A counting renaissance: combining stochastic mapping and empirical Bayes to quickly detect amino acid sites under positive selection

摘要

>Motivation: Statistical methods for comparing relative rates of synonymous and non>-synonymous substitutions maintain a central role in detecting positive selection. To identify selection, researchers often estimate the ratio of these relative rates () at individual alignment sites. Fitting a codon substitution model that captures heterogeneity in across sites provides a reliable way to perform such estimation, but it remains computationally prohibitive for massive datasets. By using crude estimates of the numbers of synonymous and non>-synonymous substitutions at each site, counting approaches scale well to large datasets, but they fail to account for ancestral state reconstruction uncertainty and to provide site-specific estimates.>Results: We propose a hybrid solution that borrows the computational strength of counting methods, but augments these methods with empirical Bayes modeling to produce a relatively fast and reliable method capable of estimating site-specific values in large datasets. Importantly, our hybrid approach, set in a Bayesian framework, integrates over the posterior distribution of phylogenies and ancestral reconstructions to quantify uncertainty about site-specific estimates. Simulations demonstrate that this method competes well with more>-principled statistical procedures and>, in some cases>, even outperforms them. We illustrate the utility of our method using human immunodeficiency virus, feline panleukopenia and canine parvovirus evolution examples.>Availability: Renaissance counting is implemented in the development branch of BEAST, freely available at . The method will be made available in the next public release of the package, including support to set up analyses in BEAUti.>Contact: or >Supplementary information: are available at Bioinformatics online.