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 03-Jul-2021 04:19:57 as follows: raxml-ng --msa /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/2_msa/P31415_trimmed_msa.fasta --data-type AA --model LG4X --prefix /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/3_mltree/P31415 --seed 2 --threads 7 --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 (7 threads), thread pinning: OFF [00:00:00] Reading alignment from file: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/2_msa/P31415_trimmed_msa.fasta [00:00:00] Loaded alignment with 255 taxa and 490 sites WARNING: Sequences tr_H2Q0E1_H2Q0E1_PANTR_9598 and tr_A0A2R9AKL1_A0A2R9AKL1_PANPA_9597 are exactly identical! WARNING: Sequences tr_A0A0H5SQK7_A0A0H5SQK7_BRUMA_6279 and tr_A0A0R3QR29_A0A0R3QR29_9BILA_42155 are exactly identical! WARNING: Sequences tr_F7EQW4_F7EQW4_MACMU_9544 and tr_A0A096MW08_A0A096MW08_PAPAN_9555 are exactly identical! WARNING: Sequences tr_F7EQW4_F7EQW4_MACMU_9544 and tr_A0A2K6B9U7_A0A2K6B9U7_MACNE_9545 are exactly identical! WARNING: Sequences tr_F7EQW4_F7EQW4_MACMU_9544 and tr_A0A2K5ZND4_A0A2K5ZND4_MANLE_9568 are exactly identical! WARNING: Sequences tr_H0ZT41_H0ZT41_TAEGU_59729 and tr_A0A218VCR0_A0A218VCR0_9PASE_299123 are exactly identical! WARNING: Sequences tr_A8X4J2_A8X4J2_CAEBR_6238 and tr_A0A2G5T0I4_A0A2G5T0I4_9PELO_1611254 are exactly identical! WARNING: Sequences tr_A0A0D9S3Q6_A0A0D9S3Q6_CHLSB_60711 and tr_A0A2K5LSC4_A0A2K5LSC4_CERAT_9531 are exactly identical! WARNING: Sequences tr_A0A2D0QFT2_A0A2D0QFT2_ICTPU_7998 and tr_A0A2D0QH73_A0A2D0QH73_ICTPU_7998 are exactly identical! WARNING: Duplicate sequences found: 9 NOTE: Reduced alignment (with duplicates and gap-only sites/taxa removed) NOTE: was saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/3_mltree/P31415.raxml.reduced.phy Alignment comprises 1 partitions and 490 patterns Partition 0: noname Model: LG4X+R4 Alignment sites / patterns: 490 / 490 Gaps: 22.48 % Invariant sites: 0.82 % NOTE: Binary MSA file created: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/3_mltree/P31415.raxml.rba Parallelization scheme autoconfig: 1 worker(s) x 7 thread(s) Parallel reduction/worker buffer size: 1 KB / 0 KB [00:00:00] Generating 20 random starting tree(s) with 255 taxa [00:00:00] Data distribution: max. partitions/sites/weight per thread: 1 / 70 / 5600 [00:00:00] Data distribution: max. searches per worker: 20 Starting ML tree search with 20 distinct starting trees [00:00:00 -115965.119059] Initial branch length optimization [00:00:00 -94189.729796] Model parameter optimization (eps = 10.000000) [00:00:08 -93725.992004] AUTODETECT spr round 1 (radius: 5) [00:00:15 -60505.565678] AUTODETECT spr round 2 (radius: 10) [00:00:23 -43323.215222] AUTODETECT spr round 3 (radius: 15) [00:00:33 -39605.725378] AUTODETECT spr round 4 (radius: 20) [00:00:45 -39473.977824] AUTODETECT spr round 5 (radius: 25) [00:00:59 -38670.280114] SPR radius for FAST iterations: 25 (autodetect) [00:00:59 -38670.280114] Model parameter optimization (eps = 3.000000) [00:01:04 -38547.849308] FAST spr round 1 (radius: 25) [00:01:13 -35595.448659] FAST spr round 2 (radius: 25) [00:01:21 -35527.285124] FAST spr round 3 (radius: 25) [00:01:27 -35523.391172] FAST spr round 4 (radius: 25) [00:01:33 -35523.391169] Model parameter optimization (eps = 1.000000) [00:01:36 -35520.572149] SLOW spr round 1 (radius: 5) [00:01:47 -35512.766295] SLOW spr round 2 (radius: 5) [00:01:57 -35512.765507] SLOW spr round 3 (radius: 10) [00:02:07 -35511.094693] SLOW spr round 4 (radius: 5) [00:02:22 -35507.349849] SLOW spr round 5 (radius: 5) [00:02:34 -35506.877473] SLOW spr round 6 (radius: 5) [00:02:44 -35506.877456] SLOW spr round 7 (radius: 10) [00:02:54 -35506.877456] SLOW spr round 8 (radius: 15) [00:03:12 -35506.877455] SLOW spr round 9 (radius: 20) [00:03:32 -35506.877455] SLOW spr round 10 (radius: 25) [00:03:55 -35506.877454] Model parameter optimization (eps = 0.100000) [00:03:56] ML tree search #1, logLikelihood: -35506.858600 [00:03:56 -114986.947927] Initial branch length optimization [00:03:56 -94323.481174] Model parameter optimization (eps = 10.000000) [00:04:06 -93940.910138] AUTODETECT spr round 1 (radius: 5) [00:04:12 -57677.380563] AUTODETECT spr round 2 (radius: 10) [00:04:20 -44750.978896] AUTODETECT spr round 3 (radius: 15) [00:04:32 -39758.615572] AUTODETECT spr round 4 (radius: 20) [00:04:44 -39687.164489] AUTODETECT spr round 5 (radius: 25) [00:04:56 -39687.153982] SPR radius for FAST iterations: 20 (autodetect) [00:04:56 -39687.153982] Model parameter optimization (eps = 3.000000) [00:05:01 -39543.386164] FAST spr round 1 (radius: 20) [00:05:10 -35828.008172] FAST spr round 2 (radius: 20) [00:05:18 -35544.546905] FAST spr round 3 (radius: 20) [00:05:24 -35513.462181] FAST spr round 4 (radius: 20) [00:05:29 -35513.462041] Model parameter optimization (eps = 1.000000) [00:05:32 -35511.989201] SLOW spr round 1 (radius: 5) [00:05:43 -35507.262083] SLOW spr round 2 (radius: 5) [00:05:52 -35504.084260] SLOW spr round 3 (radius: 5) [00:06:02 -35504.084257] SLOW spr round 4 (radius: 10) [00:06:11 -35504.083844] SLOW spr round 5 (radius: 15) [00:06:28 -35504.083844] SLOW spr round 6 (radius: 20) [00:06:47 -35504.083843] SLOW spr round 7 (radius: 25) [00:07:09 -35504.083843] Model parameter optimization (eps = 0.100000) [00:07:10] ML tree search #2, logLikelihood: -35503.999062 [00:07:10 -116271.231784] Initial branch length optimization [00:07:10 -94628.095940] Model parameter optimization (eps = 10.000000) [00:07:17 -94145.045660] AUTODETECT spr round 1 (radius: 5) [00:07:24 -61399.561867] AUTODETECT spr round 2 (radius: 10) [00:07:32 -46751.648164] AUTODETECT spr round 3 (radius: 15) [00:07:41 -40560.152771] AUTODETECT spr round 4 (radius: 20) [00:07:51 -39927.950087] AUTODETECT spr round 5 (radius: 25) [00:08:03 -39927.939055] SPR radius for FAST iterations: 20 (autodetect) [00:08:03 -39927.939055] Model parameter optimization (eps = 3.000000) [00:08:08 -39761.100530] FAST spr round 1 (radius: 20) [00:08:17 -35588.108295] FAST spr round 2 (radius: 20) [00:08:26 -35526.058153] FAST spr round 3 (radius: 20) [00:08:32 -35518.471033] FAST spr round 4 (radius: 20) [00:08:38 -35518.470830] Model parameter optimization (eps = 1.000000) [00:08:40 -35516.787085] SLOW spr round 1 (radius: 5) [00:08:52 -35509.417880] SLOW spr round 2 (radius: 5) [00:09:01 -35505.835260] SLOW spr round 3 (radius: 5) [00:09:11 -35505.300829] SLOW spr round 4 (radius: 5) [00:09:20 -35505.300829] SLOW spr round 5 (radius: 10) [00:09:30 -35504.730316] SLOW spr round 6 (radius: 5) [00:09:44 -35504.118744] SLOW spr round 7 (radius: 5) [00:09:55 -35504.118709] SLOW spr round 8 (radius: 10) [00:10:06 -35504.118296] SLOW spr round 9 (radius: 15) [00:10:24 -35504.118296] SLOW spr round 10 (radius: 20) [00:10:43 -35504.118296] SLOW spr round 11 (radius: 25) [00:11:06 -35504.118296] Model parameter optimization (eps = 0.100000) [00:11:07] ML tree search #3, logLikelihood: -35503.976864 [00:11:07 -116715.318612] Initial branch length optimization [00:11:07 -95098.441515] Model parameter optimization (eps = 10.000000) [00:11:16 -94764.320871] AUTODETECT spr round 1 (radius: 5) [00:11:23 -59603.175920] AUTODETECT spr round 2 (radius: 10) [00:11:31 -46895.058035] AUTODETECT spr round 3 (radius: 15) [00:11:41 -41738.582114] AUTODETECT spr round 4 (radius: 20) [00:11:52 -40288.415924] AUTODETECT spr round 5 (radius: 25) [00:12:03 -40047.932471] SPR radius for FAST iterations: 25 (autodetect) [00:12:03 -40047.932471] Model parameter optimization (eps = 3.000000) [00:12:09 -39903.442499] FAST spr round 1 (radius: 25) [00:12:19 -35724.442664] FAST spr round 2 (radius: 25) [00:12:27 -35530.745678] FAST spr round 3 (radius: 25) [00:12:34 -35516.121217] FAST spr round 4 (radius: 25) [00:12:40 -35515.274351] FAST spr round 5 (radius: 25) [00:12:45 -35515.274227] Model parameter optimization (eps = 1.000000) [00:12:48 -35510.792991] SLOW spr round 1 (radius: 5) [00:13:00 -35504.977637] SLOW spr round 2 (radius: 5) [00:13:10 -35504.977321] SLOW spr round 3 (radius: 10) [00:13:19 -35504.976903] SLOW spr round 4 (radius: 15) [00:13:38 -35504.976895] SLOW spr round 5 (radius: 20) [00:13:58 -35504.976888] SLOW spr round 6 (radius: 25) [00:14:20 -35504.976881] Model parameter optimization (eps = 0.100000) [00:14:21] ML tree search #4, logLikelihood: -35504.968356 [00:14:21 -116727.123694] Initial branch length optimization [00:14:21 -94153.580890] Model parameter optimization (eps = 10.000000) [00:14:29 -93765.355347] AUTODETECT spr round 1 (radius: 5) [00:14:36 -59779.967586] AUTODETECT spr round 2 (radius: 10) [00:14:44 -40795.854839] AUTODETECT spr round 3 (radius: 15) [00:14:55 -40010.952377] AUTODETECT spr round 4 (radius: 20) [00:15:08 -38676.937617] AUTODETECT spr round 5 (radius: 25) [00:15:22 -38676.885963] SPR radius for FAST iterations: 20 (autodetect) [00:15:23 -38676.885963] Model parameter optimization (eps = 3.000000) [00:15:27 -38532.777756] FAST spr round 1 (radius: 20) [00:15:36 -35664.168422] FAST spr round 2 (radius: 20) [00:15:44 -35548.938114] FAST spr round 3 (radius: 20) [00:15:51 -35514.760683] FAST spr round 4 (radius: 20) [00:15:56 -35514.734652] Model parameter optimization (eps = 1.000000) [00:15:59 -35511.401995] SLOW spr round 1 (radius: 5) [00:16:10 -35505.615525] SLOW spr round 2 (radius: 5) [00:16:20 -35505.244331] SLOW spr round 3 (radius: 5) [00:16:30 -35505.244258] SLOW spr round 4 (radius: 10) [00:16:39 -35505.244243] SLOW spr round 5 (radius: 15) [00:16:57 -35505.244237] SLOW spr round 6 (radius: 20) [00:17:16 -35505.244233] SLOW spr round 7 (radius: 25) [00:17:38 -35505.244230] Model parameter optimization (eps = 0.100000) [00:17:39] ML tree search #5, logLikelihood: -35505.220532 [00:17:39 -116535.718230] Initial branch length optimization [00:17:40 -94562.549965] Model parameter optimization (eps = 10.000000) [00:17:49 -94194.172102] AUTODETECT spr round 1 (radius: 5) [00:17:55 -57409.559194] AUTODETECT spr round 2 (radius: 10) [00:18:03 -46833.680954] AUTODETECT spr round 3 (radius: 15) [00:18:13 -40336.858091] AUTODETECT spr round 4 (radius: 20) [00:18:25 -38991.020795] AUTODETECT spr round 5 (radius: 25) [00:18:39 -38891.773309] SPR radius for FAST iterations: 25 (autodetect) [00:18:39 -38891.773309] Model parameter optimization (eps = 3.000000) [00:18:44 -38749.636057] FAST spr round 1 (radius: 25) [00:18:53 -35675.675998] FAST spr round 2 (radius: 25) [00:19:00 -35529.268558] FAST spr round 3 (radius: 25) [00:19:06 -35522.842232] FAST spr round 4 (radius: 25) [00:19:12 -35522.842040] Model parameter optimization (eps = 1.000000) [00:19:15 -35516.591303] SLOW spr round 1 (radius: 5) [00:19:26 -35510.623341] SLOW spr round 2 (radius: 5) [00:19:36 -35510.623296] SLOW spr round 3 (radius: 10) [00:19:45 -35510.623293] SLOW spr round 4 (radius: 15) [00:20:04 -35510.623293] SLOW spr round 5 (radius: 20) [00:20:23 -35510.623293] SLOW spr round 6 (radius: 25) [00:20:44 -35510.623293] Model parameter optimization (eps = 0.100000) [00:20:45] ML tree search #6, logLikelihood: -35510.599305 [00:20:45 -114190.808847] Initial branch length optimization [00:20:45 -93674.218259] Model parameter optimization (eps = 10.000000) [00:20:52 -93269.646572] AUTODETECT spr round 1 (radius: 5) [00:20:58 -61250.057132] AUTODETECT spr round 2 (radius: 10) [00:21:06 -44126.238452] AUTODETECT spr round 3 (radius: 15) [00:21:16 -39290.469134] AUTODETECT spr round 4 (radius: 20) [00:21:28 -38765.994900] AUTODETECT spr round 5 (radius: 25) [00:21:40 -38751.507952] SPR radius for FAST iterations: 25 (autodetect) [00:21:40 -38751.507952] Model parameter optimization (eps = 3.000000) [00:21:45 -38643.106634] FAST spr round 1 (radius: 25) [00:21:55 -35632.702663] FAST spr round 2 (radius: 25) [00:22:03 -35513.357150] FAST spr round 3 (radius: 25) [00:22:08 -35511.809208] FAST spr round 4 (radius: 25) [00:22:14 -35511.809183] Model parameter optimization (eps = 1.000000) [00:22:17 -35510.026887] SLOW spr round 1 (radius: 5) [00:22:28 -35505.484558] SLOW spr round 2 (radius: 5) [00:22:38 -35505.484236] SLOW spr round 3 (radius: 10) [00:22:47 -35505.484123] SLOW spr round 4 (radius: 15) [00:23:04 -35505.484122] SLOW spr round 5 (radius: 20) [00:23:22 -35505.484122] SLOW spr round 6 (radius: 25) [00:23:43 -35505.484121] Model parameter optimization (eps = 0.100000) [00:23:44] ML tree search #7, logLikelihood: -35505.468980 [00:23:44 -115907.674684] Initial branch length optimization [00:23:45 -94170.955119] Model parameter optimization (eps = 10.000000) [00:23:54 -93744.139377] AUTODETECT spr round 1 (radius: 5) [00:24:00 -61419.390245] AUTODETECT spr round 2 (radius: 10) [00:24:09 -45909.338486] AUTODETECT spr round 3 (radius: 15) [00:24:19 -41617.867348] AUTODETECT spr round 4 (radius: 20) [00:24:31 -40630.650524] AUTODETECT spr round 5 (radius: 25) [00:24:45 -40630.626159] SPR radius for FAST iterations: 20 (autodetect) [00:24:45 -40630.626159] Model parameter optimization (eps = 3.000000) [00:24:51 -40476.822112] FAST spr round 1 (radius: 20) [00:25:01 -35811.238893] FAST spr round 2 (radius: 20) [00:25:09 -35541.533759] FAST spr round 3 (radius: 20) [00:25:15 -35525.331391] FAST spr round 4 (radius: 20) [00:25:21 -35525.330643] Model parameter optimization (eps = 1.000000) [00:25:24 -35520.821983] SLOW spr round 1 (radius: 5) [00:25:35 -35514.020095] SLOW spr round 2 (radius: 5) [00:25:45 -35513.746821] SLOW spr round 3 (radius: 5) [00:25:54 -35512.735109] SLOW spr round 4 (radius: 5) [00:26:04 -35512.735043] SLOW spr round 5 (radius: 10) [00:26:14 -35512.735028] SLOW spr round 6 (radius: 15) [00:26:32 -35512.735015] SLOW spr round 7 (radius: 20) [00:26:51 -35512.735002] SLOW spr round 8 (radius: 25) [00:27:13 -35512.734991] Model parameter optimization (eps = 0.100000) [00:27:14] ML tree search #8, logLikelihood: -35512.723713 [00:27:14 -114849.373395] Initial branch length optimization [00:27:15 -93406.422456] Model parameter optimization (eps = 10.000000) [00:27:23 -93052.668254] AUTODETECT spr round 1 (radius: 5) [00:27:30 -56201.002769] AUTODETECT spr round 2 (radius: 10) [00:27:38 -40386.068622] AUTODETECT spr round 3 (radius: 15) [00:27:49 -37989.358543] AUTODETECT spr round 4 (radius: 20) [00:28:00 -37960.343567] AUTODETECT spr round 5 (radius: 25) [00:28:12 -37960.342336] SPR radius for FAST iterations: 20 (autodetect) [00:28:12 -37960.342336] Model parameter optimization (eps = 3.000000) [00:28:18 -37835.690479] FAST spr round 1 (radius: 20) [00:28:27 -35607.528337] FAST spr round 2 (radius: 20) [00:28:35 -35530.045185] FAST spr round 3 (radius: 20) [00:28:42 -35516.504272] FAST spr round 4 (radius: 20) [00:28:48 -35516.504147] Model parameter optimization (eps = 1.000000) [00:28:51 -35515.445253] SLOW spr round 1 (radius: 5) [00:29:02 -35506.200209] SLOW spr round 2 (radius: 5) [00:29:12 -35506.199574] SLOW spr round 3 (radius: 10) [00:29:22 -35505.527454] SLOW spr round 4 (radius: 5) [00:29:36 -35504.947584] SLOW spr round 5 (radius: 5) [00:29:48 -35504.947560] SLOW spr round 6 (radius: 10) [00:30:00 -35504.947558] SLOW spr round 7 (radius: 15) [00:30:19 -35504.947558] SLOW spr round 8 (radius: 20) [00:30:39 -35504.947557] SLOW spr round 9 (radius: 25) [00:31:01 -35504.947556] Model parameter optimization (eps = 0.100000) [00:31:02] ML tree search #9, logLikelihood: -35504.922749 [00:31:02 -117307.590104] Initial branch length optimization [00:31:03 -94870.567562] Model parameter optimization (eps = 10.000000) [00:31:12 -94548.792612] AUTODETECT spr round 1 (radius: 5) [00:31:19 -60554.933497] AUTODETECT spr round 2 (radius: 10) [00:31:28 -43888.899356] AUTODETECT spr round 3 (radius: 15) [00:31:38 -39143.146378] AUTODETECT spr round 4 (radius: 20) [00:31:51 -38625.068630] AUTODETECT spr round 5 (radius: 25) [00:32:05 -38625.045195] SPR radius for FAST iterations: 20 (autodetect) [00:32:05 -38625.045195] Model parameter optimization (eps = 3.000000) [00:32:10 -38486.215149] FAST spr round 1 (radius: 20) [00:32:20 -35684.907226] FAST spr round 2 (radius: 20) [00:32:28 -35529.480237] FAST spr round 3 (radius: 20) [00:32:34 -35518.820325] FAST spr round 4 (radius: 20) [00:32:40 -35512.704838] FAST spr round 5 (radius: 20) [00:32:45 -35512.704641] Model parameter optimization (eps = 1.000000) [00:32:48 -35508.817446] SLOW spr round 1 (radius: 5) [00:32:59 -35501.877121] SLOW spr round 2 (radius: 5) [00:33:09 -35501.877040] SLOW spr round 3 (radius: 10) [00:33:19 -35501.607951] SLOW spr round 4 (radius: 5) [00:33:34 -35501.607941] SLOW spr round 5 (radius: 10) [00:33:47 -35501.607940] SLOW spr round 6 (radius: 15) [00:34:04 -35501.607939] SLOW spr round 7 (radius: 20) [00:34:24 -35501.607938] SLOW spr round 8 (radius: 25) [00:34:46 -35501.607938] Model parameter optimization (eps = 0.100000) [00:34:47] ML tree search #10, logLikelihood: -35501.568109 [00:34:47 -115191.337683] Initial branch length optimization [00:34:47 -93549.259827] Model parameter optimization (eps = 10.000000) [00:34:58 -93171.780876] AUTODETECT spr round 1 (radius: 5) [00:35:04 -55378.597504] AUTODETECT spr round 2 (radius: 10) [00:35:12 -44121.984491] AUTODETECT spr round 3 (radius: 15) [00:35:24 -37980.804211] AUTODETECT spr round 4 (radius: 20) [00:35:38 -37865.362686] AUTODETECT spr round 5 (radius: 25) [00:35:50 -37861.091509] SPR radius for FAST iterations: 25 (autodetect) [00:35:50 -37861.091509] Model parameter optimization (eps = 3.000000) [00:35:54 -37735.647422] FAST spr round 1 (radius: 25) [00:36:03 -35593.493402] FAST spr round 2 (radius: 25) [00:36:11 -35524.813289] FAST spr round 3 (radius: 25) [00:36:17 -35516.798126] FAST spr round 4 (radius: 25) [00:36:23 -35513.912015] FAST spr round 5 (radius: 25) [00:36:28 -35513.911637] Model parameter optimization (eps = 1.000000) [00:36:32 -35512.504820] SLOW spr round 1 (radius: 5) [00:36:42 -35505.834541] SLOW spr round 2 (radius: 5) [00:36:52 -35505.834357] SLOW spr round 3 (radius: 10) [00:37:01 -35504.366002] SLOW spr round 4 (radius: 5) [00:37:15 -35503.796272] SLOW spr round 5 (radius: 5) [00:37:27 -35503.796199] SLOW spr round 6 (radius: 10) [00:37:37 -35503.738944] SLOW spr round 7 (radius: 15) [00:37:54 -35503.738926] SLOW spr round 8 (radius: 20) [00:38:12 -35503.738926] SLOW spr round 9 (radius: 25) [00:38:34 -35503.738926] Model parameter optimization (eps = 0.100000) [00:38:35] ML tree search #11, logLikelihood: -35503.723642 [00:38:35 -115189.086176] Initial branch length optimization [00:38:35 -94360.901842] Model parameter optimization (eps = 10.000000) [00:38:42 -93924.260280] AUTODETECT spr round 1 (radius: 5) [00:38:48 -58337.439491] AUTODETECT spr round 2 (radius: 10) [00:38:57 -43228.415336] AUTODETECT spr round 3 (radius: 15) [00:39:06 -40311.339696] AUTODETECT spr round 4 (radius: 20) [00:39:17 -39224.612321] AUTODETECT spr round 5 (radius: 25) [00:39:28 -39224.601957] SPR radius for FAST iterations: 20 (autodetect) [00:39:28 -39224.601957] Model parameter optimization (eps = 3.000000) [00:39:32 -39107.116495] FAST spr round 1 (radius: 20) [00:39:42 -35612.761117] FAST spr round 2 (radius: 20) [00:39:50 -35524.076181] FAST spr round 3 (radius: 20) [00:39:56 -35516.930822] FAST spr round 4 (radius: 20) [00:40:01 -35516.930634] Model parameter optimization (eps = 1.000000) [00:40:05 -35512.938623] SLOW spr round 1 (radius: 5) [00:40:15 -35507.766456] SLOW spr round 2 (radius: 5) [00:40:26 -35504.756816] SLOW spr round 3 (radius: 5) [00:40:35 -35504.756583] SLOW spr round 4 (radius: 10) [00:40:45 -35503.757261] SLOW spr round 5 (radius: 5) [00:40:59 -35503.515376] SLOW spr round 6 (radius: 5) [00:41:11 -35503.515333] SLOW spr round 7 (radius: 10) [00:41:21 -35503.515332] SLOW spr round 8 (radius: 15) [00:41:39 -35503.515331] SLOW spr round 9 (radius: 20) [00:41:59 -35503.515331] SLOW spr round 10 (radius: 25) [00:42:21 -35503.515330] Model parameter optimization (eps = 0.100000) [00:42:23] ML tree search #12, logLikelihood: -35503.398561 [00:42:23 -114837.445434] Initial branch length optimization [00:42:23 -93247.787088] Model parameter optimization (eps = 10.000000) [00:42:32 -92955.152010] AUTODETECT spr round 1 (radius: 5) [00:42:38 -60347.536998] AUTODETECT spr round 2 (radius: 10) [00:42:46 -44768.659698] AUTODETECT spr round 3 (radius: 15) [00:42:56 -40727.914577] AUTODETECT spr round 4 (radius: 20) [00:43:08 -40635.995831] AUTODETECT spr round 5 (radius: 25) [00:43:21 -40634.620997] SPR radius for FAST iterations: 25 (autodetect) [00:43:21 -40634.620997] Model parameter optimization (eps = 3.000000) [00:43:27 -40511.307956] FAST spr round 1 (radius: 25) [00:43:36 -35677.183611] FAST spr round 2 (radius: 25) [00:43:44 -35532.479212] FAST spr round 3 (radius: 25) [00:43:51 -35512.001073] FAST spr round 4 (radius: 25) [00:43:57 -35511.669465] FAST spr round 5 (radius: 25) [00:44:02 -35511.669392] Model parameter optimization (eps = 1.000000) [00:44:06 -35508.283854] SLOW spr round 1 (radius: 5) [00:44:17 -35503.271854] SLOW spr round 2 (radius: 5) [00:44:26 -35503.271711] SLOW spr round 3 (radius: 10) [00:44:36 -35503.271651] SLOW spr round 4 (radius: 15) [00:44:54 -35503.271647] SLOW spr round 5 (radius: 20) [00:45:12 -35503.271646] SLOW spr round 6 (radius: 25) [00:45:36 -35503.271646] Model parameter optimization (eps = 0.100000) [00:45:37] ML tree search #13, logLikelihood: -35503.246358 [00:45:37 -116027.243366] Initial branch length optimization [00:45:37 -94379.943085] Model parameter optimization (eps = 10.000000) [00:45:48 -93909.811002] AUTODETECT spr round 1 (radius: 5) [00:45:54 -60665.595253] AUTODETECT spr round 2 (radius: 10) [00:46:03 -45617.991383] AUTODETECT spr round 3 (radius: 15) [00:46:15 -39997.785098] AUTODETECT spr round 4 (radius: 20) [00:46:27 -39901.472853] AUTODETECT spr round 5 (radius: 25) [00:46:39 -39901.433133] SPR radius for FAST iterations: 20 (autodetect) [00:46:39 -39901.433133] Model parameter optimization (eps = 3.000000) [00:46:44 -39727.689498] FAST spr round 1 (radius: 20) [00:46:54 -35662.789598] FAST spr round 2 (radius: 20) [00:47:01 -35531.426696] FAST spr round 3 (radius: 20) [00:47:07 -35517.125257] FAST spr round 4 (radius: 20) [00:47:13 -35516.633460] FAST spr round 5 (radius: 20) [00:47:19 -35516.633243] Model parameter optimization (eps = 1.000000) [00:47:20 -35515.722626] SLOW spr round 1 (radius: 5) [00:47:31 -35506.237923] SLOW spr round 2 (radius: 5) [00:47:41 -35506.237684] SLOW spr round 3 (radius: 10) [00:47:51 -35506.237278] SLOW spr round 4 (radius: 15) [00:48:09 -35506.237275] SLOW spr round 5 (radius: 20) [00:48:29 -35506.237273] SLOW spr round 6 (radius: 25) [00:48:51 -35506.237271] Model parameter optimization (eps = 0.100000) [00:48:53] ML tree search #14, logLikelihood: -35506.184398 [00:48:53 -116827.255816] Initial branch length optimization [00:48:53 -95300.289461] Model parameter optimization (eps = 10.000000) [00:49:03 -94955.959112] AUTODETECT spr round 1 (radius: 5) [00:49:10 -61109.318703] AUTODETECT spr round 2 (radius: 10) [00:49:19 -44117.642029] AUTODETECT spr round 3 (radius: 15) [00:49:30 -41151.895809] AUTODETECT spr round 4 (radius: 20) [00:49:42 -39700.028763] AUTODETECT spr round 5 (radius: 25) [00:49:57 -39663.100697] SPR radius for FAST iterations: 25 (autodetect) [00:49:57 -39663.100697] Model parameter optimization (eps = 3.000000) [00:50:02 -39536.161777] FAST spr round 1 (radius: 25) [00:50:13 -35701.522127] FAST spr round 2 (radius: 25) [00:50:22 -35534.486528] FAST spr round 3 (radius: 25) [00:50:28 -35530.141803] FAST spr round 4 (radius: 25) [00:50:35 -35530.129989] Model parameter optimization (eps = 1.000000) [00:50:39 -35522.293916] SLOW spr round 1 (radius: 5) [00:50:52 -35508.857549] SLOW spr round 2 (radius: 5) [00:51:03 -35507.588438] SLOW spr round 3 (radius: 5) [00:51:15 -35503.845745] SLOW spr round 4 (radius: 5) [00:51:25 -35503.845239] SLOW spr round 5 (radius: 10) [00:51:35 -35503.845131] SLOW spr round 6 (radius: 15) [00:51:55 -35503.845094] SLOW spr round 7 (radius: 20) [00:52:16 -35503.845075] SLOW spr round 8 (radius: 25) [00:52:42 -35503.845059] Model parameter optimization (eps = 0.100000) [00:52:43] ML tree search #15, logLikelihood: -35503.794508 [00:52:43 -115863.835628] Initial branch length optimization [00:52:43 -94454.977139] Model parameter optimization (eps = 10.000000) [00:52:53 -94029.294565] AUTODETECT spr round 1 (radius: 5) [00:53:00 -60538.724205] AUTODETECT spr round 2 (radius: 10) [00:53:10 -44707.876868] AUTODETECT spr round 3 (radius: 15) [00:53:21 -40124.992129] AUTODETECT spr round 4 (radius: 20) [00:53:35 -39826.045940] AUTODETECT spr round 5 (radius: 25) [00:53:48 -39815.737062] SPR radius for FAST iterations: 25 (autodetect) [00:53:48 -39815.737062] Model parameter optimization (eps = 3.000000) [00:53:53 -39687.493623] FAST spr round 1 (radius: 25) [00:54:03 -35654.778819] FAST spr round 2 (radius: 25) [00:54:12 -35528.100705] FAST spr round 3 (radius: 25) [00:54:19 -35518.724832] FAST spr round 4 (radius: 25) [00:54:26 -35514.451695] FAST spr round 5 (radius: 25) [00:54:32 -35514.451593] Model parameter optimization (eps = 1.000000) [00:54:36 -35509.996352] SLOW spr round 1 (radius: 5) [00:54:48 -35503.234652] SLOW spr round 2 (radius: 5) [00:54:59 -35502.868267] SLOW spr round 3 (radius: 5) [00:55:09 -35502.868244] SLOW spr round 4 (radius: 10) [00:55:20 -35502.573699] SLOW spr round 5 (radius: 5) [00:55:35 -35502.573691] SLOW spr round 6 (radius: 10) [00:55:49 -35502.573691] SLOW spr round 7 (radius: 15) [00:56:08 -35502.573691] SLOW spr round 8 (radius: 20) [00:56:29 -35502.573690] SLOW spr round 9 (radius: 25) [00:56:54 -35502.573690] Model parameter optimization (eps = 0.100000) [00:56:55] ML tree search #16, logLikelihood: -35502.567540 [00:56:55 -116514.785871] Initial branch length optimization [00:56:56 -94267.471916] Model parameter optimization (eps = 10.000000) [00:57:06 -93877.350581] AUTODETECT spr round 1 (radius: 5) [00:57:13 -58165.295360] AUTODETECT spr round 2 (radius: 10) [00:57:21 -43831.204454] AUTODETECT spr round 3 (radius: 15) [00:57:31 -38743.578042] AUTODETECT spr round 4 (radius: 20) [00:57:44 -38290.387947] AUTODETECT spr round 5 (radius: 25) [00:57:57 -38277.743675] SPR radius for FAST iterations: 25 (autodetect) [00:57:57 -38277.743675] Model parameter optimization (eps = 3.000000) [00:58:03 -38140.776249] FAST spr round 1 (radius: 25) [00:58:13 -35688.237001] FAST spr round 2 (radius: 25) [00:58:23 -35538.080884] FAST spr round 3 (radius: 25) [00:58:30 -35527.365194] FAST spr round 4 (radius: 25) [00:58:36 -35527.364655] Model parameter optimization (eps = 1.000000) [00:58:39 -35522.464417] SLOW spr round 1 (radius: 5) [00:58:51 -35515.539796] SLOW spr round 2 (radius: 5) [00:59:02 -35515.538425] SLOW spr round 3 (radius: 10) [00:59:13 -35515.538005] SLOW spr round 4 (radius: 15) [00:59:32 -35515.538004] SLOW spr round 5 (radius: 20) [00:59:51 -35515.538003] SLOW spr round 6 (radius: 25) [01:00:15 -35515.538002] Model parameter optimization (eps = 0.100000) [01:00:16] ML tree search #17, logLikelihood: -35515.527638 [01:00:16 -117601.353688] Initial branch length optimization [01:00:17 -95285.081165] Model parameter optimization (eps = 10.000000) [01:00:25 -94886.586797] AUTODETECT spr round 1 (radius: 5) [01:00:32 -59910.534221] AUTODETECT spr round 2 (radius: 10) [01:00:41 -47325.005058] AUTODETECT spr round 3 (radius: 15) [01:00:52 -41411.622996] AUTODETECT spr round 4 (radius: 20) [01:01:05 -40395.855767] AUTODETECT spr round 5 (radius: 25) [01:01:19 -40386.160922] SPR radius for FAST iterations: 25 (autodetect) [01:01:19 -40386.160922] Model parameter optimization (eps = 3.000000) [01:01:24 -40215.823225] FAST spr round 1 (radius: 25) [01:01:34 -35716.075179] FAST spr round 2 (radius: 25) [01:01:42 -35522.567678] FAST spr round 3 (radius: 25) [01:01:49 -35519.954116] FAST spr round 4 (radius: 25) [01:01:55 -35519.953747] Model parameter optimization (eps = 1.000000) [01:01:59 -35515.796749] SLOW spr round 1 (radius: 5) [01:02:11 -35511.597090] SLOW spr round 2 (radius: 5) [01:02:22 -35509.130244] SLOW spr round 3 (radius: 5) [01:02:33 -35509.129891] SLOW spr round 4 (radius: 10) [01:02:43 -35509.129616] SLOW spr round 5 (radius: 15) [01:03:03 -35509.129578] SLOW spr round 6 (radius: 20) [01:03:24 -35509.129545] SLOW spr round 7 (radius: 25) [01:03:47 -35509.129514] Model