Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution

Analysis of Hi-C datasets is limited by the current existing methods for data normalization, with detection of features such as TADs and chromatin loops being inconsistent amongst different approaches. Here the authors develop Binless, a method that allows for reproducible normalization of Hi-C data...

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Autores principales:	Yannick G. Spill, David Castillo, Enrique Vidal, Marc A. Marti-Renom
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/68f2d8d52dec41c0a48e547768b92e81
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spelling	oai:doaj.org-article:68f2d8d52dec41c0a48e547768b92e812021-12-02T16:57:53ZBinless normalization of Hi-C data provides significant interaction and difference detection independent of resolution10.1038/s41467-019-09907-22041-1723https://doaj.org/article/68f2d8d52dec41c0a48e547768b92e812019-04-01T00:00:00Zhttps://doi.org/10.1038/s41467-019-09907-2https://doaj.org/toc/2041-1723Analysis of Hi-C datasets is limited by the current existing methods for data normalization, with detection of features such as TADs and chromatin loops being inconsistent amongst different approaches. Here the authors develop Binless, a method that allows for reproducible normalization of Hi-C data independent of its resolution and compare how Binless performs in comparison with other methods.Yannick G. SpillDavid CastilloEnrique VidalMarc A. Marti-RenomNature PortfolioarticleScienceQENNature Communications, Vol 10, Iss 1, Pp 1-10 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Yannick G. Spill David Castillo Enrique Vidal Marc A. Marti-Renom Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
description	Analysis of Hi-C datasets is limited by the current existing methods for data normalization, with detection of features such as TADs and chromatin loops being inconsistent amongst different approaches. Here the authors develop Binless, a method that allows for reproducible normalization of Hi-C data independent of its resolution and compare how Binless performs in comparison with other methods.
format	article
author	Yannick G. Spill David Castillo Enrique Vidal Marc A. Marti-Renom
author_facet	Yannick G. Spill David Castillo Enrique Vidal Marc A. Marti-Renom
author_sort	Yannick G. Spill
title	Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
title_short	Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
title_full	Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
title_fullStr	Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
title_full_unstemmed	Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution
title_sort	binless normalization of hi-c data provides significant interaction and difference detection independent of resolution
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/68f2d8d52dec41c0a48e547768b92e81
work_keys_str_mv	AT yannickgspill binlessnormalizationofhicdataprovidessignificantinteractionanddifferencedetectionindependentofresolution AT davidcastillo binlessnormalizationofhicdataprovidessignificantinteractionanddifferencedetectionindependentofresolution AT enriquevidal binlessnormalizationofhicdataprovidessignificantinteractionanddifferencedetectionindependentofresolution AT marcamartirenom binlessnormalizationofhicdataprovidessignificantinteractionanddifferencedetectionindependentofresolution
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Binless normalization of Hi-C data provides significant interaction and difference detection independent of resolution

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