Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Technical Report
  • Published:

An algorithm for finding protein–DNA binding sites with applications to chromatin- immunoprecipitation microarray experiments

Nature Biotechnology volume 20, pages 835–839 (2002)Cite this article

Abstract

Chromatin immunoprecipitation followed by cDNA microarray hybridization (ChIP–array) has become a popular procedure for studying genome-wide protein–DNA interactions and transcription regulation. However, it can only map the probable protein–DNA interaction loci within 1–2 kilobases resolution. To pinpoint interaction sites down to the base-pair level, we introduce a computational method, Motif Discovery scan (MDscan), that examines the ChIP–array-selected sequences and searches for DNA sequence motifs representing the protein–DNA interaction sites. MDscan combines the advantages of two widely adopted motif search strategies, word enumeration1,2,3,4 and position-specific weight matrix updating5,6,7,8,9, and incorporates the ChIP–array ranking information to accelerate searches and enhance their success rates. MDscan correctly identified all the experimentally verified motifs from published ChIP–array experiments in yeast10,11,12,13 (STE12, GAL4, RAP1, SCB, MCB, MCM1, SFF, and SWI5), and predicted two motif patterns for the differential binding of Rap1 protein in telomere regions. In our studies, the method was faster and more accurate than several established motif-finding algorithms5,8,9. MDscan can be used to find DNA motifs not only in ChIP–array experiments but also in other experiments in which a subgroup of the sequences can be inferred to contain relatively abundant motif sites. The MDscan web server can be accessed at http://BioProspector.stanford.edu/MDscan/.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. van Helden, J., Andre, B. & Collado-Vides, J. Extracting regulatory sites from the upstream region of yeast genes by computational analysis of oligonucleotide frequencies. J. Mol. Biol. 281, 827–842 (1998).

    Article  CAS  Google Scholar 

  2. Bussemaker, H.J., Li, H. & Siggia, E.D. Building a dictionary for genomes: identification of presumptive regulatory sites by statistical analysis. Proc. Natl. Acad. Sci. USA 97, 10096–10100 (2000).

    Article  CAS  Google Scholar 

  3. Sinha, S. & Tompa, M. A statistical method for finding transcription factor binding sites. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 344–354 (2000).

    CAS  PubMed  Google Scholar 

  4. Vilo, J., Brazma, A., Jonassen, I., Robinson, A. & Ukkonen, E. Mining for putative regulatory elements in the yeast genome using gene expression data. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 384–394 (2000).

    CAS  PubMed  Google Scholar 

  5. Hertz, G.Z., Hartzell, G.W. & Stormo, G.D. Identification of consensus patterns in unaligned DNA sequences known to be functionally related. Comput. Appl. Biosci. 6, 81–92 (1990).

    CAS  PubMed  Google Scholar 

  6. Bailey, T.L. & Elkan, C. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc. Int. Conf. Intell. Syst. Mol. Biol. 2, 28–36 (1994).

    CAS  PubMed  Google Scholar 

  7. Liu, J.S., Neuwald, A.F. & Lawrence, C.E. Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. J. Am. Stat. Assoc. 90, 1156–1170 (1995).

    Article  Google Scholar 

  8. Roth, F.P., Hughes, J.D., Estep, P.W. & Church, G.M. Finding DNA regulatory motifs within unaligned noncoding sequences clustered by whole-genome mRNA quantitation. Nat. Biotechnol. 16, 939–945 (1998).

    Article  CAS  Google Scholar 

  9. Liu, X., Brutlag, D.L. & Liu, J.S. BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes. Pac. Symp. Biocomput. 127–138 (2001).

  10. Ren, B. et al. Genome-wide location and function of DNA binding proteins. Science 290, 2306–2309 (2000).

    Article  CAS  Google Scholar 

  11. Iyer, V.R. et al. Genomic binding sites of the yeast cell-cycle transcription factors SBF and MBF. Nature 409, 533–538 (2001).

    Article  CAS  Google Scholar 

  12. Lieb, J.D., Liu, X., Botstein, D. & Brown, P.O. Promoter-specific binding of Rap1p revealed by genome-wide maps of protein-DNA association. Nat. Genet. 28, 327–334 (2001).

    Article  CAS  Google Scholar 

  13. Simon, I. et al. Serial regulation of transcriptional regulators in the yeast cell cycle. Cell 106, 697–708 (2001).

    Article  CAS  Google Scholar 

  14. Dolan, J.W., Kirkman, C. & Fields, S. The yeast STE12 protein binds to the DNA sequence mediating pheromone induction. Proc. Natl. Acad. Sci. USA 86, 5703–5707 (1989).

    Article  CAS  Google Scholar 

  15. Graham, I.R. & Chambers, A. Use of a selection technique to identify the diversity of binding sites for the yeast RAP1 transcription factor. Nucleic Acids Res. 22, 124–130 (1994).

    Article  CAS  Google Scholar 

  16. Buchman, A.R., Kimmerly, W.J., Rine, J. & Kornberg, R.D. Two DNA-binding factors recognize specific sequences at silencers, upstream activating sequences, autonomously replicating sequences, and telomeres in Saccharomyces cerevisiae. Mol. Cell Biol. 8, 210–225 (1988).

    Article  CAS  Google Scholar 

  17. Idrissi, F.Z. & Pina, B. Functional divergence between the half-sites of the DNA-binding sequence for the yeast transcriptional regulator Rap1p. Biochem. J. 341, 477–482 (1999).

    Article  CAS  Google Scholar 

  18. Spellman, P.T. et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol. Biol. Cell 9, 3273–3297 (1998).

    Article  CAS  Google Scholar 

  19. Lawrence, C.E. et al. Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science 262, 208–214 (1993).

    Article  CAS  Google Scholar 

  20. Liu, J.S. Monte Carlo Strategies in Scientific Computing (Springer, New York, 2001).

    Google Scholar 

Download references

Acknowledgements

The authors thank the Brown lab at Stanford (especially Jason D. Lieb) and the Young lab at MIT (especially Bing Ren) for their valuable data and scientific insight. This work is supported by National Human Genome Research Institute grants R01 HGF02235 and R01 HG02518-01, and National Science Foundation grant DMS-0094613.

Author information

Authors and Affiliations

  1. Stanford Medical Informatics, Stanford University, Stanford, 94305, CA

    X. Shirley Liu

  2. Department of Biochemistry, Stanford University, Stanford, 94305, CA

    Douglas L. Brutlag

  3. Department of Statistics, Harvard University, 1 Oxford Street, Cambridge, 02138, MA

    Jun S. Liu

Authors
  1. X. Shirley Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Douglas L. Brutlag
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jun S. Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jun S. Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, X., Brutlag, D. & Liu, J. An algorithm for finding protein–DNA binding sites with applications to chromatin- immunoprecipitation microarray experiments. Nat Biotechnol 20, 835–839 (2002). https://doi.org/10.1038/nbt717

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nbt717

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing