The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization
<|reference_start|>The Vertebrate Breed Ontology: Towards Effective Breed Data Standardization: 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.<|reference_end|>
@article{mullen2024the,
    title={The Vertebrate Breed Ontology: Towards Effective Breed Data
  Standardization},
    author={Kathleen R. Mullen (1), Imke Tammen (2), Nicolas A. Matentzoglu (3),
  Marius Mather (2), Christopher J. Mungall (4), Melissa A. Haendel (5), Frank
  W. Nicholas (2), Sabrina Toro (5), the Vertebrate Breed Ontology Consortium
  ((1) University of Colorado Anschutz Medical Campus, (2) University of
  Sydney, (3) Semanticly Ltd, (4) Lawrence Berkeley National Laboratory, (5)
  University of North Carolina at Chapel Hill)},
    journal={arXiv preprint arXiv:2406.02623},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.02623},
    primaryClass={q-bio.OT cs.DL}
}