CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks
Joint Authors
Wang, Jing
Ling, Cheng
Gao, Jingyang
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-05-28
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
Many structural variations (SVs) detection methods have been proposed due to the popularization of next-generation sequencing (NGS).
These SV calling methods use different SV-property-dependent features; however, they all suffer from poor accuracy when running on low coverage sequences.
The union of results from these tools achieves fairly high sensitivity but still produces low accuracy on low coverage sequence data.
That is, these methods contain many false positives.
In this paper, we present CNNdel, an approach for calling deletions from paired-end reads.
CNNdel gathers SV candidates reported by multiple tools and then extracts features from aligned BAM files at the positions of candidates.
With labeled feature-expressed candidates as a training set, CNNdel trains convolutional neural networks (CNNs) to distinguish true unlabeled candidates from false ones.
Results show that CNNdel works well with NGS reads from 26 low coverage genomes of the 1000 Genomes Project.
The paper demonstrates that convolutional neural networks can automatically assign the priority of SV features and reduce the false positives efficaciously.
American Psychological Association (APA)
Wang, Jing& Ling, Cheng& Gao, Jingyang. 2017. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks. BioMed Research International،Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1138072
Modern Language Association (MLA)
Wang, Jing…[et al.]. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks. BioMed Research International No. 2017 (2017), pp.1-8.
https://search.emarefa.net/detail/BIM-1138072
American Medical Association (AMA)
Wang, Jing& Ling, Cheng& Gao, Jingyang. CNNdel: Calling Structural Variations on Low Coverage Data Based on Convolutional Neural Networks. BioMed Research International. 2017. Vol. 2017, no. 2017, pp.1-8.
https://search.emarefa.net/detail/BIM-1138072
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1138072