DeepPVP: phenotype-based prioritization of causative variants using deep learning

Imane Boudellioua, Maxat Kulmanov, Paul N. Schofield, Georgios V. Gkoutos, Robert Hoehndorf

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)
185 Downloads (Pure)

Abstract

Background: Prioritization of variants in personal genomic data is a major challenge. Recently, computational methods that rely on comparing phenotype similarity have shown to be useful to identify causative variants. In these methods, pathogenicity prediction is combined with a semantic similarity measure to prioritize not only variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient's phenotype.

Results: We have developed DeepPVP, a variant prioritization method that combined automated inference with deep neural networks to identify the likely causative variants in whole exome or whole genome sequence data. We demonstrate that DeepPVP performs significantly better than existing methods, including phenotype-based methods that use similar features. DeepPVP is freely available at https://github.com/bio-ontology-research-group/phenomenet-vp.

Conclusions: DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.

Original languageEnglish
Article number65
Number of pages8
JournalBMC Bioinformatics
Volume20
Issue number1
DOIs
Publication statusPublished - 6 Feb 2019

Keywords

  • Machine learning
  • Ontology
  • Phenotype
  • Variant prioritization

ASJC Scopus subject areas

  • Structural Biology
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Applied Mathematics

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