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

• Main Authors: Imane Boudellioua, Maxat Kulmanov, Paul N. Schofield, Georgios V. Gkoutos, Robert Hoehndorf
• Format: Article
• Language: English
• Published: BMC 2019-02-01
• Series: BMC Bioinformatics
• Subjects: Variant prioritization; Phenotype; Machine learning; Ontology
• Online Access: http://link.springer.com/article/10.1186/s12859-019-2633-8

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.