Analysis | Published:

Design and analysis of ChIP-seq experiments for DNA-binding proteins

Nature Biotechnology volume 26, pages 13511359 (2008) | Download Citation

Abstract

Recent progress in massively parallel sequencing platforms has enabled genome-wide characterization of DNA-associated proteins using the combination of chromatin immunoprecipitation and sequencing (ChIP-seq). Although a variety of methods exist for analysis of the established alternative ChIP microarray (ChIP-chip), few approaches have been described for processing ChIP-seq data. To fill this gap, we propose an analysis pipeline specifically designed to detect protein-binding positions with high accuracy. Using previously reported data sets for three transcription factors, we illustrate methods for improving tag alignment and correcting for background signals. We compare the sensitivity and spatial precision of three peak detection algorithms with published methods, demonstrating gains in spatial precision when an asymmetric distribution of tags on positive and negative strands is considered. We also analyze the relationship between the depth of sequencing and characteristics of the detected binding positions, and provide a method for estimating the sequencing depth necessary for a desired coverage of protein binding sites.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    & Genome-wide analysis of protein-DNA interactions. Annu. Rev. Genomics Hum. Genet. 7, 81–102 (2006).

  2. 2.

    , , & Genome-wide mapping of in vivo protein-DNA interactions. Science 316, 1497–1502 (2007).

  3. 3.

    et al. Defining the CREB regulon: a genome-wide analysis of transcription factor regulatory regions. Cell 119, 1041–1054 (2004).

  4. 4.

    , & Active chromatin domains are defined by acetylation islands revealed by genome-wide mapping. Genes Dev. 19, 542–552 (2005).

  5. 5.

    , , , & Mapping the chromosomal targets of STAT1 by sequence tag analysis of genomic enrichment (STAGE). Genome Res. 17, 910–916 (2007).

  6. 6.

    et al. DNA methylation profiling of human chromosomes 6, 20 and 22. Nat. Genet. 38, 1378–1385 (2006).

  7. 7.

    Whole-genome re-sequencing. Curr. Opin. Genet. Dev. 16, 545–552 (2006).

  8. 8.

    et al. Model-based analysis of tiling-arrays for ChIP-chip. Proc. Natl. Acad. Sci. USA 103, 12457–12462 (2006).

  9. 9.

    et al. High-resolution computational models of genome binding events. Nat. Biotechnol. 24, 963–970 (2006).

  10. 10.

    , , , & Normalization and experimental design for ChIP-chip data. BMC Bioinformatics 8, 219 (2007).

  11. 11.

    et al. Genome-wide profiles of STAT1 DNA association using chromatin immunoprecipitation and massively parallel sequencing. Nat. Methods 4, 651–657 (2007).

  12. 12.

    , & Using quality scores and longer reads improves accuracy of Solexa read mapping. BMC Bioinformatics 9, 128 (2008).

  13. 13.

    et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).

  14. 14.

    , , , & Comparative genomics modeling of the NRSF/REST repressor network: from single conserved sites to genome-wide repertoire. Genome Res. 16, 1208–1221 (2006).

  15. 15.

    et al. Analysis of the vertebrate insulator protein CTCF-binding sites in the human genome. Cell 128, 1231–1245 (2007).

  16. 16.

    BLAT—the BLAST-like alignment tool. Genome Res. 12, 656–664 (2002).

  17. 17.

    , , & MEME: discovering and analyzing DNA and protein sequence motifs. Nucleic Acids Res. 34, W369–W373 (2006).

  18. 18.

    et al. TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 31, 374–378 (2003).

  19. 19.

    & Estimating the ratio of two Poisson rates. Comput. Stat. Data Anal. 34, 345–356 (2000).

Download references

Acknowledgements

We would like to thank Dustin Schones and Keji Zhao for providing raw data and detailed descriptions for the CTCF data set, and Ali Mortazavi and Barbara Wold for providing sequence tag data for NRSF binding. This work was supported by grants from the National Institutes of Health to P.J.P. (U01HG004258, R01GM082798, UL1RR024920).

Author information

Affiliations

  1. Center for Biomedical Informatics, Harvard Medical School, 10 Shattuck St., Boston, Massachusetts 02115, USA.

    • Peter V Kharchenko
    • , Michael Y Tolstorukov
    •  & Peter J Park
  2. Harvard-Partners Center for Genetics and Genomics, Brigham and Women's Hospital, 77 Avenue Louis Pasteur, Boston, Massachusetts 02115, USA.

    • Peter V Kharchenko
    • , Michael Y Tolstorukov
    •  & Peter J Park
  3. Harvard-MIT Health Sciences and Technology Informatics Program at Children's Hospital, 300 Longwood Ave., Boston, Massachusetts 02115, USA.

    • Peter V Kharchenko
    •  & Peter J Park

Authors

  1. Search for Peter V Kharchenko in:

  2. Search for Michael Y Tolstorukov in:

  3. Search for Peter J Park in:

Corresponding authors

Correspondence to Peter V Kharchenko or Peter J Park.

Supplementary information

PDF files

  1. 1.

    Supplementary Text and Figures

    Figures 1–20, Tables 1–3

Zip files

  1. 1.

    Supplementary Source Code

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/nbt.1508

Further reading