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SVFX: a machine-learning framework to quantify the pathogenicity of structural variants

View ORCID ProfileSushant Kumar, View ORCID ProfileArif Harmanci, Jagath Vytheeswaran, View ORCID ProfileMark B. Gerstein
doi: https://doi.org/10.1101/739474
Sushant Kumar
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
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Arif Harmanci
3Center for Precision Health, School of Biomedical Informatics, University of Texas Health Sciences Center, Houston, Texas, 77030, USA
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Jagath Vytheeswaran
4Department of Computing and Mathematical Sciences, California Institute of Technology, Pasadena, CA, 91125, USA
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Mark B. Gerstein
1Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
2Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
5Department of Computer Science, Yale University, 260/266 Whitney Avenue PO Box 208114, New Haven, CT 06520, USA
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  • For correspondence: pi@gersteinlab.org
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Abstract

A rapid decline in sequencing cost has made large-scale genome sequencing studies feasible. One of the fundamental goals of these studies is to catalog all pathogenic variants. Numerous methods and tools have been developed to interpret point mutations and small insertions and deletions. However, there is a lack of approaches for identifying pathogenic genomic structural variations (SVs). That said, SVs are known to play a crucial role in many diseases by altering the sequence and three-dimensional structure of the genome. Previous studies have suggested a complex interplay of genomic and epigenomic features in the emergence and distribution of SVs. However, the exact mechanism of pathogenesis for SVs in different diseases is not straightforward to decipher. Thus, we built an agnostic machine-learning-based workflow, called SVFX, to assign a “pathogenicity score” to somatic and germline SVs in various diseases. In particular, we generated somatic and germline training models, which included genomic, epigenomic, and conservation-based features for SV call sets in diseased and healthy individuals. We then applied SVFX to SVs in six different cancer cohorts and a cardiovascular disease (CVD) cohort. Overall, SVFX achieved high accuracy in identifying pathogenic SVs. Moreover, we found that predicted pathogenic SVs in cancer cohorts were enriched among known cancer genes and many cancer-related pathways (including Wnt signaling, Ras signaling, DNA repair, and ubiquitin-mediated proteolysis). Finally, we note that SVFX is flexible and can be easily extended to identify pathogenic SVs in additional disease cohorts.

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Posted August 19, 2019.
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SVFX: a machine-learning framework to quantify the pathogenicity of structural variants
Sushant Kumar, Arif Harmanci, Jagath Vytheeswaran, Mark B. Gerstein
bioRxiv 739474; doi: https://doi.org/10.1101/739474
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SVFX: a machine-learning framework to quantify the pathogenicity of structural variants
Sushant Kumar, Arif Harmanci, Jagath Vytheeswaran, Mark B. Gerstein
bioRxiv 739474; doi: https://doi.org/10.1101/739474

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