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) CPU E5-2690 v4 @ 2.60GHz, 28 cores, 251 GB RAM

RAxML-NG was called at 02-Jul-2021 15:13:26 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/2_msa/Q6PJG6_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/3_mltree/Q6PJG6 --seed 2 --threads 8 --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 (8 threads), thread pinning: OFF

[00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/2_msa/Q6PJG6_trimmed_msa.fasta
[00:00:00] Loaded alignment with 107 taxa and 816 sites

Alignment comprises 1 partitions and 816 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 816 / 816
Gaps: 12.08 %
Invariant sites: 0.86 %


NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/3_mltree/Q6PJG6.raxml.rba

Parallelization scheme autoconfig: 2 worker(s) x 4 thread(s)

Parallel reduction/worker buffer size: 1 KB  / 0 KB

[00:00:00] Generating 20 random starting tree(s) with 107 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 204 / 16320
[00:00:00] Data distribution: max. searches per worker: 10

Starting ML tree search with 20 distinct starting trees

[00:00:00 -124551.640735] Initial branch length optimization
[00:00:00 -102129.857100] Model parameter optimization (eps = 10.000000)
[00:00:08 -101980.692654] AUTODETECT spr round 1 (radius: 5)
[00:00:12 -75642.011868] AUTODETECT spr round 2 (radius: 10)
[00:00:18 -67058.947976] AUTODETECT spr round 3 (radius: 15)
[00:00:25 -64752.477230] AUTODETECT spr round 4 (radius: 20)
[00:00:31 -64752.448547] SPR radius for FAST iterations: 15 (autodetect)
[00:00:31 -64752.448547] Model parameter optimization (eps = 3.000000)
[00:00:39 -64528.768246] FAST spr round 1 (radius: 15)
[00:00:46 -61649.570558] FAST spr round 2 (radius: 15)
[00:00:51 -61590.578779] FAST spr round 3 (radius: 15)
[00:00:56 -61587.428973] FAST spr round 4 (radius: 15)
[00:01:01 -61584.410836] FAST spr round 5 (radius: 15)
[00:01:06 -61580.302318] FAST spr round 6 (radius: 15)
[00:01:11 -61579.860535] FAST spr round 7 (radius: 15)
[00:01:16 -61579.249758] FAST spr round 8 (radius: 15)
[00:01:21 -61578.907599] FAST spr round 9 (radius: 15)
[00:01:26 -61578.870196] Model parameter optimization (eps = 1.000000)
[00:01:29 -61575.533220] SLOW spr round 1 (radius: 5)
[00:01:39 -61570.002224] SLOW spr round 2 (radius: 5)
[00:01:49 -61569.337844] SLOW spr round 3 (radius: 5)
[00:01:58 -61569.337779] SLOW spr round 4 (radius: 10)
[00:02:08 -61565.448724] SLOW spr round 5 (radius: 5)
[00:02:22 -61565.448534] SLOW spr round 6 (radius: 10)
[00:02:33 -61565.448483] SLOW spr round 7 (radius: 15)
[00:02:47 -61565.448462] SLOW spr round 8 (radius: 20)
[00:03:00 -61565.448454] SLOW spr round 9 (radius: 25)
[00:03:05] [worker #1] ML tree search #2, logLikelihood: -61565.865606
[00:03:09 -61565.448450] Model parameter optimization (eps = 0.100000)

[00:03:11] [worker #0] ML tree search #1, logLikelihood: -61565.360801

[00:03:11 -128214.791368] Initial branch length optimization
[00:03:11 -103450.373204] Model parameter optimization (eps = 10.000000)
[00:03:17 -103355.440642] AUTODETECT spr round 1 (radius: 5)
[00:03:21 -75880.217097] AUTODETECT spr round 2 (radius: 10)
[00:03:28 -66941.537096] AUTODETECT spr round 3 (radius: 15)
[00:03:34 -64724.175962] AUTODETECT spr round 4 (radius: 20)
[00:03:40 -64714.346910] AUTODETECT spr round 5 (radius: 25)
[00:03:46 -64714.323462] SPR radius for FAST iterations: 20 (autodetect)
[00:03:46 -64714.323462] Model parameter optimization (eps = 3.000000)
[00:03:55 -64430.860082] FAST spr round 1 (radius: 20)
[00:04:02 -61653.364703] FAST spr round 2 (radius: 20)
[00:04:08 -61590.211935] FAST spr round 3 (radius: 20)
[00:04:13 -61587.693265] FAST spr round 4 (radius: 20)
[00:04:18 -61587.320796] FAST spr round 5 (radius: 20)
[00:04:23 -61587.282614] Model parameter optimization (eps = 1.000000)
[00:04:27 -61582.126262] SLOW spr round 1 (radius: 5)
[00:04:37 -61566.443599] SLOW spr round 2 (radius: 5)
[00:04:47 -61566.027573] SLOW spr round 3 (radius: 5)
[00:04:56 -61566.027525] SLOW spr round 4 (radius: 10)
[00:05:05 -61566.027521] SLOW spr round 5 (radius: 15)
[00:05:20 -61566.027519] SLOW spr round 6 (radius: 20)
[00:05:33 -61566.027518] SLOW spr round 7 (radius: 25)
[00:05:42 -61566.027517] Model parameter optimization (eps = 0.100000)

[00:05:44] [worker #0] ML tree search #3, logLikelihood: -61565.865624

[00:05:44 -129875.404571] Initial branch length optimization
[00:05:44 -102638.009581] Model parameter optimization (eps = 10.000000)
[00:05:54 -102507.090334] AUTODETECT spr round 1 (radius: 5)
[00:05:58 -74593.570448] AUTODETECT spr round 2 (radius: 10)
[00:06:04 -65773.356814] AUTODETECT spr round 3 (radius: 15)
[00:06:10 -65188.806414] AUTODETECT spr round 4 (radius: 20)
[00:06:15] [worker #1] ML tree search #4, logLikelihood: -61565.360792
[00:06:16 -65188.746703] SPR radius for FAST iterations: 15 (autodetect)
[00:06:16 -65188.746703] Model parameter optimization (eps = 3.000000)
[00:06:22 -64974.400392] FAST spr round 1 (radius: 15)
[00:06:31 -61686.022155] FAST spr round 2 (radius: 15)
[00:06:36 -61594.519588] FAST spr round 3 (radius: 15)
[00:06:42 -61591.222013] FAST spr round 4 (radius: 15)
[00:06:47 -61590.424854] FAST spr round 5 (radius: 15)
[00:06:51 -61589.805032] FAST spr round 6 (radius: 15)
[00:06:56 -61589.505194] FAST spr round 7 (radius: 15)
[00:07:01 -61589.388247] FAST spr round 8 (radius: 15)
[00:07:06 -61589.359194] Model parameter optimization (eps = 1.000000)
[00:07:10 -61581.284548] SLOW spr round 1 (radius: 5)
[00:07:20 -61576.020466] SLOW spr round 2 (radius: 5)
[00:07:30 -61576.012568] SLOW spr round 3 (radius: 10)
[00:07:39 -61574.160119] SLOW spr round 4 (radius: 5)
[00:07:54 -61574.159641] SLOW spr round 5 (radius: 10)
[00:08:04 -61574.159510] SLOW spr round 6 (radius: 15)
[00:08:18 -61574.159456] SLOW spr round 7 (radius: 20)
[00:08:31 -61574.159432] SLOW spr round 8 (radius: 25)
[00:08:40 -61574.159420] Model parameter optimization (eps = 0.100000)

[00:08:41] [worker #0] ML tree search #5, logLikelihood: -61574.106668

[00:08:41 -127305.406927] Initial branch length optimization
[00:08:41 -102689.864287] Model parameter optimization (eps = 10.000000)
[00:08:52 -102533.553405] AUTODETECT spr round 1 (radius: 5)
[00:08:53] [worker #1] ML tree search #6, logLikelihood: -61566.462384
[00:08:56 -75900.432523] AUTODETECT spr round 2 (radius: 10)
[00:09:02 -65013.121771] AUTODETECT spr round 3 (radius: 15)
[00:09:09 -64445.226997] AUTODETECT spr round 4 (radius: 20)
[00:09:16 -64435.545819] AUTODETECT spr round 5 (radius: 25)
[00:09:20 -64435.494940] SPR radius for FAST iterations: 20 (autodetect)
[00:09:20 -64435.494940] Model parameter optimization (eps = 3.000000)
[00:09:29 -64176.819983] FAST spr round 1 (radius: 20)
[00:09:37 -61757.546416] FAST spr round 2 (radius: 20)
[00:09:43 -61594.187661] FAST spr round 3 (radius: 20)
[00:09:47 -61589.691762] FAST spr round 4 (radius: 20)
[00:09:52 -61587.445977] FAST spr round 5 (radius: 20)
[00:09:57 -61586.887515] FAST spr round 6 (radius: 20)
[00:10:02 -61586.874400] Model parameter optimization (eps = 1.000000)
[00:10:04 -61585.114986] SLOW spr round 1 (radius: 5)
[00:10:15 -61566.391756] SLOW spr round 2 (radius: 5)
[00:10:24 -61566.039987] SLOW spr round 3 (radius: 5)
[00:10:34 -61566.039948] SLOW spr round 4 (radius: 10)
[00:10:43 -61566.039945] SLOW spr round 5 (radius: 15)
[00:10:58 -61566.039944] SLOW spr round 6 (radius: 20)
[00:11:11 -61566.039944] SLOW spr round 7 (radius: 25)
[00:11:20 -61566.039944] Model parameter optimization (eps = 0.100000)

[00:11:22] [worker #0] ML tree search #7, logLikelihood: -61565.865642

[00:11:22 -129228.628886] Initial branch length optimization
[00:11:22 -104061.067058] Model parameter optimization (eps = 10.000000)
[00:11:30] [worker #1] ML tree search #8, logLikelihood: -61566.029309
[00:11:32 -103847.690039] AUTODETECT spr round 1 (radius: 5)
[00:11:36 -79532.772746] AUTODETECT spr round 2 (radius: 10)
[00:11:42 -67861.903476] AUTODETECT spr round 3 (radius: 15)
[00:11:50 -65304.056209] AUTODETECT spr round 4 (radius: 20)
[00:11:56 -65304.009566] SPR radius for FAST iterations: 15 (autodetect)
[00:11:56 -65304.009566] Model parameter optimization (eps = 3.000000)
[00:12:06 -65113.659450] FAST spr round 1 (radius: 15)
[00:12:14 -61680.299001] FAST spr round 2 (radius: 15)
[00:12:19 -61615.921777] FAST spr round 3 (radius: 15)
[00:12:24 -61601.456543] FAST spr round 4 (radius: 15)
[00:12:29 -61599.290548] FAST spr round 5 (radius: 15)
[00:12:34 -61598.553367] FAST spr round 6 (radius: 15)
[00:12:38 -61598.104588] FAST spr round 7 (radius: 15)
[00:12:43 -61597.551348] FAST spr round 8 (radius: 15)
[00:12:48 -61597.258307] FAST spr round 9 (radius: 15)
[00:12:53 -61597.248839] Model parameter optimization (eps = 1.000000)
[00:12:57 -61586.355723] SLOW spr round 1 (radius: 5)
[00:13:07 -61570.682373] SLOW spr round 2 (radius: 5)
[00:13:17 -61570.029380] SLOW spr round 3 (radius: 5)
[00:13:27 -61570.029332] SLOW spr round 4 (radius: 10)
[00:13:36 -61566.005935] SLOW spr round 5 (radius: 5)
[00:13:51 -61566.005784] SLOW spr round 6 (radius: 10)
[00:14:01 -61566.005753] SLOW spr round 7 (radius: 15)
[00:14:15 -61566.005740] SLOW spr round 8 (radius: 20)
[00:14:28 -61566.005734] SLOW spr round 9 (radius: 25)
[00:14:37 -61566.005731] Model parameter optimization (eps = 0.100000)

[00:14:39] [worker #0] ML tree search #9, logLikelihood: -61565.865616

[00:14:39 -125956.226486] Initial branch length optimization
[00:14:39 -102676.429258] Model parameter optimization (eps = 10.000000)
[00:14:43] [worker #1] ML tree search #10, logLikelihood: -61565.865777
[00:14:49 -102479.361134] AUTODETECT spr round 1 (radius: 5)
[00:14:53 -76792.456166] AUTODETECT spr round 2 (radius: 10)
[00:14:59 -70375.992858] AUTODETECT spr round 3 (radius: 15)
[00:15:06 -67059.581389] AUTODETECT spr round 4 (radius: 20)
[00:15:13 -64589.489035] AUTODETECT spr round 5 (radius: 25)
[00:15:18 -64583.481830] SPR radius for FAST iterations: 25 (autodetect)
[00:15:18 -64583.481830] Model parameter optimization (eps = 3.000000)
[00:15:26 -64363.807359] FAST spr round 1 (radius: 25)
[00:15:33 -61684.311675] FAST spr round 2 (radius: 25)
[00:15:39 -61589.758109] FAST spr round 3 (radius: 25)
[00:15:44 -61587.318018] FAST spr round 4 (radius: 25)
[00:15:49 -61586.926500] FAST spr round 5 (radius: 25)
[00:15:54 -61586.781218] FAST spr round 6 (radius: 25)
[00:15:59 -61586.779493] Model parameter optimization (eps = 1.000000)
[00:16:02 -61584.101331] SLOW spr round 1 (radius: 5)
[00:16:13 -61565.493865] SLOW spr round 2 (radius: 5)
[00:16:22 -61565.493764] SLOW spr round 3 (radius: 10)
[00:16:32 -61565.493715] SLOW spr round 4 (radius: 15)
[00:16:47 -61565.493691] SLOW spr round 5 (radius: 20)
[00:17:00 -61565.493679] SLOW spr round 6 (radius: 25)
[00:17:08 -61565.493673] Model parameter optimization (eps = 0.100000)

[00:17:10] [worker #0] ML tree search #11, logLikelihood: -61565.360733

[00:17:10 -126410.220082] Initial branch length optimization
[00:17:11 -102738.531478] Model parameter optimization (eps = 10.000000)
[00:17:17 -102578.570465] AUTODETECT spr round 1 (radius: 5)
[00:17:21 -71377.676286] AUTODETECT spr round 2 (radius: 10)
[00:17:27 -64845.525069] AUTODETECT spr round 3 (radius: 15)
[00:17:35 -64222.271753] AUTODETECT spr round 4 (radius: 20)
[00:17:39] [worker #1] ML tree search #12, logLikelihood: -61565.361162
[00:17:41 -64221.511986] AUTODETECT spr round 5 (radius: 25)
[00:17:45 -64221.477474] SPR radius for FAST iterations: 20 (autodetect)
[00:17:45 -64221.477474] Model parameter optimization (eps = 3.000000)
[00:17:54 -63987.337712] FAST spr round 1 (radius: 20)
[00:18:01 -61671.177312] FAST spr round 2 (radius: 20)
[00:18:07 -61601.174185] FAST spr round 3 (radius: 20)
[00:18:13 -61590.525714] FAST spr round 4 (radius: 20)
[00:18:18 -61587.896198] FAST spr round 5 (radius: 20)
[00:18:23 -61587.612550] FAST spr round 6 (radius: 20)
[00:18:28 -61587.523963] Model parameter optimization (eps = 1.000000)
[00:18:32 -61584.433206] SLOW spr round 1 (radius: 5)
[00:18:42 -61567.112591] SLOW spr round 2 (radius: 5)
[00:18:52 -61567.092131] SLOW spr round 3 (radius: 10)
[00:19:01 -61566.025991] SLOW spr round 4 (radius: 5)
[00:19:16 -61565.928090] SLOW spr round 5 (radius: 10)
[00:19:26 -61565.917030] SLOW spr round 6 (radius: 15)
[00:19:41 -61565.911417] SLOW spr round 7 (radius: 20)
[00:19:54 -61565.908580] SLOW spr round 8 (radius: 25)
[00:20:02 -61565.907150] Model parameter optimization (eps = 0.100000)

[00:20:04] [worker #0] ML tree search #13, logLikelihood: -61565.866477

[00:20:04 -129677.620912] Initial branch length optimization
[00:20:04 -104267.479343] Model parameter optimization (eps = 10.000000)
[00:20:14 -104049.595727] AUTODETECT spr round 1 (radius: 5)
[00:20:18 -78882.754075] AUTODETECT spr round 2 (radius: 10)
[00:20:24 -68137.375863] AUTODETECT spr round 3 (radius: 15)
[00:20:31 -67293.521150] AUTODETECT spr round 4 (radius: 20)
[00:20:37 -67145.882668] AUTODETECT spr round 5 (radius: 25)
[00:20:40 -67145.854116] SPR radius for FAST iterations: 20 (autodetect)
[00:20:40 -67145.854116] Model parameter optimization (eps = 3.000000)
[00:20:47 -67017.480520] FAST spr round 1 (radius: 20)
[00:20:48] [worker #1] ML tree search #14, logLikelihood: -61565.360841
[00:20:55 -61714.931188] FAST spr round 2 (radius: 20)
[00:21:01 -61611.794892] FAST spr round 3 (radius: 20)
[00:21:06 -61604.609501] FAST spr round 4 (radius: 20)
[00:21:11 -61603.004423] FAST spr round 5 (radius: 20)
[00:21:16 -61600.756970] FAST spr round 6 (radius: 20)
[00:21:21 -61600.296575] FAST spr round 7 (radius: 20)
[00:21:26 -61600.178197] FAST spr round 8 (radius: 20)
[00:21:30 -61599.217273] FAST spr round 9 (radius: 20)
[00:21:35 -61599.216214] Model parameter optimization (eps = 1.000000)
[00:21:39 -61575.403519] SLOW spr round 1 (radius: 5)
[00:21:49 -61569.336502] SLOW spr round 2 (radius: 5)
[00:21:59 -61569.336483] SLOW spr round 3 (radius: 10)
[00:22:09 -61565.461417] SLOW spr round 4 (radius: 5)
[00:22:23 -61565.461280] SLOW spr round 5 (radius: 10)
[00:22:34 -61565.461255] SLOW spr round 6 (radius: 15)
[00:22:48 -61565.461247] SLOW spr round 7 (radius: 20)
[00:23:01 -61565.461245] SLOW spr round 8 (radius: 25)
[00:23:10 -61565.461244] Model parameter optimization (eps = 0.100000)

[00:23:12] [worker #0] ML tree search #15, logLikelihood: -61565.360700

[00:23:12 -126659.228432] Initial branch length optimization
[00:23:12 -101956.011886] Model parameter optimization (eps = 10.000000)
[00:23:22 -101812.706664] AUTODETECT spr round 1 (radius: 5)
[00:23:26 -74137.296767] AUTODETECT spr round 2 (radius: 10)
[00:23:31 -65176.170717] AUTODETECT spr round 3 (radius: 15)
[00:23:39 -64485.940543] AUTODETECT spr round 4 (radius: 20)
[00:23:45 -64480.693535] AUTODETECT spr round 5 (radius: 25)
[00:23:50 -64051.196608] SPR radius for FAST iterations: 25 (autodetect)
[00:23:50 -64051.196608] Model parameter optimization (eps = 3.000000)
[00:23:57 -63791.573629] FAST spr round 1 (radius: 25)
[00:23:59] [worker #1] ML tree search #16, logLikelihood: -61565.865717
[00:24:05 -61622.540892] FAST spr round 2 (radius: 25)
[00:24:11 -61576.746011] FAST spr round 3 (radius: 25)
[00:24:16 -61576.119889] FAST spr round 4 (radius: 25)
[00:24:21 -61575.580387] FAST spr round 5 (radius: 25)
[00:24:26 -61575.221160] FAST spr round 6 (radius: 25)
[00:24:31 -61575.107143] FAST spr round 7 (radius: 25)
[00:24:36 -61575.047058] Model parameter optimization (eps = 1.000000)
[00:24:39 -61573.152022] SLOW spr round 1 (radius: 5)
[00:24:49 -61567.559457] SLOW spr round 2 (radius: 5)
[00:24:59 -61567.144327] SLOW spr round 3 (radius: 5)
[00:25:08 -61567.144157] SLOW spr round 4 (radius: 10)
[00:25:18 -61566.110798] SLOW spr round 5 (radius: 5)
[00:25:32 -61566.013351] SLOW spr round 6 (radius: 10)
[00:25:42 -61566.002457] SLOW spr round 7 (radius: 15)
[00:25:57 -61565.996958] SLOW spr round 8 (radius: 20)
[00:26:10 -61565.994192] SLOW spr round 9 (radius: 25)
[00:26:18 -61565.992804] Model parameter optimization (eps = 0.100000)

