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Computational Analysis of ChIP-chip Data

  • Hongkai Ji
Chapter
Part of the Springer Handbooks of Computational Statistics book series (SHCS)

Abstract

Chromatin immunoprecipitation coupled with genome tiling array hybridization, also known as ChIP-chip, is a powerful technology to identify protein-DNA interactions in genomes. It is widely used to locate transcription factor binding sites and histone modifications. Data generated by ChIP-chip provide important information on gene regulation. This chapter reviews fundamental issues in ChIP-chip data analysis. Topics include data preprocessing, background correction, normalization, peak detection and motif analysis. Statistical models and principles that significantly improve data analysis are discussed. Popular software tools are briefly introduced.

Keywords

Hide Markov Model Probe Intensity Quantile Normalization Tiling Array ChIP Sample 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

This work is partially supported by the Johns Hopkins Faculty Professional Development Fund to H.J. The author would like to thank Jennifer T. Judy for helpful comments and proofreading the draft of this chapter.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.Department of BiostatisticsJohns Hopkins Bloomberg School of Public HealthBaltimoreUSA

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