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

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Title: Identifying differentially expressed transcripts from RNA-seq data with biological variation
Author: Glaus, Peter; Honkela, Antti; Rattray, Magnus
Citation: Glaus , P , Honkela , A & Rattray , M 2012 , ' Identifying differentially expressed transcripts from RNA-seq data with biological variation ' Bioinformatics , vol 28 , no. 13 , pp. 1721-1728 . , 10.1093/bioinformatics/bts260
Belongs to series: Bioinformatics
ISSN: 1367-4803
Abstract: 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 http://code.google.com/p/bitseq/, version 0.4 was used for generating results presented in this article.
Peer review status: Peer reviewed
URI: http://hdl.handle.net/10138/37377
Date: 2012

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