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Updated: Aug 28, 2025

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
Published on: February 3, 2023
SVision: a deep learning approach to resolve complex structural variants
Jiadong Lin1,2,3,4, Songbo Wang1,2,3, Peter A Audano5
1MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China.
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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