Gene ontology analysis for RNA-seq: accounting for selection bias
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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.”
From Gene expression elucidates functional impact of polygenic risk for schizophrenia (2016) · cites this paper as [111]
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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
- PMC
- PMC2872874
- 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
- gold
- doi.org
- 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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