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

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  • Wolfgang P. Lehrach
  • Dirk Husmeier

Abstract

Summary. 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.

Suggested Citation

  • 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, July.
    • Handle: RePEc:bla:jorssc:v:58:y:2009:i:3:p:307-327
    DOI: 10.1111/j.1467-9876.2008.00648.x
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    References listed on IDEAS

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    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. Suchard M.A. & Weiss R.E. & Dorman K.S. & Sinsheimer J.S., 2003. "Inferring Spatial Phylogenetic Variation Along Nucleotide Sequences: A Multiple Changepoint Model," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 427-437, January.
    3. 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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    1. Amirali Kani & Wayne S. DeSarbo & Duncan K. H. Fong, 2018. "A Factorial Hidden Markov Model for the Analysis of Temporal Change in Choice Models," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(3), pages 162-177, December.
    2. 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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