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J Biomed Inform. 2011 Dec;44 Suppl 1:S31-8. doi: 10.1016/j.jbi.2011.04.007. Epub 2011 Apr 29.

Enabling enrichment analysis with the Human Disease Ontology.

Author information

1
Stanford Center for Biomedical Informatics Research, 251 Campus Drive, Medical School Office Building, Mail Code 5479, Stanford University, Stanford, CA 94305-5479, USA. plependu@stanford.edu

Abstract

Advanced statistical methods used to analyze high-throughput data such as gene-expression assays result in long lists of "significant genes." One way to gain insight into the significance of altered expression levels is to determine whether Gene Ontology (GO) terms associated with a particular biological process, molecular function, or cellular component are over- or under-represented in the set of genes deemed significant. This process, referred to as enrichment analysis, profiles a gene set, and is widely used to make sense of the results of high-throughput experiments. Our goal is to develop and apply general enrichment analysis methods to profile other sets of interest, such as patient cohorts from the electronic medical record, using a variety of ontologies including SNOMED CT, MedDRA, RxNorm, and others. Although it is possible to perform enrichment analysis using ontologies other than the GO, a key pre-requisite is the availability of a background set of annotations to enable the enrichment calculation. In the case of the GO, this background set is provided by the Gene Ontology Annotations. In the current work, we describe: (i) a general method that uses hand-curated GO annotations as a starting point for creating background datasets for enrichment analysis using other ontologies; and (ii) a gene-disease background annotation set - that enables disease-based enrichment - to demonstrate feasibility of our method.

PMID:
21550421
PMCID:
PMC3392036
DOI:
10.1016/j.jbi.2011.04.007
[Indexed for MEDLINE]
Free PMC Article

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