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

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...

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Detalles Bibliográficos
Autores principales: Glaus, Peter, Honkela, Antti, Rattray, Magnus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3381971/
https://www.ncbi.nlm.nih.gov/pubmed/22563066
http://dx.doi.org/10.1093/bioinformatics/bts260
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author Glaus, Peter
Honkela, Antti
Rattray, Magnus
author_facet Glaus, Peter
Honkela, Antti
Rattray, Magnus
author_sort Glaus, Peter
collection PubMed
description 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. Contact: glaus@cs.man.ac.uk, antti.honkela@hiit.fi or m.rattray@sheffield.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-33819712012-06-25 Identifying differentially expressed transcripts from RNA-seq data with biological variation Glaus, Peter Honkela, Antti Rattray, Magnus Bioinformatics Original Papers 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. Contact: glaus@cs.man.ac.uk, antti.honkela@hiit.fi or m.rattray@sheffield.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2012-07-01 2012-05-03 /pmc/articles/PMC3381971/ /pubmed/22563066 http://dx.doi.org/10.1093/bioinformatics/bts260 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Glaus, Peter
Honkela, Antti
Rattray, Magnus
Identifying differentially expressed transcripts from RNA-seq data with biological variation
title Identifying differentially expressed transcripts from RNA-seq data with biological variation
title_full Identifying differentially expressed transcripts from RNA-seq data with biological variation
title_fullStr Identifying differentially expressed transcripts from RNA-seq data with biological variation
title_full_unstemmed Identifying differentially expressed transcripts from RNA-seq data with biological variation
title_short Identifying differentially expressed transcripts from RNA-seq data with biological variation
title_sort identifying differentially expressed transcripts from rna-seq data with biological variation
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3381971/
https://www.ncbi.nlm.nih.gov/pubmed/22563066
http://dx.doi.org/10.1093/bioinformatics/bts260
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