Ontology-based validation and identification of regulatory phenotypes

Handle URI:
http://hdl.handle.net/10754/627065
Title:
Ontology-based validation and identification of regulatory phenotypes
Authors:
Kulmanov, Maxat ( 0000-0003-1710-1820 ) ; Schofield, Paul N; Gkoutos, Georgios V; Hoehndorf, Robert ( 0000-0001-8149-5890 )
Abstract:
Motivation: 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. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Computer Science Program; Computational Bioscience Research Center (CBRC)
Citation:
Kulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R (2018) Ontology-based validation and identification of regulatory phenotypes. Available: http://dx.doi.org/10.1101/256529.
Publisher:
Cold Spring Harbor Laboratory
KAUST Grant Number:
URF/1/3454-01-01; FCC/1/1976-08-01
Issue Date:
31-Jan-2018
DOI:
10.1101/256529
Type:
Working Paper
Sponsors:
This 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.
Additional Links:
https://www.biorxiv.org/content/early/2018/01/30/256529
Appears in Collections:
Other/General Submission; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorKulmanov, Maxaten
dc.contributor.authorSchofield, Paul Nen
dc.contributor.authorGkoutos, Georgios Ven
dc.contributor.authorHoehndorf, Roberten
dc.date.accessioned2018-02-07T07:02:28Z-
dc.date.available2018-02-07T07:02:28Z-
dc.date.issued2018-01-31en
dc.identifier.citationKulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R (2018) Ontology-based validation and identification of regulatory phenotypes. Available: http://dx.doi.org/10.1101/256529.en
dc.identifier.doi10.1101/256529en
dc.identifier.urihttp://hdl.handle.net/10754/627065-
dc.description.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. Our method can also be applied to the rule-based prediction of phenotypes from functions. We show that the predicted phenotypes can be utilized for identification of protein-protein interactions and gene-disease associations. Based on experimental functional annotations, we predict phenotypes for 1,986 genes in mouse and 7,301 genes in human for which no experimental phenotypes have yet been determined.en
dc.description.sponsorshipThis 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.en
dc.publisherCold Spring Harbor Laboratoryen
dc.relation.urlhttps://www.biorxiv.org/content/early/2018/01/30/256529en
dc.rightsThe copyright holder for this preprint is the author/funder. It is made available under a CC-BY-NC-ND 4.0 International license.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.titleOntology-based validation and identification of regulatory phenotypesen
dc.typeWorking Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentComputer Science Programen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.eprint.versionPre-printen
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridge, Downing Street, CB2 3EG, Cambridge, United Kingdomen
dc.contributor.institutionInstitute of Biological, Environmental and Rural Sciences, Aberystwyth University, SY23 2AX, Aberystwyth, United Kingdomen
dc.contributor.institutionInstitute of Translational Medicine, University Hospitals Birmingham, NHS Foundation Trust, B15 2TT, Birmingham, United Kingdomen
dc.contributor.institutionCollege of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, B15 2TT, Birmingham, United Kingdomen
kaust.authorKulmanov, Maxaten
kaust.authorHoehndorf, Roberten
kaust.grant.numberURF/1/3454-01-01en
kaust.grant.numberFCC/1/1976-08-01en
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