The Unified Phenotype Ontology : a framework for cross-species integrative phenomics

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

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Journal
Genetics
Published
2025-03-01
DOI
https://doi.org/10.1093/genetics/iyaf027
Citations
9
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
7.25
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article

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics

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Biomedical Text Mining and Ontologies
7.25
article

The Unified Phenotype Ontology : a framework for cross-species integrative phenomics

Chris Mungall, Violeta Muñoz‐Fuentes, Erik Segerdell, Damian Smedley, Sofia Robb, Clare Pilgrim, Helen E. Parkinson, James P. Balhoff, Midori A. Harris, Sabrina Toro, Ray Stefancsik, Nomi L. Harris, Tiffany J. Callahan, Yvonne M. Bradford, Peter N. Robinson, Arwa Ibrahim, Elliot Sollis, Stacia R. Engel, Petra Fey, Nicolas Matentzoglu, Sarah Gehrke, Nicole Vasilevsky, Vinícius de Souza, Sebastian Köhler, Julie A. McMurry, David Osumi-Sutherland, Valerie Wood, Sarah M. Alghamdi, Christian A Grove, Zoë May Pendlington, Melissa Haendel, Anita R. Caron, Alayne Cuzick, Meghan A. Balk, James Seager, Malcolm E Fisher, Julius O.B. Jacobsen, Susan M. Bello, James Alastair McLaughlin, Leigh Carmody, Yasemin Bridges, Anna V. Anagnostopoulos, Laura W. Harris, Peter Hansen, Mónica Muñoz-Torres, Harry Caufield
article en
9 citations

Abstract

Phenotypic data are critical for understanding biological mechanisms and consequences of genomic variation, and are pivotal for clinical use cases such as disease diagnostics and treatment development. For over a century, vast quantities of phenotype data have been collected in many different contexts covering a variety of organisms. The emerging field of phenomics focuses on integrating and interpreting these data to inform biological hypotheses. A major impediment in phenomics is the wide range of distinct and disconnected approaches to recording the observable characteristics of an organism. Phenotype data are collected and curated using free text, single terms or combinations of terms, using multiple vocabularies, terminologies, or ontologies. Integrating these heterogeneous and often siloed data enables the application of biological knowledge both within and across species. Existing integration efforts are typically limited to mappings between pairs of terminologies; a generic knowledge representation that captures the full range of cross-species phenomics data is much needed. We have developed the Unified Phenotype Ontology (uPheno) framework, a community effort to provide an integration layer over domain-specific phenotype ontologies, as a single, unified, logical representation. uPheno comprises (1) a system for consistent computational definition of phenotype terms using ontology design patterns, maintained as a community library; (2) a hierarchical vocabulary of species-neutral phenotype terms under which their species-specific counterparts are grouped; and (3) mapping tables between species-specific ontologies. This harmonized representation supports use cases such as cross-species integration of genotype-phenotype associations from different organisms and cross-species informed variant prioritization.

GeneticsVol. 229(3)
Northwestern University (US), University of North Carolina at Chapel Hill (US), Stowers Institute for Medical Research (US), California Institute of Technology (US), Cincinnati Children's Hospital Medical Center (US), European Bioinformatics Institute (GB), Lawrence Berkeley National Laboratory (US), UN Environment Programme World Conservation Monitoring Centre (GB), Queen Mary University of London (GB), Palo Alto University (US), University of Oregon (US), University of Oslo (NO), University of Cambridge (GB), Columbia University Irving Medical Center (US), Rothamsted Research (GB), Critical Path Institute (US), Wellcome Sanger Institute (GB), Cambridge School (PT), Bridge University (SS), Arbeitsgemeinschaft Dermatologische Forschung (DE), Northwestern University (PH), Jackson Laboratory (US), Semantic Designs (United States) (US), Bioinformatics Institute (RU), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), William Harvey Research Institute (GB), University of Colorado Anschutz Medical Campus (US), Renaissance Computing Institute (US), King Abdullah University of Science and Technology (SA), Stanford University (US)
Life in Land
Openalex Percentile: Top 3%
Biomedical Text Mining and Ontologies
7.25
DOIOpen Access PDFOpenAlex
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