Journal
DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION
Volume -, Issue -, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/database/baz007
Keywords
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Categories
Funding
- UK Medical Research Council [MR/N030117/1]
- US National Institutes of Health, National Human Genome Research Institute [U41 HG000739]
- State Secretariat for Education, Research and Innovation (Swiss-Prot group)
- British Heart Foundation [RG/13/5/30112]
- Parkinson's UK [G-1307]
- National Institute for Health Research, University College London Hospitals Biomedical Research Centre
- National Human Genome Research Institute [U41 HG001315, U41 HG002223, U41 HG002273, U41 HG000330, U41 HG002659]
- National Eye Institute
- National Heart, Lung, and Blood Institute
- National Institute of Allergy and Infectious Diseases
- National Institute of Diabetes and Digestive and Kidney Diseases
- National Institute of Mental Health of the National Institutes of Health, National Human Genome Research Institute [U41 HG002273, U41 HG007822]
- National Institute of General Medical Sciences [R01GM080646, P20GM103446, U01GM120953, GM080646, GM064426, GM087371]
- Biotechnology and Biological Sciences Research Council [BB/M011674/1]
- State Secretariat for Education, Research and Innovation
- European Molecular Biology Laboratory core funds
- National Science Foundation Division of Biological Infrastructure [1458400]
- UK Wellcome Trust [104967/Z/14/Z]
- NHGRI [U41 HG02273]
- Direct For Biological Sciences
- Div Of Biological Infrastructure [1458400] Funding Source: National Science Foundation
- MRC [MR/N030117/1] Funding Source: UKRI
Ask authors/readers for more resources
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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