Statistical Applications in Genetics and Molecular Biology
Editor-in-Chief: Stumpf, Michael P.H.
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A Context Dependent Pair Hidden Markov Model for Statistical Alignment
- Universidad Carlos III de Madrid
- Laboratoire Statistique et Génome, Université d'Évry Val d'Essonne, UMR CNRS 8071, USC INRA
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.
Keywords: comparative genomics; contextual alignment; DNA sequence alignment; em algorithm; insertion deletion model; pair hidden Markov model; probabilistic alignment; sequence evolution; statistical alignment; stochastic expectation maximization algorithm