, e.g., somatic mutations / copy number variations, during cancer development. These graphical models help understand the causal structure involving events promoting cancer progression, possibly predicting complex patterns characterising genomic progression of a cancer. Reconstructed models can be used to better characterise genotype-phenotype relation, and suggest novel targets for therapy design. TRONCO (TRanslational ONCOlogy) is a R package aimed at collecting state-of-the-art algorithms to infer progression models from cross-sectional data, i.e., data collected from independent patients which does not necessarily incorporate any evident temporal information. These algorithms require a binary input matrix where: (i) each row represents a patient genome, (ii) each column an event relevant to the progression (a priori selected) and a 0/1 value models the absence/presence of a certain mutation in a certain patient. The current first version of TRONCO implements the CAPRESE algorithm (Cancer PRogression Extraction with Single Edges) to infer possible progression models arranged as trees; cfr. Inferring tree causal models of cancer progression with probability raising, L. Olde Loohuis, G. Caravagna, A. Graudenzi, D. Ramazzotti, G. Mauri, M. Antoniotti and B. Mishra. PLoS One, to appear. This vignette shows how to use TRONCO to infer a tree model of ovarian cancer progression from CGH data of copy number alterations (classified as gains or losses over chromosome’s arms). The dataset used is available in the SKY/M-FISH database.

NEWS from new and existing packages

Package maintainers can add NEWS files describing changes to their packages since the last release. The following package NEWS is available:

affxparser

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AllelicImbalance

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AnalysisPageServer

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AnnotationDbi

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NEW FEATURES and API changes

BUG FIXES AND CODE MAINTENANCE

AnnotationHub

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aroma.light

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ballgown

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bamsignals

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BiGGR

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Biobase

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BiocInstaller

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CAMERA

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canceR

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Cardinal

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ChemmineOB

Changes in version 1.6.0:

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ChemmineR

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chimera

Changes in version 1.9.2:

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ChIPpeakAnno

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ChIPseeker

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cisPath

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ClassifyR

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cleanUpdTSeq

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cleaver

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Clomial

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clusterProfiler

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Changes in version 2.1.1:

CNAnorm

1.13.1: chromosome does not start from 1

cogena

Changes in version 0.99.3 (2015-04-02):

Changes in version 0.99.2 (2015-04-01):

Changes in version 0.99.1 (2015-03-31):

Changes in version 0.99.0 (2015-03-27):

coMET

Changes in version 0.99.10 (2015-04-10):

Changes in version 0.99.9 (2015-03-10):

Changes in version 0.99.8 (2015-02-15):

Changes in version 0.99.7 (2014-12-19):

Changes in version 0.99.6 (2014-11-25):

Changes in version 0.99.5 (2014-11-06):

Changes in version 0.99.4 (2014-11-05):

Changes in version 0.99.3 (2014-10-25):

Changes in version 0.99.2 (2014-10-16):

Changes in version 0.99.1 (2014-10-16):

Changes in version 0.99.0 (2014-09-24):

compcodeR

1.3.1: Added citation file

ComplexHeatmap

Changes in version 0.99.4:

Changes in version 0.99.2:

Changes in version 0.99.1:

Changes in version 0.99.0:

CopywriteR

Changes in version 1.99.4 (2015-04-08):

Changes in version 1.99.3 (2015-04-06):

Changes in version 1.99.2 (2015-03-21):

Changes in version 1.99.0 (2015-03-05):

cpvSNP

Changes in version 0.99.0:

CRISPRseek

Changes in version 1.7.6:

NEW FEATURES

Changes in version 1.7.3:

NEW FEATURES

Changes in version 1.7.1:

BUG FIXES

csaw

Changes in version 1.1.28:

cummeRbund

2.9.3: Bugfix: - Introduced CHECK error by adding to .Rbuildignore…this is now fixed.

2.9.2: version bump to let BioC nightly build grab commit.

2.9.1: version bump for BioC devel release 3.1

2.8.2: Bugfixes: - removed reference to sqliteCloseConnection() (not exported by RSQLite 1.0.0) in vignette.

2.8.1: Bugfixes: - Made minimal changes for compatibility with RSQLite 1.0.0

Changes in version 1.5.1:

NEW FEATURES

BUG FIXES

DART

Changes in version 1.15.3:

deepSNV

Changes in version 1.99.3 (2013-07-25):

Updates

Bugfixes

Changes in version 1.99.2 (2013-07-11):

Updates

Bugfixes

Changes in version 1.99.1 (2013-06-25):

Updates

Bugfixes

Changes in version 1.99.0 (2013-04-30):

Updates

DEGreport

1.01.01: 10-17-2014 Lorena Pantano lorena.pantano@gmail.com FIX BUG WHEN ONLY ONE GENE IS DEG

derfinder

Changes in version 1.1.18:

BUG FIXES

Changes in version 1.1.17:

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

Changes in version 1.1.16:

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 1.1.15:

BUG FIXES

Changes in version 1.1.14:

BUG FIXES

Changes in version 1.1.5:

NEW FEATURES

BUG FIXES

Changes in version 1.1.3:

NEW FEATURES

BUG FIXES

derfinderPlot

Changes in version 1.1.6:

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 1.1.3:

BUG FIXES

DESeq2

Changes in version 1.8.0:

Changes in version 1.7.45:

Changes in version 1.7.43:

Changes in version 1.7.32:

Changes in version 1.7.11:

Changes in version 1.7.9:

Changes in version 1.7.3:

DiffBind

Changes in version 1.13.5:

Changes in version 1.13.4:

Changes in version 1.13.3:

Changes in version 1.13.2:

Changes in version 1.13.1:

diffHic

Changes in version 0.99.0:

DOSE

Changes in version 2.5.12:

Changes in version 2.5.11:

Changes in version 2.5.10:

Changes in version 2.5.9:

Changes in version 2.5.8:

Changes in version 2.5.7:

Changes in version 2.5.6:

Changes in version 2.5.5:

Changes in version 2.5.3:

Changes in version 2.5.1:

DSS

Changes in version 2.5.0:

easyRNASeq

Changes in version 2.3.4:

Changes in version 2.3.3:

Changes in version 2.3.2:

Changes in version 2.3.1:

Changes in version 2.3.0:

EBImage

Changes in version 4.10.0:

NEW FEATURES

SIGNIFICANT USER-VISIBLE CHANGES

PERFORMANCE IMPROVEMENTS

BUG FIXES

EBSeq

Changes in version 1.7.1:

EDASeq

Changes in version 2.1:

edgeR

Changes in version 3.10.0:

EnrichmentBrowser

Changes in version 1.1.1:

NEW FEATURES

ensembldb

Changes in version 0.99.17:

NEW FEATURES

Changes in version 0.99.16:

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

Changes in version 0.99.15:

NEW FEATURES

Changes in version 0.99.14:

BUG FIXES

Changes in version 0.99.13:

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 0.99.12:

BUG FIXES

ensemblVEP

Changes in version 1.8.0:

NEW FEATURES

MODIFICATIONS

erccdashboard

Changes in version 1.1.1:

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES AND MINOR IMPROVEMENTS

FGNet

Changes in version 3.2:

NEW FEATURES

BUGFIXES

FISHalyseR

Changes in version 0.99:

flipflop

Changes in version 1.5.15:

Changes in version 1.5.14:

Changes in version 1.5.12:

MAJOR CHANGES

NEW FEATURES

Changes in version 1.5.11:

Changes in version 1.5.10:

MAJOR CHANGES

NEW FEATURES

MINOR CHANGES

Changes in version 1.5.8:

Changes in version 1.5.7:

Changes in version 1.5.6:

MAJOR CHANGES

MINOR CHANGES

Changes in version 1.5.5:

Changes in version 1.5.4:

Changes in version 1.5.3:

NEW FEATURE

Changes in version 1.5.1:

flowcatchR

Changes in version 1.2.0:

NEW FEATURES

BUG FIXES

flowMap

Changes in version 1.5.1:

USER VISIBLE CHANGES

FlowRepositoryR

Changes in version 0.99.2:

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 0.99.0:

SIGNIFICANT USER-VISIBLE CHANGES

FlowSOM

Changes in version 0.99.4:

NEW FEATURES

Changes in version 0.99.0:

NEW FEATURES

flowType

Changes in version 2.7.0:

frma

Changes in version 1.19:

gCMAP

Changes in version 1.11.3:

