This tools identifies groups of sites in the alignments that experience substitutions along the same branches, i.g. co-evolve.
GM (Bayesian Graphical Model) uses a maximum likelihood ancestral state reconstruction to map substitution (non-synonymous only for coding data) events to branches in the phylogeny and then analyzes the joint distribution of the substitution map using a Bayesian graphical model (network). Next, a Markov chain Monte Carlo analysis is used to generate a random sample of network structures from the posterior distribution given the data. Each node in the network represents a site in the alignment, and links (edges) between nodes indicate high posterior support for correlated substitutions at the two sites over time, which implies coevolution.
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/BGM for an example)
--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/)
--max-parents The maximum number of parents allowed per node, i.e. how many sites
can directly influence substitution patterns at another site
Increasing this number scales complexity nonlinearly
default value: 1
--min-subs The minium number of substitutions per site to include it in the analysis
Filter low complexity (too few substitution) sites
default value: 1
--chains How many MCMC chains to run (does not apply to Variational-Bayes)
default value: 5
--steps MCMC chain length (does not apply to Variational-Bayes)
default value: 100,000
--burn-in MCMC chain burn in (does not apply to Variational-Bayes)
default value: 10,000
--samples MCMC samples to draw (does not apply to Variational-Bayes)
default value: 100