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Detecting homogeneous segments in DNA sequences by using hidden Markov models

Author

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  • R. J. Boys
  • D. A. Henderson
  • D. J. Wilkinson

Abstract

In recent years there has been a rapid growth in the amount of DNA being sequenced and in its availability through genetic databases. Statistical techniques which identify structure within these sequences can be of considerable assistance to molecular biologists particularly when they incorporate the discrete nature of changes caused by evolutionary processes. This paper focuses on the detection of homogeneous segments within heterogeneous DNA sequences. In particular, we study an intron from the chimpanzee α‐fetoprotein gene; this protein plays an important role in the embryonic development of mammals. We present a Bayesian solution to this segmentation problem using a hidden Markov model implemented by Markov chain Monte Carlo methods. We consider the important practical problem of specifying informative prior knowledge about sequences of this type. Two Gibbs sampling algorithms are contrasted and the sensitivity of the analysis to the prior specification is investigated. Model selection and possible ways to overcome the label switching problem are also addressed. Our analysis of intron 7 identifies three distinct homogeneous segment types, two of which occur in more than one region, and one of which is reversible.

Suggested Citation

  • 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.
    • Handle: RePEc:bla:jorssc:v:49:y:2000:i:2:p:269-285
    DOI: 10.1111/1467-9876.00191
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    Cited by:

    1. 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.
    2. 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.
    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. 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.
    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. 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.
    7. 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.
    8. 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.

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