Nease: A method for gene ontology subclassification of high-throughput gene expression data

Thomas W. Chittenden, Eleanor A. Howe, Jennifer M. Taylor, Jessica C. Mar, Martin J. Aryee, Harold Gómez, Razvan Sultana, John Braisted, Sarita J. Nair, John Quackenbush, Chris Holmes

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Summary: High-throughput technologies can identify genes whose expression profiles correlate with specific phenotypes; however, placing these genes into a biological context remains challenging. To help address this issue, we developed nested Expression Analysis Systematic Explorer (nEASE). nEASE complements traditional gene ontology enrichment approaches by determining statistically enriched gene ontology subterms within a list of genes based on co-annotation. Here, we overview an open-source software version of the nEASE algorithm. nEASE can be used either stand-alone or as part of a pathway discovery pipeline.

Original languageEnglish (US)
Article numberbts011
Pages (from-to)726-728
Number of pages3
JournalBioinformatics
Volume28
Issue number5
DOIs
StatePublished - Mar 2012

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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