
DIA-NN 2.2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on May 29 2025 15:05:00
Current date and time: Mon Apr 13 13:02:26 2026
Logical CPU cores: 128
diann-2.2.0/diann-linux --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d --f /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d --lib --threads 80 --verbose 1 --out plasma_output/diann2.2.0/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] [2:25] [2:43] [2:47] [2:49] Saving the library to plasma_output/diann2.2.0/report-lib.predicted.speclib
[2:53] Initialising library
[3:04] Loading spectral library plasma_output/diann2.2.0/report-lib.predicted.speclib
[3:06] Library annotated with sequence database(s): ProteoBenchFASTA_DDAQuantification.fasta
[3:07] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[3:07] 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 15 ppm
[3:28] Pre-processing...
[3:28] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[3:29] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[4:55] RT window set to 2.48226
[4:55] IM window set to 0.0402388
[4:55] Peak width: 3.356
[4:55] Scan window radius set to 7
[4:55] Recommended MS1 mass accuracy setting: 8 ppm
[7:13] Optimised mass accuracy: 12 ppm
[7:53] Searching decoys
[8:37] Main search
[10:03] Removing low confidence identifications
[10:08] Removing interfering precursors
[10:12] Training neural networks on 25281 target and 13850 decoy PSMs
[10:19] Training neural networks on 25281 target and 13118 decoy PSMs
[10:22] Number of IDs at 0.01 FDR: 12032
[10:23] Precursors at 1% peptidoform FDR: 11201
[10:23] Calculating protein q-values
[10:24] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[10:24] Quantification
[10:24] Precursors with scored PTMs at 1% FDR: 250 out of 268 considered
[10:24] Precursors with all scored PTM sites unoccupied at 1% FDR: 10951
[10:24] Precursors with PTMs localised (when required) with > 90% confidence: 248 out of 250
[10:25] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d.quant

[10:25] File #2/12
[10:25] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[10:31] Pre-processing...
[10:31] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[10:32] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[11:53] RT window set to 2.51473
[11:53] IM window set to 0.0437063
[11:53] Recommended MS1 mass accuracy setting: 9 ppm
[12:32] Searching decoys
[13:17] Main search
[14:49] Removing low confidence identifications
[14:53] Removing interfering precursors
[14:57] Training neural networks on 28242 target and 15674 decoy PSMs
[15:04] Training neural networks on 28242 target and 14828 decoy PSMs
[15:08] Number of IDs at 0.01 FDR: 13554
[15:08] Precursors at 1% peptidoform FDR: 12763
[15:09] Calculating protein q-values
[15:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[15:09] Quantification
[15:10] Precursors with scored PTMs at 1% FDR: 285 out of 323 considered
[15:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 12478
[15:10] Precursors with PTMs localised (when required) with > 90% confidence: 280 out of 285
[15:10] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d.quant

[15:10] File #3/12
[15:10] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[15:17] Pre-processing...
[15:17] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[15:18] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[16:39] RT window set to 2.20578
[16:39] IM window set to 0.0415852
[16:39] Recommended MS1 mass accuracy setting: 9 ppm
[17:15] Searching decoys
[17:55] Main search
[19:14] Removing low confidence identifications
[19:18] Removing interfering precursors
[19:21] Training neural networks on 28396 target and 15586 decoy PSMs
[19:29] Training neural networks on 28396 target and 15084 decoy PSMs
[19:33] Number of IDs at 0.01 FDR: 12844
[19:33] Precursors at 1% peptidoform FDR: 12240
[19:34] Calculating protein q-values
[19:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[19:34] Quantification
[19:35] Precursors with scored PTMs at 1% FDR: 277 out of 300 considered
[19:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 11963
[19:35] Precursors with PTMs localised (when required) with > 90% confidence: 273 out of 277
[19:35] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d.quant

