Benchmarking germline CNV calling tools from exome sequencing data

Abstract Whole-exome sequencing is an attractive alternative to microarray analysis because of the low cost and potential ability to detect copy number variations (CNV) of various sizes (from 1–2 exons to several Mb). Previous comparison of the most popular CNV calling tools showed a high portion of...

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Autores principales:	Veronika Gordeeva, Elena Sharova, Konstantin Babalyan, Rinat Sultanov, Vadim M. Govorun, Georgij Arapidi
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/74053e2546e143b9894809dc6ad427ac
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spelling	oai:doaj.org-article:74053e2546e143b9894809dc6ad427ac2021-12-02T18:30:39ZBenchmarking germline CNV calling tools from exome sequencing data10.1038/s41598-021-93878-22045-2322https://doaj.org/article/74053e2546e143b9894809dc6ad427ac2021-07-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-93878-2https://doaj.org/toc/2045-2322Abstract Whole-exome sequencing is an attractive alternative to microarray analysis because of the low cost and potential ability to detect copy number variations (CNV) of various sizes (from 1–2 exons to several Mb). Previous comparison of the most popular CNV calling tools showed a high portion of false-positive calls. Moreover, due to a lack of a gold standard CNV set, the results are limited and incomparable. Here, we aimed to perform a comprehensive analysis of tools capable of germline CNV calling available at the moment using a single CNV standard and reference sample set. Compiling variants from previous studies with Bayesian estimation approach, we constructed an internal standard for NA12878 sample (pilot National Institute of Standards and Technology Reference Material) including 110,050 CNV or non-CNV exons. The standard was used to evaluate the performance of 16 germline CNV calling tools on the NA12878 sample and 10 correlated exomes as a reference set with respect to length distribution, concordance, and efficiency. Each algorithm had a certain range of detected lengths and showed low concordance with other tools. Most tools are focused on detection of a limited number of CNVs one to seven exons long with a false-positive rate below 50%. EXCAVATOR2, exomeCopy, and FishingCNV focused on detection of a wide range of variations but showed low precision. Upon unified comparison, the tools were not equivalent. The analysis performed allows choosing algorithms or ensembles of algorithms most suitable for a specific goal, e.g. population studies or medical genetics.Veronika GordeevaElena SharovaKonstantin BabalyanRinat SultanovVadim M. GovorunGeorgij ArapidiNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Veronika Gordeeva Elena Sharova Konstantin Babalyan Rinat Sultanov Vadim M. Govorun Georgij Arapidi Benchmarking germline CNV calling tools from exome sequencing data
description	Abstract Whole-exome sequencing is an attractive alternative to microarray analysis because of the low cost and potential ability to detect copy number variations (CNV) of various sizes (from 1–2 exons to several Mb). Previous comparison of the most popular CNV calling tools showed a high portion of false-positive calls. Moreover, due to a lack of a gold standard CNV set, the results are limited and incomparable. Here, we aimed to perform a comprehensive analysis of tools capable of germline CNV calling available at the moment using a single CNV standard and reference sample set. Compiling variants from previous studies with Bayesian estimation approach, we constructed an internal standard for NA12878 sample (pilot National Institute of Standards and Technology Reference Material) including 110,050 CNV or non-CNV exons. The standard was used to evaluate the performance of 16 germline CNV calling tools on the NA12878 sample and 10 correlated exomes as a reference set with respect to length distribution, concordance, and efficiency. Each algorithm had a certain range of detected lengths and showed low concordance with other tools. Most tools are focused on detection of a limited number of CNVs one to seven exons long with a false-positive rate below 50%. EXCAVATOR2, exomeCopy, and FishingCNV focused on detection of a wide range of variations but showed low precision. Upon unified comparison, the tools were not equivalent. The analysis performed allows choosing algorithms or ensembles of algorithms most suitable for a specific goal, e.g. population studies or medical genetics.
format	article
author	Veronika Gordeeva Elena Sharova Konstantin Babalyan Rinat Sultanov Vadim M. Govorun Georgij Arapidi
author_facet	Veronika Gordeeva Elena Sharova Konstantin Babalyan Rinat Sultanov Vadim M. Govorun Georgij Arapidi
author_sort	Veronika Gordeeva
title	Benchmarking germline CNV calling tools from exome sequencing data
title_short	Benchmarking germline CNV calling tools from exome sequencing data
title_full	Benchmarking germline CNV calling tools from exome sequencing data
title_fullStr	Benchmarking germline CNV calling tools from exome sequencing data
title_full_unstemmed	Benchmarking germline CNV calling tools from exome sequencing data
title_sort	benchmarking germline cnv calling tools from exome sequencing data
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/74053e2546e143b9894809dc6ad427ac
work_keys_str_mv	AT veronikagordeeva benchmarkinggermlinecnvcallingtoolsfromexomesequencingdata AT elenasharova benchmarkinggermlinecnvcallingtoolsfromexomesequencingdata AT konstantinbabalyan benchmarkinggermlinecnvcallingtoolsfromexomesequencingdata AT rinatsultanov benchmarkinggermlinecnvcallingtoolsfromexomesequencingdata AT vadimmgovorun benchmarkinggermlinecnvcallingtoolsfromexomesequencingdata AT georgijarapidi benchmarkinggermlinecnvcallingtoolsfromexomesequencingdata
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Benchmarking germline CNV calling tools from exome sequencing data

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