SVision: a deep learning approach to resolve complex structural variants.

Document Type

Article

Publication Date

10-1-2022

Publication Title

Nature methods

Keywords

JGM, Deep Learning, Genome, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA

JAX Source

Nat Methods. 2022;19(10):1230-3

Volume

19

Issue

10

First Page

1230

Last Page

1233

ISSN

1548-7105

PMID

36109679

DOI

https://doi.org/10.1038/s41592-022-01609-w

Grant

We thank X. Zhao, P. Balachandran, A. Wenger, and other members of the Human Genome Structural Variation Consortium for helpful discussions on methods development and structural variants analysis. K. Y. and X. Y. are supported by National Science Foundation of China (32125009, 32070663 and 62172325), the Key Construction Program of the National ‘985’ Project, the World-Class Universities (Disciplines), the Fundamental Research Funds for the Central Universities, and the Characteristic Development Guidance Funds for the Central Universities. C. R. B., P. A. A., and J. I. F. are supported by the National Institutes of Health R35GM133600 through the NIGMS and pilot funding from the Jackson Laboratory Cancer Center (P30 CA034196). D. M. is supported by the National Science Foundation of China (61721002) and the Macao Science and Technology Development Fund under Grant (061/2020/A2).

Abstract

Complex structural variants (CSVs) encompass multiple breakpoints and are often missed or misinterpreted. We developed SVision, a deep-learning-based multi-object-recognition framework, to automatically detect and haracterize CSVs from long-read sequencing data. SVision outperforms current callers at identifying the internal structure of complex events and has revealed 80 high-quality CSVs with 25 distinct structures from an individual genome. SVision directly detects CSVs without matching known structures, allowing sensitive detection of both common and previously uncharacterized complex rearrangements.

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