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

Kent A. Shefchek, Nomi L. Harris, Michael Gargano, Nicolas Matentzoglu, Deepak Unni, Matthew Brush, Daniel Keith, Tom Conlin, Nicole Vasilevsky, Xingmin Aaron Zhang, James P. Balhoff, Larry Babb, Susan M. Bello, Hannah Blau, Yvonne Bradford, Seth Carbon, Leigh Carmody, Lauren E. Chan, Valentina Cipriani, Alayne CuzickMaria D. Rocca, Nathan Dunn, Shahim Essaid, Petra Fey, Chris Grove, Jean Phillipe Gourdine, Ada Hamosh, Midori Harris, Ingo Helbig, Maureen Hoatlin, Marcin Joachimiak, Simon Jupp, Kenneth B. Lett, Suzanna E. Lewis, Craig McNamara, Zoë M. Pendlington, Clare Pilgrim, Tim Putman, Vida Ravanmehr, Justin Reese, Erin Riggs, Sofia Robb, Paola Roncaglia, James Seager, Erik Segerdell, Morgan Similuk, Andrea L. Storm, Courtney Thaxon, Anne Thessen, Julius O.B. Jacobsen, Julie A. McMurry, Tudor Groza, Sebastian Köhler, Damian Smedley, Peter N. Robinson, Christopher J. Mungall, Melissa A. Haendel, Monica C. Munoz-Torres, David Osumi-Sutherland

Research output: Contribution to journalArticlepeer-review

18 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)D704-D715
JournalNucleic acids research
Volume48
Issue numberD1
DOIs
StatePublished - Jan 1 2020

ASJC Scopus subject areas

  • Genetics

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