Integrating phenotype ontologies with PhenomeNET
Abstract Background Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases prese...
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| Format: | Article |
| Language: | English |
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BMC
2017-12-01
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| Series: | Journal of Biomedical Semantics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13326-017-0167-4 |
| _version_ | 1819115893197635584 |
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| author | Miguel Ángel Rodríguez-García Georgios V. Gkoutos Paul N. Schofield Robert Hoehndorf |
| author_facet | Miguel Ángel Rodríguez-García Georgios V. Gkoutos Paul N. Schofield Robert Hoehndorf |
| author_sort | Miguel Ángel Rodríguez-García |
| collection | DOAJ |
| description | Abstract Background Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Results Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies. Conclusions PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease. |
| first_indexed | 2024-12-22T05:08:25Z |
| format | Article |
| id | doaj.art-22a42f40b8c547babdc92ab743e2f6ac |
| institution | Directory Open Access Journal |
| issn | 2041-1480 |
| language | English |
| last_indexed | 2024-12-22T05:08:25Z |
| publishDate | 2017-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Biomedical Semantics |
| spelling | doaj.art-22a42f40b8c547babdc92ab743e2f6ac2022-12-21T18:38:02ZengBMCJournal of Biomedical Semantics2041-14802017-12-018111110.1186/s13326-017-0167-4Integrating phenotype ontologies with PhenomeNETMiguel Ángel Rodríguez-García0Georgios V. Gkoutos1Paul N. Schofield2Robert Hoehndorf3Computational Bioscience Research Center (CBRC), King Abdullah University of Science and TechnologyCollege of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of BirminghamDepartment of Physiology, Development & Neuroscience, University of CambridgeComputational Bioscience Research Center (CBRC), King Abdullah University of Science and TechnologyAbstract Background Integration and analysis of phenotype data from humans and model organisms is a key challenge in building our understanding of normal biology and pathophysiology. However, the range of phenotypes and anatomical details being captured in clinical and model organism databases presents complex problems when attempting to match classes across species and across phenotypes as diverse as behaviour and neoplasia. We have previously developed PhenomeNET, a system for disease gene prioritization that includes as one of its components an ontology designed to integrate phenotype ontologies. While not applicable to matching arbitrary ontologies, PhenomeNET can be used to identify related phenotypes in different species, including human, mouse, zebrafish, nematode worm, fruit fly, and yeast. Results Here, we apply the PhenomeNET to identify related classes from two phenotype and two disease ontologies using automated reasoning. We demonstrate that we can identify a large number of mappings, some of which require automated reasoning and cannot easily be identified through lexical approaches alone. Combining automated reasoning with lexical matching further improves results in aligning ontologies. Conclusions PhenomeNET can be used to align and integrate phenotype ontologies. The results can be utilized for biomedical analyses in which phenomena observed in model organisms are used to identify causative genes and mutations underlying human disease.http://link.springer.com/article/10.1186/s13326-017-0167-4PhenotypePhenomeNETDisease gene prioritizationOWLAutomated reasoning |
| spellingShingle | Miguel Ángel Rodríguez-García Georgios V. Gkoutos Paul N. Schofield Robert Hoehndorf Integrating phenotype ontologies with PhenomeNET Journal of Biomedical Semantics Phenotype PhenomeNET Disease gene prioritization OWL Automated reasoning |
| title | Integrating phenotype ontologies with PhenomeNET |
| title_full | Integrating phenotype ontologies with PhenomeNET |
| title_fullStr | Integrating phenotype ontologies with PhenomeNET |
| title_full_unstemmed | Integrating phenotype ontologies with PhenomeNET |
| title_short | Integrating phenotype ontologies with PhenomeNET |
| title_sort | integrating phenotype ontologies with phenomenet |
| topic | Phenotype PhenomeNET Disease gene prioritization OWL Automated reasoning |
| url | http://link.springer.com/article/10.1186/s13326-017-0167-4 |
| work_keys_str_mv | AT miguelangelrodriguezgarcia integratingphenotypeontologieswithphenomenet AT georgiosvgkoutos integratingphenotypeontologieswithphenomenet AT paulnschofield integratingphenotypeontologieswithphenomenet AT roberthoehndorf integratingphenotypeontologieswithphenomenet |