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 01-Jul-2021 16:27:50 as follows:

raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P23141/2_msa/P23141_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P23141/3_mltree/P23141 --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/P23141/2_msa/P23141_trimmed_msa.fasta
[00:00:00] Loaded alignment with 1001 taxa and 847 sites

WARNING: Sequences tr_G3QGN3_G3QGN3_GORGO_9595 and tr_A0A2J8Q8V8_A0A2J8Q8V8_PANTR_9598 are exactly identical!
WARNING: Sequences tr_G3QGN3_G3QGN3_GORGO_9595 and tr_A0A2R9B1H5_A0A2R9B1H5_PANPA_9597 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_A0A2J8L771_A0A2J8L771_PANTR_9598 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_U3FSR0_U3FSR0_CALJA_9483 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_G7NZE2_G7NZE2_MACFA_9541 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_A0A096N102_A0A096N102_PAPAN_9555 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_A0A2K5L7H3_A0A2K5L7H3_CERAT_9531 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_A0A2K6CTK8_A0A2K6CTK8_MACNE_9545 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_A0A2K5XCK1_A0A2K5XCK1_MANLE_9568 are exactly identical!
WARNING: Sequences tr_G3QNJ3_G3QNJ3_GORGO_9595 and tr_A0A2R9A9V3_A0A2R9A9V3_PANPA_9597 are exactly identical!
WARNING: Sequences tr_G3RBW3_G3RBW3_GORGO_9595 and tr_A0A2I3RYY1_A0A2I3RYY1_PANTR_9598 are exactly identical!
WARNING: Sequences tr_G3RBW3_G3RBW3_GORGO_9595 and tr_G7NRV3_G7NRV3_MACMU_9544 are exactly identical!
WARNING: Sequences tr_G3RBW3_G3RBW3_GORGO_9595 and tr_A0A0D9RG79_A0A0D9RG79_CHLSB_60711 are exactly identical!
WARNING: Sequences tr_G3RBW3_G3RBW3_GORGO_9595 and tr_A0A2K5MHG7_A0A2K5MHG7_CERAT_9531 are exactly identical!
WARNING: Sequences tr_G3RBW3_G3RBW3_GORGO_9595 and tr_A0A2K6DL61_A0A2K6DL61_MACNE_9545 are exactly identical!
WARNING: Sequences tr_G3RBW3_G3RBW3_GORGO_9595 and tr_A0A2K5YQ23_A0A2K5YQ23_MANLE_9568 are exactly identical!
WARNING: Sequences tr_G3RBW3_G3RBW3_GORGO_9595 and tr_A0A2R9A7T8_A0A2R9A7T8_PANPA_9597 are exactly identical!
