PROTTEST (ModelTest’s relative) is a program for selecting the model of protein evolution that best fits a given set of sequences (alignment). Models included are empirical substitution matrices (such as WAG, LG, mtREV, Dayhoff, DCMut, JTT, VT, Blosum62, CpREV, RtREV, MtMam, MtArt, HIVb, and HIVw) that indicate relative rates of amino acid replacement, and specific improvements (+I:invariable sites, +G: rate heterogeneity among sites, +F: observed amino acid frequencies) to account for the evolutionary constraints impossed by conservation of protein structure and function. ProtTest uses the Akaike Information Criterion (AIC) and other statistics (AICc and BIC) to find which of the candidate models best fits the data at hand. Give ProtTest a try to fully assess its capabilities!
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Select the model that best fits the data at hand:
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ProtTest is based on Akaike’s information criterion (AIC) and Akaike’s corrected information criterion (AICc).
The AIC is useful because it can be used to evaluate the relative quality of two models. The following code shows how to run the program using a data set of two genes, with their corresponding alignments. If you run the example and click on the button «Run ProtTest», you can see that the JTT and MtMam models are the best fit to the data.
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subPlot(1,1,1)
# First example
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ProtTest
ProtTest is a tool for the rapid comparison of models of protein evolution,
based on the Akaike Information Criterion (AIC). It allows you to select
among a number of alternative models based on both their parameterization
and statistical goodness-of-fit.
In addition to calculating the AIC for various model classes,
this program also includes a number of diagnostics to detect and correct
biases in the analysis of your data. These diagnostics include:
– Log-Likelihood ratio tests, which allow you to assess the contribution
of variable sites to the overall goodness-of-fit of the model.
– Differential selection and its associated chi-square test, which allows
you to identify sites that are under positive or negative selection,
and to compute selection statistics such as Ka/Ks.
– The Bayes empirical Bayes (BEB) approach, which allows you to test
the potential presence of variable selection pressures across sites,
by allowing the class of sites considered to be variable to change
during the optimization of the model parameters.
MSWICH (Model selection using Whelan and Goldman’s method)
MSWICH is a program for selecting the most suitable model of protein evolution based on the Whelan and Goldman (WSG) test statistic. It calculates the WSG statistic for different model classes and selects the model that maximizes it.
The WSG test is based on a comparison of the log-likelihoods of two models; it is designed to assess whether there is a significant variation in evolutionary rates among sites. WSG is calculated as:
where «S» is a matrix of site-wise log-likelihood ratios (S) between two models (e.g.
What’s New in the?
A phylogenetic tree based on a group of sequences. The sequences are aligned using ClustalW2 and are concatenated to obtain a combined alignment. A Maximum Likelihood tree is then calculated using RAxML with 100 bootstrap replicates and the best model is selected based on the AIC score. ProtTest uses the AIC score to find which of the candidate models best fits the data at hand. Give ProtTest a try to fully assess its capabilities!
Source:
Description:
A phylogenetic tree based on a group of sequences. The sequences are aligned using ClustalW2 and are concatenated to obtain a combined alignment. A Maximum Likelihood tree is then calculated using RAxML with 100 bootstrap replicates and the best model is selected based on the AIC score. ProtTest uses the AIC score to find which of the candidate models best fits the data at hand. Give ProtTest a try to fully assess its capabilities!
Source:
Description:
A phylogenetic tree based on a group of sequences. The sequences are aligned using ClustalW2 and are concatenated to obtain a combined alignment. A Maximum Likelihood tree is then calculated using RAxML with 100 bootstrap replicates and the best model is selected based on the AIC score. ProtTest uses the AIC score to find which of the candidate models best fits the data at hand. Give ProtTest a try to fully assess its capabilities!
Source:
Description:
A phylogenetic tree based on a group of sequences. The sequences are aligned using ClustalW2 and are concatenated to obtain a combined alignment. A Maximum Likelihood tree is then calculated using RAxML with 100 bootstrap replicates and the best model is selected based on the AIC score. ProtTest uses the AIC score to find which of the candidate models best fits the data at hand. Give ProtTest a try to fully assess its capabilities!
Source:
Description:
A phylogenetic tree based on a group of sequences. The sequences are aligned using ClustalW2 and are concatenated to obtain a combined alignment. A Maximum Likelihood tree is then calculated using RAxML with 100 bootstrap replicates and the best model is selected based on the AIC score. ProtTest uses the A
System Requirements For ProtTest:
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