---------------------------------------------------------------------------------------------- ProtTest 3.4.2 Fast selection of the best-fit models of protein evolution (c) 2009-2016 Diego Darriba (1,2), Guillermo Taboada (2), Ram?n Doallo (2), David Posada (1) (1) Facultad de Biologia, Universidad de Vigo, 36200 Vigo, Spain (2) Facultade de Inform?tica, Universidade da Coru?a, 15071 A Coru?a, Spain ---------------------------------------------------------------------------------------------- Manual: http://darwin.uvigo.es/download/prottest_manual.pdf Homepage: http://github.com/ddarriba/prottest3 Discussion group: https://groups.google.com/group/prottest Contact: ddarriba@h-its.org, dposada@uvigo.es ---------------------------------------------------------------------------------------------- Version: 3.4.2 : 8th May 2016 Date: Mon Dec 09 16:23:50 IST 2024 OS: Linux (5.14.0-427.42.1.el9_4.x86_64) Arch: amd64 Java: 17.0.13 (Red Hat, Inc.) PhyML: /bentallab/programs/prottest-3.4.2/bin/PhyML_3.0_linux64 Citation: Darriba D, Taboada GL, Doallo R, Posada D. ProtTest 3: fast selection of best-fit models of protein evolution. Bioinformatics, 27:1164-1165, 2011 ProtTest options ---------------- Alignment file........... : input_msa.phy Tree..................... : BioNJ StrategyMode............. : BIONJ Tree Candidate models......... : Matrices............... : JTT LG MtREV Dayhoff WAG CpREV Distributions.......... : Uniform Observed frequencies... : false Statistical framework Sort models according to....: AICc Sample size.................: 117.0 Other options: Display best tree in ASCII..: false Display best tree in Newick.: false Display consensus tree......: false Verbose.....................: false ********************************************************** Observed number of invariant sites: 1 Observed aminoacid frequencies: A: 0.117 C: 0.007 D: 0.029 E: 0.028 F: 0.051 G: 0.112 H: 0.007 I: 0.085 K: 0.011 L: 0.106 M: 0.030 N: 0.010 P: 0.037 Q: 0.011 R: 0.023 S: 0.055 T: 0.099 V: 0.115 W: 0.029 Y: 0.036 ********************************************************** Model................................ : JTT Number of parameters............... : 297 (0 + 297 branch length estimates) -lnL................................ = 17150.98 (seconds)) Model................................ : LG Number of parameters............... : 297 (0 + 297 branch length estimates) -lnL................................ = 16897.93 (seconds)) Model................................ : MtREV Number of parameters............... : 297 (0 + 297 branch length estimates) -lnL................................ = 17805.70 (seconds)) Model................................ : Dayhoff Number of parameters............... : 297 (0 + 297 branch length estimates) -lnL................................ = 17287.53 (seconds)) Model................................ : WAG Number of parameters............... : 297 (0 + 297 branch length estimates) -lnL................................ = 17023.05 (seconds)) Model................................ : CpREV Number of parameters............... : 297 (0 + 297 branch length estimates) -lnL................................ = 17047.44 (seconds)) ************************************************************ Date : Mon Dec 09 16:24:20 IST 2024 Runtime: 0h:00:30 *************************************************************************** Best model according to AICc: LG Confidence Interval: 100.0 *************************************************************************** Model deltaAICc AICc AICcw -lnL --------------------------------------------------------------------------- LG 0.00 33411.89 1.00 16897.93 WAG 250.24 33662.12 0.00 17023.05 CpREV 299.03 33710.92 0.00 17047.44 JTT 506.11 33917.99 0.00 17150.98 Dayhoff 779.20 34191.09 0.00 17287.53 MtREV 1815.55 35227.44 0.00 17805.70 --------------------------------------------------------------------------- --------------------------------------------------------------------------- *********************************************** Relative importance of parameters *********************************************** alpha (+G): No +G models p-inv (+I): No +I models alpha+p-inv (+I+G): No +I+G models freqs (+F): No +F models *********************************************** Model-averaged estimate of parameters *********************************************** alpha (+G): No +G models p-inv (+I): No +I models alpha (+I+G): No +I+G models p-inv (+I+G): No +I+G models