WARNING: Sequences tr_I3MQG8_I3MQG8_ICTTR_43179 and tr_A0A2U3VNI4_A0A2U3VNI4_ODORO_9708 are exactly identical!
WARNING: Sequences tr_I3MQG8_I3MQG8_ICTTR_43179 and tr_A0A2Y9DSC6_A0A2Y9DSC6_TRIMA_127582 are exactly identical!
WARNING: Sequences tr_I3MQG8_I3MQG8_ICTTR_43179 and tr_A0A2Y9F3J5_A0A2Y9F3J5_PHYCD_9755 are exactly identical!
WARNING: Sequences tr_I3MQG8_I3MQG8_ICTTR_43179 and tr_A0A384A8J7_A0A384A8J7_BALAS_310752 are exactly identical!
WARNING: Sequences sp_Q8NFZ4_NLGN2_HUMAN_9606 and tr_A0A0D9RH57_A0A0D9RH57_CHLSB_60711 are exactly identical!
WARNING: Sequences tr_F7HPD8_F7HPD8_MACMU_9544 and tr_A0A2K6DEC1_A0A2K6DEC1_MACNE_9545 are exactly identical!
WARNING: Sequences tr_G7Q259_G7Q259_MACFA_9541 and tr_A0A2K5P6E8_A0A2K5P6E8_CERAT_9531 are exactly identical!
WARNING: Sequences tr_G7Q259_G7Q259_MACFA_9541 and tr_A0A2K6CZZ8_A0A2K6CZZ8_MACNE_9545 are exactly identical!
WARNING: Sequences tr_G7Q259_G7Q259_MACFA_9541 and tr_A0A2K5Z653_A0A2K5Z653_MANLE_9568 are exactly identical!
WARNING: Sequences tr_A0A096P2P8_A0A096P2P8_PAPAN_9555 and tr_A0A2K6B7J7_A0A2K6B7J7_MACNE_9545 are exactly identical!
WARNING: Sequences tr_A0A091FHC2_A0A091FHC2_CORBR_85066 and tr_A0A093PKH9_A0A093PKH9_9PASS_328815 are exactly identical!
WARNING: Sequences tr_A0A091V4Q2_A0A091V4Q2_NIPNI_128390 and tr_A0A093I3P5_A0A093I3P5_STRCA_441894 are exactly identical!
WARNING: Sequences tr_A0A091V4Q2_A0A091V4Q2_NIPNI_128390 and tr_A0A091GC26_A0A091GC26_9AVES_55661 are exactly identical!
WARNING: Sequences tr_A0A091V4Q2_A0A091V4Q2_NIPNI_128390 and tr_A0A0A0ASH1_A0A0A0ASH1_CHAVO_50402 are exactly identical!
WARNING: Sequences tr_A0A2R2MTQ6_A0A2R2MTQ6_LINUN_7574 and tr_A0A2R2MTU1_A0A2R2MTU1_LINUN_7574 are exactly identical!
WARNING: Sequences tr_A0A2U4C4F2_A0A2U4C4F2_TURTR_9739 and tr_A0A2Y9M7D8_A0A2Y9M7D8_DELLE_9749 are exactly identical!
WARNING: Sequences tr_A0A2U3VTB8_A0A2U3VTB8_ODORO_9708 and tr_A0A2Y9T4P8_A0A2Y9T4P8_PHYCD_9755 are exactly identical!
WARNING: Duplicate sequences found: 34

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/P23141/3_mltree/P23141.raxml.reduced.phy

Alignment comprises 1 partitions and 847 patterns

Partition 0: noname
Model: LG4X+R4
Alignment sites / patterns: 847 / 847
Gaps: 30.26 %
Invariant sites: 0.00 %


NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P23141/3_mltree/P23141.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 1001 taxa
[00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 212 / 16960
[00:00:00] Data distribution: max. searches per worker: 10

Starting ML tree search with 20 distinct starting trees

[00:00:00 -1082514.587311] Initial branch length optimization
[00:00:07 -882238.720517] Model parameter optimization (eps = 10.000000)
[00:00:53 -881492.228106] AUTODETECT spr round 1 (radius: 5)
[00:04:29 -620818.073253] AUTODETECT spr round 2 (radius: 10)
[00:08:18 -475796.547905] AUTODETECT spr round 3 (radius: 15)
[00:12:37 -380743.672140] AUTODETECT spr round 4 (radius: 20)
[00:17:34 -368221.057639] AUTODETECT spr round 5 (radius: 25)
[00:23:56 -366787.477608] SPR radius for FAST iterations: 25 (autodetect)
[00:23:56 -366787.477608] Model parameter optimization (eps = 3.000000)
[00:24:37 -365997.064053] FAST spr round 1 (radius: 25)
[00:29:16 -331294.314890] FAST spr round 2 (radius: 25)
[00:32:36 -330124.838707] FAST spr round 3 (radius: 25)
[00:35:34 -330035.263075] FAST spr round 4 (radius: 25)
[00:38:12 -330032.336483] FAST spr round 5 (radius: 25)
[00:40:42 -330032.336176] Model parameter optimization (eps = 1.000000)
[00:41:01 -330021.903179] SLOW spr round 1 (radius: 5)
[00:44:30 -329959.880591] SLOW spr round 2 (radius: 5)
[00:47:48 -329951.107370] SLOW spr round 3 (radius: 5)
[00:50:57 -329948.855013] SLOW spr round 4 (radius: 5)
[00:54:03 -329948.854919] SLOW spr round 5 (radius: 10)
[00:57:17 -329944.160826] SLOW spr round 6 (radius: 5)
[01:01:14 -329942.323031] SLOW spr round 7 (radius: 5)
[01:04:41 -329942.322920] SLOW spr round 8 (radius: 10)
[01:07:57 -329936.462619] SLOW spr round 9 (radius: 5)
[01:11:49 -329936.333665] SLOW spr round 10 (radius: 5)
[01:15:19 -329936.333575] SLOW spr round 11 (radius: 10)
[01:18:34 -329936.333574] SLOW spr round 12 (radius: 15)
[01:22:39] [worker #1] ML tree search #2, logLikelihood: -330578.123021
[01:24:05 -329936.333574] SLOW spr round 13 (radius: 20)
[01:37:51 -329936.333574] SLOW spr round 14 (radius: 25)
[01:49:39 -329936.333574] Model parameter optimization (eps = 0.100000)

[01:49:47] [worker #0] ML tree search #1, logLikelihood: -329936.268866

[01:49:47 -1084334.586077] Initial branch length optimization
[01:49:56 -888646.478948] Model parameter optimization (eps = 10.000000)
[01:50:58 -887788.222325] AUTODETECT spr round 1 (radius: 5)
[01:54:38 -630117.452581] AUTODETECT spr round 2 (radius: 10)
[01:58:28 -496974.656748] AUTODETECT spr round 3 (radius: 15)
[02:02:37 -418969.018040] AUTODETECT spr round 4 (radius: 20)
[02:07:19 -392110.304376] AUTODETECT spr round 5 (radius: 25)
[02:13:43 -378008.473265] SPR radius for FAST iterations: 25 (autodetect)
[02:13:43 -378008.473265] Model parameter optimization (eps = 3.000000)
[02:14:17 -377354.413350] FAST spr round 1 (radius: 25)
[02:19:15 -331965.236445] FAST spr round 2 (radius: 25)
[02:22:55 -330145.103284] FAST spr round 3 (radius: 25)
[02:26:02 -330061.884819] FAST spr round 4 (radius: 25)
[02:28:42 -330052.914379] FAST spr round 5 (radius: 25)
[02:31:13 -330052.913822] Model parameter optimization (eps = 1.000000)
[02:31:34 -330039.929904] SLOW spr round 1 (radius: 5)
[02:35:12 -329962.088862] SLOW spr round 2 (radius: 5)
[02:38:33 -329955.762195] SLOW spr round 3 (radius: 5)
[02:41:42 -329955.434180] SLOW spr round 4 (radius: 5)
[02:44:49 -329955.434085] SLOW spr round 5 (radius: 10)
[02:47:59 -329955.434075] SLOW spr round 6 (radius: 15)
[02:53:35 -329955.434075] SLOW spr round 7 (radius: 20)
[02:56:13] [worker #1] ML tree search #4, logLikelihood: -329941.569937
[03:02:58 -329955.434075] SLOW spr round 8 (radius: 25)
[03:15:05 -329955.434075] Model parameter optimization (eps = 0.100000)

[03:15:22] [worker #0] ML tree search #3, logLikelihood: -329954.832060

[03:15:22 -1081168.614692] Initial branch length optimization
[03:15:30 -888698.842638] Model parameter optimization (eps = 10.000000)
[03:16:37 -887864.254633] AUTODETECT spr round 1 (radius: 5)
[03:20:21 -637012.221206] AUTODETECT spr round 2 (radius: 10)
[03:24:16 -494862.258832] AUTODETECT spr round 3 (radius: 15)
[03:28:38 -391349.570669] AUTODETECT spr round 4 (radius: 20)
[03:33:17 -372838.361744] AUTODETECT spr round 5 (radius: 25)
[03:39:41 -370288.418068] SPR radius for FAST iterations: 25 (autodetect)
[03:39:41 -370288.418068] Model parameter optimization (eps = 3.000000)
[03:40:20 -369578.434648] FAST spr round 1 (radius: 25)
[03:45:11 -331131.381936] FAST spr round 2 (radius: 25)
[03:48:46 -330139.113706] FAST spr round 3 (radius: 25)
[03:51:47 -330056.568281] FAST spr round 4 (radius: 25)
[03:54:18 -330049.462326] FAST spr round 5 (radius: 25)
[03:56:43 -330045.925979] FAST spr round 6 (radius: 25)
[03:59:03 -330045.923825] Model parameter optimization (eps = 1.000000)
[03:59:19 -330039.094840] SLOW spr round 1 (radius: 5)
[04:02:43 -329979.593687] SLOW spr round 2 (radius: 5)
[04:06:08 -329961.132613] SLOW spr round 3 (radius: 5)
[04:09:27 -329956.092170] SLOW spr round 4 (radius: 5)
[04:12:34 -329955.904645] SLOW spr round 5 (radius: 5)
[04:15:41 -329955.904443] SLOW spr round 6 (radius: 10)
[04:16:58] [worker #1] ML tree search #6, logLikelihood: -329934.000833
[04:18:59 -329954.524243] SLOW spr round 7 (radius: 5)
[04:23:04 -329954.523847] SLOW spr round 8 (radius: 10)
[04:26:42 -329954.523836] SLOW spr round 9 (radius: 15)
[04:32:14 -329954.523829] SLOW spr round 10 (radius: 20)
[04:42:21 -329954.523822] SLOW spr round 11 (radius: 25)
[04:54:37 -329954.523815] Model parameter optimization (eps = 0.100000)

[04:54:47] [worker #0] ML tree search #5, logLikelihood: -329954.420658

[04:54:47 -1073062.541524] Initial branch length optimization
[04:54:55 -888659.931633] Model parameter optimization (eps = 10.000000)
[04:55:55 -887840.649341] AUTODETECT spr round 1 (radius: 5)
[04:59:32 -631942.344044] AUTODETECT spr round 2 (radius: 10)
[05:03:25 -477363.021540] AUTODETECT spr round 3 (radius: 15)
[05:07:47 -401327.117909] AUTODETECT spr round 4 (radius: 20)
[05:12:38 -377220.978011] AUTODETECT spr round 5 (radius: 25)
[05:18:21 -371404.472629] SPR radius for FAST iterations: 25 (autodetect)
[05:18:21 -371404.472629] Model parameter optimization (eps = 3.000000)
[05:18:59 -370634.212561] FAST spr round 1 (radius: 25)
[05:23:58 -331654.923609] FAST spr round 2 (radius: 25)
[05:27:40 -330200.201958] FAST spr round 3 (radius: 25)
[05:30:38 -330118.498785] FAST spr round 4 (radius: 25)
[05:33:18 -330099.013154] FAST spr round 5 (radius: 25)
[05:35:58 -330066.886332] FAST spr round 6 (radius: 25)
[05:38:19 -330066.886008] Model parameter optimization (eps = 1.000000)
[05:38:36 -330061.946095] SLOW spr round 1 (radius: 5)
[05:42:03 -329966.615069] SLOW spr round 2 (radius: 5)
[05:45:23 -329951.739412] SLOW spr round 3 (radius: 5)
[05:48:26 -329950.809183] SLOW spr round 4 (radius: 5)
[05:51:31 -329950.809104] SLOW spr round 5 (radius: 10)
[05:54:44 -329950.809095] SLOW spr round 6 (radius: 15)
[05:55:29] [worker #1] ML tree search #8, logLikelihood: -329948.167305
[06:00:30 -329950.809095] SLOW spr round 7 (radius: 20)
[06:10:03 -329950.809095] SLOW spr round 8 (radius: 25)
[06:22:07 -329950.809095] Model parameter optimization (eps = 0.100000)

[06:22:22] [worker #0] ML tree search #7, logLikelihood: -329950.476036

[06:22:22 -1073833.618310] Initial branch length optimization
[06:22:28 -882068.922570] Model parameter optimization (eps = 10.000000)
[06:23:20 -881321.853821] AUTODETECT spr round 1 (radius: 5)
[06:26:54 -636353.704548] AUTODETECT spr round 2 (radius: 10)
[06:30:40 -492970.871076] AUTODETECT spr round 3 (radius: 15)
[06:34:48 -401197.673957] AUTODETECT spr round 4 (radius: 20)
[06:39:17 -376010.713232] AUTODETECT spr round 5 (radius: 25)
[06:44:52 -370917.919939] SPR radius for FAST iterations: 25 (autodetect)
[06:44:52 -370917.919939] Model parameter optimization (eps = 3.000000)
[06:45:27 -370163.074935] FAST spr round 1 (radius: 25)
[06:50:31 -330992.288540] FAST spr round 2 (radius: 25)
[06:54:07 -330091.377132] FAST spr round 3 (radius: 25)
[06:57:13 -330016.613760] FAST spr round 4 (radius: 25)
[06:59:47 -330016.554060] Model parameter optimization (eps = 1.000000)
[07:00:12 -330003.854959] SLOW spr round 1 (radius: 5)
[07:03:50 -329953.239011] SLOW spr round 2 (radius: 5)
[07:07:13 -329944.044659] SLOW spr round 3 (radius: 5)
[07:10:23 -329943.555368] SLOW spr round 4 (radius: 5)
[07:13:29 -329943.555196] SLOW spr round 5 (radius: 10)
[07:16:42 -329939.365478] SLOW spr round 6 (radius: 5)
[07:20:40 -329937.137478] SLOW spr round 7 (radius: 5)
[07:24:07 -329937.137398] SLOW spr round 8 (radius: 10)
[07:25:12] [worker #1] ML tree search #10, logLikelihood: -329956.072097
[07:27:26 -329937.137395] SLOW spr round 9 (radius: 15)
[07:33:08 -329937.137395] SLOW spr round 10 (radius: 20)
[07:43:04 -329937.137395] SLOW spr round 11 (radius: 25)
[07:55:09 -329937.137395] Model parameter optimization (eps = 0.100000)

[07:55:17] [worker #0] ML tree search #9, logLikelihood: -329937.062481

[07:55:17 -1079123.018242] Initial branch length optimization
[07:55:23 -886799.844002] Model parameter optimization (eps = 10.000000)
[07:56:25 -885784.559538] AUTODETECT spr round 1 (radius: 5)
[08:00:03 -619014.589817] AUTODETECT spr round 2 (radius: 10)
[08:03:54 -495801.007927] AUTODETECT spr round 3 (radius: 15)
[08:08:00 -439253.289973] AUTODETECT spr round 4 (radius: 20)
[08:12:45 -397622.147739] AUTODETECT spr round 5 (radius: 25)
[08:17:59 -374071.370524] SPR radius for FAST iterations: 25 (autodetect)
[08:17:59 -374071.370524] Model parameter optimization (eps = 3.000000)
[08:18:54 -373530.803748] FAST spr round 1 (radius: 25)
[08:24:01 -331158.395733] FAST spr round 2 (radius: 25)
[08:27:44 -330140.490438] FAST spr round 3 (radius: 25)
[08:30:39 -330059.854831] FAST spr round 4 (radius: 25)
[08:33:13 -330057.611886] FAST spr round 5 (radius: 25)
[08:35:40 -330057.611502] Model parameter optimization (eps = 1.000000)
[08:36:04 -330043.411675] SLOW spr round 1 (radius: 5)
[08:39:36 -329962.203496] SLOW spr round 2 (radius: 5)
[08:43:01 -329942.423623] SLOW spr round 3 (radius: 5)
[08:46:05 -329941.210251] SLOW spr round 4 (radius: 5)
[08:49:05 -329941.210090] SLOW spr round 5 (radius: 10)
[08:52:12 -329941.210089] SLOW spr round 6 (radius: 15)
[08:55:57] [worker #1] ML tree search #12, logLikelihood: -329947.750793
[08:57:39 -329941.210089] SLOW spr round 7 (radius: 20)
[09:06:55 -329941.210089] SLOW spr round 8 (radius: 25)
[09:18:33 -329941.210089] Model parameter optimization (eps = 0.100000)

