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Segmenting bacterial and viral DNA sequence alignments with a trans-dimensional phylogenetic factorial hidden Markov model


  • Wolfgang P. Lehrach
  • Dirk Husmeier


The traditional approach to phylogenetic inference assumes that a single phylogenetic tree can represent the relationships and divergence between the taxa. However, taxa sequences exhibit varying levels of conservation, e.g. because of regulatory elements and active binding sites. Also, certain bacteria and viruses undergo interspecific recombination, where different strains exchange or transfer DNA subsequences, leading to a tree topology change. We propose a phylogenetic factorial hidden Markov model to detect recombination and rate variation simultaneously. This is applied to two DNA sequence alignments: one bacterial ("Neisseria") and another of type 1 human immunodeficiency virus. Inference is carried out in the Bayesian framework, using reversible jump Markov chain Monte Carlo sampling. Copyright (c) 2009 Royal Statistical Society.

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  • Wolfgang P. Lehrach & Dirk Husmeier, 2009. "Segmenting bacterial and viral DNA sequence alignments with a trans-dimensional phylogenetic factorial hidden Markov model," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 58(3), pages 307-327.
  • Handle: RePEc:bla:jorssc:v:58:y:2009:i:3:p:307-327

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    References listed on IDEAS

    1. Richard J. Boys & Daniel A. Henderson, 2004. "A Bayesian Approach to DNA Sequence Segmentation," Biometrics, The International Biometric Society, vol. 60(3), pages 573-581, September.
    2. R. J. Boys & D. A. Henderson & D. J. Wilkinson, 2000. "Detecting homogeneous segments in DNA sequences by using hidden Markov models," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 49(2), pages 269-285.
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    Cited by:

    1. Husmeier Dirk & Mantzaris Alexander V., 2008. "Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-41, November.

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