Probabilistic evolutionary models have revolutionized the way sequence data are handled. The success of such models in sequence analysis resides in their ability to accurately describe the stochastic evolutionary process and to incorporate key biological phenomena as they are being discovered. Codon models, in particular, have been indispensable for meaningful biological analysis of vast amount of data because they allow differentiating between the types of selective forces that operate on the genome. Specifically, codon evolutionary models are often used for two related challenges: to detect selective forces operating on protein-coding genes and to study the association of such forces with species’ characteristics. Here, I propose developing and applying novel codon models to tackle these challenges from new perspectives. My suggested models are biologically realistic, computationally feasible, and preliminary data show that they fit sequence data better than previously available models. More specifically, the newly proposed models will relax the widespread unrealistic assumption that synonymous substitutions are free from selection. This will be achieved by explicitly accounting for the baseline selection forces acting at the RNA and DNA levels, on top of which the selection at the amino-acid level operates. This has important implications not only for positive selection inference, but also for the detection of functional elements that operate at the nucleotide level. I further propose developing a unified codon model for the analysis of phenotype-genotype evolution. This novel approach would allow inferring the relative roles of selection and mutation in causing substitution rate variation of a particular gene associated with a particular trait. The use of this novel framework promises to provide a robust approach for the understanding of species adaptation at the molecular level – an enduring goal of evolutionary biology and genomic research.
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