DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.

ABSTRACT: BACKGROUND:Calling genetic variations from sequence reads is an important problem in genomics. There are many existing methods for calling various types of variations. Recently, Google developed a method for calling single nucleotide polymorphisms (SNPs) based on deep learning. Their method visualizes sequence reads in the forms of images. These images are then used to train a deep neural network model, which is used to call SNPs. This raises a research question: can deep learning be used to call more complex genetic variations such as structural variations (SVs) from sequence data? RESULTS:In this paper, we extend this high-level approach to the problem of calling structural variations. We present DeepSV, an approach based on deep learning for calling long deletions from sequence reads. D

SUBMITTER: Cai L 

PROVIDER: S-EPMC6909530 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature