While this statistical revolution has clearly had a positive impact, the proliferation of sophisticated evolutionary models has placed a burden on researchers to select the model most appropriate for their data.Īs has been repeatedly shown, an inappropriate choice of evolutionary model can affect the outcome of any phylogenetic analysis, for example, by incorrectly estimating tree topology ( Penny et al. Probabilistic modeling of sequence evolution has become the norm in phylogenetic inference ( Felsenstein 2001), dominated by maximum likelihood (ML) ( Felsenstein 1981) and Bayesian ( Yang and Rannala 1997) estimation. While the questions are diverse, those using this information are linked by a common challenge: to provide the best estimate of the evolutionary history of their data. The growing abundance of available molecular sequence data has inspired research across ecology and evolutionary biology and continues to provide novel insights into a range of biological questions. Phylogenetic inference, protein-coding sequences, substitution models Introduction These results have significant implications for phylogenetic inference of coding sequences as they make it clear that substitution models incorporating CPs not only are a computationally realistic alternative to standard models but may also frequently be statistically superior. The majority of analyzed gene alignments are best described by CP substitution models, rather than by standard nucleotide models, and without the computational cost of full codon models. We determined the most appropriate model for alignments of 177 RNA virus genes and 106 yeast genes, using 11 substitution models including one codon model and four CP models. We investigated an efficient alternative to standard nucleotide substitution models, in which codon position (CP) is incorporated into the model. This problem is exacerbated by the exclusion of codon-based models from commonly employed model selection techniques, presumably due to the computational cost associated with codon models. Although phylogenetic inference of protein-coding sequences continues to dominate the literature, few analyses incorporate evolutionary models that consider the genetic code.
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