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

Journal: BMC bioinformatics
PMID: 31830921

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?

Authors

  • Lei Cai
    Department of Information Science and Technology, Beijing University of Chemical Technology, Beijing, People's Republic of China.
  • Yufeng Wu
    Department of Computer Science and Engineering, University of Connecticut, Storrs, CT, USA.
  • Jingyang Gao
    Department of Computer Science and Technology, Beijing University of Chemical Technology, Beijing, China.

Keywords

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