A context dependent pair hidden Markov model for statistical alignment

TL;DR

This paper introduces a novel context dependent pair hidden Markov model for statistical alignment of nucleotide sequences, improving accuracy by incorporating mutation context and using a stochastic approximation EM algorithm, validated on simulated and vertebrate genome data.

Contribution

It presents a new context dependent model for sequence alignment and a specialized estimation algorithm, enhancing alignment accuracy over existing methods.

Findings

Improved alignment accuracy on simulated data.

Enhanced alignment of human pseudogene and functional gene.

Effective estimation of mutation rates in vertebrate genomes.

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.