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.
Highlights
The quest to elucidate the genetic basis of disease is hampered by the fragmented landscape of clinical and organismal data
The Monarch Initiative works closely with experts from relevant scientific data providers to ensure that the knowledge graph is useful and the data are correctly represented
This requires significant outreach with several communities of users and data providers, which takes place in the context of face-to-face workshops. Communities engaged in this way include clinicians, curators, toxicologists, exposure scientists, developmental biologists, comparative genomicists, clinical researchers, rare disease researchers and epidemiologists
Summary
The quest to elucidate the genetic basis of disease is hampered by the fragmented landscape of clinical and organismal data. Several other projects are in the process of defining SEPIO-based data models, including the Variant Interpretation in Cancer Consortium (VICC, https://cancervariants.org), and the GA4GH Genomic Knowledge Standards working group.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
