A general strategy for generating expert-guided, simplified views of ontologies
Annotation of biomedical entities with widely used, well-structured ontologies and ontology-aware tools ensures data and analyses are Findable, Accessible, Interoperable, and Reusable (FAIR). Standardized terms with synonyms support lexical search, while ontology structure enables biologically meaningful grouping of annotations, such as by location and type. However, ontologies serving diverse communities are often more complex than needed for specific applications, creating barriers to adoption by researchers and resource developers. For example, cell atlases often attempt simplifications by manually building term hierarchies linking to cell type and anatomy ontologies, but these may include relationship types unsuitable for grouping annotations. We present tools for validating human expert curated term hierarchies, developed in two human reference atlas projects, against ontology structures. The tools provide tabular statistics plus graphical views of matching and non-matching terms and relationships to support discussion and conflict resolution. The HuBMAP Human Reference Atlas (HRA) effort is used to validate the approach and tools, and the Human Developmental Cell Atlas is featured as a use case.
Authors
- Bruce Herr
- K. Borner
- Paola Roncaglia (ORCID: https://orcid.org/0000-0002-2825-0621)
- Aleix Puig-Barbe (ORCID: https://orcid.org/0000-0001-6677-8489)
- Anita R. Caron (ORCID: https://orcid.org/0000-0002-6523-4866)
- Nana-Jane Chipampe (ORCID: https://orcid.org/0000-0002-8802-0473)
- Musen Mark
- Jasmine Belfiore
- James A. McLaughlin
- James P. Balhoff
- David Osumi-Sutherland
- Josef Hardi
- Ellen M. Quardokus
- Helen Parkinson
- Huseyin Kir
Institutions
- European Bioinformatics Institute (GB)
- Wellcome Sanger Institute (GB)
- Indiana University Bloomington (US)
- Renaissance Computing Institute (US)
- Stanford University (US)
Publication Details
- Journal
- Scientific Data
- Published
- 2026-01-09
- DOI
- https://doi.org/10.1038/s41597-025-06383-w
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- European Bioinformatics Institute
- National Institutes of Health
- National Institute of Diabetes and Digestive and Kidney Diseases