title: Identifying differentially expressed transcripts from RNA-seq data with biological variation.
abstract: BACKGROUND
  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.
  
  
  BACKGROUND
  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 http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.
authors.affiliation: School of Computer Science, University of Manchester, Oxford Road, Manchester M13 9PL, UK. glaus@cs.man.ac.uk
authors.forename: Peter
authors.initials: P
authors.lastname: Glaus
authors.forename: Antti
authors.initials: A
authors.lastname: Honkela
authors.forename: Magnus
authors.initials: M
authors.lastname: Rattray
mesh_terms: D001499:Bayes Theorem; D020869:Gene Expression Profiling / Q000379:methods*; D014644:Genetic Variation; D059014:High-Throughput Nucleotide Sequencing / Q000379:methods*; D015233:Models, Statistical; D016415:Sequence Alignment; D017423:Sequence Analysis, RNA / Q000379:methods*; D012984:Software; D059467:Transcriptome