--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Thu May 10 16:41:59 WEST 2018
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=/opt/ADOPS1/DNG_N2/NS2B_3/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2B_3/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/DNG_N2/NS2B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -3539.22         -3590.13
2      -3543.61         -3585.44
--------------------------------------
TOTAL    -3539.90         -3589.44
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2B_3/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/DNG_N2/NS2B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         7.522369    0.330064    6.445980    8.689019    7.503303   1069.88   1102.57    1.000
r(A<->C){all}   0.059563    0.000169    0.035817    0.085609    0.058660    751.82    813.96    1.001
r(A<->G){all}   0.256235    0.000712    0.200860    0.305538    0.256005    478.48    554.40    1.000
r(A<->T){all}   0.088500    0.000197    0.059575    0.114151    0.087793    862.16    987.45    1.000
r(C<->G){all}   0.064357    0.000186    0.040182    0.093395    0.063934    656.21    755.86    1.000
r(C<->T){all}   0.522927    0.001035    0.464147    0.588314    0.522538    474.07    571.10    1.000
r(G<->T){all}   0.008418    0.000043    0.000006    0.021095    0.007029    881.07    915.15    1.002
pi(A){all}      0.325580    0.000247    0.295965    0.356686    0.325172   1062.48   1107.07    1.000
pi(C){all}      0.216695    0.000189    0.189690    0.243669    0.216382    858.19    968.75    1.000
pi(G){all}      0.237463    0.000212    0.209627    0.265927    0.237399    691.55    789.41    1.000
pi(T){all}      0.220262    0.000191    0.192226    0.246337    0.220087    697.15    816.62    1.000
alpha{1,2}      0.224549    0.000667    0.174714    0.274691    0.222906   1287.87   1333.52    1.000
alpha{3}        3.996541    0.884805    2.303337    5.823713    3.898486   1476.21   1488.61    1.000
pinvar{all}     0.051378    0.000565    0.008576    0.098992    0.049857   1380.00   1440.50    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	-3176.572722
Model 2: PositiveSelection	-3176.572722
Model 0: one-ratio	-3179.853132
Model 3: discrete	-3158.121053
Model 7: beta	-3159.055428
Model 8: beta&w>1	-3159.052072


Model 0 vs 1	6.560820000000604

Model 2 vs 1	0.0

Model 8 vs 7	0.006712000000334228
>C1
SWPLNEGIMAVGLVSLLGSALLKNDVPLAGPMVAGGLLLAAYVMSGSSAD
LSLEKAANVQWDEMADITGSSPIIEVKQDEDGSFSIRDVEETNMITLLVK
LALITVSGLYPLAIPVTMALWYIWQVKTQR
>C2
SWPLNEGVMAVGLVSILASSLLRNDVPMAGPLVAGGLLIACYVITGTSAD
LTVEKAADITWEEEAEQTGVSHNLMITVDDDGTMRIKDDETENILTVLLK
TALLIVSGIFPYSIPATLLVWHTWQKQTQR
>C3
SWPLNEGIMAVGVVSILLSSLLKDDVPLAGPLIAGGMLIACYVISGSSAD
LSLEKAAEVSWEEEAEHSGASHNILVEVQDDGTMKIKDEERDDTLTILLK
ATLLAVSGVYPLSIPATLFVWYFWQKKKQR
>C4
SWPLNEGIMAVGIVSILLSSLLKNDVPLAGPLIAGGMLIACYVISGSSAD
LSLEKAAVVSWEEEAEHSGASHNILVEVQDDGTMKIKDEERDDTLTILLK
ATLLAVSGVYPLSIPATLFVWYFWQKKKQR
>C5
SWPLNEAIMAVGMVSILASSLLKNDIPMTGPLVAGGLLTVCYVLTGRSAD
LELERAADVRWEEQAEISGSSPILSITISEDGSMSIKNEEEEQTLTILIR
TGLLVISGLFPVSIPITAAAWYLWEVKKQR
>C6
SWPLNEAVMAVGMVSILASSLLKNDIPMTGPLVAGGLLTVCYVLTGRSAD
LELERAADVKWEDQAEISGSSPILSITISEDGSMSIKNEEEEHTLTILIR
TGLLVISGVFPVSIPITAAAWYLWEVKKQR
>C7
SWPLNEoVMAVoLVSILASSLLRNDVPMAGPLVAGGLLIACYVITGTSAD
LTVEKAADVTWEEEAEQTGVSHNLMITVDDDGTMRIKDDETENILTVLLK
TALLIVSGIFPYSIPATLLVWHTWQKQTQR
>C8
SWPLNEGIMAIGIVSILLSSLLKNDVPLAGPLIAGGMLIACYVISGSSAD
LSLEKAAEVSWEDEAEHSGASHNILVEVQDDGTMKIKDEERDDTLTILLK
ATLLVISGV