Semantic prioritization of novel causative genomic variants

Handle URI:
http://hdl.handle.net/10754/623278
Title:
Semantic prioritization of novel causative genomic variants
Authors:
Boudellioua, Imene; Mohamad Razali, Rozaimi ( 0000-0002-8996-3975 ) ; Kulmanov, Maxat ( 0000-0003-1710-1820 ) ; Hashish, Yasmeen ( 0000-0002-9855-1139 ) ; Bajic, Vladimir B. ( 0000-0001-5435-4750 ) ; Goncalves-Serra, Eva ( 0000-0003-0360-2130 ) ; Schoenmakers, Nadia; Gkoutos, Georgios V.; Schofield, Paul N.; Hoehndorf, Robert ( 0000-0001-8149-5890 )
Abstract:
Discriminating 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.
KAUST Department:
Computational Bioscience Research Center (CBRC)
Citation:
Boudellioua 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.
Publisher:
Public Library of Science (PLoS)
Journal:
PLOS Computational Biology
Issue Date:
17-Apr-2017
DOI:
10.1371/journal.pcbi.1005500
Type:
Article
ISSN:
1553-7358
Sponsors:
This 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.
Additional Links:
http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005500
Appears in Collections:
Articles; Computational Bioscience Research Center (CBRC)

Full metadata record

DC FieldValue Language
dc.contributor.authorBoudellioua, Imeneen
dc.contributor.authorMohamad Razali, Rozaimien
dc.contributor.authorKulmanov, Maxaten
dc.contributor.authorHashish, Yasmeenen
dc.contributor.authorBajic, Vladimir B.en
dc.contributor.authorGoncalves-Serra, Evaen
dc.contributor.authorSchoenmakers, Nadiaen
dc.contributor.authorGkoutos, Georgios V.en
dc.contributor.authorSchofield, Paul N.en
dc.contributor.authorHoehndorf, Roberten
dc.date.accessioned2017-04-25T05:48:40Z-
dc.date.available2017-04-25T05:48:40Z-
dc.date.issued2017-04-17en
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.en
dc.identifier.issn1553-7358en
dc.identifier.doi10.1371/journal.pcbi.1005500en
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.en
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.en
dc.publisherPublic Library of Science (PLoS)en
dc.relation.urlhttp://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005500en
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.en
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/en
dc.titleSemantic prioritization of novel causative genomic variantsen
dc.typeArticleen
dc.contributor.departmentComputational Bioscience Research Center (CBRC)en
dc.identifier.journalPLOS Computational Biologyen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionWellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, United Kingdom.en
dc.contributor.institutionUniversity of Cambridge Metabolic Research Laboratories, Wellcome Trust-Medical Research Council, Institute of Metabolic Science, Addenbrooke's Hospital, Cambridge, United Kingdom.en
dc.contributor.institutionInstitute of Biological, Environmental and Rural Sciences, Aberystwyth University, Aberystwyth, United Kingdom.en
dc.contributor.institutionDepartment of Physiology, Development & Neuroscience, University of Cambridge, Cambridge, United Kingdom.en
kaust.authorBoudellioua, Imeneen
kaust.authorMohamad Razali, Rozaimien
kaust.authorKulmanov, Maxaten
kaust.authorHashish, Yasmeenen
kaust.authorBajic, Vladimir B.en
kaust.authorHoehndorf, Roberten
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