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Nucleic Acids Res. 2020 Jan 8;48(D1):D704-D715. doi: 10.1093/nar/gkz997.

The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species.

Author information

1
Center for Genome Research and Biocomputing, Environmental and Molecular Toxicology, Oregon State University, Corvallis, OR 97331, USA.
2
Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA 94710, USA.
3
The Jackson Laboratory For Genomic Medicine, Farmington, CT 06032, USA.
4
European Bioinformatics Institute (EMBL-EBI), European Molecular Biology Laboratory, Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
5
Oregon Clinical and Translational Research Institute, Oregon Health & Science University, Portland, OR 97239, USA.
6
Renaissance Computing Institute at UNC, Chapel Hill, NC 27517, USA.
7
Broad Institute, Cambridge, MA 02142, USA.
8
The Jackson Laboratory, Bar Harbor, ME 04609, USA.
9
Institute of Neuroscience, University of Oregon, Eugene, OR 97401, USA.
10
College of Public Health and Human Sciences, Oregon State University, Corvallis, OR 97331, USA.
11
William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London, Charterhouse Square, London EC1M 6BQ, UK.
12
Rothamsted Research, Harpenden AL5 2JQ, UK.
13
Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Sciences (NCATS), National Institutes of Health (NIH), Bethesda, MD 20892, USA.
14
dictyBase, Center for Genetic Medicine, Northwestern University, Chicago, IL 60611, USA.
15
California Institute of Technology, Pasadena, CA 91125, USA.
16
McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
17
University of Cambridge, Cambridge CB2 1TN, UK.
18
Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
19
Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.
20
Department of Neuropediatrics, Christian-Albrechts-University of Kiel, 24105 Kiel, Germany.
21
Department of Neurology, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA 19104, USA.
22
Department of Biochemistry and Molecular Biology, Oregon Health & Science University, Portland, OR 97239, USA.
23
Pryzm Health, 4215 Queensland, Australia.
24
Autism & Developmental Medicine Institute, Geisinger, Danville, PA 17837, USA.
25
Stowers Institute for Medical Research, Kansas City, MO 64110, USA.
26
Xenbase, Cincinnati Children's Hospital, Cincinnati, OH 45229, USA.
27
National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
28
University of North Carolina Medical School, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA.
29
Institute for Medical Genetics and Human Genetics, Charité-Universitätsmedizin Berlin, Augustenburger Platz 1, 13353 Berlin, Germany.

Abstract

In biology and biomedicine, relating phenotypic outcomes with genetic variation and environmental factors remains a challenge: patient phenotypes may not match known diseases, candidate variants may be in genes that haven't been characterized, research organisms may not recapitulate human or veterinary diseases, environmental factors affecting disease outcomes are unknown or undocumented, and many resources must be queried to find potentially significant phenotypic associations. The Monarch Initiative (https://monarchinitiative.org) integrates information on genes, variants, genotypes, phenotypes and diseases in a variety of species, and allows powerful ontology-based search. We develop many widely adopted ontologies that together enable sophisticated computational analysis, mechanistic discovery and diagnostics of Mendelian diseases. Our algorithms and tools are widely used to identify animal models of human disease through phenotypic similarity, for differential diagnostics and to facilitate translational research. Launched in 2015, Monarch has grown with regards to data (new organisms, more sources, better modeling); new API and standards; ontologies (new Mondo unified disease ontology, improvements to ontologies such as HPO and uPheno); user interface (a redesigned website); and community development. Monarch data, algorithms and tools are being used and extended by resources such as GA4GH and NCATS Translator, among others, to aid mechanistic discovery and diagnostics.

PMID:
31701156
DOI:
10.1093/nar/gkz997

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