Abstract
BackgroundPrioritization 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.ResultsWe 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.ConclusionsDeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
Highlights
Prioritization of variants in personal genomic data is a major challenge
DeepPVP: phenotype-based prediction using a deep artificial neural networks We developed the Deep PhenomeNET Variant Predictor (DeepPVP) as a system to identify causative variants for patients based on personal genomic data as well as phenotypes observed in the patient
To predict whether a variant is causative or not, DeepPVP uses similar features as the PVP system [7] and combines multiple pathogenicity prediction scores, a phenotype similarity computed by the PhenomeNET system, and a high-level phenotypic characterization of a patient
Summary
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 variants that are likely to be dysfunctional but those that are likely involved in the pathogenesis of a patient’s phenotype. There is a large number of methods available for ciations because they implicitly reflect interactions occurprioritizing variants in whole exome or whole genome ring within an organism across multiple levels of datasets [1] These methods commonly identify the vari- organisation [10,11,12]. The phenotypic similarity is ple variants that could possibly be pathogenic, but most of used either as a filter to remove pathogenic variants them are sub-clinical or will not result in any detectable in genes that are not associated with similar phenotypes phenotypic manifestations [5]
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
