Identifying differentially expressed transcripts from RNA-seq data with biological variation.

ABSTRACT:

Motivation

High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression (DE) estimation requires a probabilistic approach to properly account for ambiguity caused by shared exons and finite read sampling as well as the intrinsic biological variance of transcript expression.

Results

We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for DE analysis across replicates

SUBMITTER: Glaus P 

PROVIDER: S-EPMC3381971 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature