--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Nov 21 23:35:28 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/3/ACC-PG/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/ACC-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/ACC-PG/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/3/ACC-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -26623.81        -26639.48
2     -26623.45        -26639.77
--------------------------------------
TOTAL   -26623.62        -26639.64
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/ACC-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/ACC-PG/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/3/ACC-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.573065    0.001712    1.496777    1.658695    1.572405   1477.47   1489.23    1.000
r(A<->C){all}   0.082516    0.000033    0.071365    0.093408    0.082348   1004.53   1076.83    1.001
r(A<->G){all}   0.313556    0.000124    0.292219    0.335668    0.313287    778.89    806.37    1.000
r(A<->T){all}   0.113318    0.000077    0.097172    0.131894    0.113092    774.90    959.80    1.000
r(C<->G){all}   0.042225    0.000011    0.035908    0.048165    0.042172    917.21   1037.34    1.000
r(C<->T){all}   0.385594    0.000141    0.363233    0.408583    0.385214    733.41    807.78    1.001
r(G<->T){all}   0.062791    0.000024    0.053341    0.072124    0.062730   1028.76   1040.20    1.000
pi(A){all}      0.211110    0.000021    0.202020    0.219968    0.211040    783.47    845.64    1.000
pi(C){all}      0.290231    0.000024    0.280488    0.299352    0.290155    867.34    942.16    1.000
pi(G){all}      0.284951    0.000025    0.274765    0.294304    0.285019    772.15    822.17    1.000
pi(T){all}      0.213708    0.000018    0.205522    0.222166    0.213585    872.15    882.73    1.002
alpha{1,2}      0.091178    0.000010    0.085412    0.097557    0.091107   1313.85   1407.42    1.000
alpha{3}        7.775263    1.469430    5.520618   10.117090    7.653920   1422.83   1461.91    1.002
pinvar{all}     0.292319    0.000181    0.262969    0.316292    0.292429   1317.07   1409.04    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	-24565.669304
Model 2: PositiveSelection	-24565.669356
Model 0: one-ratio	-24706.841833
Model 3: discrete	-24514.70771
Model 7: beta	-24531.162641
Model 8: beta&w>1	-24523.020789


Model 0 vs 1	282.34505799999897

Model 2 vs 1	1.0399999882793054E-4

Model 8 vs 7	16.283704000001308

Additional information for M7 vs M8:
Naive Empirical Bayes (NEB) analysis
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_ACC-PG)

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

   280 N      0.655         1.196 +- 0.433
   752 S      0.734         1.265 +- 0.410
   757 Y      0.563         1.079 +- 0.499
   815 L      0.692         1.220 +- 0.437
  2136 A      0.515         1.026 +- 0.510