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arXiv · 1109.0863

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

Peter Glaus·Antti Honkela·Magnus Rattray
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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++.

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Peter Glaus, Antti Honkela, Magnus Rattray. 2012-03-05. Identifying differentially expressed transcripts from RNA-seq data with biological variation. https://doi.org/10.1093/bioinformatics%2Fbts260

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