Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data

Abstract The emergence of exome sequencing in recent years has enabled rapid and cost-effective detection of genetic variants in coding regions and offers a great opportunity to combine sequencing experiments with subsequent computational analysis for dissecting genetic basis of human inherited dise...

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Autores principales:	Mengmeng Wu, Ting Chen, Rui Jiang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5903d74ef19c4ff9ae3629c378363231
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spelling	oai:doaj.org-article:5903d74ef19c4ff9ae3629c3783632312021-12-02T16:06:17ZLeveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data10.1038/s41598-017-01834-w2045-2322https://doaj.org/article/5903d74ef19c4ff9ae3629c3783632312017-05-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-01834-whttps://doaj.org/toc/2045-2322Abstract The emergence of exome sequencing in recent years has enabled rapid and cost-effective detection of genetic variants in coding regions and offers a great opportunity to combine sequencing experiments with subsequent computational analysis for dissecting genetic basis of human inherited diseases. However, this strategy, though successful in practice, still faces such challenges as limited sample size and substantial number or diversity of candidate variants. To overcome these obstacles, researchers have been concentrated in the development of advanced computational methods and have recently achieved great progress for analysing single nucleotide variant. Nevertheless, it still remains unclear on how to analyse indels, another type of genetic variant that accounts for substantial proportion of known disease-causing variants. In this paper, we proposed an integrative method to effectively identify disease-causing indels from exome sequencing data. Specifically, we put forward a statistical method to combine five functional prediction scores, four genic association scores and a genic intolerance score to produce an integrated p-value, which could then be used for prioritizing candidate indels. We performed extensive simulation studies and demonstrated that our method achieved high accuracy in uncovering disease-causing indels. Our software is available at http://bioinfo.au.tsinghua.edu.cn/jianglab/IndelPrioritizer/.Mengmeng WuTing ChenRui JiangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mengmeng Wu Ting Chen Rui Jiang Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data
description	Abstract The emergence of exome sequencing in recent years has enabled rapid and cost-effective detection of genetic variants in coding regions and offers a great opportunity to combine sequencing experiments with subsequent computational analysis for dissecting genetic basis of human inherited diseases. However, this strategy, though successful in practice, still faces such challenges as limited sample size and substantial number or diversity of candidate variants. To overcome these obstacles, researchers have been concentrated in the development of advanced computational methods and have recently achieved great progress for analysing single nucleotide variant. Nevertheless, it still remains unclear on how to analyse indels, another type of genetic variant that accounts for substantial proportion of known disease-causing variants. In this paper, we proposed an integrative method to effectively identify disease-causing indels from exome sequencing data. Specifically, we put forward a statistical method to combine five functional prediction scores, four genic association scores and a genic intolerance score to produce an integrated p-value, which could then be used for prioritizing candidate indels. We performed extensive simulation studies and demonstrated that our method achieved high accuracy in uncovering disease-causing indels. Our software is available at http://bioinfo.au.tsinghua.edu.cn/jianglab/IndelPrioritizer/.
format	article
author	Mengmeng Wu Ting Chen Rui Jiang
author_facet	Mengmeng Wu Ting Chen Rui Jiang
author_sort	Mengmeng Wu
title	Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data
title_short	Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data
title_full	Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data
title_fullStr	Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data
title_full_unstemmed	Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data
title_sort	leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/5903d74ef19c4ff9ae3629c378363231
work_keys_str_mv	AT mengmengwu leveragingmultiplegenomicdatatoprioritizediseasecausingindelsfromexomesequencingdata AT tingchen leveragingmultiplegenomicdatatoprioritizediseasecausingindelsfromexomesequencingdata AT ruijiang leveragingmultiplegenomicdatatoprioritizediseasecausingindelsfromexomesequencingdata
_version_	1718385022686199808

Leveraging multiple genomic data to prioritize disease-causing indels from exome sequencing data

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