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A Context Dependent Pair Hidden Markov Model for Statistical Alignment

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  • Arribas-Gil Ana
  • Matias Catherine

Abstract

This article proposes a novel approach to statistical alignment of nucleotide sequences by introducing a context dependent structure on the substitution process in the underlying evolutionary model. We propose to estimate alignments and context dependent mutation rates relying on the observation of two homologous sequences. The procedure is based on a generalized pair-hidden Markov structure, where conditional on the alignment path, the nucleotide sequences follow a Markov distribution. We use a stochastic approximation expectation maximization (saem) algorithm to give accurate estimators of parameters and alignments. We provide results both on simulated data and vertebrate genomes, which are known to have a high mutation rate from CG dinucleotide. In particular, we establish that the method improves the accuracy of the alignment of a human pseudogene and its functional gene.

Suggested Citation

  • Arribas-Gil Ana & Matias Catherine, 2012. "A Context Dependent Pair Hidden Markov Model for Statistical Alignment," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(1), pages 1-29, January.
    • Handle: RePEc:bpj:sagmbi:v:11:y:2012:i:1:n:5
    DOI: 10.2202/1544-6115.1733
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    References listed on IDEAS

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    1. Ana Arribas‐Gil & Elisabeth Gassiat & Catherine Matias, 2006. "Parameter Estimation in Pair‐hidden Markov Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 651-671, December.
    2. Colin N Dewey & Peter M Huggins & Kevin Woods & Bernd Sturmfels & Lior Pachter, 2006. "Parametric Alignment of Drosophila Genomes," PLOS Computational Biology, Public Library of Science, vol. 2(6), pages 1-9, June.
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    Cited by:

    1. Sabine Mercier & Grégory Nuel, 2022. "Duality Between the Local Score of One Sequence and Constrained Hidden Markov Model," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1411-1438, September.

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    1. Arribas-Gil Ana & Matias Catherine, 2017. "A time warping approach to multiple sequence alignment," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(2), pages 133-144, April.
    2. Arribas-Gil Ana, 2010. "Parameter Estimation in Multiple-Hidden I.I.D. Models from Biological Multiple Alignment," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 9(1), pages 1-37, January.

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