A deep learning model for predicting next-generation sequencing depth from DNA sequence

DNA probes used in next generation sequencing (NGS) have variable hybridisation kinetics, resulting in non-uniform coverage. Here, the authors develop a deep learning model to predict NGS depth using DNA probe sequences and apply to human and non-human sequencing panels.

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Autores principales:	Jinny X. Zhang, Boyan Yordanov, Alexander Gaunt, Michael X. Wang, Peng Dai, Yuan-Jyue Chen, Kerou Zhang, John Z. Fang, Neil Dalchau, Jiaming Li, Andrew Phillips, David Yu Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/38cfd7ab6401440b9ae00deafd4781f0
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spelling	oai:doaj.org-article:38cfd7ab6401440b9ae00deafd4781f02021-12-02T17:56:57ZA deep learning model for predicting next-generation sequencing depth from DNA sequence10.1038/s41467-021-24497-82041-1723https://doaj.org/article/38cfd7ab6401440b9ae00deafd4781f02021-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-24497-8https://doaj.org/toc/2041-1723DNA probes used in next generation sequencing (NGS) have variable hybridisation kinetics, resulting in non-uniform coverage. Here, the authors develop a deep learning model to predict NGS depth using DNA probe sequences and apply to human and non-human sequencing panels.Jinny X. ZhangBoyan YordanovAlexander GauntMichael X. WangPeng DaiYuan-Jyue ChenKerou ZhangJohn Z. FangNeil DalchauJiaming LiAndrew PhillipsDavid Yu ZhangNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Jinny X. Zhang Boyan Yordanov Alexander Gaunt Michael X. Wang Peng Dai Yuan-Jyue Chen Kerou Zhang John Z. Fang Neil Dalchau Jiaming Li Andrew Phillips David Yu Zhang A deep learning model for predicting next-generation sequencing depth from DNA sequence
description	DNA probes used in next generation sequencing (NGS) have variable hybridisation kinetics, resulting in non-uniform coverage. Here, the authors develop a deep learning model to predict NGS depth using DNA probe sequences and apply to human and non-human sequencing panels.
format	article
author	Jinny X. Zhang Boyan Yordanov Alexander Gaunt Michael X. Wang Peng Dai Yuan-Jyue Chen Kerou Zhang John Z. Fang Neil Dalchau Jiaming Li Andrew Phillips David Yu Zhang
author_facet	Jinny X. Zhang Boyan Yordanov Alexander Gaunt Michael X. Wang Peng Dai Yuan-Jyue Chen Kerou Zhang John Z. Fang Neil Dalchau Jiaming Li Andrew Phillips David Yu Zhang
author_sort	Jinny X. Zhang
title	A deep learning model for predicting next-generation sequencing depth from DNA sequence
title_short	A deep learning model for predicting next-generation sequencing depth from DNA sequence
title_full	A deep learning model for predicting next-generation sequencing depth from DNA sequence
title_fullStr	A deep learning model for predicting next-generation sequencing depth from DNA sequence
title_full_unstemmed	A deep learning model for predicting next-generation sequencing depth from DNA sequence
title_sort	deep learning model for predicting next-generation sequencing depth from dna sequence
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/38cfd7ab6401440b9ae00deafd4781f0
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A deep learning model for predicting next-generation sequencing depth from DNA sequence

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