The UniProt-GO Annotation database in 2011

Emily Dimmer(European Bioinformatics Institute), Rachael P. Huntley(European Bioinformatics Institute), Yasmin Alam-Faruque(European Bioinformatics Institute), Tony Sawford(European Bioinformatics Institute), Claire O’Donovan(European Bioinformatics Institute), María Martin(European Bioinformatics Institute), Benoît Bely(European Bioinformatics Institute), P. Browne(European Bioinformatics Institute), W. Mun Chan(European Bioinformatics Institute), Ruth Y. Eberhardt(European Bioinformatics Institute), M. Gardner(European Bioinformatics Institute), Kati Laiho(European Bioinformatics Institute), David Legge(European Bioinformatics Institute), Michele Magrane(European Bioinformatics Institute), Klemens Pichler(European Bioinformatics Institute), Daniele Giovanni Poggioli(European Bioinformatics Institute), Harminder Sehra(European Bioinformatics Institute), Andrea H Auchincloss, Kristian B. Axelsen, Marie-Claude Blatter, Emmanuel Boutet, S. Braconi-Quintaje, Lionel Breuza, Alan Bridge, Elisabeth Coudert, Anne Estreicher, L. Famiglietti, S. Ferro-Rojas, Marc Feuermann, Arnaud Gos, Nadine Gruaz-Gumowski, Ursula Hinz, Chantal Hulo, Joachim James, Silvia Jiménez, Florence Jungo, G. Keller, P. Lemercier, Damien Lieberherr, Patrick Masson, M. Moinat, Ivo Pedruzzi, Sylvain Poux, Catherine Rivoire, Bernd Roechert, Michel Schneider, André Stutz, Suresh Sundaram, Michael Tognolli, Lydie Bougueleret, Ghislaine Argoud‐Puy(SIB Swiss Institute of Bioinformatics), Isabelle Cusin(SIB Swiss Institute of Bioinformatics), P. Duek- Roggli(SIB Swiss Institute of Bioinformatics), Ioannis Xénarios, Rolf Apweiler(European Bioinformatics Institute)
Nucleic Acids Research
November 28, 2011
10.1093/nar/gkr1048
Cited by 447Open Access
Full Text

Abstract

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.

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