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

TL;DR

BitSeq introduces a Bayesian method for estimating transcript expression and differential expression from RNA-seq data, effectively accounting for biological variance and read ambiguity, demonstrated on simulated and real datasets.

Contribution

The paper presents a novel Bayesian approach, BitSeq, for transcript expression estimation and differential analysis that incorporates biological variance and read ambiguity.

Findings

BitSeq accurately estimates transcript expression levels.

The method effectively detects differential expression with biological variance.

Demonstrated advantages over existing methods on simulated and real data.

Abstract

Motivation: High-throughput sequencing enables expression analysis at the level of individual transcripts. The analysis of transcriptome expression levels and differential expression 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 BitSeq (Bayesian Inference of Transcripts from Sequencing data), a Bayesian approach for estimation of transcript expression level from RNA-seq experiments. Inferred relative expression is represented by Markov chain Monte Carlo (MCMC) samples from the posterior probability distribution of a generative model of the read data. We propose a novel method for differential expression analysis across replicates which propagates uncertainty from the sample-level model while modelling biological variance using an expression-level-dependent prior. We demonstrate the advantages of our method using simulated data as well as an RNA-seq dataset with technical and biological replication for both studied conditions. Availability: The implementation of the transcriptome expression estimation and differential expression analysis, BitSeq, has been written in C++.