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

Bioinformatics. 2012 Jul 1;28(13):1721-8. doi: 10.1093/bioinformatics/bts260. Epub 2012 May 3.


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 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++ and Python. The software is available online from, version 0.4 was used for generating results presented in this article.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Bayes Theorem
  • Gene Expression Profiling / methods*
  • Genetic Variation
  • High-Throughput Nucleotide Sequencing / methods*
  • Models, Statistical
  • Sequence Alignment
  • Sequence Analysis, RNA / methods*
  • Software
  • Transcriptome