The TESS web page

The TESS web page

TESS
POPS
LFMM

TESS 2.3: Bayesian Clustering using tessellations and Markov models for spatial population genetics

TESS implements a Bayesian clustering algorithm for spatial population genetic analyses. It can perform both individual geographical assignment and admixture analysis. It is designed for seeking genetic discontinuities in continuous populations and estimating spatially varying individual admixture proportions. TESS returns graphical displays of geographical cluster assignments or admixture proportions (depending on the model used) and textual output of the admixture Q matrix.

Download the latest version of TESS (Updated january 2010; includes dominant markers) top

Version 1.0: Chibiao Chen (2006) and Version 2.1+: Eric Durand (2007-2010)

Download Windows TESS Version 2.3.1 Install file
Download Mac OS X TESS Version 2.3.1 Zip file
Download the source C++ code for command line programs: Without admixture version, BYM admixture version, CAR admixture version, README instructions and Makefiles are available from each archive. The programs require gcc version > 3.3 and gsl version > 1.6.1

How to display the Q matrix spatially? R Scripts are included in the program files Other explanations here

Download the program documentation

The reference manual, an example data set and R scripts are included in the TESS 2.3.1 package. For new users, we advice using the tessgui.exe program first. We also advice using CLUMPP and DISTRUCT for post-processing the program outputs. The important quantities to look at are the admixture/membership coefficients.

References:

For the admixture model: E. Durand, F. Jay, O.E. Gaggiotti, O. François (2009) Spatial inference of admixture proportions and secondary contact zones, PDF, Molecular Biology and Evolution 26:1963-1973.

For the graphic user interface and for comparisons with other programs: C. Chen, E. Durand, F. Forbes, O. François (2007) Bayesian clustering algorithms ascertaining spatial population structure: A new computer program and a comparison study, PDF, Molecular Ecology Notes 7:747-756.

A review on spatial clustering methods: O. François, E. Durand (2010) THE STATE OF THE ART - Spatially explicit Bayesian clustering models in population genetics PDF Molecular Ecology Resources, 10: 773-784.

For the hidden Markov Random Field model (without admixture): O. François, S. Ancelet, G. Guillot (2006) Bayesian clustering using hidden Markov random fields in spatial population genetics, PDF, Genetics, 174: 805-816.

Supporting Material for Chen et al. (2007) PDF, and the five and two-island data used in the simulation study five island data , d1.ZIP, d2.ZIP, d3.ZIP, d4.ZIP.

Send feedback and comments or ask assistance:
eric dot durand at berkeley dot edu
olivier dot francois at imag dot fr