Changes in version 1.11.2:

Changes in version 1.11.1:

Changes in version 1.11.0:

gCMAPWeb

Changes in version 1.7.2:

Changes in version 1.7.1:

gdsfmt

Changes in version 1.3.0-1.3.10:

NEW FEATURES

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

GeneNetworkBuilder

Changes in version 1.9.1:

NEW FEATURES

BUG FIXES

GENESIS

Changes in version 0.99.4:

Changes in version 0.99.0:

geNetClassifier

Changes in version 1.6.1:

genomation

Changes in version 0.99.9:

IMPROVEMENTS AND BUG FIXES

Changes in version 0.99.8:

IMPROVEMENTS AND BUG FIXES

Changes in version 0.99.0.2:

IMPROVEMENTS AND BUG FIXES

Changes in version 0.99.0.1:

NEW FUNCTIONS AND FEATURES

IMPROVEMENTS AND BUG FIXES

Changes in version 0.99:

NEW FUNCTIONS AND FEATURES

genomeIntervals

Changes in version 1.23.2:

Changes in version 1.23.1:

Changes in version 1.23.0:

Changes in version 1.22.3:

Changes in version 1.22.2:

Changes in version 1.22.1:

GenomicAlignments

Changes in version 1.4.0:

NEW FEATURES

SIGNIFICANT USER-LEVEL CHANGES

DEPRECATED AND DEFUNCT

BUG FIXES

GenomicFeatures

Changes in version 1.20:

NEW FEATURES

SIGNIFICANT USER-VISIBLE CHANGES

DEPRECATED AND DEFUNCT

BUG FIXES

GenomicFiles

Changes in version 1.4.0:

NEW FEATURES

MODIFICATIONS

BUG FIXES

GenomicRanges

Changes in version 1.20.0:

NEW FEATURES

SIGNIFICANT USER-LEVEL CHANGES

DEPRECATED AND DEFUNCT

BUG FIXES

GenomicTuples

Changes in version 1.1.14:

genoset

Changes in version 1.21.10:

NEW FEATURES

GGtools

Changes in version 5.3:

ggtree

Changes in version 0.99.28:

Changes in version 0.99.27:

Changes in version 0.99.26:

Changes in version 0.99.25:

Changes in version 0.99.24:

Changes in version 0.99.23:

Changes in version 0.99.22:

Changes in version 0.99.21:

Changes in version 0.99.19:

Changes in version 0.99.18:

Changes in version 0.99.17:

Changes in version 0.99.16:

Changes in version 0.99.15:

Changes in version 0.99.14:

Changes in version 0.99.13:

Changes in version 0.99.12:

Changes in version 0.99.11:

Changes in version 0.99.10:

Changes in version 0.99.9:

Changes in version 0.99.8:

Changes in version 0.99.7:

Changes in version 0.99.6:

Changes in version 0.99.5:

Changes in version 0.99.4:

Changes in version 0.99.3:

Changes in version 0.99.2:

Changes in version 0.99.1:

GOexpress

Changes in version 1.1.12:

NEW FEATURES

Changes in version 1.1.11:

NEW FEATURES

Changes in version 1.1.10:

BUG FIX

Changes in version 1.1.9:

TYPOS

Changes in version 1.1.8:

TYPOS

Changes in version 1.1.7:

BUG FIXES

Changes in version 1.1.6:

BUG FIXES

NEW FEATURES

UPDATED FEATURES

GENERAL UPDATES

Changes in version 1.1.5:

BUG FIXES

Changes in version 1.1.4:

NEW FEATURES

UPDATED FEATURES

GENERAL UPDATES

Changes in version 1.1.3:

NEW FEATURES

UPDATED FEATURES

GENERAL UPDATES

Changes in version 1.1.2:

GENERAL UPDATES

Changes in version 1.1.1:

BUG FIXES

GENERAL UPDATES

GoogleGenomics

Changes in version 1.0.0:

NEW FEATURES

GOSemSim

Changes in version 1.25.5:

Changes in version 1.25.4:

Changes in version 1.25.3:

Changes in version 1.25.2:

Changes in version 1.25.1:

graphite

Changes in version 1.13.2 (2015-04-02):

gtrellis

Changes in version 0.99.3:

