Advertisement

Kernel-Based Identification of Regulatory Modules

  • Sebastian J. Schultheiss
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 674)

Abstract

The challenge of identifying cis-regulatory modules (CRMs) is an important milestone for the ultimate goal of understanding transcriptional regulation in eukaryotic cells. It has been approached, among others, by motif-finding algorithms that identify overrepresented motifs in regulatory sequences. These methods succeed in finding single, well-conserved motifs, but fail to identify combinations of degenerate binding sites, like the ones often found in CRMs. We have developed a method that combines the abilities of existing motif finding with the discriminative power of a machine learning technique to model the regulation of genes (Schultheiss et al. (2009) Bioinformatics 25, 2126–2133). Our software is called kirmes, which stands for kernel-based identification of regulatory modules in eukaryotic sequences. Starting from a set of genes thought to be co-regulated, kirmes can identify the key CRMs responsible for this behavior and can be used to determine for any other gene not included on that list if it is also regulated by the same mechanism. Such gene sets can be derived from microarrays, chromatin immunoprecipitation experiments combined with next-generation sequencing or promoter/whole genome microarrays. The use of an established machine learning method makes the approach fast to use and robust with respect to noise. By providing easily understood visualizations for the results returned, they become interpretable and serve as a starting point for further analysis. Even for complex regulatory relationships, kirmes can be a helpful tool in directing the design of biological experiments.

Key words

Kernel methods support vector machines machine learning string kernels regulatory modules transcription factor binding motifs eukaryotic gene regulation motif finding 

References

  1. 1.
    Boser, B., Guyon, I., and Vapnik, V. (1992) A training algorithm for optimal margin classifiers. ACM Press Proceedings COLT’ 92 , 144–152.Google Scholar
  2. 2.
    Noble, W.S. (2006) What is a support vector machine? Nat Biotechnol 24, 1565–1567.PubMedCrossRefGoogle Scholar
  3. 3.
    Lawrence, C.E., Altschul, S.F., Boguski, M.S. et al. (1993) Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science 262, 208–214.PubMedCrossRefGoogle Scholar
  4. 4.
    Gupta, M., and Liu, J. (2005) De novo cis-regulatory module elicitation for eukaryotic genomes. Proc Natl Acad Sci USA 102, 7079–7084.PubMedCrossRefGoogle Scholar
  5. 5.
    Howard, M.L., and Davidson, E.H. (2004) cis-Regulatory control circuits in development. Dev Biol 271, 109–118.PubMedCrossRefGoogle Scholar
  6. 6.
    Blanchette M., Bataille, A.R., Chen, X. et al. (2006) Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression. Genome Res 16, 656–668.PubMedCrossRefGoogle Scholar
  7. 7.
    Thijs, G., Lescot, M., Marchal, K. et al. (2001) A higher order background model improves the detection of regulatory elements by Gibbs sampling. Bioinformatics 17, 1113–1122.PubMedCrossRefGoogle Scholar
  8. 8.
    Gordân, R., Narlikar, L., and Hartemink, A. (2008) A fast, alignment-free, conservation-based method for transcription factor binding site discovery. LNCS RECOMB Springer, Heidelberg 4955, 98–111.Google Scholar
  9. 9.
    Schultheiss, S. J., Busch, W., Lohmann, J. U. et al. (2009) KIRMES: kernel-based identification of regulatory modules in euchromatic sequences. Bioinformatics 25, 2126–2133.PubMedCrossRefGoogle Scholar
  10. 10.
    Das, P.M., Ramachandran, K., van Wert, J., and Singal, R. (2004) Chromatin immunoprecipitation assay. Biotechniques 37, 961–969.PubMedGoogle Scholar
  11. 11.
    Buck, M.J., and Lieb, J.D. (2004) ChIP-chip: considerations for the design, analysis, and application of genome-wide chromatin immunoprecipitation experiments. Genomics 83, 349–360.PubMedCrossRefGoogle Scholar
  12. 12.
    Barski, A., and Zhao, K. (2009) Genomic location analysis with ChIP-seq. J Cell Biochem 107, 11–18.PubMedCrossRefGoogle Scholar
  13. 13.
    Sonnenburg, S., Rätsch, G., Schäfer, C., and Schölkopf, B. (2006) Large-scale multiple kernel learning. J Mach Learn Res 7, 1531–1565.Google Scholar
  14. 14.
    Giardine, B., Riemer, C., Hardison, R.C. et al. (2005) Galaxy: a platform for interactive large-scale genome analysis. Genome Res 15, 1451–1455.PubMedCrossRefGoogle Scholar
  15. 15.
    Davis, J., and Goadrich, M. (2006) The relationship between precision-recall and ROC curves. Proceedings ICML 23, 233–240.CrossRefGoogle Scholar
  16. 16.
    Schneider, T.D., and Stephens, R.M. (1990) Sequence logos: a new way to display consensus sequences. Nucleic Acids Res 18, 6097–6100.PubMedCrossRefGoogle Scholar
  17. 17.
    Sonnenburg, S., Zien, A., Philips, P., and Rätsch, G. (2008) POIMs: positional oligomer importance matrices – understanding support vector machine-based signal detectors. Bioinformatics 24, i6–i14.PubMedCrossRefGoogle Scholar
  18. 18.
    Rätsch, G., Sonnenburg, S., and Schölkopf, B. (2005) RASE: recognition of alternatively spliced exons in C. elegans. Bioinformatics 21(Suppl. 1), i369–i377.PubMedCrossRefGoogle Scholar
  19. 19.
    Hughes, T.R., Marton, M.J., Jones, A.R. et al. (2000) Functional discovery via a compendium of expression profiles. Cell 102, 109–126.PubMedCrossRefGoogle Scholar
  20. 20.
    Smith, B., Fang, H., Pan, Y. et al. (2007) Evolution of motif variants and positional bias of the cyclic-AMP response element. BMC Evol Biol 7(Suppl. 1), S15.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2010

Authors and Affiliations

  1. 1.Friedrich Miescher Laboratory of the Max Planck SocietyTübingenGermany

Personalised recommendations