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dc.contributor.authorRodriguez-Garcia, Miguel Angel
dc.contributor.authorGkoutos, Georgios V.
dc.contributor.authorSchofield, Paul N.
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2017-12-21T13:57:04Z
dc.date.available2017-12-21T13:57:04Z
dc.date.issued2017-12-19
dc.identifier.citationRodríguez-García MÁ, Gkoutos GV, Schofield PN, Hoehndorf R (2017) Integrating phenotype ontologies with PhenomeNET. Journal of Biomedical Semantics 8. Available: http://dx.doi.org/10.1186/s13326-017-0167-4.
dc.identifier.issn2041-1480
dc.identifier.pmid29258588
dc.identifier.doi10.1186/s13326-017-0167-4
dc.identifier.urihttp://hdl.handle.net/10754/626413
dc.description.abstractBackground 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.
dc.description.sponsorshipFunding for GVG was provided by the National Science Foundation (Grant Number: IOS-1340112), the BBSRC national capability in plant phenotyping (Grant Number: BB/J004464/1) and the FP7 European Plant Phenotyping Network (Grant Agreement No. 284443). RH and MARG were supported by funding from the King Abdullah University of Science and Technology (Grant Number: FCC/1/1976-08-01).
dc.publisherSpringer Nature
dc.relation.urlhttps://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-017-0167-4
dc.rightsThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectPhenotype
dc.subjectPhenomeNET
dc.subjectDisease gene prioritization
dc.subjectOWL
dc.subjectAutomated reasoning
dc.titleIntegrating phenotype ontologies with PhenomeNET
dc.typeArticle
dc.contributor.departmentBio-Ontology Research Group (BORG)
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalJournal of Biomedical Semantics
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionInstitute of Biological, Environmental and Rural Sciences, Aberystwyth University
dc.contributor.institutionInstitute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust
dc.contributor.institutionCollege of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridge
kaust.personRodriguez-Garcia, Miguel Angel
kaust.personHoehndorf, Robert
kaust.grant.numberFCC/1/1976-08-01
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.5721523.v1
dc.relation.issupplementedbyDOI:10.6084/m9.figshare.c.3958477.v1
dc.relation.issupplementedbygithub:bio-ontology-research-group/phenomeblast
refterms.dateFOA2018-06-13T19:09:50Z
display.relations<b>Is Supplemented By:</b><br/> <ul><li><i>[Dataset]</i> <br/> RodrĂ­Guez-GarcĂ­A, M., Gkoutos, G., Schofield, P., &amp; Hoehndorf, R. (2017). Additional file 1 of Integrating phenotype ontologies with PhenomeNET. Figshare. https://doi.org/10.6084/M9.FIGSHARE.5721523.V1. DOI: <a href="https://doi.org/10.6084/m9.figshare.5721523.v1" >10.6084/m9.figshare.5721523.v1</a> Handle: <a href="http://hdl.handle.net/10754/663947" >10754/663947</a></a></li><li><i>[Dataset]</i> <br/> RodrĂ­Guez-GarcĂ­A, M., Gkoutos, G., Schofield, P., &amp; Hoehndorf, R. (2017). Integrating phenotype ontologies with PhenomeNET. Figshare. https://doi.org/10.6084/M9.FIGSHARE.C.3958477.V1. DOI: <a href="https://doi.org/10.6084/m9.figshare.c.3958477.v1" >10.6084/m9.figshare.c.3958477.v1</a> Handle: <a href="http://hdl.handle.net/10754/663948" >10754/663948</a></a></li><li><i>[Software]</i> <br/> Title: bio-ontology-research-group/phenomeblast: Source for PhenomeNET and related projects. Publication Date: 2015-03-16. github: <a href="https://github.com/bio-ontology-research-group/phenomeblast" >bio-ontology-research-group/phenomeblast</a> Handle: <a href="http://hdl.handle.net/10754/667025" >10754/667025</a></a></li></ul>
dc.date.published-online2017-12-19
dc.date.published-print2017-12


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This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Except where otherwise noted, this item's license is described as This article is distributed under the terms of the Creative Commons Attribution 4.0 International License(http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.