Quantifying the Strength of Natural Selection of a Motif Sequence

  • Chen-Hsiang Yeang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6293)


Quantification of selective pressures on regulatory sequences is a central question in studying the evolution of gene regulatory networks. Previous methods focus primarily on single sites rather than motif sequences. We propose a method of evaluating the strength of natural selection of a motif from a family of aligned sequences. The method is based on a Poisson process model of neutral sequence substitutions and derives a birth-death process of the motif occurrence frequencies. The selection coefficient is treated as a penalty for the motif death rate. We demonstrate that the birth-death model closely approximates statistics generated from simulated data and the Poisson process assumption holds in mammalian promoter sequences. Furthermore, we show that a considerably higher portion of known transcription factor binding motifs possess high selection coefficients compared to negative controls with high occurrence frequencies on promoters. Preliminary analysis supports the potential applications of the model to identify regulatory sequences under selection.


Poisson Process Motif Sequence Neutral Model Sequence Substitution Transcription Factor Binding Motif 
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  1. 1.
    Kellis, M., Patterson, N., Endrizzi, M., Birren, B., Lander, E.S.: Sequencing and comparison of yeast species to identify genes and regulatory motifs. Nature 423, 241–254 (2003)CrossRefPubMedGoogle Scholar
  2. 2.
    Yang, Z., Bielawski, J.P.: Statistical methods for detecting molecular adaptation. Trends of Ecology and Evolution 15, 496–503 (2000)CrossRefPubMedGoogle Scholar
  3. 3.
    Siepel, A., Haussler, D.: Combining phylogenetic and hidden Markov models in biosequence analysis. Journal of Computational Biology 11(2-3), 413–428 (2004)CrossRefPubMedGoogle Scholar
  4. 4.
    McDonald, J.H., Kreitman, M.: Adaptive evolution at the Adh locus in Drosophila. Nature 351, 652–654 (1991)CrossRefPubMedGoogle Scholar
  5. 5.
    Tajima, F.: Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989)PubMedPubMedCentralGoogle Scholar
  6. 6.
    Atwal, G.S., Bond, G.L., Metsuyanim, S., et al.: Haplotype structure and selection of the MDM2 oncogene in humans. Proceedings of National Academy of Science USA 104, 4525–4529 (2007)CrossRefGoogle Scholar
  7. 7.
    Raijman, D., Shamir, R., Tanay, A.: Evolution and selection in yeast promoters: analyzing the combined effect of diverse transcription factor binding sites. PLoS Computational Biology 4, 77–87 (2008)CrossRefGoogle Scholar
  8. 8.
    Zuckerandl, E., Pauling, L.: Evolutionary divergence and convergence in proteins. In: Bryson, V., Vogel, H.J. (eds.) Evolving genes and proteins, pp. 97–166. Academic Press, New York (1965)CrossRefGoogle Scholar
  9. 9.
    Felsenstein, J.: Evolutionary trees from DNA sequences: a maximum likelihood approach. Journal of Molecular Evoution 17, 368–376 (1981)CrossRefGoogle Scholar
  10. 10.
    Kendall, D.G.: On the generalized birth-death process. The Annals of Mathematical Statistics 19(1), 1–15 (1948)CrossRefGoogle Scholar
  11. 11.
    Bird, A.P.: CpG islands as gene markers in the vertebrate nucleus. Trends in Genetics 3, 342–347 (1987)CrossRefGoogle Scholar
  12. 12.
    Kuhn, R.M., Karolchik, D., Zweig, A.S., et al.: The UCSC Genome Browser Database: update 2009. Nucleic Acids Research, D755–D761 (2009)Google Scholar
  13. 13.
    Matys, V., Fricke, E., Geffers, R., Gossling, E., Haubrock, M., et al.: TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Research 31(1), 374–378 (2003)CrossRefPubMedPubMedCentralGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Chen-Hsiang Yeang
    • 1
  1. 1.Institute of Statistical ScienceAcademia SinicaTaipeiTaiwan

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