Document Type
Article
Publication Date
1-1-2019
Keywords
JMG
JAX Source
Database (Oxford) 2019 Jan 1; 2019:baz007
Volume
2019
ISSN
1758-0463
PMID
30715275
DOI
https://doi.org/10.1093/database/baz007
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.
Recommended Citation
Attrill H,
Gaudet P,
Huntley R,
Lovering R,
Engel S,
Poux S,
Van Auken K,
Georghiou G,
Chibucos M,
Berardini T,
Wood V,
Drabkin HJ,
Fey P,
Garmiri P,
Harris M,
Sawford T,
Reiser L,
Tauber R,
Toro S,
Consortium GO.
Annotation of gene product function from high-throughput studies using the Gene Ontology. Database (Oxford) 2019 Jan 1; 2019:baz007
Comments
Open access under the terms of the Creative Commons Attribution License