--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Tue Oct 31 17:22:04 WET 2017 codeml.models=0 1 2 3 7 8 mrbayes.mpich= mrbayes.ngen=1000000 tcoffee.alignMethod=CLUSTALW2 tcoffee.params= tcoffee.maxSeqs=0 codeml.bin=codeml mrbayes.tburnin=2500 codeml.dir= input.sequences= mrbayes.pburnin=2500 mrbayes.bin=mb_adops tcoffee.bin=t_coffee_ADOPS mrbayes.dir=/usr/bin/ tcoffee.dir= tcoffee.minScore=3 input.fasta= input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/Srevisao/S10_17Malus/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/Srevisao/S10_17Malus/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p": (Use the harmonic mean for Bayes factor comparisons of models) (Values are saved to the file /opt/ADOPS1/Srevisao/S10_17Malus/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -8187.19 -8205.96 2 -8186.69 -8207.35 -------------------------------------- TOTAL -8186.91 -8206.88 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/Srevisao/S10_17Malus/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/Srevisao/S10_17Malus/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p": Summaries are based on a total of 3002 samples from 2 runs. Each run produced 2001 samples of which 1501 samples were included. Parameter summaries saved to file "/opt/ADOPS1/Srevisao/S10_17Malus/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 1.500904 0.003399 1.387379 1.612350 1.498621 1501.00 1501.00 1.001 r(A<->C){all} 0.116159 0.000127 0.093586 0.137136 0.115918 797.50 966.84 1.000 r(A<->G){all} 0.304161 0.000312 0.269931 0.337823 0.304084 792.76 866.17 1.000 r(A<->T){all} 0.073605 0.000047 0.060579 0.086789 0.073365 1172.72 1191.28 1.001 r(C<->G){all} 0.153546 0.000208 0.127365 0.183627 0.153141 915.35 921.41 1.000 r(C<->T){all} 0.267489 0.000280 0.233452 0.297932 0.267719 748.52 830.83 1.001 r(G<->T){all} 0.085039 0.000075 0.068658 0.102788 0.084557 1049.55 1083.71 1.000 pi(A){all} 0.298176 0.000111 0.276450 0.318650 0.298086 981.00 1019.93 1.001 pi(C){all} 0.170038 0.000068 0.154566 0.186342 0.170000 806.43 928.97 1.000 pi(G){all} 0.188570 0.000075 0.171619 0.204928 0.188317 826.42 925.19 1.000 pi(T){all} 0.343216 0.000125 0.322118 0.364739 0.343439 927.70 969.61 1.000 alpha{1,2} 0.945389 0.018856 0.705820 1.211655 0.929334 1247.03 1299.01 1.000 alpha{3} 1.734228 0.159330 1.094733 2.534816 1.669443 1214.91 1264.17 1.000 pinvar{all} 0.035114 0.000818 0.000015 0.091155 0.028601 1225.86 1258.89 1.001 ------------------------------------------------------------------------------------------------------ * Convergence diagnostic (ESS = Estimated Sample Size); min and avg values correspond to minimal and average ESS among runs. ESS value below 100 may indicate that the parameter is undersampled. + Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman and Rubin, 1992) should approach 1.0 as runs converge. Setting sumt conformat to Simple --- CODEML SUMMARY Model 1: NearlyNeutral -4760.325842 Model 2: PositiveSelection -4722.33529 Model 0: one-ratio -4884.237773 Model 3: discrete -4722.131359 Model 7: beta -4771.204726 Model 8: beta&w>1 -4724.825834 Model 0 vs 1 247.82386199999928 Model 2 vs 1 75.98110400000041 Additional information for M1 vs M2: Naive Empirical Bayes (NEB) analysis Positively selected sites (*: P>95%; **: P>99%) (amino acids refer to 1st sequence: S10_SFBB1) Pr(w>1) post mean +- SE for w 10 N 0.997** 3.746 16 R 0.999** 3.752 20 P 0.999** 3.753 49 L 0.997** 3.746 54 F 0.815 3.245 56 E 0.958* 3.638 96 R 0.867 3.387 104 I 0.998** 3.749 106 T 0.999** 3.751 123 Q 0.644 2.773 152 T 0.898 3.472 167 E 0.754 3.077 170 C 0.987* 3.717 186 T 1.000** 3.754 188 E 0.947 3.609 Bayes Empirical Bayes (BEB) analysis (Yang, Wong & Nielsen 2005. Mol. Biol. Evol. 22:1107-1118) Positively selected sites (*: P>95%; **: P>99%) (amino acids refer to 1st sequence: S10_SFBB1) Pr(w>1) post mean +- SE for w 10 N 0.996** 3.847 +- 0.534 16 R 0.999** 3.856 +- 0.512 20 P 0.999** 3.857 +- 0.509 49 L 0.996** 3.849 +- 0.531 54 F 0.749 3.090 +- 1.277 56 E 0.947 3.706 +- 0.807 96 R 0.818 3.307 +- 1.172 104 I 0.998** 3.852 +- 0.522 106 T 0.998** 3.854 +- 0.516 123 Q 0.585 2.641 +- 1.432 152 T 0.865 3.456 +- 1.075 167 E 0.676 2.870 +- 1.347 170 C 0.983* 3.809 +- 0.623 186 T 1.000** 3.859 +- 0.504 188 E 0.930 3.654 +- 0.871 Model 8 vs 7 92.75778399999945 Additional information for M7 vs M8: Naive Empirical Bayes (NEB) analysis Positively selected sites (*: P>95%; **: P>99%) (amino acids refer to 1st sequence: S10_SFBB1) Pr(w>1) post mean +- SE for w 10 N 0.999** 3.249 11 H 0.520 2.092 16 R 1.000** 3.250 20 P 1.000** 3.250 49 L 0.999** 3.248 54 F 0.941 3.111 56 E 0.981* 3.205 58 G 0.524 2.101 69 A 0.706 2.547 81 G 0.686 2.494 93 Q 0.676 2.466 96 R 0.953* 3.138 104 I 0.999** 3.249 106 T 1.000** 3.249 123 Q 0.798 2.761 152 T 0.959* 3.154 167 E 0.918 3.057 170 C 0.995** 3.238 186 T 1.000** 3.250 188 E 0.979* 3.202 216 K 0.607 2.307 Bayes Empirical Bayes (BEB) analysis (Yang, Wong & Nielsen 2005. Mol. Biol. Evol. 22:1107-1118) Positively selected sites (*: P>95%; **: P>99%) (amino acids refer to 1st sequence: S10_SFBB1) Pr(w>1) post mean +- SE for w 10 N 0.999** 3.158 +- 0.573 11 H 0.544 1.983 +- 1.115 16 R 1.000** 3.160 +- 0.570 20 P 1.000** 3.160 +- 0.569 49 L 0.999** 3.157 +- 0.574 54 F 0.912 2.923 +- 0.820 56 E 0.976* 3.100 +- 0.662 58 G 0.547 1.993 +- 1.123 69 A 0.685 2.333 +- 1.092 81 G 0.671 2.304 +- 1.114 93 Q 0.664 2.292 +- 1.129 96 R 0.933 2.980 +- 0.780 104 I 0.999** 3.158 +- 0.572 106 T 0.999** 3.159 +- 0.570 123 Q 0.775 2.580 +- 1.059 138 Y 0.520 1.923 +- 1.080 152 T 0.946 3.020 +- 0.750 167 E 0.881 2.839 +- 0.878 170 C 0.993** 3.144 +- 0.596 186 T 1.000** 3.161 +- 0.568 188 E 0.973* 3.090 +- 0.670 216 K 0.607 2.135 +- 1.106