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- Data-specific substitution models improve protein-based phylogeneticsPublication . Brazão, João; Foster, Peter G.; J. Cox, CymonCalculating amino-acid substitution models that are specific for individual protein data sets is often difficult due to the computational burden of estimating large numbers of rate parameters. In this study, we tested the computational efficiency and accuracy of five methods used to estimate substitution models, namely Codeml, FastMG, IQ-TREE, P4 (maximum likelihood), and P4 (Bayesian inference). Data-specific substitution models were estimated from simulated alignments (with different lengths) that were generated from a known simulation model and simulation tree. Each of the resulting data-specific substitution models was used to calculate the maximum likelihood score of the simulation tree and simulated data that was used to calculate the model, and compared with the maximum likelihood scores of the known simulation model and simulation tree on the same simulated data. Additionally, the commonly-used empirical models, cpREV and WAG, were assessed similarly. Data-specific models performed better than the empirical models, which under-fitted the simulated alignments, had the highest difference to the simulation model maximum-likelihood score, clustered further from the simulation model in principal component analysis ordination, and inferred less accurate trees. Data-specific models and the simulation model shared statistically indistinguishable maximum-likelihood scores, indicating that the five methods were reasonably accurate at estimating substitution models by this measure. Nevertheless, tree statistics showed differences between optimal maximum likelihood trees. Unlike other model estimating methods, trees inferred using data-specific models generated with IQ-TREE and P4 (maximum likelihood) were not significantly different from the trees derived from the simulation model in each analysis, indicating that these two methods alone were the most accurate at estimating data-specific models. To show the benefits of using data-specific protein models several published data sets were reanalysed using IQ-TREE-estimated models. These newly estimated models were a better fit to the data than the empirical models that were used by the original authors, often inferred longer trees, and resulted in different tree topologies in more than half of the re-analysed data sets. The results of this study show that software availability and high computation burden are not limitations to generating better-fitting data-specific amino-acid substitution models for phylogenetic analyses.
- The mitochondrial phylogeny of land plants shows support for Setaphyta under composition-heterogeneous substitution modelsPublication . Sousa, Filipe; Civáň, Peter; Brazão, João; Foster, Peter G.; Cox, Cymon J.Congruence among analyses of plant genomic data partitions (nuclear, chloroplast and mitochondrial) is a strong indicator of accuracy in plant molecular phylogenetics. Recent analyses of both nuclear and chloroplast genome data of land plants (embryophytes) have, controversially, been shown to support monophyly of both bryophytes (mosses, liverworts, and hornworts) and tracheophytes (lycopods, ferns, and seed plants), with mosses and liverworts forming the clade Setaphyta. However, relationships inferred from mitochondria are incongruent with these results, and typically indicate paraphyly of bryophytes with liverworts alone resolved as the earliest-branching land plant group. Here, we reconstruct the mitochondrial land plant phylogeny from a newly compiled data set. When among-lineage composition heterogeneity is accounted for in analyses of codon-degenerate nucleotide and amino acid data, the clade Setaphyta is recovered with high support, and hornworts are supported as the earliest-branching lineage of land plants. These new mitochondrial analyses demonstrate partial congruence with current hypotheses based on nuclear and chloroplast genome data, and provide further incentive for revision of how plants arose on land.