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dc.contributor.authorBoudellioua, Imene
dc.contributor.authorMohamad Razali, Rozaimi
dc.contributor.authorKulmanov, Maxat
dc.contributor.authorHashish, Yasmeen
dc.contributor.authorBajic, Vladimir B.
dc.contributor.authorGoncalves-Serra, Eva
dc.contributor.authorSchoenmakers, Nadia
dc.contributor.authorGkoutos, Georgios V.
dc.contributor.authorSchofield, Paul N.
dc.contributor.authorHoehndorf, Robert
dc.date.accessioned2017-04-25T05:48:40Z
dc.date.available2017-04-25T05:48:40Z
dc.date.issued2017-04-17
dc.identifier.citationBoudellioua I, Mahamad Razali RB, Kulmanov M, Hashish Y, Bajic VB, et al. (2017) Semantic prioritization of novel causative genomic variants. PLOS Computational Biology 13: e1005500. Available: http://dx.doi.org/10.1371/journal.pcbi.1005500.
dc.identifier.issn1553-7358
dc.identifier.doi10.1371/journal.pcbi.1005500
dc.identifier.urihttp://hdl.handle.net/10754/623278
dc.description.abstractDiscriminating the causative disease variant(s) for individuals with inherited or de novo mutations presents one of the main challenges faced by the clinical genetics community today. Computational approaches for variant prioritization include machine learning methods utilizing a large number of features, including molecular information, interaction networks, or phenotypes. Here, we demonstrate the PhenomeNET Variant Predictor (PVP) system that exploits semantic technologies and automated reasoning over genotype-phenotype relations to filter and prioritize variants in whole exome and whole genome sequencing datasets. We demonstrate the performance of PVP in identifying causative variants on a large number of synthetic whole exome and whole genome sequences, covering a wide range of diseases and syndromes. In a retrospective study, we further illustrate the application of PVP for the interpretation of whole exome sequencing data in patients suffering from congenital hypothyroidism. We find that PVP accurately identifies causative variants in whole exome and whole genome sequencing datasets and provides a powerful resource for the discovery of causal variants.
dc.description.sponsorshipThis research used the resources of the Computational Bioscience Research Center and the Supercomputing Laboratory at King Abdullah University of Science & Technology (KAUST) in Thuwal, Saudi Arabia.
dc.publisherPublic Library of Science (PLoS)
dc.relation.urlhttp://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005500
dc.rightsThis is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.titleSemantic prioritization of novel causative genomic variants
dc.typeArticle
dc.contributor.departmentApplied Mathematics and Computational Science Program
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.journalPLOS Computational Biology
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom.
dc.contributor.institutionUniversity of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.
dc.contributor.institutionInstitute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom.
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, United Kingdom.
kaust.personBoudellioua, Imene
kaust.personMohamad Razali, Rozaimi
kaust.personKulmanov, Maxat
kaust.personHashish, Yasmeen
kaust.personBajic, Vladimir B.
kaust.personHoehndorf, Robert
refterms.dateFOA2018-06-14T04:36:30Z


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This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Except where otherwise noted, this item's license is described as This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.