
DIA-NN 2.5.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 12 2026 10:45:33
Current date and time: Thu Apr 30 11:45:28 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/MBRDIANN2.5/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 --reanalyse 

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
MBR enabled; .quant files will only be saved to disk during the first pass
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
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:24] [0:46] [7:50] [8:39] [8:45] [8:51] Saving the library to /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report-lib.predicted.speclib
[8:56] Initialising library
[9:15] Loading spectral library /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report-lib.predicted.speclib
[9:18] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[9:20] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups (targets and decoys).
[9:20] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[9:21] Annotating library proteins with information from the FASTA database
[9:21] Protein names missing for some isoforms
[9:21] Gene names missing for some isoforms
[9:21] Library contains 31680 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[9:27] Initialising library

First pass: generating a spectral library from DIA data

[9:43] File #1/12
[9:43] 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
[10:02] Pre-processing...
[10:03] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[10:04] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[14:32] RT window set to 2.2836
[14:32] IM window set to 0.0387907
[14:32] Peak width: 3.48
[14:32] Scan window radius set to 7
[14:33] Recommended MS1 mass accuracy setting: 8 ppm
[17:26] Searching decoys
[20:14] Main search
[25:37] Removing low confidence identifications
[25:46] Removing interfering precursors
[25:52] Training neural networks on 25390 target and 15268 decoy PSMs
[26:04] Training neural networks on 25390 target and 14632 decoy PSMs
[26:12] Precursors at 1% peptidoform FDR: 11712
[26:14] Number of IDs at 0.01 FDR: 12809
[26:14] Calculating protein q-values
[26:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[26:14] Quantification
[26:15] Precursors with scored PTMs at 1% FDR: 263 out of 322 considered
[26:15] Precursors with all scored PTM sites unoccupied at 1% FDR: 11656
[26:15] Precursors with PTMs localised (when required) with > 90% confidence: 256 out of 263
[26:15] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[26:15] File #2/12
[26:15] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[26:27] Pre-processing...
[26:28] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[26:29] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[30:53] RT window set to 2.43505
[30:53] IM window set to 0.0397779
[30:54] Recommended MS1 mass accuracy setting: 9 ppm
[33:53] Searching decoys
[36:55] Main search
[42:34] Removing low confidence identifications
[42:43] Removing interfering precursors
[42:49] Training neural networks on 28741 target and 17539 decoy PSMs
[43:02] Training neural networks on 28741 target and 17027 decoy PSMs
[43:12] Precursors at 1% peptidoform FDR: 12964
[43:13] Number of IDs at 0.01 FDR: 14146
[43:13] Calculating protein q-values
[43:13] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[43:13] Quantification
[43:14] Precursors with scored PTMs at 1% FDR: 301 out of 368 considered
[43:14] Precursors with all scored PTM sites unoccupied at 1% FDR: 12825
[43:14] Precursors with PTMs localised (when required) with > 90% confidence: 296 out of 301
[43:14] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[43:14] File #3/12
[43:14] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[43:26] Pre-processing...
[43:28] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[43:29] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[47:37] RT window set to 2.31987
[47:37] IM window set to 0.0392466
[47:38] Recommended MS1 mass accuracy setting: 9 ppm
[50:28] Searching decoys
[53:25] Main search
[59:03] Removing low confidence identifications
[59:11] Removing interfering precursors
[59:17] Training neural networks on 26402 target and 15522 decoy PSMs
[59:28] Training neural networks on 26402 target and 15187 decoy PSMs
[59:37] Precursors at 1% peptidoform FDR: 12096
[59:38] Number of IDs at 0.01 FDR: 13059
[59:38] Calculating protein q-values
[59:39] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[59:39] Quantification
[59:39] Precursors with scored PTMs at 1% FDR: 291 out of 352 considered
[59:39] Precursors with all scored PTM sites unoccupied at 1% FDR: 11964
[59:39] Precursors with PTMs localised (when required) with > 90% confidence: 287 out of 291
[59:40] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[59:40] File #4/12
[59:40] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[59:52] Pre-processing...
[59:53] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[59:54] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:17] RT window set to 2.15462
[64:17] IM window set to 0.0406713
[64:17] Recommended MS1 mass accuracy setting: 9 ppm
[66:37] Searching decoys
[69:21] Main search
[74:40] Removing low confidence identifications
[74:48] Removing interfering precursors
[74:53] Training neural networks on 30088 target and 18416 decoy PSMs
[75:06] Training neural networks on 30088 target and 17853 decoy PSMs
[75:15] Precursors at 1% peptidoform FDR: 13262
[75:16] Number of IDs at 0.01 FDR: 14320
[75:16] Calculating protein q-values
[75:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[75:17] Quantification
[75:18] Precursors with scored PTMs at 1% FDR: 342 out of 407 considered
[75:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 13104
[75:18] Precursors with PTMs localised (when required) with > 90% confidence: 328 out of 342
[75:18] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[75:18] File #5/12
[75:18] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[75:30] Pre-processing...
[75:31] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[75:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[79:53] RT window set to 2.42731
[79:53] IM window set to 0.0400498
[79:53] Recommended MS1 mass accuracy setting: 9 ppm
[82:49] Searching decoys
[85:53] Main search
[91:48] Removing low confidence identifications
[91:57] Removing interfering precursors
[92:03] Training neural networks on 27917 target and 16653 decoy PSMs
[92:15] Training neural networks on 27917 target and 16102 decoy PSMs
[92:24] Precursors at 1% peptidoform FDR: 12669
[92:26] Number of IDs at 0.01 FDR: 13753
[92:26] Calculating protein q-values
[92:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[92:26] Quantification
[92:27] Precursors with scored PTMs at 1% FDR: 329 out of 388 considered
[92:27] Precursors with all scored PTM sites unoccupied at 1% FDR: 12598
[92:27] Precursors with PTMs localised (when required) with > 90% confidence: 323 out of 329
[92:27] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[92:27] File #6/12
[92:27] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[92:40] Pre-processing...
[92:41] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[92:42] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[96:55] RT window set to 2.20585
[96:55] IM window set to 0.0398481
[96:55] Recommended MS1 mass accuracy setting: 9 ppm
[99:44] Searching decoys
[102:33] Main search
[107:51] Removing low confidence identifications
[108:00] Removing interfering precursors
[108:09] Training neural networks on 29785 target and 18136 decoy PSMs
[108:22] Training neural networks on 29785 target and 17523 decoy PSMs
[108:33] Precursors at 1% peptidoform FDR: 12910
[108:35] Number of IDs at 0.01 FDR: 14010
[108:35] Calculating protein q-values
[108:36] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[108:36] Quantification
[108:37] Precursors with scored PTMs at 1% FDR: 322 out of 383 considered
[108:37] Precursors with all scored PTM sites unoccupied at 1% FDR: 12721
[108:37] Precursors with PTMs localised (when required) with > 90% confidence: 313 out of 322
[108:37] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[108:37] File #7/12
[108:37] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[108:49] Pre-processing...
[108:50] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[108:51] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[113:00] RT window set to 2.584
[113:00] IM window set to 0.0380886
[113:01] Recommended MS1 mass accuracy setting: 9 ppm
[115:51] Searching decoys
[118:45] Main search
[124:14] Removing low confidence identifications
[124:24] Removing interfering precursors
[124:30] Training neural networks on 26156 target and 15374 decoy PSMs
[124:42] Training neural networks on 26156 target and 15034 decoy PSMs
[124:51] Precursors at 1% peptidoform FDR: 12442
[124:52] Number of IDs at 0.01 FDR: 13503
[124:52] Calculating protein q-values
[124:53] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[124:53] Quantification
[124:53] Precursors with scored PTMs at 1% FDR: 324 out of 379 considered
[124:53] Precursors with all scored PTM sites unoccupied at 1% FDR: 12337
[124:53] Precursors with PTMs localised (when required) with > 90% confidence: 319 out of 324
[124:54] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[124:54] File #8/12
[124:54] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[125:05] Pre-processing...
[125:06] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[125:07] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[128:59] RT window set to 2.50368
[128:59] IM window set to 0.0405478
[129:00] Recommended MS1 mass accuracy setting: 8 ppm
[131:43] Searching decoys
[134:24] Main search
[139:34] Removing low confidence identifications
[139:42] Removing interfering precursors
[139:48] Training neural networks on 28325 target and 16711 decoy PSMs
[140:02] Training neural networks on 28325 target and 15966 decoy PSMs
[140:11] Precursors at 1% peptidoform FDR: 13220
[140:12] Number of IDs at 0.01 FDR: 14392
[140:12] Calculating protein q-values
[140:13] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[140:13] Quantification
[140:13] Precursors with scored PTMs at 1% FDR: 326 out of 416 considered
[140:13] Precursors with all scored PTM sites unoccupied at 1% FDR: 13003
[140:13] Precursors with PTMs localised (when required) with > 90% confidence: 320 out of 326
[140:14] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[140:14] File #9/12
[140:14] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[140:26] Pre-processing...
[140:27] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[140:28] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[144:37] RT window set to 2.5079
[144:37] IM window set to 0.0392272
[144:37] Recommended MS1 mass accuracy setting: 9 ppm
[147:27] Searching decoys
[150:21] Main search
[156:05] Removing low confidence identifications
[156:14] Removing interfering precursors
[156:19] Training neural networks on 30513 target and 18341 decoy PSMs
[156:32] Training neural networks on 30513 target and 17759 decoy PSMs
[156:41] Precursors at 1% peptidoform FDR: 13964
[156:42] Number of IDs at 0.01 FDR: 15148
[156:42] Calculating protein q-values
[156:43] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[156:43] Quantification
[156:43] Precursors with scored PTMs at 1% FDR: 369 out of 427 considered
[156:43] Precursors with all scored PTM sites unoccupied at 1% FDR: 13756
[156:43] Precursors with PTMs localised (when required) with > 90% confidence: 357 out of 369
[156:44] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[156:44] File #10/12
[156:44] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[156:56] Pre-processing...
[156:57] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[156:58] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[161:04] RT window set to 2.49409
[161:04] IM window set to 0.0390822
[161:05] Recommended MS1 mass accuracy setting: 9 ppm
[163:54] Searching decoys
[166:45] Main search
[172:23] Removing low confidence identifications
[172:32] Removing interfering precursors
[172:37] Training neural networks on 31025 target and 18583 decoy PSMs
[172:49] Training neural networks on 31025 target and 18001 decoy PSMs
[172:58] Precursors at 1% peptidoform FDR: 13776
[172:59] Number of IDs at 0.01 FDR: 15154
[172:59] Calculating protein q-values
[173:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[173:00] Quantification
[173:00] Precursors with scored PTMs at 1% FDR: 367 out of 453 considered
[173:00] Precursors with all scored PTM sites unoccupied at 1% FDR: 13506
[173:00] Precursors with PTMs localised (when required) with > 90% confidence: 358 out of 367
[173:01] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[173:01] File #11/12
[173:01] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[173:13] Pre-processing...
[173:14] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[173:15] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[177:16] RT window set to 2.14002
[177:16] IM window set to 0.0399054
[177:17] Recommended MS1 mass accuracy setting: 9 ppm
[179:50] Searching decoys
[182:21] Main search
[187:13] Removing low confidence identifications
[187:21] Removing interfering precursors
[187:27] Training neural networks on 31369 target and 18915 decoy PSMs
[187:39] Training neural networks on 31369 target and 18196 decoy PSMs
[187:48] Precursors at 1% peptidoform FDR: 13851
[187:49] Number of IDs at 0.01 FDR: 15029
[187:49] Calculating protein q-values
[187:49] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[187:49] Quantification
[187:50] Precursors with scored PTMs at 1% FDR: 372 out of 437 considered
[187:50] Precursors with all scored PTM sites unoccupied at 1% FDR: 13758
[187:50] Precursors with PTMs localised (when required) with > 90% confidence: 358 out of 372
[187:51] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[187:51] File #12/12
[187:51] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[188:02] Pre-processing...
[188:03] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 7931928 precursors in range
[188:04] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[192:03] RT window set to 2.28294
[192:03] IM window set to 0.0400936
[192:04] Recommended MS1 mass accuracy setting: 9 ppm
[194:44] Searching decoys
[197:28] Main search
[202:50] Removing low confidence identifications
[202:58] Removing interfering precursors
[203:03] Training neural networks on 31353 target and 18873 decoy PSMs
[203:16] Training neural networks on 31353 target and 18176 decoy PSMs
[203:25] Precursors at 1% peptidoform FDR: 13990
[203:26] Number of IDs at 0.01 FDR: 15199
[203:26] Calculating protein q-values
[203:26] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[203:26] Quantification
[203:27] Precursors with scored PTMs at 1% FDR: 370 out of 426 considered
[203:27] Precursors with all scored PTM sites unoccupied at 1% FDR: 13783
[203:27] Precursors with PTMs localised (when required) with > 90% confidence: 362 out of 370
[203:27] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[203:27] Cross-run analysis
[203:27] Reading quantification information: 12 files
[203:46] Target precursors at 1% global q-value: 22693
[203:46] Quantifying peptides
[203:50] Assembling protein groups
[203:51] Quantifying proteins
[203:51] Calculating q-values for protein and gene groups
[203:54] Calculating global q-values for protein and gene groups
[203:54] Protein groups with global q-value <= 0.01: 2911
[203:54] Compressed report saved to /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[203:54] Stats report saved to /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report-first-pass.stats.tsv
[203:54] Generating spectral library:
[203:55] 24237 target and 1365 decoy precursors saved
WARNING: 1407 precursors without any fragments annotated were skipped
[203:55] Spectral library saved to /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report-lib.parquet

[203:56] Loading spectral library /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report-lib.parquet
[203:56] Spectral library loaded: 4857 protein isoforms, 4715 protein groups and 25602 precursors in 24079 elution groups (targets and decoys).
[203:56] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[203:56] Annotating library proteins with information from the FASTA database
[203:56] Gene names missing for some isoforms
[203:56] Library contains 4843 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[203:56] Initialising library
[203:57] Saving the library to /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report-lib.parquet.skyline.speclib


Second pass: using the newly created spectral library to reanalyse the data

[203:57] File #1/12
[203:57] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d
[204:08] Pre-processing...
[204:09] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[204:09] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[204:10] RT window set to 0.71775
[204:10] IM window set to 0.01
[204:10] Recommended MS1 mass accuracy setting: 10 ppm
[204:11] Searching decoys
[204:11] Main search
[204:12] Removing low confidence identifications
[204:13] Removing interfering precursors
[204:13] Training neural networks on 21706 target and 12783 decoy PSMs
[204:17] Training neural networks on 21670 target and 11304 decoy PSMs
[204:21] Precursors at 1% peptidoform FDR: 15690
[204:21] Number of IDs at 0.01 FDR: 16663
[204:21] Calculating protein q-values
[204:21] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[204:21] Quantification
[204:22] Precursors with scored PTMs at 1% FDR: 265 out of 306 considered
[204:22] Precursors with all scored PTM sites unoccupied at 1% FDR: 15595
[204:22] Precursors with PTMs localised (when required) with > 90% confidence: 262 out of 265

[204:22] File #2/12
[204:22] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[204:31] Pre-processing...
[204:32] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[204:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[204:33] RT window set to 0.712128
[204:33] IM window set to 0.01
[204:33] Recommended MS1 mass accuracy setting: 10 ppm
[204:33] Searching decoys
[204:34] Main search
[204:34] Removing low confidence identifications
[204:35] Removing interfering precursors
[204:35] Training neural networks on 21726 target and 12196 decoy PSMs
[204:40] Training neural networks on 21701 target and 11628 decoy PSMs
[204:43] Precursors at 1% peptidoform FDR: 16278
[204:43] Number of IDs at 0.01 FDR: 17185
[204:43] Calculating protein q-values
[204:43] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[204:43] Quantification
[204:44] Precursors with scored PTMs at 1% FDR: 309 out of 348 considered
[204:44] Precursors with all scored PTM sites unoccupied at 1% FDR: 15975
[204:44] Precursors with PTMs localised (when required) with > 90% confidence: 303 out of 309

[204:44] File #3/12
[204:44] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[204:57] Pre-processing...
[204:57] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[204:57] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[204:59] RT window set to 0.702091
[204:59] IM window set to 0.01
[204:59] Recommended MS1 mass accuracy setting: 10 ppm
[204:59] Searching decoys
[205:00] Main search
[205:00] Removing low confidence identifications
[205:01] Removing interfering precursors
[205:01] Training neural networks on 21741 target and 12158 decoy PSMs
[205:05] Training neural networks on 21722 target and 11631 decoy PSMs
[205:09] Precursors at 1% peptidoform FDR: 15810
[205:09] Number of IDs at 0.01 FDR: 16486
[205:09] Calculating protein q-values
[205:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[205:09] Quantification
[205:10] Precursors with scored PTMs at 1% FDR: 314 out of 351 considered
[205:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 15591
[205:10] Precursors with PTMs localised (when required) with > 90% confidence: 309 out of 314

[205:10] File #4/12
[205:10] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[205:23] Pre-processing...
[205:24] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[205:24] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[205:25] RT window set to 0.713141
[205:25] IM window set to 0.01
[205:26] Recommended MS1 mass accuracy setting: 10 ppm
[205:26] Searching decoys
[205:26] Main search
[205:27] Removing low confidence identifications
[205:28] Removing interfering precursors
[205:28] Training neural networks on 22293 target and 13740 decoy PSMs
[205:32] Training neural networks on 22254 target and 11557 decoy PSMs
[205:36] Precursors at 1% peptidoform FDR: 16766
[205:36] Number of IDs at 0.01 FDR: 17714
[205:36] Calculating protein q-values
[205:36] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[205:36] Quantification
[205:36] Precursors with scored PTMs at 1% FDR: 310 out of 351 considered
[205:36] Precursors with all scored PTM sites unoccupied at 1% FDR: 16511
[205:36] Precursors with PTMs localised (when required) with > 90% confidence: 305 out of 310

[205:36] File #5/12
[205:36] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[205:43] Pre-processing...
[205:44] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[205:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[205:46] RT window set to 0.705731
[205:46] IM window set to 0.01
[205:46] Recommended MS1 mass accuracy setting: 10 ppm
[205:46] Searching decoys
[205:46] Main search
[205:47] Removing low confidence identifications
[205:48] Removing interfering precursors
[205:48] Training neural networks on 22192 target and 13515 decoy PSMs
[205:52] Training neural networks on 22144 target and 11756 decoy PSMs
[205:56] Precursors at 1% peptidoform FDR: 16597
[205:56] Number of IDs at 0.01 FDR: 17460
[205:56] Calculating protein q-values
[205:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[205:56] Quantification
[205:57] Precursors with scored PTMs at 1% FDR: 312 out of 350 considered
[205:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 16303
[205:57] Precursors with PTMs localised (when required) with > 90% confidence: 306 out of 312

[205:57] File #6/12
[205:57] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[206:04] Pre-processing...
[206:04] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[206:04] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[206:06] RT window set to 0.705285
[206:06] IM window set to 0.01
[206:06] Recommended MS1 mass accuracy setting: 11 ppm
[206:06] Searching decoys
[206:07] Main search
[206:07] Removing low confidence identifications
[206:08] Removing interfering precursors
[206:08] Training neural networks on 22155 target and 13301 decoy PSMs
[206:13] Training neural networks on 22128 target and 11571 decoy PSMs
[206:17] Precursors at 1% peptidoform FDR: 16424
[206:17] Number of IDs at 0.01 FDR: 16994
[206:17] Calculating protein q-values
[206:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[206:17] Quantification
[206:17] Precursors with scored PTMs at 1% FDR: 311 out of 341 considered
[206:17] Precursors with all scored PTM sites unoccupied at 1% FDR: 16270
[206:17] Precursors with PTMs localised (when required) with > 90% confidence: 305 out of 311

[206:17] File #7/12
[206:17] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[206:24] Pre-processing...
[206:24] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[206:24] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[206:26] RT window set to 0.716228
[206:26] IM window set to 0.01
[206:26] Recommended MS1 mass accuracy setting: 10 ppm
[206:26] Searching decoys
[206:26] Main search
[206:27] Removing low confidence identifications
[206:28] Removing interfering precursors
[206:28] Training neural networks on 21815 target and 11918 decoy PSMs
[206:32] Training neural networks on 21795 target and 11684 decoy PSMs
[206:37] Precursors at 1% peptidoform FDR: 16469
[206:37] Number of IDs at 0.01 FDR: 17145
[206:37] Calculating protein q-values
[206:37] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[206:37] Quantification
[206:37] Precursors with scored PTMs at 1% FDR: 332 out of 359 considered
[206:37] Precursors with all scored PTM sites unoccupied at 1% FDR: 16398
[206:37] Precursors with PTMs localised (when required) with > 90% confidence: 329 out of 332

[206:37] File #8/12
[206:37] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[206:43] Pre-processing...
[206:44] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[206:44] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[206:45] RT window set to 0.700608
[206:45] IM window set to 0.0103145
[206:46] Recommended MS1 mass accuracy setting: 10 ppm
[206:46] Searching decoys
[206:46] Main search
[206:47] Removing low confidence identifications
[206:48] Removing interfering precursors
[206:48] Training neural networks on 21915 target and 12153 decoy PSMs
[206:52] Training neural networks on 21893 target and 11673 decoy PSMs
[206:56] Precursors at 1% peptidoform FDR: 16854
[206:56] Number of IDs at 0.01 FDR: 17757
[206:56] Calculating protein q-values
[206:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[206:56] Quantification
[206:56] Precursors with scored PTMs at 1% FDR: 329 out of 369 considered
[206:56] Precursors with all scored PTM sites unoccupied at 1% FDR: 16735
[206:56] Precursors with PTMs localised (when required) with > 90% confidence: 324 out of 329

[206:56] File #9/12
[206:56] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[207:03] Pre-processing...
[207:03] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[207:03] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[207:04] RT window set to 0.698099
[207:04] IM window set to 0.01
[207:04] Recommended MS1 mass accuracy setting: 11 ppm
[207:05] Searching decoys
[207:05] Main search
[207:06] Removing low confidence identifications
[207:07] Removing interfering precursors
[207:07] Training neural networks on 22153 target and 12594 decoy PSMs
[207:11] Training neural networks on 22130 target and 12011 decoy PSMs
[207:14] Precursors at 1% peptidoform FDR: 17133
[207:14] Number of IDs at 0.01 FDR: 18204
[207:14] Calculating protein q-values
[207:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[207:14] Quantification
[207:14] Precursors with scored PTMs at 1% FDR: 364 out of 391 considered
[207:14] Precursors with all scored PTM sites unoccupied at 1% FDR: 17396
[207:14] Precursors with PTMs localised (when required) with > 90% confidence: 351 out of 364

[207:15] File #10/12
[207:15] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[207:21] Pre-processing...
[207:22] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[207:22] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[207:24] RT window set to 0.698849
[207:24] IM window set to 0.0100784
[207:24] Recommended MS1 mass accuracy setting: 10 ppm
[207:24] Searching decoys
[207:24] Main search
[207:25] Removing low confidence identifications
[207:26] Removing interfering precursors
[207:26] Training neural networks on 22071 target and 12261 decoy PSMs
[207:30] Training neural networks on 22050 target and 11957 decoy PSMs
[207:34] Precursors at 1% peptidoform FDR: 17154
[207:34] Number of IDs at 0.01 FDR: 17990
[207:34] Calculating protein q-values
[207:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[207:34] Quantification
[207:34] Precursors with scored PTMs at 1% FDR: 359 out of 381 considered
[207:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 17116
[207:34] Precursors with PTMs localised (when required) with > 90% confidence: 352 out of 359

[207:34] File #11/12
[207:34] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[207:41] Pre-processing...
[207:42] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[207:42] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[207:43] RT window set to 0.692096
[207:43] IM window set to 0.01
[207:43] Recommended MS1 mass accuracy setting: 11 ppm
[207:44] Searching decoys
[207:44] Main search
[207:45] Removing low confidence identifications
[207:46] Removing interfering precursors
[207:46] Training neural networks on 22099 target and 12378 decoy PSMs
[207:50] Training neural networks on 22077 target and 11798 decoy PSMs
[207:54] Precursors at 1% peptidoform FDR: 17416
[207:54] Number of IDs at 0.01 FDR: 18182
[207:54] Calculating protein q-values
[207:54] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[207:54] Quantification
[207:54] Precursors with scored PTMs at 1% FDR: 361 out of 402 considered
[207:54] Precursors with all scored PTM sites unoccupied at 1% FDR: 17137
[207:54] Precursors with PTMs localised (when required) with > 90% confidence: 353 out of 361

[207:54] File #12/12
[207:54] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[208:01] Pre-processing...
[208:02] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 24237 precursors in range
[208:02] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[208:03] RT window set to 0.691607
[208:03] IM window set to 0.01
[208:03] Recommended MS1 mass accuracy setting: 10 ppm
[208:04] Searching decoys
[208:04] Main search
[208:05] Removing low confidence identifications
[208:06] Removing interfering precursors
[208:06] Training neural networks on 22027 target and 12413 decoy PSMs
[208:09] Training neural networks on 22004 target and 11790 decoy PSMs
[208:12] Precursors at 1% peptidoform FDR: 16981
[208:12] Number of IDs at 0.01 FDR: 18050
[208:12] Calculating protein q-values
[208:12] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[208:12] Quantification
[208:13] Precursors with scored PTMs at 1% FDR: 350 out of 418 considered
[208:13] Precursors with all scored PTM sites unoccupied at 1% FDR: 16759
[208:13] Precursors with PTMs localised (when required) with > 90% confidence: 343 out of 350

[208:13] Cross-run analysis
[208:13] Reading quantification information: 12 files
[208:13] Target precursors at 1% global q-value: 21202
[208:13] Quantifying peptides
[208:30] Quantification parameters: 0.32674, 0.00240633, 0.011241, 0.0162616, 0.116003, 0.0760434, 0.313767, 0.181442, 0.248737, 0.0148089, 0.124905, 0.023266, 0.337775, 0.27988, 0.227058, 0.0198652
[208:32] Quantifying proteins
[208:32] Calculating q-values for protein and gene groups
[208:32] Calculating global q-values for protein and gene groups
[208:32] Protein groups with global q-value <= 0.01: 2765
[208:33] Compressed report saved to /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[208:33] Stats report saved to /home/robbe/PB_output/results/MBRDIANN2.5/PYE_diaPASEF/diann_v2.5.0/report.stats.tsv