Changes in version 0.99.2:

Gviz

Changes in version 1.11.0:

NEW FEATURES

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

GWASTools

Changes in version 1.13.27:

Changes in version 1.13.26:

Changes in version 1.13.24:

Changes in version 1.13.23:

Changes in version 1.13.22:

Changes in version 1.13.21:

Changes in version 1.13.9:

Changes in version 1.13.8:

Changes in version 1.13.7:

Changes in version 1.13.6:

Changes in version 1.13.3:

HDTD

Changes in version 1.1.2 (2015-01-09):

Changes in version 1.1.1 (2014-11-04):

hiAnnotator

Changes in version 1.1.1:

HIBAG

Changes in version 1.4.0:

Changes in version 1.3.0-1.3.2:

NEW FEATURES

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

hiReadsProcessor

Changes in version 1.1.3:

HiTC

Changes in version 1.11.4:

NEW FEATURES

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

Changes in version 1.11.3:

BUG FIXES

Changes in version 1.11.1:

NEW FEATURES

BUG FIXES

hpar

Changes in version 1.9.1:

HTSFilter

Changes in version 1.7.1:

illuminaio

Changes in version 0.9.1 (2015-02-25):

Changes in version 0.9.0 (2014-10-13):

immunoClust

1.0.0: The first version caintains basically the functios and routines usesd to obtain the results of “Soerensen, T., Baumgart, S., Durek, P., Gruetzkau, A. and Haeupl, T. immunoClust - an automated analysis pipeline for the identification of immunophenotypic signatures in high-dimensional cytometric datasets. Cytometry A (accepted).” CHANGES: * The code was cleaned and modified in the C-binding calls to make it runable on R 3.1.2. * A bug in the cell.hclust function was fixed, which does not effect the general results but lead to minor differences in concrete numbers.

IMPCdata

Changes in version 1.0.1:

FEATURES

InPAS

Changes in version 0.99.7:

NEW FEATURES

Changes in version 0.99.6:

BUG FIXES

Changes in version 0.99.5:

BUG FIXES

Changes in version 0.99.4:

BUG FIXES

Changes in version 0.99.3:

BUG FIXES

Changes in version 0.99.2:

BUG FIXES

Changes in version 0.99.1:

BUG FIXES

intansv

Changes in version 1.7.1:

Notes

IRanges

Changes in version 2.2.0:

NEW FEATURES

SIGNIFICANT USER-VISIBLE CHANGES

DEPRECATED AND DEFUNCT

BUG FIXES

isobar

Changes in version 1.13.0:

kebabs

Changes in version 1.1.9:

Changes in version 1.1.8:

Changes in version 1.1.7:

Changes in version 1.1.6:

Changes in version 1.1.5:

Changes in version 1.1.4:

Changes in version 1.1.3:

Changes in version 1.1.2:

Changes in version 1.1.1:

Changes in version 1.1.0:

KEGGprofile

Changes in version 1.9.3:

Changes in version 1.9.1:

limma

Changes in version 3.24.0:

LowMACA

Changes in version 0.99.4:

Changes in version 0.99.3:

Changes in version 0.99.2:

MeSHDbi

Changes in version 1.3.2:

Changes in version 1.3.1:

metagenomeSeq

Changes in version 1.9:

metaMS

Changes in version 1.3.5:

Changes in version 1.3.4:

Changes in version 1.3.3:

Changes in version 1.3.2:

Changes in version 1.3.1:

metaseqR

Changes in version 1.5.31 (2015-03-05):

NEW FEATURES

BUG FIXES

Changes in version 1.5.21 (2015-01-09):

NEW FEATURES

BUG FIXES

Changes in version 1.5.15 (2014-12-22):

NEW FEATURES

BUG FIXES

Changes in version 1.5.2 (2014-12-30):

NEW FEATURES

BUG FIXES

Changes in version 1.5.1 (2014-10-31):

NEW FEATURES

BUG FIXES

MethylAid

Changes in version 1.1.10:

Changes in version 1.1.9:

Changes in version 1.1.5:

Changes in version 1.1.4:

BUG FIXES

Changes in version 1.1.3:

Changes in version 1.1.2:

minfi

Changes in version 1.13:

missMethyl

Changes in version 1.1.10:

