Functional genomics technologies that measure genome expression at a global scale are accelerating biological knowledge discovery. Generating these high throughput datasets is relatively easy compared to the downstream functional modelling necessary for elucidating the molecular mechanisms that govern the biology under investigation. A number of publicly available 'discovery-based' computational tools use the computationally amenable Gene Ontology (GO) for hypothesis generation. However, there are few tools that support hypothesis-based testing using the GO and none that support testing with user defined hypothesis terms.Here, we present GOModeler, a tool that enables researchers to conduct hypothesis-based testing of high throughput datasets using the GO. GOModeler summarizes the overall effect of a user defined gene/protein differential expression dataset on specific GO hypothesis terms selected by the user to describe a biological experiment. The design of the tool allows the user to complement the functional information in the GO with his/her domain specific expertise for comprehensive hypothesis testing.
Pubmed ID: 20946613 RIS Download
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A horizontally and vertically structured database that pulls scientific and medical information and describes it consistently using the Ingenuity Ontology. The Knowledge Base pulls information from journals, public molecular content databases, and textbooks. Data is curated and and integrated into the Knowledge Base .
View all literature mentionsWeb tool to search, sort, analyze, visualize and download data of interest. Along with providing details of the ontologies, gene products and annotations, features a BLAST search, Term Enrichment and GO Slimmer tools, the GO Online SQL Environment and a user help guide.Used at the Gene Ontology (GO) website to access the data provided by the GO Consortium. Developed and maintained by the GO Consortium.
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