LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data

Compared to single nucleotide variants and short indels, structural variants (SVs) are often more challenging to detect using high-throughput sequencing based methods. Here, the authors develop LinkedSV, a computational tool for SV detection using linked-read exome and genome sequencing data.

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Autores principales:	Li Fang, Charlly Kao, Michael V. Gonzalez, Fernanda A. Mafra, Renata Pellegrino da Silva, Mingyao Li, Sören-Sebastian Wenzel, Katharina Wimmer, Hakon Hakonarson, Kai Wang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/76f9da9e0bae457b92757a7d2b1f0be4
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spelling	oai:doaj.org-article:76f9da9e0bae457b92757a7d2b1f0be42021-12-02T14:39:18ZLinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data10.1038/s41467-019-13397-72041-1723https://doaj.org/article/76f9da9e0bae457b92757a7d2b1f0be42019-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13397-7https://doaj.org/toc/2041-1723Compared to single nucleotide variants and short indels, structural variants (SVs) are often more challenging to detect using high-throughput sequencing based methods. Here, the authors develop LinkedSV, a computational tool for SV detection using linked-read exome and genome sequencing data.Li FangCharlly KaoMichael V. GonzalezFernanda A. MafraRenata Pellegrino da SilvaMingyao LiSören-Sebastian WenzelKatharina WimmerHakon HakonarsonKai WangNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-15 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Li Fang Charlly Kao Michael V. Gonzalez Fernanda A. Mafra Renata Pellegrino da Silva Mingyao Li Sören-Sebastian Wenzel Katharina Wimmer Hakon Hakonarson Kai Wang LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data
description	Compared to single nucleotide variants and short indels, structural variants (SVs) are often more challenging to detect using high-throughput sequencing based methods. Here, the authors develop LinkedSV, a computational tool for SV detection using linked-read exome and genome sequencing data.
format	article
author	Li Fang Charlly Kao Michael V. Gonzalez Fernanda A. Mafra Renata Pellegrino da Silva Mingyao Li Sören-Sebastian Wenzel Katharina Wimmer Hakon Hakonarson Kai Wang
author_facet	Li Fang Charlly Kao Michael V. Gonzalez Fernanda A. Mafra Renata Pellegrino da Silva Mingyao Li Sören-Sebastian Wenzel Katharina Wimmer Hakon Hakonarson Kai Wang
author_sort	Li Fang
title	LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data
title_short	LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data
title_full	LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data
title_fullStr	LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data
title_full_unstemmed	LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data
title_sort	linkedsv for detection of mosaic structural variants from linked-read exome and genome sequencing data
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/76f9da9e0bae457b92757a7d2b1f0be4
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LinkedSV for detection of mosaic structural variants from linked-read exome and genome sequencing data

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