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Models incorporating chromatin modification data identify functionally important p53 binding sites
Genome-wide prediction of transcription factor binding sites is notoriously difficult. We have developed and applied a logistic regression approach for prediction of binding sites for the p53 transcription factor that incorporates sequence information and chromatin modification data. We tested this...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675478/ https://www.ncbi.nlm.nih.gov/pubmed/23599002 http://dx.doi.org/10.1093/nar/gkt260 |
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author | Lim, Ji-Hyun Iggo, Richard D. Barker, Daniel |
author_facet | Lim, Ji-Hyun Iggo, Richard D. Barker, Daniel |
author_sort | Lim, Ji-Hyun |
collection | PubMed |
description | Genome-wide prediction of transcription factor binding sites is notoriously difficult. We have developed and applied a logistic regression approach for prediction of binding sites for the p53 transcription factor that incorporates sequence information and chromatin modification data. We tested this by comparison of predicted sites with known binding sites defined by chromatin immunoprecipitation (ChIP), by the location of predictions relative to genes, by the function of nearby genes and by analysis of gene expression data after p53 activation. We compared the predictions made by our novel model with predictions based only on matches to a sequence position weight matrix (PWM). In whole genome assays, the fraction of known sites identified by the two models was similar, suggesting that there was little to be gained from including chromatin modification data. In contrast, there were highly significant and biologically relevant differences between the two models in the location of the predicted binding sites relative to genes, in the function of nearby genes and in the responsiveness of nearby genes to p53 activation. We propose that these contradictory results can be explained by PWM and ChIP data reflecting primarily biophysical properties of protein–DNA interactions, whereas chromatin modification data capture biologically important functional information. |
format | Online Article Text |
id | pubmed-3675478 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-36754782013-06-07 Models incorporating chromatin modification data identify functionally important p53 binding sites Lim, Ji-Hyun Iggo, Richard D. Barker, Daniel Nucleic Acids Res Computational Biology Genome-wide prediction of transcription factor binding sites is notoriously difficult. We have developed and applied a logistic regression approach for prediction of binding sites for the p53 transcription factor that incorporates sequence information and chromatin modification data. We tested this by comparison of predicted sites with known binding sites defined by chromatin immunoprecipitation (ChIP), by the location of predictions relative to genes, by the function of nearby genes and by analysis of gene expression data after p53 activation. We compared the predictions made by our novel model with predictions based only on matches to a sequence position weight matrix (PWM). In whole genome assays, the fraction of known sites identified by the two models was similar, suggesting that there was little to be gained from including chromatin modification data. In contrast, there were highly significant and biologically relevant differences between the two models in the location of the predicted binding sites relative to genes, in the function of nearby genes and in the responsiveness of nearby genes to p53 activation. We propose that these contradictory results can be explained by PWM and ChIP data reflecting primarily biophysical properties of protein–DNA interactions, whereas chromatin modification data capture biologically important functional information. Oxford University Press 2013-06 2013-04-17 /pmc/articles/PMC3675478/ /pubmed/23599002 http://dx.doi.org/10.1093/nar/gkt260 Text en © The Author(s) 2013. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Computational Biology Lim, Ji-Hyun Iggo, Richard D. Barker, Daniel Models incorporating chromatin modification data identify functionally important p53 binding sites |
title | Models incorporating chromatin modification data identify functionally important p53 binding sites |
title_full | Models incorporating chromatin modification data identify functionally important p53 binding sites |
title_fullStr | Models incorporating chromatin modification data identify functionally important p53 binding sites |
title_full_unstemmed | Models incorporating chromatin modification data identify functionally important p53 binding sites |
title_short | Models incorporating chromatin modification data identify functionally important p53 binding sites |
title_sort | models incorporating chromatin modification data identify functionally important p53 binding sites |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3675478/ https://www.ncbi.nlm.nih.gov/pubmed/23599002 http://dx.doi.org/10.1093/nar/gkt260 |
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