--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Dec 11 12:36:20 WET 2017
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=MUSCLE
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/pet/Paxi_S19_20/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/pet/Paxi_S19_20/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/pet/Paxi_S19_20/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -14047.73        -14074.62
2     -14047.44        -14069.20
--------------------------------------
TOTAL   -14047.58        -14073.94
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/pet/Paxi_S19_20/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/pet/Paxi_S19_20/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/pet/Paxi_S19_20/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         3.598672    0.012868    3.368540    3.809873    3.596539   1053.88   1106.94    1.000
r(A<->C){all}   0.139015    0.000096    0.119115    0.157120    0.138698    536.25    747.04    1.000
r(A<->G){all}   0.290778    0.000202    0.263196    0.317902    0.290555    618.79    659.38    1.000
r(A<->T){all}   0.096929    0.000042    0.084577    0.109928    0.096644    663.91    841.25    1.000
r(C<->G){all}   0.128216    0.000119    0.106659    0.148599    0.128136    528.78    592.09    1.000
r(C<->T){all}   0.266421    0.000168    0.240283    0.290463    0.265949    649.73    664.99    1.001
r(G<->T){all}   0.078641    0.000045    0.063917    0.090635    0.078496    920.81    936.48    1.000
pi(A){all}      0.307996    0.000087    0.289404    0.325417    0.308023    862.37    865.16    1.000
pi(C){all}      0.170940    0.000053    0.156548    0.185019    0.170769    801.50    850.29    1.000
pi(G){all}      0.190601    0.000061    0.175721    0.206406    0.190478    798.49    810.74    1.001
pi(T){all}      0.330462    0.000095    0.311838    0.349714    0.330531    740.28    804.73    1.000
alpha{1,2}      1.219535    0.021686    0.944315    1.502689    1.201737   1206.88   1222.70    1.000
alpha{3}        4.153370    0.614768    2.807966    5.784710    4.079843   1327.83   1376.67    1.000
pinvar{all}     0.033503    0.000354    0.000498    0.067122    0.031850   1186.02   1234.78    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	-11413.08794
Model 2: PositiveSelection	-11410.099548
Model 0: one-ratio	-11596.942392
Model 3: discrete	-11371.713279
Model 7: beta	-11389.075708
Model 8: beta&w>1	-11372.060231


Model 0 vs 1	367.7089040000028

Model 2 vs 1	5.976783999998588

Model 8 vs 7	34.03095400000166

Additional information for M7 vs M8:
Naive Empirical Bayes (NEB) analysis
Positively selected sites (*: P>95%; **: P>99%)
(amino acids refer to 1st sequence: 1_Paxillaris_S19_FBX1_AB933053)

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

    15 A      0.594         1.118
    25 T      0.742         1.244
    28 F      0.974*        1.428
    42 Y      0.966*        1.421
    44 F      0.984*        1.435
    74 S      0.536         1.081
    75 A      0.644         1.169
    77 V      0.815         1.305
    94 S      0.985*        1.435
    95 L      0.982*        1.434
    96 T      0.669         1.185
   124 S      0.554         1.088
   136 A      0.713         1.221
   149 K      0.948         1.407
   152 D      0.727         1.232
   155 M      0.928         1.392
   174 F      0.990**       1.440
   177 W      0.930         1.393
   178 L      0.658         1.176
   193 D      0.894         1.365
   194 V      0.941         1.402
   207 N      0.791         1.283
   238 M      0.747         1.250
   240 S      0.601         1.129
   243 T      0.801         1.293
   312 E      0.727         1.235
   325 A      0.972*        1.426

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: 1_Paxillaris_S19_FBX1_AB933053)

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

    28 F      0.886         1.435 +- 0.247
    42 Y      0.868         1.422 +- 0.265
    44 F      0.924         1.461 +- 0.215
    77 V      0.529         1.167 +- 0.376
    94 S      0.922         1.461 +- 0.215
    95 L      0.915         1.456 +- 0.222
   149 K      0.797         1.369 +- 0.300
   155 M      0.756         1.337 +- 0.325
   174 F      0.947         1.478 +- 0.189
   177 W      0.756         1.338 +- 0.322
   193 D      0.688         1.286 +- 0.355
   194 V      0.791         1.365 +- 0.310
   207 N      0.517         1.147 +- 0.385
   243 T      0.511         1.148 +- 0.377
   325 A      0.878         1.429 +- 0.253