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Bioinformatics. 2012 Mar 1;28(5):726-8. doi: 10.1093/bioinformatics/bts011. Epub 2012 Jan 13.

nEASE: a method for gene ontology subclassification of high-throughput gene expression data.

Author information

1
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, MA, USA.

Abstract

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.

AVAILABILITY:

nEASE is implemented within the Multiple Experiment Viewer software package available at http://www.tm4.org/mev.

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
22247278
DOI:
10.1093/bioinformatics/bts011
[Indexed for MEDLINE]

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