DeepPVP: phenotype-based prioritization of causative variants using deep learning
Research output: Contribution to journal › Article
Colleges, School and Institutes
- King Abdullah University of Science and Technology
- University of Cambridge
- University Hospitals Birmingham NHS Trust
- NIHR Experimental Cancer Medicine Centre
- NIHR Surgical Reconstruction and Microbiology Research Centre
- NIHR Biomedical Research Centre
- MRC Health Data Research UK
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.
|Number of pages||8|
|Publication status||Published - 6 Feb 2019|