parameter optimization (eps = 0.100000) [01:03:49] ML tree search #18, logLikelihood: -35509.074463 [01:03:49 -116330.355256] Initial branch length optimization [01:03:49 -93918.754937] Model parameter optimization (eps = 10.000000) [01:04:00 -93562.354473] AUTODETECT spr round 1 (radius: 5) [01:04:07 -59915.174215] AUTODETECT spr round 2 (radius: 10) [01:04:16 -45606.978227] AUTODETECT spr round 3 (radius: 15) [01:04:27 -39471.647835] AUTODETECT spr round 4 (radius: 20) [01:04:39 -39387.523710] AUTODETECT spr round 5 (radius: 25) [01:04:52 -39387.517706] SPR radius for FAST iterations: 20 (autodetect) [01:04:52 -39387.517706] Model parameter optimization (eps = 3.000000) [01:04:58 -39246.084730] FAST spr round 1 (radius: 20) [01:05:09 -35719.210249] FAST spr round 2 (radius: 20) [01:05:18 -35533.944004] FAST spr round 3 (radius: 20) [01:05:25 -35525.425419] FAST spr round 4 (radius: 20) [01:05:31 -35525.425224] Model parameter optimization (eps = 1.000000) [01:05:35 -35520.736758] SLOW spr round 1 (radius: 5) [01:05:47 -35519.264005] SLOW spr round 2 (radius: 5) [01:05:58 -35519.263950] SLOW spr round 3 (radius: 10) [01:06:09 -35519.194844] SLOW spr round 4 (radius: 15) [01:06:28 -35519.194525] SLOW spr round 5 (radius: 20) [01:06:49 -35519.194466] SLOW spr round 6 (radius: 25) [01:07:13 -35519.194445] Model parameter optimization (eps = 0.100000) [01:07:14] ML tree search #19, logLikelihood: -35519.186928 [01:07:15 -115648.569087] Initial branch length optimization [01:07:15 -94203.825474] Model parameter optimization (eps = 10.000000) [01:07:23 -93783.429162] AUTODETECT spr round 1 (radius: 5) [01:07:30 -61454.776123] AUTODETECT spr round 2 (radius: 10) [01:07:39 -45413.937733] AUTODETECT spr round 3 (radius: 15) [01:07:49 -39848.625219] AUTODETECT spr round 4 (radius: 20) [01:08:01 -39744.138042] AUTODETECT spr round 5 (radius: 25) [01:08:13 -39738.878198] SPR radius for FAST iterations: 25 (autodetect) [01:08:13 -39738.878198] Model parameter optimization (eps = 3.000000) [01:08:19 -39576.930024] FAST spr round 1 (radius: 25) [01:08:28 -35628.958023] FAST spr round 2 (radius: 25) [01:08:36 -35517.658426] FAST spr round 3 (radius: 25) [01:08:43 -35513.031072] FAST spr round 4 (radius: 25) [01:08:49 -35511.247832] FAST spr round 5 (radius: 25) [01:08:55 -35510.928970] FAST spr round 6 (radius: 25) [01:09:01 -35510.928874] Model parameter optimization (eps = 1.000000) [01:09:04 -35508.328437] SLOW spr round 1 (radius: 5) [01:09:16 -35507.792993] SLOW spr round 2 (radius: 5) [01:09:27 -35504.410106] SLOW spr round 3 (radius: 5) [01:09:37 -35502.609570] SLOW spr round 4 (radius: 5) [01:09:47 -35502.609536] SLOW spr round 5 (radius: 10) [01:09:58 -35502.609111] SLOW spr round 6 (radius: 15) [01:10:18 -35502.609109] SLOW spr round 7 (radius: 20) [01:10:39 -35502.609107] SLOW spr round 8 (radius: 25) [01:11:02 -35502.609105] Model parameter optimization (eps = 0.100000) [01:11:04] ML tree search #20, logLikelihood: -35502.602067 Optimized model parameters: Partition 0: noname Rate heterogeneity: FREE (4 cats, mean), weights&rates: (0.228793,0.489887) (0.147833,0.688110) (0.394486,0.773867) (0.228889,2.101076) 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: -35501.568109 AIC score: 72029.136218 / AICc score: 599393.136218 / BIC score: 74180.866184 Free parameters (model + branch lengths): 513 WARNING: Number of free parameters (K=513) is larger than alignment size (n=490). 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/P31415/3_mltree/P31415.raxml.bestTree All ML trees saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/3_mltree/P31415.raxml.mlTrees Optimized model saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/3_mltree/P31415.raxml.bestModel Execution log saved to: /truba/home/emrah/WORKFOLDER/PROD/300621_run/phylogeny-snakemake/results/P31415/3_mltree/P31415.raxml.log Analysis started: 03-Jul-2021 04:19:57 / finished: 03-Jul-2021 05:31:01 Elapsed time: 4264.371 seconds Consumed energy: 360.880 Wh (= 2 km in an electric car, or 9 km with an e-scooter!)