A dynamic Bayesian network for identifying protein-binding footprints from single molecule-based sequencing data.

ABSTRACT:

Motivation

A global map of transcription factor binding sites (TFBSs) is critical to understanding gene regulation and genome function. DNaseI digestion of chromatin coupled with massively parallel sequencing (digital genomic footprinting) enables the identification of protein-binding footprints with high resolution on a genome-wide scale. However, accurately inferring the locations of these footprints remains a challenging computational problem.

Results

We present a dynamic Bayesian network-based approach for the identification and assignment of statistical confidence estimates to protein-binding footprints from digital genomic footprinting data. The method, DBFP, allows footprints to be identified in a probabilistic framework and outperforms our previously described algori

SUBMITTER: Chen X 

PROVIDER: S-EPMC2881360 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature