Novel Phylogenetic Network Inference by Combining Maximum Likelihood and Hidden Markov Models

(Extended Abstract)
  • Sagi Snir
  • Tamir Tuller
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5251)


Horizontal Gene Transfer (HGT) is the event of transferring genetic material from one lineage in the evolutionary tree to a different lineage. HGT plays a major role in bacterial genome diversification and is a significant mechanism by which bacteria develop resistance to antibiotics. Although the prevailing assumption is of complete HGT, cases of partial HGT (which are also named chimeric HGT) where only part of a gene is horizontally transferred, have also been reported, albeit less frequently.

In this work we suggest a new probabilistic model for analyzing and modeling phylogenetic networks, the NET-HMM. This new model captures the biologically realistic assumption that neighboring sites of DNA or amino acid sequences are not independent, which increases the accuracy of the inference. The model describes the phylogenetic network as a Hidden Markov Model (HMM), where each hidden state is related to one of the network’s trees. One of the advantages of the NET-HMM is its ability to infer partial HGT as well as complete HGT. We describe the properties of the NET-HMM, devise efficient algorithms for solving a set of problems related to it, and implement them in software. We also provide a novel complementary significance test for evaluating the fitness of a model (NET-HMM) to a given data set.

Using NET-HMM we are able to answer interesting biological questions, such as inferring the length of partial HGT’s and the affected nucleotides in the genomic sequences, as well as inferring the exact location of HGT events along the tree branches. These advantages are demonstrated through the analysis of synthetical inputs and two different biological inputs.


