DOI: 10.5061/dryad.m4dr156
title: Data from: Improved inference of site-specific positive selection under a generalized parametric codon model when there are multinucleotide mutations and multiple nonsynonymous rates

description: 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&gt;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&gt;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
 &gt;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.
description: 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.txt