| Title: |
The Monarch Initiative in 2019: an integrative data and analytic platform connecting phenotypes to genotypes across species |
| Authors: |
Shefchek, Kent A; Harris, Nomi L; Gargano, Michael; Matentzoglu, Nicolas; Unni, Deepak; Brush, Matthew; Keith, Daniel; Conlin, Tom; Vasilevsky, Nicole; Zhang, Xingmin Aaron; Balhoff, James P; Babb, Larry; Bello, Susan M; Blau, Hannah; Bradford, Yvonne; Carbon, Seth; Carmody, Leigh; Chan, Lauren E; Cipriani, Valentina; Cuzick, Alayne; Rocca, Maria D; Dunn, Nathan; Essaid, Shahim; Fey, Petra; Grove, Chris; Gourdine, Jean-Phillipe; Hamosh, Ada; Harris, Midori; Helbig, Ingo; Hoatlin, Maureen; Joachimiak, Marcin; Jupp, Simon; Lett, Kenneth B; Lewis, Suzanna E; McNamara, Craig; Pendlington, Zoë M; Pilgrim, Clare; Putman, Tim; Ravanmehr, Vida; Reese, Justin; Riggs, Erin; Robb, Sofia; Roncaglia, Paola; Seager, James; Segerdell, Erik; Similuk, Morgan; Storm, Andrea L; Thaxon, Courtney; Thessen, Anne; Jacobsen, Julius OB; McMurry, Julie A; Groza, Tudor; Köhler, Sebastian; Smedley, Damian; Robinson, Peter N; Mungall, Christopher J; Haendel, Melissa A; Munoz-Torres, Monica C; Osumi-Sutherland, David |
| Source: |
Nucleic Acids Research, vol 48, iss D1 |
| Publisher Information: |
eScholarship, University of California |
| Publication Year: |
2020 |
| Collection: |
University of California: eScholarship |
| Subject Terms: |
Biological Sciences; Bioinformatics and Computational Biology; Genetics; Networking and Information Technology R&D (NITRD); Bioengineering; Aetiology; 2.1 Biological and endogenous factors; Generic health relevance; Good Health and Well Being; Algorithms; Animals; Biological Ontologies; Computational Biology; Databases; Genetic; Exome; Genetic Association Studies; Genetic Variation; Genomics; Genotype; Humans; Internet; Phenotype; Software; Translational Research; Biomedical; User-Computer Interface; Environmental Sciences; Information and Computing Sciences; Developmental Biology |
| Subject Geographic: |
d704 - d715 |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
application/pdf |
| Language: |
unknown |
| Relation: |
qt87m969xt; https://escholarship.org/uc/item/87m969xt; https://escholarship.org/content/qt87m969xt/qt87m969xt.pdf |
| DOI: |
10.1093/nar/gkz997 |
| Availability: |
https://escholarship.org/uc/item/87m969xt; https://escholarship.org/content/qt87m969xt/qt87m969xt.pdf; https://doi.org/10.1093/nar/gkz997 |
| Rights: |
public |
| Accession Number: |
edsbas.4F354C3D |
| Database: |
BASE |