JAMIE: A Software Tool for Jointly Analyzing Multiple ChIP-chip Experiments

  • Hao Wu
  • Hongkai JiEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 802)


Chromatin immunoprecipitation followed by genome tiling array hybridization (ChIP-chip) is a powerful approach to map transcription factor binding sites (TFBSs). Similar to other high-throughput genomic technologies, ChIP-chip often produces noisy data. Distinguishing signals from noise in these data is challenging. ChIP-chip data in public databases are rapidly growing. It is becoming more and more common that scientists can find multiple data sets for the same transcription factor in different biological contexts or data for different transcription factors in the same biological context. When these related experiments are analyzed together, binding site detection can be improved by borrowing information across data sets. This chapter introduces a computational tool JAMIE for Jointly Analyzing Multiple ChIP-chip Experiments. JAMIE is based on a hierarchical mixture model, and it is implemented as an R package. Simulation and real data studies have shown that it can significantly increase sensitivity and specificity of TFBS detection compared to existing algorithms. The purpose of this chapter is to describe how the JAMIE package can be used to perform the integrative data analysis.

Key words

Tiling array ChIP-chip Transcription factor binding site Data integration 



The authors thank Drs. Eunice Lee, Matthew Scott, and Wing H. Wong for providing the Gli data, Dr. Rafael Irizarry for providing financial support, and Dr. Thomas A. Louis for insightful discussions. This work is partly supported by National Institute of Health R01GM083084 and T32GM074906.


  1. 1.
    Ren B, Robert F, Wyrick JJ et al (2000) Genome-wide location and function of DNA binding proteins. Science 290:2306–2309PubMedCrossRefGoogle Scholar
  2. 2.
    Boyer LA, Lee TI, Cole MF et al (2005) Core transcriptional regulatory circuitry in human embryonic stem cells. Cell 122:947–956PubMedCrossRefGoogle Scholar
  3. 3.
    Cawley S, Bekiranov S, Ng HH et al (2004) Unbiased mapping of transcription factor binding sites along human chromosomes 21 and 22 points to widespread regulation of noncoding RNAs. Cell 116:499–509PubMedCrossRefGoogle Scholar
  4. 4.
    Barrett T, Troup DB, Wilhite SE et al (2009) NCBI GEO: archive for high-throughput functional genomic data. Nucleic Acids Res. 37:D885–890PubMedCrossRefGoogle Scholar
  5. 5.
    Vokes SA, Ji H, Wong WH et al (2008) A genome-scale analysis of the cis-regulatory circuitry underlying sonic hedgehog-mediated patterning of the mammalian limb. Genes Dev. 22:2651–2663PubMedCrossRefGoogle Scholar
  6. 6.
    Lee EY, Ji H, Ouyang Z et al (2010) Hedgehog pathway-regulated gene networks in cerebellum development and tumorigenesis. Proc. Natl. Acad. Sci. USA 107: 9736–9741PubMedCrossRefGoogle Scholar
  7. 7.
    Wu H, Ji H (2010) JAMIE: joint analysis of multiple ChIP-chip experiments. Bioinformatics 26:1864–1870PubMedCrossRefGoogle Scholar
  8. 8.
    The R Development Core Team (2010) R: A Language and Environment for Statistical Computing.
  9. 9.
    Kapranov P, Cawley SE, Drenkow J et al (2002) Large-scale transcriptional activity in chromosomes 21 and 22. Science 296:916–919PubMedCrossRefGoogle Scholar
  10. 10.
    Johnson WE, Li W, Meyer CA et al (2006) Model-based analysis of tiling-arrays for ChIP-chip. Proc. Natl. Acad. Sci. USA 103:12457–12462PubMedCrossRefGoogle Scholar
  11. 11.
    Ji H, Wong WH (2005) TileMap: create chromosomal map of tiling array hybridizations. Bioinformatics 21:3629–3636PubMedCrossRefGoogle Scholar
  12. 12.
    Keles S (2007) Mixture modeling for genome-wide localization of transcription factors. Biometrics 63:10–21PubMedCrossRefGoogle Scholar
  13. 13.
    Zheng M, Barrera LO, Ren B et al (2007) ChIP-chip: data, model, and analysis. Biometrics 63:787–796PubMedCrossRefGoogle Scholar
  14. 14.
    Zhang ZD, Rozowsky J, Lam HY et al (2007) Tilescope: online analysis pipeline for high-density tiling microarray data. Genome Biol. 8:R81PubMedCrossRefGoogle Scholar
  15. 15.
    Toedling J, Skylar O, Krueger T et al (2007) Ringo - an R/Bioconductor package for analyzing ChIP-chip readouts. BMC Bioinformatics 8:221PubMedCrossRefGoogle Scholar
  16. 16.
    Gottardo R, Li W, Johnson WE et al (2008) A flexible and powerful bayesian hierarchical model for ChIP-Chip experiments. Biometrics 64:468–478PubMedCrossRefGoogle Scholar
  17. 17.
    Johnson WE, Liu XS, Liu JS (2009) Doubly Stochastic Continuous-Time Hidden Markov Approach for Analyzing Genome Tiling Arrays. Ann. Appl. Stat 3:1183–1203CrossRefGoogle Scholar
  18. 18.
    Choi H, Nesvizhskii AI, Ghosh D et al (2009) Hierarchical hidden Markov model with application to joint analysis of ChIP-chip and ChIP-seq data. Bioinformatics 25:1715–1721PubMedCrossRefGoogle Scholar
  19. 19.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum Likelihood from Incomplete Data Via Em Algorithm. J. Roy. Stat. Soc. B. 39:1–38Google Scholar
  20. 20.
  21. 21.
  22. 22.
  23. 23.
  24. 24.
    Ji H, Jiang H, Ma W et al (2008) An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat Biotechnol. 26:1293–1300PubMedCrossRefGoogle Scholar
  25. 25.
  26. 26.
  27. 27.
  28. 28.
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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Biostatistics and BioinformaticsEmory UniversityAtlantaUSA
  2. 2.Department of BiostatisticsThe Johns Hopkins Bloomberg School of Public HealthBaltimoreUSA

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