FADE : FUBAR Approach to Directional Evolution

What question does this method answer?

Which site(s) in an alignment evolve towards to or away from a particular residue.

Recommended Applications

Screen protein sequence alignments where the direction of evolution can be resolved (via tree rooting, e.g. using an outgroup) to find sites which evolve differently from a standard protein model (selected by the user), or a gene-average model (GTR) to find evidence of directional selection.

Brief description

FFADE (FUBAR Approach to Directional Evolution) is a fast method to test whether or not a subset of sites in a protein alignment evolve towards a particular residue along a subset of branches at accelerated rates compared to reference model. FADE uses a random effects model and latent Dirichlet allocation (LDA) - inspired approximation methods to allocate sites to rate classes.

Input

1. A FASTA sequence alignment of protein sequences.
2. A rooted phylogenetic tree in the Newick format

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

Output

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/FADE for an example)

Further reading

http://hyphy.org/methods/selection-methods/#FADE

Tool options

--model            The baseline substitution model to use
                        [default] use GTR

--branches          Which branches should be tested for selection?
                        All [default] : test all branches

                        Internal : test only internal branches (suitable for
                        intra-host pathogen evolution for example, where terminal branches
                        may contain polymorphism data)

                        Leaves: test only terminal (leaf) branches

                        Unlabeled: if the Newick string is labeled using the {} notation,
                        test only branches without explicit labels
                        (see http://hyphy.org/tutorials/phylotree/)

--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