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A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs

Nature Protocols volume 7, pages 1551–1568 (2012)Cite this article

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Abstract

This protocol explains how to use the online integrated pipeline 'peak-motifs' (http://rsat.ulb.ac.be/rsat/) to predict motifs and binding sites in full-size peak sets obtained by chromatin immunoprecipitation–sequencing (ChIP-seq) or related technologies. The workflow combines four time- and memory-efficient motif discovery algorithms to extract significant motifs from the sequences. Discovered motifs are compared with databases of known motifs to identify potentially bound transcription factors. Sequences are scanned to predict transcription factor binding sites and analyze their enrichment and positional distribution relative to peak centers. Peaks and binding sites are exported as BED tracks that can be uploaded into the University of California Santa Cruz (UCSC) genome browser for visualization in the genomic context. This protocol is illustrated with the analysis of a set of 6,000 peaks (8 Mb in total) bound by the Drosophila transcription factor Krüppel. The complete workflow is achieved in about 25 min of computational time on the Regulatory Sequence Analysis Tools (RSAT) Web server. This protocol can be followed in about 1 h.

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Figure 1
Figure 2: Screenshot of the peak-motifs web form.
Figure 3: Input sequence treatment (top) and motif discovery (bottom) options.
Figure 4: Options for motif comparisons (top) and predicted sites visualization (bottom).
Figure 5: Sequence lengths and composition.
Figure 6: Dinucleotide composition and derived background models.
Figure 7: Reference motifs.
Figure 8: Discovered motifs grouped by algorithm.
Figure 9: Discovered motifs with motif comparisons.
Figure 10: Motif comparisons.
Figure 11: Predicted sites visualized in their genomic contexts on the UCSC genome browser.
Figure 12: Motif discovery approaches.

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Acknowledgements

This work was supported by the Alexander von Humboldt foundation to M.T.-C.; the Agence Nationale de Recherche (ANR) partner of the ERASysBio+ initiative supported under the EU ERA-NET Plus scheme in FP7 to C.H.; ANR Young Researchers Grant 'CardiHox' to C.H.; the Belgian Program on Interuniversity Attraction Poles, initiated by the Belgian Federal Science Policy Office (project P6/25 (BioMaGNet)); EU-funded Cooperation in Science and Technology (COST) action (BM1006 'SEQAHEAD—Next-Generation Sequencing Data Analysis Network'); FP7 MICROME Collaborative Project (Microbial genomics and bio-informatics', contract number 222886-2). We acknowledge the colleagues who helped to install and maintain the RSAT Web servers: R. Leplae (ULB, Belgium), R. Zayas-Lagunas (UNAM, Mexico), E. Bongcam-Rudloff (Uppsala, Sweden), F.-X. Théodule (Aix Marseille Université, France), P. Vincens (Ecole Normale Supérieure, France) and F. Joubert (Pretoria, South Africa).

Author information

Authors and Affiliations

  1. Department of Computational Molecular Biology, Max Planck Institute for Molecular Genetics, Berlin, Germany

    Morgane Thomas-Chollier

  2. Technological Advances for Genomics and Clinics, Institut National de la Santé et de la Recherche Médicale (INSERM) U928 and Université de la Méditerranée, Marseille, France

    Elodie Darbo, Carl Herrmann, Denis Thieffry & Jacques van Helden

  3. Centro de Ciencias Genomicas, Universidad Nacional Autónoma de México (UNAM), Cuernavaca, Mexico

    Matthieu Defrance

  4. Institut de Biologie de l'Ecole Normale Supérieure—Centre National de la Recherche Scientifique Unité Mixte de Recherche (CNRS UMR) 8197 and INSERM U1024, Paris, France

    Denis Thieffry

  5. Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe), Université Libre de Bruxelles, Bruxelles, Belgium

    Jacques van Helden

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Contributions

J.v.H., M.T.-C. and M.D. initiated and developed the peak-motifs software tool. E.D., C.H. and D.T. contributed to improve the tool and analyzed the study case for this protocol. All authors edited the manuscript.

Corresponding authors

Correspondence to Morgane Thomas-Chollier or Jacques van Helden.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Table 1

Comparison of software tools used for analyzing motifs in ChIP-seq peak sequences. This is an updated version of the Table 1 from the original peak-motifs publication9 summarizing the tasks, algorithms and usability properties to compare the different software options for the users. Adapted from Morgane Thomas-Chollier, Carl Herrmann, Matthieu Defrance, Olivier Sand, Denis Thieffry, Jacques van Helden, RSAT peak-motifs: motif analysis in full-size ChIP-seq datasets, Nucleic Acids Research, 2012, 40(4), by permission of Oxford University Press. (PDF 808 kb)

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Thomas-Chollier, M., Darbo, E., Herrmann, C. et al. A complete workflow for the analysis of full-size ChIP-seq (and similar) data sets using peak-motifs. Nat Protoc 7, 1551–1568 (2012). https://doi.org/10.1038/nprot.2012.088

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