Summary. We propose a unified framework for the analysis of chromatin (Ch) immunoprecipitation (IP) microarray (ChIP-chip) data for detecting transcription factor binding sites (TFBSs) or motifs. ChIP-chip assays are used to focus the genome-wide search for TFBSs by isolating a sample of DNA fragments with TFBSs and applying this sample to a microarray with probes corresponding to tiled segments across the genome. Present analytical methods use a two-step approach: (i) analyze array data to estimate IP-enrichment peaks then (ii) analyze the corresponding sequences independently of intensity information. The proposed model integrates peak finding and motif discovery through a unified Bayesian hidden Markov model (HMM) framework that accommodates the inherent uncertainty in both measurements. A Markov chain Monte Carlo algorithm is formulated for parameter estimation, adapting recursive techniques used for HMMs. In simulations and applications to a yeast RAP1 dataset, the proposed method has favorable TFBS discovery performance compared to currently available two-stage procedures in terms of both sensitivity and specificity.
|Evidence ID||Analyze ID||Interactor||Interactor Systematic Name||Interactor||Interactor Systematic Name||Type||Assay||Annotation||Action||Modification||Phenotype||Source||Reference||Note|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Gene Ontology Term||Gene Ontology Term ID||Qualifier||Aspect||Method||Evidence||Source||Assigned On||Reference||Annotation Extension|
|Evidence ID||Analyze ID||Gene||Gene Systematic Name||Phenotype||Experiment Type||Experiment Type Category||Mutant Information||Strain Background||Chemical||Details||Reference|
|Evidence ID||Analyze ID||Regulator||Regulator Systematic Name||Target||Target Systematic Name||Experiment||Conditions||Strain||Source||Reference|