RAxML-NG v. 1.0.2 released on 22.02.2021 by The Exelixis Lab. Developed by: Alexey M. Kozlov and Alexandros Stamatakis. Contributors: Diego Darriba, Tomas Flouri, Benoit Morel, Sarah Lutteropp, Ben Bettisworth. Latest version: https://github.com/amkozlov/raxml-ng Questions/problems/suggestions? Please visit: https://groups.google.com/forum/#!forum/raxml System: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 40 cores, 376 GB RAM RAxML-NG was called at 18-Jun-2021 11:37:51 as follows: raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/2_msa/P04798_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798 --seed 2 --threads 6 --tree rand{20} pars{20} Analysis options: run mode: ML tree search start tree(s): random (20) random seed: 2 tip-inner: OFF pattern compression: ON per-rate scalers: OFF site repeats: ON fast spr radius: AUTO spr subtree cutoff: 1.000000 branch lengths: proportional (ML estimate, algorithm: NR-FAST) SIMD kernels: AVX2 parallelization: coarse-grained (auto), PTHREADS (6 threads), thread pinning: OFF [00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/2_msa/P04798_trimmed_msa.fasta [00:00:00] Loaded alignment with 1001 taxa and 510 sites WARNING: Sequences tr_A0A2J8KRU7_A0A2J8KRU7_PANTR_9598 and tr_A0A2R9B5W8_A0A2R9B5W8_PANPA_9597 are exactly identical! WARNING: Sequences tr_H2QQ03_H2QQ03_PANTR_9598 and tr_A0A2R9CKC9_A0A2R9CKC9_PANPA_9597 are exactly identical! WARNING: Sequences sp_Q6GUR1_CP1A1_MACMU_9544 and tr_G7P943_G7P943_MACFA_9541 are exactly identical! WARNING: Sequences sp_Q8HYM9_CP17A_MACMU_9544 and tr_G7PDV6_G7PDV6_MACFA_9541 are exactly identical! WARNING: Sequences sp_Q8HYM9_CP17A_MACMU_9544 and tr_A0A2K6DJ66_A0A2K6DJ66_MACNE_9545 are exactly identical! WARNING: Sequences tr_A0A096MWY5_A0A096MWY5_PAPAN_9555 and tr_A0A2K5NCN0_A0A2K5NCN0_CERAT_9531 are exactly identical! WARNING: Sequences tr_A0A096MY25_A0A096MY25_PAPAN_9555 and tr_A0A2K6A157_A0A2K6A157_MANLE_9568 are exactly identical! WARNING: Sequences tr_A0A1U7Q212_A0A1U7Q212_MESAU_10036 and sp_P24455_CP2A9_MESAU_10036 are exactly identical! WARNING: Sequences tr_A0A1U7Q236_A0A1U7Q236_MESAU_10036 and sp_P24453_CP1A2_MESAU_10036 are exactly identical! WARNING: Sequences tr_A0A1U7Q3P0_A0A1U7Q3P0_MESAU_10036 and sp_Q08078_CP2CP_MESAU_10036 are exactly identical! WARNING: Sequences tr_A0A1U7Q8W9_A0A1U7Q8W9_MESAU_10036 and sp_P70687_CP17A_MESAU_10036 are exactly identical! WARNING: Sequences tr_A0A226NCX7_A0A226NCX7_CALSU_9009 and tr_A0A226PLG8_A0A226PLG8_COLVI_9014 are exactly identical! WARNING: Duplicate sequences found: 12 NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) NOTE: was saved to: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798.raxml.reduced.phy Alignment comprises 1 partitions and 510 patterns Partition 0: noname Model: LG4X+R4 Alignment sites / patterns: 510 / 510 Gaps: 7.30 % Invariant sites: 0.20 % NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798.raxml.rba Parallelization scheme autoconfig: 3 worker(s) x 2 thread(s) Parallel reduction/worker buffer size: 1 KB / 0 KB [00:00:00] Generating 20 random starting tree(s) with 1001 taxa [00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 255 / 20400 [00:00:00] Data distribution: max. searches per worker: 7 Starting ML tree search with 20 distinct starting trees [00:00:00 -995891.135619] Initial branch length optimization [00:00:06 -858380.835407] Model parameter optimization (eps = 10.000000) RAxML-NG v. 1.0.2 released on 22.02.2021 by The Exelixis Lab. Developed by: Alexey M. Kozlov and Alexandros Stamatakis. Contributors: Diego Darriba, Tomas Flouri, Benoit Morel, Sarah Lutteropp, Ben Bettisworth. Latest version: https://github.com/amkozlov/raxml-ng Questions/problems/suggestions? Please visit: https://groups.google.com/forum/#!forum/raxml System: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz, 40 cores, 376 GB RAM RAxML-NG was called at 18-Jun-2021 12:51:23 as follows: raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/2_msa/P04798_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798 --seed 2 --threads 6 --tree rand{20} pars{20} Analysis options: run mode: ML tree search start tree(s): random (20) random seed: 2 tip-inner: OFF pattern compression: ON per-rate scalers: OFF site repeats: ON fast spr radius: AUTO spr subtree cutoff: 1.000000 branch lengths: proportional (ML estimate, algorithm: NR-FAST) SIMD kernels: AVX2 parallelization: coarse-grained (auto), PTHREADS (6 threads), thread pinning: OFF WARNING: The model you specified on the command line (LG4X) will be ignored since the binary MSA file already contains a model definition. If you want to change the model, please re-run RAxML-NG with the original PHYLIP/FASTA alignment and --redo option. [00:00:00] Loading binary alignment from file: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798.raxml.rba [00:00:00] Alignment comprises 1001 taxa, 1 partitions and 510 patterns Partition 0: noname Model: LG4X+R4 Alignment sites / patterns: 510 / 510 Gaps: 7.30 % Invariant sites: 0.20 % Parallelization scheme autoconfig: 3 worker(s) x 2 thread(s) Parallel reduction/worker buffer size: 1 KB / 0 KB [00:00:00] NOTE: Resuming execution from checkpoint (logLH: -858380.84, ML trees: 0, bootstraps: 0) [00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 255 / 20400 [00:00:00] Data distribution: max. searches per worker: 7 Starting ML tree search with 20 distinct starting trees [00:00:00 -858380.835407] Model parameter optimization (eps = 10.000000) [00:02:01 -855511.277516] AUTODETECT spr round 1 (radius: 5) [00:08:45 -648040.135324] AUTODETECT spr round 2 (radius: 10) [00:16:02 -479170.896999] AUTODETECT spr round 3 (radius: 15) [00:23:31 -413562.774676] AUTODETECT spr round 4 (radius: 20) [00:34:11 -394483.589113] AUTODETECT spr round 5 (radius: 25) [00:41:01 -392307.572781] SPR radius for FAST iterations: 25 (autodetect) [00:41:01 -392307.572781] Model parameter optimization (eps = 3.000000) [00:41:43 -392187.944232] FAST spr round 1 (radius: 25) [00:46:37 -350169.361206] FAST spr round 2 (radius: 25) [00:50:01 -348973.677579] FAST spr round 3 (radius: 25) [00:52:50 -348880.236850] FAST spr round 4 (radius: 25) [00:55:17 -348840.724987] FAST spr round 5 (radius: 25) [00:57:35 -348832.507313] FAST spr round 6 (radius: 25) [00:59:47 -348832.507246] Model parameter optimization (eps = 1.000000) [01:00:07 -348822.900064] SLOW spr round 1 (radius: 5) [01:03:32 -348743.243248] SLOW spr round 2 (radius: 5) [01:06:44 -348728.137376] SLOW spr round 3 (radius: 5) [01:09:44 -348726.784701] SLOW spr round 4 (radius: 5) [01:12:41 -348726.784599] SLOW spr round 5 (radius: 10) [01:15:51 -348725.389712] SLOW spr round 6 (radius: 5) [01:19:37 -348724.973880] SLOW spr round 7 (radius: 5) [01:22:55 -348724.973836] SLOW spr round 8 (radius: 10) [01:26:11 -348724.973836] SLOW spr round 9 (radius: 15) [01:27:31] [worker #1] ML tree search #2, logLikelihood: -348779.397241 [01:32:13 -348724.973836] SLOW spr round 10 (radius: 20) [01:35:18] [worker #2] ML tree search #3, logLikelihood: -348772.705533 [01:41:27 -348724.973836] SLOW spr round 11 (radius: 25) [01:52:46 -348724.973836] Model parameter optimization (eps = 0.100000) [01:52:57] [worker #0] ML tree search #1, logLikelihood: -348724.847282 [01:52:57 -993538.974276] Initial branch length optimization [01:53:04 -853582.573647] Model parameter optimization (eps = 10.000000) [01:53:48 -851063.437655] AUTODETECT spr round 1 (radius: 5) [01:57:04 -652735.265924] AUTODETECT spr round 2 (radius: 10) [02:00:45 -478774.551385] AUTODETECT spr round 3 (radius: 15) [02:04:22 -395744.525483] AUTODETECT spr round 4 (radius: 20) [02:08:24 -386015.690537] AUTODETECT spr round 5 (radius: 25) [02:13:24 -384619.195177] SPR radius for FAST iterations: 25 (autodetect) [02:13:24 -384619.195177] Model parameter optimization (eps = 3.000000) [02:14:13 -384511.416949] FAST spr round 1 (radius: 25) [02:18:38 -350593.362491] FAST spr round 2 (radius: 25) [02:21:57 -348932.638951] FAST spr round 3 (radius: 25) [02:24:46 -348861.968407] FAST spr round 4 (radius: 25) [02:27:12 -348851.320499] FAST spr round 5 (radius: 25) [02:29:26 -348851.320499] Model parameter optimization (eps = 1.000000) [02:29:44 -348843.513435] SLOW spr round 1 (radius: 5) [02:33:15 -348759.669412] SLOW spr round 2 (radius: 5) [02:36:30 -348737.705549] SLOW spr round 3 (radius: 5) [02:39:29 -348737.705545] SLOW spr round 4 (radius: 10) [02:42:40 -348734.679715] SLOW spr round 5 (radius: 5) [02:46:29] [worker #1] ML tree search #5, logLikelihood: -348836.205900 [02:46:45 -348734.049049] SLOW spr round 6 (radius: 5) [02:50:17 -348734.049022] SLOW spr round 7 (radius: 10) [02:53:46 -348734.049022] SLOW spr round 8 (radius: 15) [03:00:01] [worker #2] ML tree search #6, logLikelihood: -348730.102434 [03:00:01 -348734.049022] SLOW spr round 9 (radius: 20) [03:09:39 -348734.049022] SLOW spr round 10 (radius: 25) [03:21:36 -348734.049022] Model parameter optimization (eps = 0.100000) [03:22:00] [worker #0] ML tree search #4, logLikelihood: -348733.651480 [03:22:00 -995815.984355] Initial branch length optimization [03:22:06 -858048.077586] Model parameter optimization (eps = 10.000000) [03:23:25 -855436.348049] AUTODETECT spr round 1 (radius: 5) [03:27:01 -636848.632885] AUTODETECT spr round 2 (radius: 10) [03:30:59 -483232.597079] AUTODETECT spr round 3 (radius: 15) [03:35:17 -407362.919732] AUTODETECT spr round 4 (radius: 20) [03:39:42 -387847.190186] AUTODETECT spr round 5 (radius: 25) [03:45:05 -383072.724352] SPR radius for FAST iterations: 25 (autodetect) [03:45:05 -383072.724352] Model parameter optimization (eps = 3.000000) [03:45:36 -382951.058718] FAST spr round 1 (radius: 25) [03:50:39 -350314.335867] FAST spr round 2 (radius: 25) [03:54:24 -348962.547500] FAST spr round 3 (radius: 25) [03:57:32 -348888.597641] FAST spr round 4 (radius: 25) [04:00:12 -348878.514188] FAST spr round 5 (radius: 25) [04:02:42 -348875.998780] FAST spr round 6 (radius: 25) [04:05:04 -348875.998779] Model parameter optimization (eps = 1.000000) [04:05:24 -348864.994618] SLOW spr round 1 (radius: 5) [04:09:05 -348802.295440] SLOW spr round 2 (radius: 5) [04:12:25 -348796.123800] SLOW spr round 3 (radius: 5) [04:15:41 -348793.611525] SLOW spr round 4 (radius: 5) [04:18:51 -348793.611474] SLOW spr round 5 (radius: 10) [04:22:16 -348790.507130] SLOW spr round 6 (radius: 5) [04:26:28 -348775.307269] SLOW spr round 7 (radius: 5) [04:26:40] [worker #1] ML tree search #8, logLikelihood: -348737.255521 [04:30:02 -348773.349102] SLOW spr round 8 (radius: 5) [04:33:22 -348772.769342] SLOW spr round 9 (radius: 5) [04:36:34 -348772.769336] SLOW spr round 10 (radius: 10) [04:37:06] [worker #2] ML tree search #9, logLikelihood: -348736.019941 [04:39:56 -348771.326735] SLOW spr round 11 (radius: 5) [04:44:00 -348765.803758] SLOW spr round 12 (radius: 5) [04:47:33 -348765.803757] SLOW spr round 13 (radius: 10) [04:50:59 -348765.803757] SLOW spr round 14 (radius: 15) [04:57:15 -348765.803757] SLOW spr round 15 (radius: 20) [05:06:59 -348765.803757] SLOW spr round 16 (radius: 25) [05:18:57 -348765.803757] Model parameter optimization (eps = 0.100000) [05:19:22] [worker #0] ML tree search #7, logLikelihood: -348765.020165 [05:19:22 -989764.692302] Initial branch length optimization [05:19:27 -854181.890760] Model parameter optimization (eps = 10.000000) [05:20:26 -851558.953354] AUTODETECT spr round 1 (radius: 5) [05:23:54 -645806.874203] AUTODETECT spr round 2 (radius: 10) [05:27:43 -473333.021396] AUTODETECT spr round 3 (radius: 15) [05:31:50 -394189.039178] AUTODETECT spr round 4 (radius: 20) [05:37:01 -386332.205576] AUTODETECT spr round 5 (radius: 25) [05:42:37 -385942.764433] SPR radius for FAST iterations: 25 (autodetect) [05:42:37 -385942.764433] Model parameter optimization (eps = 3.000000) [05:43:08 -385853.842796] FAST spr round 1 (radius: 25) [05:48:06 -350412.560192] FAST spr round 2 (radius: 25) [05:51:48 -348924.159005] FAST spr round 3 (radius: 25) [05:54:55 -348865.454609] FAST spr round 4 (radius: 25) [05:57:36 -348847.445359] FAST spr round 5 (radius: 25) [06:00:03 -348847.445337] Model parameter optimization (eps = 1.000000) [06:00:21 -348842.050736] SLOW spr round 1 (radius: 5) [06:04:13 -348759.050771] SLOW spr round 2 (radius: 5) [06:07:45 -348744.870687] SLOW spr round 3 (radius: 5) [06:11:01 -348743.779610] SLOW spr round 4 (radius: 5) [06:14:14 -348743.779591] SLOW spr round 5 (radius: 10) [06:17:39 -348743.779591] SLOW spr round 6 (radius: 15) [06:21:55] [worker #1] ML tree search #11, logLikelihood: -348810.497134 [06:24:22 -348743.779591] SLOW spr round 7 (radius: 20) [06:25:07] [worker #2] ML tree search #12, logLikelihood: -348738.277398 [06:34:22 -348743.779591] SLOW spr round 8 (radius: 25) [06:46:34 -348743.779591] Model parameter optimization (eps = 0.100000) [06:46:50] [worker #0] ML tree search #10, logLikelihood: -348743.418670 [06:46:50 -990956.634332] Initial branch length optimization [06:46:56 -853867.926774] Model parameter optimization (eps = 10.000000) [06:48:06 -851148.026361] AUTODETECT spr round 1 (radius: 5) [06:51:39 -655733.224450] AUTODETECT spr round 2 (radius: 10) [06:55:35 -487220.291177] AUTODETECT spr round 3 (radius: 15) [06:59:41 -427186.154231] AUTODETECT spr round 4 (radius: 20) [07:04:40 -395673.249257] AUTODETECT spr round 5 (radius: 25) [07:11:39 -394992.323142] SPR radius for FAST iterations: 25 (autodetect) [07:11:39 -394992.323142] Model parameter optimization (eps = 3.000000) [07:12:09 -394910.026239] FAST spr round 1 (radius: 25) [07:17:04 -350735.091741] FAST spr round 2 (radius: 25) [07:20:25 -349022.327184] FAST spr round 3 (radius: 25) [07:23:17 -348882.397719] FAST spr round 4 (radius: 25) [07:25:41 -348871.101003] FAST spr round 5 (radius: 25) [07:27:55 -348871.100933] Model parameter optimization (eps = 1.000000) [07:28:12 -348862.265441] SLOW spr round 1 (radius: 5) [07:31:34 -348756.718456] SLOW spr round 2 (radius: 5) [07:34:47 -348734.978426] SLOW spr round 3 (radius: 5) [07:37:44 -348734.978282] SLOW spr round 4 (radius: 10) [07:40:51 -348734.153191] SLOW spr round 5 (radius: 5) [07:44:35 -348733.958094] SLOW spr round 6 (radius: 5) [07:46:30] [worker #1] ML tree search #14, logLikelihood: -348723.651763 [07:47:52 -348733.958073] SLOW spr round 7 (radius: 10) [07:51:06 -348733.958073] SLOW spr round 8 (radius: 15) [07:56:59 -348733.958073] SLOW spr round 9 (radius: 20) [08:01:48] [worker #2] ML tree search #15, logLikelihood: -348766.495598 [08:06:08 -348733.958073] SLOW spr round 10 (radius: 25) [08:17:16 -348733.958073] Model parameter optimization (eps = 0.100000) [08:17:26] [worker #0] ML tree search #13, logLikelihood: -348733.692428 [08:17:26 -993923.157616] Initial branch length optimization [08:17:31 -854659.739588] Model parameter optimization (eps = 10.000000) [08:18:24 -852199.203673] AUTODETECT spr round 1 (radius: 5) [08:21:38 -646790.853648] AUTODETECT spr round 2 (radius: 10) [08:25:18 -464304.484394] AUTODETECT spr round 3 (radius: 15) [08:29:03 -398672.004311] AUTODETECT spr round 4 (radius: 20) [08:33:28 -386284.883592] AUTODETECT spr round 5 (radius: 25) [08:38:35 -384869.805849] SPR radius for FAST iterations: 25 (autodetect) [08:38:35 -384869.805849] Model parameter optimization (eps = 3.000000) [08:39:01 -384795.193751] FAST spr round 1 (radius: 25) [08:43:53 -350469.669427] FAST spr round 2 (radius: 25) [08:47:19 -348904.896935] FAST spr round 3 (radius: 25) [08:50:13 -348818.824117] FAST spr round 4 (radius: 25) [08:52:36 -348816.724885] FAST spr round 5 (radius: 25) [08:54:51 -348816.724875] Model parameter optimization (eps = 1.000000) [08:55:11 -348804.081672] SLOW spr round 1 (radius: 5) [08:58:39 -348733.937102] SLOW spr round 2 (radius: 5) [09:01:48 -348726.813431] SLOW spr round 3 (radius: 5) [09:03:07] [worker #1] ML tree search #17, logLikelihood: -348861.570004 [09:04:47 -348726.813243] SLOW spr round 4 (radius: 10) [09:07:55 -348726.813241] SLOW spr round 5 (radius: 15) [09:14:18 -348726.813241] SLOW spr round 6 (radius: 20) [09:23:35 -348726.813241] SLOW spr round 7 (radius: 25) [09:25:13] [worker #2] ML tree search #18, logLikelihood: -348761.564382 [09:34:58 -348726.813241] Model parameter optimization (eps = 0.100000) [09:35:14] [worker #0] ML tree search #16, logLikelihood: -348726.113677 [09:35:14 -991210.291936] Initial branch length optimization [09:35:19 -854184.243945] Model parameter optimization (eps = 10.000000) [09:36:19 -851497.456093] AUTODETECT spr round 1 (radius: 5) [09:39:35 -641212.507265] AUTODETECT spr round 2 (radius: 10) [09:43:16 -477857.990919] AUTODETECT spr round 3 (radius: 15) [09:46:56 -407077.031266] AUTODETECT spr round 4 (radius: 20) [09:51:17 -387624.030180] AUTODETECT spr round 5 (radius: 25) [09:56:54 -385576.384038] SPR radius for FAST iterations: 25 (autodetect) [09:56:54 -385576.384038] Model parameter optimization (eps = 3.000000) [09:57:20 -385514.741164] FAST spr round 1 (radius: 25) [10:01:55 -350556.742056] FAST spr round 2 (radius: 25) [10:05:18 -348984.754726] FAST spr round 3 (radius: 25) [10:08:13 -348870.541223] FAST spr round 4 (radius: 25) [10:10:38 -348850.867089] FAST spr round 5 (radius: 25) [10:12:53 -348847.736621] FAST spr round 6 (radius: 25) [10:15:04 -348847.736598] Model parameter optimization (eps = 1.000000) [10:15:26 -348819.868261] SLOW spr round 1 (radius: 5) [10:18:52 -348740.297857] SLOW spr round 2 (radius: 5) [10:21:59 -348735.786455] SLOW spr round 3 (radius: 5) [10:24:55 -348735.786407] SLOW spr round 4 (radius: 10) [10:28:03 -348735.786405] SLOW spr round 5 (radius: 15) [10:34:13 -348735.786405] SLOW spr round 6 (radius: 20) [10:39:08] [worker #1] ML tree search #20, logLikelihood: -348755.572141 [10:43:26 -348735.786405] SLOW spr round 7 (radius: 25) [10:54:26 -348735.786405] Model parameter optimization (eps = 0.100000) [10:54:32] [worker #0] ML tree search #19, logLikelihood: -348735.762310 Optimized model parameters: Partition 0: noname Rate heterogeneity: FREE (4 cats, mean), weights&rates: (0.137676,0.552053) (0.213504,0.558523) (0.410240,0.957509) (0.238580,1.726635) Base frequencies (model): M0: 0.147383 0.017579 0.058208 0.017707 0.026331 0.041582 0.017494 0.027859 0.011849 0.076971 0.147823 0.019535 0.037132 0.029940 0.008059 0.088179 0.089653 0.006477 0.032308 0.097931 M1: 0.063139 0.066357 0.011586 0.066571 0.010800 0.009276 0.053984 0.146986 0.034214 0.088822 0.098196 0.032390 0.021263 0.072697 0.016761 0.020711 0.020797 0.025463 0.045615 0.094372 M2: 0.062457 0.066826 0.049332 0.065270 0.006513 0.041231 0.058965 0.080852 0.028024 0.037024 0.075925 0.064131 0.019620 0.028710 0.104579 0.056388 0.062027 0.008241 0.033124 0.050760 M3: 0.106471 0.074171 0.044513 0.096390 0.002148 0.066733 0.158908 0.037625 0.020691 0.014608 0.028797 0.105352 0.007864 0.007477 0.083595 0.055726 0.047711 0.003975 0.010088 0.027159 Substitution rates (model): M 0: 0.295719 0.067388 0.253712 1.029289 0.107964 0.514644 10.868848 0.380498 0.084223 0.086976 0.188789 0.286389 0.155567 1.671061 2.132922 0.529591 0.115551 0.102453 0.916683 0.448317 0.457483 0.576016 1.741924 0.736017 0.704334 5.658311 0.123387 0.221777 93.433377 0.382175 0.235965 6.535048 0.525521 0.303537 0.641259 0.289466 0.102065 2.358429 0.251987 0.216561 0.503084 0.435271 4.873453 0.090748 0.033310 0.746537 0.128905 0.127321 0.904011 0.939733 0.435450 0.046646 0.262076 0.043986 0.189008 0.599450 109.901504 1.070052 5.229858 0.052764 0.021407 0.621146 0.081091 0.205164 5.164456 0.747330 0.308078 0.260889 0.185083 0.080708 0.029955 0.084794 1.862626 0.553477 0.151733 0.230320 0.096955 0.352526 0.590018 0.386853 1.559564 0.606648 0.587531 0.592318 0.885230 4.117654 0.246260 6.508329 0.054187 0.195703 1.669092 0.810168 0.066081 2.437439 0.165666 0.106333 0.093417 0.035149 0.072549 1.202023 1.634845 0.060194 0.069359 2.448827 0.232297 0.064822 3.537387 0.435384 0.290413 0.280695 0.105999 0.206603 0.404968 0.048984 0.069963 0.256662 0.228519 0.241077 4.320442 3.656545 0.290216 0.307466 0.096556 