In silico prediction of high-resolution Hi-C interaction matrices

Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.

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Autores principales:	Shilu Zhang, Deborah Chasman, Sara Knaack, Sushmita Roy
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/371269a5f21c4aab82d477791ec36389
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spelling	oai:doaj.org-article:371269a5f21c4aab82d477791ec363892021-12-02T17:31:53ZIn silico prediction of high-resolution Hi-C interaction matrices10.1038/s41467-019-13423-82041-1723https://doaj.org/article/371269a5f21c4aab82d477791ec363892019-12-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-13423-8https://doaj.org/toc/2041-1723Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.Shilu ZhangDeborah ChasmanSara KnaackSushmita RoyNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-18 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Shilu Zhang Deborah Chasman Sara Knaack Sushmita Roy In silico prediction of high-resolution Hi-C interaction matrices
description	Existing computational approaches to predict long-range regulatory interactions do not fully exploit high-resolution Hi-C datasets. Here the authors present a Random Forests regression-based approach to predict high-resolution Hi-C counts using one-dimensional regulatory genomic signals.
format	article
author	Shilu Zhang Deborah Chasman Sara Knaack Sushmita Roy
author_facet	Shilu Zhang Deborah Chasman Sara Knaack Sushmita Roy
author_sort	Shilu Zhang
title	In silico prediction of high-resolution Hi-C interaction matrices
title_short	In silico prediction of high-resolution Hi-C interaction matrices
title_full	In silico prediction of high-resolution Hi-C interaction matrices
title_fullStr	In silico prediction of high-resolution Hi-C interaction matrices
title_full_unstemmed	In silico prediction of high-resolution Hi-C interaction matrices
title_sort	in silico prediction of high-resolution hi-c interaction matrices
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/371269a5f21c4aab82d477791ec36389
work_keys_str_mv	AT shiluzhang insilicopredictionofhighresolutionhicinteractionmatrices AT deborahchasman insilicopredictionofhighresolutionhicinteractionmatrices AT saraknaack insilicopredictionofhighresolutionhicinteractionmatrices AT sushmitaroy insilicopredictionofhighresolutionhicinteractionmatrices
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In silico prediction of high-resolution Hi-C interaction matrices

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