
Slides day 1
Exercise 1  Parameter estimation
Exercise 2  Tree topologies
Exercise 3  Model comparison
Exercise 4  Branch support
Exercise 5  Command line
Exercise 6  Inferring ML phylogenies with codon models
Exercise 7  Inferring ML phylogenies using real datasets
Exercise 8  ReAnalyze published datasets
Exercise 1  Parameter estimation
Inferring phylogenies using maximum likelihood
In this tutorial you will be guided in using PhyML and its extension, CodonPhyML, to solve common phylogenetic problems. For some of the following exercises there might be more than one single solution.
Goal: Observing the effect of parameter estimation from data on the inferred tree topology.
In this exercise you are asked to run PhyML twice in order to compare the effect of estimating nucleotide frequencies from the used dataset vs. optimising them with a maximum likelihood (ML) approach.
First Run
Set the model to HKY+Gamma, estimating the transition/transversion ratio and the alpha parameter of the Gamma distribution by maximum likelihood (ML), nucleotide frequencies are estimated by ML.
Second Run
Set the model to HKY+Gamma, estimating the transition/transversion ratio and the alpha parameter of the Gamma distribution by maximum likelihood (ML), nucleotide frequencies are estimated empirically from the data
 Do you see much difference in the tree?
 In the likelihood value (stat file)?
 Which option is best and why do you think so?
phylogenies treeestimation maximumlikelihood parameterestimation
This exercise was prepared by Maria Anisimova
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