Year: 2019
Type: article
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.
Source: Database
Authors: 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
Institutions: University of Cambridge, SIB Swiss Institute of Bioinformatics, University College London, Stanford University, California Institute of Technology, European Bioinformatics Institute, University of Maryland, Baltimore, Phoenix Bioinformatics, Jackson Laboratory, Northwestern University, University of Oregon
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $1,235
Funders: National Science Foundation, Wellcome Trust, European Molecular Biology Laboratory, National Institute for Health and Care Research, British Heart Foundation, Parkinson's UK, University College London, National Institutes of Health, Directorate for Biological Sciences, Medical Research Council, Biotechnology and Biological Sciences Research Council, Cambridge Institute for Medical Research, University of Cambridge, Staatssekretariat für Bildung, Forschung und Innovation, National Heart, Lung, and Blood Institute, National Institute of Mental Health, National Human Genome Research Institute, National Eye Institute, National Institute of General Medical Sciences, National Institute of Allergy and Infectious Diseases, National Institute of Diabetes and Digestive and Kidney Diseases, Division of Biological Infrastructure
Awards: G1087063198, Semantic Literature Annotation and Integrative Panomics Analysis for PTM-Disease Knowledge Network Discovery, Gene Ontology Consortium, UniProt: A centralized protein sequence and function resource, G2502558408, G2508225486, G2763302649, WormBase: a core data resource for C. elegans and other nematodes, G3115553707, G3169930728, Making connections with GO: an integrative approach to highlighting medically relevant Drosophila data, Enhancing the fission yeast model organism database POMBASE and extending the benefits of community annotation., G3887245887, G3904836238, Mouse Genome Database, G3990988271, PRO: A Protein Ontology in OBO Foundry for Scalable Integration of Biomedical Knowledge, G4351133612, G4463229245, G4491286837, ABI Development: An ontology of evidence types to support biological data management, ZFIN: The Zebrafish Model Organism Database, G5004258832, FLYBASE: A DROSOPHILA GENOMIC AND GENETIC DATABASE, G7019681594, G7219047555, Genomic Resource for the Yeast Saccharomyces, Expanding the knowledge of structures and functional information through the SIFTS resource, G7979393549, G8077212762, Enhancing the fission yeast model organism database POMBASE and extending the benefits of community annotation., Delaware INBRE, The integration of biomedical research, describing the biological processes relevant to cardiovascular processes, into Gene Ontology, microRNA and protein interaction databases, G982578950
OpenAlex ID: W2913143675