Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data

Abstract Background SNP arrays, short- and long-read genome sequencing are genome-wide high-throughput technologies that may be used to assay copy number variants (CNVs) in a personal genome. Each of these technologies comes with its own limitations and biases, many of which are well-known, but not...

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Autores principales:	Ksenia Lavrichenko, Stefan Johansson, Inge Jonassen
Formato:	article
Lenguaje:	EN
Publicado:	BMC 2021
Materias:	CNV Microarrays Short reads Long reads Genome in a Bottle Biotechnology TP248.13-248.65 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/4f60f00ed0f7489ab7ca3dac99e3b042
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spelling	oai:doaj.org-article:4f60f00ed0f7489ab7ca3dac99e3b0422021-11-21T12:26:36ZComprehensive characterization of copy number variation (CNV) called from array, long- and short-read data10.1186/s12864-021-08082-31471-2164https://doaj.org/article/4f60f00ed0f7489ab7ca3dac99e3b0422021-11-01T00:00:00Zhttps://doi.org/10.1186/s12864-021-08082-3https://doaj.org/toc/1471-2164Abstract Background SNP arrays, short- and long-read genome sequencing are genome-wide high-throughput technologies that may be used to assay copy number variants (CNVs) in a personal genome. Each of these technologies comes with its own limitations and biases, many of which are well-known, but not all of them are thoroughly quantified. Results We assembled an ensemble of public datasets of published CNV calls and raw data for the well-studied Genome in a Bottle individual NA12878. This assembly represents a variety of methods and pipelines used for CNV calling from array, short- and long-read technologies. We then performed cross-technology comparisons regarding their ability to call CNVs. Different from other studies, we refrained from using the golden standard. Instead, we attempted to validate the CNV calls by the raw data of each technology. Conclusions Our study confirms that long-read platforms enable recalling CNVs in genomic regions inaccessible to arrays or short reads. We also found that the reproducibility of a CNV by different pipelines within each technology is strongly linked to other CNV evidence measures. Importantly, the three technologies show distinct public database frequency profiles, which differ depending on what technology the database was built on.Ksenia LavrichenkoStefan JohanssonInge JonassenBMCarticleCNVMicroarraysShort readsLong readsGenome in a BottleBiotechnologyTP248.13-248.65GeneticsQH426-470ENBMC Genomics, Vol 22, Iss 1, Pp 1-15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	CNV Microarrays Short reads Long reads Genome in a Bottle Biotechnology TP248.13-248.65 Genetics QH426-470
spellingShingle	CNV Microarrays Short reads Long reads Genome in a Bottle Biotechnology TP248.13-248.65 Genetics QH426-470 Ksenia Lavrichenko Stefan Johansson Inge Jonassen Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data
description	Abstract Background SNP arrays, short- and long-read genome sequencing are genome-wide high-throughput technologies that may be used to assay copy number variants (CNVs) in a personal genome. Each of these technologies comes with its own limitations and biases, many of which are well-known, but not all of them are thoroughly quantified. Results We assembled an ensemble of public datasets of published CNV calls and raw data for the well-studied Genome in a Bottle individual NA12878. This assembly represents a variety of methods and pipelines used for CNV calling from array, short- and long-read technologies. We then performed cross-technology comparisons regarding their ability to call CNVs. Different from other studies, we refrained from using the golden standard. Instead, we attempted to validate the CNV calls by the raw data of each technology. Conclusions Our study confirms that long-read platforms enable recalling CNVs in genomic regions inaccessible to arrays or short reads. We also found that the reproducibility of a CNV by different pipelines within each technology is strongly linked to other CNV evidence measures. Importantly, the three technologies show distinct public database frequency profiles, which differ depending on what technology the database was built on.
format	article
author	Ksenia Lavrichenko Stefan Johansson Inge Jonassen
author_facet	Ksenia Lavrichenko Stefan Johansson Inge Jonassen
author_sort	Ksenia Lavrichenko
title	Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data
title_short	Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data
title_full	Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data
title_fullStr	Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data
title_full_unstemmed	Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data
title_sort	comprehensive characterization of copy number variation (cnv) called from array, long- and short-read data
publisher	BMC
publishDate	2021
url	https://doaj.org/article/4f60f00ed0f7489ab7ca3dac99e3b042
work_keys_str_mv	AT ksenialavrichenko comprehensivecharacterizationofcopynumbervariationcnvcalledfromarraylongandshortreaddata AT stefanjohansson comprehensivecharacterizationofcopynumbervariationcnvcalledfromarraylongandshortreaddata AT ingejonassen comprehensivecharacterizationofcopynumbervariationcnvcalledfromarraylongandshortreaddata
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Comprehensive characterization of copy number variation (CNV) called from array, long- and short-read data

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