[00:26:20] [worker #0] ML tree search #17, logLikelihood: -61565.866252

[00:26:20 -126450.433300] Initial branch length optimization
[00:26:20 -102134.173591] Model parameter optimization (eps = 10.000000)
[00:26:29 -101982.485651] AUTODETECT spr round 1 (radius: 5)
[00:26:31] [worker #1] ML tree search #18, logLikelihood: -61565.865675
[00:26:33 -75661.378571] AUTODETECT spr round 2 (radius: 10)
[00:26:40 -65190.169331] AUTODETECT spr round 3 (radius: 15)
[00:26:46 -65056.820993] AUTODETECT spr round 4 (radius: 20)
[00:26:52 -65056.770624] SPR radius for FAST iterations: 15 (autodetect)
[00:26:52 -65056.770624] Model parameter optimization (eps = 3.000000)
[00:26:58 -64876.887660] FAST spr round 1 (radius: 15)
[00:27:06 -61716.844422] FAST spr round 2 (radius: 15)
[00:27:12 -61586.037441] FAST spr round 3 (radius: 15)
[00:27:17 -61579.253260] FAST spr round 4 (radius: 15)
[00:27:22 -61578.746834] FAST spr round 5 (radius: 15)
[00:27:27 -61578.414927] FAST spr round 6 (radius: 15)
[00:27:32 -61578.292885] FAST spr round 7 (radius: 15)
[00:27:37 -61578.239067] Model parameter optimization (eps = 1.000000)
[00:27:41 -61572.482628] SLOW spr round 1 (radius: 5)
[00:27:51 -61569.368494] SLOW spr round 2 (radius: 5)
[00:28:00 -61569.355773] SLOW spr round 3 (radius: 10)
[00:28:10 -61565.444548] SLOW spr round 4 (radius: 5)
[00:28:24 -61565.444119] SLOW spr round 5 (radius: 10)
[00:28:35 -61565.444048] SLOW spr round 6 (radius: 15)
[00:28:49 -61565.444035] SLOW spr round 7 (radius: 20)
[00:29:02 -61565.444032] SLOW spr round 8 (radius: 25)
[00:29:11 -61565.444032] Model parameter optimization (eps = 0.100000)

[00:29:12] [worker #0] ML tree search #19, logLikelihood: -61565.360798

[00:29:50] [worker #1] ML tree search #20, logLikelihood: -61565.865691

Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.235901,0.583558) (0.155871,0.721493) (0.333624,0.856736) (0.274605,1.689887) 
   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: -61565.360700

AIC score: 123564.721400 / AICc score: 123722.935446 / BIC score: 124585.579315
Free parameters (model + branch lengths): 217

WARNING: Best ML tree contains 1 near-zero branches!

Best ML tree with collapsed near-zero branches saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/3_mltree/Q6PJG6.raxml.bestTreeCollapsed
Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/3_mltree/Q6PJG6.raxml.bestTree
All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/3_mltree/Q6PJG6.raxml.mlTrees
Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/3_mltree/Q6PJG6.raxml.bestModel

Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/Q6PJG6/3_mltree/Q6PJG6.raxml.log

Analysis started: 02-Jul-2021 15:13:26 / finished: 02-Jul-2021 15:43:17

Elapsed time: 1790.647 seconds

Consumed energy: 114.596 Wh