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Gene ontology analysis for RNA-seq: accounting for selection bias
Genome biology · 2010 · Vol. 11(2) · pp. R14–R14
Matthew D. Young✉(Walter and Eliza Hall Institute of Medical Research)Matthew J. Wakefield(Walter and Eliza Hall Institute of Medical Research)Gordon K. Smyth(Walter and Eliza Hall Institute of Medical Research)Alicia Oshlack(Walter and Eliza Hall Institute of Medical Research)
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.
RNA Research and SplicingRNA modifications and cancerMolecular Biology Techniques and ApplicationsBiologyRNA-SeqGene ontologyHuman geneticsComputational biologyGeneticsSelection (genetic algorithm)GeneGenome BiologyOntology
MeSH terms
AndrogensHumansMaleProstatic NeoplasmsGene Expression Regulation, NeoplasticBiasSequence Analysis, RNACell Line, TumorGenome-Wide Association Study
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References
The development of androgen-independent prostate cancer
Nature reviews. Cancer · 2001 · 2,308 citations
Gene Ontology: tool for the unification of biology
Nature Genetics · 2000 · 43,975 citations
A scaling normalization method for differential expression analysis of RNA-seq data
Genome biology · 2010 · 8,395 citations
Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Journal of the Royal Statistical Society Series B (Statistical Methodology) · 1995 · 106,483 citations
<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data
Bioinformatics · 2009 · 43,721 citations
RNA-seq: An assessment of technical reproducibility and comparison with gene expression arrays
Genome Research · 2008 · 2,821 citations
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome
Genome biology · 2009 · 22,766 citations
Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Proceedings of the National Academy of Sciences · 2005 · 55,472 citations
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