
DIA-NN 2.3.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Sep 26 2025 02:56:25
Current date and time: Mon Apr 13 14:09:21 2026
Logical CPU cores: 128
diann-2.3.0/diann-linux --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d --lib --threads 80 --verbose 1 --out plasma_output/diann2.3.0/report.tsv --qvalue 0.01 --gen-spec-lib --predictor --fasta ProteoBenchFASTA_DDAQuantification.fasta --fasta-search --min-fr-mz 50 --max-fr-mz 2000 --met-excision --min-pep-len 6 --max-pep-len 30 --min-pr-mz 400 --max-pr-mz 1000 --min-pr-charge 1 --max-pr-charge 4 --cut K*,R* --missed-cleavages 1 --unimod4 --var-mods 1 --var-mod UniMod:35,15.994915,M --var-mod UniMod:1,42.010565,*n --peptidoforms --reanalyse --relaxed-prot-inf --rt-profiling 

Thread number set to 80
Output will be filtered at 0.01 FDR
A spectral library will be generated
Deep learning will be used to generate a new in silico spectral library from peptides provided
DIA-NN will carry out FASTA digest for in silico lib generation
Min fragment m/z set to 50
Max fragment m/z set to 2000
N-terminal methionine excision enabled
Min peptide length set to 6
Max peptide length set to 30
Min precursor m/z set to 400
Max precursor m/z set to 1000
Min precursor charge set to 1
Max precursor charge set to 4
In silico digest will involve cuts at K*,R*
Maximum number of missed cleavages set to 1
Cysteine carbamidomethylation enabled as a fixed modification
Maximum number of variable modifications set to 1
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
Modification UniMod:1 with mass delta 42.0106 at *n will be considered as variable
Peptidoform scoring enabled
MBR enabled; .quant files will only be saved to disk during the first pass
Heuristic protein grouping will be used, to reduce the number of protein groups obtained; this mode is recommended for benchmarking protein ID numbers, GO/pathway and system-scale analyses
The spectral library (if generated) will retain the original spectra but will include empirically-aligned RTs
DIA-NN will automatically optimise the mass accuracy for the first run of the experiment, use this mode for preliminary analyses only
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
The following variable modifications will be localised: UniMod:35 UniMod:1 

12 files will be processed
[0:00] Loading FASTA ProteoBenchFASTA_DDAQuantification.fasta
[0:04] Processing FASTA
[0:06] Assembling elution groups
[0:11] 5116692 precursors generated
[0:11] Protein names missing for some isoforms
[0:11] Gene names missing for some isoforms
[0:11] Library contains 31685 proteins, and 0 genes
[0:17] [0:26] [2:29] [2:47] [2:49] [2:52] Saving the library to plasma_output/diann2.3.0/report-lib.predicted.speclib
[2:55] Initialising library
[3:05] Loading spectral library plasma_output/diann2.3.0/report-lib.predicted.speclib
[3:07] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[3:08] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[3:08] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[3:08] Annotating library proteins with information from the FASTA database
[3:08] Protein names missing for some isoforms
[3:08] Gene names missing for some isoforms
[3:08] Library contains 31685 proteins, and 0 genes
[3:11] Initialising library

First pass: generating a spectral library from DIA data

[3:21] File #1/12
[3:21] 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
[3:27] Pre-processing...
[3:28] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[3:28] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[4:50] RT window set to 2.54755
[4:50] IM window set to 0.0424951
[4:50] Peak width: 3.276
[4:50] Scan window radius set to 7
[4:50] Recommended MS1 mass accuracy setting: 8 ppm
[7:19] Optimised mass accuracy: 10 ppm
[7:58] Searching decoys
[8:42] Main search
[10:08] Removing low confidence identifications
[10:13] Removing interfering precursors
[10:16] Training neural networks on 26516 target and 15059 decoy PSMs
[10:23] Training neural networks on 26516 target and 14563 decoy PSMs
[10:27] IDs at 0.01 FDR: 11802
[10:27] Precursors at 1% peptidoform FDR: 10975
[10:28] Number of IDs at 0.01 FDR: 13411
[10:28] Calculating protein q-values
[10:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[10:28] Quantification
[10:28] Precursors with scored PTMs at 1% FDR: 207 out of 311 considered
[10:28] Precursors with all scored PTM sites unoccupied at 1% FDR: 10948
[10:28] Precursors with PTMs localised (when required) with > 90% confidence: 205 out of 207
[10:29] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[10:29] File #2/12
[10:29] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[10:35] Pre-processing...
[10:36] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[10:36] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[12:02] RT window set to 2.63605
[12:02] IM window set to 0.0440225
[12:02] Recommended MS1 mass accuracy setting: 9 ppm
[12:42] Searching decoys
[13:28] Main search
[14:58] Removing low confidence identifications
[15:03] Removing interfering precursors
[15:07] Training neural networks on 29469 target and 16499 decoy PSMs
[15:14] Training neural networks on 29469 target and 15760 decoy PSMs
[15:18] IDs at 0.01 FDR: 13248
[15:19] Precursors at 1% peptidoform FDR: 12452
[15:20] Number of IDs at 0.01 FDR: 15089
[15:20] Calculating protein q-values
[15:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[15:20] Quantification
[15:21] Precursors with scored PTMs at 1% FDR: 282 out of 413 considered
[15:21] Precursors with all scored PTM sites unoccupied at 1% FDR: 12409
[15:21] Precursors with PTMs localised (when required) with > 90% confidence: 278 out of 282
[15:21] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[15:21] File #3/12
[15:21] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[15:28] Pre-processing...
[15:28] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[15:29] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[16:56] RT window set to 2.27561
[16:56] IM window set to 0.041756
[16:56] Recommended MS1 mass accuracy setting: 9 ppm
[17:33] Searching decoys
[18:13] Main search
[19:32] Removing low confidence identifications
[19:37] Removing interfering precursors
[19:41] Training neural networks on 28426 target and 15807 decoy PSMs
[19:50] Training neural networks on 28426 target and 15366 decoy PSMs
[19:54] IDs at 0.01 FDR: 12525
[19:54] Precursors at 1% peptidoform FDR: 11838
[19:55] Number of IDs at 0.01 FDR: 14374
[19:55] Calculating protein q-values
[19:55] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:55] Quantification
[19:56] Precursors with scored PTMs at 1% FDR: 274 out of 382 considered
[19:56] Precursors with all scored PTM sites unoccupied at 1% FDR: 11814
[19:56] Precursors with PTMs localised (when required) with > 90% confidence: 268 out of 274
[19:56] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[19:57] File #4/12
[19:57] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[20:03] Pre-processing...
[20:04] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[20:04] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:27] RT window set to 2.44613
[21:27] IM window set to 0.0445437
[21:28] Recommended MS1 mass accuracy setting: 9 ppm
[22:07] Searching decoys
[22:52] Main search
[24:20] Removing low confidence identifications
[24:24] Removing interfering precursors
[24:28] Training neural networks on 32092 target and 18261 decoy PSMs
[24:36] Training neural networks on 32092 target and 17626 decoy PSMs
[24:40] IDs at 0.01 FDR: 13510
[24:40] Precursors at 1% peptidoform FDR: 12893
[24:41] Number of IDs at 0.01 FDR: 15420
[24:41] Calculating protein q-values
[24:41] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[24:41] Quantification
[24:42] Precursors with scored PTMs at 1% FDR: 315 out of 449 considered
[24:42] Precursors with all scored PTM sites unoccupied at 1% FDR: 12907
[24:42] Precursors with PTMs localised (when required) with > 90% confidence: 309 out of 315
[24:42] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[24:42] File #5/12
[24:42] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[24:49] Pre-processing...
