--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sun Dec 04 15:59:01 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/351/Pkn-PL/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/351/Pkn-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/351/Pkn-PL/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/351/Pkn-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -13707.08        -13724.16
2     -13706.38        -13726.17
--------------------------------------
TOTAL   -13706.67        -13725.60
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/351/Pkn-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/351/Pkn-PL/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/351/Pkn-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.089826    0.001927    1.003344    1.173536    1.088659    915.96   1173.85    1.000
r(A<->C){all}   0.099591    0.000075    0.083574    0.117535    0.099336    692.74    965.26    1.000
r(A<->G){all}   0.290982    0.000238    0.259673    0.319223    0.291222    599.03    733.80    1.000
r(A<->T){all}   0.100159    0.000121    0.079103    0.121904    0.099814    896.06   1003.56    1.000
r(C<->G){all}   0.050952    0.000027    0.040632    0.060950    0.050816   1175.26   1281.52    1.000
r(C<->T){all}   0.397551    0.000307    0.364850    0.433190    0.397469    752.65    771.76    1.000
r(G<->T){all}   0.060764    0.000051    0.047334    0.075001    0.060332   1142.06   1152.89    1.001
pi(A){all}      0.228520    0.000040    0.215386    0.240037    0.228476    781.41    925.25    1.000
pi(C){all}      0.277916    0.000043    0.263726    0.289856    0.278098    991.75   1033.83    1.000
pi(G){all}      0.297166    0.000047    0.283859    0.310336    0.297065    830.96    941.72    1.000
pi(T){all}      0.196398    0.000032    0.185586    0.207872    0.196417   1031.72   1149.85    1.000
alpha{1,2}      0.123053    0.000043    0.111042    0.136493    0.122698   1050.19   1260.93    1.000
alpha{3}        6.477441    1.458093    4.329649    8.873616    6.359523   1323.63   1330.72    1.000
pinvar{all}     0.377676    0.000376    0.339054    0.415667    0.377809   1206.41   1215.06    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	-12679.576314
Model 2: PositiveSelection	-12676.328543
Model 0: one-ratio	-12805.798265
Model 3: discrete	-12672.776531
Model 7: beta	-12680.258515
Model 8: beta&w>1	-12669.264961


Model 0 vs 1	252.44390199999907

Model 2 vs 1	6.495542000000569

Additional information for M1 vs M2:
Naive Empirical Bayes (NEB) analysis
Positively selected sites (*: P>95%; **: P>99%)
(amino acids refer to 1st sequence: D_melanogaster_Pkn-PL)

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

   785 A      0.986*        5.176

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_Pkn-PL)

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

   162 A      0.663         1.335 +- 0.250
   372 I      0.643         1.319 +- 0.268
   598 L      0.527         1.264 +- 0.259
   784 S      0.695         1.352 +- 0.245
   785 A      0.955*        1.483 +- 0.136
   957 R      0.553         1.244 +- 0.343


Model 8 vs 7	21.987107999997534

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_Pkn-PL)

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

   784 S      0.527         2.026
   785 A      1.000**       3.543

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_Pkn-PL)

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

   155 N      0.565         1.081 +- 0.496
   162 A      0.893         1.407 +- 0.283
   372 I      0.836         1.344 +- 0.369
   598 L      0.745         1.264 +- 0.418
   708 S      0.552         1.043 +- 0.529
   734 T      0.622         1.120 +- 0.508
   774 S      0.529         0.974 +- 0.585
   784 S      0.918         1.430 +- 0.249
   785 A      0.998**       1.500 +- 0.048
   824 S      0.538         1.053 +- 0.502
   957 R      0.713         1.204 +- 0.487