Tutorial on Bayesian phylogenetic analysis
Branch: master
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Permalink
Type Name Latest commit message Commit time
Failed to load latest commit information.
JC69
K80
LICENSE
README.md

README.md

Bayesian MCMC phylogenetics tutorial in R

Fabricia F. Nascimento, Mario dos Reis and Ziheng Yang

This repository containts two tutorials that guide the user through writing simple MCMC phylogenetics software to estimate the molecular distance (and the transition/trasversion ratio) for a pairwise sequence alignment under the Jukes and Cantor (1969) (and Kimura's 1980) substitution model. The tutorials introduce concepts such as burn-in, mixing, convergence, efficiency and autocorrelation of the MCMC chain.

Directory JC69/ contains the MCMC tutorial to calculate the molecular distance under the JC69 model. The directory contains three files. File mcmc.JCd.R contains the main R code with exercises. File mcmc.JCrt.R contains the solution to exercise 7 in the previous file. File BayesianMCMC-JC.pdf contains a more detailed explanation of the theory used in the tutorial.

Directory K80/ contains the MCMC tutorial to calculate the molecular distance and the ts/tv ratio under the K80 model. The tutorial is similar to the JC69 one, but focusing on a two parameter MCMC instead. File mcmc.K80.R contains the main R code. A detailed step-by-step explanation of the R code is given at:

https://thednainus.wordpress.com/2017/03/03/tutorial-bayesian-mcmc-phylogenetics-using-r/

In the K80 tutorial the user will be able to reproduce the plots in our review:

A biologist's guide to Bayesian phylogenetic analysis
Nascimento FF, dos Reis M, Yang Z. (2017) Nature Ecology and Evolution, 1:1446-1454.

For example, the plot below shows the prior, likelihood and posterior surfaces for the distance, d, and the ts/tv ratio, k, for the K80 example:

And these are the traces for d and k obtained from an MCMC algorithm written to sample from the posterior surface above:

We hope you enjoy the tutorials. If you have any questions or comments please let us know.

Fabricia, Mario and Ziheng.