Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity

Abstract Whole Exome Sequencing (WES) is a powerful clinical diagnostic tool for discovering the genetic basis of many diseases. A major shortcoming of WES is uneven coverage of sequence reads over the exome targets contributing to many low coverage regions, which hinders accurate variant calling. I...

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Autores principales:	Qingyu Wang, Cooduvalli S. Shashikant, Matthew Jensen, Naomi S. Altman, Santhosh Girirajan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/34acd2ac6ca34b8b9c81f36ceb50a9d4
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spelling	oai:doaj.org-article:34acd2ac6ca34b8b9c81f36ceb50a9d42021-12-02T15:05:21ZNovel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity10.1038/s41598-017-01005-x2045-2322https://doaj.org/article/34acd2ac6ca34b8b9c81f36ceb50a9d42017-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01005-xhttps://doaj.org/toc/2045-2322Abstract Whole Exome Sequencing (WES) is a powerful clinical diagnostic tool for discovering the genetic basis of many diseases. A major shortcoming of WES is uneven coverage of sequence reads over the exome targets contributing to many low coverage regions, which hinders accurate variant calling. In this study, we devised two novel metrics, Cohort Coverage Sparseness (CCS) and Unevenness (UE) Scores for a detailed assessment of the distribution of coverage of sequence reads. Employing these metrics we revealed non-uniformity of coverage and low coverage regions in the WES data generated by three different platforms. This non-uniformity of coverage is both local (coverage of a given exon across different platforms) and global (coverage of all exons across the genome in the given platform). The low coverage regions encompassing functionally important genes were often associated with high GC content, repeat elements and segmental duplications. While a majority of the problems associated with WES are due to the limitations of the capture methods, further refinements in WES technologies have the potential to enhance its clinical applications.Qingyu WangCooduvalli S. ShashikantMatthew JensenNaomi S. AltmanSanthosh GirirajanNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-11 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Qingyu Wang Cooduvalli S. Shashikant Matthew Jensen Naomi S. Altman Santhosh Girirajan Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity
description	Abstract Whole Exome Sequencing (WES) is a powerful clinical diagnostic tool for discovering the genetic basis of many diseases. A major shortcoming of WES is uneven coverage of sequence reads over the exome targets contributing to many low coverage regions, which hinders accurate variant calling. In this study, we devised two novel metrics, Cohort Coverage Sparseness (CCS) and Unevenness (UE) Scores for a detailed assessment of the distribution of coverage of sequence reads. Employing these metrics we revealed non-uniformity of coverage and low coverage regions in the WES data generated by three different platforms. This non-uniformity of coverage is both local (coverage of a given exon across different platforms) and global (coverage of all exons across the genome in the given platform). The low coverage regions encompassing functionally important genes were often associated with high GC content, repeat elements and segmental duplications. While a majority of the problems associated with WES are due to the limitations of the capture methods, further refinements in WES technologies have the potential to enhance its clinical applications.
format	article
author	Qingyu Wang Cooduvalli S. Shashikant Matthew Jensen Naomi S. Altman Santhosh Girirajan
author_facet	Qingyu Wang Cooduvalli S. Shashikant Matthew Jensen Naomi S. Altman Santhosh Girirajan
author_sort	Qingyu Wang
title	Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity
title_short	Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity
title_full	Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity
title_fullStr	Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity
title_full_unstemmed	Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity
title_sort	novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/34acd2ac6ca34b8b9c81f36ceb50a9d4
work_keys_str_mv	AT qingyuwang novelmetricstomeasurecoverageinwholeexomesequencingdatasetsreveallocalandglobalnonuniformity AT cooduvallisshashikant novelmetricstomeasurecoverageinwholeexomesequencingdatasetsreveallocalandglobalnonuniformity AT matthewjensen novelmetricstomeasurecoverageinwholeexomesequencingdatasetsreveallocalandglobalnonuniformity AT naomisaltman novelmetricstomeasurecoverageinwholeexomesequencingdatasetsreveallocalandglobalnonuniformity AT santhoshgirirajan novelmetricstomeasurecoverageinwholeexomesequencingdatasetsreveallocalandglobalnonuniformity
_version_	1718388820894810112

Novel metrics to measure coverage in whole exome sequencing datasets reveal local and global non-uniformity

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