SvBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network. (19th March 2022)

• Record Type: Journal Article
• Authors: Wang, Shaoqiang; Li, Jie; Haque, A K Alvi; Zhao, Haiyong; Yang, Liying; Yuan, Xiguo
• Other Names: Ilie Lucian Academic Editor.
• Is Part Of: BioMed research international. Volume 2022(2022)
• Publication Date: 2022-03-19
• Subjects: Medicine -- Periodicals; Biology -- Periodicals; Biotechnology -- Periodicals; Life sciences -- Periodicals; 610.5
• DOI: 10.1155/2022/7196040
• Languages: English
• ISSNs: 2314-6133

Abstract

Abstract : Structural variation (SV) is an important type of genome variation and confers susceptibility to human cancer diseases. Systematic analysis of SVs has become a crucial step for the exploration of mechanisms and precision diagnosis of cancers. The central point is how to accurately detect SV breakpoints by using next-generation sequencing (NGS) data. Due to the cooccurrence of multiple types of SVs in the human genome and the intrinsic complexity of SVs, the discrimination of SV breakpoint types is a challenging task. In this paper, we propose a convolutional neural network- (CNN-) based approach, called svBreak, for the detection and discrimination of common types of SV breakpoints. The principle of svBreak is that it extracts a set of SV-related features for each genome site from the sequencing reads aligned to the reference genome and establishes a data matrix where each row represents one site and each column represents one feature and then adopts a CNN model to analyze such data matrix for the prediction of SV breakpoints. The performance of the proposed approach is tested via simulation studies and application to a real sequencing sample. The experimental results demonstrate the merits of the proposed approach when compared with existing methods. Thus, svBreak can be expected to be a supplementary approach in the field of SV analysis in human tumor genomes.