Gene ontology analysis for RNA-seq: accounting for selection bias

We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differe...

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Detalles Bibliográficos
Autores principales: Young, Matthew D, Wakefield, Matthew J, Smyth, Gordon K, Oshlack, Alicia
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2010
Materias:
Method
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872874/
https://www.ncbi.nlm.nih.gov/pubmed/20132535
http://dx.doi.org/10.1186/gb-2010-11-2-r14
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author Young, Matthew D
Wakefield, Matthew J
Smyth, Gordon K
Oshlack, Alicia
author_facet Young, Matthew D
Wakefield, Matthew J
Smyth, Gordon K
Oshlack, Alicia
author_sort Young, Matthew D
collection PubMed
description We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology.
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spelling pubmed-28728742010-05-20 Gene ontology analysis for RNA-seq: accounting for selection bias Young, Matthew D Wakefield, Matthew J Smyth, Gordon K Oshlack, Alicia Genome Biol Method We present GOseq, an application for performing Gene Ontology (GO) analysis on RNA-seq data. GO analysis is widely used to reduce complexity and highlight biological processes in genome-wide expression studies, but standard methods give biased results on RNA-seq data due to over-detection of differential expression for long and highly expressed transcripts. Application of GOseq to a prostate cancer data set shows that GOseq dramatically changes the results, highlighting categories more consistent with the known biology. BioMed Central 2010 2010-02-04 /pmc/articles/PMC2872874/ /pubmed/20132535 http://dx.doi.org/10.1186/gb-2010-11-2-r14 Text en Copyright ©2010 Young et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Method
Young, Matthew D
Wakefield, Matthew J
Smyth, Gordon K
Oshlack, Alicia
Gene ontology analysis for RNA-seq: accounting for selection bias
title Gene ontology analysis for RNA-seq: accounting for selection bias
title_full Gene ontology analysis for RNA-seq: accounting for selection bias
title_fullStr Gene ontology analysis for RNA-seq: accounting for selection bias
title_full_unstemmed Gene ontology analysis for RNA-seq: accounting for selection bias
title_short Gene ontology analysis for RNA-seq: accounting for selection bias
title_sort gene ontology analysis for rna-seq: accounting for selection bias
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2872874/
https://www.ncbi.nlm.nih.gov/pubmed/20132535
http://dx.doi.org/10.1186/gb-2010-11-2-r14
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