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 6140 CPU @ 2.30GHz, 36 cores, 251 GB RAM

RAxML-NG was called at 14-Jul-2021 16:22:14 as follows:

raxml-ng --msa /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/2_msa/P58753_trimmed_msa.fasta --data-type AA --model LG4X --prefix /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753 --seed 2 --threads 2 --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 (2 threads), thread pinning: OFF

[00:00:00] Reading alignment from file: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/2_msa/P58753_trimmed_msa.fasta
[00:00:00] Loaded alignment with 117 taxa and 85 sites

WARNING: Sequences tr_M3Y5J0_M3Y5J0_MUSPF_9669 and tr_A0A2Y9K674_A0A2Y9K674_ENHLU_391180 are exactly identical!
WARNING: Sequences tr_A0A2I3HKD7_A0A2I3HKD7_NOMLE_61853 and tr_G3RT23_G3RT23_GORGO_9595 are exactly identical!
WARNING: Sequences tr_A0A2I3HKD7_A0A2I3HKD7_NOMLE_61853 and tr_K7CIJ8_K7CIJ8_PANTR_9598 are exactly identical!
WARNING: Sequences tr_A0A2I3HKD7_A0A2I3HKD7_NOMLE_61853 and sp_P58753_TIRAP_HUMAN_9606 are exactly identical!
WARNING: Sequences tr_A0A2I3HKD7_A0A2I3HKD7_NOMLE_61853 and tr_A0A2R8Z8I1_A0A2R8Z8I1_PANPA_9597 are exactly identical!
WARNING: Sequences tr_H0YRD8_H0YRD8_TAEGU_59729 and tr_A0A218UTD6_A0A218UTD6_9PASE_299123 are exactly identical!
WARNING: Sequences tr_A0A1B8Y1S9_A0A1B8Y1S9_XENTR_8364 and tr_F6ST72_F6ST72_XENTR_8364 are exactly identical!
WARNING: Sequences tr_G7PPN6_G7PPN6_MACFA_9541 and tr_A0A2I3N4F4_A0A2I3N4F4_PAPAN_9555 are exactly identical!
WARNING: Sequences tr_G7PPN6_G7PPN6_MACFA_9541 and tr_A0A0D9S4I7_A0A0D9S4I7_CHLSB_60711 are exactly identical!
WARNING: Sequences tr_G7PPN6_G7PPN6_MACFA_9541 and tr_A0A2K5M996_A0A2K5M996_CERAT_9531 are exactly identical!
WARNING: Sequences tr_G7PPN6_G7PPN6_MACFA_9541 and tr_A0A2K6CEV6_A0A2K6CEV6_MACNE_9545 are exactly identical!
WARNING: Sequences tr_G7PPN6_G7PPN6_MACFA_9541 and tr_A0A2K6AGJ1_A0A2K6AGJ1_MANLE_9568 are exactly identical!
WARNING: Sequences tr_A0A151NG44_A0A151NG44_ALLMI_8496 and tr_A0A3Q0GFW6_A0A3Q0GFW6_ALLSI_38654 are exactly identical!
WARNING: Sequences tr_A0A151NWA5_A0A151NWA5_ALLMI_8496 and tr_A0A1U7SWX9_A0A1U7SWX9_ALLSI_38654 are exactly identical!
WARNING: Sequences tr_A0A226ML31_A0A226ML31_CALSU_9009 and tr_A0A226PMB1_A0A226PMB1_COLVI_9014 are exactly identical!
WARNING: Duplicate sequences found: 15

NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) 
NOTE: was saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753.raxml.reduced.phy

Alignment comprises 1 partitions and 85 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 85 / 85
Gaps: 1.82 %
Invariant sites: 1.18 %


NOTE: Binary MSA file created: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753.raxml.rba

Parallelization scheme autoconfig: 2 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 117 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 85 / 6800
[00:00:00] Data distribution: max. searches per worker: 10

Starting ML tree search with 20 distinct starting trees

[00:00:00 -13500.149947] Initial branch length optimization
[00:00:00 -10254.558536] Model parameter optimization (eps = 10.000000)
[00:00:03 -10223.098634] AUTODETECT spr round 1 (radius: 5)
[00:00:05 -7881.279701] AUTODETECT spr round 2 (radius: 10)
[00:00:08 -7022.059686] AUTODETECT spr round 3 (radius: 15)
[00:00:11 -6260.418366] AUTODETECT spr round 4 (radius: 20)
[00:00:15 -6203.125889] AUTODETECT spr round 5 (radius: 25)
[00:00:17 -6203.112362] SPR radius for FAST iterations: 20 (autodetect)
[00:00:17 -6203.112362] Model parameter optimization (eps = 3.000000)
[00:00:21 -6184.875739] FAST spr round 1 (radius: 20)
[00:00:24 -5834.917324] FAST spr round 2 (radius: 20)
[00:00:27 -5806.737687] FAST spr round 3 (radius: 20)
[00:00:29 -5801.800948] FAST spr round 4 (radius: 20)
[00:00:31 -5794.391736] FAST spr round 5 (radius: 20)
[00:00:33 -5794.391725] Model parameter optimization (eps = 1.000000)
[00:00:35 -5788.733768] SLOW spr round 1 (radius: 5)
[00:00:40 -5787.579764] SLOW spr round 2 (radius: 5)
[00:00:44 -5787.579682] SLOW spr round 3 (radius: 10)
[00:00:48 -5787.579681] SLOW spr round 4 (radius: 15)
[00:00:54 -5787.579681] SLOW spr round 5 (radius: 20)
[00:00:59 -5787.579681] SLOW spr round 6 (radius: 25)
[00:01:03 -5787.579681] Model parameter optimization (eps = 0.100000)

[00:01:04] [worker #0] ML tree search #1, logLikelihood: -5787.526395

[00:01:04 -13795.361201] Initial branch length optimization
[00:01:04 -10468.488258] Model parameter optimization (eps = 10.000000)
[00:01:07 -10440.473800] AUTODETECT spr round 1 (radius: 5)
[00:01:09 -7623.758148] AUTODETECT spr round 2 (radius: 10)
[00:01:12 -6966.050284] AUTODETECT spr round 3 (radius: 15)
[00:01:16 -6375.774054] AUTODETECT spr round 4 (radius: 20)
[00:01:20 -6308.906028] AUTODETECT spr round 5 (radius: 25)
[00:01:22 -6308.897019] SPR radius for FAST iterations: 20 (autodetect)
[00:01:22 -6308.897019] Model parameter optimization (eps = 3.000000)
[00:01:26 -6274.943419] FAST spr round 1 (radius: 20)
[00:01:29 -5841.822132] FAST spr round 2 (radius: 20)
[00:01:31 -5796.509549] FAST spr round 3 (radius: 20)
[00:01:33 -5788.891720] FAST spr round 4 (radius: 20)
[00:01:35 -5788.891480] Model parameter optimization (eps = 1.000000)
[00:01:36] [worker #1] ML tree search #2, logLikelihood: -5791.988029
[00:01:38 -5787.714436] SLOW spr round 1 (radius: 5)
[00:01:43 -5787.094869] SLOW spr round 2 (radius: 5)
[00:01:47 -5787.092915] SLOW spr round 3 (radius: 10)
[00:01:51 -5787.092609] SLOW spr round 4 (radius: 15)
[00:01:57 -5786.882168] SLOW spr round 5 (radius: 5)
[00:02:03 -5785.460494] SLOW spr round 6 (radius: 5)
[00:02:09 -5785.172754] SLOW spr round 7 (radius: 5)
[00:02:13 -5785.172745] SLOW spr round 8 (radius: 10)
[00:02:17 -5785.172745] SLOW spr round 9 (radius: 15)
[00:02:23 -5785.172745] SLOW spr round 10 (radius: 20)
[00:02:27 -5785.172745] SLOW spr round 11 (radius: 25)
[00:02:32 -5785.172745] Model parameter optimization (eps = 0.100000)

[00:02:33] [worker #0] ML tree search #3, logLikelihood: -5785.121102

[00:02:33 -13911.473205] Initial branch length optimization
[00:02:33 -10549.258573] Model parameter optimization (eps = 10.000000)
[00:02:36 -10515.712866] AUTODETECT spr round 1 (radius: 5)
[00:02:38 -7940.097335] AUTODETECT spr round 2 (radius: 10)
[00:02:41 -6913.316935] AUTODETECT spr round 3 (radius: 15)
[00:02:45 -6516.131366] AUTODETECT spr round 4 (radius: 20)
[00:02:48 -6481.054778] AUTODETECT spr round 5 (radius: 25)
[00:02:50 -6437.105464] SPR radius for FAST iterations: 25 (autodetect)
[00:02:50 -6437.105464] Model parameter optimization (eps = 3.000000)
[00:02:51] [worker #1] ML tree search #4, logLikelihood: -5792.933223
[00:02:54 -6395.678269] FAST spr round 1 (radius: 25)
[00:02:57 -5835.776625] FAST spr round 2 (radius: 25)
[00:02:59 -5793.906344] FAST spr round 3 (radius: 25)
[00:03:01 -5792.592433] FAST spr round 4 (radius: 25)
[00:03:03 -5792.255640] FAST spr round 5 (radius: 25)
[00:03:05 -5792.255599] Model parameter optimization (eps = 1.000000)
[00:03:07 -5791.555344] SLOW spr round 1 (radius: 5)
[00:03:11 -5790.359494] SLOW spr round 2 (radius: 5)
[00:03:15 -5790.057121] SLOW spr round 3 (radius: 5)
[00:03:19 -5790.057119] SLOW spr round 4 (radius: 10)
[00:03:23 -5790.057119] SLOW spr round 5 (radius: 15)
[00:03:28 -5790.057119] SLOW spr round 6 (radius: 20)
[00:03:33 -5790.019573] SLOW spr round 7 (radius: 25)
[00:03:37 -5790.019475] Model parameter optimization (eps = 0.100000)

[00:03:38] [worker #0] ML tree search #5, logLikelihood: -5789.949117

[00:03:38 -14067.315202] Initial branch length optimization
[00:03:38 -10550.209322] Model parameter optimization (eps = 10.000000)
[00:03:42 -10505.142884] AUTODETECT spr round 1 (radius: 5)
[00:03:44 -8400.992443] AUTODETECT spr round 2 (radius: 10)
[00:03:48 -7116.403218] AUTODETECT spr round 3 (radius: 15)
[00:03:51 -6755.185895] AUTODETECT spr round 4 (radius: 20)
[00:03:55 -6586.099188] AUTODETECT spr round 5 (radius: 25)
[00:03:58 -6585.299886] SPR radius for FAST iterations: 25 (autodetect)
[00:03:58 -6585.299886] Model parameter optimization (eps = 3.000000)
[00:04:02 -6557.168942] FAST spr round 1 (radius: 25)
[00:04:02] [worker #1] ML tree search #6, logLikelihood: -5787.019727
[00:04:04 -5853.626676] FAST spr round 2 (radius: 25)
[00:04:07 -5803.901586] FAST spr round 3 (radius: 25)
[00:04:09 -5796.596200] FAST spr round 4 (radius: 25)
[00:04:11 -5794.435952] FAST spr round 5 (radius: 25)
[00:04:13 -5794.435461] Model parameter optimization (eps = 1.000000)
[00:04:15 -5791.359320] SLOW spr round 1 (radius: 5)
[00:04:20 -5790.226300] SLOW spr round 2 (radius: 5)
[00:04:24 -5789.592892] SLOW spr round 3 (radius: 5)
[00:04:29 -5789.592736] SLOW spr round 4 (radius: 10)
[00:04:32 -5789.592734] SLOW spr round 5 (radius: 15)
[00:04:38 -5789.592734] SLOW spr round 6 (radius: 20)
[00:04:43 -5788.383982] SLOW spr round 7 (radius: 5)
[00:04:49 -5788.383966] SLOW spr round 8 (radius: 10)
[00:04:54 -5785.101470] SLOW spr round 9 (radius: 5)
[00:05:00 -5785.100250] SLOW spr round 10 (radius: 10)
[00:05:04 -5785.100220] SLOW spr round 11 (radius: 15)
[00:05:10 -5785.100220] SLOW spr round 12 (radius: 20)
[00:05:15 -5785.100220] SLOW spr round 13 (radius: 25)
[00:05:20 -5785.100220] Model parameter optimization (eps = 0.100000)

[00:05:22] [worker #0] ML tree search #7, logLikelihood: -5784.821316

[00:05:22 -13772.835397] Initial branch length optimization
[00:05:22 -10413.302459] Model parameter optimization (eps = 10.000000)
[00:05:23] [worker #1] ML tree search #8, logLikelihood: -5785.639311
[00:05:25 -10374.713371] AUTODETECT spr round 1 (radius: 5)
[00:05:27 -8293.289854] AUTODETECT spr round 2 (radius: 10)
[00:05:31 -7228.669252] AUTODETECT spr round 3 (radius: 15)
[00:05:34 -6348.492618] AUTODETECT spr round 4 (radius: 20)
[00:05:37 -6338.245428] AUTODETECT spr round 5 (radius: 25)
[00:05:40 -6338.243807] SPR radius for FAST iterations: 20 (autodetect)
[00:05:40 -6338.243807] Model parameter optimization (eps = 3.000000)
[00:05:45 -6301.094687] FAST spr round 1 (radius: 20)
[00:05:47 -5826.285952] FAST spr round 2 (radius: 20)
[00:05:50 -5803.056022] FAST spr round 3 (radius: 20)
[00:05:52 -5799.723557] FAST spr round 4 (radius: 20)
[00:05:54 -5794.381558] FAST spr round 5 (radius: 20)
[00:05:56 -5793.784958] FAST spr round 6 (radius: 20)
[00:05:58 -5793.784585] Model parameter optimization (eps = 1.000000)
[00:06:00 -5789.622076] SLOW spr round 1 (radius: 5)
[00:06:05 -5787.747962] SLOW spr round 2 (radius: 5)
[00:06:09 -5787.745313] SLOW spr round 3 (radius: 10)
[00:06:13 -5787.334107] SLOW spr round 4 (radius: 5)
[00:06:19 -5786.888535] SLOW spr round 5 (radius: 5)
[00:06:24 -5786.888484] SLOW spr round 6 (radius: 10)
[00:06:28 -5786.888475] SLOW spr round 7 (radius: 15)
[00:06:35 -5786.888473] SLOW spr round 8 (radius: 20)
[00:06:37] [worker #1] ML tree search #10, logLikelihood: -5784.912200
[00:06:40 -5786.888472] SLOW spr round 9 (radius: 25)
[00:06:44 -5786.888472] Model parameter optimization (eps = 0.100000)

