Semantic prioritization of novel causative genomic variants

Imane Boudellioua, Rozaimi Mohamad Razali, Maxat Kulmanov, Yasmeen Hashish, Vladimir B. Bajic, Eva Goncalves-Serra, Nadia Schoenmakers, Georgios V. Gkoutos, Paul N. Schofield, Robert Hoehndorf

Research output: Contribution to journalArticle

10 Citations (Scopus)

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.

Original languageEnglish
Article numbere1005500
JournalPLoS Computational Biology
Volume13
Issue number4
DOIs
Publication statusPublished - 1 Apr 2017
Externally publishedYes

Fingerprint

Exome
Prioritization
prioritization
Semantics
Genomics
Predictors
genomics
genome
Genes
Sequencing
Genome
phenotype
Phenotype
hypothyroidism
artificial intelligence
retrospective studies
Congenital Hypothyroidism
Automated Reasoning
Information Services
Learning systems

ASJC Scopus subject areas

  • Ecology, Evolution, Behavior and Systematics
  • Modelling and Simulation
  • Ecology
  • Molecular Biology
  • Genetics
  • Cellular and Molecular Neuroscience
  • Computational Theory and Mathematics

Cite this

Boudellioua, I., Mohamad Razali, R., Kulmanov, M., Hashish, Y., Bajic, V. B., Goncalves-Serra, E., ... Hoehndorf, R. (2017). Semantic prioritization of novel causative genomic variants. PLoS Computational Biology, 13(4), [e1005500]. https://doi.org/10.1371/journal.pcbi.1005500

Semantic prioritization of novel causative genomic variants. / Boudellioua, Imane; Mohamad Razali, Rozaimi; Kulmanov, Maxat; Hashish, Yasmeen; Bajic, Vladimir B.; Goncalves-Serra, Eva; Schoenmakers, Nadia; Gkoutos, Georgios V.; Schofield, Paul N.; Hoehndorf, Robert.

In: PLoS Computational Biology, Vol. 13, No. 4, e1005500, 01.04.2017.

Research output: Contribution to journalArticle

Boudellioua, I, Mohamad Razali, R, Kulmanov, M, Hashish, Y, Bajic, VB, Goncalves-Serra, E, Schoenmakers, N, Gkoutos, GV, Schofield, PN & Hoehndorf, R 2017, 'Semantic prioritization of novel causative genomic variants', PLoS Computational Biology, vol. 13, no. 4, e1005500. https://doi.org/10.1371/journal.pcbi.1005500
Boudellioua I, Mohamad Razali R, Kulmanov M, Hashish Y, Bajic VB, Goncalves-Serra E et al. Semantic prioritization of novel causative genomic variants. PLoS Computational Biology. 2017 Apr 1;13(4). e1005500. https://doi.org/10.1371/journal.pcbi.1005500
Boudellioua, Imane ; Mohamad Razali, Rozaimi ; Kulmanov, Maxat ; Hashish, Yasmeen ; Bajic, Vladimir B. ; Goncalves-Serra, Eva ; Schoenmakers, Nadia ; Gkoutos, Georgios V. ; Schofield, Paul N. ; Hoehndorf, Robert. / Semantic prioritization of novel causative genomic variants. In: PLoS Computational Biology. 2017 ; Vol. 13, No. 4.
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