Research Article
A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information
Xiaotu Ma, Ashwinikumar Kulkarni, Zhihua Zhang, Zhenyu Xuan, Robert Serfling, Michael Q. Zhang
Published:
April 01, 2012
DOI:
10.1093/nar/gkr1135
License:
© The Author(s) 2012. Published by Oxford University Press.2012This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
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 high sensitivity and specificity. Importantly, our method is much faster than several other methods for large sample sizes. Thus, we have developed an efficient and effective motif discovery method for ChIP experiments.