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

Research output: Contribution to journalArticle

23 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)e50
JournalNucleic acids research
Volume40
Issue number7
DOIs
StatePublished - Apr 1 2012
Externally publishedYes

ASJC Scopus subject areas

  • Genetics

Fingerprint Dive into the research topics of 'A highly efficient and effective motif discovery method for ChIP-seq/ChIP-chip data using positional information'. Together they form a unique fingerprint.

  • Cite this