Biological relevance of computationally predicted pathogenicity of noncoding variants

Researchers can make use of a variety of computational tools to prioritize genetic variants and predict their pathogenicity. Here, the authors evaluate the performance of six of these tools in three typical biological tasks and find generally low concordance of predictions and experimental confirmat...

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Autores principales:	Li Liu, Maxwell D. Sanderford, Ravi Patel, Pramod Chandrashekar, Greg Gibson, Sudhir Kumar
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/8f40bef121824d3e8e8c9116ae4aae2d
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spelling	oai:doaj.org-article:8f40bef121824d3e8e8c9116ae4aae2d2021-12-02T14:35:30ZBiological relevance of computationally predicted pathogenicity of noncoding variants10.1038/s41467-018-08270-y2041-1723https://doaj.org/article/8f40bef121824d3e8e8c9116ae4aae2d2019-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-08270-yhttps://doaj.org/toc/2041-1723Researchers can make use of a variety of computational tools to prioritize genetic variants and predict their pathogenicity. Here, the authors evaluate the performance of six of these tools in three typical biological tasks and find generally low concordance of predictions and experimental confirmation.Li LiuMaxwell D. SanderfordRavi PatelPramod ChandrashekarGreg GibsonSudhir KumarNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-11 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Li Liu Maxwell D. Sanderford Ravi Patel Pramod Chandrashekar Greg Gibson Sudhir Kumar Biological relevance of computationally predicted pathogenicity of noncoding variants
description	Researchers can make use of a variety of computational tools to prioritize genetic variants and predict their pathogenicity. Here, the authors evaluate the performance of six of these tools in three typical biological tasks and find generally low concordance of predictions and experimental confirmation.
format	article
author	Li Liu Maxwell D. Sanderford Ravi Patel Pramod Chandrashekar Greg Gibson Sudhir Kumar
author_facet	Li Liu Maxwell D. Sanderford Ravi Patel Pramod Chandrashekar Greg Gibson Sudhir Kumar
author_sort	Li Liu
title	Biological relevance of computationally predicted pathogenicity of noncoding variants
title_short	Biological relevance of computationally predicted pathogenicity of noncoding variants
title_full	Biological relevance of computationally predicted pathogenicity of noncoding variants
title_fullStr	Biological relevance of computationally predicted pathogenicity of noncoding variants
title_full_unstemmed	Biological relevance of computationally predicted pathogenicity of noncoding variants
title_sort	biological relevance of computationally predicted pathogenicity of noncoding variants
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/8f40bef121824d3e8e8c9116ae4aae2d
work_keys_str_mv	AT liliu biologicalrelevanceofcomputationallypredictedpathogenicityofnoncodingvariants AT maxwelldsanderford biologicalrelevanceofcomputationallypredictedpathogenicityofnoncodingvariants AT ravipatel biologicalrelevanceofcomputationallypredictedpathogenicityofnoncodingvariants AT pramodchandrashekar biologicalrelevanceofcomputationallypredictedpathogenicityofnoncodingvariants AT greggibson biologicalrelevanceofcomputationallypredictedpathogenicityofnoncodingvariants AT sudhirkumar biologicalrelevanceofcomputationallypredictedpathogenicityofnoncodingvariants
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Biological relevance of computationally predicted pathogenicity of noncoding variants

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