Journal
BIOINFORMATICS
Volume 28, Issue 13, Pages 1721-1728Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/bts260
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Funding
- European ERASysBio+ initiative project SYNERGY by the Biotechnology and Biological Sciences Research Council [BB/I004769/2]
- Academy of Finland [135311, 121179]
- IST of the European Community under the PASCAL2 Network of Excellence [IST-2007-216886]
- BBSRC [BB/I004769/2] Funding Source: UKRI
- Biotechnology and Biological Sciences Research Council [BB/I004769/2] Funding Source: researchfish
- Academy of Finland (AKA) [135311, 121179, 135311, 121179] Funding Source: Academy of Finland (AKA)
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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.
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