--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 18 13:27:03 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/285/KCNQ-PG/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/285/KCNQ-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/285/KCNQ-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/285/KCNQ-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -6479.07         -6497.62
2      -6478.86         -6499.98
--------------------------------------
TOTAL    -6478.96         -6499.37
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/285/KCNQ-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/285/KCNQ-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/285/KCNQ-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}         0.509682    0.001327    0.441842    0.586285    0.508316   1368.33   1434.66    1.000
r(A<->C){all}   0.127905    0.000285    0.097073    0.160997    0.127415   1090.24   1205.88    1.000
r(A<->G){all}   0.250264    0.000597    0.198674    0.295127    0.249883    872.96    990.44    1.000
r(A<->T){all}   0.115383    0.000388    0.076797    0.154636    0.114771    966.95   1021.19    1.000
r(C<->G){all}   0.044855    0.000073    0.029593    0.062113    0.044429    979.31   1008.56    1.000
r(C<->T){all}   0.378037    0.000820    0.321779    0.431394    0.377410    863.34    880.17    1.000
r(G<->T){all}   0.083556    0.000201    0.056766    0.111686    0.082658   1049.12   1107.41    1.000
pi(A){all}      0.234307    0.000071    0.218661    0.252239    0.234273   1050.09   1147.17    1.000
pi(C){all}      0.285917    0.000073    0.267627    0.301433    0.285727    903.70   1023.00    1.000
pi(G){all}      0.270586    0.000073    0.253784    0.286828    0.270706   1225.46   1317.25    1.000
pi(T){all}      0.209190    0.000060    0.194217    0.223914    0.209254    970.82    989.30    1.000
alpha{1,2}      0.115907    0.000182    0.088983    0.142055    0.115732   1324.41   1329.31    1.000
alpha{3}        4.107496    1.021411    2.208484    6.096073    3.997261   1356.97   1419.76    1.000
pinvar{all}     0.577750    0.000677    0.528597    0.629496    0.578229   1375.79   1383.60    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	-6008.179142
Model 2: PositiveSelection	-6008.179223
Model 0: one-ratio	-6038.626892
Model 3: discrete	-6003.915208
Model 7: beta	-6006.860589
Model 8: beta&w>1	-6006.861278


Model 0 vs 1	60.89550000000054

Model 2 vs 1	1.6200000027311035E-4

Model 8 vs 7	0.0013780000008409843