0.306067 0.204296 0.504221 1.991533 0.655465 6.799829 11.291065 0.961142 0.448965 6.227274 20.304886 0.205944 1.495537 0.091940 1.994320 0.754940 0.170343 0.050315 0.372166 0.206332 0.097050 5.381403 0.122332 3.256485 2.261319 0.848067 0.064441 0.102493 0.459041 0.133091 0.561215 0.457430 0.163849 5.260446 0.360946 0.389413 0.033291 0.115301 0.112593 1.559944 0.426508 0.132547 0.498634 0.559069 0.264728 0.693307 0.438856 0.306683 0.109129 18.392863 66.647302 0.400021 4.586081 2.099355 0.411347 0.476350 0.584622 3.634276 0.101797 0.148995 0.089177 0.034710 0.063603 0.755865 20.561407 0.133790 0.154902 M 1: 0.066142 0.590377 0.069930 9.850951 1.101363 0.150375 0.568586 0.051668 0.127170 0.292429 0.071458 1.218562 0.075144 7.169085 30.139501 13.461692 0.021372 0.045779 4.270235 0.468325 0.013688 0.302287 1.353957 0.028386 0.037750 0.262130 0.016923 0.064289 0.855973 0.079621 0.011169 0.161937 0.276530 0.161053 0.081472 0.036742 0.030342 2.851667 3.932151 8.159169 0.219934 0.421974 2.468752 0.344765 0.210724 1.172204 0.763553 0.082464 0.726566 11.149790 4.782635 0.058046 0.498072 0.258487 0.146882 0.249672 0.560142 0.046719 0.106259 0.003656 0.004200 0.014189 0.009876 0.002656 0.040244 0.267322 0.053740 0.006597 0.027639 0.012745 0.582670 0.005035 0.275844 0.098208 0.445038 1.217010 0.033969 1.988516 0.681161 0.825960 18.762977 11.949233 0.286794 0.534219 4.336817 3.054085 0.129551 4.210126 0.165753 1.088704 1.889645 3.344809 0.111063 2.067758 3.547017 2.466507 0.188236 0.203493 0.281953 0.037250 0.029788 0.008541 0.014768 0.125869 0.056702 0.004186 0.110993 0.201148 0.139705 0.009201 0.012095 0.043812 0.013513 0.002533 0.005848 0.031390 0.021612 0.004854 0.129497 0.976631 0.053397 0.019475 0.004964 0.015539 0.031779 0.064558 0.065585 0.079927 0.095591 0.196886 0.408834 0.126088 0.037226 0.452302 0.016212 7.278994 0.029917 7.918203 0.450964 0.169797 0.104288 1.578530 0.015909 0.094365 16.179952 0.042762 14.799537 1.506485 0.637893 0.123793 0.641351 0.154810 0.140750 3.416059 0.259400 0.009457 0.090576 0.292108 0.297913 0.017172 0.021976 0.032578 1.375871 0.457399 0.598048 4.418398 0.239749 0.168432 2.950318 0.143327 0.328689 0.125011 0.562720 1.414883 0.227807 3.478333 2.984862 0.061299 0.077470 1.050562 13.974326 0.154326 0.224675 0.112000 0.060703 0.123480 5.294490 0.447011 0.033381 0.045528 M 2: 0.733336 0.558955 0.503360 4.149599 1.415369 1.367574 1.263002 0.994098 0.517204 0.775054 0.763094 1.890137 0.540460 0.200122 4.972745 1.825593 0.450842 0.526135 3.839269 0.597671 0.058964 2.863355 2.872594 0.258365 0.366868 2.578946 0.358350 0.672023 5.349861 0.691594 0.063347 0.032875 0.821562 0.580847 0.661866 0.265730 0.395134 5.581680 1.279881 1.335650 0.397108 1.840061 5.739035 0.284730 0.109781 1.612642 0.466979 0.141582 0.019509 4.670980 1.967383 0.088064 0.581928 0.145401 0.225860 0.434096 2.292917 1.024707 0.821921 0.027824 0.021443 0.088850 0.060820 0.018288 0.042687 1.199607 0.420710 0.037642 0.141233 0.090101 1.043232 0.209978 0.823594 3.039380 1.463390 1.983693 0.397640 2.831098 4.102068 0.059723 5.901348 2.034980 2.600668 5.413080 4.193725 4.534772 0.377181 4.877840 0.370939 1.298542 3.509873 2.646440 0.087872 0.072299 1.139018 0.864479 0.390688 0.322761 0.625409 0.496780 0.532488 0.232460 0.169219 0.755219 0.379926 0.020447 0.023282 0.503875 0.577513 0.109318 0.153776 0.696533 0.398817 0.008940 0.043707 0.436013 0.087640 0.064863 0.036426 1.673207 0.124068 0.218118 0.039217 0.104335 0.349195 0.838324 0.888693 0.488389 1.385133 0.050226 0.962470 0.502294 1.065585 8.351808 0.377304 5.102837 0.561690 7.010411 3.054968 0.039318 0.204155 2.653232 0.564368 0.854294 15.559906 0.401070 8.929538 5.525874 0.067505 0.273372 0.437116 1.927515 0.940458 2.508169 1.357738 0.043394 0.023126 0.567639 1.048288 0.120994 0.180650 0.449074 3.135353 0.012695 0.570771 2.319555 1.856122 0.975427 3.404087 0.015631 0.458799 0.151684 4.154750 11.429924 1.457957 0.233109 0.077004 0.011074 0.026268 0.052132 8.113282 0.377578 0.429221 0.260296 0.222293 0.273138 2.903836 4.731579 0.564762 0.681215 M 3: 0.658412 0.566269 0.854111 0.884454 1.309554 1.272639 1.874713 0.552007 0.227683 0.581512 0.695190 0.967985 0.344015 0.978992 3.427163 2.333253 0.154701 0.221089 2.088785 0.540749 0.058015 5.851132 2.294145 0.182966 0.684164 3.192521 0.528161 1.128882 3.010922 1.012866 0.227296 0.156635 0.878405 0.802754 0.830884 0.431617 0.456530 3.060574 1.279257 1.438430 0.431464 2.075952 4.840271 0.644656 0.266076 2.084975 0.720060 0.291854 0.028961 4.071574 2.258357 0.073037 1.238426 0.199728 0.160296 0.482619 2.992763 1.296206 0.841829 0.031467 0.048542 0.132774 0.133055 0.056045 0.209188 0.925172 0.360522 0.094591 0.313945 0.118104 0.992259 0.086318 2.149634 5.103188 3.775817 3.954021 0.190734 1.776095 4.495841 0.264277 7.063879 2.221150 3.017954 8.558815 4.310199 2.130054 0.571406 4.137385 0.437589 2.071689 2.498630 1.763546 0.116381 0.296578 1.033710 1.283423 0.312579 0.305772 0.681277 0.507160 0.351381 0.189152 0.217780 0.767361 0.278392 0.092075 0.177263 0.451893 0.653836 0.074620 0.181992 0.752277 0.679853 0.025780 0.082005 0.326441 0.343977 0.195877 0.217424 3.057583 0.377558 0.401252 0.072258 0.241015 0.665865 1.266791 0.680174 0.717301 4.001286 0.362942 1.189259 0.964545 1.350568 12.869737 0.531100 8.904999 0.652629 10.091413 2.671718 0.086367 0.359932 4.797423 0.336801 1.021885 23.029406 0.440178 14.013035 5.069337 0.539010 0.742569 0.780580 1.331875 1.531589 4.414850 1.082703 0.091278 0.172734 0.693405 1.422571 0.068958 0.163829 0.481711 4.643214 0.121821 0.584083 4.216178 1.677263 1.575754 5.046403 0.161015 1.531223 0.599244 5.832025 33.873091 1.914768 1.287474 0.444362 0.076328 0.079916 0.466823 5.231362 0.548763 0.831890 0.382271 0.208791 0.307846 3.717971 5.910440 0.282540 0.964421 Final LogLikelihood: -348723.651763 AIC score: 701457.303526 / AICc score: 8745517.303526 / BIC score: 709947.297031 Free parameters (model + branch lengths): 2005 WARNING: Number of free parameters (K=2005) is larger than alignment size (n=510). This might lead to overfitting and compromise tree inference results! Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798.raxml.bestTree All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798.raxml.mlTrees Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798.raxml.bestModel Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/030621_run/phylogeny-snakemake/results/P04798/3_mltree/P04798.raxml.log Analysis started: 18-Jun-2021 12:51:23 / finished: 18-Jun-2021 23:45:56 Elapsed time: 39272.735 seconds (this run) / 39278.702 seconds (total with restarts) Consumed energy: 3440.361 Wh (= 17 km in an electric car, or 86 km with an e-scooter!)