EBSeq-HMM: A Bayesian approach for identifying gene-expression changes in ordered RNA-seq experiments

University of Wisconsin Madison

Contact email: nleng@wisc.edu, kendzior@biostat.wisc.edu

R/EBSeqHMM is an R package for gene and isoform expression path analysis across multiple ordered biological conditions in an RNA-seq experiment. The R/EBSeqHMM package may be downloaded below. A vignette is also available there. It provides the syntax required for identifying differentially expressed (DE) genes and isoforms in a time-course RNA-seq experiment as well for classifying DE genes into expression paths (as noted in the vignette, the commands for isoform level analysis are the same as those required for gene-level analysis).

Installation

EBSeqHMM was built under R version 3.0.1 and requires package EBSeq. R is a free and popular statistical software package that can be downloaded and installed from here. EBSeqHMM is currently available at Bioconductor (EBSeqHMM Bioconductor page).

To install, start R and within R type:

source("http://bioconductor.org/biocLite.R")

devel = "http://bioconductor.org/packages/3.2/bioc"

biocLite("EBSeqHMM", siteRepos = devel)

library(EBSeqHMM)

demo(EBSeqHMM)

The demo runs through many of the commands contained in the vignette automatically, using simulated data for illustration (it will take a few minutes). To apply EBSeqHMM to a particular dataset, the commands within the vignette may be followed once that dataset is read into R.

Vignette

EBSeqHMM_Vignette

References

Leng, N., J.A. Dawson, J.A. Thomson, V. Ruotti, A.I. Rissman, B.M.G. Smits, J.D. Haag, M.N. Gould, R.M. Stewart, and C. Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments, Bioinformatics, 2013. Paper

Li, B., V. Ruotti, R.M. Stewart, J.A. Thomson, and C. Dewey. RNA-Seq gene expression estimation with read mapping uncertainty. Bioinformatics 26(4): 493-500, 2010.

Updates

10/14/2014 EBSeqHMM is available on Bioconductor now.

06/24/2014 EBSeqHMM 0.0.4 is released