Ontology-based validation and identification of regulatory phenotypes
KAUST DepartmentBio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Online Publication Date2018-09-08
Print Publication Date2018-09-01
Permanent link to this recordhttp://hdl.handle.net/10754/627065
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AbstractMotivation Function annotations of gene products, and phenotype annotations of genotypes, provide valuable information about molecular mechanisms that can be utilized by computational methods to identify functional and phenotypic relatedness, improve our understanding of disease and pathobiology, and lead to discovery of drug targets. Identifying functions and phenotypes commonly requires experiments which are time-consuming and expensive to carry out; creating the annotations additionally requires a curator to make an assertion based on reported evidence. Support to validate the mutual consistency of functional and phenotype annotations as well as a computational method to predict phenotypes from function annotations, would greatly improve the utility of function annotations. Results We developed a novel ontology-based method to validate the mutual consistency of function and phenotype annotations. We apply our method to mouse and human annotations, and identify several inconsistencies that can be resolved to improve overall annotation quality. We also apply our method to the rule-based prediction of regulatory phenotypes from functions and demonstrate that we can predict these phenotypes with F of up to 0.647. Availability and implementation https://github.com/bio-ontology-research-group/phenogocon.
CitationKulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R (2018) Ontology-based validation and identification of regulatory phenotypes. Bioinformatics 34: i857–i865. Available: http://dx.doi.org/10.1093/bioinformatics/bty605.
SponsorsThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01 and FCC/1/1976-08-01. GVG acknowledges support from H2020-EINFRA (731075) and the National Science Foundation (IOS: 1340112) as well as support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.
PublisherCold Spring Harbor Laboratory
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