Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation

Abstract Background With the rapid development of long-read sequencing technologies, it is possible to reveal the full spectrum of genetic structural variation (SV). However, the expensive cost, finite read length and high sequencing error for long-read data greatly limit the widespread adoption of...

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Autores principales:	Tao Jiang, Shiqi Liu, Shuqi Cao, Yadong Liu, Zhe Cui, Yadong Wang, Hongzhe Guo
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	Long-read sequencing SV calling Coverage Read length Sequencing error Comprehensive evaluation Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/ed2f87bf0576446e8d9b3b0976ceda01
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spelling	oai:doaj.org-article:ed2f87bf0576446e8d9b3b0976ceda012021-11-14T12:13:15ZLong-read sequencing settings for efficient structural variation detection based on comprehensive evaluation10.1186/s12859-021-04422-y1471-2105https://doaj.org/article/ed2f87bf0576446e8d9b3b0976ceda012021-11-01T00:00:00Zhttps://doi.org/10.1186/s12859-021-04422-yhttps://doaj.org/toc/1471-2105Abstract Background With the rapid development of long-read sequencing technologies, it is possible to reveal the full spectrum of genetic structural variation (SV). However, the expensive cost, finite read length and high sequencing error for long-read data greatly limit the widespread adoption of SV calling. Therefore, it is urgent to establish guidance concerning sequencing coverage, read length, and error rate to maintain high SV yields and to achieve the lowest cost simultaneously. Results In this study, we generated a full range of simulated error-prone long-read datasets containing various sequencing settings and comprehensively evaluated the performance of SV calling with state-of-the-art long-read SV detection methods. The benchmark results demonstrate that almost all SV callers perform better when the long-read data reach 20× coverage, 20 kbp average read length, and approximately 10–7.5% or below 1% error rates. Furthermore, high sequencing coverage is the most influential factor in promoting SV calling, while it also directly determines the expensive costs. Conclusions Based on the comprehensive evaluation results, we provide important guidelines for selecting long-read sequencing settings for efficient SV calling. We believe these recommended settings of long-read sequencing will have extraordinary guiding significance in cutting-edge genomic studies and clinical practices.Tao JiangShiqi LiuShuqi CaoYadong LiuZhe CuiYadong WangHongzhe GuoBMCarticleLong-read sequencingSV callingCoverageRead lengthSequencing errorComprehensive evaluationComputer applications to medicine. Medical informaticsR858-859.7Biology (General)QH301-705.5ENBMC Bioinformatics, Vol 22, Iss 1, Pp 1-17 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Long-read sequencing SV calling Coverage Read length Sequencing error Comprehensive evaluation Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5
spellingShingle	Long-read sequencing SV calling Coverage Read length Sequencing error Comprehensive evaluation Computer applications to medicine. Medical informatics R858-859.7 Biology (General) QH301-705.5 Tao Jiang Shiqi Liu Shuqi Cao Yadong Liu Zhe Cui Yadong Wang Hongzhe Guo Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
description	Abstract Background With the rapid development of long-read sequencing technologies, it is possible to reveal the full spectrum of genetic structural variation (SV). However, the expensive cost, finite read length and high sequencing error for long-read data greatly limit the widespread adoption of SV calling. Therefore, it is urgent to establish guidance concerning sequencing coverage, read length, and error rate to maintain high SV yields and to achieve the lowest cost simultaneously. Results In this study, we generated a full range of simulated error-prone long-read datasets containing various sequencing settings and comprehensively evaluated the performance of SV calling with state-of-the-art long-read SV detection methods. The benchmark results demonstrate that almost all SV callers perform better when the long-read data reach 20× coverage, 20 kbp average read length, and approximately 10–7.5% or below 1% error rates. Furthermore, high sequencing coverage is the most influential factor in promoting SV calling, while it also directly determines the expensive costs. Conclusions Based on the comprehensive evaluation results, we provide important guidelines for selecting long-read sequencing settings for efficient SV calling. We believe these recommended settings of long-read sequencing will have extraordinary guiding significance in cutting-edge genomic studies and clinical practices.
format	article
author	Tao Jiang Shiqi Liu Shuqi Cao Yadong Liu Zhe Cui Yadong Wang Hongzhe Guo
author_facet	Tao Jiang Shiqi Liu Shuqi Cao Yadong Liu Zhe Cui Yadong Wang Hongzhe Guo
author_sort	Tao Jiang
title	Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
title_short	Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
title_full	Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
title_fullStr	Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
title_full_unstemmed	Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
title_sort	long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation
publisher	BMC
publishDate	2021
url	https://doaj.org/article/ed2f87bf0576446e8d9b3b0976ceda01
work_keys_str_mv	AT taojiang longreadsequencingsettingsforefficientstructuralvariationdetectionbasedoncomprehensiveevaluation AT shiqiliu longreadsequencingsettingsforefficientstructuralvariationdetectionbasedoncomprehensiveevaluation AT shuqicao longreadsequencingsettingsforefficientstructuralvariationdetectionbasedoncomprehensiveevaluation AT yadongliu longreadsequencingsettingsforefficientstructuralvariationdetectionbasedoncomprehensiveevaluation AT zhecui longreadsequencingsettingsforefficientstructuralvariationdetectionbasedoncomprehensiveevaluation AT yadongwang longreadsequencingsettingsforefficientstructuralvariationdetectionbasedoncomprehensiveevaluation AT hongzheguo longreadsequencingsettingsforefficientstructuralvariationdetectionbasedoncomprehensiveevaluation
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Long-read sequencing settings for efficient structural variation detection based on comprehensive evaluation

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