A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information.

ABSTRACT: Identification of DNA motifs from ChIP-seq/ChIP-chip [chromatin immunoprecipitation (ChIP)] data is a powerful method for understanding the transcriptional regulatory network. However, most established methods are designed for small sample sizes and are inefficient for ChIP data. Here we propose a new k-mer occurrence model to reflect the fact that functional DNA k-mers often cluster around ChIP peak summits. With this model, we introduced a new measure to discover functional k-mers. Using simulation, we demonstrated that our method is more robust against noises in ChIP data than available methods. A novel word clustering method is also implemented to group similar k-mers into position weight matrices (PWMs). Our method was applied to a diverse set of ChIP experiments to demonstrate its hi

SUBMITTER: Ma X 

PROVIDER: S-EPMC3326300 | biostudies-literature | 2012 Apr

REPOSITORIES: biostudies-literature