DeepSV: accurate calling of genomic deletions from high-throughput sequencing data using deep convolutional neural network.
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. DeepSV is based on a novel method of visualizing sequence reads. The visualization is designed to capture multiple sources of information in the sequence data that are relevant to long deletions. DeepSV also implements techniques for working with noisy training data. DeepSV trains a model from the visualized sequence reads and calls deletions based on this model. We demonstrate that DeepSV outperforms existing methods in terms of accuracy and efficiency of deletion calling on the data from the 1000 Genomes Project.


CONCLUSIONS
Our work shows that deep learning can potentially lead to effective calling of different types of genetic variations that are complex than SNPs.
['Cai|Lei|L|', 'Wu|Yufeng|Y|', 'Gao|Jingyang|J|http://orcid.org/0000-0003-1270-6257']
D001483:Base Sequence D002877:Chromosomes, Human D023281:Genomics D059014:High-Throughput Nucleotide Sequencing D006801:Humans D000069550:Machine Learning D016571:Neural Networks, Computer D017384:Sequence Deletion D012984:Software
High-throughput sequencing Genetic variations Deep learning Visualization Structural variations Feature extraction