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Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data

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  • Timothy Bailey
  • Pawel Krajewski
  • Istvan Ladunga
  • Celine Lefebvre
  • Qunhua Li
  • Tao Liu
  • Pedro Madrigal
  • Cenny Taslim
  • Jie Zhang

Abstract

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.

Suggested Citation

  • Timothy Bailey & Pawel Krajewski & Istvan Ladunga & Celine Lefebvre & Qunhua Li & Tao Liu & Pedro Madrigal & Cenny Taslim & Jie Zhang, 2013. "Practical Guidelines for the Comprehensive Analysis of ChIP-seq Data," PLOS Computational Biology, Public Library of Science, vol. 9(11), pages 1-8, November.
  • Handle: RePEc:plo:pcbi00:1003326
    DOI: 10.1371/journal.pcbi.1003326
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    References listed on IDEAS

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    1. Elizabeth G Wilbanks & Marc T Facciotti, 2010. "Evaluation of Algorithm Performance in ChIP-Seq Peak Detection," PLOS ONE, Public Library of Science, vol. 5(7), pages 1-12, July.
    2. Diaz Aaron & Park Kiyoub & Lim Daniel A. & Song Jun S., 2012. "Normalization, bias correction, and peak calling for ChIP-seq," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(3), pages 1-31, March.
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    Cited by:

    1. Liang-Yu Fu & Tao Zhu & Xinkai Zhou & Ranran Yu & Zhaohui He & Peijing Zhang & Zhigui Wu & Ming Chen & Kerstin Kaufmann & Dijun Chen, 2022. "ChIP-Hub provides an integrative platform for exploring plant regulome," Nature Communications, Nature, vol. 13(1), pages 1-15, December.
    2. Pedro Madrigal & Siwei Deng & Yuliang Feng & Stefania Militi & Kim Jee Goh & Reshma Nibhani & Rodrigo Grandy & Anna Osnato & Daniel Ortmann & Stephanie Brown & Siim Pauklin, 2023. "Epigenetic and transcriptional regulations prime cell fate before division during human pluripotent stem cell differentiation," Nature Communications, Nature, vol. 14(1), pages 1-23, December.
    3. Renata Bordeira-Carriço & Joana Teixeira & Marta Duque & Mafalda Galhardo & Diogo Ribeiro & Rafael D. Acemel & Panos. N. Firbas & Juan J. Tena & Ana Eufrásio & Joana Marques & Fábio J. Ferreira & Telm, 2022. "Multidimensional chromatin profiling of zebrafish pancreas to uncover and investigate disease-relevant enhancers," Nature Communications, Nature, vol. 13(1), pages 1-17, December.
    4. Shigekazu Murakami & Shannon M. White & Alec T. McIntosh & Chan D. K. Nguyen & Chunling Yi, 2023. "Spontaneously evolved progenitor niches escape Yap oncogene addiction in advanced pancreatic ductal adenocarcinomas," Nature Communications, Nature, vol. 14(1), pages 1-20, December.

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