
DIA-NN 2.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Jan 28 2025 11:23:41
Current date and time: Wed Feb 19 15:17:47 2025
CPU: GenuineIntel 13th Gen Intel(R) Core(TM) i9-13900F
SIMD instructions: AVX AVX2 FMA SSE4.1 SSE4.2 
Logical CPU cores: 32
diann.exe --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d  --f D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d  --lib  --threads 24 --verbose 1 --out D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.parquet --qvalue 0.01 --matrices --out-lib C:\DIA-NN\2.0\report-lib.parquet --gen-spec-lib --predictor --fasta D:\Proteobench_manuscript_data\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 --rt-profiling 

Thread number set to 24
Output will be filtered at 0.01 FDR
Precursor/protein x samples expression level matrices will be saved along with the main report
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
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 

6 files will be processed
[0:00] Loading FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[0:03] Processing FASTA
[0:05] Assembling elution groups
[0:10] 5116692 precursors generated
[0:10] Protein names missing for some isoforms
[0:10] Gene names missing for some isoforms
[0:10] Library contains 31685 proteins, and 0 genes
[0:14] [0:23] [8:42] [9:41] [9:45] [9:45] Saving the library to C:\DIA-NN\2.0\report-lib.predicted.speclib
[9:49] Initialising library
[10:04] Loading spectral library C:\DIA-NN\2.0\report-lib.predicted.speclib
[10:07] Library annotated with sequence database(s): D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[10:07] Spectral library loaded: 31837 protein isoforms, 51765 protein groups and 5116692 precursors in 2716663 elution groups.
[10:07] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[10:07] Annotating library proteins with information from the FASTA database
[10:07] Protein names missing for some isoforms
[10:07] Gene names missing for some isoforms
[10:07] Library contains 31685 proteins, and 0 genes
[10:10] Initialising library

First pass: generating a spectral library from DIA data

[10:25] File #1/6
[10:25] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.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
[12:08] Pre-processing...
[13:10] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 5116692 precursors in range
[13:13] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[16:54] RT window set to 2.65508
[16:54] IM window set to 0.0464085
[16:54] Peak width: 3.804
[16:54] Scan window radius set to 8
[16:54] Recommended MS1 mass accuracy setting: 12 ppm
[21:48] Optimised mass accuracy: 10 ppm
[23:43] Searching decoys
[37:48] Main search
[65:58] Removing low confidence identifications
[66:41] Removing interfering precursors
[66:46] Training neural networks on 201154 target and 131896 decoy PSMs
[68:29] Training neural networks on 201154 target and 125349 decoy PSMs
[70:08] Number of IDs at 0.01 FDR: 97255
[70:08] Precursors at 1% peptidoform FDR: 90853
[70:09] Calculating protein q-values
[70:09] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[70:09] Quantification
[70:19] Precursors with scored PTMs at 1% FDR: 1149 out of 2022 considered
[70:19] Precursors with all scored PTM sites unoccupied at 1% FDR: 89704
[70:19] Precursors with PTMs localised (when required) with > 90% confidence: 394 out of 1149
[70:24] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d.quant

[70:24] File #2/6
[70:24] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[72:02] Pre-processing...
[73:06] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 5116692 precursors in range
[73:09] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[76:12] RT window set to 2.71914
[76:12] IM window set to 0.0459442
[76:12] Recommended MS1 mass accuracy setting: 12 ppm
[77:55] Searching decoys
[90:16] Main search
[115:03] Removing low confidence identifications
[115:46] Removing interfering precursors
[115:51] Training neural networks on 202846 target and 133526 decoy PSMs
[117:32] Training neural networks on 202846 target and 126957 decoy PSMs
[119:38] Number of IDs at 0.01 FDR: 99740
[119:39] Precursors at 1% peptidoform FDR: 93262
[119:39] Calculating protein q-values
[119:40] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[119:40] Quantification
[119:56] Precursors with scored PTMs at 1% FDR: 1207 out of 2097 considered
[119:56] Precursors with all scored PTM sites unoccupied at 1% FDR: 92055
[119:56] Precursors with PTMs localised (when required) with > 90% confidence: 379 out of 1207
[119:58] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d.quant

[119:58] File #3/6
[119:58] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[121:39] Pre-processing...
[123:20] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 5116692 precursors in range
[123:24] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[127:04] RT window set to 2.76087
[127:04] IM window set to 0.044181
[127:04] Recommended MS1 mass accuracy setting: 12 ppm
[128:50] Searching decoys
[141:00] Main search
[165:26] Removing low confidence identifications
[166:08] Removing interfering precursors
[166:12] Training neural networks on 208104 target and 138140 decoy PSMs
[167:53] Training neural networks on 208104 target and 131222 decoy PSMs
[169:30] Number of IDs at 0.01 FDR: 100063
[169:30] Precursors at 1% peptidoform FDR: 93614
[169:31] Calculating protein q-values
[169:31] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[169:31] Quantification
[169:33] Precursors with scored PTMs at 1% FDR: 1267 out of 2104 considered
[169:33] Precursors with all scored PTM sites unoccupied at 1% FDR: 92347
[169:33] Precursors with PTMs localised (when required) with > 90% confidence: 443 out of 1267
[169:35] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d.quant

[169:35] File #4/6
[169:35] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[171:16] Pre-processing...
[172:16] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 5116692 precursors in range
[172:19] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[175:17] RT window set to 2.82174
[175:17] IM window set to 0.0443644
[175:17] Recommended MS1 mass accuracy setting: 11 ppm
[177:00] Searching decoys
[189:01] Main search
[213:08] Removing low confidence identifications
[213:47] Removing interfering precursors
[213:51] Training neural networks on 197663 target and 131954 decoy PSMs
[215:29] Training neural networks on 197663 target and 126002 decoy PSMs
[217:00] Number of IDs at 0.01 FDR: 97981
[217:01] Precursors at 1% peptidoform FDR: 91841
[217:01] Calculating protein q-values
[217:01] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[217:01] Quantification
[217:03] Precursors with scored PTMs at 1% FDR: 1308 out of 2093 considered
[217:03] Precursors with all scored PTM sites unoccupied at 1% FDR: 90533
[217:03] Precursors with PTMs localised (when required) with > 90% confidence: 574 out of 1308
[217:08] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d.quant

[217:08] File #5/6
[217:08] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[218:47] Pre-processing...
[219:48] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 5116692 precursors in range
[219:50] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[222:49] RT window set to 2.64581
[222:49] IM window set to 0.0467141
[222:49] Recommended MS1 mass accuracy setting: 12 ppm
[224:29] Searching decoys
[236:08] Main search
[259:28] Removing low confidence identifications
[260:10] Removing interfering precursors
[260:15] Training neural networks on 199694 target and 132698 decoy PSMs
[261:51] Training neural networks on 199694 target and 125908 decoy PSMs
[263:23] Number of IDs at 0.01 FDR: 99044
[263:24] Precursors at 1% peptidoform FDR: 92501
[263:24] Calculating protein q-values
[263:24] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[263:24] Quantification
[263:26] Precursors with scored PTMs at 1% FDR: 1184 out of 2062 considered
[263:26] Precursors with all scored PTM sites unoccupied at 1% FDR: 91317
[263:26] Precursors with PTMs localised (when required) with > 90% confidence: 370 out of 1184
[263:31] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d.quant

[263:31] File #6/6
[263:31] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[265:17] Pre-processing...
[266:18] 4348 MS1 and 104343 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 5116692 precursors in range
[266:21] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[269:22] RT window set to 3.49036
[269:22] IM window set to 0.0460693
[269:22] Recommended MS1 mass accuracy setting: 11 ppm
[271:20] Searching decoys
[285:24] Main search
[313:41] Removing low confidence identifications
[314:33] Removing interfering precursors
[314:38] Training neural networks on 198132 target and 129565 decoy PSMs
[316:14] Training neural networks on 198132 target and 123563 decoy PSMs
[317:44] Number of IDs at 0.01 FDR: 99820
[317:45] Precursors at 1% peptidoform FDR: 93909
[317:45] Calculating protein q-values
[317:46] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[317:46] Quantification
[317:48] Precursors with scored PTMs at 1% FDR: 1473 out of 2313 considered
[317:48] Precursors with all scored PTM sites unoccupied at 1% FDR: 92436
[317:48] Precursors with PTMs localised (when required) with > 90% confidence: 660 out of 1473
[317:49] Quantification information saved to D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d.quant

[317:49] Cross-run analysis
[317:49] Reading quantification information: 6 files
[318:03] Quantifying peptides
[318:24] Assembling protein groups
[318:25] Quantifying proteins
[318:25] Calculating q-values for protein and gene groups
[318:26] Calculating global q-values for protein and gene groups
[318:26] Protein groups with global q-value <= 0.01: 11387
[318:28] Compressed report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report-first-pass.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[318:28] Saving precursor levels matrix
[318:28] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report-first-pass.pr_matrix.tsv.
[318:28] Manifest saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report-first-pass.manifest.txt
[318:28] Stats report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report-first-pass.stats.tsv
[318:28] Generating spectral library:
[318:30] 122968 target and 1240 decoy precursors saved
[318:30] Spectral library saved to C:\DIA-NN\2.0\report-lib.parquet

[318:32] Loading spectral library C:\DIA-NN\2.0\report-lib.parquet
[318:33] Spectral library loaded: 13386 protein isoforms, 13204 protein groups and 124208 precursors in 114381 elution groups.
[318:33] Loading protein annotations from FASTA D:\Proteobench_manuscript_data\ProteoBenchFASTA_DDAQuantification.fasta
[318:33] Annotating library proteins with information from the FASTA database
[318:33] Gene names missing for some isoforms
[318:33] Library contains 13374 proteins, and 0 genes
[318:33] Initialising library
[318:34] Saving the library to C:\DIA-NN\2.0\report-lib.parquet.skyline.speclib


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

[318:34] File #1/6
[318:34] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d
[320:13] Pre-processing...
[321:13] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 122968 precursors in range
[321:13] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[321:17] RT window set to 0.941861
[321:17] IM window set to 0.01
[321:17] Recommended MS1 mass accuracy setting: 11 ppm
[321:19] Searching decoys
[321:27] Main search
[321:42] Removing low confidence identifications
[321:51] Removing interfering precursors
[321:52] Training neural networks on 110943 target and 68793 decoy PSMs
[322:39] Training neural networks on 110926 target and 66693 decoy PSMs
[323:24] Number of IDs at 0.01 FDR: 110380
[323:24] Precursors at 1% peptidoform FDR: 102924
[323:24] Calculating protein q-values
[323:24] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[323:24] Quantification
[323:26] Precursors with scored PTMs at 1% FDR: 1561 out of 1801 considered
[323:26] Precursors with all scored PTM sites unoccupied at 1% FDR: 101363
[323:26] Precursors with PTMs localised (when required) with > 90% confidence: 591 out of 1561

[323:27] File #2/6
[323:27] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[325:03] Pre-processing...
[326:04] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 122968 precursors in range
[326:04] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[326:08] RT window set to 0.94282
[326:08] IM window set to 0.01
[326:08] Recommended MS1 mass accuracy setting: 12 ppm
[326:10] Searching decoys
[326:17] Main search
[326:33] Removing low confidence identifications
[326:42] Removing interfering precursors
[326:43] Training neural networks on 111281 target and 69759 decoy PSMs
[327:31] Training neural networks on 111260 target and 66692 decoy PSMs
[328:16] Number of IDs at 0.01 FDR: 111666
[328:16] Precursors at 1% peptidoform FDR: 104587
[328:16] Calculating protein q-values
[328:16] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[328:16] Quantification
[328:18] Precursors with scored PTMs at 1% FDR: 1580 out of 1784 considered
[328:18] Precursors with all scored PTM sites unoccupied at 1% FDR: 103007
[328:18] Precursors with PTMs localised (when required) with > 90% confidence: 625 out of 1580

[328:19] File #3/6
[328:19] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[329:59] Pre-processing...
[331:00] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 122968 precursors in range
[331:00] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[331:05] RT window set to 0.943261
[331:05] IM window set to 0.01
[331:05] Recommended MS1 mass accuracy setting: 11 ppm
[331:06] Searching decoys
[331:14] Main search
[331:29] Removing low confidence identifications
[331:39] Removing interfering precursors
[331:40] Training neural networks on 111293 target and 70127 decoy PSMs
[332:27] Training neural networks on 111277 target and 67240 decoy PSMs
[333:13] Number of IDs at 0.01 FDR: 111730
[333:13] Precursors at 1% peptidoform FDR: 104977
[333:13] Calculating protein q-values
[333:13] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[333:13] Quantification
[333:15] Precursors with scored PTMs at 1% FDR: 1587 out of 1786 considered
[333:15] Precursors with all scored PTM sites unoccupied at 1% FDR: 103390
[333:15] Precursors with PTMs localised (when required) with > 90% confidence: 597 out of 1587

[333:16] File #4/6
[333:16] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[334:57] Pre-processing...
[335:57] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 122968 precursors in range
[335:57] Calibrating with mass accuracies 30 (MS1), 20 (MS2)
[336:01] RT window set to 0.942253
[336:01] IM window set to 0.01
[336:01] Recommended MS1 mass accuracy setting: 11 ppm
[336:03] Searching decoys
[336:10] Main search
[336:25] Removing low confidence identifications
[336:35] Removing interfering precursors
[336:36] Training neural networks on 111418 target and 69081 decoy PSMs
[337:23] Training neural networks on 111395 target and 66527 decoy PSMs
[338:08] Number of IDs at 0.01 FDR: 111908
[338:08] Precursors at 1% peptidoform FDR: 105260
[338:08] Calculating protein q-values
[338:08] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[338:08] Quantification
[338:10] Precursors with scored PTMs at 1% FDR: 1618 out of 1819 considered
[338:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 103642
[338:10] Precursors with PTMs localised (when required) with > 90% confidence: 634 out of 1618

[338:11] File #5/6
[338:11] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[339:47] Pre-processing...
[340:48] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 122968 precursors in range
[340:48] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[340:52] RT window set to 0.941013
[340:52] IM window set to 0.01
[340:52] Recommended MS1 mass accuracy setting: 11 ppm
[340:54] Searching decoys
[341:01] Main search
[341:16] Removing low confidence identifications
[341:25] Removing interfering precursors
[341:26] Training neural networks on 111283 target and 69840 decoy PSMs
[342:14] Training neural networks on 111263 target and 67041 decoy PSMs
[343:00] Number of IDs at 0.01 FDR: 111844
[343:00] Precursors at 1% peptidoform FDR: 105013
[343:00] Calculating protein q-values
[343:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[343:00] Quantification
[343:02] Precursors with scored PTMs at 1% FDR: 1592 out of 1819 considered
[343:02] Precursors with all scored PTM sites unoccupied at 1% FDR: 103421
[343:02] Precursors with PTMs localised (when required) with > 90% confidence: 612 out of 1592

[343:03] File #6/6
[343:03] Loading run D:\Proteobench_manuscript_data\Raw_diaPASEF\Marie_2025\ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[344:46] Pre-processing...
[345:48] 4348 MS1 and 104343 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 122968 precursors in range
[345:48] Calibrating with mass accuracies 30 (MS1), 19 (MS2)
[345:52] RT window set to 0.94183
[345:52] IM window set to 0.01
[345:52] Recommended MS1 mass accuracy setting: 11 ppm
[345:54] Searching decoys
[346:01] Main search
[346:16] Removing low confidence identifications
[346:26] Removing interfering precursors
[346:26] Training neural networks on 111375 target and 69200 decoy PSMs
[347:14] Training neural networks on 111358 target and 66590 decoy PSMs
[347:59] Number of IDs at 0.01 FDR: 112121
[348:00] Precursors at 1% peptidoform FDR: 105301
[348:00] Calculating protein q-values
[348:00] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[348:00] Quantification
[348:02] Precursors with scored PTMs at 1% FDR: 1583 out of 1795 considered
[348:02] Precursors with all scored PTM sites unoccupied at 1% FDR: 103718
[348:02] Precursors with PTMs localised (when required) with > 90% confidence: 616 out of 1583

[348:03] Cross-run analysis
[348:03] Reading quantification information: 6 files
[348:04] Quantifying peptides
[349:30] Quantification parameters: 0.370185, 0.00131234, 0.00438636, 0.0142981, 0.0232617, 0.015049, 0.194201, 0.0146855, 0.0629573, 0.0550705, 0.0574674, 0.0561951, 0.237257, 0.114957, 0.132582, 0.0118206
[349:40] Quantifying proteins
[349:40] Calculating q-values for protein and gene groups
[349:40] Calculating global q-values for protein and gene groups
[349:40] Protein groups with global q-value <= 0.01: 11152
[349:42] Compressed report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[349:42] Saving precursor levels matrix
[349:43] Precursor levels matrix (1% precursor and protein group FDR) saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.pr_matrix.tsv.
[349:43] Saving protein group levels matrix
[349:43] Protein groups matrix saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.pg_matrix.tsv.
[349:43] Saving gene group levels matrix
[349:43] Gene groups matrix saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.gg_matrix.tsv.
[349:43] Saving unique genes levels matrix
[349:43] Unique genes matrix saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.unique_genes_matrix.tsv.
[349:43] Manifest saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.manifest.txt
[349:43] Stats report saved to D:\Proteobench_manuscript_data\run_output_diaPASEF\diann_2.0_default\report.stats.tsv

