%0 Journal Article %1 Mungall2017Monarch %A Mungall, Christopher J. %A McMurry, Julie A. %A Köhler, Sebastian %A Balhoff, James P. %A Borromeo, Charles %A Brush, Matthew %A Carbon, Seth %A Conlin, Tom %A Dunn, Nathan %A Engelstad, Mark %A Foster, Erin %A Gourdine, J. P. %A Jacobsen, Julius O. %A Keith, Dan %A Laraway, Bryan %A Lewis, Suzanna E. %A NguyenXuan, Jeremy %A Shefchek, Kent %A Vasilevsky, Nicole %A Yuan, Zhou %A Washington, Nicole %A Hochheiser, Harry %A Groza, Tudor %A Smedley, Damian %A Robinson, Peter N. %A Haendel, Melissa A. %D 2017 %J Nucleic acids research %K dataset genotype phenotypes %N D1 %T The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species. %U http://view.ncbi.nlm.nih.gov/pubmed/27899636 %V 45 %X The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype-phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype-phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species. \copyright The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.

@article{Mungall2017Monarch, abstract = {The correlation of phenotypic outcomes with genetic variation and environmental factors is a core pursuit in biology and biomedicine. Numerous challenges impede our progress: patient phenotypes may not match known diseases, candidate variants may be in genes that have not been characterized, model organisms may not recapitulate human or veterinary diseases, filling evolutionary gaps is difficult, and many resources must be queried to find potentially significant genotype-phenotype associations. Non-human organisms have proven instrumental in revealing biological mechanisms. Advanced informatics tools can identify phenotypically relevant disease models in research and diagnostic contexts. Large-scale integration of model organism and clinical research data can provide a breadth of knowledge not available from individual sources and can provide contextualization of data back to these sources. The Monarch Initiative (monarchinitiative.org) is a collaborative, open science effort that aims to semantically integrate genotype-phenotype data from many species and sources in order to support precision medicine, disease modeling, and mechanistic exploration. Our integrated knowledge graph, analytic tools, and web services enable diverse users to explore relationships between phenotypes and genotypes across species. {\copyright} The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.}, added-at = {2018-12-02T16:09:07.000+0100}, author = {Mungall, Christopher J. and McMurry, Julie A. and K\"{o}hler, Sebastian and Balhoff, James P. and Borromeo, Charles and Brush, Matthew and Carbon, Seth and Conlin, Tom and Dunn, Nathan and Engelstad, Mark and Foster, Erin and Gourdine, J. P. and Jacobsen, Julius O. and Keith, Dan and Laraway, Bryan and Lewis, Suzanna E. and NguyenXuan, Jeremy and Shefchek, Kent and Vasilevsky, Nicole and Yuan, Zhou and Washington, Nicole and Hochheiser, Harry and Groza, Tudor and Smedley, Damian and Robinson, Peter N. and Haendel, Melissa A.}, biburl = {https://www.bibsonomy.org/bibtex/2dc1ef41a0956395901f9418c624cf454/karthikraman}, citeulike-article-id = {14380945}, citeulike-linkout-0 = {http://view.ncbi.nlm.nih.gov/pubmed/27899636}, citeulike-linkout-1 = {http://www.hubmed.org/display.cgi?uids=27899636}, day = 04, interhash = {8fab3001ba312c4e0fa39bee1eee3f24}, intrahash = {dc1ef41a0956395901f9418c624cf454}, issn = {1362-4962}, journal = {Nucleic acids research}, keywords = {dataset genotype phenotypes}, month = jan, number = {D1}, pmid = {27899636}, posted-at = {2017-06-22 13:18:37}, priority = {2}, timestamp = {2018-12-02T16:09:07.000+0100}, title = {The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species.}, url = {http://view.ncbi.nlm.nih.gov/pubmed/27899636}, volume = 45, year = 2017 }