[19:35] File #4/12
[19:35] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[19:42] Pre-processing...
[19:43] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[19:43] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[21:10] RT window set to 2.473
[21:10] IM window set to 0.0434957
[21:10] Recommended MS1 mass accuracy setting: 9 ppm
[21:52] Searching decoys
[22:39] Main search
[24:12] Removing low confidence identifications
[24:17] Removing interfering precursors
[24:21] Training neural networks on 30211 target and 16540 decoy PSMs
[24:28] Training neural networks on 30211 target and 15796 decoy PSMs
[24:33] Number of IDs at 0.01 FDR: 13890
[24:33] Precursors at 1% peptidoform FDR: 12862
[24:34] Calculating protein q-values
[24:34] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[24:34] Quantification
[24:34] Precursors with scored PTMs at 1% FDR: 306 out of 351 considered
[24:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 12556
[24:34] Precursors with PTMs localised (when required) with > 90% confidence: 301 out of 306
[24:35] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d.quant

[24:35] File #5/12
[24:35] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[24:41] Pre-processing...
[24:42] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[24:42] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[26:06] RT window set to 2.47368
[26:06] IM window set to 0.0432592
[26:07] Recommended MS1 mass accuracy setting: 9 ppm
[26:47] Searching decoys
[27:33] Main search
[29:05] Removing low confidence identifications
[29:09] Removing interfering precursors
[29:12] Training neural networks on 30256 target and 17050 decoy PSMs
[29:19] Training neural networks on 30256 target and 16238 decoy PSMs
[29:23] Number of IDs at 0.01 FDR: 13453
[29:24] Precursors at 1% peptidoform FDR: 12705
[29:24] Calculating protein q-values
[29:25] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[29:25] Quantification
[29:25] Precursors with scored PTMs at 1% FDR: 288 out of 332 considered
[29:25] Precursors with all scored PTM sites unoccupied at 1% FDR: 12417
[29:25] Precursors with PTMs localised (when required) with > 90% confidence: 285 out of 288
[29:26] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d.quant

[29:26] File #6/12
[29:26] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[29:32] Pre-processing...
[29:33] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[29:33] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[30:55] RT window set to 2.22365
[30:55] IM window set to 0.0419484
[30:56] Recommended MS1 mass accuracy setting: 9 ppm
[31:31] Searching decoys
[32:13] Main search
[33:35] Removing low confidence identifications
[33:40] Removing interfering precursors
[33:43] Training neural networks on 30645 target and 17238 decoy PSMs
[33:50] Training neural networks on 30645 target and 16420 decoy PSMs
[33:55] Number of IDs at 0.01 FDR: 13321
[33:55] Precursors at 1% peptidoform FDR: 12698
[33:55] Calculating protein q-values
[33:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[33:56] Quantification
[33:56] Precursors with scored PTMs at 1% FDR: 299 out of 318 considered
[33:56] Precursors with all scored PTM sites unoccupied at 1% FDR: 12399
[33:56] Precursors with PTMs localised (when required) with > 90% confidence: 291 out of 299
[33:57] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d.quant

[33:57] File #7/12
[33:57] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[34:03] Pre-processing...
[34:03] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[34:04] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[35:24] RT window set to 2.5005
[35:24] IM window set to 0.0412178
[35:25] Recommended MS1 mass accuracy setting: 9 ppm
[36:03] Searching decoys
[36:45] Main search
[38:09] Removing low confidence identifications
[38:13] Removing interfering precursors
[38:17] Training neural networks on 28476 target and 15718 decoy PSMs
[38:23] Training neural networks on 28476 target and 14984 decoy PSMs
[38:28] Number of IDs at 0.01 FDR: 13430
[38:28] Precursors at 1% peptidoform FDR: 12575
[38:28] Calculating protein q-values
[38:29] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[38:29] Quantification
[38:29] Precursors with scored PTMs at 1% FDR: 324 out of 369 considered
[38:29] Precursors with all scored PTM sites unoccupied at 1% FDR: 12251
[38:29] Precursors with PTMs localised (when required) with > 90% confidence: 320 out of 324
[38:29] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d.quant

