Annotation of gene product function from high-throughput studies using the Gene Ontology. (1st February 2019)

• Record Type: Journal Article
• Authors: Attrill, Helen; Gaudet, Pascale; Huntley, Rachael P; Lovering, Ruth C; Engel, Stacia R; Poux, Sylvain; Van Auken, Kimberly M; Georghiou, George; Chibucos, Marcus C; Berardini, Tanya Z; Wood, Valerie; Drabkin, Harold; Fey, Petra; Garmiri, Penelope; Harris, Midori A; Sawford, Tony; Reiser, Leonore; Tauber, Rebecca; Toro, Sabrina
• Is Part Of: Database. Volume 2019(2019)
• Publication Date: 2019-02-01
• Subjects: Biology -- Databases -- Periodicals; Bioinformatics -- Periodicals; 570.285
• DOI: 10.1093/database/baz007
• Languages: English
• ISSNs: 1758-0463

Abstract

Abstract: High-throughput studies constitute an essential and valued source of information for researchers. However, high-throughput experimental workflows are often complex, with multiple data sets that may contain large numbers of false positives. The representation of high-throughput data in the Gene Ontology (GO) therefore presents a challenging annotation problem, when the overarching goal of GO curation is to provide the most precise view of a gene's role in biology. To address this, representatives from annotation teams within the GO Consortium reviewed high-throughput data annotation practices. We present an annotation framework for high-throughput studies that will facilitate good standards in GO curation and, through the use of new high-throughput evidence codes, increase the visibility of these annotations to the research community.