Epigenetic priors for identifying active transcription factor binding sites.

ABSTRACT:

Motivation

Accurate knowledge of the genome-wide binding of transcription factors in a particular cell type or under a particular condition is necessary for understanding transcriptional regulation. Using epigenetic data such as histone modification and DNase I, accessibility data has been shown to improve motif-based in silico methods for predicting such binding, but this approach has not yet been fully explored.

Results

We describe a probabilistic method for combining one or more tracks of epigenetic data with a standard DNA sequence motif model to improve our ability to identify active transcription factor binding sites (TFBSs). We convert each data type into a position-specific probabilistic prior and combine these priors with a traditional probabilistic motif model to c

SUBMITTER: Cuellar-Partida G 

PROVIDER: S-EPMC3244768 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature