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Detecting Recombination in DNA Sequence Alignments

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Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

Summary

The underlying assumption of the phylogenetic inference methods discussed in the previous chapter is that we have one set of hierarchical relationships among the taxa. While this approach is reasonable when applied to most DNA sequence alignments, it can be violated in certain bacteria and viruses due to sporadic recombination, which is a process whereby different strains exchange or transfer DNA subsequences. The present chapter discusses the implications of recombination for phylogenetic inference and describes various methods for detecting and identifying recombinant regions in sequence alignments.

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References

  1. H. Bandelt and A. W. M. Dress. Split decomposition: a new and useful approach to phylogenetic analysis of distance data. Molecular Phylogenetics and Evolution, 1:242–252, 1992.

    Article  Google Scholar 

  2. G. Casella and E. I. George. Explaining the Gibbs sampler. The American Statistician, 46(3):167–174, 1992.

    Article  MathSciNet  Google Scholar 

  3. J. Felsenstein and G. A. Churchill. A hidden Markov model approach to variation among sites in rate of evolution. Molecular Biology and Evolution, 13(1):93–104, 1996.

    Google Scholar 

  4. W. M. Fitch and E. Margoliash. Construction of phylogenetic trees. Science, 155:279–284, 1987.

    Google Scholar 

  5. Z. Ghahramani and M. I. Jordan. Factorial hidden Markov models. Machine Learning, 29:245–273, 1997.

    Article  Google Scholar 

  6. N. C. Grassly and E. C. Holmes. A likelihood method for the detection of selection and recombination using nucleotide sequences. Molecular Biology and Evolution, 14(3):239–247, 1997.

    Google Scholar 

  7. D. Heckerman. A tutorial on learning with Bayesian networks. In M. I. Jordan, editor, Learning in Graphical Models, pages 301–354. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1998. Reprinted by MIT Press in 1999.

    Google Scholar 

  8. J. Hein. A heuristic method to reconstruct the history of sequences subject to recombination. Journal of Molecular Evolution, 36:396–405, 1993.

    Article  Google Scholar 

  9. P. G. Hoel. Introduction to Mathematical Statistics. John Wiley and Sons, Singapore, 1984.

    Google Scholar 

  10. D. Husmeier and G. McGuire. Detecting recombination with MCMC. Bioinformatics, 18(Suppl.1):S345–S353, 2002.

    Google Scholar 

  11. D. Husmeier and G. McGuire. Detecting recombination in 4-taxa DNA sequence alignments with Bayesian hidden Markov models and Markov chain Monte Carlo. Molecular Biology and Evolution, 20(3):315–337, 2003.

    Article  Google Scholar 

  12. D. Husmeier and F. Wright. Detection of recombination in DNA multiple alignments with hidden Markov models. Journal of Computational Biology, 8(4):401–427, 2001.

    Article  Google Scholar 

  13. D. Husmeier and F. Wright. Probabilistic divergence measures for detecting interspecies recombination. Bioinformatics, 17(Suppl.1):S123–S131, 2001.

    Google Scholar 

  14. D. Husmeier and F. Wright. A Bayesian approach to discriminate between alternative DNA sequence segmentations. Bioinformatics, 18(2):226–234, 2002.

    Article  Google Scholar 

  15. W. J. Krzanowski and F. H. C. Marriott. Multivariate Analysis, volume 2. Arnold, 1995. ISBN 0-340-59325-3.

    Google Scholar 

  16. D. J. C. MacKay. Introduction to Monte Carlo methods. In M. I. Jordan, editor, Learning in Graphical Models, pages 301–354. Kluwer Academic Publishers, Dordrecht, The Netherlands, 1998. Reprinted by MIT Press in 1999.

    Google Scholar 

  17. J. Maynard Smith. Analyzing the mosaic structure of genes. Journal of Molecular Evolution, 34:126–129, 1992.

    Google Scholar 

  18. G. McGuire and F. Wright. TOPAL 2.0: improved detection of mosaic sequences within multiple alignments. Bioinformatics, 16(2):130–134, 2000.

    Article  Google Scholar 

  19. G. McGuire, F. Wright, and M. Prentice. A graphical method for detecting recombination in phylogenetic data sets. Molecular Biology and Evolution, 14(11):1125–1131, 1997.

    Google Scholar 

  20. G. McGuire, F. Wright, and M. Prentice. A Bayesian method for detecting recombination in DNA multiple alignments. Journal of Computational Biology, 7(1/2):159–170, 2000.

    Article  Google Scholar 

  21. M. Moniz de Sa and G. Drouin. Phylogeny and substitution rates of angiosperm actin genes. Molecular Biology and Evolution, 13:1198–1212, 1996.

    Google Scholar 

  22. D. Posada, K. A. Crandall, and E. C. Holmes. Recombination in evolutionary genomics. Annual Review of Genetics, 36:75–97, 2002.

    Article  Google Scholar 

  23. C. P. Robert, G. Celeux, and J. Diebolt. Bayesian estimation of hidden Markov chains: A stochastic implementation. Statistics & Probability Letters, 16:77–83, 1993.

    Article  MathSciNet  Google Scholar 

  24. D. L. Robertson, P. M. Sharp, F. E. McCutchan, and B. H. Hahn. Recombination in HIV-1. Nature, 374:124–126, 1995.

    Article  Google Scholar 

  25. R. Y. Rubinstein. Simulation and the Monte Carlo Method. John Wiley & Sons, New York, 1981.

    Google Scholar 

  26. K. Strimmer and V. Moulton. Likelihood analysis of phylogenetic networks using directed graphical models. Molecular Biology and Evolution, 17(6):875–881, 2000.

    Google Scholar 

  27. K. Strimmer, C. Wiuf, and V. Moulton. Recombination analysis using directed graphical models. Molecular Biology and Evolution, 18(1):97–99, 2001.

    Google Scholar 

  28. J. Zhou and B. G. Spratt. Sequence diversity within the argF, fbp and recA genes of natural isolates of Neisseria meningitidis: interspecies recombination within the argF gene. Molecular Microbiology, 6:2135–2146, 1992.

    Google Scholar 

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© 2005 Springer-Verlag London Limited

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Husmeier, D., Wright, F. (2005). Detecting Recombination in DNA Sequence Alignments. In: Husmeier, D., Dybowski, R., Roberts, S. (eds) Probabilistic Modeling in Bioinformatics and Medical Informatics. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/1-84628-119-9_5

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  • DOI: https://doi.org/10.1007/1-84628-119-9_5

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-85233-778-0

  • Online ISBN: 978-1-84628-119-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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