Disease Ontology: improving and unifying disease annotations across species

Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficul...

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Main Authors: Susan M. Bello, Mary Shimoyama, Elvira Mitraka, Stanley J. F. Laulederkind, Cynthia L. Smith, Janan T. Eppig, Lynn M. Schriml
Format: Article
Language:English
Published: The Company of Biologists 2018-03-01
Series:Disease Models & Mechanisms
Subjects:
Rat
Online Access:http://dmm.biologists.org/content/11/3/dmm032839
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spelling doaj-64f926f6f22e4aec8dd4d63076a8662d2020-11-25T00:45:25ZengThe Company of BiologistsDisease Models & Mechanisms1754-84031754-84112018-03-0111310.1242/dmm.032839032839Disease Ontology: improving and unifying disease annotations across speciesSusan M. Bello0Mary Shimoyama1Elvira Mitraka2Stanley J. F. Laulederkind3Cynthia L. Smith4Janan T. Eppig5Lynn M. Schriml6 The Jackson Laboratory, Bar Harbor, ME, USA Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA Department of Epidemiology and Public Health, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA Department of Biomedical Engineering, Medical College of Wisconsin, Milwaukee, WI, USA The Jackson Laboratory, Bar Harbor, ME, USA The Jackson Laboratory, Bar Harbor, ME, USA Department of Epidemiology and Public Health, Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD, USA Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD's and RGD's disease term annotations identified new terms that enhance DO's representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO's domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO's usability across human data, MGD, RGD and the rest of the model organism database community.http://dmm.biologists.org/content/11/3/dmm032839Disease modelsMouseOntologiesRat
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language English
format Article
sources DOAJ
author Susan M. Bello
Mary Shimoyama
Elvira Mitraka
Stanley J. F. Laulederkind
Cynthia L. Smith
Janan T. Eppig
Lynn M. Schriml
spellingShingle Susan M. Bello
Mary Shimoyama
Elvira Mitraka
Stanley J. F. Laulederkind
Cynthia L. Smith
Janan T. Eppig
Lynn M. Schriml
Disease Ontology: improving and unifying disease annotations across species
Disease Models & Mechanisms
Disease models
Mouse
Ontologies
Rat
author_facet Susan M. Bello
Mary Shimoyama
Elvira Mitraka
Stanley J. F. Laulederkind
Cynthia L. Smith
Janan T. Eppig
Lynn M. Schriml
author_sort Susan M. Bello
title Disease Ontology: improving and unifying disease annotations across species
title_short Disease Ontology: improving and unifying disease annotations across species
title_full Disease Ontology: improving and unifying disease annotations across species
title_fullStr Disease Ontology: improving and unifying disease annotations across species
title_full_unstemmed Disease Ontology: improving and unifying disease annotations across species
title_sort disease ontology: improving and unifying disease annotations across species
publisher The Company of Biologists
series Disease Models & Mechanisms
issn 1754-8403
1754-8411
publishDate 2018-03-01
description Model organisms are vital to uncovering the mechanisms of human disease and developing new therapeutic tools. Researchers collecting and integrating relevant model organism and/or human data often apply disparate terminologies (vocabularies and ontologies), making comparisons and inferences difficult. A unified disease ontology is required that connects data annotated using diverse disease terminologies, and in which the terminology relationships are continuously maintained. The Mouse Genome Database (MGD, http://www.informatics.jax.org), Rat Genome Database (RGD, http://rgd.mcw.edu) and Disease Ontology (DO, http://www.disease-ontology.org) projects are collaborating to augment DO, aligning and incorporating disease terms used by MGD and RGD, and improving DO as a tool for unifying disease annotations across species. Coordinated assessment of MGD's and RGD's disease term annotations identified new terms that enhance DO's representation of human diseases. Expansion of DO term content and cross-references to clinical vocabularies (e.g. OMIM, ORDO, MeSH) has enriched the DO's domain coverage and utility for annotating many types of data generated from experimental and clinical investigations. The extension of anatomy-based DO classification structure of disease improves accessibility of terms and facilitates application of DO for computational research. A consistent representation of disease associations across data types from cellular to whole organism, generated from clinical and model organism studies, will promote the integration, mining and comparative analysis of these data. The coordinated enrichment of the DO and adoption of DO by MGD and RGD demonstrates DO's usability across human data, MGD, RGD and the rest of the model organism database community.
topic Disease models
Mouse
Ontologies
Rat
url http://dmm.biologists.org/content/11/3/dmm032839
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