--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Thu Nov 24 19:49:58 WET 2016
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=/opt/ADOPS/320/Ncc69-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/320/Ncc69-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/320/Ncc69-PA/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/ADOPS/320/Ncc69-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -13668.72        -13684.09
2     -13668.66        -13685.13
--------------------------------------
TOTAL   -13668.69        -13684.74
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/320/Ncc69-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/320/Ncc69-PA/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/ADOPS/320/Ncc69-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.237634    0.002669    1.140422    1.340656    1.235454   1409.20   1412.70    1.000
r(A<->C){all}   0.109507    0.000097    0.088605    0.127410    0.109276    790.70    901.12    1.000
r(A<->G){all}   0.223343    0.000209    0.196661    0.252694    0.223032    800.28    841.66    1.001
r(A<->T){all}   0.110672    0.000141    0.087439    0.133340    0.110287   1001.56   1019.13    1.000
r(C<->G){all}   0.082871    0.000049    0.069883    0.097141    0.082590   1251.88   1258.76    1.000
r(C<->T){all}   0.416121    0.000298    0.383444    0.450815    0.416032    788.22    809.47    1.000
r(G<->T){all}   0.057486    0.000049    0.045054    0.072224    0.057178   1053.93   1079.17    1.000
pi(A){all}      0.210599    0.000043    0.198463    0.223696    0.210690    752.53    811.29    1.000
pi(C){all}      0.283885    0.000045    0.271276    0.297623    0.283927    981.80   1005.58    1.000
pi(G){all}      0.286076    0.000052    0.271879    0.300178    0.286004   1152.20   1156.27    1.000
pi(T){all}      0.219440    0.000039    0.208077    0.232544    0.219455    902.81    959.12    1.000
alpha{1,2}      0.147207    0.000071    0.130573    0.163676    0.146725   1124.91   1205.27    1.000
alpha{3}        4.661658    0.869327    3.068049    6.505394    4.529564   1201.39   1281.58    1.000
pinvar{all}     0.351462    0.000459    0.308482    0.390695    0.352034   1278.79   1311.21    1.000
------------------------------------------------------------------------------------------------------
* 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	-12686.009811
Model 2: PositiveSelection	-12686.009814
Model 0: one-ratio	-12857.308482
Model 3: discrete	-12638.132263
Model 7: beta	-12656.265365
Model 8: beta&w>1	-12639.811991


Model 0 vs 1	342.59734200000094

Model 2 vs 1	5.999998393235728E-6

Model 8 vs 7	32.906747999997606

Additional information for M7 vs M8:
Naive Empirical Bayes (NEB) analysis
Positively selected sites (*: P>95%; **: P>99%)
(amino acids refer to 1st sequence: D_melanogaster_Ncc69-PA)

            Pr(w>1)     post mean +- SE for w

    35 A      0.917         1.745
    38 P      0.958*        1.809
    44 A      0.984*        1.852
    46 A      0.828         1.603
    47 G      0.658         1.331
    48 A      0.982*        1.848
    49 G      0.513         1.100
    50 A      0.985*        1.853
    52 A      1.000**       1.877
   388 A      0.833         1.611
   449 Q      0.855         1.647
   487 S      0.771         1.512
   861 V      0.826         1.599

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: D_melanogaster_Ncc69-PA)

            Pr(w>1)     post mean +- SE for w

    35 A      0.875         1.413 +- 0.323
    38 P      0.931         1.464 +- 0.273
    44 A      0.948         1.480 +- 0.242
    46 A      0.851         1.385 +- 0.364
    47 G      0.778         1.303 +- 0.443
    48 A      0.950*        1.482 +- 0.241
    49 G      0.705         1.223 +- 0.488
    50 A      0.954*        1.485 +- 0.235
    52 A      0.994**       1.519 +- 0.163
   388 A      0.839         1.376 +- 0.364
   449 Q      0.865         1.400 +- 0.350
   487 S      0.821         1.355 +- 0.392
   489 V      0.660         1.186 +- 0.483
   505 N      0.559         1.079 +- 0.506
   861 V      0.834         1.371 +- 0.369