Data from: Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates

Background: An excess of nonsynonymous substitutions, over neutrality, is considered evidence of positive Darwinian selection. Inference for proteins often relies on estimation of the nonsynonymous to synonymous ratio (ω=dN/dS) within a codon model. However, to ease computational difficulties, ω is typically estimated assuming an idealized substitution process where (i) all nonsynonymous substitutions have the same rate (regardless of impact on organism fitness) and (ii) instantaneous double and triple (DT) nucleotide mutations have zero probability (despite evidence that they can occur). It follows that estimates of ω represent an imperfect summary of the intensity of selection, and that tests based on the ω>1 threshold could be negatively impacted. Results: We developed a general-purpose parametric (GPP) modelling framework for codons. This novel approach allows specification of all possible instantaneous codon substitutions, including multiple nonsynonymous rates (MNRs) and instantaneous DT nucleotide changes. Existing codon models are specified as special cases of the GPP model. We use GPP models to implement likelihood ratio tests for ω>1 that accommodate MNRs and DT mutations. Through both simulation and real data analysis, we find that failure to model MNRs and DT mutations reduces power in some cases and inflates false positives in others. False positives under traditional M2a and M8 models were very sensitive to DT changes. This was exacerbated by the choice of frequency parameterization (GY vs. MG), with rates sometimes >90% under MG. By including MNRs and DT mutations, accuracy and power was greatly improved under the GPP framework. However, we also find that over-parameterized models can perform less well, and this can contribute to degraded performance of LRTs. Conclusions: We suggest GPP models should be used alongside traditional codon models. Further, all codon models should be deployed within an experimental design that includes (i) assessing robustness to model assumptions, and (ii) investigation of non-standard behaviour of MLEs. As the goal of every analysis is to avoid false conclusions, more work is needed on model selection methods that consider both the increase in fit engendered by a model parameter and the degree to which that parameter is affected by un-modelled evolutionary processes.Sequence data for simulation studiesMulti-sequence alignments for simulation study 1 (cases 1a-1c) simulation study 2 (cases 2a-2h) and simulation study 3 (cases 3a-3c).Simulation_sequence_data.tar.gzNewick tree files for simulation studiesNewick formatted (parenthetical notation) tree files, including branch lengths, used in each simulation study. The scale of the branch lengths is mean number of substitutions per codonSimulation_trees.txtCOLD program commands for simulation studiesCommands required to generate the sequence data of each simulation scenario using the COLD programCOLD_simulation_scripts.tar.gzNames of genes in the real data analysisFull names and ID numbers for the 24 Streptococcus transmembrane proteins analyzed.Streptococcus_gene_names.txtStreptococcus gene sequence dataMulti-sequence alignments for the 24 Streptococcus transmembrane proteins. Ambiguous alignment positions are removed.Streptococcus_gene_sequences.txtGene trees for each Streptococcus geneNewick formatted (parenthetical notation) tree files, including branch lengths, for each of the 24 Streptococcus transmembrane proteins analyzed. The scale of the branch lengths is mean number of substitutions per codonStreptococcus_trees.tx