IDEAS home Printed from https://ideas.repec.org/a/bpj/sagmbi/v7y2008i1n34.html
   My bibliography  Save this article

Addressing the Shortcomings of Three Recent Bayesian Methods for Detecting Interspecific Recombination in DNA Sequence Alignments

Author

Listed:
  • Husmeier Dirk

    (Biomathematics and Statistics Scotland)

  • Mantzaris Alexander V.

    (Biomathematics and Statistics Scotland & School of Informatics, University of Edinburgh)

Abstract

We address a potential shortcoming of three probabilistic models for detecting interspecific recombination in DNA sequence alignments: the multiple change-point model (MCP) of Suchard et al. (2003), the dual multiple change-point model (DMCP) of Minin et al. (2005), and the phylogenetic factorial hidden Markov model (PFHMM) of Husmeier (2005). These models are based on the Bayesian paradigm, which requires the solution of an integral over the space of branch lengths. To render this integration analytically tractable, all three models make the same assumption that the vectors of branch lengths of the phylogenetic tree are independent among sites. While this approximation reduces the computational complexity considerably, we show that it leads to the systematic prediction of spurious topology changes in the Felsenstein zone, that is, the area in the branch lengths configuration space where maximum parsimony consistently infers the wrong topology due to long-branch attraction. We apply two Bayesian hypothesis tests, based on an inter- and an intra-model approach to estimating the marginal likelihood. We then propose a revised model that addresses these shortcomings, and compare it with the aforementioned models on a set of synthetic DNA sequence alignments systematically generated around the Felsenstein zone.

Suggested Citation

  • 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.
    • Handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:34
    DOI: 10.2202/1544-6115.1399
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1544-6115.1399
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1544-6115.1399?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Leonardo Oliveira Martins & Hirohisa Kishino, 2010. "Distribution of distances between topologies and its effect on detection of phylogenetic recombination," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 62(1), pages 145-159, February.
    3. Andreas C. Georgiou & Alexandra Papadopoulou & Pavlos Kolias & Haris Palikrousis & Evanthia Farmakioti, 2021. "On State Occupancies, First Passage Times and Duration in Non-Homogeneous Semi-Markov Chains," Mathematics, MDPI, vol. 9(15), pages 1-17, July.
    4. Nasroallah Abdelaziz & Elkimakh Karima, 2017. "HMM with emission process resulting from a special combination of independent Markovian emissions," Monte Carlo Methods and Applications, De Gruyter, vol. 23(4), pages 287-306, December.
    5. Elkimakh Karima & Nasroallah Abdelaziz, 2020. "Hidden Markov Model with Markovian emission," Monte Carlo Methods and Applications, De Gruyter, vol. 26(4), pages 303-313, December.
    6. Yu Chuan Tai & Mark N. Kvale & John S. Witte, 2010. "Segmentation and Estimation for SNP Microarrays: A Bayesian Multiple Change-Point Approach," Biometrics, The International Biometric Society, vol. 66(3), pages 675-683, September.
    7. 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.
    8. Nur, Darfiana & Allingham, David & Rousseau, Judith & Mengersen, Kerrie L. & McVinish, Ross, 2009. "Bayesian hidden Markov model for DNA sequence segmentation: A prior sensitivity analysis," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1873-1882, March.
    9. Wilkinson, Darren J & KH Yeung, Stephen, 2004. "A sparse matrix approach to Bayesian computation in large linear models," Computational Statistics & Data Analysis, Elsevier, vol. 44(3), pages 493-516, January.
    10. 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.
    11. Chi Peter B. & Chattopadhyay Sujay & Sokurenko Evgeni V. & Lemey Philippe & Minin Vladimir N., 2015. "Synonymous and nonsynonymous distances help untangle convergent evolution and recombination," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 14(4), pages 375-389, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:7:y:2008:i:1:n:34. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.