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

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    Author
    Attrill, H.
    Gaudet, P.
    Huntley, R.P.
    Date
    2019
    Journal
    Database
    Publisher
    Oxford University Press
    Type
    Article
    
    Metadata
    Show full item record
    See at
    https://dx.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. © 2019 The Author(s).
    Sponsors
    UK Medical Research Council (MR/N030117/1 funds H.A.); US National Institutes of Health, National Human Genome Research Institute (U41 HG000739 to FlyBase); State Secretariat for Education, Research and Innovation (Swiss-Prot group); British Heart Foundation (RG/13/5/30112 funds R.P.H. and R.C.L.); Parkinson�s UK (G-1307 to R.C.L.); National Institute for Health Research, University College London Hospitals Biomedical Research Centre (R.P.H. and R.C.L.); National Human Genome Research Institute (U41 HG001315 to Saccharomyces Genome Database); National Eye Institute (UniProt Consortium); National Human Genome Research Institute (UniProt Consortium); National Heart, Lung, and Blood Institute (UniProt Consortium); National Institute of Allergy and Infectious Diseases (UniProt Consortium); National Institute of Diabetes and Digestive and Kidney Diseases (UniProt Consortium); National Institute of General Medical Sciences (UniProt Consortium); National Institute of Mental Health of the National Institutes of Health, National Human Genome Research Institute (U41 HG007822 and U41 HG002273 to UniProt Consortium); National Institute of General Medical Sciences (R01GM080646, P20GM103446 and U01GM120953 to UniProt Consortium); Biotechnology and Biological Sciences Research Council (BB/M011674/1); British Heart Foundation (RG/13/5/30112); State Secretariat for Education, Research and Innovation; European Molecular Biology Laboratory core funds; National Human Genome Research Institute (U41 HG002223 to WormBase); National Science Foundation Division of Biological Infrastructure (1458400 to Evidence and Conclusion Ontology); The Arabidopsis Information Resource (TAIR) is funded by academic institutional, corporate, and individual subscriptions. TAIR is administered by the 501(c)(3) nonprofit Phoenix Bioinformatics. UK Wellcome Trust (104967/Z/14/Z to PomBase); National Human Genome Research Institute (U41 HG002273 and U41 HG000330 to H.D.); National Institute of General Medical Sciences (GM080646 funds H.D., ); National Institute of General Medical Sciences (GM064426 and GM087371 to dictyBase); National Human Genome Research Institute (U41 HG002659 to Zebrafish Information Network); NHGRI (U41 HG02273 to Gene Ontology resource).
    Keyword
    annotation framework
    high-throughput studies
    Gene Ontology
    Data Curation
    Identifier to cite or link to this item
    https://www.scopus.com/inward/record.uri?eid=2-s2.0-85061048403&doi=10.1093%2fdatabase%2fbaz007&partnerID=40&md5=211cc2a4f5840a9f7c6e51871a6c947e; http://hdl.handle.net/10713/8714
    ae974a485f413a2113503eed53cd6c53
    10.1093/database/baz007
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