
DIA-NN 2.1.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Mar 23 2025 15:49:03
Current date and time: Wed Apr 22 09:42:23 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.1.0/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_v2.1.0/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
[0:17] 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] [0:37] [6:19] [7:02] [7:04] [7:08] Saving the library to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.1.0/report-lib.predicted.speclib
[7:12] Initialising library
[7:38] Loading spectral library /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.1.0/report-lib.predicted.speclib
[7:41] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:43] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups.
[7:43] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[7:43] Annotating library proteins with information from the FASTA database
[7:43] Protein names missing for some isoforms
[7:43] Gene names missing for some isoforms
[7:43] Library contains 31680 proteins, and 0 genes
[7:49] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[8:15] File #1/12
[8:15] 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
[8:20] Pre-processing...
[8:30] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[8:31] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[12:42] RT window set to 2.56883
[12:42] IM window set to 0.0406611
[12:42] Peak width: 3.212
[12:42] Scan window radius set to 7
[12:42] Recommended MS1 mass accuracy setting: 8 ppm
[16:59] Searching decoys
[19:42] Main search
[25:10] Removing low confidence identifications
[25:18] Removing interfering precursors
[25:24] Training neural networks on 20345 target and 10351 decoy PSMs
[25:36] Training neural networks on 20345 target and 9587 decoy PSMs
[25:41] Number of IDs at 0.01 FDR: 11707
[25:41] Precursors at 1% peptidoform FDR: 10977
[25:42] Calculating protein q-values
[25:42] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[25:42] Quantification
[25:43] Precursors with scored PTMs at 1% FDR: 224 out of 273 considered
[25:43] Precursors with all scored PTM sites unoccupied at 1% FDR: 10753
[25:43] Precursors with PTMs localised (when required) with > 90% confidence: 218 out of 224
[25:44] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[25:44] File #2/12
[25:44] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[25:48] Pre-processing...
[25:58] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[25:59] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[30:05] RT window set to 2.4165
[30:05] IM window set to 0.0410405
[30:05] Recommended MS1 mass accuracy setting: 9 ppm
[34:08] Searching decoys
[36:44] Main search
[41:47] Removing low confidence identifications
[41:54] Removing interfering precursors
[42:01] Training neural networks on 22304 target and 11332 decoy PSMs
[42:13] Training neural networks on 22304 target and 10491 decoy PSMs
[42:19] Number of IDs at 0.01 FDR: 12927
[42:19] Precursors at 1% peptidoform FDR: 12339
[42:20] Calculating protein q-values
[42:21] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[42:21] Quantification
[42:21] Precursors with scored PTMs at 1% FDR: 271 out of 306 considered
[42:21] Precursors with all scored PTM sites unoccupied at 1% FDR: 12068
[42:21] Precursors with PTMs localised (when required) with > 90% confidence: 265 out of 271
[42:22] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[42:22] File #3/12
[42:22] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[42:32] Pre-processing...
[42:43] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[42:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[46:58] RT window set to 2.1841
[46:58] IM window set to 0.0389967
[46:58] Recommended MS1 mass accuracy setting: 9 ppm
[50:52] Searching decoys
[53:16] Main search
[57:57] Removing low confidence identifications
[58:05] Removing interfering precursors
[58:12] Training neural networks on 21655 target and 10806 decoy PSMs
[58:24] Training neural networks on 21655 target and 9967 decoy PSMs
[58:30] Number of IDs at 0.01 FDR: 12422
[58:30] Precursors at 1% peptidoform FDR: 11752
[58:31] Calculating protein q-values
[58:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[58:32] Quantification
[58:32] Precursors with scored PTMs at 1% FDR: 262 out of 308 considered
[58:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 11490
[58:32] Precursors with PTMs localised (when required) with > 90% confidence: 257 out of 262
[58:33] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[58:33] File #4/12
[58:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[58:43] Pre-processing...
[58:55] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[58:56] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:08] RT window set to 2.26845
[63:08] IM window set to 0.0412407
[63:08] Recommended MS1 mass accuracy setting: 10 ppm
[67:16] Searching decoys
[69:57] Main search
[75:08] Removing low confidence identifications
[75:16] Removing interfering precursors
[75:22] Training neural networks on 22597 target and 11311 decoy PSMs
[75:33] Training neural networks on 22597 target and 10397 decoy PSMs
[75:39] Number of IDs at 0.01 FDR: 13153
[75:39] Precursors at 1% peptidoform FDR: 12573
[75:40] Calculating protein q-values
[75:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[75:40] Quantification
[75:41] Precursors with scored PTMs at 1% FDR: 291 out of 339 considered
[75:41] Precursors with all scored PTM sites unoccupied at 1% FDR: 12282
[75:41] Precursors with PTMs localised (when required) with > 90% confidence: 283 out of 291
[75:41] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[75:41] File #5/12
[75:41] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[75:52] Pre-processing...
[76:04] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[76:05] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[80:22] RT window set to 2.30265
[80:22] IM window set to 0.0415569
[80:22] Recommended MS1 mass accuracy setting: 8 ppm
[84:28] Searching decoys
[87:12] Main search
[92:27] Removing low confidence identifications
[92:34] Removing interfering precursors
[92:41] Training neural networks on 22302 target and 11151 decoy PSMs
[92:53] Training neural networks on 22302 target and 10383 decoy PSMs
[92:58] Number of IDs at 0.01 FDR: 12968
[92:59] Precursors at 1% peptidoform FDR: 12318
[93:00] Calculating protein q-values
[93:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[93:00] Quantification
[93:01] Precursors with scored PTMs at 1% FDR: 285 out of 320 considered
[93:01] Precursors with all scored PTM sites unoccupied at 1% FDR: 12033
[93:01] Precursors with PTMs localised (when required) with > 90% confidence: 280 out of 285
[93:01] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[93:01] File #6/12
[93:01] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[93:12] Pre-processing...
[93:24] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[93:25] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[97:36] RT window set to 2.19841
[97:36] IM window set to 0.0389977
[97:36] Recommended MS1 mass accuracy setting: 9 ppm
[101:38] Searching decoys
[104:10] Main search
[109:01] Removing low confidence identifications
[109:09] Removing interfering precursors
[109:15] Training neural networks on 22464 target and 11579 decoy PSMs
[109:26] Training neural networks on 22464 target and 10694 decoy PSMs
[109:31] Number of IDs at 0.01 FDR: 12675
[109:32] Precursors at 1% peptidoform FDR: 12098
[109:33] Calculating protein q-values
[109:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[109:33] Quantification
[109:34] Precursors with scored PTMs at 1% FDR: 281 out of 321 considered
[109:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 11817
[109:34] Precursors with PTMs localised (when required) with > 90% confidence: 275 out of 281
[109:34] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[109:34] File #7/12
[109:34] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[109:45] Pre-processing...
[109:55] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[109:56] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[114:02] RT window set to 2.45893
[114:02] IM window set to 0.0405327
[114:03] Recommended MS1 mass accuracy setting: 8 ppm
[118:07] Searching decoys
[120:41] Main search
[125:33] Removing low confidence identifications
[125:41] Removing interfering precursors
[125:47] Training neural networks on 21153 target and 10110 decoy PSMs
[125:58] Training neural networks on 21153 target and 9531 decoy PSMs
[126:03] Number of IDs at 0.01 FDR: 12723
[126:04] Precursors at 1% peptidoform FDR: 12143
[126:05] Calculating protein q-values
[126:05] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[126:05] Quantification
[126:06] Precursors with scored PTMs at 1% FDR: 277 out of 311 considered
[126:06] Precursors with all scored PTM sites unoccupied at 1% FDR: 11866
[126:06] Precursors with PTMs localised (when required) with > 90% confidence: 269 out of 277
[126:06] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[126:07] File #8/12
[126:07] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[126:16] Pre-processing...
[126:25] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[126:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[130:12] RT window set to 2.30191
[130:12] IM window set to 0.0392376
[130:12] Recommended MS1 mass accuracy setting: 8 ppm
[133:48] Searching decoys
[136:02] Main search
[140:21] Removing low confidence identifications
[140:29] Removing interfering precursors
[140:36] Training neural networks on 22267 target and 10933 decoy PSMs
[140:47] Training neural networks on 22267 target and 10125 decoy PSMs
[140:53] Number of IDs at 0.01 FDR: 13061
[140:53] Precursors at 1% peptidoform FDR: 12428
[140:54] Calculating protein q-values
[140:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[140:54] Quantification
[140:55] Precursors with scored PTMs at 1% FDR: 302 out of 340 considered
[140:55] Precursors with all scored PTM sites unoccupied at 1% FDR: 12126
[140:55] Precursors with PTMs localised (when required) with > 90% confidence: 300 out of 302
[140:55] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[140:55] File #9/12
[140:55] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[141:06] Pre-processing...
[141:17] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[141:18] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[145:24] RT window set to 2.23674
[145:24] IM window set to 0.0398425
[145:25] Recommended MS1 mass accuracy setting: 8 ppm
[149:20] Searching decoys
[151:45] Main search
[156:28] Removing low confidence identifications
[156:37] Removing interfering precursors
[156:44] Training neural networks on 23489 target and 11568 decoy PSMs
[156:57] Training neural networks on 23489 target and 10878 decoy PSMs
[157:03] Number of IDs at 0.01 FDR: 13883
[157:04] Precursors at 1% peptidoform FDR: 13335
[157:05] Calculating protein q-values
[157:05] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[157:05] Quantification
[157:06] Precursors with scored PTMs at 1% FDR: 312 out of 356 considered
[157:06] Precursors with all scored PTM sites unoccupied at 1% FDR: 13023
[157:06] Precursors with PTMs localised (when required) with > 90% confidence: 304 out of 312
[157:06] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[157:06] File #10/12
[157:06] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[157:17] Pre-processing...
[157:29] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[157:30] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[161:44] RT window set to 2.59538
[161:44] IM window set to 0.0406409
[161:44] Recommended MS1 mass accuracy setting: 9 ppm
[165:58] Searching decoys
[168:55] Main search
[174:35] Removing low confidence identifications
[174:43] Removing interfering precursors
[174:49] Training neural networks on 24929 target and 12585 decoy PSMs
[175:01] Training neural networks on 24929 target and 11919 decoy PSMs
[175:07] Number of IDs at 0.01 FDR: 14049
[175:07] Precursors at 1% peptidoform FDR: 13383
[175:08] Calculating protein q-values
[175:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[175:09] Quantification
[175:09] Precursors with scored PTMs at 1% FDR: 319 out of 351 considered
[175:09] Precursors with all scored PTM sites unoccupied at 1% FDR: 13064
[175:09] Precursors with PTMs localised (when required) with > 90% confidence: 313 out of 319
[175:10] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[175:10] File #11/12
[175:10] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[175:20] Pre-processing...
[175:32] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[175:33] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[179:42] RT window set to 2.31285
[179:42] IM window set to 0.0402373
[179:42] Recommended MS1 mass accuracy setting: 9 ppm
[183:46] Searching decoys
[186:23] Main search
[191:25] Removing low confidence identifications
[191:32] Removing interfering precursors
[191:39] Training neural networks on 23219 target and 10962 decoy PSMs
[191:51] Training neural networks on 23219 target and 10394 decoy PSMs
[191:56] Number of IDs at 0.01 FDR: 13515
[191:57] Precursors at 1% peptidoform FDR: 12987
[191:57] Calculating protein q-values
[191:58] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[191:58] Quantification
[191:58] Precursors with scored PTMs at 1% FDR: 329 out of 361 considered
[191:58] Precursors with all scored PTM sites unoccupied at 1% FDR: 12658
[191:58] Precursors with PTMs localised (when required) with > 90% confidence: 318 out of 329
[191:59] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[191:59] File #12/12
[191:59] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[192:10] Pre-processing...
[192:22] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[192:23] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[196:31] RT window set to 2.23074
[196:31] IM window set to 0.0393503
[196:31] Recommended MS1 mass accuracy setting: 9 ppm
[200:31] Searching decoys
[203:07] Main search
[208:03] Removing low confidence identifications
[208:11] Removing interfering precursors
[208:17] Training neural networks on 27060 target and 13873 decoy PSMs
[208:30] Training neural networks on 27060 target and 12913 decoy PSMs
[208:35] Number of IDs at 0.01 FDR: 14617
[208:35] Precursors at 1% peptidoform FDR: 13793
[208:36] Calculating protein q-values
[208:37] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[208:37] Quantification
[208:37] Precursors with scored PTMs at 1% FDR: 326 out of 360 considered
[208:37] Precursors with all scored PTM sites unoccupied at 1% FDR: 13467
[208:37] Precursors with PTMs localised (when required) with > 90% confidence: 319 out of 326
[208:38] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[208:38] Cross-run analysis
[208:38] Reading quantification information: 12 files
[208:53] Quantifying peptides
[209:19] Quantification parameters: 0.329776, 0.00265376, 0.0109072, 0.0327509, 0.146006, 0.114534, 0.333972, 0.134443, 0.219566, 0.0150113, 0.117632, 0.0465621, 0.388483, 0.222906, 0.20618, 0.0141269
[209:32] Assembling protein groups
[209:33] Quantifying proteins
[209:33] Calculating q-values for protein and gene groups
[209:36] Calculating global q-values for protein and gene groups
[209:36] Protein groups with global q-value <= 0.01: 2715
[209:37] Compressed report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.1.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[209:37] Stats report saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.1.0/report.stats.tsv
[209:37] Generating spectral library:
[209:37] 20331 target and 207 decoy precursors saved
WARNING: 187 precursors without any fragments annotated were skipped
[209:37] Spectral library saved to /home/robbe/PB_output/results/test_run/PYE_diaPASEF/diann_v2.1.0/report-lib.parquet

