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The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species
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 o...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210586/ https://www.ncbi.nlm.nih.gov/pubmed/27899636 http://dx.doi.org/10.1093/nar/gkw1128 |
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author | Mungall, Christopher J. McMurry, Julie A. Köhler, Sebastian Balhoff, James P. Borromeo, Charles Brush, Matthew Carbon, Seth Conlin, Tom Dunn, Nathan Engelstad, Mark Foster, Erin Gourdine, J.P. Jacobsen, Julius O.B. Keith, Dan Laraway, Bryan Lewis, Suzanna E. NguyenXuan, Jeremy Shefchek, Kent Vasilevsky, Nicole Yuan, Zhou Washington, Nicole Hochheiser, Harry Groza, Tudor Smedley, Damian Robinson, Peter N. Haendel, Melissa A. |
author_facet | Mungall, Christopher J. McMurry, Julie A. Köhler, Sebastian Balhoff, James P. Borromeo, Charles Brush, Matthew Carbon, Seth Conlin, Tom Dunn, Nathan Engelstad, Mark Foster, Erin Gourdine, J.P. Jacobsen, Julius O.B. Keith, Dan Laraway, Bryan Lewis, Suzanna E. NguyenXuan, Jeremy Shefchek, Kent Vasilevsky, Nicole Yuan, Zhou Washington, Nicole Hochheiser, Harry Groza, Tudor Smedley, Damian Robinson, Peter N. Haendel, Melissa A. |
author_sort | Mungall, Christopher J. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5210586 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-52105862017-01-05 The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species Mungall, Christopher J. McMurry, Julie A. Köhler, Sebastian Balhoff, James P. Borromeo, Charles Brush, Matthew Carbon, Seth Conlin, Tom Dunn, Nathan Engelstad, Mark Foster, Erin Gourdine, J.P. Jacobsen, Julius O.B. Keith, Dan Laraway, Bryan Lewis, Suzanna E. NguyenXuan, Jeremy Shefchek, Kent Vasilevsky, Nicole Yuan, Zhou Washington, Nicole Hochheiser, Harry Groza, Tudor Smedley, Damian Robinson, Peter N. Haendel, Melissa A. Nucleic Acids Res Database Issue 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. Oxford University Press 2017-01-04 2016-11-29 /pmc/articles/PMC5210586/ /pubmed/27899636 http://dx.doi.org/10.1093/nar/gkw1128 Text en © The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Database Issue Mungall, Christopher J. McMurry, Julie A. Köhler, Sebastian Balhoff, James P. Borromeo, Charles Brush, Matthew Carbon, Seth Conlin, Tom Dunn, Nathan Engelstad, Mark Foster, Erin Gourdine, J.P. Jacobsen, Julius O.B. Keith, Dan Laraway, Bryan Lewis, Suzanna E. NguyenXuan, Jeremy Shefchek, Kent Vasilevsky, Nicole Yuan, Zhou Washington, Nicole Hochheiser, Harry Groza, Tudor Smedley, Damian Robinson, Peter N. Haendel, Melissa A. The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species |
title | The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species |
title_full | The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species |
title_fullStr | The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species |
title_full_unstemmed | The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species |
title_short | The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species |
title_sort | monarch initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species |
topic | Database Issue |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5210586/ https://www.ncbi.nlm.nih.gov/pubmed/27899636 http://dx.doi.org/10.1093/nar/gkw1128 |
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