Disease Ontology: improving and unifying disease annotations across species

ABSTRACT 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. Summary: Analyzing diverse disease data requires a comprehensive, robust disease ontology to integrate annotations and retrieve accurate, interpretable results. MGD, RGD and DO are working in collaboration to achieve this goal.

[1]  Kazuhiko Ohe,et al.  Disease Compass– a navigation system for disease knowledge based on ontology and linked data techniques , 2017, Journal of Biomedical Semantics.

[2]  Anuradha Lakshminarayana,et al.  The clinical trial landscape in oncology and connectivity of somatic mutational profiles to targeted therapies , 2016, Human Genomics.

[3]  L. Schriml,et al.  The Disease Ontology: fostering interoperability between biological and clinical human disease-related data , 2015, Mammalian Genome.

[4]  Tatiana Foroud,et al.  Parkinson Disease Overview , 2014 .

[5]  Gang Feng,et al.  Disease Ontology: a backbone for disease semantic integration , 2011, Nucleic Acids Res..

[6]  Martin Ringwald,et al.  Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research. , 2017, Methods in molecular biology.

[7]  Matthew P. Campbell,et al.  UniCarbKB: New database features for integrating glycan structure abundance, compositional glycoproteomics data, and disease associations. , 2016, Biochimica et biophysica acta.

[8]  Sandeep Sahu,et al.  OncDRS: An integrative clinical and genomic data platform for enabling translational research and precision medicine , 2015, Applied & translational genomics.

[9]  J. Amberger,et al.  Searching Online Mendelian Inheritance in Man (OMIM): A Knowledgebase of Human Genes and Genetic Phenotypes , 2017, Current protocols in bioinformatics.

[10]  Riccardo Bellazzi,et al.  A Network-Based Data Integration Approach to Support Drug Repurposing and Multi-Target Therapies in Triple Negative Breast Cancer , 2016, PloS one.

[11]  Fernando Fernandez-Llimos,et al.  New pharmacy-specific Medical Subject Headings included in the 2017 database. , 2017, American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists.

[12]  F. Dhombres,et al.  Representation of rare diseases in health information systems: The orphanet approach to serve a wide range of end users , 2012, Human mutation.

[13]  Thomas C. Wiegers,et al.  Disease model curation improvements at Mouse Genome Informatics , 2012, Database J. Biol. Databases Curation.

[14]  Melinda R. Dwinell,et al.  The Disease Portals, disease–gene annotation and the RGD disease ontology at the Rat Genome Database , 2016, Database J. Biol. Databases Curation.

[15]  Yue Jiang,et al.  DisSim: an online system for exploring significant similar diseases and exhibiting potential therapeutic drugs , 2016, Scientific Reports.

[16]  Judith A. Blake,et al.  Mouse Genome Database (MGD)-2017: community knowledge resource for the laboratory mouse , 2016, Nucleic Acids Res..

[17]  Michael Gruenberger,et al.  Similarity-based search of model organism, disease and drug effect phenotypes , 2015, Journal of Biomedical Semantics.

[18]  Steven J. M. Jones,et al.  CIViC is a community knowledgebase for expert crowdsourcing the clinical interpretation of variants in cancer , 2017, Nature Genetics.

[19]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[20]  A. Rector,et al.  Relations in biomedical ontologies , 2005, Genome Biology.

[21]  S. Lewis,et al.  Uberon, an integrative multi-species anatomy ontology , 2012, Genome Biology.

[22]  Thomas C. Wiegers,et al.  MEDIC: a practical disease vocabulary used at the Comparative Toxicogenomics Database , 2012, Database J. Biol. Databases Curation.

[23]  Giorgio Valle,et al.  QueryOR: a comprehensive web platform for genetic variant analysis and prioritization , 2017, BMC Bioinformatics.

[24]  J. E. Richardson,et al.  MouseMine: a new data warehouse for MGI , 2015, Mammalian Genome.

[25]  Gang Fu,et al.  Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data , 2014, Nucleic Acids Res..

[26]  Tudor Groza,et al.  The Human Phenotype Ontology in 2017 , 2016, Nucleic Acids Res..