Ranking of non-coding pathogenic variants and putative essential regions of the human genome

Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interp...

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Autores principales:	Alex Wells, David Heckerman, Ali Torkamani, Li Yin, Jonathan Sebat, Bing Ren, Amalio Telenti, Julia di Iulio
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/55b664ef26374b7793165c7ee6e62223
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spelling	oai:doaj.org-article:55b664ef26374b7793165c7ee6e622232021-12-02T16:58:26ZRanking of non-coding pathogenic variants and putative essential regions of the human genome10.1038/s41467-019-13212-32041-1723https://doaj.org/article/55b664ef26374b7793165c7ee6e622232019-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13212-3https://doaj.org/toc/2041-1723Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.Alex WellsDavid HeckermanAli TorkamaniLi YinJonathan SebatBing RenAmalio TelentiJulia di IulioNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-9 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Alex Wells David Heckerman Ali Torkamani Li Yin Jonathan Sebat Bing Ren Amalio Telenti Julia di Iulio Ranking of non-coding pathogenic variants and putative essential regions of the human genome
description	Whole genome sequencing (WGS) holds promise to solve a subset of Mendelian disease cases for which exome sequencing did not provide a genetic diagnosis. Here, Wells et al. report a supervised machine learning model trained on functional, mutational and structural features for rank-scoring and interpreting variants in non-coding regions from WGS.
format	article
author	Alex Wells David Heckerman Ali Torkamani Li Yin Jonathan Sebat Bing Ren Amalio Telenti Julia di Iulio
author_facet	Alex Wells David Heckerman Ali Torkamani Li Yin Jonathan Sebat Bing Ren Amalio Telenti Julia di Iulio
author_sort	Alex Wells
title	Ranking of non-coding pathogenic variants and putative essential regions of the human genome
title_short	Ranking of non-coding pathogenic variants and putative essential regions of the human genome
title_full	Ranking of non-coding pathogenic variants and putative essential regions of the human genome
title_fullStr	Ranking of non-coding pathogenic variants and putative essential regions of the human genome
title_full_unstemmed	Ranking of non-coding pathogenic variants and putative essential regions of the human genome
title_sort	ranking of non-coding pathogenic variants and putative essential regions of the human genome
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/55b664ef26374b7793165c7ee6e62223
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Ranking of non-coding pathogenic variants and putative essential regions of the human genome

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