KAUST DepartmentBio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberFCC/1/1976-08-01
Online Publication Date2017-12-19
Print Publication Date2017-12
Permanent link to this recordhttp://hdl.handle.net/10754/626413
MetadataShow full item record
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.
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.
SponsorsFunding 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).
JournalJournal of Biomedical Semantics
RelationsIs Supplemented By:
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.
- PhenomeNET: a whole-phenome approach to disease gene discovery.
- Authors: Hoehndorf R, Schofield PN, Gkoutos GV
- Issue date: 2011 Oct
- Matching biomedical ontologies based on formal concept analysis.
- Authors: Zhao M, Zhang S, Li W, Chen G
- Issue date: 2018 Mar 19
- Similarity-based search of model organism, disease and drug effect phenotypes.
- Authors: Hoehndorf R, Gruenberger M, Gkoutos GV, Schofield PN
- Issue date: 2015
- Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.
- Authors: Finke MT, Filice RW, Kahn CE Jr
- Issue date: 2019 Feb 1
- Ontology-based cross-species integration and analysis of Saccharomyces cerevisiae phenotypes.
- Authors: Gkoutos GV, Hoehndorf R
- Issue date: 2012 Sep 21