Chromatin Immunoprecipitation-Sequencing (ChIP-seq) for Mapping of Estrogen Receptor-Chromatin Interactions in Breast Cancer

  • Kelly A. Holmes
  • Gordon D. Brown
  • Jason S. Carroll
Part of the Methods in Molecular Biology book series (MIMB, volume 1366)


Chromatin immunoprecipitation-sequencing (ChIP-Seq) is a powerful tool which combines the established method of ChIP with next-generation sequencing (NGS) to determine DNA-binding sites of a protein of interest on a genome-wide level, importantly, allowing for de novo discovery of binding events. Here we describe ChIP-seq using the well-established example of estrogen receptor-α mapping in the MCF7 breast cancer cell line.

Key words

Chromatin Estrogenreceptor Next-generation sequencing Promoters Enhancers Chromatin immunoprecipitation 


  1. 1.
    Ross-Innes CS, Stark R, Teschendorff AE et al (2012) Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481:389–393CrossRefGoogle Scholar
  2. 2.
    Gilmour DS, Lis JT (1984) Detecting protein-DNA interactions in vivo: distribution of RNA polymerase on specific bacterial genes. Proc Natl Acad Sci U S A 81:4275–4279CrossRefGoogle Scholar
  3. 3.
    DiRenzo J, Shang Y, Phelan M et al (2000) BRG-1 is recruited to estrogen-responsive promoters and cooperates with factors involved in histone acetylation. Mol Cell Biol 20:7541–7549CrossRefGoogle Scholar
  4. 4.
    Shang Y, Hu X, DiRenzo J et al (2000) Cofactor dynamics and sufficiency in estrogen receptor-regulated transcription. Cell 103:843–852CrossRefGoogle Scholar
  5. 5.
    Carroll JS, Liu XS, Brodsky AS et al (2005) Chromosome-wide mapping of estrogen receptor binding reveals long-range regulation requiring the forkhead protein FoxA1. Cell 122:33–43CrossRefGoogle Scholar
  6. 6.
    Carroll JS, Meyer CA, Song J et al (2006) Genome-wide analysis of estrogen receptor binding sites. Nat Genet 38:1289–1297CrossRefGoogle Scholar
  7. 7.
    Wardle FC, Odom DT, Bell GW et al (2006) Zebrafish promoter microarrays identify actively transcribed embryonic genes. Genome Biol 7:R71CrossRefGoogle Scholar
  8. 8.
    Takayama K, Kaneshiro K, Tsutsumi S et al (2007) Identification of novel androgen response genes in prostate cancer cells by coupling chromatin immunoprecipitation and genomic microarray analysis. Oncogene 26:4453–4463CrossRefGoogle Scholar
  9. 9.
    Bentley DR, Balasubramanian S, Swerdlow HP et al (2008) Accurate whole human genome sequencing using reversible terminator chemistry. Nature 456:53–59CrossRefGoogle Scholar
  10. 10.
    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 38:1767–1771CrossRefGoogle Scholar
  11. 11.
    Carroll TS, Liang Z, Salama R et al (2014) Impact of artifact removal on ChIP quality metrics in ChIP-seq and ChIP-exo data. Front Genet 5:75CrossRefGoogle Scholar
  12. 12.
    Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760CrossRefGoogle Scholar
  13. 13.
    Choe MK, Hong CP, Park J et al (2012) Functional elements demarcated by histone modifications in breast cancer cells. Biochem Biophys Res Commun 418:475–482CrossRefGoogle Scholar
  14. 14.
    Erlich Y, Mitra PP, dela Bastide M et al (2008) Alta-Cyclic: a self-optimizing base caller for next-generation sequencing. Nat Methods 5:679–682CrossRefGoogle Scholar
  15. 15.
    Menges F, Narzisi G, Mishra B (2011) TotalReCaller: improved accuracy and performance via integrated alignment and base-calling. Bioinformatics 27:2330–2337CrossRefGoogle Scholar
  16. 16.
    Schmidt D, Wilson MD, Spyrou C et al (2009) ChIP-seq: using high-throughput sequencing to discover protein-DNA interactions. Methods 48:240–248CrossRefGoogle Scholar
  17. 17.
    Laajala TD, Raghav S, Tuomela S et al (2009) A practical comparison of methods for detecting transcription factor binding sites in ChIP-seq experiments. BMC Genomics 10:618CrossRefGoogle Scholar
  18. 18.
    Xu H, Handoko L, Wei X et al (2010) A signal-noise model for significance analysis of ChIP-seq with negative control. Bioinformatics 26:1199–1204CrossRefGoogle Scholar
  19. 19.
    Bailey TL, Boden M, Buske FA et al (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37:W202–W208CrossRefGoogle Scholar
  20. 20.
    van Helden J (2003) Regulatory sequence analysis tools. Nucleic Acids Res 31:3593–3596CrossRefGoogle Scholar
  21. 21.
    McLean CY, Bristor D, Hiller M et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28:495–501CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  • Kelly A. Holmes
    • 1
  • Gordon D. Brown
    • 1
  • Jason S. Carroll
    • 1
  1. 1.Cambridge Research Institute, Cancer Research UKUniversity of CambridgeCambridgeUK

Personalised recommendations