Settings used alignment : ./infile.phy branchlengths : linked models : JC, K80, TRNEF, SYM, HKY, TRN, GTR, HKY+X, TRN+X, GTR+X, JC+G, K80+G, TRNEF+G, SYM+G, HKY+G, TRN+G, GTR+G, HKY+G+X, TRN+G+X, GTR+G+X, JC+I, K80+I, TRNEF+I, SYM+I, HKY+I, TRN+I, GTR+I, HKY+I+X, TRN+I+X, GTR+I+X, JC+I+G, K80+I+G, TRNEF+I+G, SYM+I+G, HKY+I+G, TRN+I+G, GTR+I+G, HKY+I+G+X, TRN+I+G+X, GTR+I+G+X model_selection : bic search : greedy Best partitioning scheme Scheme Name : step_42 Scheme lnL : -93466.8969116 Scheme BIC : 188348.663446 Number of params : 147 Number of sites : 15138 Number of subsets : 9 Subset | Best Model | # sites | subset id | Partition names 1 | SYM+I+G | 1225 | 8dadc32cba6129da482f56e6d1913ea5 | tRNA-Leu_2, tRNA-Leu_1, COX1_C1, tRNA-Gln, tRNA-Ile, COX2_C2, tRNA-Val, tRNA-Ser_1, tRNA-Phe 2 | GTR+I+G+X | 4497 | c5b9b0215ab7c807a69b3e78f73480f2 | tRNA-Glu, tRNA-Arg, tRNA-Asp, tRNA-Trp, tRNA-Gly, tRNA-His, tRNA-Thr, tRNA-Pro, tRNA-Met, tRNA-Ser_2, tRNA-Lys, tRNA-Asn, 16S_rRNA, tRNA-Cys, tRNA-Ala, tRNA-Tyr, ND4L_C1, COX3_C1, 12S_rRNA, ND3_C1, ND1_C1, CytB_C1 3 | GTR+I+G+X | 1631 | fb4831189ebb6cd378b1e710b6dc3c4e | ATP6_C2, CytB_C2, ND3_C2, ND4L_C2, COX2_C1, ND1_C2, COX3_C2 4 | GTR+G+X | 1627 | ca82276f7c9eb822b93f109561632634 | ND5_C1, ND2_C1, ND4_C1, ATP6_C1 5 | GTR+I+G+X | 1460 | 38f0f62df3be0ac75fbbb7486b9e31b3 | ATP8_C2, ND4_C2, ND2_C2, ND5_C2 6 | HKY+I+X | 516 | f537997c9e84ca98daca4365c202ba90 | COX1_C2 7 | TRN+G+X | 232 | 01c50ff5f6536c1ba2bb3ef35de3253d | ATP8_C1, NADH6_C2 8 | TRN+G+X | 174 | e3dc498f69c032f6d19b07aca90ea713 | NADH6_C1 9 | GTR+G+X | 3776 | 956b7fc22f11a856d6a75a41d6e77ee0 | 3CODONRY Scheme Description in PartitionFinder format Scheme_step_42 = (tRNA-Leu_2, tRNA-Leu_1, COX1_C1, tRNA-Gln, tRNA-Ile, COX2_C2, tRNA-Val, tRNA-Ser_1, tRNA-Phe) (tRNA-Glu, tRNA-Arg, tRNA-Asp, tRNA-Trp, tRNA-Gly, tRNA-His, tRNA-Thr, tRNA-Pro, tRNA-Met, tRNA-Ser_2, tRNA-Lys, tRNA-Asn, 16S_rRNA, tRNA-Cys, tRNA-Ala, tRNA-Tyr, ND4L_C1, COX3_C1, 12S_rRNA, ND3_C1, ND1_C1, CytB_C1) (ATP6_C2, CytB_C2, ND3_C2, ND4L_C2, COX2_C1, ND1_C2, COX3_C2) (ND5_C1, ND2_C1, ND4_C1, ATP6_C1) (ATP8_C2, ND4_C2, ND2_C2, ND5_C2) (COX1_C2) (ATP8_C1, NADH6_C2) (NADH6_C1) (3CODONRY); Nexus formatted character sets begin sets; charset Subset1 = 8765-8835 2372-2447 4347-5378\2 3161-3230 3089-3160 5529-5982\2 934-995 5379-5455 1-54; charset Subset2 = 11292-11362 7450-7519 5456-5527 3994-4064 7144-7215 8626-8697 10800-10870 10871-10943 3231-3299 8698-8764 5983-6055 4134-4209 996-2371 4210-4276 4065-4133 4277-4346 7520-7715\2 6622-7143\2 55-933 7216-7449\2 2448-3088\2 10036-10799\2; charset Subset3 = 6173-6621\2 10037-10799\2 7217-7449\2 7521-7715\2 5528-5982\2 2449-3088\2 6623-7143\2; charset Subset4 = 8836-10035\2 3300-3993\2 7716-8625\2 6172-6621\2; charset Subset5 = 6057-6171\2 7717-8625\2 3301-3993\2 8837-10035\2; charset Subset6 = 4348-5378\2; charset Subset7 = 6056-6171\2 10945-11291\2; charset Subset8 = 10944-11291\2; charset Subset9 = 11363-15138; charpartition PartitionFinder = Group1:Subset1, Group2:Subset2, Group3:Subset3, Group4:Subset4, Group5:Subset5, Group6:Subset6, Group7:Subset7, Group8:Subset8, Group9:Subset9; end; Nexus formatted character sets for IQtree Warning: the models written in the charpartition are just the best model found in this analysis. Not all models are available in IQtree, so you may need to set up specific model lists for your analysis #nexus begin sets; charset Subset1 = 8765-8835 2372-2447 4347-5378\2 3161-3230 3089-3160 5529-5982\2 934-995 5379-5455 1-54; charset Subset2 = 11292-11362 7450-7519 5456-5527 3994-4064 7144-7215 8626-8697 10800-10870 10871-10943 3231-3299 8698-8764 5983-6055 4134-4209 996-2371 4210-4276 4065-4133 4277-4346 7520-7715\2 6622-7143\2 55-933 7216-7449\2 2448-3088\2 10036-10799\2; charset Subset3 = 6173-6621\2 10037-10799\2 7217-7449\2 7521-7715\2 5528-5982\2 2449-3088\2 6623-7143\2; charset Subset4 = 8836-10035\2 3300-3993\2 7716-8625\2 6172-6621\2; charset Subset5 = 6057-6171\2 7717-8625\2 3301-3993\2 8837-10035\2; charset Subset6 = 4348-5378\2; charset Subset7 = 6056-6171\2 10945-11291\2; charset Subset8 = 10944-11291\2; charset Subset9 = 11363-15138; charpartition PartitionFinder = SYM+I+G:Subset1, GTR+I+G+X:Subset2, GTR+I+G+X:Subset3, GTR+G+X:Subset4, GTR+I+G+X:Subset5, HKY+I+X:Subset6, TRN+G+X:Subset7, TRN+G+X:Subset8, GTR+G+X:Subset9; end; RaxML-style partition definitions Warning: RAxML allows for only a single model of rate heterogeneity in partitioned analyses. I.e. all partitions must be assigned one of three types of model: No heterogeneity (e.g. GTR); +G (e.g. GTR+G); or +I+G (e.g. GTR+I+G). If the best models for your datasetcontain different types of model for different subsets you will need to decide on the best rate heterogeneity model before you run RAxML. If you prefer to do things more rigorously, you can run separate PartitionFinder analyses for each type of rate heterogenetity Then choose the scheme with the lowest AIC/AICc/BIC score. Note that these re-runs will be quick! DNA, Subset1 = 8765-8835, 2372-2447, 4347-5378\2, 3161-3230, 3089-3160, 5529-5982\2, 934-995, 5379-5455, 1-54 DNA, Subset2 = 11292-11362, 7450-7519, 5456-5527, 3994-4064, 7144-7215, 8626-8697, 10800-10870, 10871-10943, 3231-3299, 8698-8764, 5983-6055, 4134-4209, 996-2371, 4210-4276, 4065-4133, 4277-4346, 7520-7715\2, 6622-7143\2, 55-933, 7216-7449\2, 2448-3088\2, 10036-10799\2 DNA, Subset3 = 6173-6621\2, 10037-10799\2, 7217-7449\2, 7521-7715\2, 5528-5982\2, 2449-3088\2, 6623-7143\2 DNA, Subset4 = 8836-10035\2, 3300-3993\2, 7716-8625\2, 6172-6621\2 DNA, Subset5 = 6057-6171\2, 7717-8625\2, 3301-3993\2, 8837-10035\2 DNA, Subset6 = 4348-5378\2 DNA, Subset7 = 6056-6171\2, 10945-11291\2 DNA, Subset8 = 10944-11291\2 DNA, Subset9 = 11363-15138 MrBayes block for partition definitions Warning: MrBayes only allows a relatively small collection of models. If any model in your analysis is not one that is included in MrBayes (e.g. by setting nst = 1, 2, or 6 for DNA sequences; or is not in the available list of protein models for MrBayes)then this MrBayes block will just set that model to nst = 6 for DNA, or 'wag' for Protein. Similarly, the only additional parameters that this MrBayes block will include are +I and +G. Other parameters, such as +F and +X, are ignored. If you want to use this MrBayes block for your analysis, please make sure to check it carefully before you use it we've done our best to make it accurate, but there may be errors that remain! begin mrbayes; charset Subset1 = 8765-8835 2372-2447 4347-5378\2 3161-3230 3089-3160 5529-5982\2 934-995 5379-5455 1-54; charset Subset2 = 11292-11362 7450-7519 5456-5527 3994-4064 7144-7215 8626-8697 10800-10870 10871-10943 3231-3299 8698-8764 5983-6055 4134-4209 996-2371 4210-4276 4065-4133 4277-4346 7520-7715\2 6622-7143\2 55-933 7216-7449\2 2448-3088\2 10036-10799\2; charset Subset3 = 6173-6621\2 10037-10799\2 7217-7449\2 7521-7715\2 5528-5982\2 2449-3088\2 6623-7143\2; charset Subset4 = 8836-10035\2 3300-3993\2 7716-8625\2 6172-6621\2; charset Subset5 = 6057-6171\2 7717-8625\2 3301-3993\2 8837-10035\2; charset Subset6 = 4348-5378\2; charset Subset7 = 6056-6171\2 10945-11291\2; charset Subset8 = 10944-11291\2; charset Subset9 = 11363-15138; partition PartitionFinder = 9:Subset1, Subset2, Subset3, Subset4, Subset5, Subset6, Subset7, Subset8, Subset9; set partition=PartitionFinder; lset applyto=(1) nst=6 rates=invgamma; prset applyto=(1) statefreqpr=fixed(equal); lset applyto=(2) nst=6 rates=invgamma; lset applyto=(3) nst=6 rates=invgamma; lset applyto=(4) nst=6 rates=gamma; lset applyto=(5) nst=6 rates=invgamma; lset applyto=(6) nst=2 rates=propinv; lset applyto=(7) nst=6 rates=gamma; lset applyto=(8) nst=6 rates=gamma; lset applyto=(9) nst=6 rates=gamma; prset applyto=(all) ratepr=variable; unlink statefreq=(all) revmat=(all) shape=(all) pinvar=(all) tratio=(all); end; *Citations for this analysis* ----------------------------- If you use this analysis in your published work, please cite the following papers on which your analysis relied. For the version of PartitionFinder you used, please cite: Lanfear, R., Frandsen, P. B., Wright, A. M., Senfeld, T., Calcott, B. (2016) PartitionFinder 2: new methods for selecting partitioned models of evolution formolecular and morphological phylogenetic analyses. Molecular biology and evolution. DOI: dx.doi.org/10.1093/molbev/msw260 For the greedy algorithm you used, please cite: Lanfear, R., Calcott, B., Ho, S. Y., & Guindon, S. (2012). PartitionFinder: combined selection of partitioning schemes and substitution models for phylogenetic analyses. Molecular biology and evolution, 29(6), 1695-1701. Your analysis also used PhyML, so please cite: Guindon, S., Dufayard, J. F., Lefort, V., Anisimova, M., Hordijk, W., & Gascuel, O. (2010). New algorithms and methods to estimate maximum-likelihood phylogenies: assessing the performance of PhyML 3.0. Systematic biology, 59(3), 307-321.