Which site(s) in a gene are subject to pervasive, i.e. consistently across the entire phylogeny, diversifying selection?
The phenomenon of pervasive selection is generally most prevalent in pathogen evolution and any biological system influenced by evolutionary arms race dynamics (or balancing selection), including adaptive immune escape by viruses. As such, FUBAR is ideally suited to identify sites under positive selection which represent candidate sites subject to strong selective pressures across the entire phylogeny.
FUBAR is our recommended method for detecting pervasive selection at individual sites on large (> 500 sequences) datasets for which other methods have prohibitive runtimes, unless you have access to a computer cluster.
Perform a Fast Unbiased AppRoximate Bayesian (FUBAR) analysis of a coding sequence alignment to determine whether some sites have been subject to pervasive purifying or diversifying selection. There are three methods for estimating the posterior distribution of grid weights: collapsed Gibbs MCMC (faster), 0-th order Variation Bayes approximation (fastest), full Metropolis-Hastings (slowest).
Note: the names of sequences in the alignment must match the names of the sequences in the tree.
A JSON file with analysis results (http://hyphy.org/resources/json-fields.pdf).
A custom visualization module for viewing these results is available (see http://vision.hyphy.org/FUBAR for an example)
--code Which genetic code to use --grid The number of grid points Smaller : faster Larger : more precise posterior estimation but slower default value: 20 --method Inference method to use Variational-Bayes : 0-th order Variational Bayes approximation; fastest [default] Metropolis-Hastings : Full Metropolis-Hastings MCMC algorithm; orignal method [slowest] Collapsed-Gibbs : Collapsed Gibbs sampler [intermediate speed] --chains How many MCMC chains to run (does not apply to Variational-Bayes) default value: 5 --chain-length MCMC chain length (does not apply to Variational-Bayes) default value: 2,000,000 --burn-in MCMC chain burn in (does not apply to Variational-Bayes) default value: 1,000,000 --samples MCMC samples to draw (does not apply to Variational-Bayes) default value: 1,000 --concentration_parameter The concentration parameter of the Dirichlet prior default value: 0.5