Annotation of gene product function from high-throughput studies using the Gene Ontology

Helen Attrill; Pascale Gaudet; Rachael P Huntley; Ruth C Lovering; Stacia R Engel; Sylvain Poux; Kimberly M Van Auken; George Georghiou; Marcus C Chibucos; Tanya Z Berardini; Valerie Wood; Harold Drabkin; Petra Fey; Penelope Garmiri; Midori A Harris; Tony Sawford; Leonore Reiser; Rebecca Tauber; Sabrina Toro; Gene Ontology Consortium

doi:10.1093/database/baz007

Annotation of gene product function from high-throughput studies using the Gene Ontology

Database (Oxford). 2019 Jan 1:2019:baz007. doi: 10.1093/database/baz007.

Authors

Helen Attrill¹, Pascale Gaudet², Rachael P Huntley³, Ruth C Lovering³, Stacia R Engel⁴, Sylvain Poux⁵, Kimberly M Van Auken⁶, George Georghiou⁷, Marcus C Chibucos⁸, Tanya Z Berardini⁹, Valerie Wood¹⁰, Harold Drabkin¹¹, Petra Fey¹², Penelope Garmiri⁷, Midori A Harris¹⁰, Tony Sawford⁷, Leonore Reiser⁹, Rebecca Tauber⁸, Sabrina Toro¹³; Gene Ontology Consortium

Affiliations

¹ FlyBase, Department of Physiology, Development and Neuroscience, University of Cambridge, Downing Street, Cambridge , UK.
² CALIPHO group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, rue Michel Servet, CH Geneva, Switzerland.
³ Institute of Cardiovascular Science, University College London, London, UK.
⁴ Saccharomyces Genome Database, Department of Genetics, Stanford University, Porter Drive, Palo Alto, CA, USA.
⁵ Swiss-Prot group, SIB Swiss Institute of Bioinformatics, Centre Medical Universitaire, rue Michel Servet, CH Geneva, Switzerland.
⁶ WormBase, Division of Biology and Biological Engineering, California Institute of Technology, E California Blvd, Pasadena, CA, USA.
⁷ European Molecular Biology Laboratory, European Bioinformatics Institute, Cambridge, UK.
⁸ Evidence and Conclusion Ontology, University of Maryland School of Medicine, W Baltimore St., Baltimore, MD, USA.
⁹ The Arabidopsis Information Resource, Phoenix Bioinformatics, Redwood City, CA, USA.
¹⁰ PomBase, Cambridge Systems Biology Centre and Department of Biochemistry, University of Cambridge, Sanger Building, Tennis Court Road, Cambridge, UK.
¹¹ Mouse Genome Informatics, Department of Computational Biology and Bioinformatics, The Jackson Laboratory, Main St., Bar Harbor, ME, USA.
¹² dictyBase, Biomedical Informatics Center and Center for Genetic Medicine, Northwestern University, Feinberg School of Medicine, North Lake Shore Drive, Chicago, IL, USA.
¹³ Zebrafish Information Network, University of Oregon, Eugene, OR, USA.

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.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Databases, Genetic*
Gene Ontology*
Genomics / methods*
High-Throughput Nucleotide Sequencing
Humans
Molecular Sequence Annotation / methods*
Sequence Analysis, DNA

Abstract

Publication types

MeSH terms

Grants and funding