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 06-Jul-2021 06:29:53 as follows: raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/2_msa/A6NI86_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86 --seed 2 --threads 4 --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 (4 threads), thread pinning: OFF [00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/2_msa/A6NI86_trimmed_msa.fasta [00:00:00] Loaded alignment with 251 taxa and 192 sites WARNING: Sequences tr_A0A2I3SXA8_A0A2I3SXA8_PANTR_9598 and tr_A0A2R9C051_A0A2R9C051_PANPA_9597 are exactly identical! WARNING: Sequences tr_H2RHX1_H2RHX1_PANTR_9598 and tr_A0A2R9BI76_A0A2R9BI76_PANPA_9597 are exactly identical! WARNING: Sequences tr_A0A0M3HER8_A0A0M3HER8_HUMAN_9606 and sp_H0YKK7_GG6LS_HUMAN_9606 are exactly identical! WARNING: Sequences sp_A6NCC3_GOG8O_HUMAN_9606 and sp_F8WBI6_GOG8N_HUMAN_9606 are exactly identical! WARNING: Sequences sp_A6NDK9_GOG6C_HUMAN_9606 and sp_P0CG33_GOG6D_HUMAN_9606 are exactly identical! WARNING: Sequences tr_A0A1D5RE25_A0A1D5RE25_MACMU_9544 and tr_A0A2K6BPU3_A0A2K6BPU3_MACNE_9545 are exactly identical! WARNING: Sequences tr_A0A096P0L6_A0A096P0L6_PAPAN_9555 and tr_A0A2K5KNR9_A0A2K5KNR9_CERAT_9531 are exactly identical! WARNING: Sequences tr_A0A096P0L6_A0A096P0L6_PAPAN_9555 and tr_A0A2K5YQ72_A0A2K5YQ72_MANLE_9568 are exactly identical! WARNING: Sequences tr_A0A2K6CCY8_A0A2K6CCY8_MACNE_9545 and tr_A0A2K5XGE8_A0A2K5XGE8_MANLE_9568 are exactly identical! WARNING: Duplicate sequences found: 9 NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) NOTE: was saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86.raxml.reduced.phy Alignment comprises 1 partitions and 192 patterns Partition 0: noname Model: LG4X+R4 Alignment sites / patterns: 192 / 192 Gaps: 18.71 % Invariant sites: 0.00 % NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86.raxml.rba Parallelization scheme autoconfig: 4 worker(s) x 1 thread(s) Parallel reduction/worker buffer size: 1 KB / 0 KB [00:00:00] Generating 20 random starting tree(s) with 251 taxa [00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 192 / 15360 [00:00:00] Data distribution: max. searches per worker: 5 Starting ML tree search with 20 distinct starting trees [00:00:00 -70155.509188] Initial branch length optimization [00:00:00 -59146.653297] Model parameter optimization (eps = 10.000000) [00:00:14 -58743.319908] AUTODETECT spr round 1 (radius: 5) [00:00:26 -42985.877029] AUTODETECT spr round 2 (radius: 10) [00:00:44 -33965.015727] AUTODETECT spr round 3 (radius: 15) [00:01:08 -32829.165019] AUTODETECT spr round 4 (radius: 20) [00:01:37 -32825.775055] AUTODETECT spr round 5 (radius: 25) [00:01:57 -32825.753415] SPR radius for FAST iterations: 20 (autodetect) [00:01:57 -32825.753415] Model parameter optimization (eps = 3.000000) [00:02:06 -32711.657015] FAST spr round 1 (radius: 20) [00:02:25 -31216.579057] FAST spr round 2 (radius: 20) [00:02:41 -31081.385179] FAST spr round 3 (radius: 20) [00:02:54 -31067.876000] FAST spr round 4 (radius: 20) [00:03:05 -31067.300182] FAST spr round 5 (radius: 20) [00:03:15 -31067.255777] Model parameter optimization (eps = 1.000000) [00:03:20 -31065.778276] SLOW spr round 1 (radius: 5) [00:03:41 -31064.096686] SLOW spr round 2 (radius: 5) [00:04:00 -31062.661698] SLOW spr round 3 (radius: 5) [00:04:19 -31062.650553] SLOW spr round 4 (radius: 10) [00:04:42 -31059.930044] SLOW spr round 5 (radius: 5) [00:05:09 -31059.928267] SLOW spr round 6 (radius: 10) [00:05:37 -31059.927748] SLOW spr round 7 (radius: 15) [00:06:21 -31059.927266] SLOW spr round 8 (radius: 20) [00:06:54] [worker #1] ML tree search #2, logLikelihood: -31052.932269 [00:07:12 -31059.926786] SLOW spr round 9 (radius: 25) [00:07:42 -31059.926306] Model parameter optimization (eps = 0.100000) [00:07:44] [worker #0] ML tree search #1, logLikelihood: -31059.833132 [00:07:44 -71606.388606] Initial branch length optimization [00:07:45 -60077.093338] Model parameter optimization (eps = 10.000000) [00:07:57 -59635.150077] AUTODETECT spr round 1 (radius: 5) [00:08:05] [worker #2] ML tree search #3, logLikelihood: -31056.201231 [00:08:08] [worker #3] ML tree search #4, logLikelihood: -31071.120851 [00:08:09 -42833.186040] AUTODETECT spr round 2 (radius: 10) [00:08:25 -35813.970632] AUTODETECT spr round 3 (radius: 15) [00:08:51 -33325.570193] AUTODETECT spr round 4 (radius: 20) [00:09:21 -33193.608749] AUTODETECT spr round 5 (radius: 25) [00:09:46 -33149.725220] SPR radius for FAST iterations: 25 (autodetect) [00:09:46 -33149.725220] Model parameter optimization (eps = 3.000000) [00:09:56 -33047.521469] FAST spr round 1 (radius: 25) [00:10:16 -31205.628380] FAST spr round 2 (radius: 25) [00:10:31 -31096.388339] FAST spr round 3 (radius: 25) [00:10:45 -31070.028202] FAST spr round 4 (radius: 25) [00:10:57 -31066.089412] FAST spr round 5 (radius: 25) [00:11:07 -31066.088932] Model parameter optimization (eps = 1.000000) [00:11:12 -31064.649198] SLOW spr round 1 (radius: 5) [00:11:31 -31059.648875] SLOW spr round 2 (radius: 5) [00:11:50 -31057.538313] SLOW spr round 3 (radius: 5) [00:12:09 -31056.766611] SLOW spr round 4 (radius: 5) [00:12:26 -31056.765908] SLOW spr round 5 (radius: 10) [00:12:46 -31056.765821] SLOW spr round 6 (radius: 15) [00:13:33 -31056.765757] SLOW spr round 7 (radius: 20) [00:13:34] [worker #1] ML tree search #6, logLikelihood: -31054.889565 [00:14:20 -31056.765678] SLOW spr round 8 (radius: 25) [00:14:47 -31056.765630] Model parameter optimization (eps = 0.100000) [00:14:48] [worker #0] ML tree search #5, logLikelihood: -31056.748177 [00:14:48 -70626.213376] Initial branch length optimization [00:14:49 -59052.052120] Model parameter optimization (eps = 10.000000) [00:14:58 -58662.208767] AUTODETECT spr round 1 (radius: 5) [00:15:09 -43482.229159] AUTODETECT spr round 2 (radius: 10) [00:15:25 -34952.979353] AUTODETECT spr round 3 (radius: 15) [00:15:48 -32918.494108] AUTODETECT spr round 4 (radius: 20) [00:16:15 -32813.962266] AUTODETECT spr round 5 (radius: 25) [00:16:28] [worker #3] ML tree search #8, logLikelihood: -31069.916117 [00:16:39 -32801.823687] SPR radius for FAST iterations: 25 (autodetect) [00:16:39 -32801.823687] Model parameter optimization (eps = 3.000000) [00:16:47 -32703.499314] FAST spr round 1 (radius: 25) [00:17:06 -31298.430252] FAST spr round 2 (radius: 25) [00:17:21 -31077.796302] FAST spr round 3 (radius: 25) [00:17:33 -31069.567114] FAST spr round 4 (radius: 25) [00:17:44 -31069.159395] FAST spr round 5 (radius: 25) [00:17:54 -31069.158467] Model parameter optimization (eps = 1.000000) [00:17:59 -31067.957049] SLOW spr round 1 (radius: 5) [00:18:19 -31063.259962] SLOW spr round 2 (radius: 5) [00:18:38 -31062.678520] SLOW spr round 3 (radius: 5) [00:18:56 -31062.490775] SLOW spr round 4 (radius: 5) [00:19:13 -31062.490254] SLOW spr round 5 (radius: 10) [00:19:35 -31062.373101] SLOW spr round 6 (radius: 5) [00:20:02 -31061.104740] SLOW spr round 7 (radius: 5) [00:20:24 -31061.104629] SLOW spr round 8 (radius: 10) [00:20:36] [worker #2] ML tree search #7, logLikelihood: -31078.442665 [00:20:47 -31061.104626] SLOW spr round 9 (radius: 15) [00:21:29 -31061.104626] SLOW spr round 10 (radius: 20) [00:22:16 -31061.104626] SLOW spr round 11 (radius: 25) [00:22:47 -31061.104626] Model parameter optimization (eps = 0.100000) [00:22:48] [worker #0] ML tree search #9, logLikelihood: -31061.094980 [00:22:48 -70729.410158] Initial branch length optimization [00:22:49 -59378.012152] Model parameter optimization (eps = 10.000000) [00:23:01 -58964.622027] AUTODETECT spr round 1 (radius: 5) [00:23:13 -41882.379765] AUTODETECT spr round 2 (radius: 10) [00:23:22] [worker #1] ML tree search #10, logLikelihood: -31084.775779 [00:23:28 -34319.523166] AUTODETECT spr round 3 (radius: 15) [00:23:52 -33257.826821] AUTODETECT spr round 4 (radius: 20) [00:23:59] [worker #3] ML tree search #12, logLikelihood: -31053.280456 [00:24:20 -33251.624146] AUTODETECT spr round 5 (radius: 25) [00:24:42 -33247.488293] SPR radius for FAST iterations: 25 (autodetect) [00:24:42 -33247.488293] Model parameter optimization (eps = 3.000000) [00:24:56 -33169.357169] FAST spr round 1 (radius: 25) [00:25:15 -31258.224471] FAST spr round 2 (radius: 25) [00:25:31 -31094.087272] FAST spr round 3 (radius: 25) [00:25:43 -31078.098041] FAST spr round 4 (radius: 25) [00:25:55 -31072.226303] FAST spr round 5 (radius: 25) [00:26:04 -31072.225843] Model parameter optimization (eps = 1.000000) [00:26:11 -31058.198004] SLOW spr round 1 (radius: 5) [00:26:31 -31049.555901] SLOW spr round 2 (radius: 5) [00:26:49 -31049.320065] SLOW spr round 3 (radius: 5) [00:27:06 -31049.319214] SLOW spr round 4 (radius: 10) [00:27:27 -31049.015032] SLOW spr round 5 (radius: 5) [00:27:53 -31048.948067] SLOW spr round 6 (radius: 10) [00:28:20 -31048.947024] SLOW spr round 7 (radius: 15) [00:29:09 -31048.946998] SLOW spr round 8 (radius: 20) [00:29:40] [worker #2] ML tree search #11, logLikelihood: -31062.600153 [00:29:43] [worker #1] ML tree search #14, logLikelihood: -31050.925841 [00:29:59 -31048.946997] SLOW spr round 9 (radius: 25) [00:30:24 -31048.946997] Model parameter optimization (eps = 0.100000) [00:30:26] [worker #0] ML tree search #13, logLikelihood: -31048.877682 [00:30:26 -71112.284177] Initial branch length optimization [00:30:27 -59839.912617] Model parameter optimization (eps = 10.000000) [00:30:43 -59422.959594] AUTODETECT spr round 1 (radius: 5) [00:30:54 -42831.285590] AUTODETECT spr round 2 (radius: 10) [00:31:11 -33640.366415] AUTODETECT spr round 3 (radius: 15) [00:31:33 -32603.469163] AUTODETECT spr round 4 (radius: 20) [00:31:57 -32510.251932] AUTODETECT spr round 5 (radius: 25) [00:32:08] [worker #3] ML tree search #16, logLikelihood: -31058.114810 [00:32:23 -32510.223931] SPR radius for FAST iterations: 20 (autodetect) [00:32:23 -32510.223931] Model parameter optimization (eps = 3.000000) [00:32:31 -32405.019227] FAST spr round 1 (radius: 20) [00:32:50 -31155.103125] FAST spr round 2 (radius: 20) [00:33:06 -31086.418700] FAST spr round 3 (radius: 20) [00:33:19 -31074.134928] FAST spr round 4 (radius: 20) [00:33:32 -31067.898105] FAST spr round 5 (radius: 20) [00:33:43 -31063.998581] FAST spr round 6 (radius: 20) [00:33:53 -31063.998500] Model parameter optimization (eps = 1.000000) [00:33:56 -31063.308299] SLOW spr round 1 (radius: 5) [00:34:17 -31057.194774] SLOW spr round 2 (radius: 5) [00:34:36 -31055.429312] SLOW spr round 3 (radius: 5) [00:34:54 -31055.428863] SLOW spr round 4 (radius: 10) [00:35:17 -31054.280368] SLOW spr round 5 (radius: 5) [00:35:44 -31052.347272] SLOW spr round 6 (radius: 5) [00:36:06 -31052.347068] SLOW spr round 7 (radius: 10) [00:36:31 -31052.347061] SLOW spr round 8 (radius: 15) [00:37:18 -31052.347060] SLOW spr round 9 (radius: 20) [00:37:24] [worker #2] ML tree search #15, logLikelihood: -31055.966489 [00:38:07 -31052.347060] SLOW spr round 10 (radius: 25) [00:38:28] [worker #1] ML tree search #18, logLikelihood: -31054.459031 [00:38:40 -31052.347059] Model parameter optimization (eps = 0.100000) [00:38:42] [worker #0] ML tree search #17, logLikelihood: -31052.024852 [00:38:50] [worker #3] ML tree search #20, logLikelihood: -31057.650213 [00:46:07] [worker #2] ML tree search #19, logLikelihood: -31059.186474 Optimized model parameters: Partition 0: noname Rate heterogeneity: FREE (4 cats, mean), weights&rates: (0.051881,0.716922) (0.012913,1.328804) (0.416396,0.766949) (0.518810,1.207170) 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: -31048.877682 AIC score: 63107.755363 / AICc score: 574167.755363 / BIC score: 64752.790526 Free parameters (model + branch lengths): 505 WARNING: Number of free parameters (K=505) is larger than alignment size (n=192). This might lead to overfitting and compromise tree inference results! WARNING: Best ML tree contains 24 near-zero branches! Best ML tree with collapsed near-zero branches saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86.raxml.bestTreeCollapsed Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86.raxml.bestTree All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86.raxml.mlTrees Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86.raxml.bestModel Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/A6NI86/3_mltree/A6NI86.raxml.log Analysis started: 06-Jul-2021 06:29:53 / finished: 06-Jul-2021 07:16:01 Elapsed time: 2767.978 seconds Consumed energy: 207.393 Wh (= 1 km in an electric car, or 5 km with an e-scooter!)