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• W2170113236 abstract "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." @default.
• W2170113236 created "2016-06-24" @default.
• W2170113236 creator A5050763195 @default.
• W2170113236 creator A5055008723 @default.
• W2170113236 creator A5061778211 @default.
• W2170113236 date "2013-04-17" @default.
• W2170113236 modified "2023-09-23" @default.
• W2170113236 title "Models incorporating chromatin modification data identify functionally important p53 binding sites" @default.
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• W2170113236 doi "https://doi.org/10.1093/nar/gkt260" @default.
• W2170113236 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/3675478" @default.
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• W2170113236 hasPublicationYear "2013" @default.
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