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Related Experiment Videos

Multiple testing methods for ChIP-Chip high density oligonucleotide array data.

Sündüz Keleş1, Mark J van der Laan, Sandrine Dudoit

  • 1Department of Statistics, University of Wisconsin, Madison, Madison, WI 53706, USA. keles@stat.wisc.edu

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology
|May 19, 2006
PubMed
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This study introduces a novel statistical method for identifying transcription factor binding sites using ChIP-Chip data. The approach enhances accuracy by considering spatial data structure, improving the detection of p53 binding regions on human chromosomes.

Area of Science:

  • Genomics
  • Bioinformatics
  • Molecular Biology

Background:

  • Chromatin immunoprecipitation (ChIP) followed by hybridization (Chip) enables genome-wide identification of transcription factor binding sites.
  • Previous ChIP-Chip studies provide valuable data for understanding gene regulation.
  • Analyzing complex ChIP-Chip data requires robust statistical methodologies.

Purpose of the Study:

  • To investigate the data structure of ChIP-Chip experiments.
  • To propose and evaluate statistical methods for inferring transcription factor binding site locations from ChIP-Chip data.
  • To improve the sensitivity and accuracy of identifying binding regions.

Main Methods:

  • Development of a scan statistic incorporating spatial data structure for probe-level analysis.

Related Experiment Videos

  • Application of multiple testing procedures, including a nested-Bonferroni adjustment, to control error rates.
  • Simulation studies to assess the performance of the proposed methods.

    • Main Results:

    • The proposed methods, accounting for spatial data structure, significantly improve the sensitivity of multiple testing procedures.
    • Application to p53 ChIP-Chip data identified numerous potential binding regions on human chromosomes 21 and 22.
    • A substantial proportion of identified regions were located near gene regulatory elements (5'UTR, CpG islands) or within gene bodies, with many containing p53 consensus binding sites.

    Conclusions:

    • The developed statistical approach enhances the identification of transcription factor binding sites from ChIP-Chip data.
    • The findings provide a more comprehensive map of potential p53 regulatory targets.
    • This method offers a powerful tool for analyzing ChIP-Chip data in genomic research.