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    DeepSVP: Integration of genotype and phenotype for structural variant prioritization using deep learning

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    Name:
    2021.01.28.428557.full.pdf
    Size:
    5.042Mb
    Format:
    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Althagafi, Azza Th. cc
    Alsubaie, Lamia
    Kathiresan, Nagarajan cc
    Mineta, Katsuhiko cc
    Aloraini, Taghrid
    Almutairi, Fuad
    Alfadhel, Majid
    Gojobori, Takashi cc
    Alfares, Ahmed cc
    Hoehndorf, Robert cc
    KAUST Department
    Computer Science Program
    Computer, 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
    2021-01-28
    Permanent link to this record
    http://hdl.handle.net/10754/667141
    
    Metadata
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    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 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.sa
    Citation
    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:10.1101/2021.01.28.428557
    Publisher
    Cold Spring Harbor Laboratory
    DOI
    10.1101/2021.01.28.428557
    Additional Links
    http://biorxiv.org/lookup/doi/10.1101/2021.01.28.428557
    ae974a485f413a2113503eed53cd6c53
    10.1101/2021.01.28.428557
    Scopus Count
    Collections
    Biological and Environmental Sciences and Engineering (BESE) Division; Preprints; Bioscience Program; Computer Science Program; Computational Bioscience Research Center (CBRC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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