Chromatin Immunoprecipitation Sequencing (ChIP-Seq) for Transcription Factors and Chromatin Factors in Arabidopsis thaliana Roots: From Material Collection to Data Analysis

  • Sandra CortijoEmail author
  • Varodom Charoensawan
  • François Roudier
  • Philip A. WiggeEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 1761)


Chromatin immunoprecipitation combined with next-generation sequencing (ChIP-seq) is a powerful technique to investigate in vivo transcription factor (TF) binding to DNA, as well as chromatin marks. Here we provide a detailed protocol for all the key steps to perform ChIP-seq in Arabidopsis thaliana roots, also working on other A. thaliana tissues and in most non-ligneous plants. We detail all steps from material collection, fixation, chromatin preparation, immunoprecipitation, library preparation, and finally computational analysis based on a combination of publicly available tools.

Key words

Chromatin immunoprecipitation (ChIP) Chromatin Transcription factor (TF) Next-generation sequencing Arabidopsis Root Bioinformatics 



S.C. was supported by an EMBO long-term fellowship (ALTF 290-2013). V.C. lab is supported by the Thailand Research Fund (TRF) Grant for New Scholar (MRG6080235); Newton Advanced Fellowship through TRF (DBG60800003) and Royal Society (NA160153); the Faculty of Science, Mahidol University; and the Crown Property Bureau Foundation. The Wigge lab is supported by the Gatsby Charitable Foundation, the European Research Council, and the Biotechnology and Biological Sciences Research Council. The authors have no conflict of interest.


