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

doi:10.1093/database/baz007

. 2019 Jan 1:2019:baz007.

doi: 10.1093/database/baz007.

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

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.

PMID: 30715275
PMCID: PMC6355445
DOI: 10.1093/database/baz007

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

Helen Attrill et al. Database (Oxford). 2019.

. 2019 Jan 1:2019:baz007.

doi: 10.1093/database/baz007.

Authors

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.

PMID: 30715275
PMCID: PMC6355445
DOI: 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.

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Figures

**Figure 1**
**High-throughput evidence codes to support GO curation.** The five new evidence codes added to the Evidence and Conclusion Ontology for GO curation are shown in red boxes: the parent term `high throughput evidence used in manual assertion’ (ECO:0006056) and four child terms. Is_a relationships with other classes in the Evidence & Conclusion Ontology are shown, including the evidence equivalents used in conventional GO annotation (yellow boxes). The new high-throughput evidence codes should be used by GO curators when annotating high-throughput data in accordance with GOC annotation guidelines. The graph layout was generated using the Ontology Lookup Service (https://www.ebi.ac.uk/ols/index) OLS-graphview.

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References

1. The Gene Ontology Consortium (2018) The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res., 8, D330–D338. - PMC - PubMed
1. Chibucos M.C., Mungall C.J., Balakrishnan R. et al. (2014) Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford), 2014, bau075. - PMC - PubMed
1. Gaudet P., Škunca N., Hu J.C. et al. (2017) Primer on the Gene Ontology. Methods Mol. Biol., 1446, 25–37. - PMC - PubMed
1. Han Z., Riefler G.M., Saam J.R. et al. (2005) The C. elegans Tousled-like kinase contributes to chromosome segregation as a substrate and regulator of the Aurora B kinase. Curr. Biol., 15, 894–904. - PMC - PubMed
1. Poux S. and Gaudet P. (2017) Best practices in manual annotation with the Gene Ontology. Methods Mol. Biol., 1446, 41–54. - PubMed

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[1] The Gene Ontology Consortium (2018) The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res., 8, D330–D338. - PMC - PubMed

[2] The Gene Ontology Consortium (2018) The Gene Ontology resource: 20 years and still GOing strong. Nucleic Acids Res., 8, D330–D338. - PMC - PubMed

[3] Chibucos M.C., Mungall C.J., Balakrishnan R. et al. (2014) Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford), 2014, bau075. - PMC - PubMed

[4] Chibucos M.C., Mungall C.J., Balakrishnan R. et al. (2014) Standardized description of scientific evidence using the Evidence Ontology (ECO). Database (Oxford), 2014, bau075. - PMC - PubMed

[5] Gaudet P., Škunca N., Hu J.C. et al. (2017) Primer on the Gene Ontology. Methods Mol. Biol., 1446, 25–37. - PMC - PubMed

[6] Gaudet P., Škunca N., Hu J.C. et al. (2017) Primer on the Gene Ontology. Methods Mol. Biol., 1446, 25–37. - PMC - PubMed

[7] Han Z., Riefler G.M., Saam J.R. et al. (2005) The C. elegans Tousled-like kinase contributes to chromosome segregation as a substrate and regulator of the Aurora B kinase. Curr. Biol., 15, 894–904. - PMC - PubMed

[8] Han Z., Riefler G.M., Saam J.R. et al. (2005) The C. elegans Tousled-like kinase contributes to chromosome segregation as a substrate and regulator of the Aurora B kinase. Curr. Biol., 15, 894–904. - PMC - PubMed

[9] Poux S. and Gaudet P. (2017) Best practices in manual annotation with the Gene Ontology. Methods Mol. Biol., 1446, 41–54. - PubMed

[10] Poux S. and Gaudet P. (2017) Best practices in manual annotation with the Gene Ontology. Methods Mol. Biol., 1446, 41–54. - PubMed

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

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

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