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Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies
Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
www.exelixis-lab.org
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www.exelixis-lab.org
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The example below will automatically determine which is the best (the one with the highest likelihood score on the parsimony starting tree) protein substitution model for your dataset using the base frequencies that come with the models. It will chose among the following models: DAYHOFF, DCMUT, JTT, MTREV, WAG, RTREV, CPREV, VT, BLOSUM62, MTMAM, LG, MTART, MTZOA, PMB, HIVB, HIVW, JTTDCMUT, FLU, DUMMY, DUMMY2. These models will not be considered: LG4M, LG4X, PROT_FILE,GTR_UNLINKED, GTR!
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www.exelixis-lab.org <http://www.exelixis-lab.org>
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-- Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical
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Full Professor, Dept. of Informatics, Karlsruhe Institute
of Technology
www.exelixis-lab.org <http://www.exelixis-lab.org>
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-- Alexandros (Alexis) Stamatakis
Research Group Leader, Heidelberg Institute for Theoretical Studies
Full Professor, Dept. of Informatics, Karlsruhe Institute of Technology
www.exelixis-lab.org <http://www.exelixis-lab.org>
Alignment has 7099 distinct alignment patterns
Proportion of gaps and completely undetermined characters in this alignment: 22.28%
RAxML rapid hill-climbing mode
Using 1 distinct models/data partitions with joint branch length optimization
Executing 1 inferences on the original alignment using 1 distinct randomized MP trees
All free model parameters will be estimated by RAxML
GAMMA model of rate heterogeneity, ML estimate of alpha-parameter
GAMMA Model parameters will be estimated up to an accuracy of 0.1000000000 Log Likelihood units
Partition: 0
Alignment Patterns: 7099
Name: No Name Provided
DataType: AA
Substitution Matrix: AUTO
Using fixed base frequencies
RAxML was called as follows:
/share/eisenlab/gjospin/bin/raxmlHPC-PTHREADS -p 72992345 -T 24 -m PROTGAMMAAUTO -s concat.updated.KAMfiltered.phylip -n AUTO_7_orolo
Partition: 0 with name: No Name Provided
Base frequencies: 0.087 0.044 0.039 0.057 0.019 0.037 0.058 0.083 0.024 0.048 0.086 0.062 0.020 0.038 0.046 0.070 0.061 0.014 0.035 0.071
Automatic protein model assignment algorithm using ML criterion:
Partition: 0 best-scoring AA model: LG likelihood -19956802.701780 with fixed base frequencies
Automatic protein model assignment algorithm using ML criterion:
Partition: 0 best-scoring AA model: LG likelihood -19955936.910947 with fixed base frequencies
Automatic protein model assignment algorithm using ML criterion:
Partition: 0 best-scoring AA model: LG likelihood -19955907.359551 with fixed base frequencies
Automatic protein model assignment algorithm using ML criterion:
Partition: 0 best-scoring AA model: LG likelihood -19944415.375817 with fixed base frequencies
Automatic protein model assignment algorithm using ML criterion:
Partition: 0 best-scoring AA model: LG likelihood -19944410.766413 with fixed base frequencies
Automatic protein model assignment algorithm using ML criterion:
Partition: 0 best-scoring AA model: LG likelihood -19940563.520907 with fixed base frequencies
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