Our objective is to provide the scientific community with a consistent, robust information environment for describing, sharing, integrating and comparing the functional roles of genes, proteins and functional RNAs within and across all organisms. The Gene Ontology (GO) Consortium is an international collaboration of model organism database and genome annotation groups who have joined together to establish standards for describing gene products, to provide tools and support for the consistent application of these standards for functional annotations, and to contribute to a central bioinformatics resource to integrate and share these annotations to facilitate and enable biological research. The GO provides specific classifications consisting of well-defined, biologically descriptive terms for the domains of, molecular function, biological process and cellular component. The GO classifications are independent of a particular technology, an uncoupling of terminology from technology that encourages application of these semantic standards by organism annotation groups that utilize a wide range of technical environments. The GO has been widely adopted and used for representation of complex biological information for model organism genomes, and is increasingly used for the functional annotation of emerging genomes. With the increased use of the GO, the Consortium must actively work to ensure both the consistency and quality of annotations as well as accuracy of the ontologies so that these resources may be reliably used to draw inferences and make biological predictions. We will do so by focusing on six key aims: 1) We will provide experimental functional annotations for human and major model organisms;2) We will extend the GO into emerging genome annotation communities and for key biological domains;3) We will enable phylogenetically-based propagation of annotations;4) We will develop a Common Annotation Framework;5) We will maintain and upgrade the Gene Ontologies, and 6) We will provide annotations and ontologies to the broad genetics community, thus supporting experimental biologists, genome informaticists, and computational biologists who are using GO annotations and ontologies in their research.

Public Health Relevance

The relevance of this work for public health Is that comprehensive integration and standardization of biomedical and genomics information, such as accomplished by the Gene Ontology Consortium, is an essential component of advancing the understanding of the molecular basis underlying human health and disease outcomes.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Biotechnology Resource Cooperative Agreements (U41)
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Special Emphasis Panel (ZHG1-HGR-M (O2))
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Bonazzi, Vivien
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Jackson Laboratory
Bar Harbor
United States
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Ison, Jon; Rapacki, Kristoffer; Ménager, Hervé et al. (2016) Tools and data services registry: a community effort to document bioinformatics resources. Nucleic Acids Res 44:D38-47
Reiser, Leonore; Berardini, Tanya Z; Li, Donghui et al. (2016) Sustainable funding for biocuration: The Arabidopsis Information Resource (TAIR) as a case study of a subscription-based funding model. Database (Oxford) 2016:
Altenhoff, Adrian M; Boeckmann, Brigitte; Capella-Gutierrez, Salvador et al. (2016) Standardized benchmarking in the quest for orthologs. Nat Methods 13:425-30
Kılıç, Sefa; Sagitova, Dinara M; Wolfish, Shoshannah et al. (2016) From data repositories to submission portals: rethinking the role of domain-specific databases in CollecTF. Database (Oxford) 2016:
Mi, Huaiyu; Poudel, Sagar; Muruganujan, Anushya et al. (2016) PANTHER version 10: expanded protein families and functions, and analysis tools. Nucleic Acids Res 44:D336-42
Pundir, Sangya; Martin, Maria J; O'Donovan, Claire et al. (2016) UniProt Tools. Curr Protoc Bioinformatics 53:1.29.1-15
Foulger, R E; Denny, P; Hardy, J et al. (2016) Using the Gene Ontology to Annotate Key Players in Parkinson's Disease. Neuroinformatics 14:297-304
Diehl, Alexander D; Meehan, Terrence F; Bradford, Yvonne M et al. (2016) The Cell Ontology 2016: enhanced content, modularization, and ontology interoperability. J Biomed Semantics 7:44
Shaw, David R (2016) Searching the Mouse Genome Informatics (MGI) Resources for Information on Mouse Biology from Genotype to Phenotype. Curr Protoc Bioinformatics 56:1.7.1-1.7.16
Cheung, Kei-Hoi; Keerthikumar, Shivakumar; Roncaglia, Paola et al. (2016) Extending gene ontology in the context of extracellular RNA and vesicle communication. J Biomed Semantics 7:19

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