Paper Details

Paper Title

The Monarch Initiative: an integrative data and analytic platform connecting phenotypes to genotypes across species.

Paper Journal Title

Nucleic Acids Res

Paper Citation Count

193

Paper Publication Year

2017

Bio Mention

human, patient

Mesh Descriptor

Animals, Biological Evolution, Computational Biology, Data Curation, Databases, Genetic, Genetic Association Studies, Genotype, Humans, Phenotype, Search Engine, Software, Species Specificity, User-Computer Interface, Web Browser

Author Name	Affiliation
Christopher J Mungall	Lawrence Berkeley National Laboratory
Christopher J Mungall	Lawrence Berkeley National Laboratory
Julie A McMurry	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Julie A McMurry	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Sebastian K??hler	Institute for Medical Genetics and Human Genetics, Charite-Universitatsmedizin Berlin
Sebastian K??hler	Institute for Medical Genetics and Human Genetics, Charite-Universitatsmedizin Berlin
James P Balhoff
James P Balhoff
Charles Borromeo	University of Pittsburgh
Matthew H Brush	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Matthew H Brush	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Seth Carbon	Lawrence Berkeley National Laboratory
Tom Conlin	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Tom Conlin	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Nathan Dunn	Lawrence Berkeley National Laboratory
Mark E Engelstad	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Mark E Engelstad	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Erin Foster	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Erin Foster	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Jean-Philippe Gourdine	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Julius O B Jacobsen	William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London
Dan Keith	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Bryan Laraway	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Suzanna E Lewis	Lawrence Berkeley National Laboratory
Suzanna E Lewis	Lawrence Berkeley National Laboratory
Jeremy Nguyen-Xuan	Lawrence Berkeley National Laboratory
Kent Shefchek	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Kent Shefchek	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Nicole Vasilevsky	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Nicole Vasilevsky	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Zhou Yuan	University of Pittsburgh
Nicole L Washington	Lawrence Berkeley National Laboratory
Nicole L Washington	Lawrence Berkeley National Laboratory
Harry Hochheiser	University of Pittsburgh
Tudor Groza	Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research
Tudor Groza	Kinghorn Centre for Clinical Genomics, Garvan Institute of Medical Research
Damian Smedley	William Harvey Research Institute, Barts & The London School of Medicine & Dentistry, Queen Mary University of London
Peter N Robinson	Institute for Medical Genetics and Human Genetics, Charite-Universitatsmedizin Berlin
Peter N Robinson
Peter N Robinson	Institute for Medical Genetics and Human Genetics, Charite-Universitatsmedizin Berlin
Peter N Robinson
Melissa A Haendel	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University
Melissa A Haendel	Department of Medical Informatics and Clinical Epidemiology and OHSU Library, Oregon Health & Science University

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Dataset	Description	Source Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link
Monarch Initiative	Human disease-related phenotypes in model organisms	Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link
Monarch Initiative	Human disease-related phenotypes in model organisms	Link
Monarch Initiative	Human disease-related phenotypes in model organisms	Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link
Monarch Initiative	Human disease-related phenotypes in model organisms	Link
Monarch Initiative	The Monarch Initiative is an integrative data and analytic platform connecting phenotypes to genotypes across species, bridging basic and applied research with semantics-based analysis. The correlation of phenotypic outcomes and disease with genetic variation and environmental factors is a core pursuit in biology and biomedicine. We have created or currently contribute to many essential bio-ontologies that together enable sophisticated and semantically integrated computational analysis across gene, genotype, variant, disease, and phenotype data. We have developed algorithms and tools that are in use by multiple communities for tasks including the identification of animal models of human disease through phenotypic similarity, phenotype-driven computational support for differential diagnostics, and translational research.	Link

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