Models incorporating chromatin modification data identify functionally important p53 binding sites.

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 ther

SUBMITTER: Lim JH 

PROVIDER: S-EPMC3675478 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature