--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Fri Nov 11 23:29:57 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/2/Abl-PB/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/Abl-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-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/2/Abl-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -15203.78 -15218.98
2 -15203.96 -15219.00
--------------------------------------
TOTAL -15203.87 -15218.99
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/Abl-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-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/2/Abl-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} 0.815221 0.001131 0.752052 0.882878 0.814769 1316.65 1408.83 1.000
r(A<->C){all} 0.080261 0.000062 0.065329 0.095872 0.079916 839.19 1012.98 1.000
r(A<->G){all} 0.244529 0.000231 0.215216 0.274614 0.244025 824.78 948.15 1.001
r(A<->T){all} 0.163605 0.000241 0.134020 0.195211 0.163130 860.31 893.44 1.003
r(C<->G){all} 0.035398 0.000018 0.027270 0.043899 0.035261 1182.39 1194.97 1.000
r(C<->T){all} 0.379818 0.000323 0.343909 0.413059 0.379562 794.40 932.30 1.001
r(G<->T){all} 0.096389 0.000104 0.076763 0.116141 0.096244 798.26 866.76 1.001
pi(A){all} 0.225127 0.000031 0.213791 0.235146 0.224999 979.58 1037.68 1.000
pi(C){all} 0.324368 0.000038 0.311754 0.335911 0.324307 1005.26 1073.58 1.000
pi(G){all} 0.290062 0.000035 0.278723 0.302247 0.290141 961.30 1145.15 1.000
pi(T){all} 0.160442 0.000022 0.150930 0.168801 0.160415 1006.35 1148.06 1.000
alpha{1,2} 0.128008 0.000069 0.113133 0.145054 0.127832 1349.90 1404.08 1.000
alpha{3} 6.632409 1.647805 4.445218 9.290540 6.494378 721.90 1111.45 1.000
pinvar{all} 0.385402 0.000431 0.347709 0.429270 0.385554 1198.47 1337.79 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 -13779.593818
Model 2: PositiveSelection -13779.593818
Model 0: one-ratio -13897.267436
Model 3: discrete -13772.100306
Model 7: beta -13774.757193
Model 8: beta&w>1 -13772.261476
Model 0 vs 1 235.34723600000143
Model 2 vs 1 0.0
Model 8 vs 7 4.991433999999572
>C1
MGAQQGKDRGAHSGGGGSGAPVSCIGLSSSPVASVSPHCISSSSGVSSAP
LGGGSTLRGSRIKSSSSGVASGSGSGGGGGGSGSGLSQRSGGHKDARCNP
TVGLNIFTEHNGTKHSSFRGHPGKYHMNLEALLQSRPLPHIPAGSTAASL
LADAAELQQHQQDSGGLGLQGSSLGGGHSSTTSVFESAHRWTSKENLLAP
GPEEDDPQLFVALYDFQAGGENQLSLKKGEQVRILSYNKSGEWCEAHSDS
GNVGWVPSNYVTPLNSLEKHSWYHGPISRNAAEYLLSSGINGSFLVRESE
SSPGQRSISLRYEGRVYHYRISEDPDGKVFVTQEAKFNTLAELVHHHSVP
HEGHGLITPLLYPAPKQNKPTVFPLSPEPDEWEICRTDIMMKHKLGGGQY
GEVYEAVWKRYGNTVAVKTLKEDTMALKDFLEEAAIMKEMKHPNLVQLIG
VCTREPPFYIITEFMSHGNLLDFLRSAGRETLDAVALLYMATQIASGMSY
LESRNYIHRDLAARNCLVGDNKLVKVADFGLARLMRDDTYTAHAGAKFPI
