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

Chris Mungall(Lawrence Berkeley National Laboratory), Julie A. McMurry(Oregon Health & Science University), Sebastian Köhler(Charité - Universitätsmedizin Berlin), James P. Balhoff(RTI International), Charles Borromeo(University of Pittsburgh), Matthew Brush(Oregon Health & Science University), Seth Carbon(Lawrence Berkeley National Laboratory), Tom Conlin(Oregon Health & Science University), Nathan Dunn(Lawrence Berkeley National Laboratory), Mark Engelstad(Oregon Health & Science University), Erin D. Foster(Oregon Health & Science University), Jean-Philippe F. Gourdine(Oregon Health & Science University), Julius O.B. Jacobsen(Queen Mary University of London), Dan Keith(Oregon Health & Science University), Bryan Laraway(Oregon Health & Science University), Suzanna Lewis(Lawrence Berkeley National Laboratory), Jeremy Nguyen-Xuan(Lawrence Berkeley National Laboratory), Kent Shefchek(Oregon Health & Science University), Nicole Vasilevsky(Oregon Health & Science University), Zhou Yuan(University of Pittsburgh), Nicole Washington(Lawrence Berkeley National Laboratory), Harry Hochheiser(University of Pittsburgh), Tudor Groza(Garvan Institute of Medical Research), Damian Smedley(Queen Mary University of London), Peter N. Robinson(Jackson Laboratory), Melissa Haendel(Oregon Health & Science University)
Nucleic Acids Research
November 2, 2016
Cited by 486Open Access
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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.


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