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

doi:10.1093/bioinformatics/bts011

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 journal › Article › peer-review

6 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 language	English (US)
Article number	bts011
Pages (from-to)	726-728
Number of pages	3
Journal	Bioinformatics
Volume	28
Issue number	5
DOIs	https://doi.org/10.1093/bioinformatics/bts011
State	Published - Mar 2012
Externally published	Yes

ASJC Scopus subject areas

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

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10.1093/bioinformatics/bts011

Cite this

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title = "Nease: A method for gene ontology subclassification of high-throughput gene expression data",

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.",

author = "Chittenden, {Thomas W.} and Howe, {Eleanor A.} and Taylor, {Jennifer M.} and Mar, {Jessica C.} and Aryee, {Martin J.} and Harold G{\'o}mez and Razvan Sultana and John Braisted and Nair, {Sarita J.} and John Quackenbush and Chris Holmes",

note = "Funding Information: Funding: C.H. is supported by the Medical Research Council, UK.",

year = "2012",

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doi = "10.1093/bioinformatics/bts011",

language = "English (US)",

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T2 - A method for gene ontology subclassification of high-throughput gene expression data

AU - Chittenden, Thomas W.

AU - Howe, Eleanor A.

AU - Taylor, Jennifer M.

AU - Mar, Jessica C.

AU - Aryee, Martin J.

AU - Gómez, Harold

AU - Sultana, Razvan

AU - Braisted, John

AU - Nair, Sarita J.

AU - Quackenbush, John

AU - Holmes, Chris

N1 - Funding Information: Funding: C.H. is supported by the Medical Research Council, UK.

PY - 2012/3

Y1 - 2012/3

N2 - 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.

AB - 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.

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

Abstract

ASJC Scopus subject areas

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