Database (Oxford) 2019 Jan 1; 2019:baz007
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.
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 J.; Fey, Petra; Garmiri, Penelope; Harris, Midori A; Sawford, Tony; Reiser, Leonore; Tauber, Rebecca; Toro, Sabrina; and Consortium, Gene Ontology, "Annotation of gene product function from high-throughput studies using the Gene Ontology." (2019). Faculty Research 2019. 34.