Garli
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GARLI
GARLI (Genetic Algorithm for Rapid Likelihood Inference) performs phylogenetic searches on aligned nucleotide datasets using the maximum likelihood criterion.
Other programs andmain advantages
GARLI is loosely based on the program GAML (Lewis 1998). It uses a genetic algorithm approach to simultaneously find the topology, branch lengths and model parameters that maximize the lnL. This involves the evolution of a population of solutions termed individuals, with each individual encoding a tree topology, a set of branch lengths and a set of model parameters. Each individual is assigned a fitness based on its lnL score. Each generation random mutations are applied to some of the components of the individuals, and their fitnesses are recalculated. The individuals are then chosen to be the parents of the individuals of the next generation, in proportion to their fitnesses. This process is repeated many times, and the population of individuals evolves toward higher fitness solutions. Note that the highest fitness individual is automatically maintained in the population, ensuring that it is not lost due to chance (genetic drift).
The mutation types used by GARLI are divided into three types: topological mutations, model parameter mutations and branch-length mutations. Topological mutations consist of the standard NNI and SPR rearrangement types, as well as a localized form of SPR in which the pruned subtree may only be reattached to branches within a certain radius of its former location. Topological mutations are followed by some degree of rough branch-length optimization. Model mutations simply choose one of the model parameters and multiply it by a gamma-distributed variable with mean 1.0. When branch-length mutations are performed, a number of branches are chosen and each has its current length multiplied by a different gamma-distributed variable.
Input
You will need to input both a command file (GARLI.conf) and a sequence file in NEXUS or PHYLIP (both non-interleaved) format.
Quick start
All directions to the program are provided through a text-based configuration file, which by default is named GARLI.conf (command file). You will need to open this file in a text editor (Textedit, BBedit, TextWrangler, Notepad, vi, emacs, etc) to make changes. In the file you will see a series of fairly cryptically named options. The only thing that must be changed in the file is to enter your dataset name as the datafname (sequence file name). You should also change the ofprefix setting to give the program a prefix to be added to the beginning of output files. The default values for most settings should work well, at least for an initial exploratory run. The rest of this manual gives details on what the settings mean, which you might want to alter, and why. Make sure that you save the configuration file after making any changes.
Note: Garli has an option to run on multiple CPUs (up to 4), this is recommended for larger sequence files (30 taxa X 10,000 characters), however the default setting of 1 is more than adequate for smaller sequence files. The parallel and serial versions is called ParGarli.94 and Garli.94 respectively. MPI-version used is SCAMPI.
Output
A NEXUS tree file that can be opened in a phylogeny-viewing program e.g. treeview: http://darwin.zoology.gla.ac.uk/~rpage/treeviewx/ Other useful information
The assumed model of nucleotide substitution is the General Time Reversible (GTR) model, with gamma distributed rate heterogeneity and an estimated proportion of invariable sites. The implementation of this model is exactly equivalent to that in PAUP*, making the log likelihood (lnL) scores obtained directly comparable. All model parameters may be estimated, including the equilibrium base frequencies (which are not equal to the empirical base frequencies). The gamma model of rate heterogeneity assumes four rate categories (the default in PAUP*).
Homepage
http://www.bio.utexas.edu/faculty/antisense/garli/Garli.html
Contact person
Derrick J. Zwickl: zwickl@nescent.org
Citation
Zwickl, D. J., 2006. Genetic algorithm approaches for the phylogenetic analysis of large biological sequence datasets under the maximum likelihood criterion. Ph.D. dissertation, The University of Texas at Austin.