DeepPVP: phenotype-based prioritization of causative variants using deep learning
KAUST DepartmentBio-Ontology Research Group (BORG)
Computational Bioscience Research Center (CBRC)
Computer Science Program
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant NumberURF/1/3454-01-01
Online Publication Date2019-02-06
Print Publication Date2019-12
Permanent link to this recordhttp://hdl.handle.net/10754/627821
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AbstractPrioritization 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. 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 . DeepPVP further improves on existing variant prioritization methods both in terms of speed as well as accuracy.
CitationBoudellioua I, Kulmanov M, Schofield PN, Gkoutos GV, Hoehndorf R (2019) DeepPVP: phenotype-based prioritization of causative variants using deep learning. BMC Bioinformatics 20. Available: http://dx.doi.org/10.1186/s12859-019-2633-8.
SponsorsThis work was supported by funding from King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) under Award No. URF/1/3454-01-01, FCS/1/3657-02-01 and FCC/1/1976-08-01. GVG acknowledges support from H2020-EINFRA (731075) and the National Science Foundation (IOS:1340112) as well as support from the NIHR Birmingham ECMC, NIHR Birmingham SRMRC and the NIHR Birmingham Biomedical Research Centre and the MRC HDR UK. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Medical Research Council or the Department of Health.
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Boudellioua, I., Kulmanov, M., Schofield, P. N., Gkoutos, G. V., & Hoehndorf, R. (2019). DeepPVP: phenotype-based prioritization of causative variants using deep learning. Figshare. https://doi.org/10.6084/M9.FIGSHARE.C.4392503.V1. DOI: 10.6084/m9.figshare.c.4392503.v1 Handle: 10754/664439
Title: bio-ontology-research-group/phenomenet-vp: A phenotype-based tool for variant prioritization in WES and WGS data. Publication Date: 2016-03-14. github: bio-ontology-research-group/phenomenet-vp Handle: 10754/667044
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