Changes in version 1.1.3:

Changes in version 1.1.2:

mogsa

Changes in version 0.99.4:

NEW FEATURES

monocle

Changes in version 1.1.5:

Changes in version 1.1.1:

motifStack

Changes in version 1.11.8:

NEW FEATURES

BUG FIXES

Changes in version 1.11.7:

NEW FEATURES

BUG FIXES

Changes in version 1.11.6:

NEW FEATURES

BUG FIXES

Changes in version 1.11.5:

NEW FEATURES

BUG FIXES

Changes in version 1.11.4:

NEW FEATURES

BUG FIXES

Changes in version 1.11.3:

NEW FEATURES

BUG FIXES

Changes in version 1.11.2:

NEW FEATURES

BUG FIXES

Changes in version 1.11.1:

NEW FEATURES

BUG FIXES

msa

Changes in version 1.0.0:

MSGFgui

Changes in version 1.1.2:

MSnbase

Changes in version 1.15.18:

Changes in version 1.15.17:

Changes in version 1.15.16:

Changes in version 1.15.15:

Changes in version 1.15.14:

Changes in version 1.15.13:

Changes in version 1.15.12:

Changes in version 1.15.11:

Changes in version 1.15.10:

Changes in version 1.15.9:

Changes in version 1.15.8:

Changes in version 1.15.7:

Changes in version 1.15.6:

Changes in version 1.15.5:

Changes in version 1.15.4:

Changes in version 1.15.3:

Changes in version 1.15.2:

Changes in version 1.15.1:

Changes in version 1.15.0:

MSnID

Changes in version 1.1.6:

Changes in version 1.1.5:

Changes in version 1.1.4:

Changes in version 1.1.3:

Changes in version 1.1.2:

Changes in version 1.1.1:

Changes in version 1.1.0:

mzID

Changes in version 1.5.3:

mzR

Changes in version 2.1.12:

Changes in version 2.1.11:

Changes in version 2.1.10:

Changes in version 2.1.9:

Changes in version 2.1.8:

Changes in version 2.1.7:

Changes in version 2.1.6:

Changes in version 2.1.5:

Changes in version 2.1.4:

Changes in version 2.1.1:

nethet

0.99.5: First Bioconductor-devel release.

NetPathMiner

Changes in version 1.3.1:

npGSEA

Changes in version 1.3.8:

Changes in version 1.3.7:

Changes in version 1.3.6:

Changes in version 1.3.5:

Changes in version 1.3.4:

Changes in version 1.3.3:

Changes in version 1.3.2:

Changes in version 1.3.1:

oligo

Changes in version 1.32:

USER VISIBLE CHANGES

omicade4

Changes in version 1.7.1:

NEW FEATURES

PAA

Changes in version 1.1.1 (2015-03-13):

BUG FIXES

pandaR

Changes in version 3.2.0:

Pbase

Changes in version 0.6.13:

Changes in version 0.6.12:

Changes in version 0.6.11:

Changes in version 0.6.10:

Changes in version 0.6.9:

Changes in version 0.6.8:

Changes in version 0.6.7:

Changes in version 0.6.6:

Changes in version 0.6.5:

Changes in version 0.6.4:

Changes in version 0.6.3:

Changes in version 0.6.2:

Changes in version 0.6.1:

Changes in version 0.6.0:

pdInfoBuilder

Changes in version 1.32:

BUG FIXES

USER VISIBLE CHANGES

PhenStat

Changes in version 2.1.3:

NEW FUNCTIONALITY

COMPATIBILITY ISSUES

plethy

Changes in version 1.5.10:

Changes in version 1.5.7:

NEW FEATURES

polyester

1.2.3: change URL of example chromosome 22 sequence dataset / do not test internet-connectivity-dependent stuff during check

1.2.2: bug fix in fragment start position simulation with positional bias

1.2.1: major refactor: - reduced number of arguments for wrapper functions (simulate_experiment and simulate_experiment_countmat) - shortened source code in wrapper functions by adding several internal helper functions

1.2.1: several new features added: - GC bias in expression - empirical fragment length distribution model available - positional bias in fragmentation step - empirical error models available - custom library size factors available - simulate_experiment_empirical function added as quick way to simulate an experiment with abundances derived from a data set

prebs

Changes in version 1.7.1:

