title: An ontology-neutral framework for enrichment analysis.
abstract: Advanced statistical methods used to analyze high-throughput data (e.g. 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 relevant for and extensible to data analysis with other high-throughput measurement modalities such as proteomics, metabolomics, and tissue-microarray assays. With the availability of tools for automatic ontology-based annotation of datasets with terms from biomedical ontologies besides the GO, we need not restrict enrichment analysis to the GO. We describe, RANSUM - Rich Annotation Summarizer - which performs enrichment analysis using any ontology in the National Center for Biomedical Ontology's (NCBO) BioPortal. We outline the methodology of enrichment analysis, the associated challenges, and discuss novel analyses enabled by RANSUM.
authors.affiliation: Center for Biomedical Informatics Research, Stanford University, Stanford, CA 94305.
authors.forename: Rob
authors.initials: R
authors.lastname: Tirrell
authors.forename: Uday
authors.initials: U
authors.lastname: Evani
authors.forename: Ari E
authors.initials: AE
authors.lastname: Berman
authors.forename: Sean D
authors.initials: SD
authors.lastname: Mooney
authors.forename: Mark A
authors.initials: MA
authors.lastname: Musen
authors.forename: Nigam H
authors.initials: NH
authors.lastname: Shah
mesh_terms: D020869:Gene Expression Profiling; D040901:Proteomics