De novo and somatic structural variant discovery with SVision-pro

Nature Biotechnology, vol. 43, pp. 181–185

Abstract

Long-read-based de novo and somatic structural variant (SV) discovery remains challenging, necessitating genomic comparison between samples. We developed SVision-pro, a neural-network-based instance segmentation framework that represents genome-to-genome-level sequencing differences visually and discovers SV comparatively between genomes without any prerequisite for inference models. SVision-pro outperforms state-of-the-art approaches, in particular, the resolving of complex SVs is improved, with low Mendelian error rates, high sensitivity of low-frequency SVs and reduced false-positive rates compared with SV merging approaches.

Authors 10

1. Songbo Wang

   First Affiliated Hospital of Xi'an Jiaotong University · Xi'an Jiaotong University

   Affiliation as printed

   Department of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

2. Jiadong Lin

   Xi'an Jiaotong University

   Affiliation as printed

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

3. Peng Jia

   First Affiliated Hospital of Xi'an Jiaotong University · Xi'an Jiaotong University

   Affiliation as printed

   Department of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

4. Tun Xu

   Xi'an Jiaotong University

   Affiliation as printed

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

5. Xiujuan Li

   Xi'an Jiaotong University

   Affiliation as printed

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

6. Y Liu

   Xi'an Jiaotong University

   Affiliation as printed

   School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China

7. Dan Xu

   Xi'an Jiaotong University

   Affiliation as printed

   School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China

8. Stephen J. Bush

   Xi'an Jiaotong University

   Affiliation as printed

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

   School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

9. Deyu Meng

   Macau University of Science and Technology · Xi'an Jiaotong University

   Affiliation as printed

   MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China

   Macau Institute of Systems Engineering, Macau University of Science and Technology, Taipa, Macau

   Pazhou Laboratory (Huangpu), Guangzhou, Guangdong, China

   School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China

10. Kai Ye corresponding Aachen Faculty of Science, Leiden University

    Leiden University · First Affiliated Hospital of Xi'an Jiaotong University · Xi'an Jiaotong University

    Affiliation as printed

    Department of Gynecology and Obstetrics, Center for Mathematical Medical, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn

    Faculty of Science, Leiden University, Leiden, The Netherlands. kaiye@xjtu.edu.cn

    Genome Institute, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn

    MOE Key Lab for Intelligent Networks & Networks Security, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn

    School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn

    School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China. kaiye@xjtu.edu.cn

    School of Automation Science and Engineering, Faculty of Electronic and Information Engineering, Xi'an Jiaotong University, Xi'an, China