BUSTED error: tree_id_0 is not a supported object type in call to SetParameter

[00-kelvin]

Hi there!

I am testing out BUSTED on an alignment and I got an error which I cannot find anyone else running into online. This same error happened for 2 different alignments I tried to run. I think it may be related to the gene names that I have in my alignment and tree files (they are very long -- I'm planning to change them in the future) but I am not sure how/why. As far as I can tell, the gene names in the tree and the alignment files match one another. Do you know why I might be getting this error?

Here is the errors.log:

Error:'llKgQcaJ.tree_id_0.IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_' is not a supported object type in call to SetParameter(llKgQcaJ.tree_id_0.IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_, MODEL, busted.test);
'llKgQcaJ.tree_id_0.IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_' is not a supported object type in call to SetParameter(llKgQcaJ.tree_id_0.IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_, MODEL, busted.test);

and if it helps, here is the rest of the system output from the attempt:

 "authors":"Sergei L Kosakovsky Pond",
 "citation":"*Gene-wide identification of episodic selection*, Mol Biol Evol. 32(5):1365-71, *Synonymous Site-to-Site Substitution Rate Variation Dramatically Inflates False Positive Rates of Selection Analyses: Ignore at Your Own Peril*, Mol Biol Evol. 37(8):2430-2439",
 "contact":"spond@temple.edu",
 "info":"BUSTED (branch-site unrestricted statistical test of episodic diversification) uses a random effects branch-site model fitted  jointly to all or a subset of tree branches in order to test for alignment-wide evidence of episodic diversifying selection. Assuming there is evidence of positive selection (i.e. there is an omega > 1),  BUSTED will also perform a quick evidence-ratio style analysis to explore which individual sites may have been subject to selection. v2.0 adds support for synonymous rate variation, and relaxes the test statistic to 0.5 (chi^2_0 + chi^2_2).\n                               \nVersion 2.1 adds a grid search for the initial starting point.\n\nVersion 2.2 changes the grid search to LHC, and adds an initial search phase to use adaptive Nedler-Mead. \n\nVersion 3.0 implements the option for branch-site variation in synonymous substitution rates. \n\nVersion 3.1 adds HMM auto-correlation option for SRV, and binds SRV distributions for multiple branch sets.\n\nVersion 4.0 adds support for multiple hits (MH), ancestral state reconstruction saved to JSON, and profiling of branch-site level support for selection / multiple hits.\n\nVersion 4.2 adds calculation of MH-attributable fractions of substitutions.\n\nVersion 4.5 adds an 'error absorption' component [experimental]\n",
 "requirements":"in-frame codon alignment and a phylogenetic tree (optionally annotated with {})",
 "settings":{
  },
 "version":"4.5"
}

Analysis Description
--------------------
BUSTED (branch-site unrestricted statistical test of episodic
diversification) uses a random effects branch-site model fitted jointly
to all or a subset of tree branches in order to test for alignment-wide
evidence of episodic diversifying selection. Assuming there is evidence
of positive selection (i.e. there is an omega > 1), BUSTED will also
perform a quick evidence-ratio style analysis to explore which
individual sites may have been subject to selection. v2.0 adds support
for synonymous rate variation, and relaxes the test statistic to 0.5
(chi^2_0 + chi^2_2). Version 2.1 adds a grid search for the initial
starting point. Version 2.2 changes the grid search to LHC, and adds an
initial search phase to use adaptive Nedler-Mead. Version 3.0 implements
the option for branch-site variation in synonymous substitution rates.
Version 3.1 adds HMM auto-correlation option for SRV, and binds SRV
distributions for multiple branch sets. Version 4.0 adds support for
multiple hits (MH), ancestral state reconstruction saved to JSON, and
profiling of branch-site level support for selection / multiple hits.
Version 4.2 adds calculation of MH-attributable fractions of
substitutions. Version 4.5 adds an 'error absorption' component
[experimental] 

- __Requirements__: in-frame codon alignment and a phylogenetic tree (optionally annotated
with {})

- __Citation__: *Gene-wide identification of episodic selection*, Mol Biol Evol.
32(5):1365-71, *Synonymous Site-to-Site Substitution Rate Variation
Dramatically Inflates False Positive Rates of Selection Analyses: Ignore
at Your Own Peril*, Mol Biol Evol. 37(8):2430-2439

- __Written by__: Sergei L Kosakovsky Pond

- __Contact Information__: spond@temple.edu

- __Analysis Version__: 4.5


>code => Universal

-------
>[WARNING]
'/scratch4/agordus1/crunnel2/macse/attempt-2/N1.HOG0007354/N1.HOG0007354_NT.fasta.fixed'
contains 4 duplicate sequences. Identical sequences do not contribute
any information to the analysis and only slow down computation. Please
consider removing duplicate or 'nearly' duplicate sequences, e.g. using
https://github.com/veg/hyphy-analyses/tree/master/remove-duplicates
prior to running selection analyses
-------
/scratch4/agordus1/crunnel2/iqtree/N1.HOG0007354/N1.HOG0007354.treefile
/scratch4/agordus1/crunnel2/iqtree/N1.HOG0007354/N1.HOG0007354.treefile

>Loaded a multiple sequence alignment with **192** sequences, **1271** codons, and **1** partitions from `/scratch4/agordus1/crunnel2/macse/attempt-2/N1.HOG0007354/N1.HOG0007354_NT.fasta.fixed`

>branches => All

>srv => Yes
The number omega rate classes to include in the model (permissible range = [1,10], default value = 3, integer): 
>rates => 3

>multiple-hits => Double+Triple
The number alpha rate classes to include in the model [1-10, default 3] (permissible range = [1,10], default value = 3, integer): 
>syn-rates => 3

>error-sink => No
The number of points in the initial distributional guess for likelihood fitting (permissible range = [1,10000], default value = 250, integer): 
>grid-size => 250
The number of initial random guesses to 'seed' rate values optimization (permissible range = [1,25], default value = 1, integer): 
>starting-points => 1


### Branches to test for selection in the BUSTED analysis
* Selected 381 branches to test in the BUSTED analysis: `IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_, IBIK01030424.1.p1GENE.IBIK01030424.1__IBIK01030424.1.p1ORFtype_completelen_1192____score_228.20_Q9W391|61.429|0.0IBIK01030424.1_308-3883___, IBIK01045126.1.p1GENE.IBIK01045126.1__IBIK01045126.1.p1ORFtype_completelen_1200____score_234.21_Q9W391|61.514|0.0IBIK01045126.1_308-3907___, Node2, ICOJ01035034.1.p1GENE.ICOJ01035034.1__ICOJ01035034.1.p1ORFtype_3prime_partiallen_581_-__score_135.84_Q9W391|69.535|0.0ICOJ01035034.1_1-1740_-_, IAQZ01018679.1.p1GENE.IAQZ01018679.1__IAQZ01018679.1.p1ORFtype_completelen_1193_-__score_256.17_Q9W391|60.956|0.0IAQZ01018679.1_765-4343_-_, Node6, IAHS01025349.1.p1GENE.IAHS01025349.1__IAHS01025349.1.p1ORFtype_completelen_1192____score_237.51_Q9W391|61.270|0.0IAHS01025349.1_339-3914___, IAHS01020096.1.p1GENE.IAHS01020096.1__IAHS01020096.1.p1ORFtype_completelen_856____score_170.28_Q9W391|60.023|0.0IAHS01020096.1_339-2906___, Node17, IBRX01000794.1.p1GENE.IBRX01000794.1__IBRX01000794.1.p1ORFtype_completelen_1192____score_246.25_Q9W391|61.852|0.0IBRX01000794.1_255-3830___, IBRX01028012.1.p1GENE.IBRX01028012.1__IBRX01028012.1.p1ORFtype_completelen_594____score_120.63_Q9W391|71.655|0.0IBRX01028012.1_255-2036___, Node24, ICBF01006425.1.p1GENE.ICBF01006425.1__ICBF01006425.1.p1ORFtype_completelen_1192____score_258.97_Q9W391|61.882|0.0ICBF01006425.1_301-3876___, Node23, IAXT01005309.1.p1GENE.IAXT01005309.1__IAXT01005309.1.p1ORFtype_completelen_1192____score_237.62_Q9W391|62.440|0.0IAXT01005309.1_196-3771___, Node22, IAEU01036660.1.p1GENE.IAEU01036660.1__IAEU01036660.1.p1ORFtype_completelen_1192____score_228.79_Q9W391|61.563|0.0IAEU01036660.1_224-3799___, IAEU01034513.1.p1GENE.IAEU01034513.1__IAEU01034513.1.p1ORFtype_completelen_1201____score_227.80_Q9W391|61.765|0.0IAEU01034513.1_224-3826___, Node30, IBPZ01020432.1.p1GENE.IBPZ01020432.1__IBPZ01020432.1.p1ORFtype_completelen_1200____score_243.71_Q9W391|61.935|0.0IBPZ01020432.1_246-3845___, Node29, Node21, IAFI01033812.1.p1GENE.IAFI01033812.1__IAFI01033812.1.p1ORFtype_completelen_1189____score_271.23_Q9W391|62.141|0.0IAFI01033812.1_218-3784___, IAJV01031860.1.p1GENE.IAJV01031860.1__IAJV01031860.1.p1ORFtype_completelen_1197____score_278.69_Q9W391|61.648|0.0IAJV01031860.1_219-3809___, Node36, IAHZ01008551.1.p1GENE.IAHZ01008551.1__IAHZ01008551.1.p1ORFtype_completelen_1192_-__score_261.21_Q9W391|61.563|0.0IAHZ01008551.1_948-4523_-_, IAPM01038949.1.p1GENE.IAPM01038949.1__IAPM01038949.1.p1ORFtype_completelen_1192_-__score_268.68_Q9W391|61.753|0.0IAPM01038949.1_1015-4590_-_, IAPM01021138.1.p1GENE.IAPM01021138.1__IAPM01021138.1.p1ORFtype_completelen_1192_-__score_269.23_Q9W391|61.355|0.0IAPM01021138.1_990-4565_-_, Node41, Node39, Node35, ICEG01016407.1.p1GENE.ICEG01016407.1__ICEG01016407.1.p1ORFtype_completelen_1195____score_249.39_Q9W391|61.661|0.0ICEG01016407.1_286-3870___, Node34, Node20, Node16, ICFD01005602.1.p1GENE.ICFD01005602.1__ICFD01005602.1.p1ORFtype_completelen_1193____score_282.61_Q9W391|61.783|0.0ICFD01005602.1_236-3814___, ICFD01022791.1.p1GENE.ICFD01022791.1__ICFD01022791.1.p1ORFtype_completelen_973____score_226.01_Q9W391|62.574|0.0ICFD01022791.1_236-3154___, Node47, ICKB01027428.1.p1GENE.ICKB01027428.1__ICKB01027428.1.p1ORFtype_completelen_1137____score_240.80_Q9W391|62.248|0.0ICKB01027428.1_182-3592___, Node46, IAGL01034733.1.p1GENE.IAGL01034733.1__IAGL01034733.1.p1ORFtype_completelen_1201_-__score_294.25_Q9W391|61.142|0.0IAGL01034733.1_867-4469_-_, ICMU01000781.1.p2GENE.ICMU01000781.1__ICMU01000781.1.p2ORFtype_completelen_1193_-__score_295.77_Q9W391|61.563|0.0ICMU01000781.1_4870-8448_-_, Node51, Node45, Node15, IASB01011864.1.p2GENE.IASB01011864.1__IASB01011864.1.p2ORFtype_completelen_578_-__score_122.07_Q9W391|69.527|0.0IASB01011864.1_3000-4733_-_, IASB01029928.1.p1GENE.IASB01029928.1__IASB01029928.1.p1ORFtype_completelen_1192_-__score_263.95_Q9W391|61.514|0.0IASB01029928.1_788-4363_-_, Node59, IASB01011864.1.p1GENE.IASB01011864.1__IASB01011864.1.p1ORFtype_completelen_614_-__score_139.10_Q9W391|54.586|0.0IASB01011864.1_822-2663_-_, Node58, IAET01006820.1.p2GENE.IAET01006820.1__IAET01006820.1.p2ORFtype_completelen_455____score_104.10_Q9W391|72.562|0.0IAET01006820.1_242-1606___, IAET01006820.1.p1GENE.IAET01006820.1__IAET01006820.1.p1ORFtype_completelen_638____score_136.59_Q9W391|54.571|0.0IAET01006820.1_1980-3893___, IAET01022678.1.p1GENE.IAET01022678.1__IAET01022678.1.p1ORFtype_completelen_1192____score_255.63_Q9W391|61.992|0.0IAET01022678.1_242-3817___, Node65, Node63, Node57, IBFJ01026216.1.p2GENE.IBFJ01026216.1__IBFJ01026216.1.p2ORFtype_completelen_1192_-__score_269.36_Q9W391|62.121|0.0IBFJ01026216.1_6310-9885_-_, Node56, IBHY01030644.1.p1GENE.IBHY01030644.1__IBHY01030644.1.p1ORFtype_completelen_1192____score_279.70_Q9W391|61.532|0.0IBHY01030644.1_278-3853___, Node55, IBHV01023419.1.p1GENE.IBHV01023419.1__IBHV01023419.1.p1ORFtype_completelen_1193_-__score_236.80_Q9W391|61.404|0.0IBHV01023419.1_867-4445_-_, ICDG01040823.1.p2GENE.ICDG01040823.1__ICDG01040823.1.p2ORFtype_completelen_1201____score_238.62_Q9W391|60.428|0.0ICDG01040823.1_197-3799___, Node77, IBVS01042725.1.p1GENE.IBVS01042725.1__IBVS01042725.1.p1ORFtype_completelen_1193_-__score_246.45_Q9W391|60.478|0.0IBVS01042725.1_577-4155_-_, Node76, GITO01017760.1.p1GENE.GITO01017760.1__GITO01017760.1.p1ORFtype_completelen_593____score_91.88_Q9W391|71.127|0.0GITO01017760.1_176-1954___, GITO01017758.1.p1GENE.GITO01017758.1__GITO01017758.1.p1ORFtype_completelen_1194____score_207.44_Q9W391|60.717|0.0GITO01017758.1_176-3757___, Node86, GITQ01086551.1.p1GENE.GITQ01086551.1__GITQ01086551.1.p1ORFtype_completelen_1194____score_205.92_Q9W391|60.478|0.0GITQ01086551.1_223-3804___, GITN01020168.1.p1GENE.GITN01020168.1__GITN01020168.1.p1ORFtype_completelen_1194____score_202.84_Q9W391|60.478|0.0GITN01020168.1_260-3841___, Node89, Node85, GITR01029024.1.p1GENE.GITR01029024.1__GITR01029024.1.p1ORFtype_completelen_1194____score_202.54_Q9W391|60.876|0.0GITR01029024.1_183-3764___, GITM01107266.1.p1GENE.GITM01107266.1__GITM01107266.1.p1ORFtype_completelen_1194____score_203.09_Q9W391|60.717|0.0GITM01107266.1_171-3752___, Node93, GITP01097532.1.p1GENE.GITP01097532.1__GITP01097532.1.p1ORFtype_completelen_1194____score_202.73_Q9W391|60.558|0.0GITP01097532.1_167-3748___, Node92, Node84, IAHF01009952.1.p2GENE.IAHF01009952.1__IAHF01009952.1.p2ORFtype_completelen_599_-__score_74.75_Q9W391|69.527|0.0IAHF01009952.1_2519-4315_-_, IAHF01042293.1.p1GENE.IAHF01042293.1__IAHF01042293.1.p1ORFtype_completelen_1202_-__score_190.59_Q9W391|61.094|0.0IAHF01042293.1_460-4065_-_, IAHF01009952.1.p1GENE.IAHF01009952.1__IAHF01009952.1.p1ORFtype_completelen_635_-__score_110.59_Q9W391|53.035|0.0IAHF01009952.1_456-2360_-_, Node99, Node97, Node83, ICMP01040724.1.p1GENE.ICMP01040724.1__ICMP01040724.1.p1ORFtype_completelen_1194_-__score_201.85_Q9W391|61.226|0.0ICMP01040724.1_2281-5862_-_, ICFC01035680.1.p1GENE.ICFC01035680.1__ICFC01035680.1.p1ORFtype_completelen_1026____score_200.67_Q9W391|60.385|0.0ICFC01035680.1_180-3257___, ICFC01014002.1.p1GENE.ICFC01014002.1__ICFC01014002.1.p1ORFtype_completelen_1194____score_230.88_Q9W391|60.876|0.0ICFC01014002.1_180-3761___, ICFC01032190.1.p2GENE.ICFC01032190.1__ICFC01032190.1.p2ORFtype_completelen_979____score_196.91_Q9W391|61.100|0.0ICFC01032190.1_180-3116___, Node107, Node105, Node103, IAJJ01023981.1.p1GENE.IAJJ01023981.1__IAJJ01023981.1.p1ORFtype_completelen_1202_-__score_165.87_Q9W391|60.697|0.0IAJJ01023981.1_2301-5906_-_, Node102, Node82, IBMZ01017010.1.p1GENE.IBMZ01017010.1__IBMZ01017010.1.p1ORFtype_completelen_1190____score_211.64_Q9W391|60.797|0.0IBMZ01017010.1_182-3751___, IBMZ01023404.1.p2GENE.IBMZ01023404.1__IBMZ01023404.1.p2ORFtype_completelen_748____score_126.51_Q9W391|66.327|0.0IBMZ01023404.1_182-2425___, Node112, IBMZ01027999.1.p2GENE.IBMZ01027999.1__IBMZ01027999.1.p2ORFtype_completelen_1198____score_219.55_Q9W391|61.173|0.0IBMZ01027999.1_182-3775___, IBMZ01023404.1.p3GENE.IBMZ01023404.1__IBMZ01023404.1.p3ORFtype_completelen_452____score_77.75_Q9W391|60.996|0.0IBMZ01023404.1_2380-3735___, Node115, Node111, Node81, Node75, IBTE01003235.1.p1GENE.IBTE01003235.1__IBTE01003235.1.p1ORFtype_completelen_1161_-__score_213.46_Q9W391|58.767|0.0IBTE01003235.1_2401-5883_-_, IBTE01026773.1.p1GENE.IBTE01026773.1__IBTE01026773.1.p1ORFtype_completelen_1194_-__score_221.15_Q9W391|60.783|0.0IBTE01026773.1_2402-5983_-_, Node126, ICCR01048429.1.p2GENE.ICCR01048429.1__ICCR01048429.1.p2ORFtype_completelen_424____score_80.22_Q9W391|71.703|0.0ICCR01048429.1_230-1501___, ICCR01013010.1.p1GENE.ICCR01013010.1__ICCR01013010.1.p1ORFtype_completelen_1205____score_228.85_Q9W391|60.842|0.0ICCR01013010.1_230-3844___, Node132, ICCR01001062.1.p1GENE.ICCR01001062.1__ICCR01001062.1.p1ORFtype_completelen_1197____score_227.97_Q9W391|60.543|0.0ICCR01001062.1_280-3870___, Node131, 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IARK01007357.1.p1GENE.IARK01007357.1__IARK01007357.1.p1ORFtype_internallen_151_-__score_32.69_P18826|66.225|5.44e-67IARK01007357.1_3-452_-_, Node325, GIWQ01331976.1.p1GENE.GIWQ01331976.1__GIWQ01331976.1.p1ORFtype_5prime_partiallen_111____score_19.72_Q9W391|76.768|1.74e-47GIWQ01331976.1_3-335___, GIWQ01046471.1.p1GENE.GIWQ01046471.1__GIWQ01046471.1.p1ORFtype_completelen_1195_-__score_287.27_Q9W391|61.022|0.0GIWQ01046471.1_1478-5062_-_, Node336, Node324, Node266, IBBP01002027.1.p1GENE.IBBP01002027.1__IBBP01002027.1.p1ORFtype_completelen_1194____score_292.65_Q9W391|61.116|0.0IBBP01002027.1_212-3793___, ICOI01015237.1.p1GENE.ICOI01015237.1__ICOI01015237.1.p1ORFtype_completelen_704____score_155.73_Q9W391|65.896|0.0ICOI01015237.1_167-2278___, ICOI01009904.1.p1GENE.ICOI01009904.1__ICOI01009904.1.p1ORFtype_completelen_1194____score_293.40_Q9W391|61.146|0.0ICOI01009904.1_167-3748___, Node341, Node339, Node265, ICFE01009168.1.p1GENE.ICFE01009168.1__ICFE01009168.1.p1ORFtype_completelen_1204_-__score_211.04_Q9W391|61.190|0.0ICFE01009168.1_256-3867_-_, ICFE01038298.1.p1GENE.ICFE01038298.1__ICFE01038298.1.p1ORFtype_completelen_1191_-__score_206.41_Q9W391|60.717|0.0ICFE01038298.1_256-3828_-_, Node346, ICEN01012359.1.p1GENE.ICEN01012359.1__ICEN01012359.1.p1ORFtype_completelen_1204____score_205.73_Q9W391|61.319|0.0ICEN01012359.1_182-3793___, ICEN01030437.1.p1GENE.ICEN01030437.1__ICEN01030437.1.p1ORFtype_completelen_1191____score_198.79_Q9W391|60.845|0.0ICEN01030437.1_182-3754___, Node349, Node345, IBPI01002076.1.p1GENE.IBPI01002076.1__IBPI01002076.1.p1ORFtype_completelen_1191_-__score_235.66_Q9W391|61.080|0.0IBPI01002076.1_425-3997_-_, IAOP01032820.1.p1GENE.IAOP01032820.1__IAOP01032820.1.p1ORFtype_completelen_1066____score_251.06_Q9W391|60.450|0.0IAOP01032820.1_365-3562___, IAOP01002745.1.p1GENE.IAOP01002745.1__IAOP01002745.1.p1ORFtype_completelen_1206____score_298.02_Q9W391|60.831|0.0IAOP01002745.1_365-3982___, Node355, Node353, GJZJ01023528.1.p1GENE.GJZJ01023528.1__GJZJ01023528.1.p1ORFtype_completelen_1194____score_177.32_Q9W391|61.098|0.0GJZJ01023528.1_178-3759___, Node352, Node344, Node264, ICBW01001470.1.p1GENE.ICBW01001470.1__ICBW01001470.1.p1ORFtype_completelen_1049____score_234.15_Q9W391|56.440|0.0_Q9W391|79.167|3.60e-134ICBW01001470.1_202-3348___, ICBW01018528.1.p1GENE.ICBW01018528.1__ICBW01018528.1.p1ORFtype_completelen_1202____score_264.33_Q9W391|61.642|0.0ICBW01018528.1_202-3807___, Node359, Node263, Node205, Node73, IALE01046619.1.p1GENE.IALE01046619.1__IALE01046619.1.p1ORFtype_3prime_partiallen_309____score_65.53_Q9W391|75.896|1.48e-167IALE01046619.1_177-1100___, IALE01007658.1.p1GENE.IALE01007658.1__IALE01007658.1.p1ORFtype_5prime_partiallen_902_-__score_144.30_Q9W391|55.901|0.0IALE01007658.1_284-2989_-_, Node362, Node72, IATW01018331.1.p1GENE.IATW01018331.1__IATW01018331.1.p1ORFtype_completelen_1194_-__score_279.83_Q9W391|61.355|0.0IATW01018331.1_1404-4985_-_, IATW01032803.1.p1GENE.IATW01032803.1__IATW01032803.1.p1ORFtype_completelen_1194_-__score_279.83_Q9W391|61.355|0.0IATW01032803.1_1465-5046_-_, Node365, Node71, ICOF01009191.1.p1GENE.ICOF01009191.1__ICOF01009191.1.p1ORFtype_completelen_1192_-__score_272.97_Q9W391|61.373|0.0ICOF01009191.1_393-3968_-_, ICOE01011277.1.p1GENE.ICOE01011277.1__ICOE01011277.1.p1ORFtype_completelen_1192_-__score_268.12_Q9W391|61.373|0.0ICOE01011277.1_293-3868_-_, Node368, Node70, Node54, Node14, IBDY01029920.1.p1GENE.IBDY01029920.1__IBDY01029920.1.p1ORFtype_completelen_1192_-__score_258.19_Q9W391|61.404|0.0IBDY01029920.1_706-4281_-_, IBDY01001491.1.p1GENE.IBDY01001491.1__IBDY01001491.1.p1ORFtype_completelen_1203_-__score_256.96_Q9W391|61.667|0.0IBDY01001491.1_710-4318_-_, Node371, Node13, ICOJ01008057.1.p1GENE.ICOJ01008057.1__ICOJ01008057.1.p1ORFtype_5prime_partiallen_644_-__score_152.84_Q9W391|54.043|0.0ICOJ01008057.1_603-2534_-_, Node12, IBZG01030073.1.p1GENE.IBZG01030073.1__IBZG01030073.1.p1ORFtype_completelen_1192_-__score_277.92_Q9W391|62.121|0.0IBZG01030073.1_2135-5710_-_, IBZG01010977.1.p1GENE.IBZG01010977.1__IBZG01010977.1.p1ORFtype_completelen_1052_-__score_226.15_Q9W391|56.680|0.0_Q9W391|72.800|4.10e-49IBZG01010977.1_2011-5166_-_, Node375, Node11, ICCG01001627.1.p1GENE.ICCG01001627.1__ICCG01001627.1.p1ORFtype_completelen_1192_-__score_254.17_Q9W391|62.121|0.0ICCG01001627.1_853-4428_-_, Node10, ICHT01003247.1.p2GENE.ICHT01003247.1__ICHT01003247.1.p2ORFtype_completelen_1192_-__score_232.85_Q9W391|61.772|0.0ICHT01003247.1_4994-8569_-_, ICHT01037393.1.p2GENE.ICHT01037393.1__ICHT01037393.1.p2ORFtype_completelen_1200_-__score_232.45_Q9W391|61.508|0.0ICHT01037393.1_5008-8607_-_, Node379, Node9, Node5`


