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Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human
Deep sequencing of 5′ capped transcripts has revealed a variety of transcription initiation patterns, from narrow, focused promoters to wide, broad promoters. Attempts have already been made to model empirically classified patterns, but virtually no quantitative models for transcription initiation h...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2012
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3387189/ https://www.ncbi.nlm.nih.gov/pubmed/22768038 http://dx.doi.org/10.1371/journal.pone.0038112 |
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author | Zhang, Zhihua Ma, Xiaotu Zhang, Michael Q. |
author_facet | Zhang, Zhihua Ma, Xiaotu Zhang, Michael Q. |
author_sort | Zhang, Zhihua |
collection | PubMed |
description | Deep sequencing of 5′ capped transcripts has revealed a variety of transcription initiation patterns, from narrow, focused promoters to wide, broad promoters. Attempts have already been made to model empirically classified patterns, but virtually no quantitative models for transcription initiation have been reported. Even though both genetic and epigenetic elements have been associated with such patterns, the organization of regulatory elements is largely unknown. Here, linear regression models were derived from a pool of regulatory elements, including genomic DNA features, nucleosome organization, and histone modifications, to predict the distribution of transcription start sites (TSS). Importantly, models including both active and repressive histone modification markers, e.g. H3K4me3 and H4K20me1, were consistently found to be much more predictive than models with only single-type histone modification markers, indicating the possibility of “bivalent-like” epigenetic control of transcription initiation. The nucleosome positions are proposed to be coded in the active component of such bivalent-like histone modification markers. Finally, we demonstrated that models trained on one cell type could successfully predict TSS distribution in other cell types, suggesting that these models may have a broader application range. |
format | Online Article Text |
id | pubmed-3387189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-33871892012-07-05 Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human Zhang, Zhihua Ma, Xiaotu Zhang, Michael Q. PLoS One Research Article Deep sequencing of 5′ capped transcripts has revealed a variety of transcription initiation patterns, from narrow, focused promoters to wide, broad promoters. Attempts have already been made to model empirically classified patterns, but virtually no quantitative models for transcription initiation have been reported. Even though both genetic and epigenetic elements have been associated with such patterns, the organization of regulatory elements is largely unknown. Here, linear regression models were derived from a pool of regulatory elements, including genomic DNA features, nucleosome organization, and histone modifications, to predict the distribution of transcription start sites (TSS). Importantly, models including both active and repressive histone modification markers, e.g. H3K4me3 and H4K20me1, were consistently found to be much more predictive than models with only single-type histone modification markers, indicating the possibility of “bivalent-like” epigenetic control of transcription initiation. The nucleosome positions are proposed to be coded in the active component of such bivalent-like histone modification markers. Finally, we demonstrated that models trained on one cell type could successfully predict TSS distribution in other cell types, suggesting that these models may have a broader application range. Public Library of Science 2012-06-29 /pmc/articles/PMC3387189/ /pubmed/22768038 http://dx.doi.org/10.1371/journal.pone.0038112 Text en This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication. https://creativecommons.org/publicdomain/zero/1.0/ This is an open-access article distributed under the terms of the Creative Commons Public Domain declaration, which stipulates that, once placed in the public domain, this work may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. |
spellingShingle | Research Article Zhang, Zhihua Ma, Xiaotu Zhang, Michael Q. Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human |
title | Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human |
title_full | Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human |
title_fullStr | Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human |
title_full_unstemmed | Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human |
title_short | Bivalent-Like Chromatin Markers Are Predictive for Transcription Start Site Distribution in Human |
title_sort | bivalent-like chromatin markers are predictive for transcription start site distribution in human |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3387189/ https://www.ncbi.nlm.nih.gov/pubmed/22768038 http://dx.doi.org/10.1371/journal.pone.0038112 |
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