PAML 4.8a / PAMLX 1.3.1 – Phylogenetic Analysis by Maximum Likelihood

PAML 4.8a / PAMLX 1.3.1

:: DESCRIPTION

PAML (Phylogenetic Analysis by Maximum Likelihood) is a package of programs for phylogenetic analyses of DNA or protein sequences using maximum likelihood.The programs may be used to compare and test phylogenetic trees, but their main strengths lie in the rich repertoire of evolutionary models implemented, which can be used to estimate parameters in models of sequence evolution and to test interesting biological hypotheses. Uses of the programs include estimation of synonymous and nonsynonymous rates (dN and dS) between two protein-coding DNA sequences, inference of positive Darwinian selection through phylogenetic comparison of protein-coding genes, reconstruction of ancestral genes and proteins for molecular restoration studies of extinct life forms, combined analysis of heterogeneous data sets from multiple gene loci, and estimation of species divergence times incorporating uncertainties in fossil calibrations. This note discusses some of the major applications of the package, which includes example data sets to demonstrate their use.

PAML-X: A GUI for PAML

::DEVELOPER

Ziheng Yang

:: SCREENSHOTS

:: REQUIREMENTS

  • Windows / MacOsX / Linux

:: DOWNLOAD

PAML , PAML-X

:: MORE INFORMATION

Citation:

Yang, Z. 2007.
PAML 4: a program package for phylogenetic analysis by maximum likelihood.
Molecular Biology and Evolution 24: 1586-1591

Mol Biol Evol. 2013 Oct 24.
PAMLX: A Graphical User Interface for PAML.
Xu B, Yang Z.

PMB matrix – Data file to use with Ziheng Yang’s PAML for Protein Sequence Analysis.

PMB matrix

:: DESCRIPTION

PMB matrix is a implementation of PMB in Pseq-gen and Data file to use with Ziheng Yang’s PAML for protein sequence analysis.

::DEVELOPER

The Tillier Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 PMB matrix

:: MORE INFORMATION

Citation:

A transition probability model for amino acid substitutions from BLOCKS
Shalini Veerassamy, Andrew Smith and Elisabeth R. M. Tillier,
J. Computational Biology (2003) 10(6): 997-1010