ProCoNA

Changes in version 1.4.1:

pRoloc

Changes in version 1.7.13:

Changes in version 1.7.12:

Changes in version 1.7.11:

Changes in version 1.7.10:

Changes in version 1.7.9:

Changes in version 1.7.8:

Changes in version 1.7.7:

Changes in version 1.7.6:

Changes in version 1.7.5:

Changes in version 1.7.4:

Changes in version 1.7.3:

Changes in version 1.7.2:

Changes in version 1.7.1:

Changes in version 1.7.0:

pRolocGUI

Changes in version 1.1.5:

Changes in version 1.1.4:

Changes in version 1.1.3:

Changes in version 1.1.2:

Changes in version 1.1.1:

proteoQC

Changes in version 1.3.2:

Changes in version 1.3.1:

ProtGenerics

Changes in version 0.99.3:

Changes in version 0.99.2:

Changes in version 0.99.1:

Changes in version 0.99.0:

qcmetrics

Changes in version 1.5.1:

QDNAseq

Changes in version 1.2.4 (2015-01-21):

OTHER

Changes in version 1.2.3 (2015-01-20):

BUG FIXES

Changes in version 1.2.2 (2014-12-23):

IMPROVEMENTS

Changes in version 1.2.1 (2014-11-01):

IMPROVEMENTS

qpgraph

Changes in version 2.20:

USER VISIBLE CHANGES

BUG FIXES

QuartPAC

Changes in version 0.99.0:

QuasR

Changes in version 1.8.0:

PUBLICATION

NEW FEATURES

qvalue

Changes in version 1.99:

This update of the qvalue package includes

R3CPET

2015.02.1:

r3Cseq

Changes in version 1.13.1 (2015-01-26):

Rbowtie

Changes in version 1.7.9:

NEW FEATURES

ReactomePA

Changes in version 1.11.9:

Changes in version 1.11.8:

Changes in version 1.11.7:

Changes in version 1.11.6:

Changes in version 1.11.5:

Changes in version 1.11.2:

Changes in version 1.11.1:

RedeR

Changes in version 1.15.0:

regionReport

Changes in version 1.1.9:

NEW FEATURES

Changes in version 1.1.8:

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 1.1.7:

NEW FEATURES

Changes in version 1.1.3:

BUG FIXES

ReportingTools

Changes in version 2015-3-27:

rGREAT

Changes in version 0.99.5:

Changes in version 0.99.4:

rgsepd

Changes in version 0.99.15:

BUG FIXES

Changes in version 0.99.12:

NEW FEATURES

Changes in version 0.99.11:

BUG FIXES

Changes in version 0.99.10:

BUG FIXES

Changes in version 0.99.4:

SIGNIFICANT USER-VISIBLE CHANGES

rhdf5

Changes in version 2.12.0:

NEW FEATURES

BUG FIXES

Risa

1.9.1: 1. Added citation information. 2. The identification of investigation files now avoids editor backup files (ignoring files like “i_Investigation~”) 3. Fixed issue where the investigation file wasn’t fully read, due to the number of columns in higher rows being greater than the first five rows (as used by read.table) 4. Moved inst/doc to vignettes folder as required since R 3.1.0 5. Imported packages in Depends 6. Removed ‘library’ or ‘require’ calls to packages already attached by Depends. 7. Fixed no visible binding for global variable.

RNAprobR

Changes in version 0.99.4:

NEW FEATURES

RnaSeqSampleSize

Changes in version 0.99.8 (2015-04-12):

BUG FIXES

Changes in version 0.99.7 (2015-04-06):

BUG FIXES

Changes in version 0.99.5 (2015-04-06):

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 0.99.4:

NEW FEATURES

BUG FIXES

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 0.99.3 (2014-11-23):

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 0.99.2:

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 0.99.1 (2014-10-19):

SIGNIFICANT USER-VISIBLE CHANGES

Changes in version 0.99.0 (2014-10-16):

SIGNIFICANT USER-VISIBLE CHANGES

RnBeads

Changes in version 0.99.22:

Changes in version 0.99.20:

Changes in version 0.99.19:

Changes in version 0.99.18:

