
DIA-NN 2.2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on May 29 2025 15:05:00
Current date and time: Wed Apr 22 05:40:08 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.2.0/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_v2.2.0/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
[0:06] Processing FASTA
[0:09] Assembling elution groups
[0:18] 8103720 precursors generated
[0:18] Protein names missing for some isoforms
[0:18] Gene names missing for some isoforms
[0:18] Library contains 31680 proteins, and 0 genes
[0:24] [0:38] [6:26] [7:10] [7:12] [7:16] Saving the library to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.2.0/report-lib.predicted.speclib
[7:21] Initialising library
[7:34] Loading spectral library /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.2.0/report-lib.predicted.speclib
[7:37] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:39] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups.
[7:39] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:39] Annotating library proteins with information from the FASTA database
[7:39] Protein names missing for some isoforms
[7:39] Gene names missing for some isoforms
[7:39] Library contains 31680 proteins, and 0 genes
[7:45] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[8:00] File #1/12
[8:00] 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 15 ppm
[8:06] Pre-processing...
[8:07] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[8:08] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[11:37] RT window set to 2.52446
[11:37] IM window set to 0.0399599
[11:37] Peak width: 3.536
[11:37] Scan window radius set to 7
[11:37] Recommended MS1 mass accuracy setting: 8 ppm
[14:09] Searching decoys
[17:06] Main search
[22:53] Removing low confidence identifications
[23:00] Removing interfering precursors
[23:05] Training neural networks on 20656 target and 10497 decoy PSMs
[23:15] Training neural networks on 20656 target and 9731 decoy PSMs
[23:20] Number of IDs at 0.01 FDR: 11851
[23:20] Precursors at 1% peptidoform FDR: 11276
[23:21] Calculating protein q-values
[23:22] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[23:22] Quantification
[23:22] Precursors with scored PTMs at 1% FDR: 242 out of 275 considered
[23:22] Precursors with all scored PTM sites unoccupied at 1% FDR: 11034
[23:22] Precursors with PTMs localised (when required) with > 90% confidence: 235 out of 242
[23:23] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[23:23] File #2/12
[23:23] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[23:30] Pre-processing...
[23:31] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[23:31] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[27:00] RT window set to 2.53389
[27:00] IM window set to 0.0402525
[27:01] Recommended MS1 mass accuracy setting: 9 ppm
[29:36] Searching decoys
[32:41] Main search
[38:41] Removing low confidence identifications
[38:47] Removing interfering precursors
[38:53] Training neural networks on 23729 target and 12154 decoy PSMs
[39:03] Training neural networks on 23729 target and 11457 decoy PSMs
[39:08] Number of IDs at 0.01 FDR: 13520
[39:09] Precursors at 1% peptidoform FDR: 12748
[39:10] Calculating protein q-values
[39:10] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[39:10] Quantification
[39:10] Precursors with scored PTMs at 1% FDR: 271 out of 315 considered
[39:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 12477
[39:10] Precursors with PTMs localised (when required) with > 90% confidence: 266 out of 271
[39:11] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[39:11] File #3/12
[39:11] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[39:18] Pre-processing...
[39:19] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[39:20] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[42:46] RT window set to 2.28514
[42:46] IM window set to 0.0393618
[42:47] Recommended MS1 mass accuracy setting: 8 ppm
[45:09] Searching decoys
[47:56] Main search
[53:20] Removing low confidence identifications
[53:27] Removing interfering precursors
[53:32] Training neural networks on 22149 target and 11218 decoy PSMs
[53:41] Training neural networks on 22149 target and 10420 decoy PSMs
[53:46] Number of IDs at 0.01 FDR: 12418
[53:47] Precursors at 1% peptidoform FDR: 11939
[53:47] Calculating protein q-values
[53:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[53:48] Quantification
[53:48] Precursors with scored PTMs at 1% FDR: 269 out of 293 considered
[53:48] Precursors with all scored PTM sites unoccupied at 1% FDR: 11670
[53:48] Precursors with PTMs localised (when required) with > 90% confidence: 265 out of 269
[53:49] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[53:49] File #4/12
[53:49] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[53:56] Pre-processing...
[53:57] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[53:58] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[57:30] RT window set to 2.34085
[57:30] IM window set to 0.0405278
[57:30] Recommended MS1 mass accuracy setting: 9 ppm
[60:00] Searching decoys
[62:59] Main search
[68:50] Removing low confidence identifications
[68:57] Removing interfering precursors
[69:02] Training neural networks on 24631 target and 12625 decoy PSMs
[69:12] Training neural networks on 24631 target and 11694 decoy PSMs
[69:17] Number of IDs at 0.01 FDR: 13638
[69:18] Precursors at 1% peptidoform FDR: 13035
[69:18] Calculating protein q-values
[69:19] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[69:19] Quantification
[69:19] Precursors with scored PTMs at 1% FDR: 311 out of 341 considered
[69:19] Precursors with all scored PTM sites unoccupied at 1% FDR: 12724
[69:19] Precursors with PTMs localised (when required) with > 90% confidence: 301 out of 311
[69:20] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[69:20] File #5/12
[69:20] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[69:27] Pre-processing...
[69:28] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[69:29] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[73:00] RT window set to 2.4143
[73:00] IM window set to 0.0406901
[73:01] Recommended MS1 mass accuracy setting: 9 ppm
[75:36] Searching decoys
[78:42] Main search
[84:45] Removing low confidence identifications
[84:52] Removing interfering precursors
[84:57] Training neural networks on 24435 target and 12457 decoy PSMs
[85:07] Training neural networks on 24435 target and 11677 decoy PSMs
[85:12] Number of IDs at 0.01 FDR: 13419
[85:13] Precursors at 1% peptidoform FDR: 12861
[85:14] Calculating protein q-values
[85:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[85:14] Quantification
[85:15] Precursors with scored PTMs at 1% FDR: 305 out of 328 considered
[85:15] Precursors with all scored PTM sites unoccupied at 1% FDR: 12556
[85:15] Precursors with PTMs localised (when required) with > 90% confidence: 300 out of 305
[85:16] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[85:16] File #6/12
[85:16] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[85:22] Pre-processing...
[85:24] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[85:24] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[88:50] RT window set to 2.24971
[88:50] IM window set to 0.0399668
[88:50] Recommended MS1 mass accuracy setting: 9 ppm
[91:17] Searching decoys
[94:11] Main search
[99:47] Removing low confidence identifications
[99:54] Removing interfering precursors
[99:59] Training neural networks on 24218 target and 12506 decoy PSMs
[100:10] Training neural networks on 24218 target and 11659 decoy PSMs
[100:15] Number of IDs at 0.01 FDR: 13146
[100:16] Precursors at 1% peptidoform FDR: 12607
[100:17] Calculating protein q-values
[100:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[100:17] Quantification
[100:17] Precursors with scored PTMs at 1% FDR: 297 out of 322 considered
[100:17] Precursors with all scored PTM sites unoccupied at 1% FDR: 12310
[100:17] Precursors with PTMs localised (when required) with > 90% confidence: 288 out of 297
[100:18] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[100:18] File #7/12
[100:18] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[100:25] Pre-processing...
[100:26] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[100:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[103:55] RT window set to 2.42426
[103:55] IM window set to 0.0395421
[103:56] Recommended MS1 mass accuracy setting: 9 ppm
[106:25] Searching decoys
[109:15] Main search
[114:49] Removing low confidence identifications
[114:55] Removing interfering precursors
[115:00] Training neural networks on 23723 target and 11669 decoy PSMs
[115:11] Training neural networks on 23723 target and 11187 decoy PSMs
[115:16] Number of IDs at 0.01 FDR: 13388
[115:16] Precursors at 1% peptidoform FDR: 12826
[115:17] Calculating protein q-values
[115:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[115:18] Quantification
[115:18] Precursors with scored PTMs at 1% FDR: 290 out of 316 considered
[115:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 12536
[115:18] Precursors with PTMs localised (when required) with > 90% confidence: 284 out of 290
[115:19] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[115:19] File #8/12
[115:19] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[115:25] Pre-processing...
[115:26] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[115:27] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[118:48] RT window set to 2.4251
[118:48] IM window set to 0.0387765
[118:49] Recommended MS1 mass accuracy setting: 8 ppm
[121:11] Searching decoys
[123:54] Main search
[129:10] Removing low confidence identifications
[129:17] Removing interfering precursors
[129:22] Training neural networks on 24090 target and 11916 decoy PSMs
[129:32] Training neural networks on 24090 target and 11251 decoy PSMs
[129:37] Number of IDs at 0.01 FDR: 13811
[129:38] Precursors at 1% peptidoform FDR: 13092
[129:38] Calculating protein q-values
[129:39] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[129:39] Quantification
[129:39] Precursors with scored PTMs at 1% FDR: 307 out of 338 considered
[129:39] Precursors with all scored PTM sites unoccupied at 1% FDR: 12785
[129:39] Precursors with PTMs localised (when required) with > 90% confidence: 302 out of 307
[129:40] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[129:40] File #9/12
[129:40] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[129:47] Pre-processing...
[129:48] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[129:49] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[133:14] RT window set to 2.65896
[133:14] IM window set to 0.0388117
[133:15] Recommended MS1 mass accuracy setting: 9 ppm
[135:48] Searching decoys
[138:51] Main search
[144:55] Removing low confidence identifications
[145:03] Removing interfering precursors
[145:09] Training neural networks on 23782 target and 11456 decoy PSMs
[145:20] Training neural networks on 23782 target and 10849 decoy PSMs
[145:25] Number of IDs at 0.01 FDR: 14089
[145:25] Precursors at 1% peptidoform FDR: 13556
[145:26] Calculating protein q-values
[145:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[145:27] Quantification
[145:27] Precursors with scored PTMs at 1% FDR: 324 out of 357 considered
[145:27] Precursors with all scored PTM sites unoccupied at 1% FDR: 13232
[145:27] Precursors with PTMs localised (when required) with > 90% confidence: 316 out of 324
[145:28] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[145:28] File #10/12
[145:28] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[145:35] Pre-processing...
[145:36] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[145:37] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[149:06] RT window set to 2.46779
[149:06] IM window set to 0.0392273
[149:07] Recommended MS1 mass accuracy setting: 9 ppm
[151:35] Searching decoys
[154:31] Main search
[160:18] Removing low confidence identifications
[160:24] Removing interfering precursors
[160:29] Training neural networks on 26801 target and 13639 decoy PSMs
[160:39] Training neural networks on 26801 target and 12825 decoy PSMs
[160:44] Number of IDs at 0.01 FDR: 14632
[160:44] Precursors at 1% peptidoform FDR: 14031
[160:45] Calculating protein q-values
[160:46] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[160:46] Quantification
[160:46] Precursors with scored PTMs at 1% FDR: 344 out of 373 considered
[160:46] Precursors with all scored PTM sites unoccupied at 1% FDR: 13687
[160:46] Precursors with PTMs localised (when required) with > 90% confidence: 339 out of 344
[160:47] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[160:47] File #11/12
[160:47] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[160:54] Pre-processing...
[160:55] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[160:55] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[164:29] RT window set to 2.32984
[164:29] IM window set to 0.0405483
[164:29] Recommended MS1 mass accuracy setting: 9 ppm
[166:56] Searching decoys
[169:47] Main search
[175:23] Removing low confidence identifications
[175:30] Removing interfering precursors
[175:35] Training neural networks on 24985 target and 12260 decoy PSMs
[175:45] Training neural networks on 24985 target and 11677 decoy PSMs
[175:50] Number of IDs at 0.01 FDR: 14062
[175:51] Precursors at 1% peptidoform FDR: 13549
[175:52] Calculating protein q-values
[175:52] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[175:52] Quantification
[175:53] Precursors with scored PTMs at 1% FDR: 344 out of 371 considered
[175:53] Precursors with all scored PTM sites unoccupied at 1% FDR: 13205
[175:53] Precursors with PTMs localised (when required) with > 90% confidence: 334 out of 344
[175:53] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[175:53] File #12/12
[175:53] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[176:00] Pre-processing...
[176:01] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[176:02] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[179:28] RT window set to 2.25798
[179:28] IM window set to 0.0394571
[179:28] Recommended MS1 mass accuracy setting: 9 ppm
[181:52] Searching decoys
[184:42] Main search
[190:18] Removing low confidence identifications
[190:26] Removing interfering precursors
[190:33] Training neural networks on 28219 target and 14587 decoy PSMs
[190:45] Training neural networks on 28219 target and 13623 decoy PSMs
[190:51] Number of IDs at 0.01 FDR: 14861
[190:52] Precursors at 1% peptidoform FDR: 14037
[190:53] Calculating protein q-values
[190:53] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[190:53] Quantification
[190:54] Precursors with scored PTMs at 1% FDR: 341 out of 361 considered
[190:54] Precursors with all scored PTM sites unoccupied at 1% FDR: 13696
[190:54] Precursors with PTMs localised (when required) with > 90% confidence: 336 out of 341
[190:55] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[190:55] Cross-run analysis
[190:55] Reading quantification information: 12 files
[191:11] Quantifying peptides
[191:42] Quantification parameters: 0.335829, 0.0026084, 0.0110738, 0.0146022, 0.183804, 0.132544, 0.33854, 0.159504, 0.233418, 0.0144606, 0.127216, 0.0494383, 0.405396, 0.212421, 0.200849, 0.0134611
[191:57] Assembling protein groups
[191:58] Quantifying proteins
[191:58] Calculating q-values for protein and gene groups
[192:01] Calculating global q-values for protein and gene groups
[192:01] Protein groups with global q-value <= 0.01: 2818
[192:02] Compressed report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.2.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[192:02] Stats report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.2.0/report.stats.tsv
[192:02] Generating spectral library:
[192:02] 20834 target and 214 decoy precursors saved
WARNING: 201 precursors without any fragments annotated were skipped
[192:02] Spectral library saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.2.0/report-lib.parquet

