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MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets
ChIP-Seq is widely used to characterize genome-wide binding patterns of transcription factors and other chromatin-associated proteins. Although comparison of ChIP-Seq data sets is critical for understanding cell type-dependent and cell state-specific binding, and thus the study of cell-specific gene...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439967/ https://www.ncbi.nlm.nih.gov/pubmed/22424423 http://dx.doi.org/10.1186/gb-2012-13-3-r16 |
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author | Shao, Zhen Zhang, Yijing Yuan, Guo-Cheng Orkin, Stuart H Waxman, David J |
author_facet | Shao, Zhen Zhang, Yijing Yuan, Guo-Cheng Orkin, Stuart H Waxman, David J |
author_sort | Shao, Zhen |
collection | PubMed |
description | ChIP-Seq is widely used to characterize genome-wide binding patterns of transcription factors and other chromatin-associated proteins. Although comparison of ChIP-Seq data sets is critical for understanding cell type-dependent and cell state-specific binding, and thus the study of cell-specific gene regulation, few quantitative approaches have been developed. Here, we present a simple and effective method, MAnorm, for quantitative comparison of ChIP-Seq data sets describing transcription factor binding sites and epigenetic modifications. The quantitative binding differences inferred by MAnorm showed strong correlation with both the changes in expression of target genes and the binding of cell type-specific regulators. |
format | Online Article Text |
id | pubmed-3439967 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-34399672012-09-14 MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets Shao, Zhen Zhang, Yijing Yuan, Guo-Cheng Orkin, Stuart H Waxman, David J Genome Biol Method ChIP-Seq is widely used to characterize genome-wide binding patterns of transcription factors and other chromatin-associated proteins. Although comparison of ChIP-Seq data sets is critical for understanding cell type-dependent and cell state-specific binding, and thus the study of cell-specific gene regulation, few quantitative approaches have been developed. Here, we present a simple and effective method, MAnorm, for quantitative comparison of ChIP-Seq data sets describing transcription factor binding sites and epigenetic modifications. The quantitative binding differences inferred by MAnorm showed strong correlation with both the changes in expression of target genes and the binding of cell type-specific regulators. BioMed Central 2012-03-16 /pmc/articles/PMC3439967/ /pubmed/22424423 http://dx.doi.org/10.1186/gb-2012-13-3-r16 Text en Copyright © 2012 Shao et al.; licensee BioMed Central Ltd. https://creativecommons.org/licenses/by/2.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0 (https://creativecommons.org/licenses/by/2.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Method Shao, Zhen Zhang, Yijing Yuan, Guo-Cheng Orkin, Stuart H Waxman, David J MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets |
title | MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets |
title_full | MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets |
title_fullStr | MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets |
title_full_unstemmed | MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets |
title_short | MAnorm: a robust model for quantitative comparison of ChIP-Seq data sets |
title_sort | manorm: a robust model for quantitative comparison of chip-seq data sets |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3439967/ https://www.ncbi.nlm.nih.gov/pubmed/22424423 http://dx.doi.org/10.1186/gb-2012-13-3-r16 |
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