Changes in version 0.99.17:

Changes in version 0.99.16:

Changes in version 0.99.15:

Changes in version 0.99.13:

Changes in version 0.99.12:

Changes in version 0.99.10:

Changes in version 0.99.9:

Changes in version 0.99.8:

Changes in version 0.99.7:

Changes in version 0.99.6:

Changes in version 0.99.0:

rpx

Changes in version 1.3.1:

Rqc

Changes in version 1.2:

NEW FEATURES

USER VISIBLE CHANGES

BUG FIXES

Rsamtools

Changes in version 1.19:

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

RUVcorr

Changes in version 0.99.1:

Changes in version 0.99.0:

RUVSeq

Changes in version 1.1:

SeqArray

Changes in version 1.7.1-1.7.5:

SeqVarTools

Changes in version 1.5.1:

SGSeq

Changes in version 1.2.0:

ShortRead

Changes in version 1.25:

SIGNIFICANT USER-VISIBLE CHANGES

BUG FIXES

SigCheck

Changes in version 2.0.0:

SIMAT

Changes in version 0.99.3:

Changes in version 0.99.2:

Changes in version 0.99.1:

Changes in version 0.99.0:

SNPRelate

Changes in version 1.1.0-1.1.11:

specL

Changes in version 1.1.17:

USER UNVISIBLE CHANGES

Changes in version 1.1.16:

USER UNVISIBLE CHANGES

Changes in version 1.1.15:

USER UNVISIBLE CHANGES

Changes in version 1.1.14:

USER UNVISIBLE CHANGES

Changes in version 1.1.13:

USER VISIBLE CHANGES

Changes in version 1.1.12:

USER VISIBLE CHANGES

Changes in version 1.1.11:

USER VISIBLE CHANGES

Changes in version 1.1.10:

USER VISIBLE CHANGES

Changes in version 1.1.9:

USER VISIBLE CHANGES

USER UNVISIBLE CHANGES

Changes in version 1.1.8:

USER VISIBLE CHANGES

USER UNVISIBLE CHANGES

Changes in version 1.1.7:

USER VISIBLE CHANGES

Changes in version 1.1.6:

USER VISIBLE CHANGES

Changes in version 1.1.5:

USER VISIBLE CHANGES

Changes in version 1.1.4:

USER VISIBLE CHANGES

USER UNVISIBLE CHANGES

Changes in version 1.1.3:

USER VISIBLE CHANGES

Changes in version 1.1.2:

USER VISIBLE CHANGES

Changes in version 1.1.1:

USER VISIBLE CHANGES

SRAdb

Changes in version 1.21.11 (2015-03-22):

Changes in version 1.21.8 (2014-12-12):

supraHex

Changes in version 1.5.1:

NEW FEATURES

synapter

Changes in version 1.9.5:

Changes in version 1.9.4:

Changes in version 1.9.3:

Changes in version 1.9.2:

Changes in version 1.9.1:

Changes in version 1.9.0:

TargetSearch

Changes in version 1.24.0:

BUG FIXES

TCC

Changes in version 1.7.13:

TFBSTools

Changes in version 1.5.0:

NEW FEATURES

TPP

Changes in version 1.0.0:

trackViewer

Changes in version 1.3.4:

NEW FEATURES

BUG FIXES

Changes in version 1.3.3:

NEW FEATURES

BUG FIXES

Changes in version 1.3.2:

NEW FEATURES

BUG FIXES

Changes in version 1.3.1:

NEW FEATURES

BUG FIXES

unifiedWMWqPCR

Changes in version 1.3.1:

SIGNIFICANT USER-VISIBLE CHANGES

VariantAnnotation

Changes in version 1.14.0:

NEW FEATURES

MODIFICATIONS

BUG FIXES

VariantFiltering

Changes in version 1.4:

USER VISIBLE CHANGES

BUG FIXES

xcms

Changes in version 1.43.3:

BUG FIXES

Changes in version 1.43.2:

BUG FIXES

Changes in version 1.43.1:

NEW FEATURE

xps

Changes in version 3.2:

VERSION xps-1.27.1

Packages removed since the last release.

The following packages are no longer in Bioconductor:

asmn, COPDSexualDimorphism, DNaseR, flowFlowJo, flowPhyto