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PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls

Nature Biotechnology volume 27, pages 66–75 (2009)Cite this article

Abstract

Chromatin immunoprecipitation (ChIP) followed by tag sequencing (ChIP-seq) using high-throughput next-generation instrumentation is fast, replacing chromatin immunoprecipitation followed by genome tiling array analysis (ChIP-chip) as the preferred approach for mapping of sites of transcription-factor binding and chromatin modification. Using two deeply sequenced data sets for human RNA polymerase II and STAT1, each with matching input-DNA controls, we describe a general scoring approach to address unique challenges in ChIP-seq data analysis. Our approach is based on the observation that sites of potential binding are strongly correlated with signal peaks in the control, likely revealing features of open chromatin. We develop a two-pass strategy called PeakSeq to compensate for this. A two-pass strategy compensates for signal caused by open chromatin, as revealed by inclusion of the controls. The first pass identifies putative binding sites and compensates for genomic variation in the 'mappability' of sequences. The second pass filters out sites not significantly enriched compared to the normalized control, computing precise enrichments and significances. Our scoring procedure enables us to optimize experimental design by estimating the depth of sequencing required for a desired level of coverage and demonstrating that more than two replicates provides only a marginal gain in information.

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Figure 1: ChIP-seq signal profile maps.
Figure 2: PeakSeq scoring procedure.
Figure 3: ChIP-seq target list scaling.
Figure 4: ChIP-seq versus ChIP-chip signal tracks and target binding sites for Pol II and STAT1.
Figure 5: Depth of sequencing and value of replicates.

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Acknowledgements

This work was done with support by grants from the National Institutes of Health (NIH) and made use of the Yale University Life Sciences Computing Center (NIH grant RR19895). We acknowledge Mike Wilson's assistance with submission of data to GEO.

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Authors and Affiliations

  1. Molecular Biophysics & Biochemistry Dept., Yale University, PO Box 208114, New Haven, 06520-8114, Connecticut, USA

    Joel Rozowsky, Zhengdong D Zhang, Theodore Gibson, Michael Snyder & Mark B Gerstein

  2. Molecular, Cellular & Developmental Biology Dept, Yale University, New Haven, 06520, Connecticut, USA

    Ghia Euskirchen & Michael Snyder

  3. Program in Computational Biology and Bioinformatics, Yale University, New Haven, 06520, Connecticut, USA

    Raymond K Auerbach & Mark B Gerstein

  4. Department of Computer Science, Yale University, New Haven, 06520, Connecticut, USA

    Robert Bjornson, Nicholas Carriero & Mark B Gerstein

Authors
  1. Joel Rozowsky
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  2. Ghia Euskirchen
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  4. Zhengdong D Zhang
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  5. Theodore Gibson
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  6. Robert Bjornson
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  8. Michael Snyder
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  9. Mark B Gerstein
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Contributions

J.R. conceived and developed the scoring methodology, analyzed the data presented in the paper and wrote the manuscript. G.E. generated the experimental data. R.K.A. assisted with the analysis in the paper as well as editing the manuscript. Z.D.Z. was involved in the conceptualization of the scoring methodology. T.G. assisted in the coding of the PeakSeq scoring procedure. R.B. and N.C. developed the code for generating indexed mappability maps of a genome and assisted with analysis. M.S. helped conceive of the scoring methodology and with the editing of the manuscript. M.B.G. also helped conceive of the scoring methodology as well as supervised the analysis and writing of the manuscript.

Corresponding authors

Correspondence to Joel Rozowsky or Mark B Gerstein.

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Supplementary Figure 1, Supplementary Tables 1 and 2 and Supplementary Notes (PDF 717 kb)

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Rozowsky, J., Euskirchen, G., Auerbach, R. et al. PeakSeq enables systematic scoring of ChIP-seq experiments relative to controls. Nat Biotechnol 27, 66–75 (2009). https://doi.org/10.1038/nbt.1518

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