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The Effect of RNA Substitution Models on Viroid and RNA Virus Phylogenies.

ABSTRACT: Many viroids and RNA viruses have genomes that exhibit secondary structure, with paired nucleotides forming stems and loops. Such structures violate a key assumption of most methods of phylogenetic reconstruction, that sequence change is independent among sites. However, phylogenetic analyses of these transmissible agents rarely use evolutionary models that account for RNA secondary structure. Here, we assess the effect of using RNA-specific nucleotide substitution models on the phylogenetic inference of viroids and RNA viruses. We obtained data sets comprising full-genome nucleotide sequences from six viroid and ten single-stranded RNA virus species. For each alignment, we inferred consensus RNA secondary structures, then evaluated different DNA and RNA substitution models. We used model selection to choose the best-fitting model and evaluate estimated Bayesian phylogenies. Further, for each data set we generated and compared Robinson-Foulds (RF) statistics in order to test whether the distributions of trees generated under alternative models are notably different to each other. In all alignments, the best-fitting model was one that considers RNA secondary structure: RNA models that allow a nonzero rate of double substitution (RNA16A and RNA16C) fitted best for both viral and viroid data sets. In 14 of 16 data sets, the use of an RNA-specific model led to significantly longer tree lengths, but only in three cases did it have a significant effect on RFs. In conclusion, using RNA model when undertaking phylogenetic inference of viroids and RNA viruses can provide a better model fit than standard approaches and model choice can significantly affect branch length estimates.

SUBMITTER: Patino-Galindo JA

PROVIDER: S-EPMC5814974 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Likelihood-based approaches can reconstruct evolutionary processes in greater detail and with better precision from larger data sets. The extremely large comparative genomic data sets that are now being generated thus create new opportunities for understanding molecular evolution, but analysis of such large quantities of data poses escalating computational challenges. Recently developed Markov chain Monte Carlo methods that augment substitution histories are a promising approach to alleviate these computational costs. We analyzed the computational costs of several such approaches, considering how they scale with model and data set complexity. This provided a theoretical framework to understand the most important computational bottlenecks, leading us to combine novel variations of our conditional pathway integration approach with recent advances made by others. The resulting technique ("partial sampling" of substitution histories) is considerably faster than all other approaches we considered. It is accurate, simple to implement, and scales exceptionally well with dimensions of model complexity and data set size. In particular, the time complexity of sampling unobserved substitution histories using the new method is much faster than previously existing methods, and model parameter and branch length updates are independent of data set size. We compared the performance of methods on a 224-taxon set of mammalian cytochrome-b sequences. For a simple nucleotide substitution model, partial sampling was at least 10 times faster than the PhyloBayes program, which samples substitutions in continuous time, and about 100 times faster than when using fully integrated substitution histories. Under a general reversible model of amino acid substitution, the partial sampling method was 1,600 times faster than when using fully integrated substitution histories, confirming significantly improved scaling with model state-space complexity. Partial sampling of substitutions thus dramatically improves the utility of likelihood approaches for analyzing complex evolutionary processes on large data sets.

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The Effect of RNA Substitution Models on Viroid and RNA Virus Phylogenies.

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