--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Wed Nov 02 16:19:50 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/138/CG4041-PB/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/138/CG4041-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/138/CG4041-PB/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/138/CG4041-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -8743.57         -8759.25
2      -8742.85         -8758.87
--------------------------------------
TOTAL    -8743.14         -8759.08
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/138/CG4041-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/138/CG4041-PB/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/138/CG4041-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.125608    0.003456    1.012300    1.238781    1.124718   1255.60   1378.30    1.000
r(A<->C){all}   0.110229    0.000159    0.085477    0.133683    0.109739    703.41    948.32    1.000
r(A<->G){all}   0.325434    0.000476    0.284291    0.368762    0.324470    745.92    919.03    1.001
r(A<->T){all}   0.070509    0.000176    0.041849    0.093428    0.070208   1034.05   1115.79    1.001
r(C<->G){all}   0.076605    0.000069    0.059995    0.092097    0.076593   1134.15   1168.20    1.000
r(C<->T){all}   0.375732    0.000531    0.330738    0.419131    0.375815    773.62    893.63    1.000
r(G<->T){all}   0.041491    0.000059    0.026306    0.056353    0.041233    750.94    840.70    1.002
pi(A){all}      0.194796    0.000054    0.181164    0.210306    0.194729   1011.26   1012.81    1.001
pi(C){all}      0.308385    0.000075    0.291639    0.324636    0.308485    898.38    943.52    1.002
pi(G){all}      0.284304    0.000071    0.268195    0.300622    0.284211   1127.47   1139.46    1.000
pi(T){all}      0.212515    0.000056    0.199366    0.227770    0.212494    972.50   1024.05    1.000
alpha{1,2}      0.146732    0.000111    0.126989    0.167362    0.146080   1120.72   1277.67    1.000
alpha{3}        5.137991    1.266969    3.121084    7.418113    5.004754   1324.97   1359.18    1.000
pinvar{all}     0.333675    0.000733    0.280649    0.385864    0.333933   1291.06   1396.03    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	-8117.810688
Model 2: PositiveSelection	-8115.931491
Model 0: one-ratio	-8234.821549
Model 3: discrete	-8101.610916
Model 7: beta	-8115.571572
Model 8: beta&w>1	-8101.499091


Model 0 vs 1	234.02172200000132

Model 2 vs 1	3.7583939999985887

Model 8 vs 7	28.144962000000305

Additional information for M7 vs M8:
Naive Empirical Bayes (NEB) analysis
Positively selected sites (*: P>95%; **: P>99%)
(amino acids refer to 1st sequence: D_melanogaster_CG4041-PB)

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

   234 Q      0.961*        2.841
   259 E      0.940         2.787
   262 I      0.990*        2.917
   265 E      0.997**       2.936
   267 L      0.998**       2.939
   278 P      0.955*        2.827

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_CG4041-PB)

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

   129 S      0.531         1.069 +- 0.542
   234 Q      0.953*        1.585 +- 0.429
   259 E      0.942         1.574 +- 0.447
   262 I      0.971*        1.604 +- 0.422
   265 E      0.982*        1.613 +- 0.412
   267 L      0.983*        1.615 +- 0.410
   275 M      0.688         1.264 +- 0.526
   278 P      0.941         1.575 +- 0.449
   279 E      0.760         1.357 +- 0.574
   280 T      0.536         1.077 +- 0.548
   367 A      0.507         0.996 +- 0.679

>C1
MGTRERERECRLCAVTFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLKE
LQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAMRHPPLAIAQILRIF
YQVACGINVLSRHHLVAHNVEPKHILLSSDGQRVKLFNYGLHHMTKGGAY
VPFPIGNIRYMAPERLLGLNGNVKSDVWSLALVMVELILQIELWPKLKLS
NVVRKILAFGKSNGALEKIAREHQCHERYVQMDQRLRQLLESCLSVLPKR
RPLPGELLEHPIFEEVLLDLKKQKMQPLSPETEHLPLLLRCPLSQIYHLW
QLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDDR
VVPLRLKALLQRLSGLPAAVYFPLLHSPRFPAHFARELQELPLVIREKDI
EYQFQRVRLFARLLQGYPHTAEQLQREAAVDVPPLLRGPIWAALLEVVPN
GSYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWVT
AHPQYVYWQGLDSLTAPFLYLNFNNEELAFLSLFKFIPKYLQWFFLKDNS
AVIKEYLSKFSQLTAFHEPLLAQHLASISFIPELFAIPWFLTMFSHVFPL
HKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLPD
IVMDGCVLESQKMYEATPKSITHRQHALRLQPPQALDIGVADVELKHLQQ
EQCPRISAKDVQFLLDNSPAELALIDLRSVVEFGRVHVPHSINIPFATVQ
LGEQRLEALQVPQLEAQLRGKIVVCVSNIHQHSVEFSHFLVACGVQRTCI
LHKGFNVLHSIEPNILISNo
>C2
MGTRERERECRLCAVTFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLKE
LQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAMRHPPLTIAQILRIF
YQVACGINVLSRHHLVAHNLEPKHILLSSDGQRVKLFNYGLHHMTKGGAY
VPFPIGNIRYMAPERLLGLNGNVKSDVWSLALVMVELILQIELWPKLKLS
NVVRKILAFGKSNGALEKIAREHQCHERYVQMDQRLRQLLESCLSVLPKR
RPLPGELLEHPIFEEVLLDLKKQKMEPLSPETDHLPLLLRCPLSQIYHLW
QLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDDR
VVPLRLKALLQRLSGLPAAVYFPLLHSPRFPAHFARELQELPLVIREKDI
EYQFQRVRLFTRLLQGYPHTAEQLQREAAVDVPPLLRGPIWAALLEVVPN
GSYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWVT
AHPQYVYWQGLDSLTAPFLYLNFNNEELAFLSLFKFIPKYLQWFFLKDNS
AVIKEYLSKFSQLTAFHEPLLAQHLASISFIPELFAIPWFLTMFSHVFPL
HKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLPD
IVMDGCVLESQKMYEATPKSITHRQHALRLQPPQALDIGVADVELKHLQQ
EQCPRISAKDVQFLLDNSPAELALVDLRSVVEFGRVHVPHSINIPFATVQ
LGEQRLEALQVPQLEAQLRGKIVVCVSNIHQHSVEFSHFLVACGVQRTCI
LHKGFNVLHSIEPNILISNo
>C3
MGTRERERECRLCAVTFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLKE
LQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAMRHPPLAIAQILRIF
YQVACGINVLSRHHLVAHNVEPKHILLSSDGQRVKLFNYGLHHMTKGGAY
VPFPIGNIRYMAPERLLGLNGNVKSDVWSLALVMVELILQIELWPKLKLS
NVVRKILAFGKSNGALEKIAREHQCHERYVQMDQRLRQLLESCLSVLPKR
RPLPGELLEHPIFEEVLLDLKKQKMEPLSLETDHLPLLLRCPLSQIYHLW
QLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDDR
VVPLRLKALLQRLSGLPAAVYFPLLHSPRFPAHFARELQELPLVIREKDI
EYQFQRVRLFTRLLQGYPHTAEQLQREAAVDVPPLLRGPIWAALLEVVPN
GSYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWVT
AHPQYVYWQGLDSLTAPFLYLNFNNEELAFLSLFKFIPKYLQWFFLKDNS
AVIKEYLSKFSQLTAFHEPLLAQHLASISFIPELFAIPWFLTMFSHVFPL
HKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLPD
IVMDGCVLESQKMYEATPKSITHRQHALRLQPPQALDIGVADVELKHLQQ
EQCPRISAKDVQFLLDNSPAELALVDLRSVVEFGRVHVPHSINIPFATVQ
LGEQRLEALQVPQLEAQLRGKIVVCVSNIHQHSVEFSHFLVAGGVQRTCI
LHKGFNVLHSIEPNILISNo
>C4
MATRERDRECRLCAVTFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLKE
LQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAMRHPPLAIPQILRIF
YQVACGINVLSRHHLVAHNVEPKHILLSSDGQRVKLFNYGLHHMTKGGAY
VPFPIGNIRYMAPERLLGLNGNVKSDVWSLALVMVEPILQIELWPKLKLS
NVVRKILAFGKSNGVLEKIAREHQCHERYVQLDQRLRQLLESCLSVLPKR
RPLPWELLDHPVFEEVLLDLKKQKMQPSSPETEHLPLLLRCPLSQIYHLW
QLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDDR
VVPLCLKALLQRLSGLPAAVYFPLLHSPRFPAHFARELQELPLVIREKDI
EYQFQRVRLFTRLLQGYPHTAEQLQREAAVDVPPLLRGPIWAALLEVLPN
GSYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWVT
AHPQYVYWQGLDSLTAPFLYLNYNNEELAFLSLFKFIPKYLQWFFLKDNS
AVIKEYLSKFSQLTAFHEPLLAQHLASISFIPELFAIPWFLTMFSHVFPL
HKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLPD
IVMDGCVLESQKMYEATPKSITYRQHALRLQPPQALDIGVADVELKHLQQ
EQCPRISAKDVQFLLDNSPAELALVDLRSVVEFGRVHVPHSINIPFATVQ
LGEQRLEALQVPQLEAQLRGKIVVCVSNIHQHSVEFSHFLVACGVQRTCI
LHKGFNVLHSIEPNILISNo
>C5
MATRERERECRLCAVTFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLKE
LQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAMRHPPLAIAQILRIF
YQVACGINVLCRHHLVAHNVEPKHILLSSDGQRVKLFNYGLHHMTKGGAY
VPFPIGNIRYMAPERLLGLNGNVKSDVWSLALVMVELILQIELWPKLKLS
NVVRKILAFGRSNGVLEKIAREHQCHERYVQMDQRLRQLLESCLSVLPKR
RPLPWELLDHPIFEEVILDLKKQKMQPLSPETEHLPLLLRCPLSQIYHLW
QLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDDR
VVPLRLKALLQRLSGLPAAVYFPLLHSPRFPAHFARELQELPLVIREKDI
EYQFQRVRLFTRLIQGYPHTAEQLQREAAVDVPPLLRGPIWAALLEVVPN
GSYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWVT
AHPQYVYWQGLDSLTAPFLYLNFNNEELAFLSLFKFIPKYLQWFFLKDNS
AVIKEYLSKFSQLTAFHEPLLAQHLASISFIPELFAIPWFLTMFSHVFPL
HKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLPD
IVMDGCVLESQKMYEATPKSITHRQHALRLQPPRALDIGVADVELKHLQQ
EQCPRISAKDVQFLLDNSPAELILVDLRSVVEFGRVHVPHSINIPFATVQ
LGEQRLEALQVPQLEAQLRGKIVVCVSNIHQHSVEFSHFLVACGVQRTCI
LHKGFNVLHSIEPNILISNo
>C6
MGSTRERERESRLCAVTFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLK
ELQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAKRHPPLAIAQILRI
FYQVACGIKVLSQHHLVAHNLEPKHVLISTDGQRVKLFNYGLHHMTKGGA
YVPFPIGNIRYMAPERLLGLNGNVKSDIWALALMMVELLFQIELWPKLKL
SNVVRKILAFGRSNGVLEKIAREHQCHERYAEMDSSLRQLLESCLSVLPK
RRPLPEELLSHPVFEGLLLSLQKQSSQPETLEHLPLLLRCPLSQIYHLWQ
LAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDDRV
VPLRLKALLQRLSRLPAAVYFPLLHSPRFPAHFARELQELPLVIREKDIE
YQFQRVRLFTRLLQGYPHTSEQLRSEAAVDVPPLLRGPIWAALLEVVPNG
SYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWVTA
HPQYVYWQGLDSLTAPFLYLNFNNEELAFLSLFKFIPKYLQWFFLKDNSA
VIKEYLSKFSQLTAFHEPLLAQHLASISFIPELFAIPWFLTMFSHVFPLH
KILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLPDI
VMDGCVLESQKMYEATPKSITHRQHALRLQPPQALDIGVADVELKHLQLE
QCPRISAKDVHFLLIHSPAELILVDLRSVVEFGRVHVPHSINIPFATVQL
GEQRLEALQVPQLEALLRGKIVVCVSNIHQHSVEFSHFLVACGVQRTCIL
HKGFNVLHSIEPNILISNoo
>C7
MGSTRERERESRLCAVTFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLK
ELQDDHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAKRHPPLAIAQILRI
FYQVACGINVLSQHHLVAHNLEPKHVLISSDGLRVKLFNYGLHHMTKGGA
YVPFPIGNIRYMAPERLLGLNGNVKSDVWALAMIVVELVFQIELWPKLKL
