Accessing the relative performance of fast molecular dating methods for phylogenomic data

Abstract Due to advances in genome sequencing techniques, there was a significant growth of phylogenomic datasets. This massive amount of data represents a computational challenge for molecular dating with the Bayesian approach that relax the assumption of rate constancy. To overcome these issues, over the last few decades, rapid molecular dating methods have been proposed. However, comparative evaluation of their relative performances on empirical data sets is lacking. We analyzed 23 empirical phylogenomic datasets to investigate the performance of two commonly employed fast dating methodologies, the penalized likelihood (PL), implemented in treePL, and the relative rate framework (RRF), implemented in RelTime. They were compared to Bayesian analysis under the same models and calibration settings. We found that the RRF was computationally faster and generally provided node age estimates statistically equivalent to Bayesian divergence times. Furthermore, contrasted to Bayesian dating, PL time estimates were excessively precise. To approximate Bayesian approaches, RelTime is an efficient method with significantly lower computational demand, being up to more than 100 times faster than treePL. Thus, to alleviate the computational burden of Bayesian divergence time inference in the era of massive genomic data, molecular dating can be facilitated using the RRF, so that evolutionary hypotheses can be tested more quickly and efficiently.