Bioconductor R packages for the analysis of RNA-seq Data

ArrayExpressHTS – ArrayExpress High Throughput Sequencing Processing Pipeline. RNA-Seq processing pipeline for public ArrayExpress experiments or local datasets

BitSeq – Transcript expression inference and differential expression analysis for RNA-seq data. The BitSeq package is targeted for transcript expression analysis and differential expression analysis of RNA-seq data in two stage process. In the first stage it uses Bayesian inference methodology to infer expression of individual transcripts from individual RNA-seq experiments. The second stage of BitSeq embraces the differential expression analysis of transcript expression. Providing expression estimates from replicates of multiple conditions, Log-Normal model of the estimates is used for inferring the condition mean transcript expression and ranking the transcripts based on the likelihood of differential expression.

cqn – Conditional quantile normalization. A normalization tool for RNA-Seq data, implementing the conditional quantile normalization method.

cummeRbund – Analysis, exploration, manipulation, and visualization of Cufflinks high-throughput sequencing data. Allows for persistent storage, access, exploration, and manipulation of Cufflinks high-throughput sequencing data. In addition, provides numerous plotting functions for commonly used visualizations.

DEGseq – DEGseq is an R package to identify differentially expressed genes from RNA-Seq data.

DESeq –   Differential gene expression analysis based on the negative binomial distribution. Estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution

DEXSeq – Inference of differential exon usage in RNA-Seq. The package is focused on finding differential exon usage using RNA-seq exon counts between samples with different experimental designs. It provides functions that allows the user to make the necessary statistical tests based on a model that uses the negative binomial distribution to estimate the variance between biological replicates and generalized linear models for testing. The package also provides functions for the visualization and exploration of the results.

easyRNASeq – Count summarization and normalization for RNA-Seq data.

EDASeq –   Empirical analysis of digital gene expression data in R. Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization

gage – Generally Applicable Gene-set Enrichment for Pathway Analysis. GAGE is a published method for gene set or pathway analysis. GAGE is generally applicable independent of microarray data attributes including sample sizes, experimental designs, microarray platforms, and other types of heterogeneity, and consistently achieves superior performance over other frequently used methods. In gage package, we provide functions for basic GAGE analysis, result processing and presentation. We have also built pipeline routines for of multiple GAGE analyses in a batch, comparison between parallel analyses, and combined analysis of heterogeneous data from different sources/studies. In addition, we provide demo microarray data and commonly used gene set data based on KEGG pathways and GO terms. These funtions and data are also useful for gene set analysis using other methods.

goseq –   Gene Ontology analyser for RNA-seq and other length biased data. Detects Gene Ontology and/or other user defined categories which are over/under represented in RNA-seq data

iASeq –   iASeq: integrating multiple sequencing datasets for detecting allele-specific events. It fits correlation motif model to multiple RNAseq or ChIPseq studies to improve detection of allele-specific events and describe correlation patterns across studies.

manta –   Microbial Assemblage Normalized Transcript Analysis. Tools for robust comparative metatranscriptomics.

oneChannelGUI – A graphical interface designed to facilitate analysis of microarrays and miRNA/RNA-seq data on laptops. This package was developed to simplify the use of Bioconductor tools for beginners having limited or no experience in writing R code. This library provides a graphical interface for microarray gene and exon level analysis as well as miRNA/mRNA-seq data analysis.

rnaSeqMap – rnaSeq secondary analyses, provides means of analysis for RNAseq data, used together with genomic annotation. Requires a set of BAM files on the input or alternatively, an xmapcore database in MySQL as a back-end, which is also a storage for sequencing reads. Front-end analyses include transformations of the coverage function, splicing analysis, finding irreducible regions with the two-sliding-windows algorithm and genomic region visualizations.

TSSi – Transcription Start Site Identification. Identify and normalize transcription start sites in high-throughput sequencing data.

tweeDEseq – RNA-seq data analysis using the Poisson-Tweedie family of distributions

 

 

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