Cookies on this website
We use cookies to ensure that we give you the best experience on our website. If you click 'Continue' we'll assume that you are happy to receive all cookies and you won't see this message again. Click 'Find out more' for information on how to change your cookie settings.

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

Original publication

DOI

10.1093/bioinformatics/bts011

Type

Journal article

Journal

Bioinformatics (Oxford, England)

Publication Date

03/2012

Volume

28

Pages

726 - 728

Addresses

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

Keywords

Humans, Neoplasms, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Algorithms, Software, Vocabulary, Controlled