[38:30] File #8/12
[38:30] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[38:35] Pre-processing...
[38:36] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[38:36] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[39:55] RT window set to 2.53491
[39:55] IM window set to 0.0417976
[39:55] Recommended MS1 mass accuracy setting: 9 ppm
[40:32] Searching decoys
[41:14] Main search
[42:36] Removing low confidence identifications
[42:41] Removing interfering precursors
[42:44] Training neural networks on 31414 target and 17418 decoy PSMs
[42:51] Training neural networks on 31414 target and 16665 decoy PSMs
[42:55] Number of IDs at 0.01 FDR: 14070
[42:56] Precursors at 1% peptidoform FDR: 13174
[42:56] Calculating protein q-values
[42:56] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[42:57] Quantification
[42:57] Precursors with scored PTMs at 1% FDR: 335 out of 378 considered
[42:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 12839
[42:57] Precursors with PTMs localised (when required) with > 90% confidence: 331 out of 335
[42:57] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d.quant

[42:57] File #9/12
[42:57] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[43:04] Pre-processing...
[43:04] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[43:05] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[44:26] RT window set to 2.4614
[44:26] IM window set to 0.0416315
[44:26] Recommended MS1 mass accuracy setting: 9 ppm
[45:03] Searching decoys
[45:47] Main search
[47:13] Removing low confidence identifications
[47:17] Removing interfering precursors
[47:20] Training neural networks on 31714 target and 17596 decoy PSMs
[47:28] Training neural networks on 31714 target and 16912 decoy PSMs
[47:32] Number of IDs at 0.01 FDR: 14824
[47:32] Precursors at 1% peptidoform FDR: 14006
[47:33] Calculating protein q-values
[47:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[47:33] Quantification
[47:34] Precursors with scored PTMs at 1% FDR: 367 out of 400 considered
[47:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 13639
[47:34] Precursors with PTMs localised (when required) with > 90% confidence: 357 out of 367
[47:34] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d.quant

[47:34] File #10/12
[47:34] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[47:41] Pre-processing...
[47:41] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[47:42] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[49:04] RT window set to 2.56958
[49:04] IM window set to 0.0416559
[49:04] Recommended MS1 mass accuracy setting: 9 ppm
[49:42] Searching decoys
[50:28] Main search
[51:58] Removing low confidence identifications
[52:03] Removing interfering precursors
[52:07] Training neural networks on 31879 target and 17477 decoy PSMs
[52:14] Training neural networks on 31879 target and 16803 decoy PSMs
[52:19] Number of IDs at 0.01 FDR: 14672
[52:19] Precursors at 1% peptidoform FDR: 13538
[52:20] Calculating protein q-values
[52:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[52:20] Quantification
[52:20] Precursors with scored PTMs at 1% FDR: 362 out of 408 considered
[52:20] Precursors with all scored PTM sites unoccupied at 1% FDR: 13176
[52:20] Precursors with PTMs localised (when required) with > 90% confidence: 355 out of 362
[52:21] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d.quant

[52:21] File #11/12
[52:21] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[52:27] Pre-processing...
[52:28] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[52:28] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[53:50] RT window set to 2.60021
[53:50] IM window set to 0.0432779
[53:50] Recommended MS1 mass accuracy setting: 10 ppm
[54:28] Searching decoys
[55:14] Main search
[56:47] Removing low confidence identifications
[56:51] Removing interfering precursors
[56:54] Training neural networks on 30591 target and 16313 decoy PSMs
[57:01] Training neural networks on 30591 target and 15655 decoy PSMs
[57:06] Number of IDs at 0.01 FDR: 14334
[57:06] Precursors at 1% peptidoform FDR: 13664
[57:07] Calculating protein q-values
[57:07] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[57:07] Quantification
[57:07] Precursors with scored PTMs at 1% FDR: 371 out of 396 considered
[57:07] Precursors with all scored PTM sites unoccupied at 1% FDR: 13293
[57:07] Precursors with PTMs localised (when required) with > 90% confidence: 366 out of 371
[57:08] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d.quant

[57:08] File #12/12
[57:08] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[57:15] Pre-processing...
[57:15] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 5116692 precursors in range
[57:16] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[58:37] RT window set to 2.21011
[58:37] IM window set to 0.0425118
[58:38] Recommended MS1 mass accuracy setting: 10 ppm
[59:09] Searching decoys
[59:50] Main search
[61:14] Removing low confidence identifications
[61:18] Removing interfering precursors
[61:22] Training neural networks on 32047 target and 17395 decoy PSMs
[61:29] Training neural networks on 32047 target and 16633 decoy PSMs
[61:34] Number of IDs at 0.01 FDR: 14693
[61:34] Precursors at 1% peptidoform FDR: 13987
[61:35] Calculating protein q-values
[61:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[61:35] Quantification
[61:35] Precursors with scored PTMs at 1% FDR: 386 out of 411 considered
[61:35] Precursors with all scored PTM sites unoccupied at 1% FDR: 13601
[61:35] Precursors with PTMs localised (when required) with > 90% confidence: 381 out of 386
[61:36] Quantification information saved to /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d.quant

[61:36] Cross-run analysis
[61:36] Reading quantification information: 12 files
[61:45] Quantifying peptides
[62:12] Assembling protein groups
[62:13] Quantifying proteins
[62:13] Calculating q-values for protein and gene groups
[62:15] Calculating global q-values for protein and gene groups
[62:15] Protein groups with global q-value <= 0.01: 2982
[62:16] Compressed report saved to plasma_output/diann2.2.0/report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[62:16] Stats report saved to plasma_output/diann2.2.0/report-first-pass.stats.tsv
[62:16] Generating spectral library:
[62:16] 21394 target and 219 decoy precursors saved
[62:16] Spectral library saved to plasma_output/diann2.2.0/report-lib.parquet

[62:19] Loading spectral library plasma_output/diann2.2.0/report-lib.parquet
[62:19] Spectral library loaded: 3848 protein isoforms, 3655 protein groups and 21613 precursors in 20485 elution groups.
[62:19] Loading protein annotations from FASTA ProteoBenchFASTA_DDAQuantification.fasta
[62:19] Annotating library proteins with information from the FASTA database
[62:19] Gene names missing for some isoforms
[62:19] Library contains 3817 proteins, and 0 genes
[62:19] Initialising library
[62:20] Saving the library to plasma_output/diann2.2.0/report-lib.parquet.skyline.speclib


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

[62:20] File #1/12
[62:20] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R1.d
[62:26] Pre-processing...
[62:26] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[62:26] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[62:27] RT window set to 0.716867
[62:27] IM window set to 0.010622
[62:27] Recommended MS1 mass accuracy setting: 10 ppm
[62:27] Searching decoys
[62:28] Main search
[62:28] Removing low confidence identifications
[62:28] Removing interfering precursors
[62:29] Training neural networks on 19184 target and 11562 decoy PSMs
[62:31] Training neural networks on 19178 target and 10731 decoy PSMs
[62:33] Number of IDs at 0.01 FDR: 16234
[62:33] Precursors at 1% peptidoform FDR: 14735
[62:33] Calculating protein q-values
[62:33] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:33] Quantification
[62:34] Precursors with scored PTMs at 1% FDR: 354 out of 382 considered
[62:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 14381
[62:34] Precursors with PTMs localised (when required) with > 90% confidence: 350 out of 354

[62:34] File #2/12
[62:34] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R2.d
[62:40] Pre-processing...
[62:40] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[62:40] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[62:41] RT window set to 0.702142
[62:41] IM window set to 0.0110287
[62:41] Recommended MS1 mass accuracy setting: 10 ppm
[62:41] Searching decoys
[62:41] Main search
[62:42] Removing low confidence identifications
[62:42] Removing interfering precursors
[62:43] Training neural networks on 19439 target and 11729 decoy PSMs
[62:45] Training neural networks on 19435 target and 10994 decoy PSMs
[62:48] Number of IDs at 0.01 FDR: 17030
[62:48] Precursors at 1% peptidoform FDR: 15955
[62:48] Calculating protein q-values
[62:48] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[62:48] Quantification
[62:48] Precursors with scored PTMs at 1% FDR: 370 out of 405 considered
[62:48] Precursors with all scored PTM sites unoccupied at 1% FDR: 15585
[62:48] Precursors with PTMs localised (when required) with > 90% confidence: 366 out of 370

