SvBreak: A New Approach for the Detection of Structural Variant Breakpoints Based on Convolutional Neural Network.

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 sequ

SUBMITTER: Wang S 

PROVIDER: S-EPMC8957449 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature