veg / hyphy

HyPhy: Hypothesis testing using Phylogenies
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BUSTED error: tree_id_0 is not a supported object type in call to SetParameter #1710

Closed 00-kelvin closed 4 months ago

00-kelvin commented 4 months ago

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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IAUC01042471.1.p1GENE.IAUC01042471.1__IAUC01042471.1.p1ORFtype_completelen_1202____score_291.27_Q9W391|60.726|0.0IAUC01042471.1_187-3792___, Node315, Node301, Node267, IBHE01015111.1.p1GENE.IBHE01015111.1__IBHE01015111.1.p1ORFtype_completelen_1201_-__score_246.02_Q9W391|61.094|0.0IBHE01015111.1_1785-5387_-_, IBHE01019068.1.p1GENE.IBHE01019068.1__IBHE01019068.1.p1ORFtype_completelen_1194_-__score_244.92_Q9W391|61.434|0.0IBHE01019068.1_1776-5357_-_, Node328, IBHE01043807.1.p1GENE.IBHE01043807.1__IBHE01043807.1.p1ORFtype_completelen_952_-__score_200.82_Q9W391|61.765|0.0IBHE01043807.1_1447-4302_-_, Node327, IARK01011757.1.p1GENE.IARK01011757.1__IARK01011757.1.p1ORFtype_5prime_partiallen_654_-__score_127.02_Q9W391|52.941|0.0IARK01011757.1_1736-3697_-_, IARK01023945.1.p1GENE.IARK01023945.1__IARK01023945.1.p1ORFtype_5prime_partiallen_645_-__score_126.68_Q9W391|53.399|0.0IARK01023945.1_1738-3672_-_, Node332, Node326, 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 commented 4 months ago

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 commented 4 months ago

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 commented 4 months ago

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.

image

Best, Sergei

00-kelvin commented 4 months ago

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 commented 4 months ago

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 commented 4 months ago

Dear @00-kelvin,

I forgot to attach it before, oops.

Best, Sergei

N1.HOG0007354.nex.BUSTED.json.gz

00-kelvin commented 3 months ago

Thank you so much for your help, Sergei! I have another couple of questions for you:

  1. I am curious what software you are using to visualize alignments, as shown in the screenshot you sent on 5/16? It looks like a great way to visually inspect the quality of alignments and I'd like to download it!

  2. I noticed that other Hyphy programs that I am interested in running on the same data sets (specifically RELAX and MEME) do not have the --error-sink option that BUSTED has for filtering out alignment errors. I was wondering if you had any recommendation for software that could reproduce the effects of the --error-sink flag by removing small stretches of error post-alignment before applying HyPhy tests? Some options I am looking into are TAPER, HMMCleaner, BMGE or trimAl; have you used any of these or others with success before selection analyses?

Thank you again! I know I am getting a little off topic here, so if you would rather communicate directly via email going forward that's fine by me: crunnel2@jh.edu

spond commented 3 months ago

Dear @00-kelvin,

  1. The alignment viewer is shown at the bottom of the Observable BUSTED visualization notebook (https://observablehq.com/@spond/busted)

  2. I usually don't do that much data cleaning, but in my experience even heavily cleaned alignments using the tools you mention let some residual errors bleed through. Whatever you use is fine :) My original idea for filtering within HyPhy goes something like this

(a). Run BUSTED with --error-sink Yes.

For example

$hyphy busted --alignment tests/data/HIVvif.nex --error-sink Yes

...

* Log(L) = -3377.71, AIC-c =  6917.78 (80 estimated parameters)
* For *test* branches, the following rate distribution for branch-site combinations was inferred

|          Selection mode           |     dN/dS     |Proportion, %|               Notes               |
|-----------------------------------|---------------|-------------|-----------------------------------|
|        Negative selection         |     0.444     |   45.820    |                                   |
|        Negative selection         |     0.956     |   46.886    |                                   |
|      Diversifying selection       |     1.012     |    7.249    |                                   |
|         Error absorption          |   1908.156    |    0.045    |                                   |

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

|               Rate                | Proportion, % |               Notes               |
|-----------------------------------|---------------|-----------------------------------|
|               0.294               |    54.428     |                                   |
|               1.162               |    32.700     |                                   |
|               3.575               |    12.872     |                                   |

Performing the constrained (dN/dS > 1 not allowed) model fit
* Log(L) = -3377.71, AIC-c =  6915.73 (79 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         |     0.443     |   45.866    |                                   |
|        Negative selection         |     0.957     |   44.071    |                                   |
|         Neutral evolution         |     1.000     |   10.019    |       Collapsed rate class        |
|         Error absorption          |   1908.156    |    0.045    |                                   |

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

|               Rate                | Proportion, % |               Notes               |
|-----------------------------------|---------------|-----------------------------------|
|               0.294               |    54.428     |                                   |
|               1.161               |    32.700     |                                   |
|               3.574               |    12.872     |                                   |

----
## Branch-site unrestricted statistical test of episodic diversification [BUSTED]
Likelihood ratio test for episodic diversifying positive selection, **p =   0.5000**.

(b). Run hyphy error-filter using the BUSTED .json.

$hyphy error-filter --json tests/data/HIVvif.nex.BUSTED.json --output tests/data/HIVvif-filtered.nex --output-json tests/data/HIVvif-filtered.json

### Filtering results

|                     Sequence                     |   Filtered Sites   |Filtered Proportion, %|
|--------------------------------------------------|--------------------|----------------------|
|                      B_LAI                       |         0          |           0.000      |
|                      B_P896                      |         0          |           0.000      |
|                      B_YU2                       |         1          |           0.052      |
|                      B_YU10                      |         1          |           0.052      |
|                      B_3_7                       |         0          |           0.000      |
|                       B_MN                       |         1          |           0.052      |
|                     B_C18MBC                     |         0          |           0.000      |
|                     B_U39_32                     |         1          |           0.052      |
|                     B_U13_2                      |         1          |           0.052      |
|                      B_GMK1                      |         0          |           0.000      |
|                      B_8_18                      |         0          |           0.000      |
|                     B_IFA10                      |         0          |           0.000      |
|                     B_JRCSF                      |         0          |           0.000      |
|                      B_JRFL                      |         0          |           0.000      |
|                    B_PA_3799                     |         0          |           0.000      |
|                     B_BCSG3C                     |         0          |           0.000      |
|                       B_I2                       |         0          |           0.000      |
|                      B_SF2                       |         0          |           0.000      |
|                    B_3202A12                     |         1          |           0.052      |
|                      B_F12                       |         0          |           0.000      |
|                      B_OYI                       |         2          |           0.104      |
|                    B_WEAU160                     |         0          |           0.000      |
|                      B_NL43                      |         1          |           0.052      |
|                      B_CAM1                      |         0          |           0.000      |
|                      B_D31                       |         0          |           0.000      |
|                      B_MANC                      |         0          |           0.000      |
|                      B_HAN                       |         1          |           0.052      |
|                      B_AD8                       |         0          |           0.000      |
|                       B_RF                       |         3          |           0.156      |

Masked a total of **13** or    0.233% sites

(c). Take the filtered results alignment file from step (b) and feed it into MEME or other analyses.

ORIGINAL MEME

$hyphy meme --alignment tests/data/HIVvif.nex --pvalue 0.05

....

|   Codon    | Partition  |   alpha    |non-syn rate (beta) distribution, rates : weights|    LRT     |Episodic selection detected?| # branches |         List of most common codon substitutions at this site          |
|:----------:|:----------:|:----------:|:-----------------------------------------------:|:----------:|:--------------------------:|:----------:|:---------------------------------------------------------------------:|
|     6      |     1      |    0.000   |            0.00/1949.24 : 1.00/0.00             |   14.135   |      Yes, p =  0.0004      |     1      |                              [1]CAG>GCA                               |
|     31     |     1      |    4.705   |             2.75/82.96 : 0.38/0.62              |    5.463   |      Yes, p =  0.0297      |     2      |             [4]Att>Gtt|[1]aTT>aAG,aTt>aCt,atT>atC,ATt>TGt             |
|     33     |     1      |    4.863   |             3.38/465.67 : 0.73/0.27             |    6.129   |      Yes, p =  0.0211      |     0      |           [4]aGg>aAg|[2]Agg>Ggg|[1]agG>agA,Ggg>Agg,Ggg>Cgg            |
|     37     |     1      |    9.429   |             4.12/254.03 : 0.43/0.57             |    4.632   |      Yes, p =  0.0456      |     0      |  [1]aaA>aaC,aAa>aGa,AaA>GaC,GGa>AAa,Gga>Aga,ggA>ggG,ggA>ggT,GGT>AAG   |
|     62     |     1      |    0.000   |             0.00/123.59 : 0.64/0.36             |    6.166   |      Yes, p =  0.0207      |     2      |                          [1]gCT>gAA,gCt>gGt                           |
|    101     |     1      |    0.000   |             0.00/10.26 : 0.00/1.00              |    4.914   |      Yes, p =  0.0394      |     4      |                     [4]gAc>gGc|[2]Gac>Aac,gaC>gaA                     |
|    109     |     1      |    0.000   |             0.00/89.18 : 0.10/0.90              |   14.480   |      Yes, p =  0.0003      |     4      |                     [2]CtG>AtA|[1]CTG>ACA,cTg>cGg                     |
|    124     |     1      |    0.000   |             0.00/77.80 : 0.48/0.52              |    5.293   |      Yes, p =  0.0324      |     5      |                     [3]Ata>Tta|[1]Ata>Cta,ATa>TCa                     |

FILTERED MEME (notice how some of the sites drop off compared to the original alignment)

$hyphy meme --alignment tests/data/HIVvif-filtered.nex --pvalue 0.05

|   Codon    | Partition  |   alpha    |non-syn rate (beta) distribution, rates : weights|    LRT     |Episodic selection detected?| # branches |         List of most common codon substitutions at this site          |
|:----------:|:----------:|:----------:|:-----------------------------------------------:|:----------:|:--------------------------:|:----------:|:---------------------------------------------------------------------:|
|     33     |     1      |    3.823   |             3.82/452.52 : 0.81/0.19             |    6.823   |      Yes, p =  0.0148      |     0      |           [4]aGg>aAg|[2]Agg>Ggg|[1]agG>agA,Ggg>Agg,Ggg>Cgg            |
|     37     |     1      |   11.600   |             5.09/263.32 : 0.00/1.00             |    4.576   |      Yes, p =  0.0469      |     0      |  [1]aaA>aaC,aAa>aGa,AaA>GaC,GGa>AAa,Gga>Aga,ggA>ggG,ggA>ggT,GGT>AAG   |
|    101     |     1      |    0.000   |             0.00/11.20 : 0.00/1.00              |    4.937   |      Yes, p =  0.0390      |     4      |                     [4]gAc>gGc|[2]Gac>Aac,gaC>gaA                     |
|    109     |     1      |    0.000   |             0.00/47.12 : 0.00/1.00              |    9.048   |      Yes, p =  0.0048      |     2      |                         [2]CtG>AtA|[1]cTg>cGg                         |

Best, Sergei

00-kelvin commented 3 months ago

Sergei,

Thank you as always for your quick replies! I will definitely try your strategy for filtering within Hyphy.

Calvin