TESS @ BioHPC

TESS implements a Bayesian clustering algorithm for spatial population genetics analysis. It performs individual geographical assigment and admixture analysis. It is designed for seeking genetic discontinuities in continuous populations, and for correcting for stratification in association studies. Given individual geographical locations, the program builds a prior (spatial) network structure indicating genetic relatedness. TESS returns graphical display of geographical cluster assignments and textual output of the admixture Q matrix. For more information, please consult the TESS web page.

Calculations will be carried out on the BioHPC compute cluster at CBSU. You will receive e-mail notifications when the job is submitted, when it starts, and when it is finished. Output will be available via links embedded in the notification e-mails. For more information about this program and BioHPC interface in general, please visit our Frequently Asked Questions page.

This interface will launch the command-line version of TESS. Graphical front-end of TESS is currently not available. You will need to upload a data file, supply the command-line options, and specify how many times the run should be repeated. Upon job completion, a zipped archive datafile_rslts.zip of the job will be available for htpp and ftp download via a link in a notification e-mail. This file will decompress into directories run1, run2, etc., containing data for the individual "repetitions". During the run, you will be able to monitor the job's progress through a special link in notification emails.

E-mail:
(only guests need to use this field, registered users should log in)

Job name: (will be set to the name of data file)

Upload your data file

Upload geographic distances file (optional)

Select model
NO admixture WITH admixture;

Enter command line options (no newlines, please!)
Options -F, -g, -m (if applicabe) may be skipped - they will be supplied automatically at submission

How many times to repeat the run?
Repetitions will run in parallel using 2 or 4 processors (depending on the cluster).

Cluster: ( Show timeout info )