[00:06:46] [worker #0] ML tree search #9, logLikelihood: -5786.730287

[00:06:46 -13370.180027] Initial branch length optimization
[00:06:46 -10338.069075] Model parameter optimization (eps = 10.000000)
[00:06:51 -10300.187415] AUTODETECT spr round 1 (radius: 5)
[00:06:53 -7983.526482] AUTODETECT spr round 2 (radius: 10)
[00:06:56 -6712.153706] AUTODETECT spr round 3 (radius: 15)
[00:07:00 -6594.484835] AUTODETECT spr round 4 (radius: 20)
[00:07:03 -6471.027555] AUTODETECT spr round 5 (radius: 25)
[00:07:06 -6470.992686] SPR radius for FAST iterations: 20 (autodetect)
[00:07:06 -6470.992686] Model parameter optimization (eps = 3.000000)
[00:07:10 -6433.254891] FAST spr round 1 (radius: 20)
[00:07:14 -5841.674039] FAST spr round 2 (radius: 20)
[00:07:17 -5801.410774] FAST spr round 3 (radius: 20)
[00:07:19 -5799.943906] FAST spr round 4 (radius: 20)
[00:07:21 -5798.692396] FAST spr round 5 (radius: 20)
[00:07:23 -5798.691994] Model parameter optimization (eps = 1.000000)
[00:07:25 -5796.767192] SLOW spr round 1 (radius: 5)
[00:07:30 -5795.286336] SLOW spr round 2 (radius: 5)
[00:07:34 -5795.022330] SLOW spr round 3 (radius: 5)
[00:07:38 -5794.820921] SLOW spr round 4 (radius: 5)
[00:07:41 -5794.820920] SLOW spr round 5 (radius: 10)
[00:07:46 -5794.547301] SLOW spr round 6 (radius: 5)
[00:07:52 -5794.545798] SLOW spr round 7 (radius: 10)
[00:07:57 -5792.537174] SLOW spr round 8 (radius: 5)
[00:08:03 -5792.536892] SLOW spr round 9 (radius: 10)
[00:08:06] [worker #1] ML tree search #12, logLikelihood: -5791.914853
[00:08:08 -5792.536881] SLOW spr round 10 (radius: 15)
[00:08:13 -5792.536880] SLOW spr round 11 (radius: 20)
[00:08:18 -5792.536880] SLOW spr round 12 (radius: 25)
[00:08:22 -5792.536880] Model parameter optimization (eps = 0.100000)

[00:08:24] [worker #0] ML tree search #11, logLikelihood: -5792.151135

[00:08:24 -13626.965028] Initial branch length optimization
[00:08:24 -10268.601185] Model parameter optimization (eps = 10.000000)
[00:08:28 -10241.451783] AUTODETECT spr round 1 (radius: 5)
[00:08:30 -8405.893760] AUTODETECT spr round 2 (radius: 10)
[00:08:33 -7042.537547] AUTODETECT spr round 3 (radius: 15)
[00:08:36 -6545.591321] AUTODETECT spr round 4 (radius: 20)
[00:08:40 -6137.439950] AUTODETECT spr round 5 (radius: 25)
[00:08:43 -6137.438297] SPR radius for FAST iterations: 20 (autodetect)
[00:08:43 -6137.438297] Model parameter optimization (eps = 3.000000)
[00:08:46 -6110.819810] FAST spr round 1 (radius: 20)
[00:08:49 -5805.589774] FAST spr round 2 (radius: 20)
[00:08:52 -5796.152082] FAST spr round 3 (radius: 20)
[00:08:54 -5796.086420] Model parameter optimization (eps = 1.000000)
[00:08:56 -5793.814124] SLOW spr round 1 (radius: 5)
[00:09:00 -5792.825901] SLOW spr round 2 (radius: 5)
[00:09:02] [worker #1] ML tree search #14, logLikelihood: -5790.163020
[00:09:04 -5792.823930] SLOW spr round 3 (radius: 10)
[00:09:08 -5790.082246] SLOW spr round 4 (radius: 5)
[00:09:14 -5787.977173] SLOW spr round 5 (radius: 5)
[00:09:19 -5787.976848] SLOW spr round 6 (radius: 10)
[00:09:23 -5787.976844] SLOW spr round 7 (radius: 15)
[00:09:29 -5787.976844] SLOW spr round 8 (radius: 20)
[00:09:34 -5787.976844] SLOW spr round 9 (radius: 25)
[00:09:38 -5787.976844] Model parameter optimization (eps = 0.100000)

[00:09:39] [worker #0] ML tree search #13, logLikelihood: -5787.936887

[00:09:39 -13486.727253] Initial branch length optimization
[00:09:39 -10295.656507] Model parameter optimization (eps = 10.000000)
[00:09:43 -10264.792673] AUTODETECT spr round 1 (radius: 5)
[00:09:45 -8071.983815] AUTODETECT spr round 2 (radius: 10)
[00:09:48 -6884.061895] AUTODETECT spr round 3 (radius: 15)
[00:09:52 -6503.745673] AUTODETECT spr round 4 (radius: 20)
[00:09:55 -6447.378948] AUTODETECT spr round 5 (radius: 25)
[00:09:58 -6447.289802] SPR radius for FAST iterations: 20 (autodetect)
[00:09:58 -6447.289802] Model parameter optimization (eps = 3.000000)
[00:10:03 -6417.546873] FAST spr round 1 (radius: 20)
[00:10:07 -5867.019309] FAST spr round 2 (radius: 20)
[00:10:09 -5811.848984] FAST spr round 3 (radius: 20)
[00:10:12 -5805.070649] FAST spr round 4 (radius: 20)
[00:10:14 -5803.654513] FAST spr round 5 (radius: 20)
[00:10:16 -5803.653829] Model parameter optimization (eps = 1.000000)
[00:10:18 -5791.301961] SLOW spr round 1 (radius: 5)
[00:10:23 -5789.925039] SLOW spr round 2 (radius: 5)
[00:10:27 -5789.783791] SLOW spr round 3 (radius: 5)
[00:10:28] [worker #1] ML tree search #16, logLikelihood: -5788.084225
[00:10:32 -5789.783272] SLOW spr round 4 (radius: 10)
[00:10:36 -5788.520190] SLOW spr round 5 (radius: 5)
[00:10:42 -5788.058257] SLOW spr round 6 (radius: 5)
[00:10:47 -5788.057976] SLOW spr round 7 (radius: 10)
[00:10:51 -5788.057973] SLOW spr round 8 (radius: 15)
[00:10:58 -5788.057973] SLOW spr round 9 (radius: 20)
[00:11:03 -5788.057973] SLOW spr round 10 (radius: 25)
[00:11:07 -5788.057973] Model parameter optimization (eps = 0.100000)

[00:11:08] [worker #0] ML tree search #15, logLikelihood: -5787.266798

[00:11:08 -13632.684972] Initial branch length optimization
[00:11:08 -10334.116495] Model parameter optimization (eps = 10.000000)
[00:11:12 -10299.100185] AUTODETECT spr round 1 (radius: 5)
[00:11:14 -8328.474591] AUTODETECT spr round 2 (radius: 10)
[00:11:17 -7067.991325] AUTODETECT spr round 3 (radius: 15)
[00:11:20 -6516.917308] AUTODETECT spr round 4 (radius: 20)
[00:11:24 -6428.706444] AUTODETECT spr round 5 (radius: 25)
[00:11:26 -6427.082388] SPR radius for FAST iterations: 25 (autodetect)
[00:11:26 -6427.082388] Model parameter optimization (eps = 3.000000)
[00:11:30 -6398.169936] FAST spr round 1 (radius: 25)
[00:11:33 -5850.034767] FAST spr round 2 (radius: 25)
[00:11:35 -5819.100249] FAST spr round 3 (radius: 25)
[00:11:38 -5802.960436] FAST spr round 4 (radius: 25)
[00:11:40 -5801.141066] FAST spr round 5 (radius: 25)
[00:11:42 -5801.140436] Model parameter optimization (eps = 1.000000)
[00:11:44 -5794.551937] SLOW spr round 1 (radius: 5)
[00:11:49 -5794.547259] SLOW spr round 2 (radius: 10)
[00:11:52 -5791.499993] SLOW spr round 3 (radius: 5)
[00:11:58 -5791.499985] SLOW spr round 4 (radius: 10)
[00:12:01] [worker #1] ML tree search #18, logLikelihood: -5787.274025
[00:12:03 -5791.499985] SLOW spr round 5 (radius: 15)
[00:12:09 -5791.499985] SLOW spr round 6 (radius: 20)
[00:12:14 -5791.499985] SLOW spr round 7 (radius: 25)
[00:12:19 -5791.499985] Model parameter optimization (eps = 0.100000)

[00:12:20] [worker #0] ML tree search #17, logLikelihood: -5791.444602

[00:12:20 -13692.619008] Initial branch length optimization
[00:12:20 -10353.342753] Model parameter optimization (eps = 10.000000)
[00:12:23 -10317.301153] AUTODETECT spr round 1 (radius: 5)
[00:12:25 -7775.794116] AUTODETECT spr round 2 (radius: 10)
[00:12:29 -6682.149333] AUTODETECT spr round 3 (radius: 15)
[00:12:33 -6427.087104] AUTODETECT spr round 4 (radius: 20)
[00:12:36 -6417.394297] AUTODETECT spr round 5 (radius: 25)
[00:12:38 -6417.386016] SPR radius for FAST iterations: 20 (autodetect)
[00:12:38 -6417.386016] Model parameter optimization (eps = 3.000000)
[00:12:41 -6383.769686] FAST spr round 1 (radius: 20)
[00:12:44 -5827.442303] FAST spr round 2 (radius: 20)
[00:12:47 -5802.159213] FAST spr round 3 (radius: 20)
[00:12:49 -5802.158430] Model parameter optimization (eps = 1.000000)
[00:12:50 -5801.730710] SLOW spr round 1 (radius: 5)
[00:12:54 -5801.728875] SLOW spr round 2 (radius: 10)
[00:12:58 -5799.600210] SLOW spr round 3 (radius: 5)
[00:13:04 -5799.600074] SLOW spr round 4 (radius: 10)
[00:13:09 -5799.600069] SLOW spr round 5 (radius: 15)
[00:13:09] [worker #1] ML tree search #20, logLikelihood: -5791.986787
[00:13:14 -5799.600069] SLOW spr round 6 (radius: 20)
[00:13:19 -5795.134481] SLOW spr round 7 (radius: 5)
[00:13:26 -5795.011738] SLOW spr round 8 (radius: 5)
[00:13:31 -5795.011385] SLOW spr round 9 (radius: 10)
[00:13:35 -5795.011379] SLOW spr round 10 (radius: 15)
[00:13:41 -5795.011379] SLOW spr round 11 (radius: 20)
[00:13:46 -5795.011379] SLOW spr round 12 (radius: 25)
[00:13:51 -5791.418154] SLOW spr round 13 (radius: 5)
[00:13:58 -5787.380672] SLOW spr round 14 (radius: 5)
[00:14:03 -5787.379692] SLOW spr round 15 (radius: 10)
[00:14:07 -5787.379485] SLOW spr round 16 (radius: 15)
[00:14:13 -5787.379439] SLOW spr round 17 (radius: 20)
[00:14:18 -5787.379428] SLOW spr round 18 (radius: 25)
[00:14:23 -5787.379426] Model parameter optimization (eps = 0.100000)

[00:14:24] [worker #0] ML tree search #19, logLikelihood: -5786.734767


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.304603,0.396875) (0.383299,0.564157) (0.148348,1.645560) (0.163750,2.557281) 
   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: -5784.821316

AIC score: 12043.642633 / AICc score: 124855.642633 / BIC score: 12622.550980
Free parameters (model + branch lengths): 237

WARNING: Number of free parameters (K=237) is larger than alignment size (n=85).
         This might lead to overfitting and compromise tree inference results!


WARNING: Best ML tree contains 23 near-zero branches!

Best ML tree with collapsed near-zero branches saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753.raxml.bestTreeCollapsed
Best ML tree saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753.raxml.bestTree
All ML trees saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753.raxml.mlTrees
Optimized model saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753.raxml.bestModel

Execution log saved to: /cta/users/eakkoyun/WORKFOLDER/PROD/run_300621/phylogeny-snakemake/results/P58753/3_mltree/P58753.raxml.log

Analysis started: 14-Jul-2021 16:22:14 / finished: 14-Jul-2021 16:36:39

Elapsed time: 864.948 seconds