
DIA-NN 2.5.0 Academia  (Data-Independent Acquisition by Neural Networks)
Compiled on Apr 12 2026 10:45:33
Current date and time: Sat Apr 18 17:36:07 2026
Logical CPU cores: 128
/home/robbe/bin/diann-2.5.0/diann-linux --f /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d --f /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d --f /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d --f /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d --f /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d --f /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d --fasta /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta --out /home/robbe/PB_output/results/test_run/HYE_diaPASEF/diann_v2.5.0/report.tsv --threads 32 --missed-cleavages 2 --min-pep-len 7 --max-pep-len 30 --mass-acc 20 --mass-acc-ms1 20 --qvalue 0.01 --protein-qvalue 0.01 --min-pr-charge 2 --max-pr-charge 4 --min-pr-mz 400 --max-pr-mz 1200 --min-fr-mz 200 --max-fr-mz 2000 --unimod4 --var-mod UniMod:35,15.994915,M --gen-spec-lib --fasta-search 

Thread number set to 32
Maximum number of missed cleavages set to 2
Min peptide length set to 7
Max peptide length set to 30
Output will be filtered at 0.01 FDR
Output will be filtered at 0.01 protein-level FDR
Min precursor charge set to 2
Max precursor charge set to 4
Min precursor m/z set to 400
Max precursor m/z set to 1200
Min fragment m/z set to 200
Max fragment m/z set to 2000
Cysteine carbamidomethylation enabled as a fixed modification
Modification UniMod:35 with mass delta 15.9949 at M will be considered as variable
A spectral library will be generated
DIA-NN will carry out FASTA digest for in silico lib generation
Mass accuracy will be fixed to 2e-05 (MS2) and 2e-05 (MS1)
WARNING: FASTA digest mode enabled and raw data are provided, turning on deep learning spectra/RT/IM prediction
WARNING: incorrect settings, the in silico-predicted library must be generated in a separate pipeline step and then used to process the raw data, now without activating FASTA digest
WARNING: peptidoform scoring enabled because variable modifications have been declared; to disable, use --no-peptidoforms
The following variable modifications will be localised: UniMod:35 

6 files will be processed
[0:00] Loading FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[0:06] Processing FASTA
[0:09] Assembling elution groups
[0:18] 8103720 precursors generated
[0:18] Protein names missing for some isoforms
[0:18] Gene names missing for some isoforms
[0:18] Library contains 31680 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[0:25] [0:39] [7:45] [8:40] [8:44] [8:48] Saving the library to /home/robbe/PB_output/results/test_run/HYE_diaPASEF/diann_v2.5.0/report-lib.predicted.speclib
[8:53] Initialising library
[9:10] Loading spectral library /home/robbe/PB_output/results/test_run/HYE_diaPASEF/diann_v2.5.0/report-lib.predicted.speclib
[9:14] Library annotated with sequence database(s): /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[9:16] Spectral library loaded: 31832 protein isoforms, 43199 protein groups and 8103720 precursors in 3825450 elution groups (targets and decoys).
[9:16] Loading protein annotations from FASTA /public/local/ProteoBench/fastas/ProteoBenchFASTA_MixedSpecies_HYE.fasta
[9:16] Annotating library proteins with information from the FASTA database
[9:16] Protein names missing for some isoforms
[9:16] Gene names missing for some isoforms
[9:16] Library contains 31680 proteins, and 0 genes
WARNING: no gene information in the FASTA or library: consider using --ids-to-names
[9:22] Initialising library
WARNING: it is strongly recommended to enable MBR when analysing with a large library, if this is a quantitative analysis

[9:38] File #1/6
[9:38] Loading run /public/local/ProteoBench/HYE_diaPASEF/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 10-15 ppm
[11:07] Pre-processing...
[11:13] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 7127359 precursors in range
[11:14] Calibrating with mass accuracies 24 (MS1), 25 (MS2)
[12:39] RT window set to 2.50808
[12:39] IM window set to 0.0433049
[12:39] Peak width: 4.068
[12:39] Scan window radius set to 8
[12:39] Recommended MS1 mass accuracy setting: 11 ppm
[13:22] Searching decoys
[22:11] Main search
[39:53] Removing low confidence identifications
[40:34] Removing interfering precursors
[40:46] Training neural networks on 192048 target and 145619 decoy PSMs
[41:35] Training neural networks on 192048 target and 142016 decoy PSMs
[42:19] Precursors at 1% peptidoform FDR: 92893
[42:20] Number of IDs at 0.01 FDR: 95857
[42:20] Calculating protein q-values
[42:21] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[42:21] Quantification
[42:27] Precursors with scored PTMs at 1% FDR: 760 out of 935 considered
[42:27] Precursors with all scored PTM sites unoccupied at 1% FDR: 92133
[42:27] Precursors with PTMs localised (when required) with > 90% confidence: 738 out of 760
[42:29] Quantification information saved to /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_01_11494.d.quant

[42:30] File #2/6
[42:30] Loading run /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d
[43:48] Pre-processing...
[43:55] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 7127359 precursors in range
[43:56] Calibrating with mass accuracies 24 (MS1), 25 (MS2)
[45:34] RT window set to 2.59168
[45:34] IM window set to 0.0432488
[45:34] Recommended MS1 mass accuracy setting: 11 ppm
[46:22] Searching decoys
[55:29] Main search
[73:14] Removing low confidence identifications
[73:57] Removing interfering precursors
[74:10] Training neural networks on 197520 target and 150159 decoy PSMs
[74:58] Training neural networks on 197520 target and 147002 decoy PSMs
[75:43] Precursors at 1% peptidoform FDR: 96525
[75:45] Number of IDs at 0.01 FDR: 99441
[75:45] Calculating protein q-values
[75:46] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[75:46] Quantification
[75:50] Precursors with scored PTMs at 1% FDR: 917 out of 1068 considered
[75:50] Precursors with all scored PTM sites unoccupied at 1% FDR: 95608
[75:50] Precursors with PTMs localised (when required) with > 90% confidence: 887 out of 917
[75:52] Quantification information saved to /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_02_11500.d.quant

[75:52] File #3/6
[75:52] Loading run /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d
[77:17] Pre-processing...
[77:23] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 7127359 precursors in range
[77:24] Calibrating with mass accuracies 24 (MS1), 25 (MS2)
[78:51] RT window set to 2.67922
[78:51] IM window set to 0.0418055
[78:51] Recommended MS1 mass accuracy setting: 12 ppm
[79:36] Searching decoys
[88:44] Main search
[106:59] Removing low confidence identifications
[107:41] Removing interfering precursors
[107:52] Training neural networks on 201495 target and 153370 decoy PSMs
[108:37] Training neural networks on 201495 target and 149544 decoy PSMs
[109:17] Precursors at 1% peptidoform FDR: 96098
[109:19] Number of IDs at 0.01 FDR: 99150
[109:19] Calculating protein q-values
[109:20] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[109:20] Quantification
[109:23] Precursors with scored PTMs at 1% FDR: 698 out of 888 considered
[109:23] Precursors with all scored PTM sites unoccupied at 1% FDR: 95400
[109:23] Precursors with PTMs localised (when required) with > 90% confidence: 669 out of 698
[109:25] Quantification information saved to /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_A_Sample_Alpha_03_11506.d.quant

[109:25] File #4/6
[109:25] Loading run /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d
[110:50] Pre-processing...
[110:56] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 7127359 precursors in range
[110:57] Calibrating with mass accuracies 24 (MS1), 25 (MS2)
[112:29] RT window set to 2.5651
[112:29] IM window set to 0.0411208
[112:29] Recommended MS1 mass accuracy setting: 12 ppm
[113:12] Searching decoys
[121:52] Main search
[139:03] Removing low confidence identifications
[139:43] Removing interfering precursors
[139:55] Training neural networks on 191784 target and 143747 decoy PSMs
[140:43] Training neural networks on 191784 target and 139474 decoy PSMs
[141:28] Precursors at 1% peptidoform FDR: 93304
[141:30] Number of IDs at 0.01 FDR: 96042
[141:30] Calculating protein q-values
[141:30] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[141:30] Quantification
[141:34] Precursors with scored PTMs at 1% FDR: 707 out of 892 considered
[141:34] Precursors with all scored PTM sites unoccupied at 1% FDR: 92597
[141:34] Precursors with PTMs localised (when required) with > 90% confidence: 685 out of 707
[141:35] Quantification information saved to /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_01_11496.d.quant

[141:35] File #5/6
[141:35] Loading run /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d
[142:58] Pre-processing...
[143:04] 4348 MS1 and 104340 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 7127359 precursors in range
[143:05] Calibrating with mass accuracies 24 (MS1), 25 (MS2)
[144:36] RT window set to 2.54092
[144:36] IM window set to 0.0423338
[144:36] Recommended MS1 mass accuracy setting: 12 ppm
[145:20] Searching decoys
[153:57] Main search
[170:44] Removing low confidence identifications
[171:22] Removing interfering precursors
[171:35] Training neural networks on 184946 target and 139374 decoy PSMs
[172:21] Training neural networks on 184946 target and 135236 decoy PSMs
[173:05] Precursors at 1% peptidoform FDR: 93641
[173:06] Number of IDs at 0.01 FDR: 96619
[173:06] Calculating protein q-values
[173:07] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[173:07] Quantification
[173:10] Precursors with scored PTMs at 1% FDR: 710 out of 888 considered
[173:10] Precursors with all scored PTM sites unoccupied at 1% FDR: 92931
[173:10] Precursors with PTMs localised (when required) with > 90% confidence: 691 out of 710
[173:11] Quantification information saved to /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_02_11502.d.quant

[173:11] File #6/6
[173:11] Loading run /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d
[174:35] Pre-processing...
[174:41] 4348 MS1 and 104343 MS2 scans in 4348 (inferred) and 4348 (encoded) cycles, 7127359 precursors in range
[174:42] Calibrating with mass accuracies 24 (MS1), 25 (MS2)
[176:14] RT window set to 2.74225
[176:14] IM window set to 0.0425039
[176:14] Recommended MS1 mass accuracy setting: 11 ppm
[177:00] Searching decoys
[185:41] Main search
[203:21] Removing low confidence identifications
[204:03] Removing interfering precursors
[204:13] Training neural networks on 193268 target and 146669 decoy PSMs
[204:57] Training neural networks on 193268 target and 143321 decoy PSMs
[205:33] Precursors at 1% peptidoform FDR: 95772
[205:35] Number of IDs at 0.01 FDR: 98856
[205:35] Calculating protein q-values
[205:35] Number of genes identified at 1% FDR: 0 (precursor-level), 0 (protein-level) (inference performed using proteotypic peptides only)
[205:35] Quantification
[205:38] Precursors with scored PTMs at 1% FDR: 712 out of 876 considered
[205:38] Precursors with all scored PTM sites unoccupied at 1% FDR: 95060
[205:38] Precursors with PTMs localised (when required) with > 90% confidence: 689 out of 712
[205:40] Quantification information saved to /public/local/ProteoBench/HYE_diaPASEF/ttSCP_diaPASEF_Condition_B_Sample_Alpha_03_11508.d.quant

[205:40] Cross-run analysis
[205:40] Reading quantification information: 6 files
[206:07] Target precursors at 1% global q-value: 122410
[206:07] Quantifying peptides
[207:23] Quantification parameters: 0.35486, 0.00136486, 0.00338017, 0.109333, 0.139219, 0.127811, 0.20933, 0.014391, 0.0359129, 0.13085, 0.0899937, 0.103921, 0.18887, 0.0782852, 0.0902135, 0.0117727
[207:29] Assembling protein groups
[207:31] Quantifying proteins
[207:31] Calculating q-values for protein and gene groups
[207:33] Calculating global q-values for protein and gene groups
[207:33] Protein groups with global q-value <= 0.01: 11728
[207:36] Compressed report saved to /home/robbe/PB_output/results/test_run/HYE_diaPASEF/diann_v2.5.0/report.parquet. Use R 'arrow' or Python 'PyArrow' package to process
[207:36] Stats report saved to /home/robbe/PB_output/results/test_run/HYE_diaPASEF/diann_v2.5.0/report.stats.tsv
[207:37] Generating spectral library:
[207:39] 134082 target and 7583 decoy precursors saved
WARNING: 5665 precursors without any fragments annotated were skipped
[207:40] Spectral library saved to /home/robbe/PB_output/results/test_run/HYE_diaPASEF/diann_v2.5.0/report-lib.parquet

