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

ABSTRACT: 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.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.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.

SUBMITTER: Glaus P

PROVIDER: S-EPMC3381971 | biostudies-literature | 2012 Jul

REPOSITORIES: biostudies-literature

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

Glaus Peter P Honkela Antti A Rattray Magnus M

Bioinformatics (Oxford, England) 20120503 13

<h4>Motivation</h4>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.<h4>Results</h4>We present Bayesian inference of transcripts from sequencing data (BitSeq), a Bayesian appro ...[more]

PMID: 22563066

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Project description:BackgroundNowadays, both customers and producers prefer thin-tailed fat sheep. To effectively breed for this phenotype, it is important to identify candidate genes and uncover the genetic mechanism related to tail fat deposition in sheep. Accumulating evidence suggesting that post-transcriptional modification events of precursor-messenger RNA (pre-mRNA), including alternative splicing (AS) and alternative polyadenylation (APA), may regulate tail fat deposition in sheep. Differentially expressed transcripts (DETs) analysis is a way to identify candidate genes related to tail fat deposition. However, due to the technological limitation, post-transcriptional modification events in the tail fat of sheep and DETs between thin-tailed and fat-tailed sheep remains unclear.MethodsIn the present study, we applied pooled PacBio isoform sequencing (Iso-Seq) to generate transcriptomic data of tail fat tissue from six sheep (three thin-tailed sheep and three fat-tailed sheep). By comparing with reference genome, potential gene loci and novel transcripts were identified. Post-transcriptional modification events, including AS and APA, and lncRNA in sheep tail fat were uncovered using pooled Iso-Seq data. Combining Iso-Seq data with six RNA-sequencing (RNA-Seq) data, DETs between thin- and fat-tailed sheep were identified. Protein protein interaction (PPI) network, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were implemented to investigate the potential functions of DETs.ResultsIn the present study, we revealed the transcriptomic complexity of the tail fat of sheep, result in 9,001 potential novel gene loci, 17,834 AS events, 5,791 APA events, and 3,764 lncRNAs. Combining Iso-Seq data with RNA-Seq data, we identified hundreds of DETs between thin- and fat-tailed sheep. Among them, 21 differentially expressed lncRNAs, such as ENSOART00020036299, ENSOART00020033641, ENSOART00020024562, ENSOART00020003848 and 9.53.1 may regulate tail fat deposition. Many novel transcripts were identified as DETs, including 15.527.13 (DGAT2), 13.624.23 (ACSS2), 11.689.28 (ACLY), 11.689.18 (ACLY), 11.689.14 (ACLY), 11.660.12 (ACLY), 22.289.6 (SCD), 22.289.3 (SCD) and 22.289.14 (SCD). Most of the identified DETs have been enriched in GO and KEGG pathways related to extracellular matrix (ECM). Our result revealed the transcriptome complexity and identified many candidate transcripts in tail fat, which could enhance the understanding of molecular mechanisms behind tail fat deposition.

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

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

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