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

Mineta, Katsuhiko

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

Alfares, Ahmed

Hoehndorf, Robert

KAUST Department
Computer Science ProgramComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Computational Scientists
Computational Bioscience Research Center (CBRC)
Bioscience Program
Biological and Environmental Sciences and Engineering (BESE) Division
Date
2021-01-28Permanent link to this record
http://hdl.handle.net/10754/667141
Metadata
Show full item recordAbstract
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 information with information about gene functions. 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/DeepSVP Contact: robert.hoehndorf@kaust.edu.saCitation
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. doi:10.1101/2021.01.28.428557Publisher
Cold Spring Harbor LaboratoryAdditional Links
http://biorxiv.org/lookup/doi/10.1101/2021.01.28.428557ae974a485f413a2113503eed53cd6c53
10.1101/2021.01.28.428557