BreakNet: detecting deletions using long reads and a deep learning approach.

ABSTRACT:

Background

Structural variations (SVs) occupy a prominent position in human genetic diversity, and deletions form an important type of SV that has been suggested to be associated with genetic diseases. Although various deletion calling methods based on long reads have been proposed, a new approach is still needed to mine features in long-read alignment information. Recently, deep learning has attracted much attention in genome analysis, and it is a promising technique for calling SVs.

Results

In this paper, we propose BreakNet, a deep learning method that detects deletions by using long reads. BreakNet first extracts feature matrices from long-read alignments. Second, it uses a time-distributed convolutional neural network (CNN) to integrate and map the feature matrices to f

SUBMITTER: Luo J 

PROVIDER: S-EPMC8641175 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature