CNV-P: a machine-learning framework for predicting high confident copy number variations

Background Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs f...

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Autores principales:	Taifu Wang, Jinghua Sun, Xiuqing Zhang, Wen-Jing Wang, Qing Zhou
Formato:	article
Lenguaje:	EN
Publicado:	PeerJ Inc. 2021
Materias:	Copy number variant Machine learning Genome sequencing Medicine R
Acceso en línea:	https://doaj.org/article/ad731120167f4781abc75638c1c55012
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spelling	oai:doaj.org-article:ad731120167f4781abc75638c1c550122021-12-04T15:05:07ZCNV-P: a machine-learning framework for predicting high confident copy number variations10.7717/peerj.125642167-8359https://doaj.org/article/ad731120167f4781abc75638c1c550122021-12-01T00:00:00Zhttps://peerj.com/articles/12564.pdfhttps://peerj.com/articles/12564/https://doaj.org/toc/2167-8359Background Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement. Methods Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier. Results The prediction results on several real biological datasets showed that our models could accurately classify the CNVs at over 90% precision rate and 85% recall rate, which greatly improves the performance of state-of-the-art algorithms. Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs and the platforms of sequencing. Conclusions Our framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.Taifu WangJinghua SunXiuqing ZhangWen-Jing WangQing ZhouPeerJ Inc.articleCopy number variantMachine learningGenome sequencingMedicineRENPeerJ, Vol 9, p e12564 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Copy number variant Machine learning Genome sequencing Medicine R
spellingShingle	Copy number variant Machine learning Genome sequencing Medicine R Taifu Wang Jinghua Sun Xiuqing Zhang Wen-Jing Wang Qing Zhou CNV-P: a machine-learning framework for predicting high confident copy number variations
description	Background Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement. Methods Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier. Results The prediction results on several real biological datasets showed that our models could accurately classify the CNVs at over 90% precision rate and 85% recall rate, which greatly improves the performance of state-of-the-art algorithms. Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs and the platforms of sequencing. Conclusions Our framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.
format	article
author	Taifu Wang Jinghua Sun Xiuqing Zhang Wen-Jing Wang Qing Zhou
author_facet	Taifu Wang Jinghua Sun Xiuqing Zhang Wen-Jing Wang Qing Zhou
author_sort	Taifu Wang
title	CNV-P: a machine-learning framework for predicting high confident copy number variations
title_short	CNV-P: a machine-learning framework for predicting high confident copy number variations
title_full	CNV-P: a machine-learning framework for predicting high confident copy number variations
title_fullStr	CNV-P: a machine-learning framework for predicting high confident copy number variations
title_full_unstemmed	CNV-P: a machine-learning framework for predicting high confident copy number variations
title_sort	cnv-p: a machine-learning framework for predicting high confident copy number variations
publisher	PeerJ Inc.
publishDate	2021
url	https://doaj.org/article/ad731120167f4781abc75638c1c55012
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CNV-P: a machine-learning framework for predicting high confident copy number variations

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