Evolution of GALA Proteins All ML phylogenies were inferred using PHYML program [42]. The phylogenies used to perform the selection tests was inferred using coding sequences under HKY+c, and all phylogenies used for testing LGT hypothesis were inferred under WAG model [43]. Phylogenies for testing LGT hypothesis were also inferred using fast neighbor-joining method implemented in BIONJ [44,45]. Branch supports were inferred using approximate LRT [45] and 100 bootstrap replicates where computation permitted. Supporting Information Table S1 Maximum likelihood estimates of selection parameters for codon models Found at: doi:10.1371/journal.pone.0001694.s001 (0.06 MB DOC) Figure S1 Phylogenetic tree supplemental file phymlGALA.tre, which can be viewed with ATV-forester from www.phylosoft.org/ atv/ Found at: doi:10.1371/journal.pone.0001694.s002 (0.04 MB TXT) Positions of positive selection in GALA2 at: doi:10.1371/journal.pone.0001694.s003 (0.03 Positions of positive selection in GALA2 at: doi:10.1371/journal.pone.0001694.s004 (0.02 MB MB sequence. Inconsistencies in estimates under different models may be a sign that the algorithm has not converged to a global optimum. To insure proper convergence, we performed repeated runs for each model (with different starting values) and confirmed that the distribution of selective pressure described by estimates under models M2a and M8 were compatible with the distribution estimated under M3 for all datasets analyzed. Where a LRT for positive selective pressure was significant, we used the Bayesian inference to calculate posterior probabilities that a site belongs to a particular site class. The posterior distribution of the parameter of interest (in our case v) is proportional to the product of its assumed prior distribution and the likelihood of the observed data given this prior. In this study we used the Bayesian Empirical Bayesian approach [28], where the posteriors are obtained by integrating over the prior distribution of selection- related parameters, while setting other model parameters to their maximum likelihood estimates. Sites with high posteriors proba- bilities (.0.95) of coming from a class with v.1 are likely to have evolved under positive selection. Anisimova et al. [39] showed that the major factor affecting the accuracy Bayesian site prediction is the diversity of the data set and the number of sequences used. 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