[24:50] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[24:50] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:13] RT window set to 2.49906
[26:13] IM window set to 0.0436305
[26:14] Recommended MS1 mass accuracy setting: 9 ppm
[26:54] Searching decoys
[27:39] Main search
[29:09] Removing low confidence identifications
[29:14] Removing interfering precursors
[29:17] Training neural networks on 30047 target and 16673 decoy PSMs
[29:25] Training neural networks on 30047 target and 16143 decoy PSMs
[29:29] IDs at 0.01 FDR: 13265
[29:29] Precursors at 1% peptidoform FDR: 12638
[29:30] Number of IDs at 0.01 FDR: 15226
[29:30] Calculating protein q-values
[29:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[29:30] Quantification
[29:31] Precursors with scored PTMs at 1% FDR: 293 out of 408 considered
[29:31] Precursors with all scored PTM sites unoccupied at 1% FDR: 12698
[29:31] Precursors with PTMs localised (when required) with > 90% confidence: 290 out of 293
[29:31] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[29:31] File #6/12
[29:31] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[29:38] Pre-processing...
[29:39] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[29:39] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[31:02] RT window set to 2.55976
[31:02] IM window set to 0.0433383
[31:02] Recommended MS1 mass accuracy setting: 9 ppm
[31:41] Searching decoys
[32:26] Main search
[33:57] Removing low confidence identifications
[34:01] Removing interfering precursors
[34:05] Training neural networks on 30946 target and 17517 decoy PSMs
[34:12] Training neural networks on 30946 target and 16853 decoy PSMs
[34:16] IDs at 0.01 FDR: 12996
[34:17] Precursors at 1% peptidoform FDR: 12100
[34:17] Number of IDs at 0.01 FDR: 14826
[34:17] Calculating protein q-values
[34:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[34:18] Quantification
[34:18] Precursors with scored PTMs at 1% FDR: 280 out of 402 considered
[34:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 12042
[34:18] Precursors with PTMs localised (when required) with > 90% confidence: 273 out of 280
[34:19] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[34:19] File #7/12
[34:19] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[34:25] Pre-processing...
[34:25] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[34:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[35:47] RT window set to 2.40233
[35:47] IM window set to 0.0391752
[35:47] Recommended MS1 mass accuracy setting: 9 ppm
[36:22] Searching decoys
[37:00] Main search
[38:16] Removing low confidence identifications
[38:20] Removing interfering precursors
[38:23] Training neural networks on 27444 target and 14730 decoy PSMs
[38:30] Training neural networks on 27444 target and 14369 decoy PSMs
[38:34] IDs at 0.01 FDR: 12925
[38:34] Precursors at 1% peptidoform FDR: 12066
[38:35] Number of IDs at 0.01 FDR: 14655
[38:35] Calculating protein q-values
[38:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[38:35] Quantification
[38:36] Precursors with scored PTMs at 1% FDR: 312 out of 464 considered
[38:36] Precursors with all scored PTM sites unoccupied at 1% FDR: 12019
[38:36] Precursors with PTMs localised (when required) with > 90% confidence: 310 out of 312
[38:36] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[38:36] File #8/12
[38:36] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[38:42] Pre-processing...
[38:42] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[38:43] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[40:01] RT window set to 2.77166
[40:01] IM window set to 0.0428188
[40:01] Recommended MS1 mass accuracy setting: 9 ppm
[40:38] Searching decoys
[41:22] Main search
[42:48] Removing low confidence identifications
[42:53] Removing interfering precursors
[42:56] Training neural networks on 32582 target and 18425 decoy PSMs
[43:04] Training neural networks on 32582 target and 17610 decoy PSMs
[43:08] IDs at 0.01 FDR: 13602
[43:09] Precursors at 1% peptidoform FDR: 12459
[43:09] Number of IDs at 0.01 FDR: 15747
[43:09] Calculating protein q-values
[43:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[43:10] Quantification
[43:10] Precursors with scored PTMs at 1% FDR: 341 out of 499 considered
[43:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 12297
[43:10] Precursors with PTMs localised (when required) with > 90% confidence: 337 out of 341
[43:11] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[43:11] File #9/12
[43:11] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[43:17] Pre-processing...
[43:18] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[43:18] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[44:40] RT window set to 2.60286
[44:40] IM window set to 0.0428052
[44:40] Recommended MS1 mass accuracy setting: 9 ppm
[45:19] Searching decoys
[46:03] Main search
[47:30] Removing low confidence identifications
[47:35] Removing interfering precursors
[47:38] Training neural networks on 32562 target and 18599 decoy PSMs
[47:46] Training neural networks on 32562 target and 17656 decoy PSMs
[47:51] IDs at 0.01 FDR: 14366
[47:51] Precursors at 1% peptidoform FDR: 13578
[47:52] Number of IDs at 0.01 FDR: 16498
[47:52] Calculating protein q-values
[47:52] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[47:52] Quantification
[47:53] Precursors with scored PTMs at 1% FDR: 365 out of 505 considered
[47:53] Precursors with all scored PTM sites unoccupied at 1% FDR: 13566
[47:53] Precursors with PTMs localised (when required) with > 90% confidence: 359 out of 365
[47:53] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[47:53] File #10/12
[47:53] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[48:00] Pre-processing...
[48:01] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[48:01] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[49:23] RT window set to 2.70185
[49:23] IM window set to 0.0412577
[49:23] Recommended MS1 mass accuracy setting: 9 ppm
[50:02] Searching decoys
[50:47] Main search
[52:17] Removing low confidence identifications
[52:22] Removing interfering precursors
[52:25] Training neural networks on 30826 target and 17023 decoy PSMs
[52:32] Training neural networks on 30826 target and 16150 decoy PSMs
[52:37] IDs at 0.01 FDR: 13922
[52:37] Precursors at 1% peptidoform FDR: 13204
[52:38] Number of IDs at 0.01 FDR: 15848
[52:38] Calculating protein q-values
[52:38] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[52:38] Quantification
[52:38] Precursors with scored PTMs at 1% FDR: 382 out of 514 considered
[52:38] Precursors with all scored PTM sites unoccupied at 1% FDR: 13102
[52:38] Precursors with PTMs localised (when required) with > 90% confidence: 372 out of 382
[52:39] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[52:39] File #11/12
[52:39] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[52:46] Pre-processing...
[52:46] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[52:47] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[54:07] RT window set to 2.56985
[54:07] IM window set to 0.0427265
[54:07] Recommended MS1 mass accuracy setting: 10 ppm
[54:38] Searching decoys
[55:21] Main search
[56:48] Removing low confidence identifications
[56:53] Removing interfering precursors
[56:56] Training neural networks on 32172 target and 17498 decoy PSMs
[57:03] Training neural networks on 32172 target and 17108 decoy PSMs
[57:07] IDs at 0.01 FDR: 14040
[57:07] Precursors at 1% peptidoform FDR: 13296
[57:08] Number of IDs at 0.01 FDR: 16340
[57:08] Calculating protein q-values
[57:08] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[57:08] Quantification
[57:09] Precursors with scored PTMs at 1% FDR: 367 out of 501 considered
[57:09] Precursors with all scored PTM sites unoccupied at 1% FDR: 13313
[57:09] Precursors with PTMs localised (when required) with > 90% confidence: 362 out of 367
[57:09] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[57:09] File #12/12
[57:09] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[57:16] Pre-processing...
[57:17] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[57:17] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[58:39] RT window set to 2.28351
[58:39] IM window set to 0.0419911
[58:39] Recommended MS1 mass accuracy setting: 9 ppm
[59:09] Searching decoys
[59:50] Main search
[61:10] Removing low confidence identifications
[61:15] Removing interfering precursors
[61:19] Training neural networks on 33810 target and 18512 decoy PSMs
[61:26] Training neural networks on 33810 target and 17994 decoy PSMs
[61:31] IDs at 0.01 FDR: 14564
[61:31] Precursors at 1% peptidoform FDR: 13695
[61:32] Number of IDs at 0.01 FDR: 16664
[61:32] Calculating protein q-values
[61:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[61:32] Quantification
[61:32] Precursors with scored PTMs at 1% FDR: 372 out of 495 considered
[61:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 13674
[61:32] Precursors with PTMs localised (when required) with > 90% confidence: 366 out of 372
[61:33] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[61:33] Cross-run analysis
[61:33] Reading quantification information: 12 files
[61:43] Quantifying peptides
[61:46] Assembling protein groups
[61:47] Quantifying proteins
[61:47] Calculating q-values for protein and gene groups
[61:49] Calculating global q-values for protein and gene groups
[61:49] Protein groups with global q-value <= 0.01: 2948
[61:50] Compressed report saved to plasma_output/diann2.3.0/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[61:50] Stats report saved to plasma_output/diann2.3.0/report-first-pass.stats.tsv
[61:50] Generating spectral library:
[61:50] 22548 target and 235 decoy precursors saved
[61:50] Spectral library saved to plasma_output/diann2.3.0/report-lib.parquet

[61:51] Loading spectral library plasma_output/diann2.3.0/report-lib.parquet
[61:51] Spectral library loaded: 3559 protein isoforms, 3393 protein groups and 22783 precursors in 21640 elution groups.
[61:51] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[61:51] Annotating library proteins with information from the FASTA database
[61:51] Gene names missing for some isoforms
[61:51] Library contains 3528 proteins, and 0 genes
[61:51] Initialising library
[61:52] Saving the library to plasma_output/diann2.3.0/report-lib.parquet.skyline.speclib


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

[61:52] File #1/12
[61:52] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d
[61:58] Pre-processing...
[61:59] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[61:59] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[61:59] RT window set to 0.714657
[61:59] IM window set to 0.0112297
[61:59] Recommended MS1 mass accuracy setting: 10 ppm
[61:59] Searching decoys
[62:00] Main search
[62:00] Removing low confidence identifications
[62:00] Removing interfering precursors
[62:01] Training neural networks on 20224 target and 11425 decoy PSMs
[62:03] Training neural networks on 20212 target and 11324 decoy PSMs
[62:06] IDs at 0.01 FDR: 16229
[62:06] Precursors at 1% peptidoform FDR: 14751
[62:06] Number of IDs at 0.01 FDR: 17095
[62:06] Calculating protein q-values
[62:06] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:06] Quantification
[62:06] Precursors with scored PTMs at 1% FDR: 342 out of 392 considered
[62:06] Precursors with all scored PTM sites unoccupied at 1% FDR: 14791
[62:06] Precursors with PTMs localised (when required) with > 90% confidence: 338 out of 342

[62:06] File #2/12
[62:06] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[62:12] Pre-processing...
[62:13] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[62:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[62:13] RT window set to 0.705898
[62:13] IM window set to 0.011383
[62:13] Recommended MS1 mass accuracy setting: 11 ppm
[62:14] Searching decoys
[62:14] Main search
[62:14] Removing low confidence identifications
[62:15] Removing interfering precursors
[62:15] Training neural networks on 20436 target and 11693 decoy PSMs
[62:18] Training neural networks on 20424 target and 11642 decoy PSMs
[62:20] IDs at 0.01 FDR: 17414
[62:20] Precursors at 1% peptidoform FDR: 16257
[62:20] Number of IDs at 0.01 FDR: 18096
[62:20] Calculating protein q-values
[62:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:20] Quantification
[62:21] Precursors with scored PTMs at 1% FDR: 368 out of 405 considered
[62:21] Precursors with all scored PTM sites unoccupied at 1% FDR: 16250
[62:21] Precursors with PTMs localised (when required) with > 90% confidence: 365 out of 368

[62:21] File #3/12
[62:21] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[62:27] Pre-processing...
[62:28] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[62:28] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[62:28] RT window set to 0.699483
[62:28] IM window set to 0.0101655
[62:28] Recommended MS1 mass accuracy setting: 10 ppm
[62:29] Searching decoys
[62:29] Main search
[62:29] Removing low confidence identifications
[62:30] Removing interfering precursors
[62:30] Training neural networks on 20476 target and 11905 decoy PSMs
[62:32] Training neural networks on 20461 target and 11606 decoy PSMs
[62:35] IDs at 0.01 FDR: 16922
[62:35] Precursors at 1% peptidoform FDR: 15332
[62:35] Number of IDs at 0.01 FDR: 17921
[62:35] Calculating protein q-values
[62:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:35] Quantification
[62:35] Precursors with scored PTMs at 1% FDR: 359 out of 411 considered
[62:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 15422
[62:35] Precursors with PTMs localised (when required) with > 90% confidence: 354 out of 359

[62:35] File #4/12
[62:35] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[62:42] Pre-processing...
[62:43] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[62:43] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[62:43] RT window set to 0.704833
[62:43] IM window set to 0.0102957
[62:43] Recommended MS1 mass accuracy setting: 11 ppm
[62:43] Searching decoys
[62:44] Main search
[62:44] Removing low confidence identifications
[62:45] Removing interfering precursors
[62:45] Training neural networks on 20522 target and 11817 decoy PSMs
[62:47] Training neural networks on 20510 target and 11688 decoy PSMs
[62:50] IDs at 0.01 FDR: 17643
[62:50] Precursors at 1% peptidoform FDR: 16402
[62:50] Number of IDs at 0.01 FDR: 18329
[62:50] Calculating protein q-values
[62:50] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:50] Quantification
[62:50] Precursors with scored PTMs at 1% FDR: 387 out of 421 considered
[62:50] Precursors with all scored PTM sites unoccupied at 1% FDR: 16369
[62:50] Precursors with PTMs localised (when required) with > 90% confidence: 381 out of 387

[62:51] File #5/12
[62:51] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[62:57] Pre-processing...
[62:58] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[62:58] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[62:58] RT window set to 0.701003
[62:58] IM window set to 0.0104638
[62:58] Recommended MS1 mass accuracy setting: 11 ppm
[62:59] Searching decoys
[62:59] Main search
[62:59] Removing low confidence identifications
[63:00] Removing interfering precursors
[63:00] Training neural networks on 20550 target and 12093 decoy PSMs
[63:03] Training neural networks on 20532 target and 11714 decoy PSMs
[63:05] IDs at 0.01 FDR: 17732
[63:05] Precursors at 1% peptidoform FDR: 16150
[63:05] Number of IDs at 0.01 FDR: 18285
[63:05] Calculating protein q-values
[63:05] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:05] Quantification
[63:06] Precursors with scored PTMs at 1% FDR: 377 out of 412 considered
[63:06] Precursors with all scored PTM sites unoccupied at 1% FDR: 15992
[63:06] Precursors with PTMs localised (when required) with > 90% confidence: 374 out of 377

[63:06] File #6/12
[63:06] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[63:13] Pre-processing...
[63:13] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[63:13] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:14] RT window set to 0.700823
[63:14] IM window set to 0.0102469
[63:14] Recommended MS1 mass accuracy setting: 11 ppm
[63:14] Searching decoys
[63:14] Main search
[63:14] Removing low confidence identifications
[63:15] Removing interfering precursors
[63:15] Training neural networks on 20463 target and 11943 decoy PSMs
[63:18] Training neural networks on 20454 target and 11586 decoy PSMs
[63:20] IDs at 0.01 FDR: 17423
[63:20] Precursors at 1% peptidoform FDR: 15814
[63:20] Number of IDs at 0.01 FDR: 18107
[63:20] Calculating protein q-values
[63:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:20] Quantification
[63:21] Precursors with scored PTMs at 1% FDR: 372 out of 418 considered
[63:21] Precursors with all scored PTM sites unoccupied at 1% FDR: 15732
[63:21] Precursors with PTMs localised (when required) with > 90% confidence: 367 out of 372

[63:21] File #7/12
[63:21] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[63:27] Pre-processing...
[63:27] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[63:27] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:28] RT window set to 0.709158
[63:28] IM window set to 0.0113171
[63:28] Recommended MS1 mass accuracy setting: 11 ppm
[63:28] Searching decoys
[63:28] Main search
[63:28] Removing low confidence identifications
[63:29] Removing interfering precursors
[63:29] Training neural networks on 20503 target and 11422 decoy PSMs
[63:32] Training neural networks on 20489 target and 11503 decoy PSMs
[63:34] IDs at 0.01 FDR: 17647
[63:34] Precursors at 1% peptidoform FDR: 16164
[63:34] Number of IDs at 0.01 FDR: 18458
[63:34] Calculating protein q-values
[63:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:34] Quantification
[63:35] Precursors with scored PTMs at 1% FDR: 417 out of 442 considered
[63:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 16149
[63:35] Precursors with PTMs localised (when required) with > 90% confidence: 410 out of 417

[63:35] File #8/12
[63:35] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[63:41] Pre-processing...
[63:41] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[63:41] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:41] RT window set to 0.709932
[63:41] IM window set to 0.0115911
[63:42] Recommended MS1 mass accuracy setting: 11 ppm
[63:42] Searching decoys
[63:42] Main search
[63:42] Removing low confidence identifications
[63:43] Removing interfering precursors
[63:43] Training neural networks on 20590 target and 12005 decoy PSMs
[63:46] Training neural networks on 20578 target and 11695 decoy PSMs
[63:48] IDs at 0.01 FDR: 18270
[63:48] Precursors at 1% peptidoform FDR: 16620
[63:48] Number of IDs at 0.01 FDR: 18931
[63:48] Calculating protein q-values
[63:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:48] Quantification
[63:49] Precursors with scored PTMs at 1% FDR: 408 out of 453 considered
[63:49] Precursors with all scored PTM sites unoccupied at 1% FDR: 16534
[63:49] Precursors with PTMs localised (when required) with > 90% confidence: 402 out of 408

[63:49] File #9/12
[63:49] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[63:55] Pre-processing...
[63:56] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[63:56] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:56] RT window set to 0.697436
[63:56] IM window set to 0.0104235
[63:56] Recommended MS1 mass accuracy setting: 10 ppm
[63:56] Searching decoys
[63:56] Main search
[63:57] Removing low confidence identifications
[63:57] Removing interfering precursors
[63:58] Training neural networks on 20716 target and 11964 decoy PSMs
[64:00] Training neural networks on 20701 target and 11694 decoy PSMs
[64:03] IDs at 0.01 FDR: 18847
[64:03] Precursors at 1% peptidoform FDR: 17386
[64:03] Number of IDs at 0.01 FDR: 19312
[64:03] Calculating protein q-values
[64:03] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:03] Quantification
[64:03] Precursors with scored PTMs at 1% FDR: 430 out of 451 considered
[64:03] Precursors with all scored PTM sites unoccupied at 1% FDR: 17192
[64:03] Precursors with PTMs localised (when required) with > 90% confidence: 423 out of 430

[64:03] File #10/12
[64:03] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[64:10] Pre-processing...
[64:10] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[64:10] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:11] RT window set to 0.699954
[64:11] IM window set to 0.0102572
[64:11] Recommended MS1 mass accuracy setting: 10 ppm
[64:11] Searching decoys
[64:12] Main search
[64:12] Removing low confidence identifications
[64:12] Removing interfering precursors
[64:13] Training neural networks on 20749 target and 12187 decoy PSMs
[64:15] Training neural networks on 20738 target and 11717 decoy PSMs
[64:18] IDs at 0.01 FDR: 18351
[64:18] Precursors at 1% peptidoform FDR: 17216
[64:18] Number of IDs at 0.01 FDR: 19207
[64:18] Calculating protein q-values
[64:18] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:18] Quantification
[64:18] Precursors with scored PTMs at 1% FDR: 436 out of 461 considered
[64:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 17253
[64:18] Precursors with PTMs localised (when required) with > 90% confidence: 429 out of 436

[64:18] File #11/12
[64:18] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[64:25] Pre-processing...
[64:26] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[64:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:26] RT window set to 0.691036
[64:26] IM window set to 0.0110908
[64:26] Recommended MS1 mass accuracy setting: 11 ppm
[64:27] Searching decoys
[64:27] Main search
[64:27] Removing low confidence identifications
[64:28] Removing interfering precursors
[64:28] Training neural networks on 20682 target and 12024 decoy PSMs
[64:30] Training neural networks on 20669 target and 11707 decoy PSMs
[64:33] IDs at 0.01 FDR: 18448
[64:33] Precursors at 1% peptidoform FDR: 17344
[64:33] Number of IDs at 0.01 FDR: 19068
[64:33] Calculating protein q-values
[64:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:33] Quantification
[64:33] Precursors with scored PTMs at 1% FDR: 432 out of 454 considered
[64:33] Precursors with all scored PTM sites unoccupied at 1% FDR: 17238
[64:33] Precursors with PTMs localised (when required) with > 90% confidence: 424 out of 432

[64:33] File #12/12
[64:33] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[64:40] Pre-processing...
[64:41] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 22548 precursors in range
[64:41] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:41] RT window set to 0.694172
[64:41] IM window set to 0.0108702
[64:41] Recommended MS1 mass accuracy setting: 11 ppm
[64:41] Searching decoys
[64:42] Main search
[64:42] Removing low confidence identifications
[64:43] Removing interfering precursors
[64:43] Training neural networks on 20629 target and 11686 decoy PSMs
[64:45] Training neural networks on 20622 target and 11602 decoy PSMs
[64:48] IDs at 0.01 FDR: 18385
[64:48] Precursors at 1% peptidoform FDR: 17157
[64:48] Number of IDs at 0.01 FDR: 19029
[64:48] Calculating protein q-values
[64:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:48] Quantification
[64:48] Precursors with scored PTMs at 1% FDR: 437 out of 459 considered
[64:48] Precursors with all scored PTM sites unoccupied at 1% FDR: 17025
[64:48] Precursors with PTMs localised (when required) with > 90% confidence: 433 out of 437

[64:49] Cross-run analysis
[64:49] Reading quantification information: 12 files
[64:49] Quantifying peptides
[65:04] Quantification parameters: 0.365732, 0.00250123, 0.0138639, 0.0151521, 0.0132425, 0.0131871, 0.351739, 0.246851, 0.291196, 0.0129766, 0.0136064, 0.0139093, 0.401266, 0.380514, 0.317704, 0.0386533
[65:06] Quantifying proteins
[65:06] Calculating q-values for protein and gene groups
[65:06] Calculating global q-values for protein and gene groups
[65:06] Protein groups with global q-value <= 0.01: 2704
[65:07] Compressed report saved to plasma_output/diann2.3.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[65:07] Stats report saved to plasma_output/diann2.3.0/report.stats.tsv

