nEASE: a method for gene ontology subclassification of high-throughput gene expression data
Author(s) -
Thomas W. Chittenden,
Eleanor Howe,
Jennifer M. Taylor,
Jessica C. Mar,
Martin J. Aryee,
Harold Gómez,
Răzvan Sultana,
John Braisted,
Sarita J. Nair,
John Quackenbush,
Chris Holmes
Publication year - 2012
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/bts011
Subject(s) - pipeline (software) , annotation , gene ontology , computer science , context (archaeology) , ontology , software , throughput , gene , gene annotation , computational biology , data mining , gene expression , biology , programming language , genetics , artificial intelligence , genome , operating system , paleontology , epistemology , wireless , philosophy
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.
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