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

Azza Th. Althagafi, Lamia Alsubaie, Nagarajan Kathiresan, Katsuhiko Mineta, Taghrid Aloraini, Fuad Almutairi, Majid Alfadhel, Takashi Gojobori, Ahmad Alfares, Robert Hoehndorf

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


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
Original languageEnglish (US)
StatePublished - Dec 24 2021

Bibliographical note

KAUST Repository Item: Exported on 2022-01-26
Acknowledged KAUST grant number(s): FCC/1/1976-08-01, OSR, URF/1/3790-01-01, URF/1/4355-01-01
Acknowledgements: 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].

ASJC Scopus subject areas

  • Biochemistry
  • Computational Theory and Mathematics
  • Computational Mathematics
  • Molecular Biology
  • Statistics and Probability
  • Computer Science Applications


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