[09:18:49] [worker #0] ML tree search #11, logLikelihood: -329940.793037

[09:18:49 -1076585.144486] Initial branch length optimization
[09:18:57 -882499.362667] Model parameter optimization (eps = 10.000000)
[09:19:59 -881725.155848] AUTODETECT spr round 1 (radius: 5)
[09:23:36 -635219.843191] AUTODETECT spr round 2 (radius: 10)
[09:27:28 -494529.128950] AUTODETECT spr round 3 (radius: 15)
[09:31:36 -426122.824867] AUTODETECT spr round 4 (radius: 20)
[09:36:37 -379803.413421] AUTODETECT spr round 5 (radius: 25)
[09:41:46 -370844.203821] SPR radius for FAST iterations: 25 (autodetect)
[09:41:46 -370844.203821] Model parameter optimization (eps = 3.000000)
[09:42:32 -369805.753138] FAST spr round 1 (radius: 25)
[09:47:09 -331972.926583] FAST spr round 2 (radius: 25)
[09:50:36 -330164.855813] FAST spr round 3 (radius: 25)
[09:53:30 -330042.619431] FAST spr round 4 (radius: 25)
[09:56:06 -330014.907400] FAST spr round 5 (radius: 25)
[09:58:35 -330007.677176] FAST spr round 6 (radius: 25)
[10:01:04 -330000.930977] FAST spr round 7 (radius: 25)
[10:03:26 -330000.930900] Model parameter optimization (eps = 1.000000)
[10:03:39 -329996.465563] SLOW spr round 1 (radius: 5)
[10:06:56 -329943.639355] SLOW spr round 2 (radius: 5)
[10:10:09 -329936.245141] SLOW spr round 3 (radius: 5)
[10:13:16 -329934.120241] SLOW spr round 4 (radius: 5)
[10:16:19 -329934.119732] SLOW spr round 5 (radius: 10)
[10:19:31 -329929.895275] SLOW spr round 6 (radius: 5)
[10:23:26 -329929.895222] SLOW spr round 7 (radius: 10)
[10:26:56 -329929.895221] SLOW spr round 8 (radius: 15)
[10:32:09 -329929.895221] SLOW spr round 9 (radius: 20)
[10:35:11] [worker #1] ML tree search #14, logLikelihood: -329938.622226
[10:41:35 -329929.895221] SLOW spr round 10 (radius: 25)
[10:53:02 -329929.895221] Model parameter optimization (eps = 0.100000)

