Skip to main content

Chromatin Immunoprecipitation Sequencing (ChIP-seq) for Detecting Histone Modifications and Modifiers

  • Protocol
  • First Online:
Epigenomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 2577))

Abstract

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the most widely used method for analyzing genome-wide DNA–protein interactions. Because there is considerable variation in the modes and strengths of DNA–protein interactions, chromatin immunoprecipitation (ChIP) protocols have been diversified and optimized for different needs. Here, we describe protocols for detecting histone modifications and modifiers using various crosslinking and immunoprecipitation conditions. We provide a complete ChIP-seq workflow covering sample preparation, immunoprecipitation, next-generation sequencing (NGS) library preparation, and data analyses.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Protocol
USD 49.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bernstein BE, Humphrey EL, Erlich RL et al (2002) Methylation of histone H3 Lys 4 in coding regions of active genes. Proc Natl Acad Sci 99:8695–8700. https://doi.org/10.1073/pnas.082249499

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Barski A, Cuddapah S, Cui K et al (2007) High-resolution profiling of histone methylations in the human genome. Cell 129:823–837. https://doi.org/10.1016/j.cell.2007.05.009

    Article  CAS  PubMed  Google Scholar 

  3. 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. https://doi.org/10.1038/nature06008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Zeng P-Y, Vakoc CR, Chen Z-C et al (2006) In vivo dual cross-linking for identification of indirect DNA-associated proteins by chromatin immunoprecipitation. BioTechniques 41:694–698. https://doi.org/10.2144/000112297

    Article  CAS  PubMed  Google Scholar 

  5. Ye SK, Agata Y, Lee HC et al (2001) The IL-7 receptor controls the accessibility of the TCRgamma locus by Stat5 and histone acetylation. Immunity 15:813–823. https://doi.org/10.1016/s1074-7613(01)00230-8

    Article  CAS  PubMed  Google Scholar 

  6. Anan K, Hino S, Shimizu N et al (2018) LSD1 mediates metabolic reprogramming by glucocorticoids during myogenic differentiation. Nucleic Acids Res 46:5441–5454. https://doi.org/10.1093/nar/gky234

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ewels P, Magnusson M, Lundin S et al (2016) MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32:3047–3048. https://doi.org/10.1093/bioinformatics/btw354

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. 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. https://doi.org/10.1186/gb-2009-10-3-r25

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9:357–359. https://doi.org/10.1038/nmeth.1923

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Li H, Durbin R (2009) Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25:1754–1760. https://doi.org/10.1093/bioinformatics/btp324

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Danecek P, Bonfield JK, Liddle J et al (2021) Twelve years of SAMtools and BCFtools. Gigascience 10:giab008. https://doi.org/10.1093/gigascience/giab008

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Shen L, Shao N, Liu X et al (2014) ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15:284. https://doi.org/10.1186/1471-2164-15-284

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Thorvaldsdóttir H, Robinson JT, Mesirov JP (2013) Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform 14:178–192. https://doi.org/10.1093/bib/bbs017

    Article  CAS  PubMed  Google Scholar 

  14. Zhang Y, Liu T, Meyer CA et al (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9:R137. https://doi.org/10.1186/gb-2008-9-9-r137

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. McLean CY, Bristor D, Hiller M et al (2010) GREAT improves functional interpretation of cis-regulatory regions. Nat Biotechnol 28:495–501. https://doi.org/10.1038/nbt.1630

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Heinz S, Benner C, Spann N et al (2010) Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38:576–589. https://doi.org/10.1016/j.molcel.2010.05.004

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Bailey TL, Johnson J, Grant CE et al (2015) The MEME suite. Nucleic Acids Res 43:W39–W49. https://doi.org/10.1093/nar/gkv416

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Fornes O, Castro-Mondragon JA, Khan A et al (2020) JASPAR 2020: update of the open-access database of transcription factor binding profiles. Nucleic Acids Res 48:D87–D92. https://doi.org/10.1093/nar/gkz1001

    Article  CAS  PubMed  Google Scholar 

  19. Oki S, Ohta T, Shioi G et al (2018) ChIP-Atlas: a data-mining suite powered by full integration of public ChIP-seq data. EMBO Rep 19:e46255. https://doi.org/10.15252/embr.201846255

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Hino S, Sakamoto A, Nagaoka K et al (2012) FAD-dependent lysine-specific demethylase-1 regulates cellular energy expenditure. Nat Commun 3:758. https://doi.org/10.1038/ncomms1755

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by the following funding sources: JSPS KAKENHI Grant Numbers 20H04108 and 21 K19513 (S.H.) and 21H02686 and 20KK0185 (M.N.), Takeda Science Foundation (S.H. and M.N.), and the Japan Agency for Medical Research and Development (21gk0210029h0101 (S.H.) and JP19gm4010003 (M.N.)). We would like to thank Editage (www.editage.com) for English language editing.

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Shinjiro Hino or Mitsuyoshi Nakao .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature

About this protocol

Check for updates. Verify currency and authenticity via CrossMark

Cite this protocol

Hino, S., Sato, T., Nakao, M. (2023). Chromatin Immunoprecipitation Sequencing (ChIP-seq) for Detecting Histone Modifications and Modifiers. In: Hatada, I., Horii, T. (eds) Epigenomics. Methods in Molecular Biology, vol 2577. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2724-2_4

Download citation

  • DOI: https://doi.org/10.1007/978-1-0716-2724-2_4

  • Published:

  • Publisher Name: Humana, New York, NY

  • Print ISBN: 978-1-0716-2723-5

  • Online ISBN: 978-1-0716-2724-2

  • eBook Packages: Springer Protocols

Publish with us

Policies and ethics