### Obtaining branch lengths and nucleotide substitution biases under the nucleotide GTR model

>kill-zero-lengths => Yes

### Deleted 20 zero-length internal branches: `Node112, Node131, Node132, Node138, Node160, Node170, Node171, Node179, Node193, Node198, Node216, Node228, Node233, Node241, Node243, Node287, Node294, Node328, Node59, Node65`
* Log(L) = -126699.48, AIC-c = 254177.37 (389 estimated parameters)
* 1 partition. Total tree length by partition (subs/site)  7.868

### Obtaining the global omega estimate based on relative GTR branch lengths and nucleotide substitution biases
* Log(L) =     -inf, AIC-c =      inf (376 estimated parameters)
* 1 partition. Total tree length by partition (subs/site)  6.574
* non-synonymous/synonymous rate ratio for *test* =   0.9165

### Improving branch lengths, nucleotide substitution biases, and global dN/dS ratios under a full codon model
* Log(L) =     -inf, AIC-c =      inf (376 estimated parameters)
* non-synonymous/synonymous rate ratio for *test* =   0.8430

### Performing the full (dN/dS > 1 allowed) branch-site model fit
Error:
'llKgQcaJ.tree_id_0.IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_' is not a supported object type in call to SetParameter(llKgQcaJ.tree_id_0.IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_, MODEL, busted.test);

Function call stack
1 :  SetParameter(llKgQcaJ.tree_id_0.IANO01021913.1.p1GENE.IANO01021913.1__IANO01021913.1.p1ORFtype_completelen_1192_-__score_234.59_Q9W391|61.398|0.0IANO01021913.1_889-4464_-_, MODEL, busted.test);
-------
2 :  ExecuteCommands("SetParameter (`id`."+model.ApplyModelToTree.list[model.ApplyModelToTree.b]+",MODEL,"+model.ApplyModelToTree.apply_model+")", /home/crunnel2/anaconda3/envs/selection/share/hyphy/TemplateBatchFiles/libv3/models/);
-------
3 :  [namespace = llKgQcaJ] model.ApplyModelToTree(lf_components[2*i+1],tree[i],None,model_map[i]);
-------
4 :  busted.grid_search.results=estimators.FitLF(busted.filter_names,busted.trees,busted.model_map,busted.final_partitioned_mg_results,busted.model_object_map,{terms.run_options.retain_lf_object:TRUE,terms.run_options.proportional_branch_length_scaler:busted.global_scaler_list,terms.run_options.optimization_settings:{"OPTIMIZATION_METHOD":"nedler-mead","MAXIMUM_OPTIMIZATION_ITERATIONS":500,"OPTIMIZATION_PRECISION":busted.nm.precision},terms.search_grid:busted.initial_grid,terms.search_restarts:busted.N.initial_guesses});
-------

Check errors.log for execution error details.

Thank you so much in advance for your help!
Calvin

[spond]

Dear @00-kelvin,

This is most likely due to a sequence name with "odd" characters. Would you please share the input alignment and tree so I can check? Also, please confirm your hyphy version (hyphy --version).

Best,
Sergei

[00-kelvin]

HYPHY 2.5.60(MP) for Linux on x86_64 x86 SSE4 SIMD zlib (v1.2.13)

Alignment and tree attached as a zip file. Let me know if you can't open them for whatever reason. Many thanks for your quick reply!

00-kelvin-hyphy.zip

[spond]

Dear @00-kelvin,

Yes, a sequence name issue. I wrote a little utility script to help with this, and also to allow you to map slighlty non-matching IDs in the alignment / tree files (in your case you have = and _ mismtatches for some names). See https://github.com/veg/hyphy-analyses/tree/master/clean-names with a specific use example for your files there.

The resulting .nex file and .BUSTED.json file are attached.

$hyphy busted --alignment test/N1.HOG0007354.nex --error-sink Yes

....


* Log(L) = -98776.92, AIC-c = 198333.08 (389 estimated parameters)
* For *test* branches, the following rate distribution for branch-site combinations was inferred

|          Selection mode           |     dN/dS     |Proportion, %|               Notes               |
|-----------------------------------|---------------|-------------|-----------------------------------|
|        Negative selection         |     0.000     |   79.080    |                                   |
|        Negative selection         |     0.102     |   20.838    |                                   |
|      Diversifying selection       |     1.777     |    0.027    |                                   |
|         Error absorption          |9999999171.5...|    0.056    |                                   |

* The following rate distribution for site-to-site **synonymous** rate variation was inferred

|               Rate                | Proportion, % |               Notes               |
|-----------------------------------|---------------|-----------------------------------|
|               0.129               |    83.843     |                                   |
|               0.339               |    15.348     |                                   |
|              103.850              |     0.809     |                                   |

Performing the constrained (dN/dS > 1 not allowed) model fit
* Log(L) = -98776.99, AIC-c = 198331.22 (388 estimated parameters)
* For *test* branches under the null (no dN/dS > 1 model), the following rate distribution for branch-site combinations was inferred

|          Selection mode           |     dN/dS     |Proportion, %|               Notes               |
|-----------------------------------|---------------|-------------|-----------------------------------|
|        Negative selection         |     0.000     |   79.079    |                                   |
|        Negative selection         |     0.102     |   20.838    |                                   |
|         Neutral evolution         |     1.000     |    0.027    |                                   |
|         Error absorption          |9999999171.5...|    0.057    |                                   |

* The following rate distribution for site-to-site **synonymous** rate variation was inferred

|               Rate                | Proportion, % |               Notes               |
|-----------------------------------|---------------|-----------------------------------|
|               0.128               |    83.843     |                                   |
|               0.339               |    15.348     |                                   |
|              103.844              |     0.809     |                                   |

----
## Branch-site unrestricted statistical test of episodic diversification [BUSTED]
Likelihood ratio test for episodic diversifying positive selection, **p =   0.4643**.

The "error-absorption" component allows you to filter out local alignment issue (use https://observablehq.com/@spond/busted to load the JSON and look around).

Many automated alignment procedures leave things like the folllowing in place (amino-acids 1000-1050 in your example file), which would "light up" as selection, but are more likely misalignments/misannotation.

Best,
Sergei

[00-kelvin]

Many thanks, Sergei! I will definitely use the error-sink flag in the future, that seems very useful. Thank you for sorting this out for me. The clean-names tool will come in handy too, I'm sure.

[00-kelvin]

Hi Sergei! Sorry to bother you again -- I could not find where you attached the .json file. Would you be able to re-send? I was able to re-run the analysis with the error sink and get results, but I just wanted to check that they matched your results. Thank you!

[spond]

Dear @00-kelvin,

I forgot to attach it before, oops.

Best,
Sergei

N1.HOG0007354.nex.BUSTED.json.gz