TESS 2.3.1 – Bayesian Clustering using Tessellations and Markov models for Spatial Population Genetics

TESS 2.3.1

:: DESCRIPTION

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.

::DEVELOPER

the Computational and Mathematical Biology group in Grenoble

:: SCREENSHOTS

TESS

:: REQUIREMENTS

  • MacOsX / Windows

:: DOWNLOAD

 TESS 

:: MORE INFORMATION

Citation

C. Chen, E. Durand, F. Forbes, O. François (2007)
Bayesian clustering algorithms ascertaining spatial population structure: A new computer program and a comparison study,
Molecular Ecology Notes 7:747-756.

TESS 1.0 – Predict Transcription Factor Binding Sites in DNA sequence

TESS 1.0

:: DESCRIPTION

TESS (Transcription Element Search System) reads (selected) PWMs (Partial Weight Matrices) from a file and predicts binding sites on DNA sequences read from another file.

::DEVELOPER

the Computational Biology and Informatics Laboratory at the University of Pennsylvania

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX
  • C Compiler

:: DOWNLOAD

 TESS 

:: MORE INFORMATION

Citation:

Curr Protoc Bioinformatics. 2008 Mar;Chapter 2:Unit 2.6. doi: 10.1002/0471250953.bi0206s21.
Using TESS to predict transcription factor binding sites in DNA sequence.
Schug J.

TESS 2.1.0 – Simulation of Reconstructed Phylogenetic Trees under Time-dependent Birth-death Processes

TESS 2.1.0

:: DESCRIPTION

TESS is an R-package for simulation of reconstructed phylogenetic trees under global, time-dependent birth-death processes. Speciation and extinction rates can be any function of time and mass-extinction events at specific times can be provided. Trees can be simulated either conditioned on the number of species, the time of the process, or both. Additionally, the likelihood equations are implemented for convenience and can be used for Maximum Likelihood (ML) estimation and Bayesian inference.

::DEVELOPER

HöhnaLab

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux/ MacOsX / Windows
  • R package

:: DOWNLOAD

  TESS

:: MORE INFORMATION

Citation

TESS: An R package for efficiently simulating phylogenetic trees and performing Bayesian inference of lineage diversification rates.
Höhna S, May MR, Moore BR.
Bioinformatics. 2015 Nov 4. pii: btv651.

Bioinformatics. 2013 Jun 1;29(11):1367-74. doi: 10.1093/bioinformatics/btt153
Fast simulation of reconstructed phylogenies under global, time-dependent birth-death processes.
Höhna S.