Hide Markov Model Horizontal Gene Transfer Edge Length Segment Length Horizontal Transfer 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Addario-Berry, L., Hallett, M., Lagergren, J.: Towards identifying lateral gene transfer events. In: PSB 2003, pp. 279–290 (2003)Google Scholar
  2. 2.
    Bergthorsson, U., Adams, K., Thomason, B., Palmer, J.: Widespread horizontal transfer of mitochondrial genes in flowering plants. Nature 424, 197–201 (2003)CrossRefGoogle Scholar
  3. 3.
    Birin, H., Gal-Or, Z., Elias, I., Tuller, T.: Inferring horizontal transfers in the presence of rearrangements by the minimum evolution criterion. Bioinformatics 24(6), 826–832 (2008)CrossRefGoogle Scholar
  4. 4.
    Boc, A., Makarenkov, V.: New efficient algorithm for detection of horizontal gene transfer events. In: Benson, G., Page, R.D.M. (eds.) WABI 2003. LNCS (LNBI), vol. 2812, pp. 190–201. Springer, Heidelberg (2003)Google Scholar
  5. 5.
    Delwiche, C., Palmer, J.: Rampant horizontal transfer and duplication of rubisco genes in eubacteria and plastids. Mol. Biol. Evol. 13(6) (1996)Google Scholar
  6. 6.
    Doolittle, W.F., Boucher, Y., Nesbo, C.L., Douady, C.J., Andersson, J.O., Roger, A.J.: How big is the iceberg of which organellar genes in nuclear genomes are but the tip? Phil. Trans. R. Soc. Lond. B. Biol. Sci. 358, 39–57 (2003)CrossRefGoogle Scholar
  7. 7.
    Durbin, R., Eddy, S.R., Krogh, A., Mitchison, G.: Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press, Cambridge (1999)Google Scholar
  8. 8.
    Paulsen, I.T., et al.: Role of mobile DNA in the evolution of Vacomycin-resistant Enterococcus faecalis. Science 299(5615), 2071–2074 (2003)CrossRefGoogle Scholar
  9. 9.
    Felsenstein, J.: Evolutionary trees from DNA sequences: A maximum likelihood approach. J. Mol. Evol. 17, 368–376 (1981)CrossRefGoogle Scholar
  10. 10.
    Hallett, M., Lagergren, J.: Efficient algorithms for lateral gene transfer problems. In: RECOMB 2001, pp. 149–156. ACM Press, New York (2001)CrossRefGoogle Scholar
  11. 11.
    Hallett, M., Lagergren, J., Tofigh, A.: Simultaneous identification of duplications and lateral transfers. In: Proceedings of the eighth annual international conference on Research in computational molecular biology, pp. 347–356 (2004)Google Scholar
  12. 12.
    Hein, J.: Reconstructing evolution of sequences subject to recombination using parsimony. Math. Biosci. 98, 185–200 (1990)zbMATHCrossRefMathSciNetGoogle Scholar
  13. 13.
    Husmeier, D., McGuire, G.: Detecting recombination with MCMC. Bioinformatics 18, 345–353 (2002)CrossRefGoogle Scholar
  14. 14.
    Huson, D.H., Bryant, D.: Application of phylogenetic networks in evolutionary studies. Mol. Biol. Evol. 23(2), 254–267 (2006)CrossRefGoogle Scholar
  15. 15.
    Lawrence, J.G., Ochman, H.: Amelioration of bacterial genomes: rates of change and exchange. J. Mol. Evol. 44(4), 383–397 (1997)CrossRefGoogle Scholar
  16. 16.
    Jin, G., Nakhleh, L., Snir, S., Tuller, T.: Maximum likelihood of phylogenetic networks. Bioinformatics 22(21), 2604–2611 (2006)CrossRefGoogle Scholar
  17. 17.
    Jin, G., Nakhleh, L., Snir, S., Tuller, T.: Inferring phylogenetic networks by the maximum parsimony criterion: A case study. Mol. Biol. Evol. 24(1), 324–337 (2007)CrossRefGoogle Scholar
  18. 18.
    Jin, G., Nakhleh, L., Snir, S., Tuller, T.: A new linear-time heuristic algorithm for computing the parsimony score of phylogenetic networks: Theoretical bounds and empirical performance. In: Măndoiu, I.I., Zelikovsky, A. (eds.) ISBRA 2007. LNCS (LNBI), vol. 4463, pp. 61–72. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  19. 19.
    Jin, G., Nakhleh, L., Snir, S., Tuller, T.: Parsimony score of phylogenetic networks: Hardness results and a linear-time heuristic (submitted, 2008)Google Scholar
  20. 20.
    Judd, W.S., Olmstead, R.G.: A survey of tricolpate (eudicot) phylogenetic relationships. Am. J. Bot. 91, 1627–1644 (2004)CrossRefGoogle Scholar
  21. 21.
    Jukes, T., Cantor, C.: Evolution of protein molecules. In: Munro, H.N. (ed.) Mammalian protein metabolism, pp. 21–132 (1969)Google Scholar
  22. 22.
    Matte-Tailliez, O., Brochier, C., Forterre, P., Philippe, H.: Archaeal phylogeny based on ribosomal proteins. Mol. Biol. Evol. 19(5), 631–639 (2002)Google Scholar
  23. 23.
    Nakhleh, L., Ruths, D., Wang, L.S.: RIATA-HGT: A Fast and Accurate Heuristic for Reconstructing Horizontal Gene Transfer. In: Wang, L. (ed.) COCOON 2005. LNCS, vol. 3595, pp. 84–93. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  24. 24.
    Pupko, T., Huchon, D., Cao, Y., Okada, N., Hasegawa, M.: Combining multiple datasets in a likelihood analysis: which models are best. Mol. Biol. Evol. 19(12), 2294–2307 (2002)Google Scholar
  25. 25.
    Ragan, M.A.: On surrogate methods for detecting lateral gene transfer. FEMS Microbiol. Lett. 201(2), 187–191 (2001)CrossRefGoogle Scholar
  26. 26.
    Richardson, A.O., Palmer, J.D.: Horizontal gene transfer in plants. J. Exp. Bot. 58(1), 1–9 (2007)CrossRefGoogle Scholar
  27. 27.
    Siepel, A., Haussler, D.: Combining phylogenetic and hidden markov models in biosequence analysis. In: RECOMB 2003, pp. 277–286 (2003)Google Scholar
  28. 28.
    Strimmer, K., Moulton, V.: Likelihood analysis of phylogenetic networks using directed graphical models. Mol. Biol. Evol. 17(6), 875–881 (2000)Google Scholar
  29. 29.
    von Haeseler, A., Churchill, G.A.: Network models for sequence evolution. J. Mol. Evol. 37, 77–85 (1993)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Sagi Snir
    • 1
  • Tamir Tuller
    • 2
  1. 1.Dept. of Mathematics and Computer ScienceNetanya Academic CollegeNetanya
  2. 2.School of Computer ScienceTel Aviv University 

Personalised recommendations