CodABC: a computational framework to coestimate recombination, substitution, and molecular adaptation rates by approximate Bayesian computation

Abstract

The estimation of substitution and recombination rates can provide important insights into the molecular evolution of protein-coding sequences. Here, we present a new computational framework, called CodABC, to jointly estimate recombination, substitution and synonymous and non-synonymous rates from coding data. CodABC uses approximate Bayesian computation (ABC) with and without regression adjustment and implements a variety of codon models, intracodon recombination and longitudinal sampling. CodABC can provide accurate joint parameter estimates from recombining coding sequences, often outperforming maximum likelihood methods based on more approximate models. In addition, CodABC allows for the inclusion of several nuisance parameters such as those representing codon frequencies, transition matrices, heterogeneity across sites or invariable sites. CodABC is freely available from http://code.google.com/p/codabc/, includes a GUI, extensive documentation and ready-touse examples, and can run in parallel on multicore machines.