Kathleen R. MullenUniversity of Colorado Anschutz Medical Campus, Imke TammenUniversity of Sydney, Nicolas A. MatentzogluSemanticly Ltd, Marius MatherUniversity of Sydney, Christopher J. MungallLawrence Berkeley National Laboratory, Melissa A. HaendelUniversity of North Carolina at Chapel Hill, Frank W. NicholasUniversity of Sydney, Sabrina ToroUniversity of North Carolina at Chapel Hill, the Vertebrate Breed Ontology Consortium
{"title":"The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization","authors":"Kathleen R. MullenUniversity of Colorado Anschutz Medical Campus, Imke TammenUniversity of Sydney, Nicolas A. MatentzogluSemanticly Ltd, Marius MatherUniversity of Sydney, Christopher J. MungallLawrence Berkeley National Laboratory, Melissa A. HaendelUniversity of North Carolina at Chapel Hill, Frank W. NicholasUniversity of Sydney, Sabrina ToroUniversity of North Carolina at Chapel Hill, the Vertebrate Breed Ontology Consortium","doi":"arxiv-2406.02623","DOIUrl":null,"url":null,"abstract":"Background: Limited universally adopted data standards in veterinary science\nhinders data interoperability and therefore integration and comparison; this\nultimately impedes application of existing information-based tools to support\nadvancement in veterinary diagnostics, treatments, and precision medicine. Objectives: Creation of a Vertebrate Breed Ontology (VBO) as a single,\ncoherent logic-based standard for documenting breed names in animal health,\nproduction and research-related records will improve data use capabilities in\nveterinary and comparative medicine. Animals: No live animals were used in this study. Methods: A list of breed names and related information was compiled from\nrelevant sources, organizations, communities, and experts using manual and\ncomputational approaches to create VBO. Each breed is represented by a VBO term\nthat includes all provenance and the breed's related information as metadata.\nVBO terms are classified using description logic to allow computational\napplications and Artificial Intelligence-readiness. Results: VBO is an open, community-driven ontology representing over 19,000\nlivestock and companion animal breeds covering 41 species. Breeds are\nclassified based on community and expert conventions (e.g., horse breed, cattle\nbreed). This classification is supported by relations to the breeds' genus and\nspecies indicated by NCBI Taxonomy terms. Relationships between VBO terms, e.g.\nrelating breeds to their foundation stock, provide additional context to\nsupport advanced data analytics. VBO term metadata includes common names and\nsynonyms, breed identifiers or codes, and attributed cross-references to other\ndatabases. Conclusion and clinical importance: Veterinary data interoperability and\ncomputability can be enhanced by the adoption of VBO as a source of standard\nbreed names in databases and veterinary electronic health records.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"34 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.02623","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Limited universally adopted data standards in veterinary science
hinders data interoperability and therefore integration and comparison; this
ultimately impedes application of existing information-based tools to support
advancement in veterinary diagnostics, treatments, and precision medicine. Objectives: Creation of a Vertebrate Breed Ontology (VBO) as a single,
coherent logic-based standard for documenting breed names in animal health,
production and research-related records will improve data use capabilities in
veterinary and comparative medicine. Animals: No live animals were used in this study. Methods: A list of breed names and related information was compiled from
relevant sources, organizations, communities, and experts using manual and
computational approaches to create VBO. Each breed is represented by a VBO term
that includes all provenance and the breed's related information as metadata.
VBO terms are classified using description logic to allow computational
applications and Artificial Intelligence-readiness. Results: VBO is an open, community-driven ontology representing over 19,000
livestock and companion animal breeds covering 41 species. Breeds are
classified based on community and expert conventions (e.g., horse breed, cattle
breed). This classification is supported by relations to the breeds' genus and
species indicated by NCBI Taxonomy terms. Relationships between VBO terms, e.g.
relating breeds to their foundation stock, provide additional context to
support advanced data analytics. VBO term metadata includes common names and
synonyms, breed identifiers or codes, and attributed cross-references to other
databases. Conclusion and clinical importance: Veterinary data interoperability and
computability can be enhanced by the adoption of VBO as a source of standard
breed names in databases and veterinary electronic health records.