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 nonsynonymous rates from coding data. CodABC uses approximate Bayesian computation 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-to-use examples, and can run in parallel on multicore machines.

Advances in Computer Simulation of Genome Evolution: Toward More Realistic Evolutionary Genomics Analysis by Approximate Bayesian Computation

“…These methods provide promising alternative analytical strategies and can generate very accurate inferences because of their joint consideration of different evolutionary processes. For example, they have already outperformed approximate maximum likelihood methods based on more approximate models (Lopes et al 2014;Arenas et al 2015). Nevertheless, ABC approaches definitely require extensive computer simulation with evolutionary frameworks that must be able to model the evolutionary process in a way that is as realistic as possible.…”

ABC random forests for Bayesian parameter inference

“…Since their introduction in population genetics (Tavaré et al, 1997;Pritchard et al, 1999;Beaumont et al, 2002), ABC methods have been used in an ever increasing range of applications, corresponding to different types of complex models in diverse scientific fields (see, e.g., Beaumont, 2008;Toni et al, 2009;Beaumont, 2010;Csilléry et al, 2010;Theunert et al, 2012;Chan et al, 2014;Arenas et al, 2015;Sisson et al, 2018). Posterior distributions are the cornerstone of any Bayesian analysis as they constitute both a sufficient summary of the data and a means to deliver all aspects of inference, from point estimators to predictions and uncertainty quantification.…”

LDJump: Estimating variable recombination rates from population genetic data

“…Further well established frameworks to estimate recombination rates include Lamarc (Kuhner, ), OmegaMap (Wilson & McVean, ), RDP (Martin, Murrell, Golden, Khoosal, & Muhire, ), and CodABC (Arenas, Lopes, Beaumont, & Posada, ). The latter method (Arenas et al., ) applies approximate Bayesian computation (ABC) using 26 summary statistics to estimate constant recombination rates for simulated regions of size up to 300 codons for 100 alignments. With the GUI of RDP (Martin et al., ) overall patterns of recombination and testing for hot and cold spots is performed with help from LDhat (McVean et al., ).…”