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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2021-12-01
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| Series: | PeerJ |
| Subjects: | |
| Online Access: | https://peerj.com/articles/12564.pdf |
| _version_ | 1827608196563337216 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-09T07:07:36Z |
| format | Article |
| id | doaj.art-ad731120167f4781abc75638c1c55012 |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-03-09T07:07:36Z |
| publishDate | 2021-12-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ |
| spelling | doaj.art-ad731120167f4781abc75638c1c550122023-12-03T09:28:10ZengPeerJ Inc.PeerJ2167-83592021-12-019e1256410.7717/peerj.12564CNV-P: a machine-learning framework for predicting high confident copy number variationsTaifu Wang0Jinghua Sun1Xiuqing Zhang2Wen-Jing Wang3Qing Zhou4BGI-Shenzhen, Shenzhen, ChinaBGI-Shenzhen, Shenzhen, ChinaBGI-Shenzhen, Shenzhen, ChinaBGI-Shenzhen, Shenzhen, ChinaBGI-Shenzhen, Shenzhen, ChinaBackground 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.https://peerj.com/articles/12564.pdfCopy number variantMachine learningGenome sequencing |
| spellingShingle | Taifu Wang Jinghua Sun Xiuqing Zhang Wen-Jing Wang Qing Zhou CNV-P: a machine-learning framework for predicting high confident copy number variations PeerJ Copy number variant Machine learning Genome sequencing |
| title | 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_short | 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 |
| topic | Copy number variant Machine learning Genome sequencing |
| url | https://peerj.com/articles/12564.pdf |
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