Bayesian semiparametric regression models to characterize molecular evolution.

ABSTRACT:

Background

Statistical models and methods that associate changes in the physicochemical properties of amino acids with natural selection at the molecular level typically do not take into account the correlations between such properties. We propose a Bayesian hierarchical regression model with a generalization of the Dirichlet process prior on the distribution of the regression coefficients that describes the relationship between the changes in amino acid distances and natural selection in protein-coding DNA sequence alignments.

Results

The Bayesian semiparametric approach is illustrated with simulated data and the abalone lysin sperm data. Our method identifies groups of properties which, for this particular dataset, have a similar effect on evolution. The model also provide

SUBMITTER: Datta S 

PROVIDER: S-EPMC3577475 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature