
DIA-NN 2.5.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 12 2026 10:45:33
Current date and time: Sat Apr 18 17:48:23 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.5.0/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_v2.5.0/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
[0:07] Processing FASTA
[0:11] Assembling elution groups
[0:20] 8103720 precursors generated
[0:20] Protein names missing for some isoforms
[0:20] Gene names missing for some isoforms
[0:20] Library contains 31680 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:27] [0:44] [8:00] [8:52] [8:55] [9:00] Saving the library to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.5.0/report-lib.predicted.speclib
[9:06] Initialising library
[9:24] Loading spectral library /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.5.0/report-lib.predicted.speclib
[9:28] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[9:30] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups (targets and decoys).
[9:30] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[9:30] Annotating library proteins with information from the FASTA database
[9:30] Protein names missing for some isoforms
[9:30] Gene names missing for some isoforms
[9:30] Library contains 31680 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[9:37] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[9:54] File #1/12
[9:54] 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
[10:08] Pre-processing...
[10:10] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[10:10] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[14:14] RT window set to 2.2836
[14:14] IM window set to 0.0387907
[14:14] Peak width: 3.48
[14:14] Scan window radius set to 7
[14:15] Recommended MS1 mass accuracy setting: 8 ppm
[16:54] Searching decoys
[19:35] Main search
[24:55] Removing low confidence identifications
[25:05] Removing interfering precursors
[25:12] Training neural networks on 25390 target and 15268 decoy PSMs
[25:26] Training neural networks on 25390 target and 14632 decoy PSMs
[25:39] Precursors at 1% peptidoform FDR: 11712
[25:40] Number of IDs at 0.01 FDR: 12809
[25:40] Calculating protein q-values
[25:41] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[25:41] Quantification
[25:42] Precursors with scored PTMs at 1% FDR: 263 out of 322 considered
[25:42] Precursors with all scored PTM sites unoccupied at 1% FDR: 11656
[25:42] Precursors with PTMs localised (when required) with > 90% confidence: 256 out of 263
[25:43] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[25:43] File #2/12
[25:43] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[26:04] Pre-processing...
[26:06] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[26:07] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[30:21] RT window set to 2.43505
[30:21] IM window set to 0.0397779
[30:22] Recommended MS1 mass accuracy setting: 9 ppm
[33:18] Searching decoys
[36:25] Main search
[42:34] Removing low confidence identifications
[42:45] Removing interfering precursors
[42:52] Training neural networks on 28741 target and 17539 decoy PSMs
[43:07] Training neural networks on 28741 target and 17027 decoy PSMs
[43:18] Precursors at 1% peptidoform FDR: 12964
[43:19] Number of IDs at 0.01 FDR: 14146
[43:19] Calculating protein q-values
[43:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[43:20] Quantification
[43:20] Precursors with scored PTMs at 1% FDR: 301 out of 368 considered
[43:20] Precursors with all scored PTM sites unoccupied at 1% FDR: 12825
[43:20] Precursors with PTMs localised (when required) with > 90% confidence: 296 out of 301
[43:21] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[43:21] File #3/12
[43:21] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[43:38] Pre-processing...
[43:40] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[43:41] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[48:19] RT window set to 2.31987
[48:19] IM window set to 0.0392466
[48:20] Recommended MS1 mass accuracy setting: 9 ppm
[51:21] Searching decoys
[54:19] Main search
[60:08] Removing low confidence identifications
[60:18] Removing interfering precursors
[60:25] Training neural networks on 26402 target and 15522 decoy PSMs
[60:40] Training neural networks on 26402 target and 15187 decoy PSMs
[60:54] Precursors at 1% peptidoform FDR: 12096
[60:56] Number of IDs at 0.01 FDR: 13059
[60:56] Calculating protein q-values
[60:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[60:56] Quantification
[60:57] Precursors with scored PTMs at 1% FDR: 291 out of 352 considered
[60:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 11964
[60:57] Precursors with PTMs localised (when required) with > 90% confidence: 287 out of 291
[60:58] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[60:58] File #4/12
[60:58] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[61:16] Pre-processing...
[61:17] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[61:18] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[65:33] RT window set to 2.15462
[65:33] IM window set to 0.0406713
[65:34] Recommended MS1 mass accuracy setting: 9 ppm
[68:00] Searching decoys
[70:53] Main search
[76:42] Removing low confidence identifications
[76:53] Removing interfering precursors
[77:00] Training neural networks on 30088 target and 18416 decoy PSMs
[77:16] Training neural networks on 30088 target and 17853 decoy PSMs
[77:28] Precursors at 1% peptidoform FDR: 13262
[77:29] Number of IDs at 0.01 FDR: 14320
[77:29] Calculating protein q-values
[77:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[77:30] Quantification
[77:30] Precursors with scored PTMs at 1% FDR: 342 out of 407 considered
[77:30] Precursors with all scored PTM sites unoccupied at 1% FDR: 13104
[77:30] Precursors with PTMs localised (when required) with > 90% confidence: 328 out of 342
[77:31] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[77:31] File #5/12
[77:31] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[77:49] Pre-processing...
[77:51] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[77:52] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[82:38] RT window set to 2.42731
[82:38] IM window set to 0.0400498
[82:39] Recommended MS1 mass accuracy setting: 9 ppm
[85:56] Searching decoys
[89:15] Main search
[95:38] Removing low confidence identifications
[95:51] Removing interfering precursors
[96:00] Training neural networks on 27917 target and 16653 decoy PSMs
[96:17] Training neural networks on 27917 target and 16102 decoy PSMs
[96:28] Precursors at 1% peptidoform FDR: 12669
[96:30] Number of IDs at 0.01 FDR: 13753
[96:30] Calculating protein q-values
[96:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[96:31] Quantification
[96:32] Precursors with scored PTMs at 1% FDR: 329 out of 388 considered
[96:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 12598
[96:32] Precursors with PTMs localised (when required) with > 90% confidence: 323 out of 329
[96:32] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[96:33] File #6/12
[96:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[96:50] Pre-processing...
[96:51] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[96:52] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[101:25] RT window set to 2.20585
[101:25] IM window set to 0.0398481
[101:26] Recommended MS1 mass accuracy setting: 9 ppm
[104:34] Searching decoys
[107:39] Main search
[113:42] Removing low confidence identifications
[113:53] Removing interfering precursors
[114:01] Training neural networks on 29785 target and 18136 decoy PSMs
[114:17] Training neural networks on 29785 target and 17523 decoy PSMs
[114:29] Precursors at 1% peptidoform FDR: 12910
[114:30] Number of IDs at 0.01 FDR: 14010
[114:30] Calculating protein q-values
[114:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[114:31] Quantification
[114:32] Precursors with scored PTMs at 1% FDR: 322 out of 383 considered
[114:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 12721
[114:32] Precursors with PTMs localised (when required) with > 90% confidence: 313 out of 322
[114:33] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[114:33] File #7/12
[114:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[114:49] Pre-processing...
[114:50] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[114:51] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[119:25] RT window set to 2.584
[119:25] IM window set to 0.0380886
[119:25] Recommended MS1 mass accuracy setting: 9 ppm
[122:35] Searching decoys
[125:45] Main search
[131:30] Removing low confidence identifications
[131:40] Removing interfering precursors
[131:46] Training neural networks on 26156 target and 15374 decoy PSMs
[131:59] Training neural networks on 26156 target and 15034 decoy PSMs
[132:11] Precursors at 1% peptidoform FDR: 12442
[132:12] Number of IDs at 0.01 FDR: 13503
[132:12] Calculating protein q-values
[132:13] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[132:13] Quantification
[132:13] Precursors with scored PTMs at 1% FDR: 324 out of 379 considered
[132:13] Precursors with all scored PTM sites unoccupied at 1% FDR: 12337
[132:13] Precursors with PTMs localised (when required) with > 90% confidence: 319 out of 324
[132:14] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[132:14] File #8/12
[132:14] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[132:28] Pre-processing...
[132:29] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[132:30] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[136:41] RT window set to 2.50368
[136:41] IM window set to 0.0405478
[136:41] Recommended MS1 mass accuracy setting: 8 ppm
[139:42] Searching decoys
[142:38] Main search
[148:25] Removing low confidence identifications
[148:37] Removing interfering precursors
[148:43] Training neural networks on 28325 target and 16711 decoy PSMs
[148:58] Training neural networks on 28325 target and 15966 decoy PSMs
[149:08] Precursors at 1% peptidoform FDR: 13220
[149:09] Number of IDs at 0.01 FDR: 14392
[149:09] Calculating protein q-values
[149:10] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[149:10] Quantification
[149:10] Precursors with scored PTMs at 1% FDR: 326 out of 416 considered
[149:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 13003
[149:10] Precursors with PTMs localised (when required) with > 90% confidence: 320 out of 326
[149:11] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[149:11] File #9/12
[149:11] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[149:29] Pre-processing...
[149:30] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[149:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[154:27] RT window set to 2.5079
[154:27] IM window set to 0.0392272
[154:28] Recommended MS1 mass accuracy setting: 9 ppm
[157:45] Searching decoys
[160:45] Main search
[166:37] Removing low confidence identifications
[166:49] Removing interfering precursors
[166:58] Training neural networks on 30513 target and 18341 decoy PSMs
[167:15] Training neural networks on 30513 target and 17759 decoy PSMs
[167:28] Precursors at 1% peptidoform FDR: 13964
[167:30] Number of IDs at 0.01 FDR: 15148
[167:30] Calculating protein q-values
[167:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[167:31] Quantification
[167:31] Precursors with scored PTMs at 1% FDR: 369 out of 427 considered
[167:31] Precursors with all scored PTM sites unoccupied at 1% FDR: 13756
[167:31] Precursors with PTMs localised (when required) with > 90% confidence: 357 out of 369
[167:32] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[167:32] File #10/12
[167:32] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[167:49] Pre-processing...
[167:51] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[167:52] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[172:25] RT window set to 2.49409
[172:25] IM window set to 0.0390822
[172:25] Recommended MS1 mass accuracy setting: 9 ppm
[175:33] Searching decoys
[178:46] Main search
[185:04] Removing low confidence identifications
[185:15] Removing interfering precursors
[185:22] Training neural networks on 31025 target and 18583 decoy PSMs
[185:39] Training neural networks on 31025 target and 18001 decoy PSMs
[185:50] Precursors at 1% peptidoform FDR: 13776
[185:52] Number of IDs at 0.01 FDR: 15154
[185:52] Calculating protein q-values
[185:52] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[185:53] Quantification
[185:53] Precursors with scored PTMs at 1% FDR: 367 out of 453 considered
[185:53] Precursors with all scored PTM sites unoccupied at 1% FDR: 13506
[185:53] Precursors with PTMs localised (when required) with > 90% confidence: 358 out of 367
[185:54] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[185:54] File #11/12
[185:54] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[186:09] Pre-processing...
[186:11] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[186:12] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[190:29] RT window set to 2.14002
[190:29] IM window set to 0.0399054
[190:29] Recommended MS1 mass accuracy setting: 9 ppm
[193:10] Searching decoys
[195:36] Main search
[201:38] Removing low confidence identifications
[201:50] Removing interfering precursors
[201:57] Training neural networks on 31369 target and 18915 decoy PSMs
[202:14] Training neural networks on 31369 target and 18196 decoy PSMs
[202:26] Precursors at 1% peptidoform FDR: 13851
[202:28] Number of IDs at 0.01 FDR: 15029
[202:28] Calculating protein q-values
[202:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[202:28] Quantification
[202:29] Precursors with scored PTMs at 1% FDR: 372 out of 437 considered
[202:29] Precursors with all scored PTM sites unoccupied at 1% FDR: 13758
[202:29] Precursors with PTMs localised (when required) with > 90% confidence: 358 out of 372
[202:30] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[202:30] File #12/12
[202:30] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[202:49] Pre-processing...
[202:51] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[202:51] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[207:53] RT window set to 2.28294
[207:53] IM window set to 0.0400936
[207:54] Recommended MS1 mass accuracy setting: 9 ppm
[211:27] Searching decoys
[214:29] Main search
[221:14] Removing low confidence identifications
[221:25] Removing interfering precursors
[221:32] Training neural networks on 31353 target and 18873 decoy PSMs
[221:51] Training neural networks on 31353 target and 18176 decoy PSMs
[222:05] Precursors at 1% peptidoform FDR: 13990
[222:07] Number of IDs at 0.01 FDR: 15199
[222:07] Calculating protein q-values
[222:08] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[222:08] Quantification
[222:09] Precursors with scored PTMs at 1% FDR: 370 out of 426 considered
[222:09] Precursors with all scored PTM sites unoccupied at 1% FDR: 13783
[222:09] Precursors with PTMs localised (when required) with > 90% confidence: 362 out of 370
[222:10] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[222:10] Cross-run analysis
[222:10] Reading quantification information: 12 files
[222:31] Target precursors at 1% global q-value: 22693
[222:31] Quantifying peptides
[222:48] Quantification parameters: 0.339867, 0.00255383, 0.011244, 0.0141637, 0.154201, 0.109875, 0.315389, 0.192323, 0.245307, 0.0140513, 0.113708, 0.0197052, 0.436127, 0.229968, 0.22149, 0.0132874
[222:51] Assembling protein groups
[222:51] Quantifying proteins
[222:51] Calculating q-values for protein and gene groups
[222:54] Calculating global q-values for protein and gene groups
[222:54] Protein groups with global q-value <= 0.01: 2911
[222:55] Compressed report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.5.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[222:55] Stats report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.5.0/report.stats.tsv
[222:55] Generating spectral library:
[222:56] 24237 target and 1365 decoy precursors saved
WARNING: 1407 precursors without any fragments annotated were skipped
[222:56] Spectral library saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.5.0/report-lib.parquet