[10:53:12] [worker #0] ML tree search #13, logLikelihood: -329929.832741

[10:53:12 -1078868.454833] Initial branch length optimization
[10:53:21 -881787.762779] Model parameter optimization (eps = 10.000000)
[10:54:11 -881077.068671] AUTODETECT spr round 1 (radius: 5)
[10:57:47 -627875.994256] AUTODETECT spr round 2 (radius: 10)
[11:01:39 -477912.672700] AUTODETECT spr round 3 (radius: 15)
[11:05:49 -419695.525811] AUTODETECT spr round 4 (radius: 20)
[11:11:03 -372741.508843] AUTODETECT spr round 5 (radius: 25)
[11:16:49 -369546.318480] SPR radius for FAST iterations: 25 (autodetect)
[11:16:49 -369546.318480] Model parameter optimization (eps = 3.000000)
[11:17:04 -369448.708808] FAST spr round 1 (radius: 25)
[11:22:07 -332246.235024] FAST spr round 2 (radius: 25)
[11:25:50 -330816.361502] FAST spr round 3 (radius: 25)
[11:28:56 -330722.234301] FAST spr round 4 (radius: 25)
[11:31:31 -330710.567907] FAST spr round 5 (radius: 25)
[11:33:55 -330709.028545] FAST spr round 6 (radius: 25)
[11:36:14 -330709.028482] Model parameter optimization (eps = 1.000000)
[11:36:24 -330707.820069] SLOW spr round 1 (radius: 5)
[11:39:50 -330662.867397] SLOW spr round 2 (radius: 5)
[11:43:03 -330650.032381] SLOW spr round 3 (radius: 5)
[11:45:56 -330649.698277] SLOW spr round 4 (radius: 5)
[11:48:58 -330649.698177] SLOW spr round 5 (radius: 10)
[11:52:07 -330649.698136] SLOW spr round 6 (radius: 15)
[11:57:47 -330649.698136] SLOW spr round 7 (radius: 20)
[12:07:05 -330649.698136] SLOW spr round 8 (radius: 25)
[12:07:17] [worker #1] ML tree search #16, logLikelihood: -329948.669816
[12:18:55 -330649.698136] Model parameter optimization (eps = 0.100000)

[12:19:06] [worker #0] ML tree search #15, logLikelihood: -330649.426033

[12:19:06 -1080720.165686] Initial branch length optimization
[12:19:15 -889307.194660] Model parameter optimization (eps = 10.000000)
[12:20:17 -888569.294756] AUTODETECT spr round 1 (radius: 5)
[12:23:55 -644438.198708] AUTODETECT spr round 2 (radius: 10)
[12:27:41 -494810.292752] AUTODETECT spr round 3 (radius: 15)
[12:31:37 -430862.427837] AUTODETECT spr round 4 (radius: 20)
[12:36:22 -390203.369399] AUTODETECT spr round 5 (radius: 25)
[12:41:48 -381044.251157] SPR radius for FAST iterations: 25 (autodetect)
[12:41:48 -381044.251157] Model parameter optimization (eps = 3.000000)
[12:42:20 -380343.762040] FAST spr round 1 (radius: 25)
[12:47:19 -331331.305777] FAST spr round 2 (radius: 25)
[12:50:39 -330230.252545] FAST spr round 3 (radius: 25)
[12:53:33 -330158.557919] FAST spr round 4 (radius: 25)
[12:55:51 -330142.417614] FAST spr round 5 (radius: 25)
[12:58:03 -330141.849681] FAST spr round 6 (radius: 25)
[13:00:12 -330141.848848] Model parameter optimization (eps = 1.000000)
[13:00:33 -330136.400257] SLOW spr round 1 (radius: 5)
[13:03:31 -330010.453438] SLOW spr round 2 (radius: 5)
[13:06:29 -329979.252713] SLOW spr round 3 (radius: 5)
[13:09:19 -329977.921020] SLOW spr round 4 (radius: 5)
[13:12:06 -329977.162481] SLOW spr round 5 (radius: 5)
[13:14:52 -329977.162423] SLOW spr round 6 (radius: 10)
[13:17:39 -329977.162407] SLOW spr round 7 (radius: 15)
[13:22:15 -329977.162400] SLOW spr round 8 (radius: 20)
[13:30:15 -329977.162394] SLOW spr round 9 (radius: 25)
[13:35:52] [worker #1] ML tree search #18, logLikelihood: -329969.644930
[13:40:28 -329977.162387] Model parameter optimization (eps = 0.100000)

[13:40:34] [worker #0] ML tree search #17, logLikelihood: -329977.072240

[13:40:34 -1080314.565915] Initial branch length optimization
[13:40:40 -884174.130159] Model parameter optimization (eps = 10.000000)
[13:41:50 -883372.194375] AUTODETECT spr round 1 (radius: 5)
[13:44:55 -628848.333847] AUTODETECT spr round 2 (radius: 10)
[13:47:59 -493195.504917] AUTODETECT spr round 3 (radius: 15)
[13:51:35 -420728.941664] AUTODETECT spr round 4 (radius: 20)
[13:56:03 -401112.299475] AUTODETECT spr round 5 (radius: 25)
[14:01:34 -381451.778415] SPR radius for FAST iterations: 25 (autodetect)
[14:01:34 -381451.778415] Model parameter optimization (eps = 3.000000)
[14:01:50 -381365.907208] FAST spr round 1 (radius: 25)
[14:06:34 -332390.003830] FAST spr round 2 (radius: 25)
[14:09:50 -330700.261315] FAST spr round 3 (radius: 25)
[14:12:38 -330562.378567] FAST spr round 4 (radius: 25)
[14:15:11 -330539.813456] FAST spr round 5 (radius: 25)
[14:17:26 -330531.818521] FAST spr round 6 (radius: 25)
[14:19:34 -330531.815157] Model parameter optimization (eps = 1.000000)
[14:19:42 -330531.239355] SLOW spr round 1 (radius: 5)
[14:22:58 -330453.220315] SLOW spr round 2 (radius: 5)
[14:26:02 -330436.896252] SLOW spr round 3 (radius: 5)
[14:28:54 -330434.955378] SLOW spr round 4 (radius: 5)
[14:31:39 -330434.954938] SLOW spr round 5 (radius: 10)
[14:34:32 -330431.008152] SLOW spr round 6 (radius: 5)
[14:38:10 -330425.583732] SLOW spr round 7 (radius: 5)
[14:41:18 -330424.425850] SLOW spr round 8 (radius: 5)
[14:44:13 -330424.425527] SLOW spr round 9 (radius: 10)
[14:47:05 -330424.425527] SLOW spr round 10 (radius: 15)
[14:49:38] [worker #1] ML tree search #20, logLikelihood: -329944.131435
[14:51:58 -330424.425527] SLOW spr round 11 (radius: 20)
[15:00:04 -330424.425527] SLOW spr round 12 (radius: 25)
[15:10:13 -330424.425527] Model parameter optimization (eps = 0.100000)

[15:10:24] [worker #0] ML tree search #19, logLikelihood: -330422.858211


Optimized model parameters:

   Partition 0: noname
   Rate heterogeneity: FREE (4 cats, mean),  weights&rates: (0.099778,0.240806) (0.132615,0.346307) (0.402080,0.903700) (0.365526,1.550331) 
   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: -329929.832741

AIC score: 663869.665482 / AICc score: 8707929.665482 / BIC score: 673376.775375
Free parameters (model + branch lengths): 2005

WARNING: Number of free parameters (K=2005) is larger than alignment size (n=847).
         This might lead to overfitting and compromise tree inference results!


Best ML tree saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P23141/3_mltree/P23141.raxml.bestTree
All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P23141/3_mltree/P23141.raxml.mlTrees
Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P23141/3_mltree/P23141.raxml.bestModel

Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P23141/3_mltree/P23141.raxml.log

Analysis started: 01-Jul-2021 16:27:50 / finished: 02-Jul-2021 07:38:15

Elapsed time: 54625.072 seconds

Consumed energy: 4647.111 Wh (= 23 km in an electric car, or 116 km with an e-scooter!)