DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learning
Type
ArticleAuthors
Althagafi, Azza Th.
Alsubaie, Lamia
Kathiresan, Nagarajan
Mineta, Katsuhiko

Aloraini, Taghrid
Almutairi, Fuad
Alfadhel, Majid
Gojobori, Takashi

Alfares, Ahmad
Hoehndorf, Robert

KAUST Department
Bio-Ontology Research Group (BORG)Bioscience Program
Computational Bioscience Research Center (CBRC)
Computational Scientists
Computer Science Program
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
KAUST Grant Number
FCC/1/1976-08-01OSR
URF/1/3790-01-01
URF/1/4355-01-01
Date
2021-12-24Submitted Date
2021-03-29Permanent link to this record
http://hdl.handle.net/10754/667141
Metadata
Show full item recordAbstract
Abstract Motivation Structural genomic variants account for much of human variability and are involved in several diseases. Structural variants are complex and may affect coding regions of multiple genes, or affect the functions of genomic regions in different ways from single nucleotide variants. Interpreting the phenotypic consequences of structural variants relies on information about gene functions, haploinsufficiency or triplosensitivity, and other genomic features. Phenotype-based methods to identifying variants that are involved in genetic diseases combine molecular features with prior knowledge about the phenotypic consequences of altering gene functions. While phenotype-based methods have been applied successfully to single nucleotide variants as well as short insertions and deletions, the complexity of structural variants makes it more challenging to link them to phenotypes. Furthermore, structural variants can affect a large number of coding regions, and phenotype information may not be available for all of them. Results We developed DeepSVP, a computational method to prioritize structural variants involved in genetic diseases by combining genomic and gene functions information. We incorporate phenotypes linked to genes, functions of gene products, gene expression in individual celltypes, and anatomical sites of expression, and systematically relate them to their phenotypic consequences through ontologies and machine learning. DeepSVP significantly improves the success rate of finding causative variants in several benchmarks and can identify novel pathogenic structural variants in consanguineous families. Availability https://github.com/bio-ontology-research-group/DeepSVPCitation
Althagafi, A., Alsubaie, L., Kathiresan, N., Mineta, K., Aloraini, T., Almutairi, F., … Hoehndorf, R. (2021). DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learning. Bioinformatics. doi:10.1093/bioinformatics/btab859Sponsors
This work was supported by the King Abdullah University of Science and Technology (KAUST) Office of Sponsored Research (OSR) [Award Nos URF/1/3790-01-01, URF/1/4355-01-01, FCC/1/1976-08-01 and FCC/1/1976-08-08].Publisher
Oxford University Press (OUP)Journal
BioinformaticsPubMed ID
34951628Additional Links
https://academic.oup.com/bioinformatics/advance-article/doi/10.1093/bioinformatics/btab859/6482742Relations
Is Supplemented By:- [Software]
Title: bio-ontology-research-group/DeepSVP: Prioritizing Copy Number Variants (CNV) using Phenotype and Gene Functional Similarity. Publication Date: 2020-06-08. github: bio-ontology-research-group/DeepSVP Handle: 10754/667966
ae974a485f413a2113503eed53cd6c53
10.1093/bioinformatics/btab859
Scopus Count
Except where otherwise noted, this item's license is described as This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
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