[62:48] File #3/12
[62:48] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R3.d
[62:54] Pre-processing...
[62:55] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[62:55] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[62:55] RT window set to 0.698644
[62:55] IM window set to 0.01
[62:55] Recommended MS1 mass accuracy setting: 11 ppm
[62:56] Searching decoys
[62:56] Main search
[62:56] Removing low confidence identifications
[62:57] Removing interfering precursors
[62:57] Training neural networks on 19426 target and 11724 decoy PSMs
[62:59] Training neural networks on 19420 target and 10902 decoy PSMs
[63:02] Number of IDs at 0.01 FDR: 16969
[63:02] Precursors at 1% peptidoform FDR: 15552
[63:02] Calculating protein q-values
[63:02] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:02] Quantification
[63:02] Precursors with scored PTMs at 1% FDR: 375 out of 416 considered
[63:02] Precursors with all scored PTM sites unoccupied at 1% FDR: 15177
[63:02] Precursors with PTMs localised (when required) with > 90% confidence: 368 out of 375

[63:02] File #4/12
[63:02] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R4.d
[63:09] Pre-processing...
[63:09] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[63:09] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:10] RT window set to 0.699575
[63:10] IM window set to 0.0103556
[63:10] Recommended MS1 mass accuracy setting: 11 ppm
[63:10] Searching decoys
[63:10] Main search
[63:11] Removing low confidence identifications
[63:11] Removing interfering precursors
[63:12] Training neural networks on 19529 target and 11985 decoy PSMs
[63:14] Training neural networks on 19523 target and 11077 decoy PSMs
[63:17] Number of IDs at 0.01 FDR: 17279
[63:17] Precursors at 1% peptidoform FDR: 15956
[63:17] Calculating protein q-values
[63:17] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:17] Quantification
[63:17] Precursors with scored PTMs at 1% FDR: 387 out of 415 considered
[63:17] Precursors with all scored PTM sites unoccupied at 1% FDR: 15569
[63:17] Precursors with PTMs localised (when required) with > 90% confidence: 381 out of 387

[63:17] File #5/12
[63:17] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R5.d
[63:24] Pre-processing...
[63:24] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[63:24] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:25] RT window set to 0.689866
[63:25] IM window set to 0.0108257
[63:25] Recommended MS1 mass accuracy setting: 10 ppm
[63:25] Searching decoys
[63:25] Main search
[63:26] Removing low confidence identifications
[63:26] Removing interfering precursors
[63:26] Training neural networks on 19562 target and 11886 decoy PSMs
[63:29] Training neural networks on 19558 target and 11133 decoy PSMs
[63:31] Number of IDs at 0.01 FDR: 17182
[63:32] Precursors at 1% peptidoform FDR: 15866
[63:32] Calculating protein q-values
[63:32] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:32] Quantification
[63:32] Precursors with scored PTMs at 1% FDR: 374 out of 404 considered
[63:32] Precursors with all scored PTM sites unoccupied at 1% FDR: 15492
[63:32] Precursors with PTMs localised (when required) with > 90% confidence: 369 out of 374

[63:32] File #6/12
[63:32] Loading run /public/local/ProteoBench/PYE_diaPASEF/A9_G_DIA_nLC_tTOF_R6.d
[63:39] Pre-processing...
[63:39] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[63:39] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:40] RT window set to 0.685003
[63:40] IM window set to 0.0102257
[63:40] Recommended MS1 mass accuracy setting: 10 ppm
[63:40] Searching decoys
[63:40] Main search
[63:40] Removing low confidence identifications
[63:41] Removing interfering precursors
[63:41] Training neural networks on 19465 target and 11760 decoy PSMs
[63:44] Training neural networks on 19460 target and 10938 decoy PSMs
[63:46] Number of IDs at 0.01 FDR: 17005
[63:46] Precursors at 1% peptidoform FDR: 15636
[63:46] Calculating protein q-values
[63:46] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[63:46] Quantification
[63:46] Precursors with scored PTMs at 1% FDR: 375 out of 410 considered
[63:46] Precursors with all scored PTM sites unoccupied at 1% FDR: 15261
[63:46] Precursors with PTMs localised (when required) with > 90% confidence: 367 out of 375

[63:46] File #7/12
[63:46] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R1.d
[63:52] Pre-processing...
[63:53] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[63:53] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[63:53] RT window set to 0.703856
[63:53] IM window set to 0.0109555
[63:53] Recommended MS1 mass accuracy setting: 10 ppm
[63:54] Searching decoys
[63:54] Main search
[63:54] Removing low confidence identifications
[63:55] Removing interfering precursors
[63:55] Training neural networks on 19467 target and 11596 decoy PSMs
[63:57] Training neural networks on 19461 target and 10846 decoy PSMs
[64:00] Number of IDs at 0.01 FDR: 17382
[64:00] Precursors at 1% peptidoform FDR: 16305
[64:00] Calculating protein q-values
[64:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:00] Quantification
[64:00] Precursors with scored PTMs at 1% FDR: 426 out of 446 considered
[64:00] Precursors with all scored PTM sites unoccupied at 1% FDR: 15879
[64:00] Precursors with PTMs localised (when required) with > 90% confidence: 419 out of 426

[64:00] File #8/12
[64:00] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R2.d
[64:06] Pre-processing...
[64:06] 1886 MS1 and 49019 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[64:06] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:07] RT window set to 0.700254
[64:07] IM window set to 0.0111662
[64:07] Recommended MS1 mass accuracy setting: 10 ppm
[64:07] Searching decoys
[64:07] Main search
[64:08] Removing low confidence identifications
[64:08] Removing interfering precursors
[64:08] Training neural networks on 19544 target and 11864 decoy PSMs
[64:11] Training neural networks on 19539 target and 10941 decoy PSMs
[64:14] Number of IDs at 0.01 FDR: 17625
[64:14] Precursors at 1% peptidoform FDR: 16437
[64:14] Calculating protein q-values
[64:14] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:14] Quantification
[64:14] Precursors with scored PTMs at 1% FDR: 420 out of 439 considered
[64:14] Precursors with all scored PTM sites unoccupied at 1% FDR: 16017
[64:14] Precursors with PTMs localised (when required) with > 90% confidence: 414 out of 420

[64:14] File #9/12
[64:14] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R3.d
[64:20] Pre-processing...
[64:21] 1886 MS1 and 49013 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[64:21] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:21] RT window set to 0.679319
[64:21] IM window set to 0.01
[64:21] Recommended MS1 mass accuracy setting: 10 ppm
[64:22] Searching decoys
[64:22] Main search
[64:22] Removing low confidence identifications
[64:23] Removing interfering precursors
[64:23] Training neural networks on 19611 target and 11780 decoy PSMs
[64:26] Training neural networks on 19606 target and 10972 decoy PSMs
[64:28] Number of IDs at 0.01 FDR: 18206
[64:28] Precursors at 1% peptidoform FDR: 17185
[64:28] Calculating protein q-values
[64:28] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:28] Quantification
[64:28] Precursors with scored PTMs at 1% FDR: 450 out of 461 considered
[64:28] Precursors with all scored PTM sites unoccupied at 1% FDR: 16735
[64:28] Precursors with PTMs localised (when required) with > 90% confidence: 442 out of 450

[64:29] File #10/12
[64:29] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R4.d
[64:35] Pre-processing...
[64:36] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[64:36] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:36] RT window set to 0.679936
[64:36] IM window set to 0.0100867
[64:36] Recommended MS1 mass accuracy setting: 10 ppm
[64:37] Searching decoys
[64:37] Main search
[64:37] Removing low confidence identifications
[64:38] Removing interfering precursors
[64:38] Training neural networks on 19650 target and 11722 decoy PSMs
[64:40] Training neural networks on 19646 target and 11033 decoy PSMs
[64:42] Number of IDs at 0.01 FDR: 18175
[64:43] Precursors at 1% peptidoform FDR: 17134
[64:43] Calculating protein q-values
[64:43] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:43] Quantification
[64:43] Precursors with scored PTMs at 1% FDR: 443 out of 458 considered
[64:43] Precursors with all scored PTM sites unoccupied at 1% FDR: 16691
[64:43] Precursors with PTMs localised (when required) with > 90% confidence: 439 out of 443

[64:43] File #11/12
[64:43] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R5.d
[64:50] Pre-processing...
[64:50] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[64:50] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[64:51] RT window set to 0.678751
[64:51] IM window set to 0.0100145
[64:51] Recommended MS1 mass accuracy setting: 11 ppm
[64:51] Searching decoys
[64:51] Main search
[64:51] Removing low confidence identifications
[64:52] Removing interfering precursors
[64:52] Training neural networks on 19636 target and 11781 decoy PSMs
[64:54] Training neural networks on 19630 target and 10955 decoy PSMs
[64:57] Number of IDs at 0.01 FDR: 17909
[64:57] Precursors at 1% peptidoform FDR: 16978
[64:57] Calculating protein q-values
[64:57] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[64:57] Quantification
[64:57] Precursors with scored PTMs at 1% FDR: 438 out of 456 considered
[64:57] Precursors with all scored PTM sites unoccupied at 1% FDR: 16540
[64:57] Precursors with PTMs localised (when required) with > 90% confidence: 431 out of 438

[64:57] File #12/12
[64:57] Loading run /public/local/ProteoBench/PYE_diaPASEF/B9_G_DIA_nLC_tTOF_R6.d
[65:04] Pre-processing...
[65:05] 1886 MS1 and 49016 MS2 scans in 1886 (inferred) and 1886 (encoded) cycles, 21394 precursors in range
[65:05] Calibrating with mass accuracies 21 (MS1), 25 (MS2)
[65:05] RT window set to 0.66779
[65:05] IM window set to 0.01
[65:05] Recommended MS1 mass accuracy setting: 11 ppm
[65:06] Searching decoys
[65:06] Main search
[65:06] Removing low confidence identifications
[65:07] Removing interfering precursors
[65:07] Training neural networks on 19552 target and 11732 decoy PSMs
[65:09] Training neural networks on 19547 target and 10919 decoy PSMs
[65:12] Number of IDs at 0.01 FDR: 18092
[65:12] Precursors at 1% peptidoform FDR: 16979
[65:12] Calculating protein q-values
[65:12] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[65:12] Quantification
[65:12] Precursors with scored PTMs at 1% FDR: 437 out of 463 considered
[65:12] Precursors with all scored PTM sites unoccupied at 1% FDR: 16542
[65:12] Precursors with PTMs localised (when required) with > 90% confidence: 432 out of 437

[65:12] Cross-run analysis
[65:12] Reading quantification information: 12 files
[65:13] Quantifying peptides
[65:44] Quantification parameters: 0.358878, 0.00248678, 0.0133319, 0.0132418, 0.0132089, 0.0131654, 0.338, 0.250456, 0.286039, 0.0133828, 0.0139584, 0.0137371, 0.39667, 0.394658, 0.322372, 0.0133009
[66:01] Quantifying proteins
[66:01] Calculating q-values for protein and gene groups
[66:01] Calculating global q-values for protein and gene groups
[66:01] Protein groups with global q-value <= 0.01: 2729
[66:02] Compressed report saved to plasma_output/diann2.2.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[66:02] Stats report saved to plasma_output/diann2.2.0/report.stats.tsv

