Background: Statistical methods for identifying positively selected sites in protein coding regions are one of the most commonly used tools in evolutionary bioinformatics. However, they have been limited by not taking the physiochemical properties of amino acids into account. Results: We develop a new codon-based likelihood model for detecting site-specific selection pressures acting on specific physicochemical properties. Nonsynonymous substitutions are divided into substitutions that differ with respect to the physicochemical properties of interest, and those that do not. The substitution rates of these two types of changes, relative to the synonymous substitution rate, are then described by two parameters, and respectively. The new model allows us to perform likelihood ratio tests for positive selection acting on specific physicochemical properties of interest. The new method is first used to analyze simulated data and is shown to have good power and accuracy in detecting physico...
Wendy S. W. Wong, Raazesh Sainudiin, Rasmus Nielse