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Annual International Conference on Research in Computational Molecular Biology

RECOMB 2006: Research in Computational Molecular Biology pp 458–472Cite as

Motif Yggdrasil: Sampling from a Tree Mixture Model

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Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 3909))

Abstract

In phylogenetic foot-printing, putative regulatory elements are found in upstream regions of orthologous genes by searching for common motifs. Motifs in different upstream sequences are subject to mutations along the edges of the corresponding phylogenetic tree, consequently taking advantage of the tree in the motif search is an appealing idea. We describe the Motif Yggdrasil sampler; the first Gibbs sampler based on a general tree that uses unaligned sequences. Previous tree-based Gibbs samplers have assumed a star-shaped tree or partially aligned upstream regions. We give a probabilistic model describing upstream sequences with regulatory elements and build a Gibbs sampler with respect to this model. We apply the collapsing technique to eliminate the need to sample nuisance parameters, and give a derivation of the predictive update formula. The use of the tree achieves a substantial increase in nucleotide level correlation coefficient both for synthetic data and 37 bacterial lexA genes.

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References

  1. Lawrence, C.E., Altschul, S.F., Boguski, M.S., Liu, J.S., Neuwald, A.F., Wootton, J.C.: Detecting subtle sequence signals: a Gibbs sampling strategy for multiple alignment. Science 262, 208–214 (1993)

    Article  Google Scholar 

  2. Liu, J., Neuwald, A., Lawrence, C.: Bayesian models for multiple local sequence alignment and Gibbs sampling strategies. Journal of the American Statistical Association 90, 1156–1170 (1995)

    Article  MATH  Google Scholar 

  3. Eskin, E., Pevzner, P.: Finding composite regulatory patterns in DNA sequences. Bioinformatics 18(Suppl. 1), S354–363 (2002)

    Google Scholar 

  4. Eskin, E.: From profiles to patterns and back again: a branch and bound algorithm for finding near optimal motif profiles. In: Proceedings of the eigth International Conference on Computational Molecular Biology (RECOMB 2004), pp. 115–124. ACM Press, New York (2004)

    Google Scholar 

  5. Keich, U., Pevzner, P.: Subtle motifs: defining the limits of motif finding algorithms. Bioinformatics 18, 1382–1390 (2002)

    Article  Google Scholar 

  6. Buhler, J., Tompa, M.: Finding motifs using random projections. J. Comput. Biol. 9, 225–242 (2002)

    Article  Google Scholar 

  7. Blanchette, M., Schwikowski, B., Tompa, M.: An exact algorithm to identify motifs in orthologous sequences from multiple species. Proc. Int. Conf. Intell. Syst. Mol. Biol. 8, 37–45 (2000)

    Google Scholar 

  8. Blanchette, M.: Algorithms for phylogenetic footprinting. In: Proceedings of the Fifth International Conference on Computational Molecular Biology (RECOMB 2001), pp. 49–58. ACM Press, New York (2001)

    Google Scholar 

  9. Moses, A., Chiang, D., Eisen, M.: Phylogenetic motif detection by expectation-maximization on evolutionary mixtures, 324–335 (2004)

    Google Scholar 

  10. Siddhartan, R., van Nimwegen, E., Siggia, E.D.: PhyloGibbs: A Gibbs sampler incorporating phylogenetic information. In: Eskin, E., Workman, C. (eds.) RECOMB 2004 Satellite Workshop on Regulatory Genomics, pp. 30–41 (2005)

    Google Scholar 

  11. Li, X., Wong, W.: Sampling motifs on phylogenetic trees. Proc. Natl. Acad. Sci. USA 102, 9481–9486 (2005)

    Article  MATH  MathSciNet  Google Scholar 

  12. Wray, G.A., Hahn, M.W., Abouheif, E., Balhoff, J.P., Pizer, M., Rockman, M.V., Romano, L.A.: The evolution of transcriptional regulation in eukaryotes. Mol. Biol. Evol. 20, 1377–1419 (2003)

    Article  Google Scholar 

  13. Moses, A.M., Chiang, D.Y., Kellis, M., Lander, E.S., Eisen, M.B.: Position specific variation in the rate of evolution in transcription factor binding sites. BMC Evol. Biol. 3, 19 (2003)

    Article  Google Scholar 

  14. Liu, X., Brutlag, D., Liu, J.: BioProspector: discovering conserved DNA motifs in upstream regulatory regions of co-expressed genes, 127–138 (2001)

    Google Scholar 

  15. Liu, J.: The collapsed Gibbs sampler with applications to a gene regulation problem. Journal of the American Statistical Association 89 (1994)

    Google Scholar 

  16. Jensen, S.T., Liu, J.S.: Biooptimizer: a bayesian scoring function approach to motif discovery. Bioinformatics 20, 1557–1564 (2004)

    Article  Google Scholar 

  17. Vavouri, T., Elgar, G.: Prediction of cis-regulatory elements using binding site matrices–the successes, the failures and the reasons for both. Curr. Opin. Genet. Dev. 15, 395–402 (2005)

    Article  Google Scholar 

  18. Tompa, M., Li, N., Bailey, T., Church, G., De Moor, B., Eskin, E., Favorov, A., Frith, M., Fu, Y., Kent, W., Makeev, V., Mironov, A., Noble, W., Pavesi, G., Pesole, G., Regnier, M., Simonis, N., Sinha, S., Thijs, G., van Helden, J., Vandenbogaert, M., Weng, Z., Workman, C., Ye, C., Zhu, Z.: Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol. 23, 137–144 (2005)

    Article  Google Scholar 

  19. Gilks, W.R., Richardson, S., Spiegelhalter, D.J.: Markov Chain Monte Carlo in Practice. Chapman and Hall, Boca Raton (1996)

    MATH  Google Scholar 

  20. Liu, J.S.: Monte Carlo strategies in Scientific Computing. Springer, New York (2003)

    Google Scholar 

  21. Durbin, R., Eddy, S., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probablistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1998)

    Book  Google Scholar 

  22. Rambaut, A., Grassly, N.: Seq-Gen: an application for the Monte Carlo simulation of DNA sequence evolution along phylogenetic trees. Comput. Appl. Biosci. 13, 235–238 (1997)

    Google Scholar 

  23. Mazon, G., Erill, I., Campoy, S., Cortes, P., Forano, E., Barbe, J.: Reconstruction of the evolutionary history of the LexA-binding sequence. Microbiology 150, 3783–3795 (2004)

    Article  Google Scholar 

  24. Wingender, E., Chen, X., Fricke, E., Geffers, R., Hehl, R., Liebich, I., Krull, M., Matys, V., Michael, H., Ohnhauser, R., Pruss, M., Schacherer, F., Thiele, S., Urbach, S.: The TRANSFAC system on gene expression regulation. Nucleic Acids Res. 29, 281–283 (2001)

    Article  Google Scholar 

  25. Erill, I., Jara, M., Salvador, N., Escribano, M., Campoy, S., Barbe, J.: Differences in LexA regulon structure among Proteobacteria through in vivo assisted comparative genomics. Nucleic Acids Res. 32, 6617–6626 (2004)

    Article  Google Scholar 

  26. Guindon, S., Gascuel, O.: A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52, 696–704 (2003)

    Article  Google Scholar 

  27. Hannenhalli, S., Wang, L.: Enhanced position weight matrices using mixture models. Bioinformatics 21(Suppl. 1), i204–i212 (2005)

    Google Scholar 

  28. Huson, D.: Splitstree: analyzing and visualizing evolutionary data. Bioinformatics 14, 68–73 (1998)

    Article  Google Scholar 

  29. Bryant, D., Moulton, V.: Neighbor-net: an agglomerative method for the construction of phylogenetic networks. Mol. Biol. Evol. 21, 255–265 (2004)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Stockholm Bioinformatics Center and School of Computer Science and Communication, Royal Institute of Technology, SE-100 44, Stockholm, Sweden

    Samuel A. Andersson & Jens Lagergren

Authors
  1. Samuel A. Andersson
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  2. Jens Lagergren
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Editor information

Editors and Affiliations

  1. Georgia Institute of Technology and Università di Padova,  

    Alberto Apostolico

  2. Topic Chairs, P.O. Box

    Concettina Guerra

  3. Center for Molecular Biology and Computer Sciecne Department, Brown University, 115 Waterman St., 02912, Providence, RI, USA

    Sorin Istrail

  4. University of California, San Diego, USA

    Pavel A. Pevzner

  5. Department of Molecular and Computational Biology, University of Southern California, 1050 Childs Way, 90089-2910, Los Angeles, CA, USA

    Michael Waterman

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© 2006 Springer-Verlag Berlin Heidelberg

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Andersson, S.A., Lagergren, J. (2006). Motif Yggdrasil: Sampling from a Tree Mixture Model. In: Apostolico, A., Guerra, C., Istrail, S., Pevzner, P.A., Waterman, M. (eds) Research in Computational Molecular Biology. RECOMB 2006. Lecture Notes in Computer Science(), vol 3909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11732990_39

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  • DOI: https://doi.org/10.1007/11732990_39

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33295-4

  • Online ISBN: 978-3-540-33296-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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