A supervised learning framework for chromatin loop detection in genome-wide contact maps

Predicting chromatin loops from genome-wide interaction matrices such as Hi-C data provides insight into gene regulation events. Here, the authors present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps, and apply it to systematically pr...

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Autores principales:	Tarik J. Salameh, Xiaotao Wang, Fan Song, Bo Zhang, Sage M. Wright, Chachrit Khunsriraksakul, Yijun Ruan, Feng Yue
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/d358d96ed7504e06836b668c15d107b4
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id	oai:doaj.org-article:d358d96ed7504e06836b668c15d107b4
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spelling	oai:doaj.org-article:d358d96ed7504e06836b668c15d107b42021-12-02T15:23:19ZA supervised learning framework for chromatin loop detection in genome-wide contact maps10.1038/s41467-020-17239-92041-1723https://doaj.org/article/d358d96ed7504e06836b668c15d107b42020-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17239-9https://doaj.org/toc/2041-1723Predicting chromatin loops from genome-wide interaction matrices such as Hi-C data provides insight into gene regulation events. Here, the authors present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps, and apply it to systematically predict chromatin loops in 56 Hi-C datasets, with results available at the 3D Genome Browser.Tarik J. SalamehXiaotao WangFan SongBo ZhangSage M. WrightChachrit KhunsriraksakulYijun RuanFeng YueNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Tarik J. Salameh Xiaotao Wang Fan Song Bo Zhang Sage M. Wright Chachrit Khunsriraksakul Yijun Ruan Feng Yue A supervised learning framework for chromatin loop detection in genome-wide contact maps
description	Predicting chromatin loops from genome-wide interaction matrices such as Hi-C data provides insight into gene regulation events. Here, the authors present Peakachu, a Random Forest classification framework that predicts chromatin loops from genome-wide contact maps, and apply it to systematically predict chromatin loops in 56 Hi-C datasets, with results available at the 3D Genome Browser.
format	article
author	Tarik J. Salameh Xiaotao Wang Fan Song Bo Zhang Sage M. Wright Chachrit Khunsriraksakul Yijun Ruan Feng Yue
author_facet	Tarik J. Salameh Xiaotao Wang Fan Song Bo Zhang Sage M. Wright Chachrit Khunsriraksakul Yijun Ruan Feng Yue
author_sort	Tarik J. Salameh
title	A supervised learning framework for chromatin loop detection in genome-wide contact maps
title_short	A supervised learning framework for chromatin loop detection in genome-wide contact maps
title_full	A supervised learning framework for chromatin loop detection in genome-wide contact maps
title_fullStr	A supervised learning framework for chromatin loop detection in genome-wide contact maps
title_full_unstemmed	A supervised learning framework for chromatin loop detection in genome-wide contact maps
title_sort	supervised learning framework for chromatin loop detection in genome-wide contact maps
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/d358d96ed7504e06836b668c15d107b4
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A supervised learning framework for chromatin loop detection in genome-wide contact maps

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