The UniProt-GO Annotation database in 2011
By E. Dimmer, Rachael P. Huntley, Y. Alam-Faruque, T. Sawford, C. O’Donovan, Maria Jesus Martin, B. Bely, P. Browne, Wei Mun Chan, R. Y. Eberhardt and 45 more. 2011
440 citations recorded by the source corpus.
The GO annotation dataset provided by the UniProt Consortium (GOA: http://www.ebi.ac.uk/GOA) is a comprehensive set of evidenced-based associations between terms from the Gene Ontology resource and UniProtKB proteins. Currently supplying over 100 million annotations to 11 million proteins in more than 360 000 taxa, this resource has increased 2-fold over the last 2 years and has benefited from a wealth of checks to improve annotation correctness and consistency as well as now supplying a greater information content enabled by GO Consortium annotation format developments. Detailed, manual GO annotations obtained from the curation of peer-reviewed papers are directly contributed by all UniProt curators and supplemented with manual and electronic annotations from 36 model organism and domain-focused scientific resources. The inclusion of high-quality, automatic annotation predictions ensures the UniProt GO annotation dataset supplies functional information to a wide range of proteins, including those from poorly characterized, non-model organism species. UniProt GO annotations are freely available in a range of formats accessible by both file downloads and web-based views. In addition, the introduction of a new, normalized file format in 2010 has made for easier handling of the complete UniProt-GOA data set.
Subfields: Molecular Biology
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In Molecular Biology
- Maryam Bagherian
- Zaneta Nikolovska-Coleska
- Stephen Fitzgerald
- Leo Gordon
- S. Searle
- Javier Herrero
- Kathryn M Beal
- Maureen A. Sartor
Citations in the corpus
- Ensembl comparative genomics resources (2016, background): “Despite recent concerns on the orthology conjecture (8), orthologues tend to be more similar in function than paralogues (9) and are widely used in gene annotation (10, 11).”
- Machine learning approaches and databases for prediction of drug–target interaction: a survey paper (2020, methodology): “In total, 11 molecular interaction databases (including IntAct) were incorporated into IntAct including AgBase [ 266–269], MINT [ 270–273], UniProt [274][41], I2D [275], MBINFO, MatrixDB [276], Molecular Connections, InnateDN [ 277], IMEx [278] and GOA.”
- SingleCellSignalR: inference of intercellular networks from single-cell transcriptomics (2019, methodology): “We also extracted LR pairs, with respective participants annotated as ligand or receptor in GO (31), from Reactome (32) pathways.”