  1. 1.
    Bannister AJ, Kouzarides T (2011) Regulation of chromatin by histone modifications. Cell Res 21(3):381–395CrossRefPubMedPubMedCentralGoogle Scholar
  2. 2.
    Morohashi K, Xie Z, Grotewold E (2009) Gene-specific and genome-wide ChIP approaches to study plant transcriptional networks. Methods Mol Biol 553:3–12CrossRefPubMedGoogle Scholar
  3. 3.
    Chow BY, Kay SA (2013) Global approaches for telling time: omics and the Arabidopsis circadian clock. Semin Cell Dev Biol 24(5):383–392CrossRefPubMedPubMedCentralGoogle Scholar
  4. 4.
    Valouev A, Johnson DS, Sundquist A, Medina C, Anton E, Batzoglou S, Myers RM, Sidow A (2008) Genome-wide analysis of transcription factor binding sites based on ChIP-Seq data. Nat Methods 5(9):829–834CrossRefPubMedPubMedCentralGoogle Scholar
  5. 5.
    Ferrier T, Matus JT, Jin J, Riechmann JL (2011) Arabidopsis paves the way: genomic and network analyses in crops. Curr Opin Biotechnol 22(2):260–270CrossRefPubMedGoogle Scholar
  6. 6.
    Solomon MJ, Varshavsky A (1985) Formaldehyde-mediated DNA-protein crosslinking: a probe for in vivo chromatin structures. Proc Natl Acad Sci U S A 82(19):6470–6474CrossRefPubMedPubMedCentralGoogle Scholar
  7. 7.
    Ghavi-Helm Y, Zhao B, Furlong EE (2016) Chromatin immunoprecipitation for analyzing transcription factor binding and histone modifications in Drosophila. Methods Mol Biol 1478:263–277CrossRefPubMedGoogle Scholar
  8. 8.
    Ballare C, Castellano G, Gaveglia L, Althammer S, Gonzalez-Vallinas J, Eyras E, Le Dily F, Zaurin R, Soronellas D, Vicent GP, Beato M (2013) Nucleosome-driven transcription factor binding and gene regulation. Mol Cell 49(1):67–79CrossRefPubMedGoogle Scholar
  9. 9.
    Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat Methods 9(4):357–359CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Robinson JT, Thorvaldsdottir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011) Integrative genomics viewer. Nat Biotechnol 29(1):24–26CrossRefPubMedPubMedCentralGoogle Scholar
  11. 11.
    Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS (2008) Model-based analysis of ChIP-Seq (MACS). Genome Biol 9(9):R137CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    Zang C, Schones DE, Zeng C, Cui K, Zhao K, Peng W (2009) A clustering approach for identification of enriched domains from histone modification ChIP-Seq data. Bioinformatics 25(15):1952–1958CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Huang W, Loganantharaj R, Schroeder B, Fargo D, Li L (2013) PAVIS: a tool for Peak Annotation and Visualization. Bioinformatics 29(23):3097–3099CrossRefPubMedPubMedCentralGoogle Scholar
  14. 14.
    Du Z, Zhou X, Ling Y, Zhang Z, Su Z (2010) agriGO: a GO analysis toolkit for the agricultural community. Nucleic Acids Res 38(Web Server issue):W64–W70CrossRefPubMedPubMedCentralGoogle Scholar
  15. 15.
    Eden E, Lipson D, Yogev S, Yakhini Z (2007) Discovering motifs in ranked lists of DNA sequences. PLoS Comput Biol 3(3):e39CrossRefPubMedPubMedCentralGoogle Scholar
  16. 16.
    Eden E, Navon R, Steinfeld I, Lipson D, Yakhini Z (2009) GOrilla: a tool for discovery and visualization of enriched GO terms in ranked gene lists. BMC Bioinformatics 10:48CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT, Harris MA, Hill DP, Issel-Tarver L, Kasarskis A, Lewis S, Matese JC, Richardson JE, Ringwald M, Rubin GM, Sherlock G (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25(1):25–29CrossRefPubMedPubMedCentralGoogle Scholar
  18. 18.
    Gene Ontology Consortium (2015) Gene Ontology Consortium: going forward. Nucleic Acids Res 43(Database issue):D1049–D1056CrossRefGoogle Scholar
  19. 19.
    Bailey TL, Elkan C (1994) Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc Int Conf Intell Syst Mol Biol 2:28–36PubMedGoogle Scholar
  20. 20.
    Bailey TL, Boden M, Buske FA, Frith M, Grant CE, Clementi L, Ren J, Li WW, Noble WS (2009) MEME SUITE: tools for motif discovery and searching. Nucleic Acids Res 37(Web Server issue):W202–W208CrossRefPubMedPubMedCentralGoogle Scholar
  21. 21.
    Franco-Zorrilla JM, Lopez-Vidriero I, Carrasco JL, Godoy M, Vera P, Solano R (2014) DNA-binding specificities of plant transcription factors and their potential to define target genes. Proc Natl Acad Sci U S A 111(6):2367–2372CrossRefPubMedPubMedCentralGoogle Scholar
  22. 22.
    O’Malley RC, Huang SS, Song L, Lewsey MG, Bartlett A, Nery JR, Galli M, Gallavotti A, Ecker JR (2016) Cistrome and epicistrome features shape the regulatory DNA landscape. Cell 165(5):1280–1292CrossRefPubMedPubMedCentralGoogle Scholar
  23. 23.
    Chen K, Xi Y, Pan X, Li Z, Kaestner K, Tyler J, Dent S, He X, Li W (2013) DANPOS: dynamic analysis of nucleosome position and occupancy by sequencing. Genome Res 23(2):341–351CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Ramírez F, Ryan DP, Grüning B, Bhardwaj V, Kilpert F, Richter AS, Heyne S, Dündar F, Manke T (2016) deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res 44(W1):W160–W165CrossRefPubMedPubMedCentralGoogle Scholar
  25. 25.
    Tian B, Yang J, Brasier AR (2012) Two-step cross-linking for analysis of protein-chromatin interactions. Methods Mol Biol 809:105–120CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2018

Authors and Affiliations

  1. 1.The Sainsbury LaboratoryUniversity of CambridgeCambridgeUK
  2. 2.Department of Biochemistry, Faculty of ScienceMahidol UniversityBangkokThailand
  3. 3.Integrative Computational BioScience (ICBS) CenterMahidol UniversityNakhon PathomThailand
  4. 4.Systems Biology of Diseases Research Unit, Faculty of ScienceMahidol UniversityNakhon PathomThailand
  5. 5.Laboratoire de Reproduction et Développement des Plantes – ENS LyonLyon Cedex 07France

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