Gene ontology analysis for RNA-seq: accounting for selection bias
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)
Cited by 7,794Open Access
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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