INSnet: a method for detecting insertions based on deep learning network.

ABSTRACT:

Background

Many studies have shown that structural variations (SVs) strongly impact human disease. As a common type of SV, insertions are usually associated with genetic diseases. Therefore, accurately detecting insertions is of great significance. Although many methods for detecting insertions have been proposed, these methods often generate some errors and miss some variants. Hence, accurately detecting insertions remains a challenging task.

Results

In this paper, we propose a method named INSnet to detect insertions using a deep learning network. First, INSnet divides the reference genome into continuous sub-regions and takes five features for each locus through alignments between long reads and the reference genome. Next, INSnet uses a depthwise separable convolutional n

SUBMITTER: Gao R 

PROVIDER: S-EPMC9990265 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature