Cipres Science Gateway | GARLI Conf Creator 2.0 Information

Using GARLI.conf Creator 2.0:

This interface is designed to create garli.conf files. It was created primarily to allow users to configure portioned analyses using the CSG; but it can be used to configure nearly any run on GARLI.
The usage of this tool is slightly quirky, so a brief set of instructions are provided:

Start configuring your run by selecting the GARLI.conf Creator interface.

Although GARLI.conf Creator is a simple file creation interface, you must select an input file, because all CSG jobs require an input file.
You may select any file in your active folder as input, because this file will not be acted upon.

Open the parameter page. There are 5 “Simple Parameters” that help activate the interface to configure the runs correctly.

If you do not wish to evaluate multiple partitions under separate models,or you have no partitions, a single active pane will appear in the “Advanced Parameters” Section for configuring your run.

If your data set is partitioned, and you wish to analyze the partitions separately, you will find the appropriate interface sections (up to five partitions) are active when you expand the “Advanced Parameters” pane.

To add more partitions to your file, do the following:

Find the last partition configuration, noted by a header as below. The last partition begin with the header [modeln] where n is an integer, and end with the header [master] e.g.:
[model5]
numratecats = 4
ratehetmodel = gamma
datatype = nucleotide
ratematrix = (1.0000 1.2674 0.1631 0.1631 1.2674 1.0000)
numratecats=4
invariantsites = estimate
statefrequencies = equal
[master]

On the line above the header [master], add a new header, incrementing the previous header by one, e.g
[model6]

Then add statements below the new header as required to configure the model for that data partition, e.g
numratecats =
ratehetmodel =
datatype =
ratematrix =
numratecats=
invariantsites =
statefrequencies =

If you use GARLI, please cite:

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. (pdf)

If there is a tool or a feature you need, please let us know.