Mohamad Razali, Rozaimi
Bajic, Vladimir B.
Gkoutos, Georgios V.
Schofield, Paul N.
KAUST DepartmentComputational Bioscience Research Center (CBRC)
MetadataShow full item record
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.
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.
SponsorsThis 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.
PublisherPublic Library of Science (PLoS)
JournalPLOS Computational Biology
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