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

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.

Agency
National Institute of Health (NIH)
Institute
National Human Genome Research Institute (NHGRI)
Type
Biotechnology Resource Cooperative Agreements (U41)
Project #
5U41HG002273-15
Application #
8839808
Study Section
Special Emphasis Panel (ZHG1-HGR-M (O2))
Program Officer
Di Francesco, Valentina
Project Start
2001-01-19
Project End
2017-02-28
Budget Start
2015-03-01
Budget End
2016-02-29
Support Year
15
Fiscal Year
2015
Total Cost
$3,738,905
Indirect Cost
$318,803
Name
Jackson Laboratory
Department
Type
DUNS #
042140483
City
Bar Harbor
State
ME
Country
United States
Zip Code
04609
Christie, Karen R; Blake, Judith A (2018) Sensing the cilium, digital capture of ciliary data for comparative genomics investigations. Cilia 7:3
Müller, H-M; Van Auken, K M; Li, Y et al. (2018) Textpresso Central: a customizable platform for searching, text mining, viewing, and curating biomedical literature. BMC Bioinformatics 19:94
Pichler, Klemens; Warner, Kate; Magrane, Michele et al. (2018) SPIN: Submitting Sequences Determined at Protein Level to UniProt. Curr Protoc Bioinformatics 62:e52
Chibucos, Marcus C; Siegele, Deborah A; Hu, James C et al. (2017) The Evidence and Conclusion Ontology (ECO): Supporting GO Annotations. Methods Mol Biol 1446:245-259
Roncaglia, Paola; van Dam, Teunis J P; Christie, Karen R et al. (2017) The Gene Ontology of eukaryotic cilia and flagella. Cilia 6:10
Poux, Sylvain; Arighi, Cecilia N; Magrane, Michele et al. (2017) On expert curation and scalability: UniProtKB/Swiss-Prot as a case study. Bioinformatics 33:3454-3460
Munoz-Torres, Monica; Carbon, Seth (2017) Get GO! Retrieving GO Data Using AmiGO, QuickGO, API, Files, and Tools. Methods Mol Biol 1446:149-160
Denaxas, Spiros C (2017) Integrating Bio-ontologies and Controlled Clinical Terminologies: From Base Pairs to Bedside Phenotypes. Methods Mol Biol 1446:275-287
Ruch, Patrick (2017) Text Mining to Support Gene Ontology Curation and Vice Versa. Methods Mol Biol 1446:69-84
Pesquita, Catia (2017) Semantic Similarity in the Gene Ontology. Methods Mol Biol 1446:161-173

Showing the most recent 10 out of 88 publications