KWTAPEGLAYNKFSTKSDVWAFGVLLWEIATYGMSPYPAIDLTDVYHKLD
KGYRMERPPGCPPEVYDLMRQCWQWDATDRPTFKSIHHALEHMFQESSIT
EAVEKQLNANATSASSSAPSTSGVATGGGATTTTAASGCASSSSATASLS
LTPQMVKKGLPGGQALTPNAHHNDPHQQQASTPMSETGSTSTKLSTFSSQ
GKGNVQMRRTTNKQGKQAPAPPKRTSLLSSSRDSTYREEDPANARCNFID
DLSTNGLARDINSLTQRYDSETDPAADPDTDATGDSLEQSLSQVIAAPVT
NKMQHSLHSGGGGGGIGPRSSQQHSSFKRPTGTPVMGNRGLETRQSKRSQ
LHSQAPGPGPPSTQPHHGNNGVVTSAHPITVGALDVMNVKQVVNRYGTLP
KGARIGAYLDSLEDSSEAAPALPATAPSLPPANGHATPPAARLNPKASPI
PPQQMIRSNSSGGVTMQNNAAASLNKLQRHRTTTEGTMMTFSSFRAGGSS
SSPKRSASGVASGVQPALANLEFPPPPLDLPPPPEEFEGGPPPPPPAPES
AVQAIQQHLHAQLPNNGNISNGNGTNNNDSSHNDVSNIAPSVEEASSRFG
VSLRKREPSTDSCSSLGSPPEDLKEKLITEIKAAGKDTAPASHLANGSGI
AVVDPVSLLVTELAESMNLPKPPPQQQQKLTNGNSTGSGFKAQLKKVEPK
KMSAPMPKAEPANTIIDFKAHLRRVDKEKEPATPAPAPATVAVANNANCN
TTGTLNRKEDGSKKFSQAMQKTEIKIDVTNSNVEADAGAAGEGDLGKRRS
TGSINSLKKLWEQQPPAPDYATSTILQQQPSVVNGGGTPNAQLSPKYGMK
SGAINTVGTLPAKLGNKQPPAAPPPPPPNCTTSNSSTTSISTSSRDCTSR
QQASSTIKTSHSTQLFTDDEEQSHTEGLGSGGQGSADMTQSLYEQKPQIQ
QKPAVPHKPTKLTIYATPIAKLTEPASSASSTQISRESILELVGLLEGSL
KHPVNAIAGSQWLQLSDKLNILHNSCVIFAENGAMPPHSKFQFRELVTRV
EAQSQHLRSAGSKNVQDNERLVAEVGQSLRQISNALNRoooooooooooo
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>C2
MGAQQGKDRGAHSGGGGSGAPVSCIGLSSSPVASVSPHCISSSSGVSSAP
LGGGSTLRGSRIKSSSSGVASGSGSGGGGGGSGSGLSQRSGGHKDARCNP
TVGLNIFTEHNGTKHSSFRGHPGKYHMNLEALLQSRPLPHIPAGSTAASL
LADAAELQQHQQDSGGLGLQGSSLGGGHSSTTSVFESAHRWTSKENLLAP
GPEEDDPQLFVALYDFQAGGENQLSLKKGEQVRILSYNKSGEWCEAHSDS
GNVGWVPSNYVTPLNSLEKHSWYHGPISRNAAEYLLSSGINGSFLVRESE
SSPGQRSISLRYEGRVYHYRISEDPDGKVFVTQEAKFNTLAELVHHHSVP
HEGHGLITPLLYPAPKQNKPTVFPLSPEPDEWEICRTDIMMKHKLGGGQY
GEVYEAVWKRYGNTVAVKTLKEDTMALKDFLEEAAIMKEMKHPNLVQLIG
VCTREPPFYIITEFMSHGNLLDFLRSAGRETLDAVALLYMATQIASGMSY
LESRNYIHRDLAARNCLVGDNKLVKVADFGLARLMRDDTYTAHAGAKFPI
KWTAPEGLAYNKFSTKSDVWAFGVLLWEIATYGMSPYPGIDLTDVYHKLE
KGYRMERPPGCPPEVYDLMRQCWQWDATDRPTFKSIHHALEHMFQESSIT
EAVEKQLNANATSASSSAPSTSGVATGGGATTTTAASGCASSSSATASLS
LTPQMVKKGLSGGQSLTPNAHHNDPHQQQASTPMSETGSTSTKLSTFSSQ
GKGNVQMRRTTNKQGKQAPAPPKRTSLLSSSRDSTYREEDPANARCNFID
DLSTNGLARDINSLTQRYDSETDPAGDPDTDATGDSLEQSLSQVIAAPAT
NKMQHSLHSGGGGGGIGPRSSQQHSSFKRPTGTPVMGNRGLETRQSKRSQ
HHPQAPGPGPPSTQPHHGNNGVLTSAHPITVGALEVMNVKQVVNRYGTLP
KGARIGAYLDSLEDSTEAAPPLPATAPSLPPANGHATPPSARLNPKASPI
PPQQMIRSNSSGGVTMQNNAAASLNKLQRHRTTTEGTMMTFSSFRAGGSS
SSPKRSASGLASGVQPALANLEFPPPPLDLPPPPEEFEGGPPPPPPAPES
AVQAIQQHLHAQLPNNGNISNGNGSNNNDSSHNDVSNIAPSVEEASSRFG
VSLRKREPSTDSCSSLGSPPEDLKEKLITEIKAAGKESAPASHLANGSGI
AVVDPVSLLVTELAESMNLPKSPPQQQQKLTNGNGTGSGFKAQLKKVEPK
KMSAPMPKAEPASTIIDFKAHLRRVDKEKEPAAPAPAPVAVANNANCNTT
GTLNRKEDSSKKFSQAMQKTEIKIDVTNSNVEADAGATGEGDLGKRRSTG
SINSLKKLWEQQPPASDYATSTILQQQPVVNGGGTQTAQLSPKYGMKSGA
INTAGTLPAKLGNKPPPAAPPPPPPNCTTSNSSTTSISTSSRDCTSRQQA
SSTIKTSHSTQLFADDEEQSHTEGLGSG
