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ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data

A cell’s epigenome arises from interactions among regulatory factors—transcription factors and histone modifications—co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditiona...

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Autores principales: Lundberg, Scott M., Tu, William B., Raught, Brian, Penn, Linda Z., Hoffman, Michael M., Lee, Su-In
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4852466/
https://www.ncbi.nlm.nih.gov/pubmed/27139377
http://dx.doi.org/10.1186/s13059-016-0925-0
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author Lundberg, Scott M.
Tu, William B.
Raught, Brian
Penn, Linda Z.
Hoffman, Michael M.
Lee, Su-In
author_facet Lundberg, Scott M.
Tu, William B.
Raught, Brian
Penn, Linda Z.
Hoffman, Michael M.
Lee, Su-In
author_sort Lundberg, Scott M.
collection PubMed
description A cell’s epigenome arises from interactions among regulatory factors—transcription factors and histone modifications—co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditional-dependence relationships among a large number of ChIP-seq data sets. We applied ChromNet to all available 1451 ChIP-seq data sets from the ENCODE Project, and showed that ChromNet revealed previously known physical interactions better than alternative approaches. We experimentally validated one of the previously unreported interactions, MYC–HCFC1. An interactive visualization tool is available at http://chromnet.cs.washington.edu. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0925-0) contains supplementary material, which is available to authorized users.
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spelling pubmed-48524662016-05-03 ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data Lundberg, Scott M. Tu, William B. Raught, Brian Penn, Linda Z. Hoffman, Michael M. Lee, Su-In Genome Biol Method A cell’s epigenome arises from interactions among regulatory factors—transcription factors and histone modifications—co-localized at particular genomic regions. We developed a novel statistical method, ChromNet, to infer a network of these interactions, the chromatin network, by inferring conditional-dependence relationships among a large number of ChIP-seq data sets. We applied ChromNet to all available 1451 ChIP-seq data sets from the ENCODE Project, and showed that ChromNet revealed previously known physical interactions better than alternative approaches. We experimentally validated one of the previously unreported interactions, MYC–HCFC1. An interactive visualization tool is available at http://chromnet.cs.washington.edu. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13059-016-0925-0) contains supplementary material, which is available to authorized users. BioMed Central 2016-04-30 /pmc/articles/PMC4852466/ /pubmed/27139377 http://dx.doi.org/10.1186/s13059-016-0925-0 Text en © Lundberg et al. 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Method
Lundberg, Scott M.
Tu, William B.
Raught, Brian
Penn, Linda Z.
Hoffman, Michael M.
Lee, Su-In
ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
title ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
title_full ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
title_fullStr ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
title_full_unstemmed ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
title_short ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data
title_sort chromnet: learning the human chromatin network from all encode chip-seq data
topic Method
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4852466/
https://www.ncbi.nlm.nih.gov/pubmed/27139377
http://dx.doi.org/10.1186/s13059-016-0925-0
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