Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

Accurate estimations of the frequency distribution of rare variants are needed to quantify the discovery power and guide large-scale human sequencing projects. This study describes an algorithm called UnseenEst to estimate the distribution of genetic variations using tens of thousands of exomes.

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Autores principales:	James Zou, Gregory Valiant, Paul Valiant, Konrad Karczewski, Siu On Chan, Kaitlin Samocha, Monkol Lek, Shamil Sunyaev, Mark Daly, Daniel G. MacArthur
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2016
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/030ddc69bcf0485d89f5ce1d1a30988b
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spelling	oai:doaj.org-article:030ddc69bcf0485d89f5ce1d1a30988b2021-12-02T17:32:08ZQuantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects10.1038/ncomms132932041-1723https://doaj.org/article/030ddc69bcf0485d89f5ce1d1a30988b2016-10-01T00:00:00Zhttps://doi.org/10.1038/ncomms13293https://doaj.org/toc/2041-1723Accurate estimations of the frequency distribution of rare variants are needed to quantify the discovery power and guide large-scale human sequencing projects. This study describes an algorithm called UnseenEst to estimate the distribution of genetic variations using tens of thousands of exomes.James ZouGregory ValiantPaul ValiantKonrad KarczewskiSiu On ChanKaitlin SamochaMonkol LekShamil SunyaevMark DalyDaniel G. MacArthurNature PortfolioarticleScienceQENNature Communications, Vol 7, Iss 1, Pp 1-5 (2016)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q James Zou Gregory Valiant Paul Valiant Konrad Karczewski Siu On Chan Kaitlin Samocha Monkol Lek Shamil Sunyaev Mark Daly Daniel G. MacArthur Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
description	Accurate estimations of the frequency distribution of rare variants are needed to quantify the discovery power and guide large-scale human sequencing projects. This study describes an algorithm called UnseenEst to estimate the distribution of genetic variations using tens of thousands of exomes.
format	article
author	James Zou Gregory Valiant Paul Valiant Konrad Karczewski Siu On Chan Kaitlin Samocha Monkol Lek Shamil Sunyaev Mark Daly Daniel G. MacArthur
author_facet	James Zou Gregory Valiant Paul Valiant Konrad Karczewski Siu On Chan Kaitlin Samocha Monkol Lek Shamil Sunyaev Mark Daly Daniel G. MacArthur
author_sort	James Zou
title	Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_short	Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_full	Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_fullStr	Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_full_unstemmed	Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
title_sort	quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects
publisher	Nature Portfolio
publishDate	2016
url	https://doaj.org/article/030ddc69bcf0485d89f5ce1d1a30988b
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Quantifying unobserved protein-coding variants in human populations provides a roadmap for large-scale sequencing projects

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