--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 03 21:35:47 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/revmuscle/S25/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/revmuscle/S25/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/revmuscle/S25/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -7218.95         -7236.64
2      -7218.11         -7234.69
--------------------------------------
TOTAL    -7218.44         -7236.08
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/revmuscle/S25/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/revmuscle/S25/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/revmuscle/S25/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.411342    0.003582    1.296463    1.533146    1.411036   1274.94   1387.97    1.000
r(A<->C){all}   0.117456    0.000142    0.095376    0.141581    0.117231    842.04    916.37    1.002
r(A<->G){all}   0.299580    0.000338    0.266143    0.338348    0.299210    624.49    695.07    1.000
r(A<->T){all}   0.076804    0.000058    0.063491    0.092613    0.076514    969.55   1055.09    1.000
r(C<->G){all}   0.164638    0.000246    0.133911    0.196458    0.164346    865.43    995.16    1.000
r(C<->T){all}   0.255648    0.000290    0.222222    0.287928    0.254916    574.24    659.61    1.000
r(G<->T){all}   0.085873    0.000086    0.067098    0.103020    0.085761   1156.12   1182.96    1.000
pi(A){all}      0.298985    0.000121    0.277162    0.319558    0.298770   1035.82   1045.97    1.000
pi(C){all}      0.172981    0.000074    0.157056    0.190855    0.172902    888.54    982.33    1.000
pi(G){all}      0.190271    0.000083    0.173064    0.208007    0.190062    794.39    884.76    1.000
pi(T){all}      0.337763    0.000131    0.315453    0.360256    0.337689    702.62    877.55    1.000
alpha{1,2}      0.755680    0.011754    0.556234    0.961197    0.743714   1103.79   1143.33    1.000
alpha{3}        1.718519    0.182976    1.014866    2.563881    1.650783   1126.97   1157.92    1.000
pinvar{all}     0.042998    0.001138    0.000003    0.110555    0.035041   1036.51   1078.62    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	-1305.49625
Model 2: PositiveSelection	-1292.608656
Model 0: one-ratio	-1332.90378
Model 3: discrete	-1292.605245
Model 7: beta	-1307.095987
Model 8: beta&w>1	-1292.523632


Model 0 vs 1	54.81506000000036

Model 2 vs 1	25.775187999999616

Additional information for M1 vs M2:
Naive Empirical Bayes (NEB) analysis
Positively selected sites (*: P>95%; **: P>99%)
(amino acids refer to 1st sequence: S25_SFBB1)

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

     7 Q      0.848         3.699
    19 Q      0.986*        4.142
    22 Y      0.791         3.519
    26 A      0.767         3.443
    36 T      0.856         3.726
    51 E      0.989*        4.150
    54 C      1.000**       4.185

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: S25_SFBB1)

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

     7 Q      0.829         3.831 +- 1.653
    19 Q      0.984*        4.434 +- 1.267
    22 Y      0.714         3.257 +- 1.629
    26 A      0.688         3.160 +- 1.635
    36 T      0.800         3.628 +- 1.594
    51 E      0.977*        4.369 +- 1.249
    54 C      1.000**       4.488 +- 1.203


Model 8 vs 7	29.144710000000032

Additional information for M7 vs M8:
Naive Empirical Bayes (NEB) analysis
Positively selected sites (*: P>95%; **: P>99%)
(amino acids refer to 1st sequence: S25_SFBB1)

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

     7 Q      0.888         3.487
    19 Q      0.990**       3.782
    22 Y      0.876         3.453
    26 A      0.866         3.425
    36 T      0.908         3.544
    51 E      0.994**       3.793
    54 C      1.000**       3.810

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: S25_SFBB1)

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

     7 Q      0.922         3.405 +- 1.110
    19 Q      0.993**       3.628 +- 0.909
    20 Y      0.561         2.261 +- 1.383
    22 Y      0.908         3.314 +- 1.075
    26 A      0.898         3.282 +- 1.098
    36 T      0.931         3.408 +- 1.053
    37 T      0.603         2.337 +- 1.287
    51 E      0.995**       3.629 +- 0.893
    54 C      1.000**       3.646 +- 0.885