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.

RANSUM - Rich Annotation Summarizer - performs enrichment analysis using any ontology in the National Center for Biomedical Ontology's (NCBO) BioPortal and is described in Tirrell et al 2010

The STOP tool by Sean Mooney's group expands on the ideas in RANSUM to create a user friendly tool to form hypothesis about disease associations of large sets of genes or proteins.

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