Bayesian Molecular Clock Dating Using Genome-Scale Datasets.

Area of Science:

• Computational Biology
• Evolutionary Biology
• Genomics

Background:

• Bayesian molecular clock dating has advanced significantly, incorporating relaxed-clock models and flexible fossil calibration densities.
• Next-generation sequencing provides vast genome-scale datasets, enabling new approaches to dating the tree of life.

Purpose of the Study:

• To provide a protocol for using the MCMCTree program for Bayesian inference of divergence times.
• To demonstrate the application of these methods to genome-scale datasets.

Main Methods:

• Utilizing Bayesian inference with relaxed-clock models for evolutionary rate variation.
• Employing flexible fossil calibration densities to account for uncertainty in node ages.
• Using the MCMCTree program for phylogenetic dating with genome-scale data.

Main Results:

• The protocol details setting up analyses, defining priors, and diagnosing Markov Chain Monte Carlo (MCMC) algorithms.
• An exemplar analysis using a ten-species primate phylogeny with a large molecular alignment is presented.
• The study facilitates obtaining posterior estimates of divergence times and evolutionary rates from genome-scale data.

Conclusions:

• Genome-scale data and advanced Bayesian methods, like MCMCTree, are making the dating of the tree of life feasible.
• The protocol provides a practical guide for researchers applying these powerful computational tools in evolutionary studies.