Along with the promotion and application of genetic techniques, desiring to improve the efficiency of calling copy number variation (CNV) in next-generation sequencing analysis technics (NGS) data turns into a requirement. A single existing method is insufficient for calling all potential CNVs. In this paper, we present cnnCNV, a convolutional neural network (CNV) based framework for calling CNV. Firstly, cnnCNV merges the output of existing CNV calling tools as candidates; secondly, generates images of each candidate region from aligned reads based on multiple detection theories; finally, a trained model can be used to classify candidates into true and false. Our approach was tested on simulated data comparing with existing tools, including Breakdancer, Control-FREEC, CNVnator, Delly and readDepth. Results show that cnnCNV improve precision and sensitivity of CNV calling remarkably. Pseudo-code of image generation can be download at: The pseudo-code of image creation can be downloaded at: https://github.com/StudMs/cnnCNV.
基金：
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61472026]; Beijing Natural Science FoundationBeijing Natural Science Foundation [5182018]
语种：
外文
被引次数：
WOS：
第一作者：
第一作者单位：[1]Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing, Peoples R China
通讯作者：
推荐引用方式(GB/T 7714)：
Ding Miaosen,Gao Jingyang,Ling Cheng,et al.cnnCNV: A Sensitive and Efficient Method for Detecting Copy Number Variation based on Convolutional Neural Networks[J].PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE on BIOINFORMATICS and BIOMEDICINE (BIBM).2018,2744-2746.
APA：
Ding, Miaosen,Gao, Jingyang,Ling, Cheng&Gao, Liwei.(2018).cnnCNV: A Sensitive and Efficient Method for Detecting Copy Number Variation based on Convolutional Neural Networks.PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE on BIOINFORMATICS and BIOMEDICINE (BIBM),,
MLA：
Ding, Miaosen,et al."cnnCNV: A Sensitive and Efficient Method for Detecting Copy Number Variation based on Convolutional Neural Networks".PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE on BIOINFORMATICS and BIOMEDICINE (BIBM) .(2018):2744-2746
