Epigenetic Analysis: ChIP-chip and ChIP-seq

  • Matteo PellegriniEmail author
  • Roberto Ferrari
Part of the Methods in Molecular Biology book series (MIMB, volume 802)


The access of transcription factors and the replication machinery to DNA is regulated by the epigenetic state of chromatin. In eukaryotes, this complex layer of regulatory processes includes the direct methylation of DNA, as well as covalent modifications to histones. Using next-generation sequencers, it is now possible to obtain profiles of epigenetic modifications across a genome using chromatin immunoprecipitation followed by sequencing (ChIP-seq). This technique permits the detection of the binding of proteins to specific regions of the genome with high resolution. It can be used to determine the target sequences of transcription factors, as well as the positions of histones with specific modification of their N-terminal tails. Antibodies that selectively bind methylated DNA may also be used to determine the position of methylated cytosines. Here, we present a data analysis pipeline for processing ChIP-seq data, and discuss the limitations and idiosyncrasies of these approaches.

Key words

ChIP-seq Chromatin immunoprecipitation Transcription factor binding sites Peak calling Histone modification DNA methylation Next-generation sequencing Poisson statistics 



The authors would like to thank Professor Bernard L. Mirkin for development of the drug-resistant models of human neuroblastoma cells and for his advice and encouragement, and Jesse Moya for technical assistance. This work was supported by Broad Stem Cell Research Center and Institute of Genomics and Proteomics at UCLA.


  1. 1.
    Jenuwein T, Allis CD (2001) Translating the histone code. Science 293:1074–1080.PubMedCrossRefGoogle Scholar
  2. 2.
    Nelson JD, Denisenko O, Bomsztyk K (2006) Protocol for the fast chromatin immunoprecipitation (ChIP) method. Nat Protoc 1:179–185.PubMedCrossRefGoogle Scholar
  3. 3.
    Buck MJ, Lieb JD (2004) ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 83:349–360.PubMedCrossRefGoogle Scholar
  4. 4.
    Valouev A, Johnson DS, Sundquist A et al (2008) Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 5:829–834.PubMedCrossRefGoogle Scholar
  5. 5.
    Mardis ER (2008) The impact of next-generation sequencing technology on genetics. Trends Genet 24:133–141.PubMedCrossRefGoogle Scholar
  6. 6.
    Cock PJ, Fields CJ, Goto N et al (2010) The Sanger FASTQ file format for sequences with quality scores, and the Solexa/Illumina FASTQ variants. Nucleic Acids Res 38:1767–1771.PubMedCrossRefGoogle Scholar
  7. 7.
    Langmead B, Trapnell C, Pop M et al (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol 10:R25.PubMedCrossRefGoogle Scholar
  8. 8.
  9. 9.
    Li R, Li Y, Kristiansen K et al (2008) SOAP: short oligonucleotide alignment program. Bioinformatics 24:713–714.PubMedCrossRefGoogle Scholar
  10. 10.
    Nicol JW, Helt GA, Blanchard SG Jr et al (2009) The Integrated Genome Browser: free software for distribution and exploration of genome-scale datasets. Bioinformatics 25:2730–2731.PubMedCrossRefGoogle Scholar
  11. 11.
    Rhead B, Karolchik D, Kuhn RM et al (2010) The UCSC Genome Browser database: update 2010. Nucleic Acids Res 38:D613–619.PubMedCrossRefGoogle Scholar
  12. 12.
    Clement NL, Snell Q, Clement MJ et al (2010) The GNUMAP algorithm: unbiased probabilistic mapping of oligonucleotides from next-generation sequencing. Bioinformatics 26:38–45.PubMedCrossRefGoogle Scholar
  13. 13.
    Pevzner PA, Tang H (2001) Fragment assembly with double-barreled data. Bioinformatics 17:S225–233.PubMedCrossRefGoogle Scholar
  14. 14.
    Auerbach RK, Euskirchen G, Rozowsky J et al (2009) Mapping accessible chromatin regions using Sono-Seq. Proc Natl Acad Sci U S A 106:14926–14931.PubMedCrossRefGoogle Scholar
  15. 15.
    Mikkelsen TS, Ku M, Jaffe DB et al (2007) Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature 448:553–560.PubMedCrossRefGoogle Scholar
  16. 16.
    Benjamini Y, Drai D, Elmer G et al (2001) Controlling the false discovery rate in behavior genetics research. Behav Brain Res 125:279–284.PubMedCrossRefGoogle Scholar
  17. 17.
    Muir WM, Rosa GJ, Pittendrigh BR et al (2009) A mixture model approach for the analysis of small exploratory microarray experiments. Comput Stat Data Anal 53:1566–1576.PubMedCrossRefGoogle Scholar
  18. 18.
  19. 19.
    Cokus SJ, Feng S, Zhang X et al (2008) Shotgun bisulphite sequencing of the Arabidopsis genome reveals DNA methylation patterning. Nature 452:215–219.PubMedCrossRefGoogle Scholar
  20. 20.
  21. 21.
    Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137.PubMedCrossRefGoogle Scholar
  22. 22.
    Spyrou C, Stark R, Lynch AG et al (2009) BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinformatics 10:299.PubMedCrossRefGoogle Scholar
  23. 23.
  24. 24.
    Chin MH, Mason MJ, Xie W et al (2009) Induced pluripotent stem cells and embryonic stem cells are distinguished by gene expression signatures. Cell Stem Cell 5:111–123.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Molecular, Cell and DevelopmentalUniversity of CaliforniaLos AngelesUSA
  2. 2.Department of Biological ChemistryUniversity of CaliforniaLos AngelesUSA

Personalised recommendations