Identifying noncoding risk variants using disease-relevant gene regulatory networks

Current methods for prioritization of non-coding genetic risk variants are based on sequence and chromatin features. Here, Gao et al. develop ARVIN, which predicts causal regulatory variants using disease-relevant gene-regulatory networks, and validate this approach in reporter gene assays.

Guardado en:

Detalles Bibliográficos
Autores principales:	Long Gao, Yasin Uzun, Peng Gao, Bing He, Xiaoke Ma, Jiahui Wang, Shizhong Han, Kai Tan
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/5472bfb79e7346c78601c129c375ba8e
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:5472bfb79e7346c78601c129c375ba8e
record_format	dspace
spelling	oai:doaj.org-article:5472bfb79e7346c78601c129c375ba8e2021-12-02T16:56:53ZIdentifying noncoding risk variants using disease-relevant gene regulatory networks10.1038/s41467-018-03133-y2041-1723https://doaj.org/article/5472bfb79e7346c78601c129c375ba8e2018-02-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-03133-yhttps://doaj.org/toc/2041-1723Current methods for prioritization of non-coding genetic risk variants are based on sequence and chromatin features. Here, Gao et al. develop ARVIN, which predicts causal regulatory variants using disease-relevant gene-regulatory networks, and validate this approach in reporter gene assays.Long GaoYasin UzunPeng GaoBing HeXiaoke MaJiahui WangShizhong HanKai TanNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-12 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Long Gao Yasin Uzun Peng Gao Bing He Xiaoke Ma Jiahui Wang Shizhong Han Kai Tan Identifying noncoding risk variants using disease-relevant gene regulatory networks
description	Current methods for prioritization of non-coding genetic risk variants are based on sequence and chromatin features. Here, Gao et al. develop ARVIN, which predicts causal regulatory variants using disease-relevant gene-regulatory networks, and validate this approach in reporter gene assays.
format	article
author	Long Gao Yasin Uzun Peng Gao Bing He Xiaoke Ma Jiahui Wang Shizhong Han Kai Tan
author_facet	Long Gao Yasin Uzun Peng Gao Bing He Xiaoke Ma Jiahui Wang Shizhong Han Kai Tan
author_sort	Long Gao
title	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_short	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_full	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_fullStr	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_full_unstemmed	Identifying noncoding risk variants using disease-relevant gene regulatory networks
title_sort	identifying noncoding risk variants using disease-relevant gene regulatory networks
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/5472bfb79e7346c78601c129c375ba8e
work_keys_str_mv	AT longgao identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks AT yasinuzun identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks AT penggao identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks AT binghe identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks AT xiaokema identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks AT jiahuiwang identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks AT shizhonghan identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks AT kaitan identifyingnoncodingriskvariantsusingdiseaserelevantgeneregulatorynetworks
_version_	1718382679788879872

Identifying noncoding risk variants using disease-relevant gene regulatory networks

Ejemplares similares