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

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...

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Bibliographic Details
Main Authors: Lei Cai, Yufeng Wu, Jingyang Gao
Format: Article
Language:English
Published: BMC 2019-12-01
Series:BMC Bioinformatics
Subjects:
Structural variations
Deep learning
Feature extraction
Visualization
Genetic variations
High-throughput sequencing
Online Access:https://doi.org/10.1186/s12859-019-3299-y

Internet

https://doi.org/10.1186/s12859-019-3299-y

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