The PREP suite: predictive RNA editors for plant mitochondrial genes, chloroplast genes and user-defined alignments

RNA editing alters plant mitochondrial and chloroplast transcripts by converting specific cytidines to uridines, which usually results in a change in the amino acid sequence of the translated protein. Systematic studies have experimentally identified sites of RNA editing in organellar transcriptomes from several species, but these analyses have not kept pace with rate of genome sequencing. The PREP (predictive RNA editors for plants) suite was developed to computationally predict sites of RNA editing based on the well-known principle that editing in plant organelles increases the conservation of proteins across species. The PREP suite provides predictive RNA editors for plant mitochondrial genes (PREP-Mt), for chloroplast genes (PREP-Cp), and for alignments submitted by the user (PREP-Aln). These servers require minimal input, are very fast, and are highly accurate on all seed plants examined to date. PREP-Mt has proved useful in several research studies and the newly developed PREP-Cp and PREP-Aln servers should be of further assistance for analyses that require knowledge of the location of sites of RNA editing. The PREP suite is freely available at http://prep.unl.edu/

Molecular dating, evolutionary rates, and the age of the grasses.

Many questions in evolutionary biology require an estimate of divergence times but, for groups with a sparse fossil record, such estimates rely heavily on molecular dating methods. The accuracy of these methods depends on both an adequate underlying model and the appropriate implementation of fossil evidence as calibration points. We explore the effect of these in Poaceae (grasses), a diverse plant lineage with a very limited fossil record, focusing particularly on dating the early divergences in the group. We show that molecular dating based on a data set of plastid markers is strongly dependent on the model assumptions. In particular, an acceleration of evolutionary rates at the base of Poaceae followed by a deceleration in the descendants strongly biases methods that assume an autocorrelation of rates. This problem can be circumvented by using markers that have lower rate variation, and we show that phylogenetic markers extracted from complete nuclear genomes can be a useful complement to the more commonly used plastid markers. However, estimates of divergence times remain strongly affected by different implementations of fossil calibration points. Analyses calibrated with only macrofossils lead to estimates for the age of core Poaceae ∼51-55 Ma, but the inclusion of microfossil evidence pushes this age to 74-82 Ma and leads to lower estimated evolutionary rates in grasses. These results emphasize the importance of considering markers from multiple genomes and alternative fossil placements when addressing evolutionary issues that depend on ages estimated for important groups

A Bayesian approach for evaluating the impact of historical events on rates of diversification

Evolutionary biologists often wish to explore the impact of a particular historical event (e.g., the origin of a novel morphological trait, an episode of biogeographic dispersal, or the onset of an ecological association) on rates of diversification (speciation minus extinction). We describe a Bayesian approach for evaluating the correlation between such events and differential rates of diversification that relies on cross-validation predictive densities. This approach exploits estimates of the marginal posterior probability for the rate of diversification (in the unaffected part of the tree) and the marginal probability for the timing of the event to generate a predictive distribution of species diversity that would be expected had the event not occurred. The realized species diversity can then be compared to this predictive diversity distribution to assess whether rates of diversification associated with the event are significantly higher or lower than expected. Although simple, this Bayesian approach provides a robust inference framework that accommodates various sources of uncertainty, including error associated with estimates of divergence times, diversification-rate parameters, and event history. Furthermore, the proposed approach is relatively flexible, allowing exploration of various types of events (including changes in discrete morphological traits, episodes of biogeographic movement, etc.) under both hypothesis-testing and data-exploration inference scenarios. Importantly, the cross-validation predictive densities approach facilitates evaluation of both replicated and unique historical events. We demonstrate this approach with empirical examples concerning the impact of morphological and biogeographic events on rates of diversification in Adoxaceae and Lupinus, respectively