
DIA-NN 2.3.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Sep 26 2025 02:56:25
Current date and time: Wed Apr 22 01:32:51 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.3.0/diann-linux --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta --out /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.3.0/report.tsv --threads 32 --missed-cleavages 2 --min-pep-len 7 --max-pep-len 30 --mass-acc 20 --mass-acc-ms1 20 --qvalue 0.01 --protein-qvalue 0.01 --min-pr-charge 2 --max-pr-charge 4 --min-pr-mz 400 --max-pr-mz 1200 --min-fr-mz 200 --max-fr-mz 2000 --unimod4 --var-mod UniMod:35,15.994915,M --gen-spec-lib --fasta-search 

Thread number set to 32
Maximum number of missed cleavages set to 2
Min peptide length set to 7
Max peptide length set to 30
Output will be filtered at 0.01 FDR
Output will be filtered at 0.01 protein-level FDR
Min precursor charge set to 2
Max precursor charge set to 4
Min precursor m/z set to 400
Max precursor m/z set to 1200
Min fragment m/z set to 200
Max fragment m/z set to 2000
Cysteine carbamidomethylation enabled as a fixed modification
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
A spectral library will be generated
DIA-NN will carry out FASTA digest for in silico lib generation
Mass accuracy will be fixed to 2e-05 (MS2) and 2e-05 (MS1)
WARNING: FASTA digest mode enabled and raw data are provided, turning on deep learning spectra/RT/IM prediction
WARNING: incorrect settings, the in silico-predicted library must be generated in a separate pipeline step and then used to process the raw data, now without activating FASTA digest
WARNING: peptidoform scoring enabled because variable modifications have been declared; to disable, use --no-peptidoforms
The following variable modifications will be localised: UniMod:35 

12 files will be processed
[0:00] Loading FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[0:05] Processing FASTA
[0:09] Assembling elution groups
[0:17] 8103720 precursors generated
[0:17] Protein names missing for some isoforms
[0:17] Gene names missing for some isoforms
[0:17] Library contains 31680 proteins, and 0 genes
[0:23] [0:39] [6:32] [7:14] [7:17] [7:20] Saving the library to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.3.0/report-lib.predicted.speclib
[7:25] Initialising library
[7:39] Loading spectral library /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.3.0/report-lib.predicted.speclib
[7:42] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:44] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups.
[7:44] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:44] Annotating library proteins with information from the FASTA database
[7:44] Protein names missing for some isoforms
[7:44] Gene names missing for some isoforms
[7:44] Library contains 31680 proteins, and 0 genes
[7:51] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[8:06] File #1/12
[8:06] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d
WARNING: for the vast majority of timsTOF datasets it is better to manually fix both the MS1 and MS2 mass accuracies to 10-15 ppm
[8:17] Pre-processing...
[8:18] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[8:19] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[11:44] RT window set to 2.46002
[11:44] IM window set to 0.0371328
[11:44] Peak width: 3.54
[11:44] Scan window radius set to 7
[11:44] Recommended MS1 mass accuracy setting: 8 ppm
[14:17] Searching decoys
[17:11] Main search
[22:52] Removing low confidence identifications
[22:58] Removing interfering precursors
[23:03] Training neural networks on 19205 target and 9299 decoy PSMs
[23:12] Training neural networks on 19205 target and 8727 decoy PSMs
[23:17] IDs at 0.01 FDR: 11447
[23:17] Precursors at 1% peptidoform FDR: 11082
[23:18] Number of IDs at 0.01 FDR: 12234
[23:18] Calculating protein q-values
[23:19] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[23:19] Quantification
[23:19] Precursors with scored PTMs at 1% FDR: 259 out of 333 considered
[23:19] Precursors with all scored PTM sites unoccupied at 1% FDR: 11032
[23:19] Precursors with PTMs localised (when required) with > 90% confidence: 253 out of 259
[23:20] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[23:20] File #2/12
[23:20] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[23:31] Pre-processing...
[23:32] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[23:33] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:55] RT window set to 2.37272
[26:55] IM window set to 0.0386258
[26:56] Recommended MS1 mass accuracy setting: 9 ppm
[29:28] Searching decoys
[32:25] Main search
[38:11] Removing low confidence identifications
[38:17] Removing interfering precursors
[38:22] Training neural networks on 23580 target and 11951 decoy PSMs
[38:33] Training neural networks on 23580 target and 11283 decoy PSMs
[38:38] IDs at 0.01 FDR: 13343
[38:38] Precursors at 1% peptidoform FDR: 12867
[38:39] Number of IDs at 0.01 FDR: 14481
[38:39] Calculating protein q-values
[38:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[38:40] Quantification
[38:40] Precursors with scored PTMs at 1% FDR: 291 out of 392 considered
[38:40] Precursors with all scored PTM sites unoccupied at 1% FDR: 12831
[38:40] Precursors with PTMs localised (when required) with > 90% confidence: 286 out of 291
[38:41] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[38:41] File #3/12
[38:41] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[38:52] Pre-processing...
[38:53] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[38:54] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[42:17] RT window set to 2.31976
[42:17] IM window set to 0.0388627
[42:17] Recommended MS1 mass accuracy setting: 9 ppm
[44:43] Searching decoys
[47:36] Main search
[53:13] Removing low confidence identifications
[53:19] Removing interfering precursors
[53:24] Training neural networks on 22507 target and 11192 decoy PSMs
[53:33] Training neural networks on 22507 target and 10606 decoy PSMs
[53:38] IDs at 0.01 FDR: 12607
[53:38] Precursors at 1% peptidoform FDR: 12064
[53:39] Number of IDs at 0.01 FDR: 13588
[53:39] Calculating protein q-values
[53:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[53:40] Quantification
[53:40] Precursors with scored PTMs at 1% FDR: 285 out of 385 considered
[53:40] Precursors with all scored PTM sites unoccupied at 1% FDR: 11989
[53:40] Precursors with PTMs localised (when required) with > 90% confidence: 278 out of 285
[53:41] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[53:41] File #4/12
[53:41] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[53:52] Pre-processing...
[53:53] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[53:54] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[57:21] RT window set to 2.24463
[57:21] IM window set to 0.0408701
[57:22] Recommended MS1 mass accuracy setting: 9 ppm
[59:49] Searching decoys
[62:45] Main search
[68:29] Removing low confidence identifications
[68:36] Removing interfering precursors
[68:41] Training neural networks on 24625 target and 12797 decoy PSMs
[68:51] Training neural networks on 24625 target and 12033 decoy PSMs
[68:57] IDs at 0.01 FDR: 13519
[68:57] Precursors at 1% peptidoform FDR: 13020
[68:58] Number of IDs at 0.01 FDR: 14542
[68:58] Calculating protein q-values
[68:58] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[68:58] Quantification
[68:59] Precursors with scored PTMs at 1% FDR: 329 out of 429 considered
[68:59] Precursors with all scored PTM sites unoccupied at 1% FDR: 12921
[68:59] Precursors with PTMs localised (when required) with > 90% confidence: 316 out of 329
[69:00] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[69:00] File #5/12
[69:00] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[69:11] Pre-processing...
[69:12] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[69:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[72:41] RT window set to 2.46228
[72:41] IM window set to 0.0411472
[72:42] Recommended MS1 mass accuracy setting: 9 ppm
[75:18] Searching decoys
[78:32] Main search
[84:49] Removing low confidence identifications
[84:55] Removing interfering precursors
[85:00] Training neural networks on 22800 target and 11389 decoy PSMs
[85:10] Training neural networks on 22800 target and 10811 decoy PSMs
[85:15] IDs at 0.01 FDR: 12929
[85:15] Precursors at 1% peptidoform FDR: 12361
[85:16] Number of IDs at 0.01 FDR: 13928
[85:16] Calculating protein q-values
[85:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[85:17] Quantification
[85:17] Precursors with scored PTMs at 1% FDR: 304 out of 422 considered
[85:17] Precursors with all scored PTM sites unoccupied at 1% FDR: 12269
[85:17] Precursors with PTMs localised (when required) with > 90% confidence: 300 out of 304
[85:18] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[85:18] File #6/12
[85:18] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[85:30] Pre-processing...
[85:31] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[85:31] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[88:57] RT window set to 2.21521
[88:57] IM window set to 0.0393219
[88:58] Recommended MS1 mass accuracy setting: 9 ppm
[91:21] Searching decoys
[94:16] Main search
[99:56] Removing low confidence identifications
[100:03] Removing interfering precursors
[100:08] Training neural networks on 24348 target and 12412 decoy PSMs
[100:18] Training neural networks on 24348 target and 11697 decoy PSMs
[100:23] IDs at 0.01 FDR: 13360
[100:24] Precursors at 1% peptidoform FDR: 12785
[100:25] Number of IDs at 0.01 FDR: 14595
[100:25] Calculating protein q-values
[100:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[100:26] Quantification
[100:26] Precursors with scored PTMs at 1% FDR: 319 out of 401 considered
[100:26] Precursors with all scored PTM sites unoccupied at 1% FDR: 12690
[100:26] Precursors with PTMs localised (when required) with > 90% confidence: 312 out of 319
[100:27] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[100:27] File #7/12
[100:27] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[100:38] Pre-processing...
[100:39] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[100:40] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[104:02] RT window set to 2.41652
[104:02] IM window set to 0.0383805
[104:02] Recommended MS1 mass accuracy setting: 8 ppm
[106:25] Searching decoys
[109:17] Main search
[115:00] Removing low confidence identifications
[115:07] Removing interfering precursors
[115:12] Training neural networks on 22520 target and 11246 decoy PSMs
[115:22] Training neural networks on 22520 target and 10814 decoy PSMs
[115:27] IDs at 0.01 FDR: 12794
[115:28] Precursors at 1% peptidoform FDR: 12228
[115:29] Number of IDs at 0.01 FDR: 13928
[115:29] Calculating protein q-values
[115:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[115:30] Quantification
[115:30] Precursors with scored PTMs at 1% FDR: 309 out of 394 considered
[115:30] Precursors with all scored PTM sites unoccupied at 1% FDR: 12148
[115:30] Precursors with PTMs localised (when required) with > 90% confidence: 302 out of 309
[115:31] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[115:31] File #8/12
[115:31] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[115:41] Pre-processing...
[115:42] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[115:43] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[119:00] RT window set to 2.57934
[119:00] IM window set to 0.0393934
[119:00] Recommended MS1 mass accuracy setting: 8 ppm
[121:34] Searching decoys
[124:29] Main search
[130:15] Removing low confidence identifications
[130:22] Removing interfering precursors
[130:27] Training neural networks on 24287 target and 12046 decoy PSMs
[130:37] Training neural networks on 24287 target and 11400 decoy PSMs
[130:42] IDs at 0.01 FDR: 13887
[130:43] Precursors at 1% peptidoform FDR: 13080
[130:44] Number of IDs at 0.01 FDR: 14931
[130:44] Calculating protein q-values
[130:44] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[130:45] Quantification
[130:45] Precursors with scored PTMs at 1% FDR: 309 out of 424 considered
[130:45] Precursors with all scored PTM sites unoccupied at 1% FDR: 12864
[130:45] Precursors with PTMs localised (when required) with > 90% confidence: 302 out of 309
[130:46] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[130:46] File #9/12
[130:46] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[130:57] Pre-processing...
[130:58] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[130:59] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[134:23] RT window set to 2.34415
[134:23] IM window set to 0.0396369
[134:23] Recommended MS1 mass accuracy setting: 9 ppm
[136:25] Searching decoys
[139:20] Main search
[145:06] Removing low confidence identifications
[145:12] Removing interfering precursors
[145:17] Training neural networks on 22218 target and 10764 decoy PSMs
[145:27] Training neural networks on 22218 target and 9989 decoy PSMs
[145:32] IDs at 0.01 FDR: 13625
[145:33] Precursors at 1% peptidoform FDR: 13187
[145:34] Number of IDs at 0.01 FDR: 14337
[145:34] Calculating protein q-values
[145:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[145:34] Quantification
[145:35] Precursors with scored PTMs at 1% FDR: 348 out of 423 considered
[145:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 12975
[145:35] Precursors with PTMs localised (when required) with > 90% confidence: 341 out of 348
[145:35] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[145:35] File #10/12
[145:35] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[145:47] Pre-processing...
[145:48] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[145:48] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[149:16] RT window set to 2.43672
[149:16] IM window set to 0.039427
[149:17] Recommended MS1 mass accuracy setting: 9 ppm
[151:55] Searching decoys
[154:59] Main search
[161:01] Removing low confidence identifications
[161:08] Removing interfering precursors
[161:13] Training neural networks on 26060 target and 13182 decoy PSMs
[161:24] Training neural networks on 26060 target and 12450 decoy PSMs
[161:29] IDs at 0.01 FDR: 14475
[161:30] Precursors at 1% peptidoform FDR: 13698
[161:31] Number of IDs at 0.01 FDR: 15628
[161:31] Calculating protein q-values
[161:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[161:31] Quantification
[161:32] Precursors with scored PTMs at 1% FDR: 356 out of 485 considered
[161:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 13513
[161:32] Precursors with PTMs localised (when required) with > 90% confidence: 346 out of 356
[161:32] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[161:33] File #11/12
[161:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[161:44] Pre-processing...
[161:45] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[161:46] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[165:12] RT window set to 2.14659
[165:12] IM window set to 0.0389702
[165:12] Recommended MS1 mass accuracy setting: 9 ppm
[167:49] Searching decoys
[170:48] Main search
[176:20] Removing low confidence identifications
[176:27] Removing interfering precursors
[176:32] Training neural networks on 25621 target and 12576 decoy PSMs
[176:42] Training neural networks on 25621 target and 12333 decoy PSMs
[176:48] IDs at 0.01 FDR: 14051
[176:49] Precursors at 1% peptidoform FDR: 13591
[176:50] Number of IDs at 0.01 FDR: 15420
[176:50] Calculating protein q-values
[176:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[176:50] Quantification
[176:51] Precursors with scored PTMs at 1% FDR: 350 out of 454 considered
[176:51] Precursors with all scored PTM sites unoccupied at 1% FDR: 13524
[176:51] Precursors with PTMs localised (when required) with > 90% confidence: 341 out of 350
[176:51] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[176:52] File #12/12
[176:52] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[177:03] Pre-processing...
[177:04] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[177:05] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[180:29] RT window set to 2.07449
[180:29] IM window set to 0.0389637
[180:30] Recommended MS1 mass accuracy setting: 9 ppm
[182:28] Searching decoys
[185:09] Main search
[190:29] Removing low confidence identifications
[190:36] Removing interfering precursors
[190:41] Training neural networks on 26038 target and 13230 decoy PSMs
[190:52] Training neural networks on 26038 target and 12669 decoy PSMs
[190:57] IDs at 0.01 FDR: 14310
[190:58] Precursors at 1% peptidoform FDR: 13639
[190:59] Number of IDs at 0.01 FDR: 15439
[190:59] Calculating protein q-values
[190:59] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[190:59] Quantification
[191:00] Precursors with scored PTMs at 1% FDR: 353 out of 473 considered
[191:00] Precursors with all scored PTM sites unoccupied at 1% FDR: 13475
[191:00] Precursors with PTMs localised (when required) with > 90% confidence: 344 out of 353
[191:00] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[191:00] Cross-run analysis
[191:00] Reading quantification information: 12 files
[191:16] Quantifying peptides
[191:32] Quantification parameters: 0.339085, 0.00255934, 0.0111847, 0.0150586, 0.171303, 0.112832, 0.367633, 0.139541, 0.244228, 0.014856, 0.132477, 0.0276351, 0.446905, 0.233967, 0.211445, 0.0138463
[191:34] Assembling protein groups
[191:35] Quantifying proteins
[191:35] Calculating q-values for protein and gene groups
[191:39] Calculating global q-values for protein and gene groups
[191:39] Protein groups with global q-value <= 0.01: 2911
[191:40] Compressed report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.3.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[191:40] Stats report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.3.0/report.stats.tsv
[191:40] Generating spectral library:
[191:41] 21306 target and 211 decoy precursors saved
WARNING: 852 precursors without any fragments annotated were skipped
[191:41] Spectral library saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.3.0/report-lib.parquet

