Cited by 7,107Bioinformatics and Genomic NetworksGenomics and Phylogenetic StudiesGene expression and cancer classification

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

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Young, Matthew D, Wakefield, Matthew J, Smyth, Gordon K, Oshlack, Alicia·2010·Genome Biology

Abstract

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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  • For RNA-seq, GOSeq is advantageous because it models the detection bias affecting long and highly expressed transcripts.

    “Compared to the hypergeometric and Fisher’s tests, GOSeq has an advantage for RNA-seq data in that it explicitly accounts for the detection bias of long and highly expressed transcripts.”

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Authors
Young, M. D., Wakefield, M. J., Smyth, G. K., & Oshlack, A.
Affiliations
Walter and Eliza Hall Institute of Medical Research (AU)
Corresponding author
Oshlack, Alicia
Venue
Genome Biology
Published
2010-02-04
Volume
11
Issue
2
Pages
R14-R14
ISSN
  • 1465-6906 (ISSN-L)
  • 1474-760X
OpenAlex
W2107060225
Cited by
7,107
References
30
Citations by year
2026: 284 · 2025: 495 · 2024: 690 · 2023: 713 · 2022: 918 · 2021: 1008 · 2020: 849 · 2019: 657 · 2018: 476 · 2017: 385 · 2016: 229 · 2015: 160 · 2014: 95 · 2013: 58 · 2012: 44
FWCI
18.77
Type
journal article
Language
en
Open access
Locations
  • Genome biology (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
gene ontology enrichment, RNA-seq, selection bias, differential gene expression, prostate cancer, gene expression analysis
MeSH terms
Androgens, Humans, Male, Prostatic Neoplasms, Gene Expression Regulation, Neoplastic, Bias, Sequence Analysis, RNA, Cell Line, Tumor, Genome-Wide Association Study

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