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HyPhy-FUBAR (version 2.5.47+galaxy0)
If the input file type is NEXUS and it includes a valid newick tree, that tree will override an uploaded newick tree

FUBAR : Faste Unbiased Bayesian AppRoximation

What question does this method answer?

Which site(s) in a gene are subject to pervasive, i.e. consistently across the entire phylogeny, diversifying selection?

Brief description

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).


  1. A FASTA sequence alignment.
  2. A phylogenetic tree in the Newick format

Note: the names of sequences in the alignment must match the names of the sequences in the tree.


A JSON file with analysis results (

A custom visualization module for viewing these results is available (see for an example)

Tool options

--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

                    The concentration parameter of the Dirichlet prior
                    default value: 0.5