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

Nucleic Acids Res. 2012 Apr;40(7):e50. doi: 10.1093/nar/gkr1135. Epub 2012 Jan 6.

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.

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Animals
  • Binding Sites
  • CCCTC-Binding Factor
  • Chromatin Immunoprecipitation*
  • Cluster Analysis
  • Computer Simulation
  • Drosophila melanogaster / genetics
  • Embryonic Stem Cells / metabolism
  • Gene Regulatory Networks
  • High-Throughput Nucleotide Sequencing
  • Mice
  • Nucleotide Motifs
  • Oligonucleotide Array Sequence Analysis
  • Regulatory Elements, Transcriptional*
  • Repressor Proteins
  • Sequence Analysis, DNA
  • Software*
  • Transcription Factors / metabolism*

Substances

  • CCCTC-Binding Factor
  • Ctcf protein, mouse
  • Repressor Proteins
  • Transcription Factors