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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...

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Detalles Bibliográficos
Autores principales: Lim, Ji-Hyun, Iggo, Richard D., Barker, Daniel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
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.
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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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