SNVVRKILAFGRSNGVLEKIAREHQCHERYAQMDASLRQLLESCLSVLPK
RRPLPGELIGHPAFEAVHLDVQKEKLQPLHEGAEHLPLLLRCPLSQIYHL
WQLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDD
RVVPLRLKALLQRLSRLPAAVYFPLLHSPRFPAHFARELQELPLVIRERD
IEYQFQRVRLFTRLLQGYPHTSEQLRREAAVDVPPLLRGPIWAALLEVVP
NGSYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWV
TAHPQYVYWQGLDSLTAPFLYLNFNNEELAFLSLFKFIPKYLQWFFLKDN
SAVIKEYLSKFSQLTAFHEPLLARHLASISFIPELFAIPWFLTMFSHVFP
LHKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLP
DIVMDGCVLESQKMYEATPKSITHRQHALRLQPPQALDIGVADVELKHLQ
QEQCPRISAKDVQFLLDNSPAELALVDLRSVVEFGRVHVPHSINIPFATV
QLGEQRLEALQVPQLEALLRGRIVVCVSNIHQHSVEFSHFLVACGVQRTC
ILHKGFNVLHSIEPNILISN
>C8
MGSTRERERESRLCAVTFFARLHPGDVCGSNGLPLTPNSIAILGRAQKLK
ELQDETLCQYLDVIRGKHERTIVVSEYLGMSLEDYAKRHPPLAIAQILRI
FYQVACGINVLSQHHLVAHNLEPKHVLISNDGLRVKLFNYGLHHMTKGGA
YVPFPIGNIRYMAPERLLGLNGNVKSDVWALAMMVVELVFQIELWPKLKL
SNVVRKILAFGRSNGVLEKIAREHQCHERYAEMDANLRQLLESCLSVLPK
RRPLPGELLSHPIFETVCTDLKKEKLEALGEGAEHVPLLLRCPLSQIYHL
WQLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDD
RVVPLRLKALLQRLSRLPAGVYFPLLHSPRFPAHFARELQELPLVIRERD
IEYQFQRVRLFTRLLQGYPHTSEQLRSEAAVDVPPLLRGPIWAALLEVVP
NGSYAKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWV
TAHPQYVYWQGLDSLTAPFLYLNFNNEELAFLSLFKFIPKYLQWFFLKDN
SAVIKEYLSKFSQLTAFHEPLLARHLASISFIPELFAIPWFLTMFSHVFP
LHKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLP
DIVMDGCVLESQKMYEATPKSITHRQHAQRLQPPQALDIGVADVELKHLQ
QEQCPRISAKDVQFLLDHSPAELALVDLRSVVEFGRVHVPHSINIPFATV
QLGDQRLEALQVPQLEALLRGRIVVCVSNIHQHSVEFSHFLVACGVQRTC
ILHKGFNVLHSIEPNILISN
>C9
MGSTRERERESRLCAITFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLK
ELQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAKRHPPLAIAQILRI
FYQVACGIDVLSQHHLVAHNLEPKHILISNDSLRVKLFNYGLHHMTKDGA
YVPFPIGNIRYMAPERLLGLNGNVKSDVWSLALLMVELIFQIELWPKLKL
SNVIRKILALSRSSGVLEKIAREHQCHERYIEMNKNLRQLLESCLSVLPK
RRPLPGELLNDEVFENIRLDLNKEKIQPLGNEIEHLPLLLRCPLSQIYHL
WQLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDD
RVVPLRLKALLQRLSRLPAGVYFPLLHSPRFPAHFSRELQELPLVIREKD
IEYQFQRVRLFTRLLQGYPHTAEQLQREAAVDVPPLLRGPIWAALLEVVP
NGSYSKIDKFTSTSTDRQIEVDIPRCHQYDELLSSPDGHRKLRRLLKAWV
TAHPQYVYWQGLDSLTAPFLFLNFNNEELAFLSLFKFIPKYLQWFFLKDN
SAVIKEYLCKFSQLTAFHEPLLAQHLASISFIPELFAIPWFLTMFSHVFP
LHKILHLWDKLMLGDSSYPLFIGIAILRQLRSTLLTSGFNECILLFSDLP
DIVMDGCVLESQKMYEATPKSITHRQHALRLHPQQALDIGVTDVELKHLQ
QEQCPRISAKDVQFMLDNSPSELALVDLRSVVEFGRVHVPHSINIPFATV
QLGDQRLDALQVPQLEAQLRGRIVVCVSNIHQHSVEFSHFLVACGVQRTC
ILHKGFNVLHSIEPNILISN
>C10
MGSTRERERESRLCAITFFAKLHPGDVCGSNGLPLTPNSIAILGRAQKLK
ELQDEHLCQYLDVIRGKHERTIVVSEYLGLSLEDYAKRHPPLAIAQILRI
FYQVACGIDVLSQHHLVAHNLEPKHVLLSDDCQRVKLFNYGLHHMTKGGA
YVPFPIGNIRYMAPERLLGLNGNVKSDIWSLAMLIVELIFQIELWPKLKI
SNVIRKILAFGRSNGVLEKIAREHQCHERYAEMDSSLRELLESCLSVLPK
RRPQPEELIKHALFEAVILDLKKEKTQALSVETEHLPLLLRCPLSQIYHL
WQLAGGDVQAELKKEGLIRSEAPILGLPQIVRLSGASVCPGRSQAQLMDD
RVVPLRLKALLQRLSRLPAAVYFPLLHSPRFPAHFARELQDLPLVIREKD
IEYQFQRVRLFTRLLQGYPHTAEHLQREAA