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Codon EvolutionMechanisms and Models$
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Gina M. Cannarozzi and Adrian Schneider

Print publication date: 2012

Print ISBN-13: 9780199601165

Published to Oxford Scholarship Online: May 2015

DOI: 10.1093/acprof:osobl/9780199601165.001.0001

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Monte Carlo computational approaches in Bayesiancodon-substitution modelling

Monte Carlo computational approaches in Bayesiancodon-substitution modelling

Chapter:
(p.45) Chapter 4 Monte Carlo computational approaches in Bayesiancodon-substitution modelling
Source:
Codon Evolution
Author(s):

Nicolas Rodrigue

Nicolas Lartillot

Publisher:
Oxford University Press
DOI:10.1093/acprof:osobl/9780199601165.003.0004

This chapter reviews Markov Chain Monte Carlo (MCMC) approaches in codon-substitution modelling. It outlines the process of data analysis using the Bayesian framework. It describes the algorithms for sampling from posterior distributions under the more well-known models inspired from Muse and Gaut (1994) and Goldman and Yang (1994). The chapter also illustrates how parameter expansion and data-augmentation sampling methodologies can be employed to develop models that apply the Dirichlet process for capturing across-site heterogeneity (e.g. Huelsenbeck et al., 2006), and models that allow for dependence among codons due to protein structure (e.g. Robinson et al., 2003). It describes the thermodynamic integration approaches for computing Bayes factors in these contexts, and highlights the diversity of models that can be implemented and directly compared with such approaches.

Keywords:   codon-substitution modelling, Markov Chain Monte Carlo, Bayesian framework, thermodynamic integration, Dirichlet process

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