Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.

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Autores principales:	Dimitrios Vitsios, Ryan S. Dhindsa, Lawrence Middleton, Ayal B. Gussow, Slavé Petrovski
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/4a6d20095a0b4f49bb6cbd6b031c99c8
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spelling	oai:doaj.org-article:4a6d20095a0b4f49bb6cbd6b031c99c82021-12-02T13:33:01ZPrioritizing non-coding regions based on human genomic constraint and sequence context with deep learning10.1038/s41467-021-21790-42041-1723https://doaj.org/article/4a6d20095a0b4f49bb6cbd6b031c99c82021-03-01T00:00:00Zhttps://doi.org/10.1038/s41467-021-21790-4https://doaj.org/toc/2041-1723Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.Dimitrios VitsiosRyan S. DhindsaLawrence MiddletonAyal B. GussowSlavé PetrovskiNature PortfolioarticleScienceQENNature Communications, Vol 12, Iss 1, Pp 1-14 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Dimitrios Vitsios Ryan S. Dhindsa Lawrence Middleton Ayal B. Gussow Slavé Petrovski Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
description	Intolerance to variation is a strong indicator of disease relevance for coding regions of the human genome. Here, the authors present JARVIS, a deep learning method integrating intolerance to variation in non-coding regions and sequence-specific annotations to infer non-coding variant pathogenicity.
format	article
author	Dimitrios Vitsios Ryan S. Dhindsa Lawrence Middleton Ayal B. Gussow Slavé Petrovski
author_facet	Dimitrios Vitsios Ryan S. Dhindsa Lawrence Middleton Ayal B. Gussow Slavé Petrovski
author_sort	Dimitrios Vitsios
title	Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_short	Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_full	Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_fullStr	Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_full_unstemmed	Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
title_sort	prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/4a6d20095a0b4f49bb6cbd6b031c99c8
work_keys_str_mv	AT dimitriosvitsios prioritizingnoncodingregionsbasedonhumangenomicconstraintandsequencecontextwithdeeplearning AT ryansdhindsa prioritizingnoncodingregionsbasedonhumangenomicconstraintandsequencecontextwithdeeplearning AT lawrencemiddleton prioritizingnoncodingregionsbasedonhumangenomicconstraintandsequencecontextwithdeeplearning AT ayalbgussow prioritizingnoncodingregionsbasedonhumangenomicconstraintandsequencecontextwithdeeplearning AT slavepetrovski prioritizingnoncodingregionsbasedonhumangenomicconstraintandsequencecontextwithdeeplearning
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Prioritizing non-coding regions based on human genomic constraint and sequence context with deep learning

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