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 02-Jul-2021 06:17:14 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/2_msa/Q9BZG2_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/3_mltree/Q9BZG2 --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/300621_run/phylogeny-snakemake/results/Q9BZG2/2_msa/Q9BZG2_trimmed_msa.fasta
[00:00:00] Loaded alignment with 680 taxa and 409 sites

WARNING: Sequences tr_G3S7R3_G3S7R3_GORGO_9595 and sp_Q9BZG2_PPAT_HUMAN_9606 are exactly identical!
WARNING: Sequences tr_G2HFX5_G2HFX5_PANTR_9598 and tr_A0A2R9CBQ4_A0A2R9CBQ4_PANPA_9597 are exactly identical!
WARNING: Sequences tr_F7ENJ9_F7ENJ9_MACMU_9544 and tr_G7PYF3_G7PYF3_MACFA_9541 are exactly identical!
WARNING: Sequences tr_A0A096MPE7_A0A096MPE7_PAPAN_9555 and tr_A0A2K6BUL8_A0A2K6BUL8_MACNE_9545 are exactly identical!
WARNING: Sequences tr_A0A096MPE7_A0A096MPE7_PAPAN_9555 and tr_A0A2K6ABM3_A0A2K6ABM3_MANLE_9568 are exactly identical!
WARNING: Sequences tr_A0A0A0MW08_A0A0A0MW08_PAPAN_9555 and tr_A0A2K5LU59_A0A2K5LU59_CERAT_9531 are exactly identical!
WARNING: Sequences tr_A0A0A0MW08_A0A0A0MW08_PAPAN_9555 and tr_A0A2K5XLY3_A0A2K5XLY3_MANLE_9568 are exactly identical!
WARNING: Sequences tr_A0A0V1CQ06_A0A0V1CQ06_TRIBR_45882 and tr_A0A0V0VAG4_A0A0V0VAG4_9BILA_181606 are exactly identical!
WARNING: Sequences tr_A0A0V0X8A2_A0A0V0X8A2_9BILA_92179 and tr_A0A0V1L750_A0A0V1L750_9BILA_6335 are exactly identical!
WARNING: Sequences tr_A0A2D0Q6F6_A0A2D0Q6F6_ICTPU_7998 and tr_A0A2D0QA68_A0A2D0QA68_ICTPU_7998 are exactly identical!
WARNING: Duplicate sequences found: 10

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/Q9BZG2/3_mltree/Q9BZG2.raxml.reduced.phy

Alignment comprises 1 partitions and 409 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 409 / 409
Gaps: 18.45 %
Invariant sites: 0.00 %


NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/3_mltree/Q9BZG2.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 680 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 205 / 16400
[00:00:00] Data distribution: max. searches per worker: 7

Starting ML tree search with 20 distinct starting trees

[00:00:00 -482402.544625] Initial branch length optimization
[00:00:06 -406431.759987] Model parameter optimization (eps = 10.000000)
[00:00:39 -405486.786976] AUTODETECT spr round 1 (radius: 5)
[00:02:10 -331014.384981] AUTODETECT spr round 2 (radius: 10)
[00:04:00 -260878.436992] AUTODETECT spr round 3 (radius: 15)
[00:05:53 -220104.564724] AUTODETECT spr round 4 (radius: 20)
[00:08:17 -207278.268009] AUTODETECT spr round 5 (radius: 25)
[00:11:45 -205344.145321] SPR radius for FAST iterations: 25 (autodetect)
[00:11:45 -205344.145321] Model parameter optimization (eps = 3.000000)
[00:12:04 -205115.142647] FAST spr round 1 (radius: 25)
[00:15:00 -188373.630971] FAST spr round 2 (radius: 25)
[00:16:57 -187652.253111] FAST spr round 3 (radius: 25)
[00:18:31 -187596.153455] FAST spr round 4 (radius: 25)
[00:19:46 -187594.855130] FAST spr round 5 (radius: 25)
[00:20:57 -187594.855072] Model parameter optimization (eps = 1.000000)
[00:21:12 -187587.895549] SLOW spr round 1 (radius: 5)
[00:23:02 -187546.130422] SLOW spr round 2 (radius: 5)
[00:24:40 -187543.704468] SLOW spr round 3 (radius: 5)
[00:26:14 -187543.704434] SLOW spr round 4 (radius: 10)
[00:28:01 -187543.704433] SLOW spr round 5 (radius: 15)
[00:32:29 -187543.704433] SLOW spr round 6 (radius: 20)
[00:38:16 -187543.704432] SLOW spr round 7 (radius: 25)
[00:44:44 -187543.704432] Model parameter optimization (eps = 0.100000)

[00:44:50] [worker #0] ML tree search #1, logLikelihood: -187543.646730

[00:44:50 -485938.409221] Initial branch length optimization
[00:44:54 -406469.233175] Model parameter optimization (eps = 10.000000)
[00:45:22 -405574.370953] AUTODETECT spr round 1 (radius: 5)
[00:46:52 -333368.779529] AUTODETECT spr round 2 (radius: 10)
[00:48:39 -264396.549614] AUTODETECT spr round 3 (radius: 15)
[00:49:05] [worker #2] ML tree search #3, logLikelihood: -187540.439728
[00:50:43 -215855.810378] AUTODETECT spr round 4 (radius: 20)
[00:53:19 -208951.655636] AUTODETECT spr round 5 (radius: 25)
[00:56:34 -206187.512907] SPR radius for FAST iterations: 25 (autodetect)
[00:56:34 -206187.512907] Model parameter optimization (eps = 3.000000)
[00:56:54 -205912.356937] FAST spr round 1 (radius: 25)
[00:59:31 -188253.173393] FAST spr round 2 (radius: 25)
[01:01:20 -187634.277089] FAST spr round 3 (radius: 25)
[01:02:52 -187608.373138] FAST spr round 4 (radius: 25)
[01:04:09 -187597.187323] FAST spr round 5 (radius: 25)
[01:05:20 -187597.187084] Model parameter optimization (eps = 1.000000)
[01:05:30 -187595.331494] SLOW spr round 1 (radius: 5)
[01:07:17 -187549.561333] SLOW spr round 2 (radius: 5)
[01:08:25] [worker #1] ML tree search #2, logLikelihood: -187530.491029
[01:08:59 -187543.196155] SLOW spr round 3 (radius: 5)
[01:10:37 -187541.490243] SLOW spr round 4 (radius: 5)
[01:12:12 -187541.490093] SLOW spr round 5 (radius: 10)
[01:13:57 -187540.067661] SLOW spr round 6 (radius: 5)
[01:16:04 -187539.066017] SLOW spr round 7 (radius: 5)
[01:17:51 -187539.065798] SLOW spr round 8 (radius: 10)
[01:19:43 -187539.065676] SLOW spr round 9 (radius: 15)
[01:23:50 -187539.065555] SLOW spr round 10 (radius: 20)
[01:29:07 -187539.065434] SLOW spr round 11 (radius: 25)
[01:35:22 -187539.065313] Model parameter optimization (eps = 0.100000)

[01:35:34] [worker #0] ML tree search #4, logLikelihood: -187538.713654

[01:35:34 -484938.188798] Initial branch length optimization
[01:35:38 -406227.354045] Model parameter optimization (eps = 10.000000)
[01:36:08 -405338.430135] AUTODETECT spr round 1 (radius: 5)
[01:37:39 -327738.691518] AUTODETECT spr round 2 (radius: 10)
[01:39:30 -257674.789794] AUTODETECT spr round 3 (radius: 15)
[01:41:18] [worker #2] ML tree search #6, logLikelihood: -187556.750812
[01:41:39 -214847.628658] AUTODETECT spr round 4 (radius: 20)
[01:44:00 -205147.501479] AUTODETECT spr round 5 (radius: 25)
[01:46:52 -203066.721360] SPR radius for FAST iterations: 25 (autodetect)
[01:46:52 -203066.721360] Model parameter optimization (eps = 3.000000)
[01:47:13 -202761.535657] FAST spr round 1 (radius: 25)
[01:50:03 -188291.828597] FAST spr round 2 (radius: 25)
[01:52:01 -187679.408518] FAST spr round 3 (radius: 25)
[01:53:33 -187595.411378] FAST spr round 4 (radius: 25)
[01:54:51 -187589.731702] FAST spr round 5 (radius: 25)
[01:56:04 -187588.766949] FAST spr round 6 (radius: 25)
[01:57:14 -187588.766417] Model parameter optimization (eps = 1.000000)
[01:57:28 -187585.819735] SLOW spr round 1 (radius: 5)
[01:59:17 -187556.282974] SLOW spr round 2 (radius: 5)
[02:00:56 -187554.085735] SLOW spr round 3 (radius: 5)
[02:02:34 -187553.555876] SLOW spr round 4 (radius: 5)
[02:04:00] [worker #1] ML tree search #5, logLikelihood: -187537.801194
[02:04:10 -187553.555673] SLOW spr round 5 (radius: 10)
[02:05:59 -187553.555551] SLOW spr round 6 (radius: 15)
[02:10:10 -187552.945228] SLOW spr round 7 (radius: 5)
[02:12:23 -187552.405606] SLOW spr round 8 (radius: 5)
[02:14:14 -187552.405488] SLOW spr round 9 (radius: 10)
[02:16:13 -187552.405400] SLOW spr round 10 (radius: 15)
[02:20:09 -187552.254164] SLOW spr round 11 (radius: 5)
[02:22:21 -187551.239643] SLOW spr round 12 (radius: 5)
[02:24:15 -187549.737368] SLOW spr round 13 (radius: 5)
[02:26:00 -187548.294012] SLOW spr round 14 (radius: 5)
[02:27:37 -187548.293657] SLOW spr round 15 (radius: 10)
[02:27:51] [worker #2] ML tree search #9, logLikelihood: -187544.252104
[02:29:27 -187547.229172] SLOW spr round 16 (radius: 5)
[02:31:36 -187546.316520] SLOW spr round 17 (radius: 5)
[02:33:26 -187546.315315] SLOW spr round 18 (radius: 10)
[02:35:22 -187546.315285] SLOW spr round 19 (radius: 15)
[02:39:20 -187546.315282] SLOW spr round 20 (radius: 20)
[02:44:59 -187546.315281] SLOW spr round 21 (radius: 25)
[02:51:24 -187546.315280] Model parameter optimization (eps = 0.100000)

[02:51:36] [worker #0] ML tree search #7, logLikelihood: -187546.155455

[02:51:36 -483602.153038] Initial branch length optimization
[02:51:40 -406711.799780] Model parameter optimization (eps = 10.000000)
[02:52:12 -405807.278534] AUTODETECT spr round 1 (radius: 5)
[02:53:45 -335447.492425] AUTODETECT spr round 2 (radius: 10)
[02:55:39 -267793.927827] AUTODETECT spr round 3 (radius: 15)
[02:57:41 -223806.502819] AUTODETECT spr round 4 (radius: 20)
[03:00:36 -212039.702792] AUTODETECT spr round 5 (radius: 25)
[03:03:59 -210543.230047] SPR radius for FAST iterations: 25 (autodetect)
[03:03:59 -210543.230047] Model parameter optimization (eps = 3.000000)
[03:04:17 -210306.747559] FAST spr round 1 (radius: 25)
[03:07:06 -188536.291681] FAST spr round 2 (radius: 25)
[03:09:09 -187686.361330] FAST spr round 3 (radius: 25)
[03:10:49 -187618.793037] FAST spr round 4 (radius: 25)
[03:12:07 -187613.622052] FAST spr round 5 (radius: 25)
[03:13:19 -187613.621692] Model parameter optimization (eps = 1.000000)
[03:13:32 -187608.939920] SLOW spr round 1 (radius: 5)
[03:13:36] [worker #1] ML tree search #8, logLikelihood: -187521.758448
[03:14:39] [worker #2] ML tree search #12, logLikelihood: -187533.200458
[03:15:24 -187554.041522] SLOW spr round 2 (radius: 5)
[03:17:09 -187548.439319] SLOW spr round 3 (radius: 5)
[03:18:46 -187548.438942] SLOW spr round 4 (radius: 10)
[03:20:35 -187548.438771] SLOW spr round 5 (radius: 15)
[03:24:53 -187547.851408] SLOW spr round 6 (radius: 5)
[03:27:03 -187547.850875] SLOW spr round 7 (radius: 10)
[03:29:20 -187547.850708] SLOW spr round 8 (radius: 15)
[03:33:07 -187547.850563] SLOW spr round 9 (radius: 20)
[03:38:59 -187547.850422] SLOW spr round 10 (radius: 25)
[03:45:30 -187547.850283] Model parameter optimization (eps = 0.100000)

[03:45:39] [worker #0] ML tree search #10, logLikelihood: -187547.619623

[03:45:39 -484435.232256] Initial branch length optimization
[03:45:43 -406602.522088] Model parameter optimization (eps = 10.000000)
[03:46:12 -405619.939202] AUTODETECT spr round 1 (radius: 5)
[03:47:44 -325212.592917] AUTODETECT spr round 2 (radius: 10)
[03:49:33 -262460.952770] AUTODETECT spr round 3 (radius: 15)
[03:51:31 -225623.249072] AUTODETECT spr round 4 (radius: 20)
[03:54:06 -209303.369467] AUTODETECT spr round 5 (radius: 25)
[03:57:58 -208694.178350] SPR radius for FAST iterations: 25 (autodetect)
[03:57:58 -208694.178350] Model parameter optimization (eps = 3.000000)
[03:58:21 -208478.004221] FAST spr round 1 (radius: 25)
[04:01:30 -188429.918632] FAST spr round 2 (radius: 25)
[04:03:23] [worker #1] ML tree search #11, logLikelihood: -187532.492940
[04:03:30 -187644.252050] FAST spr round 3 (radius: 25)
[04:05:10 -187602.855118] FAST spr round 4 (radius: 25)
[04:06:28 -187596.182772] FAST spr round 5 (radius: 25)
[04:07:42 -187595.534291] FAST spr round 6 (radius: 25)
[04:08:53 -187594.417841] FAST spr round 7 (radius: 25)
[04:10:04 -187591.451578] FAST spr round 8 (radius: 25)
[04:11:13 -187591.450378] Model parameter optimization (eps = 1.000000)
[04:11:21 -187586.361224] SLOW spr round 1 (radius: 5)
[04:13:11 -187541.603373] SLOW spr round 2 (radius: 5)
[04:14:53 -187536.267115] SLOW spr round 3 (radius: 5)
[04:15:16] [worker #2] ML tree search #15, logLikelihood: -187533.560297
[04:16:31 -187535.988411] SLOW spr round 4 (radius: 5)
[04:18:07 -187535.987514] SLOW spr round 5 (radius: 10)
[04:19:55 -187535.987378] SLOW spr round 6 (radius: 15)
[04:24:30 -187535.987253] SLOW spr round 7 (radius: 20)
[04:30:47 -187535.987128] SLOW spr round 8 (radius: 25)
[04:37:36 -187535.987004] Model parameter optimization (eps = 0.100000)

[04:37:45] [worker #0] ML tree search #13, logLikelihood: -187535.405448

[04:37:45 -486962.180852] Initial branch length optimization
[04:37:49 -408407.051489] Model parameter optimization (eps = 10.000000)
[04:38:44 -407445.534491] AUTODETECT spr round 1 (radius: 5)
[04:40:16 -337373.352929] AUTODETECT spr round 2 (radius: 10)
[04:42:04 -268025.526660] AUTODETECT spr round 3 (radius: 15)
[04:44:06 -224931.493511] AUTODETECT spr round 4 (radius: 20)
[04:46:41 -212044.041605] AUTODETECT spr round 5 (radius: 25)
[04:50:27 -210082.966917] SPR radius for FAST iterations: 25 (autodetect)
[04:50:27 -210082.966917] Model parameter optimization (eps = 3.000000)
[04:50:46 -209828.146362] FAST spr round 1 (radius: 25)
[04:51:11] [worker #1] ML tree search #14, logLikelihood: -187544.452744
[04:53:37 -188525.154808] FAST spr round 2 (radius: 25)
[04:55:35 -187654.612056] FAST spr round 3 (radius: 25)
[04:57:15 -187600.806092] FAST spr round 4 (radius: 25)
[04:58:32 -187600.515040] FAST spr round 5 (radius: 25)
[04:59:44 -187600.514576] Model parameter optimization (eps = 1.000000)
[04:59:58 -187598.422809] SLOW spr round 1 (radius: 5)
[05:01:47 -187544.637551] SLOW spr round 2 (radius: 5)
[05:03:27 -187544.590905] SLOW spr round 3 (radius: 10)
[05:05:16 -187544.589404] SLOW spr round 4 (radius: 15)
[05:08:39] [worker #2] ML tree search #18, logLikelihood: -187539.400308
[05:09:37 -187544.588792] SLOW spr round 5 (radius: 20)
[05:15:07 -187544.588467] SLOW spr round 6 (radius: 25)
[05:21:21 -187544.588251] Model parameter optimization (eps = 0.100000)

[05:21:26] [worker #0] ML tree search #16, logLikelihood: -187544.560028

[05:21:26 -484581.083375] Initial branch length optimization
[05:21:31 -407572.871460] Model parameter optimization (eps = 10.000000)
[05:22:00 -406653.223999] AUTODETECT spr round 1 (radius: 5)
[05:23:31 -327928.512030] AUTODETECT spr round 2 (radius: 10)
[05:25:21 -258956.506710] AUTODETECT spr round 3 (radius: 15)
[05:27:21 -217047.565192] AUTODETECT spr round 4 (radius: 20)
[05:30:14 -204062.063160] AUTODETECT spr round 5 (radius: 25)
[05:33:31 -203025.118728] SPR radius for FAST iterations: 25 (autodetect)
[05:33:31 -203025.118728] Model parameter optimization (eps = 3.000000)
[05:33:50 -202746.908299] FAST spr round 1 (radius: 25)
[05:36:42 -188266.961284] FAST spr round 2 (radius: 25)
[05:38:46 -187645.212921] FAST spr round 3 (radius: 25)
[05:40:02] [worker #1] ML tree search #17, logLikelihood: -187529.071107
[05:40:19 -187610.659137] FAST spr round 4 (radius: 25)
[05:41:36 -187609.679626] FAST spr round 5 (radius: 25)
[05:42:52 -187606.137231] FAST spr round 6 (radius: 25)
[05:44:02 -187606.137036] Model parameter optimization (eps = 1.000000)
[05:44:17 -187603.029198] SLOW spr round 1 (radius: 5)
[05:46:08 -187551.330824] SLOW spr round 2 (radius: 5)
[05:47:49 -187546.456911] SLOW spr round 3 (radius: 5)
[05:49:29 -187545.007428] SLOW spr round 4 (radius: 5)
[05:51:05 -187545.005712] SLOW spr round 5 (radius: 10)
[05:52:54 -187544.405620] SLOW spr round 6 (radius: 5)
[05:55:01 -187543.862136] SLOW spr round 7 (radius: 5)
[05:56:51 -187543.859844] SLOW spr round 8 (radius: 10)
[05:58:49 -187543.859203] SLOW spr round 9 (radius: 15)
[06:03:02 -187543.858937] SLOW spr round 10 (radius: 20)
[06:09:11 -187543.858763] SLOW spr round 11 (radius: 25)
[06:16:21 -187543.858612] Model parameter optimization (eps = 0.100000)

[06:16:26] [worker #0] ML tree search #19, logLikelihood: -187543.797926

[06:24:56] [worker #1] ML tree search #20, logLikelihood: -187529.499146

Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.114224,0.407427) (0.169111,0.476059) (0.384235,0.879137) (0.332430,1.609843) 
   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: -187521.758448

AIC score: 377769.516896 / AICc score: 4096033.516896 / BIC score: 383240.210654
Free parameters (model + branch lengths): 1363

WARNING: Number of free parameters (K=1363) is larger than alignment size (n=409).
         This might lead to overfitting and compromise tree inference results!


WARNING: Best ML tree contains 46 near-zero branches!

Best ML tree with collapsed near-zero branches saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/3_mltree/Q9BZG2.raxml.bestTreeCollapsed
Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/3_mltree/Q9BZG2.raxml.bestTree
All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/3_mltree/Q9BZG2.raxml.mlTrees
Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/3_mltree/Q9BZG2.raxml.bestModel

Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q9BZG2/3_mltree/Q9BZG2.raxml.log

Analysis started: 02-Jul-2021 06:17:14 / finished: 02-Jul-2021 12:42:11

Elapsed time: 23096.555 seconds

Consumed energy: 2045.334 Wh (= 10 km in an electric car, or 51 km with an e-scooter!)