Work

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

Year: 2011

Type: article

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.

Source: Nucleic Acids Research

DOI: 10.1093/nar/gkr1135

Authors: Xiaotu Ma, Ashwinikumar Kulkarni, Zhihua Zhang, Zhenyu Xuan, Robert Serfling, Michael Q. Zhang

Institutions: The University of Texas at Dallas, Tsinghua University

Language: English

Topic: Genomics and Chromatin Dynamics

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: gold

Estimated APC paid: $3,992